Bhajan Lal
Cornelius Borecho Bavoh
Jai Krishna Sahith Sayani Editors
Machine
Learning and
Flow Assurance
in Oil and Gas
Production
Machine Learning and Flow Assurance in Oil
and Gas Production
Bhajan Lal · Cornelius Borecho Bavoh ·
Jai Krishna Sahith Sayani
Editors
Machine Learning and Flow
Assurance in Oil and Gas
Production
Editors
Bhajan Lal
Chemical Engineering Department
Universiti Teknologi Petronas
Seri Iskandar, Perak, Malaysia
Cornelius Borecho Bavoh
Chemical Engineering Department
Universiti Teknologi PETRONAS
Bandar Seri Iskandar, Malaysia
Jai Krishna Sahith Sayani
Department of Chemical and Bioprocess
Engineering
University College Dublin
Belfield, Ireland
ISBN 978-3-031-24230-4
ISBN 978-3-031-24231-1 (eBook)
https://doi.org/10.1007/978-3-031-24231-1
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
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Preface
The use of digital or artificial intelligence methods in flow assurance has increased
recently to effectively achieve fast results without any thorough training. Generally,
flow assurance covers all risks associated with maintaining the flow of oil and gas
during any stage in the petroleum industry. Flow assurance in the oil and gas industry
covers the anticipation, limitation, and/or prevention of hydrates, wax, asphaltenes,
scale, and corrosion during operation. The occurrence of flow assurance challenges
mostly leads to stoppage of production or plugs, damage of pipelines or production
facilities, economic losses, and in severe cases blowouts and loss of human lives.
A combination of several chemical and non-chemical techniques is mostly used to
prevent flow assurance issues in the industry.
However, the use of models to anticipate, limit, and/or prevent flow assurance
problems is recommended as the best and suitable practice. The existing proposed
flow assurance models on hydrates, wax, asphaltenes, scale, and corrosion management are challenged with accuracy and precision. They are not also limited by
several parametric assumptions. Recently, machine learning methods have gained
much attention as the best practice for predicting flow assurance issues. Examples
of these machine learning models include conventional approaches such as Artificial Neural Network, Support Vector Machine (SVM), Least Squares Support Vector
Machine (LSSVM), Random Forest (RF), and hybrid models. The use of machine
learning in flow assurance is growing, and thus relevant knowledge and guidelines
on their application methods and effectiveness are needed for academic, industrial,
and research purposes.
In this book, we focused on the use and abilities of various machine learning
methods in flow assurance. Initially, basic definitions and use of machine learning in
flow assurance are discussed in a broader scope within the oil and gas industry. The
rest of the chapters discuss the use of machine learning in various flow assurance areas
such as hydrates, wax, asphaltenes, scale, and corrosion. Also, the use of machine
learning in practical field applications is discussed to understand the practical use of
v
vi
Preface
machine learning in flow assurance. This book is useful to flow assurance engineers,
students, and industries who wish to be flow assurance authorities in the twenty-firstcentury smart oil and gas industry.
Seri Iskandar, Malaysia
Bandar Seri Iskandar, Malaysia
Belfield, Ireland
Bhajan Lal
Cornelius Borecho Bavoh
Jai Krishna Sahith Sayani
Contents
1
Machine Learning and Flow Assurance Issues . . . . . . . . . . . . . . . . . . .
Cornelius Borecho Bavoh and Bhajan Lal
1
2
Machine Learning in Oil and Gas Industry . . . . . . . . . . . . . . . . . . . . . .
Jai Krishna Sahith Sayani and Bhajan Lal
7
3
Multiphase Flow Systems and Potential of Machine Learning
Approaches in Cutting Transport and Liquid Loading
Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Muhammad Saad Khan, Abinash Barooah, Bhajan Lal,
and Mohammad Azizur Rahman
27
4
Machine Learning in Corrosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jai Krishna Sahith Sayani and Bhajan Lal
59
5
Machine Learning in Asphaltenes Mitigation . . . . . . . . . . . . . . . . . . . .
Ali Qasim and Bhajan Lal
81
6
Machine Learning for Scale Deposition in Oil and Gas Industry . . . 105
Sirisha Nallakukkala and Bhajan Lal
7
Machine Learning in CO2 Sequestration . . . . . . . . . . . . . . . . . . . . . . . . 119
Amirun Nissa Rehman and Bhajan Lal
8
Machine Learning in Wax Deposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Ihtisham Ul Haq and Bhajan Lal
9
Machine Learning Application in Gas Hydrates . . . . . . . . . . . . . . . . . . 155
Ali Qasim and Bhajan Lal
10 Machine Learning Application Guidelines in Flow Assurance . . . . . 175
Cornelius Borecho Bavoh and Bhajan Lal
vii
Chapter 1
Machine Learning and Flow Assurance
Issues
Cornelius Borecho Bavoh and Bhajan Lal
Abstract This chapter briefly discusses the main challenges facing the flow assurance related areas in the oil and gas industry. It also provide simple fundamental
definitions to machine learning vocabulary to introduce to machine learning terms.
Keywords Machine learning vocabularies · Flow assurance · Hydrates · Scales ·
Corrosion
1.1 Introduction
Flow assurance issues has increased recently owing to the recent increase in the
production of oil and gas in harsh environments [1]. Most of such issue arise from
the production and transportation pipelines in the oil and gas industry. The term flow
assurance is the process of successfully ensuring the economic flow of hydrocarbon
stream from the reservoir to the point of sale. The major oil and gas flow assurance issues include wax, hydrates, asphaltenes, scales, and corrosion, etc. [2]. The
knowledge of multiple discipline studies with a combination of chemistry, multiphase hydrodynamics, thermodynamics, kinetics, and materials science. The main
flow assurance issues are briefly discussed in this section for the better understanding
of the entire book [2, 3].
C. B. Bavoh
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
B. Lal (B)
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar Perak, Malaysia
e-mail: bhajan.lal@utp.edu.my
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_1
1
2
C. B. Bavoh and B. Lal
1.2 Flow Assurances Challenges
1.2.1 Wax Deposition
The deposition of wax is a very common flow assurance issue in the petroleum
industry. The deposition of wax occurs because of the precipitation of paraffin
constituents in crude oil. The paraffins are mostly alkanes with carbon numbers
greater than 20 [4, 5]. The precipitation of wax in crude oil flow is controlled by the
operating pressure and temperature, the crude oil compositions, the flow rate, shear
stress, and the crude viscosity. Wax precipitation occurance in pipelines can lead
to stoppage in crude flow, leading to pipeline blockage and stoppage in production.
Usually, the use of pigging, and chemical additives is preferable for wax precipitation
prevention and/or removal [5].
1.2.2 Corrosion
The deteriorating of metals due to chemical reactions between the metal and its
surrounding is known as corrosion. The rate and form of corrosion is controlled by
the type of environment (especially the presence of gaseous systems) and metals
under consideration. Pipeline corrosion is one of the main causes of subsea pipeline
failure. Therefore, it is very necessary to regulate and control pipeline condition to
effectively predict likely failures during operations [6].
1.2.3 Asphaltene
Asphaltenes are basically compounds that are polar and soluble in nature and are
present in heavy crude oil fractions. Just like hydrates, asphaltene has the tendency
of plugging pipelines and reservoir pores. Mostly asphaltene precipitation occurs as
a result of thermodynamic conditions variations, crude viscosity, and the constituent
of the crude oil. Asphaltene that contains chemical components such as carbon,
hydrogen, nitrogen, oxygen and sulfide, and has aromatic cyclic structures are chemical asphaltene. Similar to hydrates, asphaltene precipitation is more likely to occur
in an undersaturated, light reservoir fluid than a heavy hydrocarbon system. When
asphaltenes form in pipelines and reservoirs, they mostly lead to oil production
declines, higher viscosity and water–oil emulsion [7].
1 Machine Learning and Flow Assurance Issues
3
1.2.4 Scales
Scales are water forming deposits formed during oil and gas transportation. This
water forming deposits are formed as crystals from insoluble salt or oxides trapped
in the water molecules. The precipitation of scales is a result of the incompatibility
of salts molecules in owing to water their individual solubility in exceeded in water
[8, 9]. These molecules become difficult to travel out, hence they precipitate and
in severe cases lead to plugging. Similar to other flow assurance issues, scales
formation is thermodynamically driven. Other factors such as scales forming species
concentration, water quality, hydrodynamic conditions, and pH also influence scale
formation. There are several existing techniques used to reduce scale formation.
These are mechanical means, chemical dissolution and the use of scale inhibitors.
The formation and deposition of scales can lead to formation damage and production
equipment failure during the development of a reservoir. In case of waterflooding,
complex geochemical processes between the injection water, formation water,
formation rock, and the concentration of ions can cause operational challenges.
Major contribution of scale control concentrates on understanding the conditions
for scale formation and its inhibition [8, 9].
1.2.5 Hydrates
Hydrates are solid inclusion crystalline substance that are icelike in nature and are
formed at high pressure and low temperature conditions. Hydrate is formed in the
presences of high pressure, low temperature, the presence of water, and the presence
of hydrate forming molecules (gas or liquid, but most hydrate formers are gaseous)
[10–12]. Common gas hydrate formers are light hydrocarbons (e.g. methane, ethane,
propane, isobutane) and inorganic molecules such as CO2 and H2 S. Despite some
interesting potential of gas hydrates in sea water desalination, carbon capture and
storage, gas transformation and future energy source, their formation in flow assurance systems or operations are strictly prohibited and unwanted. Aside gas dominated systems, Oil dominated flowlines are proven to be prone to hydrate formation
at subsea low temperatures and high-pressure conditions, thereby leading to hydraterelated flow assurance issues. The formation of hydrates in pipelines leads to production stoppage, pipeline blockage, and in very serious scenarios lead to loss of lives
[13]. Therefore, for a smother flow assurance process, all these floe assurance related
issues need to be avoided.
4
C. B. Bavoh and B. Lal
1.3 Machine Learning Vocabulary
Since this book focuses of the use of machines learning algorithms in flow assurance,
it is proper to first understand some few machine learning vocabularies. This will set
a bases for readers to effectively follow the discussed in the rest of the book [14].
Some important machine learning vocabularies used in this book are explained as
follows.
• Algorithm: an algorithm consists of a set of commands issued to an AI, neural
network to conduct a task specific job. The part of a system or neural network that
engage in problem solving is the algorithm.
• Artificial Intelligence: It is the capability of a system to imitate human behavior.
• Artificial Neural Network: This consist of several models that learn like the human
brain in systems for solving complex problems.
• Big Data: Extremely large data sets that are analyzed to reveal patterns, trends,
and associations related to human interaction.
• Classification: This is basically algorithms used to assign categorical data set in
a software.
• Deep Learning: A subset of machine learning that uses specialized algorithms to
model, understand and mimic complex behavior structures and relationships in
data.
• Genetic algorithm: An algorithm based on principles of genetics used to efficiently
and quickly find solutions.
• Machine Intelligence: The overall concept of machine learning, deep learning,
and classical learning algorithms.
• Machine Learning: An application of AI that provides systems the ability to learn
and improve from experience without any specific instructions being programmed
into it.
• Machine Translation: Applying NLP to translate language whether via text or
voice.
• Pattern Recognition: Recognizing regularities in data and using them efficiently.
• Recurrent Neural Network: A type of neural network that identifies sequential
patterns and makes sense of sequential information.
• Supervised Learning: Output datasets provide machines with information on
producing a desirable result/ algorithm.
• Transfer Learning: A system that uses previously learned data to do a new set of
tasks.
• Unsupervised Learning: A machine learning technique where the model works
independently to discover information. As the name suggests, no supervision is
required. The most common supervised learning method is cluster analysis.
1 Machine Learning and Flow Assurance Issues
5
References
1. Bavoh CB, Lal B, Keong LK (2020) Introduction to gas hydrates. Chem Addit Gas Hydrates.
https://doi.org/10.1007/978-3-030-30750-9_1
2. Theyab M, Theyab MA, Petroleum SJ, Muhammad *, Theyab A (2018) Fluid flow assurance
issues: literature review fluid flow assurance issues: literature review view project scifed journal
of petroleum fluid flow assurance issues: literature review. SciFed J Pet
3. Gao H (2022) Flow assurance overview and challenges in oil and gas production
4. Wang J, Zhou F, Zhang L, Huang Y, Yao E, Zhang L, Wang F, Fan F (2019) Experimental study
of wax deposition pattern concerning deep condensate gas in Bozi block of Tarim oilfield and
its application. Thermochim Acta 671:1–9
5. Yang J, Lu Y, Daraboina N, Sarica C (2020) Wax deposition mechanisms: is the current
description sufficient? Fuel. https://doi.org/10.1016/j.fuel.2020.117937
6. Theyab MA (2018) Fluid flow assurance issues : literature review. SciFed J Pet 2:1–11
7. Mohammed I, Mahmoud M, Al Shehri D, El-Husseiny A, Alade O (2021) Asphaltene
precipitation and deposition: a critical review. J Pet Sci Eng 197:107956
8. Olajire AA (2015) A review of oilfield scale management technology for oil and gas production.
J Pet Sci Eng 135:723–737
9. Jamaluddin AKM, Kabir CS (2012) Flow assurance: managing flow dynamics and production
chemistry. J Pet Sci Eng 100:106–116
10. B. Bavoh C, Ntow Ofei T, Lal B, (2020) investigating the potential cuttings transport behavior
of ionic liquids in drilling mud in the presence of sII hydrates. Energy Fuels 34:2903–2915
11. Bavoh C, Nashed O, Rehman A, Othaman NA, Lal B, Sabil khalik Ionic liquids as gas hydrate
thermodynamic inhibitors. Ind Eng Chem Res 60:15835–15873
12. Nissa Rehman A, B. Bavoh C, Pendyala R, Lal B, (2021) Research advances, maturation,
and challenges of hydrate-based CO2 sequestration in porous media. ACS Sustain Chem Eng
9:15075–15108
13. Bavoh CB, Lal B, Osei H, Sabil KM, Mukhtar H (2019) A review on the role of amino acids
in gas hydrate inhibition, CO2 capture and sequestration, and natural gas storage. J Nat Gas
Sci Eng 64:52–71
14. Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learnin. MIT Press.
https://doi.org/10.1007/s00362-019-01124-9
Chapter 2
Machine Learning in Oil and Gas
Industry
Jai Krishna Sahith Sayani and Bhajan Lal
Abstract In the chapter, the use of machine learning in the oil and gas industry
is briefly presented with emphases on the current trends in the oil and gas models.
Also, the use of machine learning in the oil and gas upstream is discussed with
highlights on the recent advancement on the use of AI in the oil and gas industry.
The challenges facing the application of machine learning in the oil and gas industry
is also presented.
Keywords Machine learning · Artificial intelligence · Oil and gas industry ·
Upstream
2.1 Introduction
The oil and gas industry is a massive business that includes upstream, midstream,
and downstream sectors (Fig. 2.1). It is a difficult yet vital industry all over the
world. It has a direct or indirect influence on our everyday lives by enabling services
such as transportation, heating, and electricity, as well as various petrochemical
products ranging from household items to clothing. It’s no surprise that petroleum
is sometimes referred to as “black gold” because of its worth and value in human
existence and the global economy [1]. At the moment, OGI organisations are looking
at new methods to reduce operating costs, improve efficiency, and increase output.
The challenges and dangers connected with activities and operations across the supply
chain must be thoroughly handled, which necessitates the evolution of technology
J. K. S. Sayani
Department of Chemical and Bioprocess Engineering, University College Dublin (UCD), Dublin,
Ireland
B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar Perak, Malaysia
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_2
7
8
J. K. S. Sayani and B. Lal
inside the OGI, particularly for data processing and administration. Digitalization of
oil fields, real-time optimization of drilling operations, the use of nanotechnology,
wireless sensor networks to improve gauging, reservoir modelling, and diagnostics
are some examples of such technologies. According to industry research, there are
enough resources available to sustain present oil production levels for at least another
50 years. Thus, the key problems that the OGI has are increasing productivity and
delivering products to end customers at the lowest feasible cost. Given its worldwide
character and the complexities of contracting with governments, joint ventures, and
other stakeholders, the OGI also requires technology solutions for more effective
data management [2].
Systems for oil field exploration, reservoir engineering, drilling, and production
engineering are all part of the petroleum business. Other chemicals, such as prescription medications, solvents, fertilisers, insecticides, and polymers, are also powered by
oil and gas [3]. If fossil fuel costs continue to climb, fossil fuel businesses will need to
create new technologies and enhance operations in order to boost efficiency and build
on current capabilities. However, as the oil fields age, they are now producing more
water than oil due to waterfront arrival at shore, channeling, coning, or water breakthrough. This makes economically producing petroleum from the formation difficult.
Fig. 2.1 Breakdown of oil and gas industry
2 Machine Learning in Oil and Gas Industry
9
Furthermore, because the price of oil has not yet stabilised, fairly expensive engineering or equipment is of little interest to any oil and gas corporation. The simplest
way to conserve efficiency and productivity is to optimise cumulative extraction
with effective and smart technology, such as Inflow Control Devices (ICD) or Inflow
Control Valves (ICV) as well as downhole sensor systems. Improved control in huge
oilfields necessitates quick decision-making while keeping continuing difficulties
in mind. The Smart Oilfield will do this by constructing a comprehensive oilfield
technological infrastructure through the digitization of instrumentation equipment
and the creation of a network-based information exchange in order to improve the
production process [4].
It has become abundantly evident that digital technology has a major impact
on business and society. With the passage of time, it has become clear that digital
transformation is now regarded as the “fourth industrial revolution,” characterised
by the convergence of technologies that blur the boundaries between the physical, digital, and biological realms, such as artificial intelligence, robotics, and selfdriving vehicles. Artificial intelligence (AI) technologies are gaining popularity due
to their quick response times and powerful potential for generalization [5]. Machine
learning has shown promising results in helping and improving traditional reservoir engineering techniques in a wide spectrum of reservoir engineering difficulties [6]. Various research use advanced machine-learning methods for classification and regression issues, such as Fuzzy Logic (FL), Artificial Neural Networks
(ANN), Supporting Vector Machines (SVM), and Response Surface Model (RSM)
[7]. Several machine-learning methods utilised in reservoir engineering are within
the supervised learning category. The majority of reservoir engineering implementations include evolutionary optimization approaches such as Genetic Algorithm (GA)
and Particle Swarm Optimization (PSO).
The goal of the study should be to construct analytical processes by merging
forward and reverse-looking AI models in order to predict the accurate solution of
an inverse problem. Rana et al., for example, used forward-looking Gaussian proxy
designs, Bayesian optimization, and numerical models of high-fidelity procedures to
structure AI-assisted common platform workflows [8]. The new method is being used
to address a problem with a coal seam degasification effort from the past. Bayesian
optimization It may discover a variety of reservoir characteristic distribution solutions
that suit the provided field data [9]. The authors also created a specialty approach
based on ANN that uses field data from a part of the Marcellus shale gas field to help
the history-matching method. It evaluated several hydraulic fracturing designs. Costa
et al. employed ANN models and optimization programming to tackle a contextappropriate oilfield problem [10]. Throughout this method, forward-thinking ANN
expertise systems are outfitted to replicate quantitative high-fidelity simulations in
order to anticipate output data throughout the historical field period. Indeed, machine
learning is employed in the petroleum business to investigate data-related issues.
The educational programme was created to teach petroleum engineers via the use of
machine learning algorithms and artificial intelligence tools. This gives suggestions
for increasing productivity while lowering costs [3].
10
J. K. S. Sayani and B. Lal
The Gulf of Mexico oil and gas sectors have explored the impact of technology
transition in oil and natural gas inspection using a specific collection of data types in
a microstructured grid. The findings indicate that the technical shift with adaption of
this technology has played a significant role in the offshore oil and gas business during
the previous 50 years, with increased deposits and decreasing costs. Even while saturation impact remained important in the first two decades, its influence on technical
advancement was adequate to compensate for the capital loss of more than 50 years of
research [11]. Improving knowledge for future technological improvements through
testing and innovation may result in improved predicting strategies for delivering oil
and gas. Despite the fact that significant advances are possible, developers are testing
field as well as regional-level systems to understand the consequences of changes
in exploration technology. The progress of technology effects the discovery of new
deposits, which are economically feasible sections of existing deposits, as well as the
rental of capital; our research is critical for estimating mineral resources across the
government revenue accounts [12]. Aside from technological advancements, certain
circumstances may have a significant impact on the cost efficiency of the Clean
Electricity Production from Offshore Natural Gas (CEPONG) framework [13].
Sami and Ibrahim investigated three different machine learning algorithms in order
to anticipate multiphase flowing bottom hole pressure [14]. The model is constructed
and tested using real-world data from an open literature collection. To evaluate the
procedure accurately of the BHPs obtained using ML models and verify the work’s
usefulness, a range of datasets were employed to check the correctness of the recommended models. Hazbeh et al. examined the precision and computational performance of machine learning techniques for rate of penetration in directional well
drilling [15]. Hassanvand et al. estimated the rock uniaxial strength characteristics
for an Iranina carbonate oil deposit using an artificial neural network [16]. Priyanka
et al. did a review study on cloud computing-based smart grid technologies in the oil
pipeline sensor network system [17].
The understanding of block chain technology in the oil and gas industry answers
the opportunities, obstacles, risks, and developments that are analysed in this area.
Block chain technology will provide several benefits to the whole oil and gas industry,
including reduced payments and increased accountability and performance. The
evolution of block chain technology in the oil and gas sector would then move to
a modified block chain network, cross-chain, and modified smart contracts, as well
as more interdisciplinary expertise [18, 19]. Casing drilling technology; modern
innovations, enhanced oil recovery; synthetic, thermic, physical, and chemical techniques Microbial Enhanced Oil Recovery (MOER) and water alternating gas (WAG)
processes are examples of technical changes brought about by the implementation
of the block chain method in this sector.
This Chapter describes cutting-edge research on the application of Machine
Learning and AI approaches in the oil and gas upstream business. The primary goal
of this article is to demonstrate the benefits of AI and machine learning approaches
in various upstream areas. The study describes procedures that use machine learning
and AI for effective computation and decision making based on a thorough understanding of this industry. This research examines how a handshake between the
2 Machine Learning in Oil and Gas Industry
11
petroleum industry and a numerical simulator with an intelligent system facilitates
work and increases production.
2.2 Machine Learning in Upstream
The term “upstream” refers to the production of crude oil and natural gas. It entails
locating possible underground or underwater crude oil and natural gas deposits,
drilling exploratory wells, and then drilling and operating the wells utilised to bring
the crude oil or raw natural gas to the surface. The upstream segment is of special
significance since it is the most capital-intensive and crucial of the three segments
in the oil and gas industry. The increase in data processing capacities improves the
performance of electronic gadgets. Computing power should be used for production
and exploration in the oil and gas sectors [20].
Companies in this industry deal with large uncertainties that are managed manually and rely on specialist knowledge rather than real facts. The adage “one rock, two
geologists, three viewpoints” encapsulates the enormous uncertainties and hazards
that oil and gas firms face. When making multibillion-dollar decisions on where
and how to spend over the next 5–20 years, the uncertainties must be addressed.
Nonetheless, despite the sector’s complex and unpredictable management difficulties, single-criterion techniques have historically dominated decision-making [21].
To account for uncertainties associated with practitioners’ subjective perception and
decision-making based on experience, the initial steps in employing artificial intelligence and machine learning, which are becoming increasingly popular, are made
in the upstream.
The report draws on findings from hundreds of AI initiatives completed with the
authors’ assistance for top oil and gas upstream firms throughout the world over
the previous three years. The programmes encompassed AI solutions for the whole
spectrum of upstream activities, including reservoir geological evaluation, drilling
optimization, reservoir engineering/field development, and production optimization.
More information may be found in Table 2.1.
The domination of “difficult-to-recover” oil and gas reserves during the last ten
years necessitates new operational approaches and economic models in hydrocarbon
exploration and production, aimed at assuring acceptable oil and gas production profitability. Both well-developed (brownfields) and recently discovered (greenfields)
subterranean hydrocarbon reserves fall within this category. Despite the fact that the
vast majority of brownfields are reasonably large geometrically and have adequate
transport and storage qualities (porosity and permeability), the volume of oil and gas
recoverable with cheap waterflooding is fairly little. All traditional brownfields, in
theory, produce more water than oil. To maintain production levels, operators must
spend enough money on one of the following operations: additional drilling, well
treatment (e.g., hydraulic fracturing), or field-scale enhanced oil recovery procedures
(e.g., increasing the mobility of remaining oil in the reservoir with chemical cocktails
12
J. K. S. Sayani and B. Lal
Table 2.1 Non-confidential overview of projects completed with the authors’ direct involvement
Upstream
activity
Developed tool
Geological
assessment
A tool for
automatically
mapping
reservoir rock
parameters over
an oil area
AI approach
Non gradient
optimization +
interpolation
techniques
Main effect
Acceleration
De-risking
Speeded up the
manual mapping
procedure from
several weeks to
several seconds
Removing human
mistakes that cause
incorrect mapping
Means a more
precise
determination of
the proper
hydrocarbon targets
Geological
Gradient
information may boosting
be extracted from
well records
using this tool
100 + times
speedup
Rock typing
software based
on photographs
of rock samples
taken from wells
~1.000.000 +
times speedup
Deep neural
networks
Drilling
Detects the kind Combination of
of drilled rock
machine learning
and likely failure algorithms
using real-time
drilling telemetry
Up to 20% time
saving and up to
15% money
savings at well
construction
Reservoir
engineering
A tool for
speeding up
traditional
reservoir
simulations
Deep neural
networks
Accelerating by a Making it feasible
factor of 200–2000 to filter through a
significantly larger
number of field
development
scenarios in order
to find the best one
Production
optimization
Data-driven
technique for
predicting the
efficacy of well
treatment
initiatives
objectively
Gradient
boosting +
expert-based
feature selection
100 + times faster
estimation of the
well treatment
effect
Increasing the
amount of contact
between the
wellbore and the
pay zone
Up to a 20%
increase in the
marginality of
campaign
investments
injection). In many situations, the money spent on these activities does not pay off,
allowing brownfields to die slowly.
The situation isn’t much better in terms of new discoveries. Almost all
of the recently discovered hydrocarbon sources are difficult to access. It’s possible
they’re [22]:
• In harsh environments (e.g., the Arctic shelf)
• Complex in geometry (e.g., thin and winding layers of oil-saturated rocks with
many cracks)
2 Machine Learning in Oil and Gas Industry
13
• Under a thick layer of seawater and salt minerals (e.g., offshore Brazil)
• Poor in permeability (so that the hydrocarbon is nearly immobile within the
reservoir rock).
The development of these greenfields necessitates costly technologies, putting the
profitability of future oil production in jeopardy.
Decision-makers manually and based on specialist knowledge, not actual data,
handle uncertainties associated to long-term and high-value investments in the oil and
gas upstream. In this situation, they must respond to two major questions. First and
foremost, is this a unique asset perspective? Will we spend money on geophysical
research to evaluate the asset’s potential? This topic is often answered through a
geological modelling and reservoir modelling workflow, which can take anywhere
from a few months to several years, depending on the need for further geophysical
surveys and the complexity of in-house techniques. The second question is whether I
should invest in increasing oil output at my asset. If that’s the case, which technologies
are worth investing in? Experts are mostly in charge of this issue, and they are backed
up by evidence. By using traditional reservoir modelling tools, we can get a good idea
of how much water is in the reservoir. A strong reliance on professional opinion, as
well as a lack of suitable input data for the traditional modelling techniques produce
skewed and ambiguous results.
AI systems that have been trained with the appropriate field data can help with both
questions by speeding up the asset assessment process and making it more objective
or expert independent. The next part discusses the first steps in this direction as well
as future potential.
2.2.1 Exploration
Exploration for hydrocarbons is fraught with danger. Explorationists must accurately
identify subsurface potential in order to drill and utilise hydrocarbons. Limited 2D
seismic data were used in the early twenty-first century to pinpoint drilling locations
based on subsurface mapping. Because it is fraught with danger, the odds of success
were 1:7. With the passage of time, new data was collected in each of the leases that
were curved out for investigation. With advances in seismic and well data gathering,
processing, and interpretation, this vast volume of data was dubbed “big data,” and
it was stored in Terabytes of memory space. The machine learning idea was used
to analyse the vast data. The goal of using big data and applying machine learning
during collecting and processing is to enhance the signal to noise ratio. Using several
robust methods, the clean data was used to interpret 2D, 3D, and 4D seismic. By
combining well logging with accurate mapping of multiple subsurface horizons, an
interpreter was able to prepare subsurface volume maps and turn them into amplitude,
porosity, and saturation maps. To interpret the data parameters from the subsurface
models, inversion techniques were used [22].
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Machine learning techniques assisted in the construction of horizon and window
based features in order to grasp the sweet spots over time. Machine learning has
produced new qualities such as coherency, edge map, spectral decomposition, and
relief map. Understanding fault polygons, mapping complex fault structures, and
facies mapping utilising striatal slicing increased subsurface prospect understanding.
Prospects were converted into drillable prospects using machine learning algorithms, increasing the possibility of success to 1:3. The use of 4D seismic or repeat
seismic assisted an interpreter in determining the hydrocarbon transport following
drilling [23].
Artificial neural networks and heuristic methods are increasingly widely used to
refine target prospects, their size, and hydrocarbon volume. Monte Carlo simulation
and Evolutionary programming techniques are used to calculate a stochastic range
of hydrocarbon in the subsurface and how much can be extracted and brought to
the surface. In short, machine learning has ushered in a paradigm shift in India’s
and the world’s exploration and production regimes. The use of AI in the oil and
gas business is fast evolving, as the concept of AI pervades several stages of the
industry, including intelligent drilling, intelligent development, intelligent pipelines,
intelligent processing, and so on, and it will become a promising research path.
Using artificial intelligence algorithms, developers have built a spectrum of realistic
application technologies in research and production. Using artificial intelligence
algorithms, developers have built a spectrum of realistic application technologies in
research and production.
The adoption of the ANN technique has already generated significant results in
terms of minimising exploration risks and raising exploration well success rates in
the field of exploration. Drilling quality has substantially increased and expenses
have decreased because to new drilling equipment such as an automated drilling
rig and an intelligent drill pipe. The most common use of AI technology in oilfield
development is to refine the development plan based on past production data [24, 25].
A regression model can be used to investigate field planning and well site planning.
Unsupervised learning can be used to understand data characteristics. Kumar et al.,
proposed a framework that was shown to be beneficial for shales due to its ability to
manage massive amounts of data. The linearized rock physics inversion method can
be used to tackle the problem of rock physics. Although this model can yield correct
physical parameters, it is ineffective for highly nonlinearized rock physics. The
suggested machine learning approach can generate accurate and cost-effective well
logs, it can be concluded. When compared to the Long Short Term Memory Method,
the Shift Window method can provide better pressure prediction (LSTM) [26, 27].
Diersen et al. employed artificial intelligence to reduce human effort in seismic
whole wave tomography processing and analysis. This is accomplished through the
use of artificial intelligence and the Complex Wavelet Transform (CWT). CWT is a
wavelet-based transformation that aids in the understanding of waveforms’ time and
frequency domains. Inside the full-wave tomography technique, an Artificial Neural
Network and a Knowledge based Artificial Neural Network can be employed to pick
suitable seismic window pieces [28].
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2.2.2 Reservoir Engineering
Fluid flow through porous medium, production forecasts, and field optimization are
all aspects of reservoir engineering. Preparing subsurface property maps and PVT
analysis necessitates numerical simulations, modelling, and experiments. To prepare
static and dynamic models, modelling is done on large amounts of data. For appraisal
planning and stochastic field development plans, data from seismic, well log, core
analysis, and past reservoir performance are combined using machine learning algorithms. Artificial Neural Network, Genetic Algorithm, Response Surface Model
(RSM), and other algorithms are used to perform complex pressure transient analysis
and deconvolution of pressure data. These GA models are highly useful for matching
reservoir histories and preparing P90, P50, and P10 production profiles following
Project Resource Management Systems principles (PRMS). Large amounts of data
are used to create reservoir maps, which are refined repeatedly as new data is added
to the database.
For many years, ANN has been used to estimate reservoir parameters such as
permeability and porosity. To forecast reservoir fluid properties, many machine
learning approaches such as K Nearest Neighbours (KNN), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Adaptive Boosting, and Collaborative
Filtering can be used. Onwuchekwa discovered that collaborative filtering, which
was originally built for a consumer product recommendation system, worked well
in their reservoir study [29]. The synthetic reservoir model can be used to simulate
reservoir oil numerically. Teixeira and Secchi employed an optimization technique
to find the best control for increasing total oil production [30]. The parametric study
compares different machine learning algorithms for predicting permeability, seismic
characteristics, and wireline data. In terms of permeability prediction, the Superior
Vector Mechanism (SVM) outperformed other approaches. Anifowose et al., developed an intelligent model based on injector wells that used the Extreme Gradient
Boosting method to forecast reservoir response. In a heterogeneous reservoir with
complex topography, Nwachukwu et al. chose five cases: homogeneous reservoir
water flood, channelized reservoir water flood, 20-model ensemble water flood, and
CO2 flood [31].
2.2.3 Drilling Engineering
Drilling has a number of issues, including stick sleep vibrations, loss of circulation,
bit wear, excessive torque, borehole instability, and so on. Machine learning has
the ability to solve these issues [32]. Aliouane and Ouadfeul suggested a machine
learning approach for generating a poisson’s ratio map, which is beneficial for determining drilling direction and rock features [33]. Castineria et al. used the machine
learning method to verify the quality of huge drilling data, retrieve critical information, and anticipate non-productive time [34]. This strategy saved money by reducing
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the amount of time it took to assess the quality of vast amounts of drilling data. The
Byesian network (BN) can be used for Managed Pressure Drilling (MPD) and Under
Balanced Drilling (UBD) operations in deep sea drilling. According to Bhandari
et al., the BN may be utilised efficiently for risk analysis and failure prediction in
the offshore business [35]. Automation was used to control drilling parameters such
as the Weight of Bit (WOB), Rotary Speed (RPM), and Rate of Penetration (ROP).
A machine learning method can collect information such as alternative bit or rig
equipment upgrades, estimated abrasive wear, and predicted bit wear [36].
2.2.4 Production Engineering
The use of advanced machine learning technologies results in a revolutionary workflow that relieves the burden on engineers. In the oil and gas industry, machine
learning has a variety of applications in production engineering. One of the most
difficult tasks is analyzing massive amounts of data in a short amount of time in
order to make decisions. Machine learning technologies can be used to recognize
production patterns in data. With active learning, Subrahmanya et al. were able to
obtain the data point with the maximum information value. With semi-supervised
learning, data from wells was integrated from labelled and unlabeled sources. Algorithms were used to check, verify, and recover the data. The study looked at well
logging data rectification, quality control of physical and chemical fluid parameters, and separation between base production and well interventions [37, 38]. With
the help of data patterns, the ANN model can forecast closure pressure. To reduce
mistake, the output data is usually compared to the real outcomes. According to
Nande, an ANN model is capable of accurately predicting closure pressure [39].
Shen et al. employed the Support Vector Regression Model to forecast wrinkling in
mechanically lined pipelines [40]. The importance of edge analytics for the oil and
gas industries was explained by Saghir et al. For electric submersible pump driven
wells, edge analytics was used to detect anomalies in real time [41].
Another key use in the oil and gas business is the Continuous Integration/Continuous Deployment (CICD) methods in machine learning. An advanced
CICD should contain a precise and repeatable Machine Learning (ML) workflow
with tracking, model lineage, and version control systems. This is especially useful
in recognizing conceptual drift, which occurs when the performance of a statistical
model degrades over time due to changes in data and previously modelled input–
output relationships [42]. Another key use in the oil and gas business is the Continuous Integration/Continuous Deployment (CICD) methods in machine learning. An
advanced CICD should contain a precise and repeatable Machine Learning (ML)
workflow with tracking, model lineage, and version control systems. This is especially useful in recognizing conceptual drift, which occurs when the performance of a
statistical model degrades over time due to changes in data and previously modelled
input–output relationships. By employing LiDAR to create 3D point clouds and
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analytics for plant building, experts can predict the behavior of a structure and calculate its maintenance needs, considerably prolonging its lifetime. These simulations,
on the other hand, are fairly “static,” in the sense that they don’t account for all
of the changes in an asset’s actual, real-world physical variables that can affect its
performance over time. To combine data from IIoT sensors concerning actual environmental loads with a virtual duplicate of the asset, new control methods have been
developed.
2.3 Machine Learning Advancements in the Oil and Gas
Industry
Companies are actively pursuing creative techniques to be more efficient through
simplifying production, cutting costs, and enhancing worker safety, among other
things, as the oil and gas business grows more competitive and unpredictable. Many
CEOs are turning to digitization to protect businesses from market shocks, stay profitable at reduced oil prices, and gain a competitive edge during the rebound. Artificial intelligence (AI) and machine learning-based technologies, which are rapidly
maturing and being deployed across the value chain, are the way forward. Numerous
sectors have recognised the advantages of these new technologies, and we will
continue to see more AI applications created in the future. Let’s look at some real-life
AI applications in the oil and gas sector.
2.3.1 Total S.A. With Google Cloud–Optimize Subsurface
Data Analysis
Before and after drilling into the Earth, oil and gas companies must collect and analyse
a large amount of data. They need to be able to solve complicated exploration and
production challenges before they waste money drilling into an unproductive well to
increase efficiency in day-to-day operations. In 2018, Total S.A., a French oil and gas
firm, teamed up with Google Cloud to create AI solutions that improve subsurface
data processing for exploration and production. If you go back a few decades, you’ll
see that Total is no stranger to AI implementation. In the 1990s, the business began
using AI and machine learning algorithms to describe oil and gas areas. In 2013, they
deployed predictive maintenance technologies for turbines, pumps, and compressors,
which resulted in hundreds of millions of dollars in savings. With Google Cloud,
they’re taking it to the next level. Their combined technologies will make it possible
to use computer vision to understand subsurface images from seismic studies.
Furthermore, their AI solutions will use natural language processing to automate
the study of technical texts. Total will be able to explore and analyse oil and gas areas
significantly faster and more effectively as a result of these solutions.
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2.3.2 ExxonMobil and MIT Collaborate to Detect Oil Seeps
with AI-Powered Robots
ExxonMobil is one of the most well-known oil and gas companies in the world. They
also put their money into some interesting AI projects. The industry behemoth partnered up with the Massachusetts Institute of Technology (MIT) in 2016 to develop
artificial intelligence (AI) robots for ocean research. One of the major members of this
deep-sea endeavour is Brian Williams, an MIT professor and a core designer of the
software for NASA’s Mars Curiosity Rover, adding to the cool factor. ExxonMobil
intends to employ this deep-sea AI robot to improve its natural seep detection capabilities. Natural oil seeps from the bottom are the most common source of oil into the
world’s oceans, accounting for over half of all oil discharged each year, according to
the National Oceanic and Atmospheric Administration. ExxonMobil’s AI-powered
robots will be able to detect these oil seeps, reducing the risk of exploration and
minimising the impact on marine life.
Researchers and engineers from ExxonMobil are partnering with MIT’s Computer
Science and Artificial Intelligence Laboratory (CSAIL) to build self-learning,
submersible artificial intelligence robots for ocean subsurface exploration. The
robots’ programming, or “intelligence,” will allow them to perform independently
in severe environments like those encountered on Mars, as well as modify mission
parameters on their own to examine unforeseen anomalies. Observing the waters,
charting deep areas, researching how they evolve over time, and assessing their state
would be a promising application for the new technology.
2.3.3 Shell—Machine Learning Algorithms for Precision
Drilling
Shell is another corporate behemoth that is experimenting with AI applications.
Shell is utilising reinforcement learning to operate its drilling equipment this time
around, effectively implementing a reward system based on the AI’s decisions. For
example, to drive the drill into the subsurface, a machine learning model is trained on
historical data from Shell’s comprehensive drilling records, as well as simulations. It
also considers data from seismic surveys, as well as temperature, pressure, and other
drill bit data points. The geosteerer, or the person running the drilling machine, can
then provide feedback via reward or punishment functions, allowing the machinery
to adjust to changing subsurface conditions. This allows the geosteerer to have a
greater understanding of the area in which they are working, resulting in faster, more
precise results and fewer machinery damage.
However, innovation does not end there. Shell is continuously on the lookout
for major ideas that will push the oil and gas industry’s boundaries. Shell conducts
regular calls for AI proposals centred on machine learning from people and startups all across the world through their Shell Game Changer project. Shell is leading
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the way in helping to tackle some of the industry’s biggest difficulties, whether it’s
investing in these ideas or simply partnering on a project.
However, innovation does not end there. Shell is continuously on the lookout
for major ideas that will push the oil and gas industry’s boundaries. Shell conducts
regular calls for AI proposals centred on machine learning from people and startups all across the world through their Shell Game Changer project. Shell is leading
the way in helping to tackle some of the industry’s biggest difficulties, whether it’s
investing in these ideas or simply partnering on a project. Algorithms for guiding
drills through the subsurface are being created utilising data from Shell’s drilling
history as well as data from simulated investigations. Mechanical data from the
drill bit, such as pressures and temperatures, are included, as well as data from
subsurface seismic research. As a result, a Shell geosteerer, the drilling machine’s
human programmer, is capable of comprehending the circumstances in which they
are working, resulting in faster results and less wear and tear on machinery.
2.3.4 Aker BP and Spark Cognition—Predictive
Maintenance Increases Productivity
Unplanned downtime can be a costly nightmare for offshore oil and gas platforms,
with catastrophic asset breakdowns costing as much as $2e3 million in a single
day. Too many businesses still rely on antiquated ways, causing some to place a
greater emphasis on data and analytics when making maintenance choices. Aker BP,
a Norwegian independent upstream oil and gas firm, teamed with Spark Cognition
to implement an AI-powered predictive maintenance solution to their unmanned
Tambar platform, where problems with a crucial multi-phase pump cause a large
amount of unplanned downtime.
Spark Cognition built and integrated a multi-phase pump normal behaviour model
into its AI-powered predictive maintenance software, which subsequently flagged
deviations from normal subsystem behaviour. The AI programme notified Aker BP
operators and SMEs to a probable multi-phase pump trip caused by a deteriorating
seal over the course of six months, with previous failures costing over $10 million
in lost production. Aker BP and Spark Cognition were able to avert pump failure,
resulting in hundreds of thousands of dollars in additional production for each day
of downtime avoided.
Spark Cognition built and integrated a multi-phase pump normal behaviour model
into its AI-powered predictive maintenance software, which subsequently flagged
deviations from normal subsystem behaviour. The AI programme notified Aker BP
operators and SMEs to a probable multi-phase pump trip caused by a deteriorating
seal over the course of six months, with previous failures costing over $10 million
in lost production. Aker BP and Spark Cognition were able to avert pump failure,
resulting in hundreds of thousands of dollars in additional production for each day
of downtime avoided. SparkPredict uses machine learning techniques to analyse
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sensor data in order to detect inefficient processes and impending issues before they
occur. Installing SparkPredict on Aker BP’s offshore production platforms will boost
productivity and efficiency, furthering the company’s goal of providing unrivalled
value to its clients around the world.
2.4 Challenges
Companies To reduce uncertainty, the first step is to develop a system that can handle
multiple hypotheses in order to arrive at an optimal solution. Anifowose et al. developed an effective AI and machine learning technique to tackle this challenge. Hybrid
Intelligent System (HIS) was created to address this issue in machine learning. It had
been demonstrated that the HIS has enormous potential for improving oil field reserve
estimates, resulting in better finding, considerably more efficient extraction, wider
development, and highly productive use of energy supplies [43]. Given the current
state of the oil industry, machine learning appears to have become more widely used
in the previous five years, particularly in the alleviation of drilling issues in real
time, as well as in oil drilling automation and technology. Machine learning has also
shown promise in achieving higher rates of penetration (ROP) and lower CPF levels,
as well as many other performance metrics like drilling 10,000 m per day [32].
Hawedi et al. proposed a data-driven framework for evaluating well performance
in two scenarios: predicting only current well performance and forecasting future well
performance [44]. The entire method is much more detailed than the step-by-step
regression evaluation in that it includes additional data sources such as geological
map details, output restrictions such as tube head pressure, and positions that represent dynamic reservoir characterisation of non-traditional wells without requiring
a current model [45]. Machine learning (ML) will significantly improve oil exploration and interpretation of seismic data, as well as develop extraction strategies to
make it more efficient. The biggest issue confronting the oil industry today is the
environmental risk associated with both oil extraction and production. However, the
goal is that other systems will be constructed using modern technical approaches that
are considerably more environmentally sustainable. Although the Artificial Neuro
Fuzzy Inference System (ANFIS) improves performance slightly, the prediction is
not necessarily changed when ANN is used because the neural network is already
capable of providing a genuine working formula [46].
While certain oil and gas firms, such as ONGC, OIL, Reliance, and Shell, are
accelerating their AI activities by investing heavily in startups and R&D, many
obstacles are keeping them from implementing AI in the exploration and production
of oil and gas on a large scale and quickly. This isn’t a problem exclusive to the oil
and gas industry; it’s a regular occurrence in the early stages of AI’s development.
The main problems, according to studies, are related to the type of people the
sector requires, the value of data, and the necessity for open communication. These
three difficulties will be explained further down.
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2.4.1 Manpower
Artificial intelligence’s success is inextricably linked to human intelligence. AI solutions aren’t general, and they can’t be purchased off the shelf. Even when produced
by third parties, AI solutions must be tailored to a company’s business context and
database. Companies must build in-house teams of data and AI specialists to actively
use AI in processes and products. These groups should be able to assist in the development of AI infrastructure (algorithms and datasets) and, at the very least, customise
tools that businesses will use in the future. That means that, in the next ten years,
oil and gas companies will become (partially) data-driven, and AI specialists will
become indispensable in supporting practically all innovation efforts in the industry.
Finding and keeping AI talent, on the other hand, is a difficult undertaking. There is
a severe scarcity of AI talent on the employment market, and with more corporations
investing in AI and developing their own AI departments, the outlook for the next
decade is bleak. This is particularly true for oil and gas firms.
Next, in order to compete with tech giants like Google, Yandex, IBM, and Amazon
for the same talent, oil and gas businesses must combat negative opinions regarding
fossil fuel industries by partnering with great institutions and cool startups throughout
the world. That is neither a simple nor a cheap task.
Despite the fact that AI’s entry into the oil and gas business heralds “the end of
petroleum engineering as we know it,” petroleum engineers will not vanish. The only
thing that will change is their function and the ability that will be needed of them. To
succeed in the AI era, oil and gas firms will need petroleum engineers with a good
understanding of data science and the capacity to identify and design jobs that can
be addressed by AI, in addition to data scientists. Their job will be to make sure that
the relevant challenges for AI are recognised, that the right data is collected, and that
solutions are tailored to the physical and process realities.
This will become increasingly important over time, as otherwise, the wrong questions may be asked, and existing human errors may be compounded, as was the
case with Google’s mammogram-based breast cancer screening system. As a result
of the use of AI, it is not simply data science and AI skills that are in need, but a
new way of thinking about the difficulties that oil and gas firms face, anchored in a
thorough grasp of the processes and the underlying logic of activities. As a result,
petroleum engineers’ new job will become increasingly important. Some universities, such as Russia’s Skolkovo Institute of Science and Technology and the United
States’ West Virginia University, have already begun to implement specific educational programmes that combine data science with petroleum studies to prepare the
future generation of petroleum engineers.
Petroleum engineers will need to learn how to interact with AI assistants, which
are akin to Alexa and Siri but focused on industry applications, in addition to working
with data and data scientists. The goal in these new partnerships will be to combine
the best of both sides’ capabilities: AI’s capacity to deal with large amounts of data,
detect patterns and relationships, and petroleum engineers’ deep industrial domain
knowledge. Although AI is projected to mostly be utilised by humans to supplement
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rather than replace their decision-making abilities, this will be a difficult undertaking
because numerous questions about trust and the fear of losing jobs may arise. There is
also an unresolved issue with people’s legal perspectives on AI’s recommendations.
There may be instances where an AI tool proposes a course of action that results in
a loss of money, output, or even serious health or environmental problems.
In this instance, there is no clear understanding of who is responsible for what:
the AI algorithm, the AI algorithm user, or the AI algorithm developer. With the
advancement of AI capabilities, this subject will become increasingly relevant. As a
result, the legal foundation will be established in simultaneously. According to the
practise, the algorithms and their developers are not liable, but the decisionmakers
who receive advise from AI and AI users bear accountability. As a result, in order
to take advantage of the chance to greatly expand decision-making capabilities,
businesses will need to develop not just AI strategies, but AI strategies as well.
2.4.2 Data Availability
To be taught and subsequently perform successfully in the operational mode, AI
systems require high-quality data in a sufficient volume. While smarter algorithms
may aid in obtaining better results from datasets of restricted size, there is no way to
improve faulty data. As a result, access to large amounts of high-quality data is both
an advantage and a barrier to the development of AI applications. Large volumes
of raw data are generated in oil and gas sectors. Even said, there are acknowledged
challenges with the quality and accuracy of field data, as well as a general shortage
of huge volumes of labelled data in the oil and gas business, so it is not a guarantee
of success. Training datasets must be properly acquired over the course of a multiyear procedure that follows a well-planned methodology and is situation-specific.
Oil and gas firms will have to adapt and adjust their organisational structures and
procedures to increase the value of the data they have or can access. Technical efforts
in enhancing AI systems and their subsequent practical use in oil and gas exploration
and production are driven by data difficulties (across industries, not only in the oil
and gas industry).
2.4.3 Opportunities and Facilities for Collaboration
Artificial intelligence emerges in an open and collaborative environment as a result
of academia’s decades of leadership in AI research, almost free of commercial influences. This spawned a culture of open sharing and publishing, which businesses
from all industries (and all corners of the globe) were forced to adopt as a standard
in order to compete in the AI era. While open innovation is becoming the norm
in the digital industry, oil and gas corporations aren’t known for their collaborative
industry projects, especially between competitors and especially in crucial fields like
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23
artificial intelligence. Despite the fact that many organisations have announced the
open-source of some of their data and assert the importance of cross-company and
cross-border data exchange, the reality is currently negative.
The oil and gas industry in the United Kingdom One of the first significant open
data releases in the oil and gas industry was the National Data Repository. It holds
130 gigabytes of geophysical, infrastructure, field, and well data, with approximately
12,500 wellbores, 5000 seismic surveys, and 3000 pipelines represented (Oil and Gas
Authority, 2019). On the basis of accessible data, potential for machine learning and
artificial intelligence applications are outlined. Oil and gas firms should reconsider
their methods for partnering and interacting with universities because university labs
are another significant source of novel AI technology and AI skills.
2.5 COVID-19’s Impact on the Oil and Gas Industry,
and AI as a Solution Companies
Lower crude prices and geopolitical difficulties are resulting in excess supply and
some major industry advances as the oil and gas sector enters a new normal of
pandemic condition.
Despite the fact that consumption is likely to rise as the world recovers from the
epidemic and normalises its relations and output quotas, industry participants must
be flexible enough to adjust to the new reality. They need to focus on strengthening
their supply chain and activities in order to reduce manufacturing, distribution, and
transportation expenses.
Artificial intelligence (AI) has the potential to transform the value chain in
the oil and gas industry. Artificial intelligence (AI) models are frequently used as
isolated point solutions with little overall benefit. As benefits begin to plateau fast,
dissatisfaction with performance has an impact on future plans.
As it incorporates crossdomain data, the industry continues to focus on different
aspects of reservoir, geology, geophysics, engineering, and drilling. With a single
staff in charge of all geotechnical needs, these divisions were developed to boost
productivity across the organisation. This operational division, which was established
in the past to meet cost-cutting requirements, precludes the oil and gas industry from
implementing broader cross-functional AI applications.
2.6 Summary
This chapter presents examples of machine learning applications in exploration,
reservoir, drilling, and production. The oil and gas business, according to the literature analysis, is well-positioned to gain from machine learning because of its ability
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to process large amounts of data quickly. Throughout this work, a variety of monitored learning approaches have been defined and described. Machine learning has
the ability to fundamentally alter the myriad vital decisions made by administrators
and engineers in the oil and gas industry on a daily basis. If proper approaches are
employed to incorporate diverse data kinds or structures and convert them into valuable information that leads to intelligent judgments, the future benefits of information
can be realised. Many of these methods, which use ANN, ALM, supervised learning,
fuzzy logic, linear regression, and PCA, may be implemented to address numerous
challenges in the oil and gas industry and aid in the development of successful strategies. Machine learning’s use is expected to grow fast in the future years, and its worth
will be widely recognised in the oil and gas industries.
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Chapter 3
Multiphase Flow Systems and Potential
of Machine Learning Approaches
in Cutting Transport and Liquid Loading
Scenarios
Muhammad Saad Khan, Abinash Barooah, Bhajan Lal,
and Mohammad Azizur Rahman
Abstract Multiphase flow is a primary concern in flow assurance applications,
mainly dealing with cutting transport, hydrate formation, and liquid loading issues.
The conventional prediction model includes empirical equations and complex
physics-based models; however, they are limited within the experimental ranges.
Due to complex physics and the limitations of numerical methods, new techniques
of collecting and evaluating multiphase behavior in these pipelines is essential, which
is reviewed in this chapter. The review covers the overview of different multiphase
systems, followed by cutting transport and existing models for accurate prediction of
cutting transport. Also, the available literature on machine learning applications in
cutting transport is included in it. Moreover, the chapter also demonstrates a liquid
loading issue and their available prediction methods. The available literature on
machine learning in liquid loading applications is also part of this chapter. The final
part of the chapter includes the case studies of machine learning in multiphase flow
systems to provide the field applicability of this modern prediction technique which
can provide an avenue for future applications.
Keywords Cutting transport · Flow regime maps · Liquid loading · Machine
learning · Multiphase flow
M. S. Khan · B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
A. Barooah
Petroleum Engineering Department, Texas A&M University, College Station, USA
M. A. Rahman
Petroleum Engineering Department, Texas A&M University at Qatar, Ar-Rayyan, Qatar
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_3
27
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M. S. Khan et al.
3.1 Introduction to Multiphase
Two-phase flow is the interaction of two distinct phases in a channel that share
common interfaces, with each phase representing a mass or volume of matter. Both
phases are capable of coexisting as solids, gases, or liquids. Although three-phase
multiphase flow occurs, the bulk of multiphase engineering applications uses a twophase flow. Planning, development, and optimization are required for flow in any
channel. Based on the interfaces generated between the phases, predicting the flow
phases and flow regimes is critical, i.e., typical flow patterns. Based on the flow rate,
channel size, and operating conditions, this knowledge allows for predicting pressure drop and heat transfer characteristics. The appropriate flow parameters can be
determined based on the pressure drop data to reduce the occurrence of corrosion,
erosion, or scale development, all of which can lead to excessive friction. Predicting
flow regimes, such as oil and gas flow in pipelines or chemical process industries, allows avoiding excessive gas pressure buildup, meltdowns, and potentially
hazardous explosions. The results of heat transfer analysis can be utilized to design
flow channels and operating conditions and forecast and avoid flow instabilities.
The heat transfer community and practicing engineers are predominantly interested in two-phase flow, which involves phase transition between a single substance’s
liquid and vapor phases. Two-phase flow refers to the fluid flow of a two-phase
mixture, which can be (1) liquid–vapor flow, (2) liquid–liquid, (3) liquid–solid particles, or (4) gas–solid particles. This category includes forced convective condensation
and boiling. Solving two-phase flow and heat transfer problems is extremely difficult
because each phase has its own properties, velocity, and temperature.
The conveyance of multiphase well fluids is extremely prevalent in the oil industry.
When the fluid from an oil and gas well reaches the wellhead, it forms a three-phase
(oil, water, and gas) or two-phase (oil and gas) mixture. A single-phase liquid or gas
flow at the wellhead is an extreme example.
The multiphase fluid flow analysis is used to mimic the behavior and interactions
of two fluid phases. The study uses the Volume of Fluid (VoF) method, a typical
multiphase system computing method. Because of the variety in flow regimes, multiphase flow is substantially more difficult than single-phase flow (or flow patterns).
Fluid distribution varies dramatically throughout flow regimes, influencing pressure
gradients inside and outside the drill string.
3.1.1 Gas–Liquid Flow Systems
Gas–liquid systems are often encountered in gas wells where produced water
increases with increased production, causing serious production threats like hydrate
formations [1–9] and liquid loading [10–13]. Flow Regime maps are handy tools for
understanding the flow behavior that might be expected for a given set of input data.
Each map, however, is not broad enough to apply to other data sets. It describes the
3 Multiphase Flow Systems and Potential of Machine Learning …
29
geometrical distribution of a multiphase fluid passing through a pipe. This distribution is described using various flow regimes, distinguishing between qualitative and
somewhat arbitrary. The flow mapping or flow analysis indicates the onsite visualization of fluid flow behavior encountered mainly in production lines is gas–liquid,
which is depicted in Fig. 3.1. The significant gas–liquid flow regimes in a horizontal
annulus can be described as the bubbly flow, plug flow, slug flow, wavy flow, and
annular flow with the increased flow rates of gases phase (see Fig. 3.1).
Fig. 3.1 Gas–liquid flow regimes orientation in the horizontal annulus: a Schematic b pictorial
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M. S. Khan et al.
3.1.2 Liquid–Liquid Flow Systems
Many engineering applications accurately predict oil–water flow properties such as
flow pattern, water holdup, and pressure gradient. Flows of two immiscible liquids
occur in a wide variety of processes and equipment. Oil and water combinations are
delivered through pipes across great distances via pipelines [14–16]. Despite their
importance, liquid–liquid fluxes have not been studied as thoroughly as gas–liquid
flows. In fact, gas–liquid systems are a subset of two-fluid systems distinguished
by a low density to viscosity ratio. The density difference between the phases in
liquid–liquid systems is quite small (Fig. 3.2).
3.1.3 Solid–Liquid Flow Systems
Solid–liquid flows, which are generally encountered during the drilling operation,
require effective cutting transport for effective hole cleaning [2, 17–19]. Different
types of flow behaviors like stationary bed, slowly moving bed, rolling bed, and
dispersed moving beds are generally encountered based on the increased flow rate
of liquid flow, as presented in Fig. 3.3.
3.1.4 Solid–Liquid–Gas Three-Phase Flow Systems
Figure 3.4 shows the schematic and actual flow representation of multiphase flow
(Solid–liquid–gas) systems in cutting transportation perspectives [2, 17]. Like the
solid–liquid system, the three-phase system also possesses a similar flow pattern,
and solid transport increases with a rise in liquid flow rates. The stationary solid bed
breaks up in a different pattern of moving dunes. Some solid particles roll on the,
while the other solid accumulates in the lower part of the dunes. This mechanism
pushes the solids in the flow direction (Fig. 3.5).
3.2 Flow Assurance Issues in Drilling Applications (Cutting
Transport)
Solid particles (cuttings) are produced in petroleum drilling by the drill bit when
it is forced downhole at a specific penetration rate (ROP). The cuttings are then
conveyed to the surface by the often shear-thinning drilling fluid through the annular
gap (formed by the drill pipe1 in a wellbore).
3 Multiphase Flow Systems and Potential of Machine Learning …
31
Fig. 3.2 Liquid–liquid flow regimes orientation in the horizontal annulus: a pictorial b Schematic
3.3 Introduction of Cutting Transport Issues
Among the many roles of drilling fluid, hole cleaning, or the movement of cuttings
from the bit to the surface, is a critical function that impacts the drilling process’s
efficiency. Hole cleaning removes drill cuttings from a borehole and transports them
to the surface. Across the last 30 years, cutting transportation has gotten much attention, and experts worldwide have researched it. It is mostly determined by the drilling
mud’s viscosity and gel strength. If the drilling mud lacks viscosity, it will be unable to
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M. S. Khan et al.
Fig. 3.3 Solid–liquid flow regimes orientation in the horizontal annulus: a Schematic b pictorial
pull the cuttings from the bottom of the borehole to the top. Again, if the drilling mud
has sufficient gel strength, it will be unable to suspend the cuttings once the mud pump
is turned off. However, the effect of viscosity in horizontal wells is different compared
to vertical wells [2, 20]. It has been observed that cutting transport or hole cleaning is
more difficult in horizontal and direction wells than in vertical wells. Cutting transport becomes significantly more difficult as wellbore angles exceed 30°. The cause
of the hampered transportation is the formation of stagnant beds, which dramatically
increase friction and, as a result, restrict the annular area of the cutting to move
or carry. These drill cuttings can cause various issues, including drill pipe sticking,
increased torque, drag, fluid circulation loss, loss of control over mud density. On
top of that, it significantly increases the cost of drilling operations, resulting in the
waste of hundreds of millions of dollars worldwide. An assessment conducted by
3 Multiphase Flow Systems and Potential of Machine Learning …
33
Fig. 3.4 Gas–solid-liquid flow regimes orientation in the horizontal annulus: a Schematic b
pictorial
Amoco experts that focused on 70% of its lost time due to unexpected events revealed
that clogged pipe problems were the primary source of wasted time. Even though
extensive research has been undertaken to identify how different parameters affect
the cutting transport process in directional and horizontal wells.
Adequate cuttings transport is essential for appropriate hole cleaning, i.e., the
absence of a critical cuttings bed to avoid costly drilling downtimes due to clogged
pipes. Many parameters influence the quality of solids transport [21, 22], two of
which are drill pipe rotation and eccentricity. Several experimental research studies
have been conducted because of the importance of cuttings transport in the drilling
sector [23].
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Fig. 3.5 Classification of existing cutting transport models
Understanding the mechanism of cutting transport in the annulus is critical. There
is no accumulation of drill cuttings when the annular velocity (flow rate) is high
enough. The critical transport velocity is the speed at which there is no accumulation of bed height, and all cuttings can efficiently transfer to the surface [24]. The
bed height increases during sub-critical transport velocity as the annular flow area
decrease, resulting in a steady increase in cuts’ local velocity. Mud continues to accumulate until the local velocity reaches a threshold number. Then, an equilibrium bed
height is reached at that particular sub-critical flow transport velocity or flow rate.
As a result, equilibrium bed height depends on critical velocity [24]. The constant
buildup of cut depositions results in various bed formations, such as moving beds
and stationary beds. When stationary bed formation occurs, two potential mechanisms can cause solid transport: saltation behavior. The lift force induced by the
fluid overcomes the gravitational force and sliding phenomenon, in which viscous
force is more dominant than friction between the solid cuttings and pipe wall.
Figure 3.5 depicts a generic classification of several sorts of models. Since its
inception, the development of various cutting transport models has been centered on
three distinct directions: empirical models, mechanical models, and, more recently,
numerical models. Empirical correlations are generated using various experimental
data, whereas mechanical models consider the physical mechanics of cutting transit.
On the other hand, numerical models rely on mesh generation and accurate analysis
of individual mesh sizes. Empirical models are rarely utilized because they require
many variables, some of which are difficult to simulate at the lab scale, limiting their
application [25]. Again, mechanical models are classified as 1D (one dimension),
2D (two dimensions), and 3D (three dimensions). In 1D models, velocity is only
considered in the Z direction (along with the wellbore axis).
3 Multiphase Flow Systems and Potential of Machine Learning …
35
Two-dimensional and three-dimensional models are rarely employed since
solving a three-dimensional (unsteady-state equation); on fast workstations, the timedependent problem would take hours to solve and are practically useless in field
applications [26]. In contrast, in 2D and 3D models, velocity is considered in the
Z, X, and Y directions, respectively (velocity gradient both along and across the
wellbore). These are further subdivided based on the number of annulus layers, i.e.,
two-layer and three-layer models. The next section offers a complete summary of
the many models in historical sequence.
3.4 Evolution of Various Cutting Transport Models
3.4.1 Layer Model
Wicks [26] created one of the first two-layer cutting transport models. Wicks’ idea
was based on saltation and backed by experiments. His paper focused on lift force
in the X direction (across the wellbore) instead of the Z direction (along the wellbore). Tomren and coworkers [27] discovered the following observations during their
experimental investigation of cutting transfer: (1) When the vertical variation is less
than 10°, cuttings transport is very comparable. (2) A cuttings bedforms at low flow
rates. (3) The bed thickness grows with deviation until it is independent of the deviation angle. (4) Drill pipe eccentricity affects bed thickness but not fluid viscosity,
depending on deviation and flow rate. To salt, a particle from the bed, drag, and lift
must overcome gravity to rock it over its contact sites. As the particle falls through the
fluid, its settling velocity reflects the balance of gravity and drag. The fluid velocity
near the particle must be larger than the settling velocity to move a particle. So
the liquid layer’s average fluid velocity must be several times the particle’s settling
velocity. This model only applies to fine solids. Especially for coarse particles, high
fluid velocities can cause bed sliding [28]. Gavignet and Sobéy [29] proposed a
two-layer model with a stationary layer at the annulus’ bottom and a heterogeneous
layer above. One of the first models that work for both fine and coarse particles. The
diffusivity equation was used to calculate the solid composition of the heterogeneous
layer [30]. Brown et al. [31] experimentally compared Gavignet’s model utilizing
Dodge and Metzner’s Reynolds number for turbulent non-Newtonian flow [32]. This
allowed them to calculate a cutting wall friction factor of 0.2 empirically. Instead
of clear mud, Martins and Santana offered a two-layer model with a heterogeneous
layer. It permitted particles to be suspended in the upper layer, unlike Gavignet and
Sobey’s concept. Iyoho and Takahashi [33] observed spatial changes in cuttings bed
height along the wellbore using a similar approach. All previous models assumed
that the bed height remains constant along the wellbore. In this case, a two-layer
model with triangular dunes was adopted. The model requires knowledge of pressure fluctuations: sliding and heterogeneous layer model by Clark [24] to forecast
bed heights. The wellbore angle determines the primary mechanism. This is one of
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M. S. Khan et al.
the first devices to recognize that high angles require a rotating cutting bed. A lifting
device transfers clippings at a churning, moving cuttings bed. Particle settling determines almost vertical transport. Based on various wellbore angles. Also, Kamp [34]
used mass balance and momentum models to predict cutting transport behavior. In
terms of closure terms, the model looks to be similar to Doron et al. Quantitatively,
two-layered models outperform correlation-based models. At a given mud flow rate,
the model overestimates cuttings transfer.
3.4.2 Layer Model
Doron and Barnea [35] proposed a three-layer concept with a fixed bed, a moving
bed, and a mixed layer in 1993. It was an extension of Doron’s two-layer model
[30] proposed in 1987. The two-layer model’s fundamental flaw is its inability to
reliably forecast the presence of a stationary bed at low flow rates. The three-layer
model outperformed the two-layer model for low flow rates. According to Nguyen
and Rahman [36], three layers of cuts with a uniform concentration and velocity.
On top of that layer is a distributed cuttings layer with a Wilson-like velocity
gradient [37]. The top layer is clear mud. Ramadan et al. [38] proposed a threelayer model for inclined channel flows. Experiments did not validate this model.
Although the three-layer cutting transport model has been developed, it is still limited
to Newtonian fluid flows in horizontal and nearly horizontal channels [37]. The prior
modeling studies’ flaw of no-slip between particles and fluid was also eliminated.
However, the no-slip assumption restricts the model to horizontal and near-horizontal
situations [39].
3.5 Empirical Model
Research Centre Sunbury, comparing the effects of major parameters such as hole
angle, penetration rate, mud characteristics, and flow regime. On the other hand,
Zamora and Hanson [40] proposed an empirical model based on experimental and
field observations to improve hole cleaning efficiency for high angle wellbore. Luo
and Bern [41] proposed a model for determining the minimum flow rate required
to clear or prevent stationary cuttings beds on the low-side of a deviated wellbore.
The model was validated using experimental data from an 8” wellbore simulator at
BP. Larsen et al. [40] created a model for strongly inclined wellbores (50–90°). The
model forecasts the critical velocity and cuttings-bed thickness when the flow rate
is below the critical flow. Based on empirical correlations from a 35 ft long 5-inch
diameter flow loop. Valid for holes 500° or more or where cutting beds may occur.
Hopkins [41] discovered that increasing drilling fluid density reduced bit cutting slip
velocity and quantified this effect using a novel equation with modifications. The
density of drilling fluid can only be increased so far. If the Equivalent mud density
3 Multiphase Flow Systems and Potential of Machine Learning …
37
exceeds the formation pressure, the formation may fracture. To estimate the critical
or minimal velocity to lift the cutting from the wellbore, Rudi [42] established a
new model with equations. This model develops Moore’s slip velocity of a vertical
well for usage in inclined and horizontal wells with Larsen’s slip velocity method
modifications for rotating speed and mud weight. The minimal mud rate of Larsen’s
approach, Larsen’s experiment data, and Peden’s experiment data do not differ much
from this new equation in the publication.
However, at an inclination angle of 45°, the new equation produces a larger mud
minimum rate than the previous techniques. Tobenna [43] used Bern-approach Lou’s
and a correction factor in computing the critical flow rate for deviated wells. In
2017, Mohamad Ishaq et al. [44] examined the Rudi-Shindu [42], Hopkins [41],
and Tobenna [43] models. Hopkins’ model [41] is modified by cutting size and
RPM (RPM). The Tobenna [43] model’s minimum flow rate is only affected by well
inclination, drilling fluid weight, and rheological characteristics. Rudi’s model is
restricted to inclinations exceeding 45°. The explored models are not suited for horizontal wells because they do not contain lateral section effects [44]. Several writers
have developed other empirical models optimized using experimental data sets [22,
45–48]. They were designed for a limited range of drilling settings and are dependent
on the experimental setup. Extending the range affects its accuracy [48]. Recently,
Khaled and colleagues [17] created a generalized dimensionless model for accurate
prediction of cuttings concentration in deviated wells using data from many sources,
including experimental work, computational fluid dynamics (CFD) simulation, and
experimental data acquired from the literature. The model can predict the cutting
transport behavior with a resounding accuracy of 90% for various hydrodynamic
conditions.
3.6 Transient Model
During the drill operations, the cuttings bed distribution changes in real-time. The
conventional models can only calculate the unchanging height of the cuttings bed
at the end of the formation, but not the changing cuttings bed distribution during
the process. The annulus pressure drop is uncertain if the cuttings bed distribution
is unknown. Similarly, removing the cuttings bed from the borehole takes time. The
steady models can’t characterize the process and calculate the hydraulics. Iyoho
et al. [49] introduced transient models for cutting removal in 1987. Subsequently,
Martins et al. [52] developed a two-layer transient model for horizontal wells based
on mass and momentum conservation equations. Shigemi Naganawa [50] devised
a modified two-layer model to simulate cuttings transport along an extended-reach
well’s complete trajectory in wellbore drilling. Other factors as cuttings concentration
and bed height were also predicted in nonstationary flow [39]. Most of them are made
up of nonlinear equations that are difficult to solve. So, real-time calculation in fields
is difficult [51].
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M. S. Khan et al.
We can conclude from the evolution of the different types of cutting transport
models presented in chronological order and classified form in terms of their underlying mechanisms. It was observed that although the 2D and 3D models provide a
better representation of actual cutting transport, 1D models are more comfortable to
develop, solve, and take less time. However, there is still a need for improvement
in the existing models. The Empirical models are not used for field application as
many variables involved impose restrictions. Therefore, applying the latest predictive
technique like machine learning is very important in this perspective.
3.7 Machine Learning Approaches for Cutting Transport
Accurate cutting transport or hole cleaning is imperative for a successful drilling
operation. It has been seen that although it is easier in the case of vertical wells,
however, it is more difficult in the case of horizontal and extended reach well due
to the accumulation of cuttings at the lower side of the borehole due to gravity.
Over the years, several different techniques such as empirical, Dimensional analysis,
mechanical and numerical models have been used for predicting cutting transport
phenomenon. However, Artificial Intelligence (AI), especially the machine learning
(ML) technique, is still very limited in this field [52].
Artificial neural networks (ANNs) are mathematical models inspired by studies
on brain systems, consisting of highly interconnected processing nodes or elements
(artificial neurons) under a pre-specified topology (sequence of layers or slabs with
full or random connections between the layers). Ozbayoglu and coauthors [55] were
one of the first who used Back Propagation Neural Network (BPNN) in Artificial
Neural Network (ANN) for Horizontal and Inclined wells. They developed three nondimensional parameters, i.e., Reynold number, Froude number, and cutting concentration at the bit, to predict the cuttings bed height of the stationary bed. Experimental data was used for developing the model using the traditional least-square fit
and BPNN method. They revealed that ANN showed higher accuracy than the least
square fit method.
Rooki and coauthors [53] compared the BPNN and Multi Linear Regression
(MLR) methods for predicting the cutting concentration during foam drilling for
a Horizontal Well. Like Ozbayoglu [54], they revealed that BPNN showed a lower
Average Absolute Percentage (AAPE) than the MLR Method, highlighting ANN
technology’s effectiveness. Later on, Rookie and Rakhshkhorshid [55] used the
Radial Basis Function Network (RBFN) method to predict the cutting concentration
in a horizontal well. They [55] recommended that RBFN is a better prediction tool
than the BPNN method, which showed higher accuracy, faster training, and simpler
network architecture. Al-Azane and coauthors [56] used the Support Vector Machine
(SVM) network for predicting the cutting concentration for horizontal wells.
Aliouane and Ouadfeul [57] proposed a machine-learning algorithm to generate a
poisson’s ratio map, which can be used to predict drilling direction and rock properties. Castineria et al. [58] used machine learning to assess the quality of large amounts
3 Multiphase Flow Systems and Potential of Machine Learning …
39
of drilling data, obtain critical information, and forecast non-productive time. This
method helped reduce labor costs when checking the quality of large amounts of
drilling data. Deepwater drilling can use the Bayesian network (BN) for Managed
Pressure Drilling (MPD) and Under Balanced Drilling (UBD) operations. According
to Bhandari et al. (2015), the BN can be used effectively for offshore industry risk
analysis and failure prediction. Automation was used to control drilling parameters such as Weight of Bit (WOB), Rotary Speed (RPM), and Rate of Penetration
(ROP). A machine learning algorithm can collect information on potential bit or rig
equipment upgrades and estimate abrasive and anticipated bit wear [59].
Two-phase flow is the interaction of two distinct phases in a channel that share
common interfaces, with each phase representing a mass or volume of matter. Both
phases are capable of coexisting as solids, gases, or liquids. Although three-phase
multiphase flow occurs, the bulk of multiphase engineering applications uses a twophase flow. Planning, development, and optimization are required for flow in any
channel. Based on the interfaces generated between the phases, predicting the flow
phases and flow regimes is critical, i.e., typical flow patterns. Based on the flow rate,
channel size, and operating conditions, this knowledge allows for predicting pressure drop and heat transfer characteristics. The appropriate flow parameters can be
determined based on the pressure drop data to reduce the occurrence of corrosion,
erosion, or scale development, all of which can lead to excessive friction. Predicting
flow regimes, such as oil and gas flow in pipelines or chemical process industries, allows avoiding excessive gas pressure buildup, meltdowns, and potentially
hazardous explosions. The results of heat transfer analysis can be utilized to design
flow channels and operating conditions and forecast and avoid flow instabilities.
The heat transfer community and practicing engineers are predominantly interested in two-phase flow, which involves phase transition between a single substance’s
liquid and vapor phases. Two-phase flow refers to the fluid flow of a two-phase
mixture, which can be (1) liquid–vapor flow, (2) liquid–liquid, (3) liquid–solid particles, or (4) gas–solid particles. This category includes forced convective condensation
and boiling. Solving two-phase flow and heat transfer problems is extremely difficult
because each phase has its own properties, velocity, and temperature.
The conveyance of multiphase well fluids is extremely prevalent in the oil industry.
When the fluid from an oil and gas well reaches the wellhead, it forms a three-phase
(oil, water, and gas) or two-phase (oil and gas) mixture. A single-phase liquid or gas
flow at the wellhead is an extreme example.
The multiphase fluid flow analysis is used to mimic the behavior and interactions
of two fluid phases. The study uses the Volume of Fluid (VoF) method, a typical
multiphase system computing method. Because of the variety in flow regimes, multiphase flow is substantially more difficult than single-phase flow (or flow patterns).
Fluid distribution varies dramatically throughout flow regimes, influencing pressure
gradients inside and outside the drill string.
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M. S. Khan et al.
3.8 Flow Assurance issues in Liquid Loading Applications
3.8.1 Introduction of Liquid Loading Issue
Most natural gas wells have enough reservoir pressure when they are first completed
to flow formation fluids (liquid hydrocarbons and water) to the surface and the
produced gas. As production continues, reservoir pressure falls, and the flowing
fluid’s velocity in the well tubing begins to fall. Eventually, the gas velocity up
to the production tubing is insufficient to lift liquid droplets to the surface. This
phenomenon occurs when the upward gas velocity in the well falls below a critical
value, known as critical velocity [66, 67]. The critical velocity is when the produced
liquids, primarily flowing upwards, begin to drop back, causing liquid loading problems. This downhole liquid accumulation affects hydrostatic back-pressure on the
reservoir, lowering production rates, disrupting the multiphase flow in the gas well
(due to flow regime changes), and, in extreme cases, destroying natural gas wells
presented in Fig. 3.6.
Aside from dry natural gas wells, liquid loading concerns in the wellbore for
volatile gases like condensates gas wells might be seen. When the pressure falls
below the dew point pressure, gas condensates in the form of liquid form, imposing
liquid loading. The typical liquid loading behavior in vertical and horizontal gas
wells is depicted in Fig. 3.7.
3.8.2 Flow Pattern Analysis for Liquid Loading System
Different flow patterns can occur when two phases (gas–liquid) pass through a wellbore/pipeline. Common flow distributions are bubbly, stratified, slug, annular, and
Fig. 3.6 Evolution of the
liquid loading in a natural
gas well
3 Multiphase Flow Systems and Potential of Machine Learning …
41
Fig. 3.7 The characteristic liquid loading behavior in vertical and horizontal gas wells [11]
separated flows. Small bubbles are dispersed with a liquid phase in bubbly flow,
whereas more oversized liquid slugs or larger gas pockets can form in slug flow.
In stratified flow, the liquid and gas phases are separated, and the gas flows on top
because it is lighter than liquid; and annular flow, where liquid flows as a film on the
inner surface of the tubing. Mandhane et al. [68] offered a typical flow pattern map in
the literature. The flow map attempts to anticipate the many sorts of flow zones. The
flow map developed by Mandhane et al. [68] has been the most commonly used flow
map for gas/Newtonian flow. They generated this flow map for horizontal two-phase
flow using around 1400 experimental data from the AGA-API two-phase flow data
bank.
The most important flow pattern transitions in the prediction of liquid loading are
linked with patterns of slug-to-churn and churn-to-annular flow. Even with the advent
of modern instrumentation, it is now possible to obtain high-frequency measurements
of critical flow parameters (e.g., phase fraction, pressure, temperature, velocity). The
observed regime’s interpretation remains highly subjective [69]. The minimum gas
flow rate model developed by Nosseir et al. [69] was based on the impact of flow
regimes and variations in flow circumstances in natural gas wells. Moreover, Guo
et al. [70] developed a closed-form analytical solution for determining the minimum
gas flow rate for continuous liquid removal from a gas well. The following are the
typical symptoms of liquid loading:
• Unanticipated production losses
• The production of slugs or plugs in the fluid stream.
• Unanticipated variance in pressure gradients.
• Low-temperature spikes in the temperature profiles at the wellhead.
It is difficult to determine the beginning liquid loading point because it is not
always obvious [71, 72]. A comprehensive well diagnostic data analysis must be
performed to discover the origin of the liquid loading problem [73]. The most subjective liquid loading symptom is identified using flow mapping [74, 75]. Another critical criterion is to monitor changes in the gas wells’ pressure gradient or pressure
42
M. S. Khan et al.
profile. Understanding the components that influence liquid loading is critical for
overcoming it. The superficial gas velocities (Vg,s) and superficial liquid velocities
(Vl,s) have the greatest influence on creating different flow regimes in a natural gas
well. Figures 3.8 and 3.9 represent the liquid holdup with a typical flow pattern in a
horizontal and vertical gas well.
Fig. 3.8 A schematic representation of liquid holdup in horizontal gas wells with a typical flow
pattern [76]
Fig. 3.9 A schematic representation of liquid holdup in vertical gas wells with a typical flow pattern
[77]
3 Multiphase Flow Systems and Potential of Machine Learning …
43
3.8.3 Prediction Models for Liquid Loading
Although several established techniques are used to alleviate the effects of liquid
loading, the industry still lacks reliable predictive models to help select the best remedial option for a particular loading occurrence. Gas wells must operate at or above a
predetermined minimum rate to avoid liquid loading. “The critical gas rate” is defined
as the minimum stable gas rate (MSGR) required to produce condensate liquid or
water to the surface without the accumulation of liquids below the surface. There
are various correlations for estimating the critical gas rate, the majority of which
are based on Turner’s [78] pioneering work conducted in 1969, with improvements
presented by Coleman et al. [79], Li et al. [80], and many others.
Most generalized critical gas velocity correlations include the following correlating parameters: bottom-hole flowing pressure “Pwf ,” psi, tubing radius “rw,” in
inches, gas density “g,” lb/ft3 , liquid density “L,” lb/ft3 , surface tension “g–L,”
dyne/cm, bottom-hole temperature “T,” °R. The fundamental force balance acting
on liquid droplets’ balance includes the drag force of gas flowing upward and the
downward force of the droplet’s weight. The drag force should be consistently higher
than the gravity force.
Fd = Fg
(3.1)
Fd =
π d2
1
ρG C D VC2
2gc
4
(3.2)
Fg =
g
π d3
(ρ L − ρG )
gc
6
(3.3)
with
after substitution
/
VC =
4g (ρ L − ρG ) d
3
ρG
CD
(3.4)
The variables in the expression to evaluate the critical velocity include the drag
coefficient (Cd), which depends on the Reynolds number, the diameter of the droplet,
gas, and liquid densities in hydrocarbon condensate systems change significantly
offshore flowlines due to immense pressure and temperature gradients.
Though significant efforts have been made to predict the flowing conditions at
which the well remains out of the liquid loading region using the so-called “Turner’s
criteria,” these do not capture the dynamics of the loading sequence. Operators
frequently use Turner’s criteria to design a production system to remove all liquids
from the well at gas rates high enough to do so. It’s impossible to tell how much
of a problem the loading is or how quickly it will slow down production with the
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M. S. Khan et al.
help of these tools. From a flow mechanics perspective, It is critical to comprehend flow patterns and flow pattern transitions. The shape and behavior of the interface between phases in a multiphase mixture determine the “flow regime” or “flow
pattern.“ Within the multiphase fluid, competing forces or mechanisms are at work
simultaneously, and the balance determines the flow pattern. Additionally, the proportions and properties of the phases coexisting in the well/tubing must be determined
to model multiphase flows associated with liquid loading in gas wells.
A turbulent gas core with numerous entrained liquid droplets exists in an annular
flow at the well’s center, while a brittle liquid layer (m to mm) exists at the tubing’s
surface. At the bottom of the well, a turbulent gas core generates frictional pressure
drop, while a thin liquid coating imposes hydrostatic pressure load. As a result,
boosting gas production to minimize liquid loading may not solve a natural gas
well’s liquid loading.
Modeling methodologies used to characterize liquid loading in gas wells include
empirical, phenomenological, two-fluid, and drift-flux models. Particular emphasis
is placed on these approaches’ ability to capture the ephemeral aspect of the loading
process. Phenomenological models are flow-pattern-based models that rely on identifying a specific flow regime and the use of adhoc pressure drop computations.
Examples of phenomenological models include those based on the unit cell concept
for slug flow in vertical pipes, which take into account the actual flow configuration
(long Taylor bubbles separated by aerated liquid slugs) in the formulation of ad hoc
models for calculating the void fraction, wall shear stress [81–84]. Data for frictional
pressure gradient and void fraction are connected to system variables using empirical
equations in empirical models. Many trials are required to duplicate a specific situation to develop a reliable empirical model, but this can be costly. The model is only
applicable to a limited set of conditions unless the dimensional analysis is performed.
There are several advantages to relying on empirical models rather than fundamental
physical mechanisms. The two-fluid model does not work well with intermittent flow
patterns like slug flow. Liquid flows upward in the Taylor bubble zone but downwards in vertical upward slug flow, making averaging liquid properties difficult. The
relative motion between phases in most separated flows is closely proportional to
the pressure and velocity gradients in the two phases. A drift flux model cannot
account for this. The two-fluid technique also does not adequately capture churn
flow. This flow pattern can be considered an annular flow with periodic reversals in
the liquid film. Waves have developed, carrying the liquids upwards. A film travels
downhill between the waves, replenishing the next upstream wave from which liquid
is draining. The result is net upward liquid transfer.
The two-fluid model does not account for this flow intermittency. Waltrich et al.
[85] collected steady-state and transient two-phase flow experimental data at Texas
A&M University’s TowerLAB facility in the United States, using a transparent
vertical test Sect. 42 m in length and 0.048 m ID. As working fluids, air and water were
used. Comparisons were made between a benchmark algorithm for multiphase flow
in pipes, experimental data, and HyTAF, showing the need for additional research
in this area. Alves et al. [86] demonstrated a satisfactory agreement between the
observed pressure gradients of Waltrich et al. [85] and those predicted by HyTAF for
3 Multiphase Flow Systems and Potential of Machine Learning …
45
an induced transient example, from steady-state annular flow to steady-state churn
flow.
Most models used to forecast and diagnose liquid loading issues are based on
steady-state analysis. As a result, they cannot deal with the transient phenomena
associated with liquid loadings, such as the gradual buildup of the static fluid column
in the well and the resulting increase in back-pressure on the formation. In business
and academia, efforts are being made to link observed well dynamics with the intermittent reservoir response typical of liquid loading in gas wells. Even when transient
multiphase wellbore models are used, the problem remains incorrectly described
because the reservoir is characterized using a steady-state Inflow Performance Relationship (IPR), resulting in inaccurate boundary conditions between the well and
the reservoir. Yang et al. [87] provided a new set of methods for determining the
critical velocity for inclined wells that consider the various forces acting on a liquid
droplet. They thought that the droplet constantly rises through the tubing’s midsection and is unaffected by the wall. Ming et al. [88] created a model that considers the
degree of deviation and the influence of the Reynolds number. Their [88] findings
suggested that the Reynolds number had a favorable impact on the critical liquid
carrying velocity; however, the degree of variations had a negative impact.
The standard pressure profile in the near-wellbore region of a flowing reservoir cannot characterize the transitory phenomena that occur during liquid loading.
Because of a combination of inertia and compressibility effects, the reservoir reaction to wellbore phase redistribution effects is not instantaneous. A more reliable
technique would be to utilize a transient multiphase flow wellbore model using the
transient properties of the reservoir’s near-wellbore region as boundary conditions.
Despite the several two-phase flow modeling methodologies available, it is critical
to capture the transition from annular flow to churn flow, then to slug flow, and
finally to bubble flow, which may result in the well’s demise. More study is needed
in this area to develop credible predictive models that can assist in selecting the
optimal remedial solution for a given incident of liquid loading. Table 3.1 summarizes
the gas/liquid flow investigations on various types of fluxes. As previously stated,
liquid loading is a common concern in gas wells, necessitating the development of
a modified analytical model for predicting its occurrence and critical gas velocity.
Monitoring the pipe’s appearance and position of deviated wells will necessitate
greater experimental efforts.
As highlighted from the brief literature review, liquid loading in the gas wells is
one of the most problematic scenarios encountered during the production stages of
the reservoir [13, 69, 71, 73, 89, 90]. The smooth gas production from the gas wells
hinders the liquid loading issues. The undesired condensate and water are entrained
in the gas phase below the dew point as the liquid phase goes below the bubble point
pressure. The problem could even be worsened when solid particles are encountered
in the gas wells. Therefore, it is highly desired to adopt the sophisticated and latest
computational approach such as machine learning to accurately predict the onset of
liquid loading (Tables 3.2 and 3.3).
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M. S. Khan et al.
Table 3.1 Literature for ML technique applied in hole cleaning
Author and Year
Well Type
ML Technique
Parameter Studied Error
Ozbayoglu et al.
[54], 2002
Horizontal and
highly inclined
wells
BPNN
Cuttings bed
height
Error < 10%
BPNN and MLR
Cutting
concentration
during foam
drilling
AAPE for ANN <
6%, AAPE for
MLR < 9%
Rooki et al. [53], Horizontal
annulus
2014
Rooki and
Rakhshkhorshid
[55], 2017
Horizontal
annulus
RBFN
Cutting
concentration in
underbalanced
drilling
AAPE = 5.7%
Al-Azane et al.
[56], 2018
Horizontal and
Deviated wells
SVM
Cutting
concentration
AAE < 5%
Al-Azani et al.
[60], 2019
Horizontal and
Deviated wells
ANN and SVM
Cutting
concentration
AAE < 5%
Sorgun et al.
[61], 2015
Horizontal
Annulus
Support Vector
Regression
Pressure loss
AAE < 6%
Osman [62],
(2004)
Deviated well
BPNN
Flow regime
Identification and
calculate Liquid
hold up
AAE < 10%
Sorgun and ulcer Horizontal pipe TrainGdx–Tansig
[63], 2016
Pressure drop
AAE < 20%
Ozbayoglu et al.
[64], 2009
Horizontal
wellbore
Several ANN
techniques
Fluid pattern and
pressure drop
AAE for fluid
patter < 5%, AAE
for pressure drop
< 20%
Ashena and
Moghadasi [65],
2011
Vertical
wellbore
BPNN, Ant
Colony
Optimization
(ACO), Genetic
Algorithm (GA.)
Bottom hole
pressure
estimation
ACO and GA
performs better as
compared to
BPNN
3.8.4 Machine Learning Approaches for Liquid Loading
or Gas/liquid Flow
Determining multiphase flow parameters like flow pattern, pressure drop, and the
liquid holdup is a difficult and valuable problem in the chemical, oil, and gas industries, especially during transportation. There are two basic approaches to solving this
problem in the literature: data-driven algorithms and mechanistic models. Although
data-driven methods are more accurate in forecasting, they do not reveal the twophase characteristics (i.e., pressure gradient, gas and local liquid velocities, liquid
holdup, etc.). Machine Learning is the latest prediction technology for efficiently
3 Multiphase Flow Systems and Potential of Machine Learning …
47
Table 3.2 Gas/liquid two-phase flow studies
Authors and
Year
Type of Fluid
Geometry
Dimension
Studied
Parameters
Ansari et al.
[91], 1994
Gas wells
Vertical and
directional wells
4.57– 17.78 cm
ID length,
Wellhead
pressure
Developed
mechanistic
model
Vertical
7.62 cm ID/18 m Minimum flow
length
rate
Coleman et al. Gas wells
[79], 1991
D. Barnea
[92],1987
Air and water
Vertical-Inclined
5 cm ID,
3.45 bar–
43.13 bar
Hakim et al.
[93], 2018
Air and water
Vertical
7.62 cm ID/40 m Pressure gradient
length
and liquid holdup
Goal and
Valkó [94],
2016
Air and water
Vertical
4.8 cm ID/42 m
length
Ikpeka and
Okolo [95],
2019
Air and water
Vertical and
deviated
3.1 cm ID/9.5 m A new model to
length, Wellhead predict liquid
pressure =
loading
40 bar
Guo et al.
[96], 2005
Gas wells with
oil and water
Vertical wells
Different tubing
sizes, Wellhead
pressure
Develop a new
correlation
Pedro
Air and water
Cavalcanti De
Sousa [97],
2013
Vertical
6.03 cm
ID/13.97 cm
OD/42 m length
Investigate
unexpected
periodic release of
liquid slugs
Paulo Jose
Waltrich [85],
2011
Air and water
Vertical
50 cm ID/42 m
length
Visualize
two-phase flow
regimes and
measure pressure
and liquid holdup
Riza et al.
[98], 2016
Air and water
Vertical
50 cm/42 m
length
Study of liquid
loading
phenomena
Slightly inclined
(0.65° and 2.1°)
5 cm ID/26 m
length
Flow regime
Andreussi and Air and water
Person [99],
1987
Alsaadi et al.
[100], 2015
Air and water
Operative
equation and
dimensionless
map
Flow regime and
validate a liquid
content model
Vertical-Directional 7.62 cm ID/18 m Pressure gradient
(60° to 88°)
length
and liquid holdup
(continued)
48
M. S. Khan et al.
Table 3.2 (continued)
Authors and
Year
Type of Fluid
Geometry
Dimension
Studied
Parameters
Guner et al.
[101], 2015
Air and water
Vertical-Inclined
(90° to 45°)
7.62 cm ID/18 m Pressure gradient,
length
liquid holdup, and
visualization
Skopich et al.
[102], 2015
Air and water
Vertical
5.08 cm and
10.16 cm
ID/167.1 m
length
Pressure gradient
and liquid holdup
Dinaryanto
et al. [103],
2017
Air and water
horizontal pipe
2.6 cm ID/10 m
length at 8 bar
pressure
Flow regime map
and visualization
Kong and Kim Air and water
[75], 2017
horizontal pipe
3.1 cm ID/9.5 m Flow regime
length
transition and
Frictional pressure
drop analysis
Kesana et al.
[104], 2017
Air and water/air horizontal
and Aqueous
Carboxymethyl
Cellulose (CMC)
solution
7.62 cm ID/18 m Flow pattern map
length
and effect of
liquid viscosity on
slug flow
distributions
Kong et al.
[76], 2017
Air and water
3.81 cm
ID/9.5 m length
horizontal
Flow regime
mapping, the void
fraction, bubble
velocity analysis
was also
performed
predicting gas–liquid systems. For instance, Shaban et al. [105] employed an elastic
mapping technique to produce a flow pattern map from differential pressure signals.
Different flow patterns for air–water two-phase flow in a vertical pipe were examined. The elastic mapping method is a machine learning technology that reduces the
number of data dimensions displayed in a two-dimensional map. The produced map
displayed well-defined and clustered data of the same types of flow patterns.
Al-Naser et al. [106] detected the flow pattern in a horizontal pipe using MATLAB
and its built-in neural network technique. The authors generated input for various
flow patterns using the unified model simulator. They [106] created a universal 3-D
flow pattern map for various dimensions and fluid parameters based on the liquid
Reynolds number, gas Reynolds number, and pressure drop multiplier. They showed
that the model, based on the natural logarithm of the three dimensions, can correctly
predict up to 97 percent of the flow patterns.
Deep machine learning was applied by Ezzatabadipour et al. [107] for pipes with
diameters of 1 or 2 inches and varying pipe inclinations; to predict annular, bubble,
scattered bubble, intermittent, stratified smooth, and stratified wavy flows. They used
a feedforward neural network known as a multilayer perceptron (MLP) to train 60
3 Multiphase Flow Systems and Potential of Machine Learning …
49
Table 3.3 Machine learning approaches applied on gas/liquid systems
Authors and year
Application
Machine learning technique
Al-Naser et al. [106], 2016
Flow pattern detection in
horizontal conduit
ANN technique
Ezzatabadipour et al. [107],
2017
Flow regimes predictions for
2-inch pipelines
Deep learning approach
Amaya-Gómez et al. [108],
2019
Predictions of flow regimes
maps
Bayesian supervised method
Alhashem [113], 2019
Forecasted multiphase flow
regimes in horizontal pipelines
Decision Tree, Random
Forest, Logistic Regression,
Support Vector Machine
(SVM), and Neural Network
Multi-Layer Perceptron were
the methods evaluated in the
study
Hernandez et al. [109], 2019
Classification of various flow
Data-driven methodology for
regimes for horizontal pipelines decision tree-based model
Almalkai and Ahmed [110],
2020
Flow pattern analysis from
downstream of the orifice
Classification Learner
approach in MATLAB
Mask et al. [111], 2019
Flow pattern analysis via
dimensionless numbers
XGB tree model (machine
learning) approach
Sami and Ibrahim [112], 2021 Flowing bottom-hole pressure Random forest, K-Nearest
(FBHP) prediction for gas wells Neighbors (K.N.N.), and
artificial neural network
(ANN)
percent of a total of 5676 data points, with 20 percent for validation and 20 percent
for testing. The outcomes showed that the strategy could predict the flow pattern
accurately. Amaya-Gómez et al. [108] suggested a Bayesian supervised method with
a novel visualization tool for flow pattern maps.
Hernandez and coworkers [109] introduced a machine learning-based data-driven
approach for flow pattern classification. A decision tree-based model was developed
and then validated for flow pattern prediction using a set of 9224 observations using
the data-driven methodology. Over a wide range of two-phase flow conditions, the
closure relationship selection model correctly classified flow regimes. The intermittent flow was found to be the most accurate (86.32 percent), while the annular flow
was found to be the least accurate (49.11 percent). The results show that less than
10% of global accuracy is lost compared to direct data-based algorithms, which is
explained by the poor performance for unusual values and zones close to flowing
pattern borders.
The local pressure drop has been the focus of much research on two-phase flow via
orifices. Despite the topic’s relevance, flow patterns downstream of an orifice have
gotten comparatively little research. As the mixture passes through the aperture,
the momentum of the phases increases considerably. Almalkai and Ahmed [110]
introduced the ML technique for the flow pattern behavior analysis downstream
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M. S. Khan et al.
of an orifice in a horizontal pipe. Findings revealed that the Classification Learner
environment, a Machine Learning Algorithm in MATLAB, provides a total error of
9%, which indicates its prediction ability for flow pattern downstream of the orifice.
Fluid characteristics, liquid and gas flow rates, flow conduit shape, and mechanical factors influence the flow pattern. Mask et al. [111] used hydraulic basics and
dimensional analysis to reduce the number of freedom dimensions to produce three
upscaling numbers. They used machine learning techniques on dimensionless variables to dramatically enhance their predicted accuracy to 90% or more than the usual
dimensional model using experimental data, which gave an accuracy of 80%.
Oil and gas production wells are evaluated based on the flow of bottom-hole pressure (FBHP). Oil production optimization, oil lifting costs, and workover operation
assessment necessitate accurate FBHP predictions in the petroleum sector. Sami and
Ibrahim [112] used three machine learning techniques to forecast the multiphase
FBHP: random forest, K-Nearest Neighbors (K.N.N.), and artificial neural network
(ANN). The results showed that using an artificial neural network model resulted in
a 2.5 percent error in estimating the FBHP, which was less than the 3.6 percent and 4
percent errors in the random forest and K-nearest neighbor models. Alhashem [113]
forecasted multiphase flow regimes in horizontal air, water, and oil pipelines using
supervised machine learning (ML) approaches. The input features used were water
cut (the proportion of water), gas superficial velocity, and liquid superficial velocity.
The output was projected to be one of six different flow regimes. The methods evaluated in the study were Decision Tree, Random Forest, Logistic Regression, Support
Vector Machine (SVM), and Neural Network Multi-Layer Perceptron (MLP). Their
findings revealed that the random forest algorithm, with high accuracy of up to 90.8
percent and a short training time (0.13 s), is the best candidate for the dataset to
increase the size of the data and features.
3.9 Case Studies in Multiphase Flow Assurance
Petroleum companies are actively looking for new ways to become more efficient,
such as streamlining production, lowering costs, and improving worker safety, among
other things. Several administrators look to digitize to protect their businesses with
lower oil prices and a recovering economy. The ML or AI are implemented in various
fields within the industry, including production optimization, detecting oil seep,
precision drilling, boosting production with predictive maintenance. The following
paragraph only focuses on the case study in the cutting transport area.
Shell Industry is a leader in exciting AI applications. Shell uses reinforcement
learning to control its drilling equipment, essentially rewarding the AI’s choices
[52]. A machine learning model, for example, is trained on historical drilling data
and simulations to guide the drill into the subsurface. It also takes seismic data,
temperature, pressure, and drill bit data into account. The steerer can then use reward
and punishment functions to assist the drilling machine in adapting to changing
subsurface conditions. This allows the geosteerer to understand the environment
3 Multiphase Flow Systems and Potential of Machine Learning …
51
better, resulting in faster results and less machine damage. In any case, progress is
being made. Shell is always looking for big ideas to push the boundaries of the oil
and gas industry. Shell game-changer regularly solicits machine learning-focused AI
proposals from individuals and start-ups worldwide. Shell is leading to help solve
some of the industry’s biggest challenges by investing in these ideas and collaborating
on projects. Throughout the process, AI is being implemented or tested.
The company recently implemented semi-supervised reinforcement learning to
manage its drill rigs. However, machine learning can work with unlabeled data (supervised or unsupervised learning). Reinforcement learning strikes a middle ground by
rewarding the AI’s “choices” based on their success. Shell’s drilling history and
simulated explorations create algorithms that guide drills through the subsurface. It
includes drill bit mechanical data like pressures and temperatures and subsurface
seismic data. As a result, a Shell geosteerer is the human programmer of the drilling
machinery, resulting in faster results and less wear and tear on machinery.
3.9.1 Conclusion
Since This chapter provided a high-level overview of the multiphase system,
including cutting transport and liquid loading in the flow assurance domain. The
first sections discussed the process and existing prediction methods and their limitations. The research also includes a literature review on machine learning applications
in the cutting transport and liquid loading fields. According to the machine learning
literature, the application of ML is still in its early stages, especially for multiphase
systems. Furthermore, the accuracy of predictive algorithms can be improved by
gaining experience, either by increasing the size of the dataset or by adding relevant
features. The oil and gas sector is entering a different normal of pandemic situation.
Industry players must adapt to the new reality and improve their supply chain and
activities. In this perspective, the appropriately trained ML model and logic can be
tailored to the industry to provide automated flow control or install prediction and
mitigation solutions for pipelines and field operations, potentially changing the oil
industry’s value chain.
References
1. Qasim A, Saad Khan M, Lal B, Mohd. Shariff A, Che Ismail M, (2021) Evaluation of tetramethylammonium acetate as corrosion suppressor for flow assurance applications. Mater Today
Proc. https://doi.org/10.1016/j.matpr.2021.03.568
2. Khan MS, Barooh A, Rahman MA, Hassan I, Hasan R (2021) Investigation of Flowzan as nonnewtonian cutting transport fluid in directional drilling applications via electrical resistance
tomography method. In: ASME 2021 40th international conference on ocean, offshore and
arctic engineering
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Chapter 4
Machine Learning in Corrosion
Jai Krishna Sahith Sayani and Bhajan Lal
Abstract This chapter presents the advances and progress on the use of machine
learning in corrosion. Specifically, the use of machine learning to predict corrosion
rate in pipelines.
Keywords Machine learning · Flow assurance · Corrosion · Predictions · Model
4.1 Introduction
Corrosion is a common problem that occurs in every industrial sector. Corrosion has
become a major crisis in each sector due to its effect that can influence the profit and
loss in any production business. This is because when corrosion occurs, it can affect
the quality and the quantity of the product produced thus, it requires maintenance
to back to its original condition. More critical if corrosion becomes uncontrol, any
unpredicted accident can happen that can cause events such as sudden burst, leaking,
explosion, and a more serious case can involve a fatality.
This chapter will include the corrosion mechanisms, factors that influence corrosion, and types of corrosion that can happen in the oil and gas industry. Besides, it
also describes the mitigation procedures that can be done to minimize and control
the corrosion as it is quite impossible to completely avoid the corrosion from keep
occurring. Next, in implementing all the possible corrosion controls, it is important
to determine the corrosion rate of the equipment in order to decide which method
J. K. S. Sayani
Department of Chemical and Bioprocess Engineering, University College Dublin (UCD), Dublin,
Ireland
B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_4
59
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J. K. S. Sayani and B. Lal
is the most suitable and the correct amount of corrosion inhibitor requires to minimize the corrosion rate if applicable. The corrosion rate can be determined based on
experimental or computer simulation methods. These methods are also described in
this chapter.
Over the years, corrosion has become the main problem faced by every sector.
Alternatives in determining the corrosion rate in terms of simulation have been developed to tackle this problem. Various corrosion prediction models have been developed
based on the different operating conditions of the process involved. HYDROCOR,
Cassandra, De Waard, NORSOK, Lipucor, ECE, and KSC are examples of the
corrosion prediction model includes in this chapter.
4.2 Corrosion in Oil and Gas Industry
4.2.1 Corrosion Mechanism
Corrosion has been a major crisis that occurs not only in the oil and gas industry
but also in every processing plant. It can occur in every piece of equipment such
as storage tank, pipeline, reactor, and distillation column whenever the corrosion
factors exist. Some of the main factors of corrosion are oxygen, metal ion, and water
vapour. Corrosion is the degradation process of material strength and its critical
properties as a result of chemical, electrochemical, and other processes that involve
the reaction between the exposed material surface and the surrounding environment.
As corrosion can happen easily regardless of its severity level, a large amount of
revenue has been allocated only for corrosion as whenever it happened, the recovery
cost will include the maintenance, compensation, and installation of a new part of
the affected equipment.7/8847 For example, in 2018 around $1.4 billion was spent
by US oil and gas exploration and production industry only for corrosion purposes
as reported by NACE International also known National Association of Corrosion
Engineers. Therefore, this crisis must be overcome by implementing the steps in
reducing the corrosion rate to avoid losing a large amount of revenue.
It occurs when the reduction–oxidation (redox) reaction takes place as the
substance transforms to a more stable form. The selection of which metal to undergo
whether oxidation or reduction will be based on the standard electrode potential.
For the metal with higher standard electrode potential, it will undergo reduction
at the cathode side while the metal with lower standard electrode potential will
likely undergo oxidation at the anode site. The anodic metal will likely become
thinner throughout the redox reaction as it loses some of its electrons whereas, for
the cathodic metal, it will become thicker due to the gaining electron process.
2M → 2M2+ + 4e−
O2 + 2H2 O + 4e− → 4OH−
(Anodic reaction)
(Cathodic reaction)
(4.1)
(4.2)
4 Machine Learning in Corrosion
61
4.2.2 Corrosion Factors
Environmental factor has been the main influence of corrosion to occur. Various
types of corrosion can take place due to the existence of certain factors such as pH,
pressure, temperature, and the presence of sulfides, acid, and carbon dioxide [1].
Increases in temperature can result in increasing corrosion rate since the collision
frequency of ions increases. Even so, the corrosion rate increases with temperature,
whenever the temperature reaches its peak point, the corrosion rate starts to decrease
due to the formation of corrosion product on the metal surface avoiding the reaction
between the metal surface with the corrosion agent. Next, an increase in pressure will
result in increase flow velocity and directly lead to an increase in corrosion rate. This
is because, at this condition, higher speed of the fluid able to remove the protective
layer formed on the metal surface causing a more exposed area of metal and allowing
corrosion to happen [2].
In addition, the presence of certain elements able to corrosion to happen. For
example, an increase in the oxygen concentration is able to increase the corrosion
rate since oxygen will likely undergo a cathodic reaction during the electrochemical
process. However, for metals such as stainless steel, titanium, and aluminium, the
presence of oxygen is able to maintain the protective oxide layer on these metals [3].
Therefore, the presence of oxygen can result in an increase and decrease of corrosion
rate depending on the surrounding conditions. Moreover, the presence of carbon
dioxide or hydrogen sulfide that dominant an area can classify whether it undergoes
sweet corrosion or sour corrosion, respectively. This classification of corrosion can
help in determining the suitable corrosion control to be implemented. Besides, the
partial pressure of carbon dioxide also can be used to determine the corrosion severity
of the equipment. For example, for carbon dioxide partial pressure (PPCO2 ) < 48 kPa,
it can be considered as non-corrosive, 48 kPa < PPCO2 < 207 kPa can be considered as
medium corrosion, and PPCO2 > 207 kPa can be considered as severe corrosion [4].
4.2.3 Types of Corrosion
General corrosion or also known as uniform corrosion or general attack corrosion
is a type of corrosion that occurs at the same rate over the entire surface area of the
metal that is exposed to the corrosion-causing conditions (Fig. 4.1). This happens
due to the presence of acids, salts, and when oxidation and reduction reactions occur
uniformly [1]. The uniformly occur electrochemical reaction results in thinning of
the wall or surface thickness until failure occurs. However, the corrosion can be well
managed by increasing the thickness of the exposed surface. Next, galvanic corrosion
is corrosion that happens when the two dissimilar metals are placed together in
a corrosive electrolyte (Fig. 4.2). For the metal with high corrosion resistance, it
will become the cathode while the other metal will become the anode. This type of
corrosion can happen due to the presence of acids, water, and the deposition of salts
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Fig. 4.1 General corrosion on a metal surface [4]
from sulfides and chlorides [1]. In the anode–cathode extend, the further the position
of the two metals in the galvanic series, the lower the reactivity between the metals
thus minimizing the galvanic corrosion.
In addition, another type of corrosion that can happen is pitting corrosion
(Fig. 4.3). This corrosion can be observed when there are holes, cavities, or pits
formed on the metal surface. It is considered more dangerous than general corrosion
due to its difficulty to be traced and predicted. This difficulty is because of the formation of corrosion product that forms on the metal surface that covers the holes and
pits resulting in the unnoticeable pits. This type of corrosion involves three stages
which are the formation of the surface layer on the metal surface, initiation of pits
at localized regions where layer breakdown occurs, and followed by pit propagation
and final material penetration. Moreover, intergranular corrosion is corrosion with
the localized attack that occurs at grain boundaries or directly next to grain boundaries, leaving the majority of the grains intact. If this corrosion occurs, it may lead
to loss of tensile properties and loss of cross-sectional thickness of the materials.
This corrosion is likely to occur due to a fabrication process that involves heating
or cooling where some of the material content can be reduced when undergoes the
process. Lastly, many other types of corrosion can happen in the oil and gas industry
such as crevice corrosion, microbial induced corrosion (MIC), erosion corrosion,
stress corrosion, and intergranular corrosion.
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Fig. 4.2 Galvanic corrosion
at three lead pipe joints [4]
Fig. 4.3 Pitting corrosion
on a metal surface [5]
4.2.4 Corrosion Control
The selection of the most suitable corrosion control can be influenced by the chemical
involved in the corrosion and the environmental conditions. The first thing that should
be considered in the selection of construction material. There is no material that is
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J. K. S. Sayani and B. Lal
able to fulfill all the requirements. The most common material 0020 used is carbon
steel due to its cheapness but the usage of carbon steel, especially in natural gas
systems, must come with the application of the coating, cathodic protection, and
injection of corrosion inhibitors to minimize the corrosion rate. Other than carbon
steel, titanium alloy also can be used in natural gas as it is highly corrosion resistant
due to the formation of TiO2 passive layer and also highly resistant to H2 S, CO2, and
brine [6].
Besides, for the injection of corrosion inhibitor, it will be adsorbed and covered
the metal surface. By increasing the concentration of the corrosion inhibitor, it
able to reduce the metal surface exposed to the corrosion agent. There are multiple
types of corrosion inhibitors that can be utilized such as anodic inhibitors, cathodic
inhibitors, organic and inorganic inhibitors based on the purpose of the usage [3].
Prior to injecting the corrosion inhibitor into the equipment, it recommends doing
some chemical cleaning such as pigging. For the reason that the effectiveness of
the inhibitors can be reduced when the suspended solids and sand present as it will
attract the molecules of the inhibitors. Apart from these two alternatives, there are
other ways that can be utilized to minimize the corrosion rate such as cathodic protection and coating. All of these corrosion controls can be done based on the behaviour
of the corrosion agents and the internal or external environmental conditions of the
equipment involved.
4.3 Mitigation Procedures
The severity of corrosion can be influenced by the material of construction. The
selection of materials for the construction of the equipment is very vital as it will
influence the capital expenditures (CAPEX) and operational expenditure (OPEX).
Therefore, it is required to keep both balances by implementing the mitigation activities. Carbon steel is the most commonly used material in construction such as a
pipeline. However, this application is only applicable with the implementation of
corrosion control such as injection of inhibitors and cathodic protection. Other than
carbon steel, materials such as stainless steel, alloy, and PVC have also been used as
construction materials depending on the operating conditions of the process and the
characteristics of the substances involved.
4.3.1 Pigging
Pigging is a process where a device known as ‘pig’ is inserted in a pipeline that used
the flowing fluid as the driving force. It can be categorized into three types based
on its functionality such as cleaning pigs, sealing pigs, and in-line inspection (ILI).
Cleaning pigs are used to remove the solid deposition, debris, wax, asphaltenes, and
biofilms from the pipe whereas cleaning pigs are used to seal, sweep liquid (mainly
4 Machine Learning in Corrosion
65
water), apply corrosion inhibitors, coat the pipe, and separate products. Lastly, inline inspection (ILI) pigs are a tool that is utilized to inspect pipelines and are also
known as intelligent pigs. It can be done on a variety sizes of pipelines as it will not
interrupt and stop the flow of the materials flowing through the pipeline.
Besides, pigging can be implemented in both metallic and non-metallic pipelines.
Pigging is used to remove accumulated water and sediment in non-metallic pipes.
During construction, operation and maintenance, inspection, repair and rehabilitation, and decommissioning, a pigging operation is carried out. Pigs exist in
various shapes which used for different applications such as a sphere, cast, and
multi-diameter.
4.3.2 Corrosion Inhibitor
In addition, the usage of corrosion inhibitors has been one of the commonly used
applications for corrosion control. Most construction in the oil and gas industry
implement the usage of carbon steel for the equipment that is only possible with the
assistance of corrosion inhibitors. This application is the most trusted, time-tested,
and proven method that can be used especially in the production, transportation, and
refinery sectors in the oil and gas industry. The characteristics of organic corrosion
inhibitors that are commonly used in the oil and gas industry are organic, polar, and
surface-active molecules. It is made up of mixtures containing active ingredients for
inhibitors, hydrate inhibitors, antifoaming agents, and more but rarely made up of
pure substance.
The application of corrosion inhibitors is by injecting a small concentration of
the chemical substance into the corrosive media to decrease or prevent the reaction
between the metal and the media. Then, the inhibitors will operate by the adsorption
of ions or molecules onto the metal surface. The injection of corrosion inhibitors to
the corrosive media is able to reduce the corrosion rate of the media by increasing
or decreasing the anodic and/or cathodic reaction, decreasing the diffusion rate for
the reactant to the surface of the metal, and decreasing the electrical resistance of
the metal surface. In addition, the selection of corrosion inhibitors depends on some
factors such as cost, availability, toxicity, and biodegradability. The usage of the
natural product as the corrosion inhibitor has been considered previously but the
usage has already been banned due to its toxicity towards human beings and the
environment. This case same goes for the organic compound that contains nitrogen,
oxygen, or sulfur that has significant inhibition efficiency but is not being used due
to expensive and toxic to the environment. Therefore, the existence of plant extract
as a corrosion inhibitor has been one of the options of corrosion inhibitor. This
is because plant extract is a renewable source of inhibitor, readily available, and
environment-friendly other than it has very high inhibition efficiency. It is believed
that the presence of heterocyclic constituents like alkaloids and flavonoids causes
the possibility of plant extract to be a corrosion inhibitor.
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J. K. S. Sayani and B. Lal
Based on the study done on the effectiveness of non-ionic surfactants as corrosion
inhibitors which are aniline and ethoxylated aromatic amine, it is found that by
increasing the concentration of corrosion inhibitor, the corrosion rate decreases and
the inhibition efficiency also increases [7].
4.3.3 Internal Coating
Coating can be one of the options to control the corrosion rate. Some confusion
might happen from the words of liner and coating which liner can be referred to as
the materials used to protect the internal surface whereas coating can be referred to
as the materials used to protect the external surface. In minimizing or preventing the
corrosion rate, both protection from the internal coating and external coating can be
done.
Various types of coating can be used for the internal protection of the surface. For
examples are polymeric liners, clad materials, and refractive index. The consideration
of polymeric liner as an internal coating is due to its properties such as adhesion, abrasion resistance, ability to withstand rapid changes of pressure without blistering or
flickering, elasticity to withstand stresses during construction, and ability to withstand
welding temperature. Moreover, properties like electrical resistivity, aging, water and
moisture resistance, flexibility, resistance to a salt solution, solvent, hydrocarbon, and
wet H2 S gas are other properties that are also taken into account during the selection
of polymeric liner. Different types of polymeric liners have different properties that
are suitable for only certain functions. The most widely used liners in the oil and gas
industry are epoxies and vinyls other than phenolics, furans, neoprenes, and alkyds.
Refractive index is another internal coating that can be used for reducing the
corrosion rate. This liner typically is used for the surface material that is exposed to
high temperatures such as in refineries. This liner typically has a range of thickness
between 25 and 100 mm. The performance of this liner can be measured from its
installation, materials, process, and fabrication. Some examples of the materials used
for refractories are silica, chromium oxide, silicon nitride, and alumina.
4.3.4 External Coating
Pipeline corrosion can happen in the external and internal of the pipeline due to
exposure to the corrosion factors. As for corrosion that occurs outside the pipeline,
the corrosion control can divide whether exposed to the atmosphere or exposed to
the underground environment. For the corrosion that happened due to atmospheric
exposure, the corrosion can be controlled by implementing the electrically insulated
coating. The various coating can be used, for example, polymetric coating, metallic
or thermal spray coating, girth weld coating, insulator, and concrete coating.
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Polymetric coating is an anti-corrosion coating that is applied to the above-ground
structure and to buried infrastructures. Some of the usages of this coating are in the
1980s, extruded three-layers coatings were used with an inner fusion bonded epoxy
(FBE) layer, an adhesive layer, and an outer polyolefin (polyethylene or polypropylene) layer. In the 1990s, the composite coatings were used, with an inner FBE, a
graded layer of FBE, and modified polyethylene, and followed by an outer polyolefin layer. Some examples of the materials used as polymetric coating are coal tar,
asphalt, and extruded polyolefins.
Next, girth weld coating is used during pipeline construction as a protective
fluid coating that is placed to the metal surface of two pipes that have been
linked throughout their circumference. It is also known as joint coatings since it
is commonly applied to protect weld-joint. Some types of girth-weld coatings that
have been used in the oil and gas industry are tape coating, heat shrinkable coating,
polyolefin, and powder epoxy. Tape coating can be applied based on the type of
application whether it is a hot or cold application with different maximum operating
temperatures. Each of the applications has its tape that can be used as each tape has
its own characteristics. For example, the bituminous tape is a single and multi-layer
tape that can be applied with or without primer whereas petrolatum is a single and
multi-layer tape that must be applied with primer. It also has a typical maximum
operating temperature of 30 °C. Next, heat shrinkable coating is a protective coating
in form of a wraparound or tubular sleeve for the pipeline that consists of an adhesive
and an extruded polyolefin outer layer. This coating will shrink in size and adapt
to the surface it is surrounded by whenever the heat is applied to it. The shrinking
process of the coating is caused by the polyolefin contained in the sleeve which
also the uniqueness of this coating. The types of heat-shrinkable coating can be
classified into two, whether with primer or without primer. Each of the tapes has its
maximum operating temperature as an indicator for the selection of tape during the
decision-making process. For example, polyethylene with no primer has a maximum
operating temperature of 50 °C. In addition, liquid epoxy, urethane, vinylester, wax,
elastomeric, and visco-elastic coatings are the other types of girth weld coating.
Besides, metallic coating or also known as thermal spray coating is used for
applications such as wear resistance, corrosion resistance, bioactivity, and dielectric
properties of light metal [8]. This coating protects the steel from these problems by
acting as a barrier coating or sacrificial anode It is also widely used in industrial
sectors such as biomedical, automotive, and aerospace as a protective coating due to
its suitability to be applied to all types of materials such as pure metals, metal alloys,
oxide ceramics, cermets, plastics, and hard metals (carbides). However, materials
that can easily decompose irreversibly during melting, not stable during melting,
and vaporize excessively in the spray process are not suitable for this coating [9].
Silicon ceramics and magnesia are examples of materials that are not suitable for
this type of coating.
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4.3.5 Cathodic Protection
Cathodic protection is an electrical method for corrosion control for the metallic
structure that is widely used in the underground system. For examples of the system
are lead cables, water treatment equipment, steel piling, well casing, and water storage
tank. In this method, there is the involvement of current that flows from the more
negative potential metal to more positive potential metal through an electrolyte.
Some corrosion types that can be related to this protection method are electrolytic
corrosion and galvanic corrosion. Electrolytic corrosion occurs due to the existence
of current from an external source that enters and leaves a particular metallic structure through an electrolyte. This type of corrosion mechanism is also called stray
current corrosion in the underground work. Next, galvanic corrosion occurs due to the
differences in the electrolyte such as soil resistivity, oxygen concentration, moisture
content, and various ion concentration.
The cathodic protection mechanism requires an anode, a cathode, and an electrolyte to occur. For the cathode or the structure to be protected, the commonly
used materials are made up of iron, steel, and stainless steel however, there are
other metals that also can be used as a cathode such as lead, sheathed cable, copper,
and aluminium piping, galvanized steel, and cast iron. Moreover, the widely used
electrolyte is soil or water but there are also some unusual electrolytes that can
be used successfully which are concrete, calcium chloride, and caustic soda. In
conducting the cathodic protection, there are two methods that can be applied which
are impressed current cathodic protection and sacrificial anode cathodic protection.
Firstly, impressed current cathodic protection requires the anode to be energized by
an external direct current (DC) power source which the anode will be then installed
in an electrolyte and connected to the positive terminal of the DC power source. For
the structure that to be protected, it will be connected to the negative terminal of the
power source. This method is also known as a rectifier system. Next, for the anode
of this method, it requires the one that has longer life as it will corrode during the
process. Examples of the anode are graphite, high silicon cast iron, and precious metal
oxide coated titanium. Secondly, the sacrificial anode cathodic protection requires
the usage of the galvanic anode with a higher energy level or potential compares to
the structure to be protected. In addition, the anode of this method can be whether
magnesium or zinc whereas the structure to be protected can be made up of material
such as iron or steel. However, the selection of the anode and structure can be done
based on the electromotive force series where the lower material in the ranking will
be the anode.
4.3.6 Process Optimization
Process optimization is another method that can be used for corrosion control. This
method is implemented by altering the environment of the equipment or pipeline to
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69
make it not corrosion favorable. The operating condition such as pH, temperature,
pressure, and flowrate will play the important role in this method. However, this
method can be said to be expensive and sometimes impractical environment.
4.4 Corrosion Prediction Models
The Corrosion prediction models are used to estimate and predict the corrosion rate
of the equipment of the pipeline. This can be done during the designing of the equipment in order to plan or strategize the corrosion control measure that needs to be
implemented to minimize the corrosion that might occur. Several corrosion predictions models have been developed with different environmental factors, limitations,
and philosophies during the development of the models. The prediction model can
be classified as a mechanistic, empirical, or semi-empirical model. For a mechanistic
model, it has a strong theoretical background and considers the mechanisms of basic
reactions such as chemical, electrochemical, and transport processes. Whereas the
empirical model has a very little theoretical background with a foundation of a set
of basic empirical correlations. For the semi-empirical model, it is partly based on
well-established theoretical hypotheses. They are expanded for practical reasons to
regions where there is insufficient theoretical knowledge in such as way that the extra
phenomena may be explained empirically [10]. It is observed that for the cases of
lack of corrosion or very low corrosion rate, it happened because of the formation of
protective films and/or oil wetting factors [11].
Institute for Energy Technology (IFE) has conducted several discussions with
corrosion experts from different oil companies such as Saudi Aramco, Shell, Chevron,
and BP. The outcome from these meetings is a guideline for the use of a CO2 corrosion
prediction tool that can be used to estimate the corrosion rate in the oil and gas
industry. Some of the prediction models that have been evaluated from the IFE joint
projects are as follows:
• HYDROCOR
• CORPLUS
• KSC MODEL
• MULTICORP
• NORSOK model
• De Waard model
• Cassandra
• ECE model
• PREDICT
• Corpos
• SweetCor
• Tulsa model
• OLI model
• ULL model
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4.4.1 Hydrocor
HYDROCOR model is a mechanistic model developed by Shell. This model is a
combination of corrosion and fluid flow modeling with a few assumptions. The flow
considered for this model is extended to slug flow which brought to a mean that it
ignores the bubble collapsing in the slug mixing zone [12]. It also assumed that there
is no protection film during the presence of water formation and weak protection
film for condensed water cases [13]. Oil wetting factor is considered when the water
cut is below 40% and liquid velocity is above 1.5 m/s however, this consideration
is only valid for crude oil systems but not for gas condensate [14]. For this model,
it requires the inlet pressure and temperature, mole percentage of CO2 and H2 S,
production rates, bicarbonate, organic acid, glycol content, and pipe diameter as the
main input parameters. Some additional information that is also required is oil type
(gas condensate or crude oil) and water type (condensed water or formation water).
This model considers the small effect of protective films and less sensitivity of the
variation of pH.
4.4.2 Cassandra
Cassandra is a predictive tool from BP that considered the corrosion in multiphase
flow. For this model, it considered a weaker effect of protective layer at high temperature than in the de Waard model but does not specify the effect for the cases whether
with or without the water formation [12]. This model requires temperature, total
pressure, liquid velocity, water chemistry, and mole percentage of CO2 as the main
input parameters. Other information that also can be used if available are glycol
concentration, oil type (condensate or crude oil), water type (condensed water or
formation water), hydraulic diameter, and presence of acetic acid. This model also
considers the relatively little effect of protective films and pH but not the oil wetting
effect [14].
4.4.3 De Waard
The De Waard model is a semi-empirical model that was originally developed by De
Waard and Milliams in 1975. It has been revised several times due to the correction
factors added to the original correlation that considered the effect of pH and little
consideration on the formation of the protective film [13]. For the first version that
developed in 1975, the dependency of the correlation is temperature and CO2 partial
pressure. Some correction factors have been introduced in the 1991 version and
1993 version, which respected the effect of pH and corrosion product, and effect
of fluid velocity, respectively. For the correlation that was revised in 1995, it takes
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into consideration the effect of mass transport, fluid velocity, and steel composition.
The requirement of main input parameters only takes water cut, temperature, liquid
velocity, CO2 partial pressure, and pH. For the information that can be used depending
on its availability are hydraulic parameters, oil type (condensate or crude oil), water
type (condensed water or formation water), total pressure, and glycol concentration.
The consideration of protective films is only when no presence of formation water
as it is neglected because of the possibility to break down when with the presence
of formation water. It also assumed that for water cut below 30% and liquid velocity
above 1 m/s, oil wetting and no corrosion occurs but only for crude oil system, not
for condensate [14].
4.4.4 NORSOK
NORSOK model is an empirical model developed by the Norwegian oil companies
Statoil, Norsk Hydro, and Saga Petroleum. It takes into account the effect of protective
layers especially at higher temperatures and higher pH. The range of temperature
that can be applied to this correlation has been wider than the original one as it
considers temperature from 5 to 150 °C [15]. The major input parameters required
by this model are glycol concentration, pH, temperature, total pressure, CO2 content,
and wall shear stress. The calculation of pH can be based on three ways; pH will
be based on temperature and CO2 partial pressure for the condensed water without
corrosion product, pH can be calculated for the pH in condensed water saturated with
iron carbonate produced, and pH will be based on specified bicarbonate content and
ionic strength from water analysis for pH of formation water [14]. This model does
not consider the effect of oil wetting Scales.
4.4.5 Lipucor
Lipucor model is an empirical model developed by Total that has two versions.
The first version is a point model that the calculation of the flow regime will be
at one point in a pipeline or well and a version that is based on fluid flow model
which developed by the collaboration of Total, Elf, and Institute Français du Pétrole
[14]. The parameters such as mole percentage of CO2 and H2 S, water chemistry,
total pressure, pipe diameter, production rates, and temperature are the major input
required. For the other information that can be used depending on its availability
are steel composition, oil density, and gas molar weight. For this model, the pH
has a relatively small effect on the corrosion rate, yet the calculation of pH will be
based on the water chemistry, temperature, and CO2 content. The oil wetting factor
is considered in this model. By utilizing this model, it is capable to indicate the
corrosion type (general or localized) and the severity of the corrosion that might
happen.
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4.4.6 ECE
Electronic Corrosion Engineer or ECE model is developed by Intetech which inspired
by the De Waard 95 model but includes a module for calculating pH from water
chemistry and bicarbonate produced by corrosion, as well as an additional correlation
for the influence oil wetting. The oil wetting factor is considered in this model which
is based on a light crude oil field’s tube corrosion and determined by the density of
oil, flow inclination, and liquid flow velocity [14].
4.4.7 KSC
KSC model is a mechanistic model that developed at Institute for Energy Technology.
Electrochemical reactions at the steel surface, chemical reactions in the liquid phase,
species diffusion to and from the bulk phase, diffusion via porous corrosion films, and
precipitation of iron carbonate in the corrosion film are all simulated in this model.
The corrosion rate that calculated by this model can be with and without protective
films but for the calculation without protective film, it will be used for the calculation
for risk of mesa attack [14]. The main input parameters required by this model are
CO2 partial pressure, pH, total pressure liquid flow velocity, and temperature. This
model does not consider the oil wetting factor but the protective layer factors that
are considered is sensitive to pH and temperature.
4.5 Machine Learning in Corrosion
TIn the oil and gas industry, corrosion can occur in any sector whether upstream,
midstream, or downstream. Any area of metal that is exposed to corrosion factors such
as oxygen, hydrogen sulfide, and carbon dioxide can lead to a corrosive environment.
Since the upstream sector involves the exploration and production process of crude
oil and natural gas, corrosion tends to occur during the drilling process when the
equipment exposes to any corrosion factors. Moreover, in order to sustain the oil
production, the reservoir will be injected with water to increase the internal pressure.
Excess exposure towards high water cut, temperature, and pressure can lead to a
corrosive environment resulting in corrosion to occur.
Next, the midstream sector relates to the transportation of raw materials, products,
and transition substances to downstream sectors. This transportation includes crude
oil, natural gas liquid (NGL), liquid petroleum products, oilfield steam, condensate,
oilfield water, multiphase fluids, gas, and liquid or dense phase CO2 . In the midstream
sector, pitting & crevice corrosion have a high chance to occur in the equipment such
as pipeline, fittings, flanges, valves, bolting, and gaskets. As the transportation of
products involves various types of fluid, the factors that influence corrosion to occur
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are also different. For example, corrosion factors for oil pipelines are water holdup,
solid deposition, bacteria, velocity, CO2 , H2 S, and oxygen ingress whereas corrosion
factors for the gas pipelines are solid deposition, methanol, elemental sulfur, water
holdup, critical gas velocity, polysulfides, H2 S, CO2 , oxygen ingress, bacteria, and
chlorides. Lastly, corrosion factors for water pipelines are microorganisms, scale
formation, oxygen ingress, solid accumulation, CO2 (aq), and H2 S (aq).
All the raw materials found in upstream will be transported to downstream through
midstream. In downstream, all the raw materials will be processed prior to the purpose
of the sale. This is the sector where the crude oil refineries, petrochemical plants, and
petroleum product distribution take place and if the corrosion is not well controlled,
it can affect the production period and cost. A scheduled shutdown or turnaround is
a common thing that happens in downstream in order to perform maintenance and
repair the equipment however if uncontrol corrosion happens, frequent unscheduled
shutdown can lead to an increase in the cost of maintenance and production loss.
Moreover, as some equipment requires to be opened during maintenance, it will
increase the chances of the internal equipment to be exposed to air and moisture
which can influence corrosion and stress corrosion cracking (SCC) to occur [4].
In investigating and determining the corrosion rate can be done by experimental
method or computer simulation method. For the experimental method, the corrosion
rate can be determined by observing the weight loss experienced by the materials
or using the Tafel extrapolation method while for the computer simulation method,
a machine learning application can be implemented to predict and determine the
corrosion rate.
4.5.1 Weight Loss Method
The weight loss method is one of the methods that can be used to determine the
corrosion rate experimentally. However, this method is time-consuming as there
is no driving force applied during the experiment. The change of weight of the
metal specimen after being removed from the environment will be weighed and the
corrosion rate will be calculated based on the formula as follows [16].
Corr osion Rate(C R) =
KW
ρ At
where K = constant, 534
W = weight loss in milligram (mg).
ρ = density of the metal sample in gram per cubic centimetre (g/cm3 ).
A = sample area in square inch (in2 ).
t = time of immersion (exposure) in hours (h).
CR = corrosion rate in milli-inch per year.
(4.3)
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4.5.2 Tafel Extrapolation Method
The Tafel extrapolation method is another method to determine the corrosion rate of
material by estimating the corrosion current or corrosion potential in an electrochemical cell. This method is faster than the weight loss method is it involves an electric
current that can act as the driving force. It is an accurate method as it considers
all the reactions that involve in the corrosion. This consideration is made based on
the assumption of simple electrochemical kinetics, to represent the mixed potential
theory, predict the corrosion rate and potential based on the kinetics and thermodynamics of all the reactions occurring on the electrode surface. In determining the
corrosion rate, a Tafel graph is plotted in order to determine the Tafel slope that will
be required in the calculation of corrosion rate based on the formula as follow:
1
)
i corr = (
)(
c
2.303R p ββaa ×β
+βc
)
(
i corr × t × M
× 10
V =
F ×S×d
(4.4)
(4.5)
where i corr = corrosion current density in amperes per square centimetre (A/cm2 )
R p = corrosion resistance in ohms square centimetre (Ω cm2 ).
βa and βc = Tafel slopes.
V = corrosion rate of polarization in millimetre per year (mm/year).
t = immersion time.
M = equivalent molar weight in gram per mol (g/mol).
F = Faraday constant (96,500 C.mol−1 ).
S = surface area of electrode.
d = density of iron.
4.5.3 Machine Learning
Over the years, various studies have been done related to predicting corrosion rate or
corrosion behaviour using the machine learning approach. Both machine learning and
experimental approaches have been used however, the machine learning approach
has been a hot topic lately due to the world migration into Artificial Intelligence (AI)
and more accurate outcomes compared to experiments.
Most of the studies done on this corrosion topics majority are using neural network
technique for the machine learning part. According to the study of prediction of
pipeline internal corrosion profile, it was found that the technique used was feedforward neural networks which was a part of the neural network learning approach. De
Masi et al. [17] proposed the usage of the Levenberg–Marquardt back propagation
algorithm in predicting the internal corrosion profile of a pipeline and in identifying
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75
the pipeline sections that were critically exposed to the corrosion risk. In this study,
three types of input variables were alternated with another three output variables in
achieving the objectives of the study. The three types of input variables were geometrical pipeline characteristics, fluid dynamic multiphase variables, and deterministic
models whereas the three output variables were corrosion rate, metal loss, and area
of defects. In this study, the correlations used to estimate the corrosion rates were de
Waard and NORSOK model.
Next, referring to the study done by Canonaco et al. [18] that observed the
behaviour of three types of machine learning on internal corrosion of pipeline, all
the input data used which were geometrical variables and fluid dynamical variables
were supplied from the Pipeline Inspection Gauge (PIG) and dynamic multiphase
flow simulator (OLGA), respectively. For the performance of the learning algorithms
which were Support Vector Machine (SVM), Neural Network, and XGBoost, it split
the data into two sets with 80% for the training set and 20% for the test set. It
also used cross-validation on the training set to maximize the performance of the
learning algorithm. In this study, it showed that Support Vector Machine (SVM) was
able to capture all of the phenomenon’s components better than XGBoost and Neural
Network.
In addition, the study of corrosion rate prediction using ANN in a carbonated mixture of MDEA-based solution showed that the ANN model showed a
better performance compared to the SVM model [8]. In this study, the Levenberg–
Marquardt BP algorithm was also chosen due to its smallest mean square error
(MSE) and the lowest training time. From the results obtained, it showed that pH,
solution type, and conductivity of the solution showed a higher correlation coefficient
compared to the PZ, DEA, and MEA concentrations to corrosion rate.
Based on the study on the identification of the crucial factors of corrosion done by
De Masi et al. [17], it used an artificial neural network (ANN) as it was the most useful
application for the prediction of internal corrosion. The input and out parameters used
in this study were based on the historical and current operating data of the pipeline.
For the input parameters, it used four categories of data which were hydrocarbon characteristics, geometrical pipeline characteristics, fluid dynamic multiphase variables,
and deterministic models whereas the output parameter was corrosion volume loss.
For the first category of the input parameters, it only included CO2 partial pressure,
the second category includes elevation, inclination, and concavity of each segment,
the third category includes flow regime, hold-up, pressure, gas flow, total flow, liquid
velocity, and gas velocity, and lastly, the fourth category includes de Waard and
NORSOK. For the evaluation of the dependency between the observed corrosion
and the variables, Mutual Information (MI) analysis was used. This study concluded
that at the finest scale of the region exposed to corrosion that at 10 m, it was found
that hold-up, concavity, liquid velocity, and gas velocity had the highest influence
on corrosion which were at 0.84, 0.64, 0.60, and 0.50 of the Mutual Information.
From the studies, all the findings obtained show that in determining the most
suitable and accurate algorithms and the number of hidden neurons, a trial and error
method is used as each of the options has its preferable environment to operate with.
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This machine learning method also can be the fastest method to be used in predicting
the corrosion rate of equipment depending on the size of the dataset used.
4.6 Case Study
Throughout the years, there are many studies done on the prediction of corrosion rate
using the machine learning approach. Various studies were done on this corrosion rate
prediction using the machine learning approach with different operating conditions
and the algorithms utilized in achieving the objectives.
For the study done on the prediction of the lifetime of a pipe by Shaik et al. [19],
it showed that corrosion played the most important role in the factor that influence
the lifetime of the pipeline using the Artificial Neural Network (ANN) approach. In
this study, there were two sections which were the machine learning development
and sensitivity analysis. For the machine learning part, it was performed based on the
corrosion, wall thinning, age, external diameter, pressure, thickness, and product type
as the input, and the pipe condition target was the output. The data used in this study
was based on the real-time operating data from the risk inspection date. In conducting
the machine learning, the data samples were divided into three groups with 70%,
20%, and 10% for the training set, testing set, and validation set purposes. As for
the training algorithm implemented in this study, it used the Levenberg–Marquardt
algorithm as it was the fastest Back Propagation (BP) algorithm. As ANN consisting
of three layers that included input, hidden, and output layers, the machine learning
model was developed by implementing 7 neurons in the input layer that represented
all the input parameters, 10 hidden neurons in the hidden layer, and 1 neuron in the
output layer that represented the pipe condition target as the output of the machine
learning model.
In addition, the sensitivity analysis was done with the purpose to determine the
correlation or the impact of each factor of the machine learning’s input on the pipe life
condition which was the output of the machine learning. From this analysis, it found
that corrosion was the factor with the highest negative impact on the pipe condition
with 44% compared to other factors. It can be explained that increasing corrosion will
affect decreasing pipe conditions. This study concluded that the machine learning
developed was considered as successful reflecting on the coefficient of determination
(R2 ) value obtained from the training, testing, and validation performed which were
0.9927, 0.9953, and 0.9842, respectively.
Next, based on the study done by Diao et al. [20] on the influence of the chemical
composition of low-alloy steel and environmental factors on the corrosion rate, it
was found that both factors affect the corrosion rate of the pipe. In determining the
key factors of the corrosion rate, the combination of gradient boosting decision tree
(GBDT) and Kendall correlations were used due to its higher coefficient of determination (R2 ) in the training and testing sets compared to the principal component analysis (PCA) method. The R2 values for both training and testing sets of the preferred
method were 0.94 and 0.92, respectively. Based on this method, the key factors that
4 Machine Learning in Corrosion
77
had the highest influence on corrosion rate were maximum dissolved oxygen content,
maximum seawater salinity, minimum pH value, and mean temperature for the environmental factors whereas Cr, C, Si, P, and S contents for the materials factors. After
the determination of key factors, machine learning was developed with all the key
factors as the input and corrosion rate as the output.
For the machine learning development, it used Phyton and random forest algorithm as the modeling algorithm. This was because the random forest algorithm had
the highest coefficient of determination (R2 ) and root-mean-square error (RMSE)
compared to the k-nearest neighbor model, multilayer perceptron model, support
vector regression model with radial basis function kernel, linear kernel, and polynomial kernel. In the machine learning development, the data samples were divided
into training and testing sets. The division of the groups was based on 40%, 50%,
60%, 70%, 80%, and 90% for the training set and the remaining for the testing sets.
In this study, the development of machine learning implemented the random forest
approach with 80% for the training set and 10% for the testing set. This was due to
above 80% of the training set causing the accuracy of the model started to decrease
which was believed to be caused by overfitting. Lastly, for the prediction of corrosion
rate based on the key factors, the features λ– , V – , EN p– and ΔH f– under method II
were selected for the prediction of corrosion rate due to its higher accuracy in terms
of RMSE and R2 compared to the method I that utilized the usage of EN s , K, Rm,
and Rc features. The R2 of method II for the training and testing sets were 0.91 and
0.90, respectively. Thus, based on the values of R2 and RMSE for the chosen method
used, it showed that the machine learning developed was reliable for the corrosion
rate prediction.
Besides, Pei et al. [21] proposed the usage of the random forest machine learning
approach to study the relation of corrosion rate on the environmental factors. Among
all the listed environmental factors investigated which were temperature, relative
humidity, rainfall, sulfur dioxide, nitrogen dioxide carbon monoxide, ozone, and
particulate pollutant, only the first three parameters were considered as the key
factors that influence the corrosion rate. This was due to its higher importance index
compared to others. The importance index was determined using the random forest
method. for the machine learning development, the data samples were split into two
groups which were 90% for the training set and the remaining 10% for the testing
set. This study also included the results for the Artificial Neural Network (ANN)
and support vector regression (SVR) other than the random forest for comparison
purposes. By considering the three key factors as the input and atmospheric corrosion
monitoring current (IACM ) as the output of the machine learning, the random forest
had the highest accuracy in terms of R2 compared to ANN and SVR. The R2 values
for the training and testing samples were 0.526 and 0.457, respectively.
In addition, another machine learning model was developed known as the
corrected model. This corrected model included the formation of rust on the atmospheric corrosion monitoring (ACM) sensor as one of the inputs. This consideration
caused the R2 of the random forest to increase from 0.526 to 0.940. For this corrected
model, the random forest method still had the highest R2 than ANN and SVR with
0.94 for both training and testing samples. The consideration of rust formation was
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due to when the atmospheric corrosion occurred, the layer of the steel electrode
became thicker and the roughness of the surface increased due to the rust formation.
This situation led to enhancement ability by the sensor surface to collect moisture
and decrease in the critical relative humidity to generate the IACM output. This study
concluded that the corrected random forest model was a better machine learning
model to be used for the prediction of corrosion rate as the number of the samples
correctly predicted and the accuracy rate was higher than the first random forest
model developed with 6196/6540 and 94.7%, respectively.
References
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industry. Koroze a Ochr Mater 61:100–117
4. Al-Janabi YT (2020) An overview of corrosion in oil and gas industry: upstream, midstream,
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17. De Masi G, Vichi R, Gentile M, Bruschi R, Gabetta G (2014) A neural network predictive model
of pipeline internal corrosion profile. In: 1st International conference on systems informatics,
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18. Canonaco G, Roveri M, Alippi C, Podenzani F, Bennardo A, Conti M, Mancini N (2021) A
machine-learning approach for the prediction of internal corrosion in pipeline infrastructures.
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19. Shaik N, Pedapati S, Dzubir FAB (2022) Remaining useful life prediction of a piping system
using artificial neural networks: a case study. Ain Shams Eng J 13:101535
20. Diao Y, Yan L, Gao K (2021) Improvement of the machine learning-based corrosion rate
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Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor
via machine learning. Corros Sci 170:108697
Chapter 5
Machine Learning in Asphaltenes
Mitigation
Ali Qasim and Bhajan Lal
Abstract The issue of Asphaltenes formation inside the pipeline is a major concern
in flow assurance industry. These are complex polar molecules with high molecular weights. Asphaltenes mitigation is required as they disrupt the normal operation of the pipeline. Industry employs mechanical, ultrasonic, thermal, bacterial
and chemical treatments to mitigate asphaltenes deposition. For asphaltenes prediction, preceding studies have used thermodynamic solubility technique, colloidal
based models. Currently researchers have focused on machine learning techniques
to predict the conditions of asphaltenes formation. The machine and deep learning
methods included Bayesian belief network (BBN), Least-squares support vector
machine (LSSVM), Support vector regression (SVR) and Genetic algorithm-support
vector regression (GA-SVR). It was found that the use machine learning and deep
learning approaches predicted accurately about the onset of asphaltenes precipitation
and deposition. In future, the utilisation of machine learning approaches in the field
of asphaltenes mitigation can be studied further.
Keywords Machine learning · Flow assurance · Asphaltene · AI
5.1 Introduction
Asphaltenes are complex molecules with the largest molecular weight known in
crude oils, are far more hazardous than typical wax deposition. These are essentially
polar molecules [1, 2]. Figure 5.1 depicts a part of asphaltene that originates in the
pipeline.
In terms of chemical composition, asphaltenes are made up of heteroatoms such
as carbon, nitrogen, hydrogen, sulphide, and oxygen. Aromatic structures (cyclic
A. Qasim
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
B. Lal (B)
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
e-mail: bhajan.lal@utp.edu.my
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_5
81
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A. Qasim and B. Lal
Fig. 5.1 Asphaltenes formation inside pipeline
in shape) are also produced. In a variety of solvents, asphaltenes have different
solubility ratios. It is highly soluble in some solvents, such as benzene and toluene,
but insoluble at room temperature in n-pentane and n-heptane. Asphaltene can form
in heavy oil when temperature and pressure conditions, as well as the makeup of the
oil’s phases, alter. The compounds in this chemical cause clogging inside oil pipelines
and huge reservoir wells, making it dangerous. Because of its proclivity to associate
and aggregate, asphaltenes have been termed the “Cholesterol of Petroleum”. They
are a viscous substance that boils at a high temperature [3, 4].
Asphaltene content in oils can range from less than a percent to as much as
20% [5, 6]. A range of analytical methodologies have been used to characterise
the structure and size of asphaltene aggregates. The structure of the asphaltene
molecule, on the other hand, has remained a mystery. Structural characterization
includes determining the chemical components, molecular weight and size, and
molecule architecture. Heavy metals like Nickel and Vanadium have the potential
to change the reactivity of asphaltene molecules [7].
5.2 Asphaltene Precipitation and Deposition in Oil and Gas
Industry
The point at which asphaltenes begin to separate from oil is known as the commencement of asphaltene precipitation. One of the first stages in preventing asphaltene
precipitation is to understand the asphaltenic fluid and its behaviour under various
operating conditions from a thermodynamic aspect. Asphaltene precipitation occurs
inside the asphaltene precipitation envelope (APE), which is defined by sufficient
5 Machine Learning in Asphaltenes Mitigation
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temperature and pressure conditions [6, 8]. Figure 5.1 depicts a typical asphaltene
phase envelope. APE can provide useful information on the way to adapt production
settings to avoid asphaltene precipitation from reservoir fluid under high pressure
and temperature. When reservoir pressure falls below the start of asphaltene precipitation pressure, which is lower than reservoir pressure but usually much higher
than the saturation pressure of the active reservoir fluid, asphaltenes precipitate as
shown in Fig. 5.2. As pressure decreases below the beginning precipitation pressure,
the amount of asphaltene precipitation increases, reaching its maximum value as
pressure approaches the bubble point pressure.
As a result, the presence of gas components effects asphaltene precipitation, and
the overall system’s composition and pressure control the stability of these precipitated particles. The operational pressure and temperature are represented by the
P–T diagram. When modelling asphaltene precipitation with thermodynamic phase
equilibrium, it is necessary to determine the energy associated with molecules as
well as the parameters of interaction functional groups in the molecules [1, 9]. This
emphasises the need of identifying and establishing the structure and properties of
asphaltenes at the molecular level. Due to the improvements in research and the
use of sophisticated analytical instruments, studying asphaltenes at the molecular
level has resulted in the development of better thermodynamic prediction methods.
Using equations of state, the structural analysis findings are applied to forecast the
APE. Flow assurance engineers use this APE data to design production systems that
maximise oil production while minimising asphaltene precipitation in reservoirs and
flowlines [10]. However, precipitation is not the only factor that effects deposition;
in order to form a layer, particles must contact the wall and adhere to it [11].
Asphaltene deposition is a serious flow assurance issue in oil and gas production,
and resolving it takes a tremendous amount of capital. It is developed when the
stability of asphaltene is affected by pressure, temperature, and composition of the
involved system. The higher the asphaltene onset pressure, the lower the pressure at
Fig. 5.2 Asphaltene
precipitation envelope [6]
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which asphaltene forms which is also known as Upper Asphaltenes Onset Pressure
(UAOP) [12]. This phenomenon can exist near the wellbore as well as along the flow
lines, expanding the potential deposition zone from the reservoir and near-wellbore
regions to surface facilities. The lower asphaltene onset pressure is the pressure below
the bubble point at which asphaltenes dissolve in a solution (LAOP). Asphaltene
deposition is estimated the use of De Boer plot: This technique was suggested by De
Boer et al. to assess when oil begins to form a solid phase, resulting in a reduction
in flow rate. As revealed in Fig. 5.3, the De Boer plot shows the pressure differential
between the starting pressure and the bubble point pressure against the initial density
of the oil sample. There are three distinct zones depicted in this diagram: (1) Region
A, which has a high risk of serious flow problems, (2) Region B, which faces moderate
issues, and (3) Region C, which confronts minimal or minor flow issues [13].
When researching asphaltene flocculation and deposition in a pipe, the electro
kinetic influence must be taken into account. This impact is caused by the electrical
potential difference in the conduit caused by the movement of colloidal particles
in the oil mixture, which promotes asphaltene deposition and clogging. Some of
the parameters that have a considerable impact on this occurrence are the conduit’s
characteristics include electrical, thermal, and wettability characteristics, pressure,
temperature, flow regime, and oil mixture quality.
Broseta et al. used a stainless-steel capillary to test the onset point and rate of
asphaltene deposition when pressure, temperature, and composition were varied. This
was accomplished by lowering the pressure across the capillary tube, which revealed
the creation of a deposition layer. They also altered the standard light transmission
mechanism to make it more receptive to fluids with a trace amount of asphaltene in
them. Asphaltene deposition happened when the asphaltene was unstable under the
process conditions. This procedure was also utilised to see if asphaltene deposition
could be reversed [15].
Fig. 5.3 De Boer plot for Bubble Point [14]
5 Machine Learning in Asphaltenes Mitigation
85
Fig. 5.4 Cylindrical Taylor-Couette (TC) cell [14]
Taylor-Couette (TC) cell is a high-pressure cell that can be used in real-world oil
fields to manufacture organic deposition including asphaltenes and waxes [14]. The
Taylor-Couette (TC) cell is depicted schematically in Fig. 5.4. The middle half of the
cell is made up of a spinning spindle that can simulate pipe flow (Fig. 5.4, part b).
The cell also has a cooling/heating bath that adjusts the temperature by circulating
the coolant around cylinder. Akbarzadeh et al. [16] used the TC cell, but with a few
modifications to reduce the cell’s shortcomings. To address the cell’s inability to
function with a low level of asphaltene, they used the fresh sample of oil. The most
critical component in the deposition rate was found to be the shear rate. There were
also more deposits at the reservoir oil’s bubble point pressure.
5.3 Asphaltene Mitigation Procedures
5.3.1 Mechanical Method
The mechanical method is to scrape the asphaltene sediment out of the wells mechanically. The most common approach is to use a wire line unit; however, this method is
slow and expensive when dealing with a long and tough deposit. Another approach is
to utilise a Coiled tubing device and a hydro-blasting equipment to drill the deposit.
This device, however, has the disadvantage of limiting operating pressure, making
cleaning more difficult. Another method of cleaning is to apply pressure to the deposit
in order to create a differential pressure across it [17].
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5.3.2 Ultrasonic Treatment
Ultrasonic technology can be used to clean asphaltene-clogged portions of oil wells
and reservoirs. Ultrasonic vibrations appear to disturb the maltenes that form a continuous phase to provide adhesive and ductile properties to the scattered asphaltenes
[13]. Time, power, frequency, and the type of radiation necessary to remove asphaltene deposits all have an impact on the performance of ultrasonic waves. This method
was tested on asphaltene clusters and permeability damage in carbonate porous media
in reservoir rocks produced by asphaltene deposition. The use of ultrasonic radiation
in that crude oil resulted in the disintegration of asphaltene clusters into fine sizes,
as well as an improvement in reservoir rock permeability damage [18].
5.3.3 Thermal Treatment
There are a variety of thermal methods for treating asphaltene deposition, including
hot oiling, which includes injecting a hot oil into the well to remove asphaltene
deposits. This method, however, can be harmful to the formation [19]. A down hole
heater can be used for a short amount of time to melt asphaltene deposits in the
wellbore or on tubing, which can then be pumped up with the created oil. The application of this approach is limited, however, due to the high cost of maintenance and
the shortage of electric power. The use of heat-liberating chemicals, which requires
pumping down a solution of equimolar ammonium chloride and sodium nitrate,
is another thermal approach for eliminating asphaltene deposits. Using a buffer,
the exothermic reaction is postponed until the fluid reaches the bottom-hole with a
significant amount of nitrogen gas. However, in comparison to the traditional thermal
method, this technology is quite expensive [20].
5.3.4 Bacterial Treatment
In this treatment, bacteria are used to remediate asphaltene build-up. Almehaideb
et al. [21] chose specialised bacteria with a high endurance for the extreme temperature and salinity conditions observed in the reservoir rock they studied in the lab. They
exposed damaged cores to microorganisms in their experiment after they lost early
oil permeability due to asphaltene accumulation. On average, bacterial stimulation
resulted in a 54% improvement in defective core permeability.
5 Machine Learning in Asphaltenes Mitigation
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5.3.5 Chemical Treatment
Chemical treatment with solvents is required when mechanical treatments fail to
remove asphaltene deposits. Asphaltene solvents are mostly made up of aromatic
solvents. Alternatives to chemical solvents, such as de-asphaltened oils with high
aromatics content were also used at low cost; xylene, with a low flash point of 28 °C.
Toluene is much more volatile than benzene with a flashpoint of 5 °C, yet it is less
effective. However, for a variety of reasons, including corrosion, safety concerns due
to their low flash point, and environmental concerns, the use of these hydrocarbon
solvents is restricted [5].
5.4 Asphaltene Prediction Models
The asphaltene prediction models include the models for the precipitation and deposition. They are based on thermodynamic solubility techniques, colloidal method
and deposition modelling. The details of these models are discussed in the following
sub-sections.
5.4.1 Thermodynamic Solubility Technique
Thomas et al. [22] formulated a regular solution theory for multicomponent systems
to characterise the asphaltene precipitation behaviours. They used Prausnitz’s GE
= UE equation for a multicomponent solid phase, where GE is the excess Gibbs
free energy accounting for the liquid system’s non-ideality and UE is the excess
internal energy. To simulate asphaltene precipitation in Western Canadian bitumens,
Alboudwarej et al. [23] employed a regular solution theory of the liquid–liquid
equilibrium. They investigated the effects of several solvents on precipitation. As
the system pressure was above the bubble point, a liquid–liquid equilibrium was
hypothesised. They divided bitumens into four basic pseudo-components based on
the Saturate, Aromatic, Resin and Asphaltene (SARA) fraction to overcome the categorization challenge of the regular solution theory: saturates, aromatics, resins, and
asphaltenes. The distribution of molar mass, molar volume, and solubility characteristics of asphaltene was a major challenge when utilising regular solution theory
to simulate asphaltene precipitation. The polydispersity of asphaltene, the difficulty
of lumping asphaltenes as a single component, and determining the precise and reliable amount of precipitation were all compelling reasons to use the Schultz-Zimm
molar mass distribution to partition asphaltene into variable molar mass fractions.
The proposed method was successful in mimicking the commencement of asphaltene precipitation as well as the amount of asphaltene precipitation. At intermediate
solvent/bitumen ratios, however, the model underpredicted simulation results.
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Afterwards, Akbarzadeh et al. [24] updated the molar mass distribution function
of the model employed by Thomas et al. with the gamma function and found the
improved performance. According to the association model, asphaltene and resin
both have active sites that allow them to bind to similar molecules and cause aggregation. Saturate and aromatic components are also considered neutral. Terminators
and propagators are terms used to describe compounds that have one or more active
sites. Finally, a combination of terminators and propagators make up asphaltene
and resin. Thermal and hydrocracking processes modify the distribution function
of molar weight, volume, and solubility, which are key characteristics in asphaltene
precipitation, according to the researchers.
The Flory–Huggins hypothesis was utilised by Hirschberg et al. [25] to model the
amount of asphaltene precipitation. They employed Soave–Redlich–Kwong (SRK)
equation of state (EoS) to simulate a vapour-liquid equilibrium thermodynamic
condition. The liquid phase was made up of the solvent which was asphaltene-free and
asphalt phases which contained asphaltene and resin. The Flory Huggins model had
several discrepancies. First, there was an overestimation of asphaltene solubility in
the presence of a large volume of solvent. Second, the model was unable to predict
the amount of precipitation at pressures greater than 200 bar. Thirdly, asphaltene
was thought to be homogenous. Rassamdana et al. [26] investigated the effects of nalkanes on a light oil sample using the Hirschberg model. They found a discrepancy
between the model predictions and the experimental data. They argued that the difference originated from the concept of reversible precipitation events. A two-component
Asphaltene Solubility Model (ASM) was later proposed based on the Flory–Huggins
theory. Unlike previous implementations of the theory, the composition of the phases
after asphaltene precipitation was not assumed, and the phases were defined by a link
between refractive index and solubility properties. Despite its success in predicting
the emergence of asphaltene, the ASM had a number of flaws discussed in literature
[27, 28]. Changing the solubility parameter and molar volume, for example, required
an optimization technique. Alternative solvents including pentane and pentadecane
were also investigated using the same extent of solubility criteria. The Asphaltene
Instability Trend (ASIST) was proposed based on the same premise, and the solubility parameters for numerous liquid solvents were plotted against the molar volume.
In fact, ASIST found a linear relationship between the solubility parameter and the
molar volume at the onset condition. Although some previous studies investigated the
method’s usefulness for predicting asphaltene instability and the model was unable
to effectively forecast the amount of precipitation [28, 29].
One of the simplest models in asphaltene precipitation is the cubic equation of state
(EoS), which combines the solid phase fugacity correlation and the Peng-Robinson
(PR) EoS. The asphaltene precipitated phase is assumed to be a pure solid in the
model. The model is basic, but it is not exact enough, and it must be updated every
time the components are altered [22]. To calculate the fugacity of components in
a vapour-liquid equilibrium, Ngheim et al. [30] also used PR EoS. In their model,
the heavy fraction of crude oil was divided into two categories: precipitating and
non-precipitating. Dissociated asphaltene/resins made up the first percentage, while
heavy paraffins and non-dissociable asphaltene/resins made up the latter. Although it
5 Machine Learning in Asphaltenes Mitigation
89
was dependent on the precise partitioning of the heavy fractions and their characteristics, the suggested model was successful in forecasting asphaltene phase separation.
Furthermore, in the presence of a large number of solvents, the model failed to
account for asphaltene precipitation. Other studies used PR EoS to investigate the
asphaltene phase behaviour induced by n-alkanes, even designating thirty divisions
to match the molar mass distribution of asphaltene. On the other hand, the effect of
resins and aromatics on asphaltene stability has not been applied further [31, 32].
Based on Wertheim’s perturbation theory to the mixture, Chapman and associates developed Statistical Associating Fluid Theory, SAFT, a common model for
modelling asphaltene phase behaviour. Perturbed Chain Statistical Associating Fluid
Theory, PC-SAFT, appeared to be more suitable for simulating asphaltene behaviour
than prior SAFT model variations [33]. Because the SAFT model’s effectiveness is
heavily reliant on segment lumping and features, such as binary interaction factors,
various methods of lumping in an oil mixture have been reported in the literature.
Molecules are separated into a spherical chain of connected parts according to the
SAFT theory. The number of segments per chain, segment diameter, and van der
Waals energy of segments are all critical parameters that must be adjusted for saturated liquid densities and vapour pressures. These criteria are related to the molecular
weight of a segment.
PC-SAFT EoS was used by Gonzalez et al. [11] and Vargas et al. [34] to conduct
substantial research on the modelling of asphaltene phase behaviour. They observed
the effect of gas injection on the onset of asphaltene precipitation as well as the
pressure at which bubbles form. They looked at how gas injections of methane,
ethane, CO2 , and N2 affected the formation of asphaltene.
To account for the polydispersity of asphaltene, it was split into three to nine
subtractions and the SAFT parameters were modified [34]. Asphaltene precipitation
was exaggerated in the proposed model since it took into consideration the polydispersity effect. Later, Alhammadi et al. [36] compared the PC-SAFT with the CPA,
which contained the Peneloux shift parameter in its physical element, in terms of
prediction performance. Although the Peneloux shift parameter was included to the
cubic EoSs (SRK, PR, and the physical half of CPA), the PC-SAFT fared better in
thermodynamic property modelling at equilibrium. To model the onset of asphaltene
precipitation and bubble point pressure with and without gas injection, Arya et al.
used PCSAFT EoS with the association contribution. They compared PC-SAFT
with the association (WA) to PC-SAFT EoS without the association (WOA) and
CPA EoS outcomes. They used the models to examine six reservoir fluids and found
encouraging results, allowing the commencement of asphaltene precipitation to be
adequately modelled. However, for two of the fluid samples, the PC-SAFT (WOA)
was unable to correlate the higher onset pressure [35] as shown in Fig. 5.5.
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Fig. 5.5 Correlation and prediction of Upper Asphaltene Onset (UOP) and Bubble Point (BP)
pressures, a without gas injection or GI, b with 5 mol% gas injection, c with 10 mol% gas injection,
and d with 15 mol% gas injection [35]
5.4.2 Colloidal Technique
Li and Firoozabadi [37] used PR EoS within the CPA framework to model the asphaltene phase behaviour. A physical component based on PR EoS and an association
component based on Wertheim’s thermodynamic perturbation theory made up the
CPA model. Oil was considered to include three spurious components: saturate,
aromatic/resin, and asphaltene. The single variable in the model is the association
energy parameter. Furthermore, the model succeeded of modelling precipitation incidence but not so much of forecasting the onset point. This problem can be traced
back to a change in the association energy parameter. Shirani et al. [38] used a similar
paradigm to study a real oil sample with fifteen pseudo-components, ignoring the
5 Machine Learning in Asphaltenes Mitigation
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interaction of asphaltene with other components and focusing only on self-association
of asphaltene particles. For the physical portion of the model, they looked at both
SRK and PR EoSs and discovered that SRK EoS performed better than PR EoS
and colloidal models. According to CPA research for modelling asphaltene phase
behaviour, the chemical interaction of monomers leads to association due to the
increase of new species. Some temperature-dependent variables were assumed to be
constant, which is a flaw in the method [37].
The first instance colloidal theory was applied to asphaltene precipitation was by
Pfeiffer and Saal [39]. It was claimed that lighter hydrocarbons in the oil combination
peptize asphaltene, and a lack of resins to cover the asphaltene heavy particles caused
asphaltene precipitation. Asphaltene aggregation and precipitation are caused by the
transfer of peptizing agent which is a resin from the asphaltene phase to the oil phase
and vice versa. The outer layer stabilises the asphaltene due to steric repulsion. As
a result, adding a solvent (n-alkane) causes peptizing agents in the outer layer to
transition to the oil phase, increasing the polarity of the asphaltene phase while
decreasing the polarity of the oil phase, increasing the polarity of the asphaltene
phase while decreasing the polarity of the oil phase. The polarity of the asphaltene
phase reaches a point where the asphaltene micelles become linked and grow in size
at first.
One of the earliest applications of the colloidal approach to model asphaltene
precipitation was by Leontaritis and Mansoori [40]. The particles of asphaltene were
seen as a solid phase surrounded by resins. When an n-alkane solvent was applied
to the oil, asphaltene precipitated. The precipitation condition was modelled using a
Vapour-Liquid Equilibrium (VLE) calculation method. The critical resin concentration was changed based on the experimental findings. The model, however, is only
valid when asphaltene precipitation is reversible i.e. asphaltene dissociation occurs.
Other researchers [41, 42] used the colloidal structure and tweaked it slightly. For
example, Pan and Firoozabadi [42] investigated two phases: a precipitated phase
(asphaltene and resin) and a liquid phase. The thermodynamic parameters of the
species were calculated using EoS, and phase compositions were determined by
minimising the Gibbs free energy. The model has several problems, including the
requirement for a large number of parameters and the absence of a clear relationship between the parameters and thermodynamic conditions such as pressure,
temperature, and composition.
5.4.3 Asphaltene Deposition Modelling
Asphaltene precipitation at a certain pressure and temperature facilitates flow assurance induced problems formation in the reservoir. When the solid phase evolves in the
porous structure, the permeability of the rocks diminishes, lowering inflow performance. The models in the literature, as shown in Fig. 5.6, tend to assume the following
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Fig. 5.6 Asphaltene-deposition-induced rock impairment mechanisms [6]
mechanisms: (1) adsorption of asphaltene nano-aggregates in the reservoir’s capillary pores, forming a deposited layer that partially restricts flow, (2) clogging of pore
throats by larger aggregates caused by particle deposition, and (3) shear removal of
asphaltene particles, increasing the permeability of the rock.
The flow impairment caused by asphaltene-induced formation damage was
detected by researchers using several advanced mathematical models that accounted
for the diffusivity of distinct phases in porous environments. The suspension of
asphaltene nano-aggregates in liquid is considered in most of these models (oil
phase). Gruesbeck and Collins [43] were the first to model the deposition of fines,
up to 5 m, in porous media, indicating major changes in the permeability of the
formation rock and accounting for the reduction in productivity.
The diffusivity model was constructed by Wang and Civan [44], who used an
empirical correlation to account for the deposition of asphaltene. The liquid influx
terms were computed using the Darcy law, whereas the adsorption and clogging
mechanisms in the deposition were described using empirical components. The
overall mass balance of the asphaltene phase is represented by Eq. 5.1, and the
correlation utilised to calculate deposition via adsorption, deposition removal as a
result of drag force, and pore clogging is represented by Eq. 5.2.
ρa
∂wa L
∂
(ϕρa Cnp + ϕρ L wa L μ f ) = ρa
∂t
∂t
(5.1)
∂wa L
= αϕCnp − βwa L (μ L − μ L ,crit ) + ς Cnp μ f
∂t
(5.2)
where ϕ is the porosity, ρ a is the density of asphaltene, C np is the concentration
of nanoparticles, ρ L is the density of the liquid phase, waL is the mass fraction of
dissolved asphaltene in the liquid phase, uf is the mean fluid velocity represented by
the Darcy law, uL is the interstitial velocity of the liquid phase, uL,crit is the critical
5 Machine Learning in Asphaltenes Mitigation
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interstitial velocity of the liquid phase, α is the empirical constant for deposition, β is
empirical constant indicating removal of the deposit, and ζ is the empirical constant
accounting for plug clogging. The constants were not dependent on particle size and
obtained experimentally by core flooding.
For the four phases of gas, oil, water, and asphaltene, Preceding researches undertook comprehensive reservoir simulations in cylindrical coordinates, accounting for
reservoir damage induced by asphaltene deposition [45, 46]. The black oil model was
used to estimate the fluid’s properties. Soulgani et al. [47] proposed a new adsorption
model based on the Arrhenius kinetic equation that avoided Brownian particle diffusion along the pores’ walls. The rate of deposition and the mean velocity of fluids
through the pores was found to be inversely dependent.
Kord et al. [48] revised the plugging and adsorption terms in the Wang and Civan
model using Arrhenius rate equations. The model outperformed the previous models
when compared to flow loop studies. Behbahani et al. [49] coupled the asphaltene
precipitation from equilibrium thermodynamics and the convection diffusion equations with Wang and Civan’s rock impairment model to study multilayer adsorption
of asphaltene in the pores. The deposition process, on the other hand, was limited
by thermodynamic restrictions, resulting in a minor contribution to the overall mass
balance.
Ramirez et al. [50], proposed one of the earliest asphaltene deposition model in
flowlines. They stated molecular diffusion as the primary process for asphaltenes to
accumulate on pipe walls. Similar to wax deposition, the asphaltene molecules moved
toward the pipe walls as a result of the thermal difference between the bulk fluid
temperature and the pipe walls. According to the model, the molecules diffused in
the dissolved state and precipitated on the pipe wall, which is at a lower temperature
than the bulk fluid. Near the wall, however, oil components diffuse into the main
stream, causing asphaltene and oil molecules to diffuse in the reverse way. In addition
to the mechanism, shear forces were expected to remove a portion of the deposit in
the model.
Mirzayi et al. [51] investigated at other mechanisms such gravity settling, thermophoresis, buoyancy, Brownian diffusion, drag force, and shear removal. Particle
movement in both laminar and turbulent flows can be studied using these forces.
Shirdel et al. [52] evaluated the deposition rate of asphaltene particles using
typical particulate deposition principles in various flow regimes. Friedlander and
Johnstone98 presented classical models of particle flow in turbulent conditions with
eddy diffusion, which were compiled in this study. In addition, as recommended by
Cleaver and Yates [53], a stochastic approach to determining the deposition was used.
As seen in Eq. 5.3, the flow regime was determined on the basis of particle relaxation time. The model performances were compared to experimental data from the
literature on iron particles, air flow, and oil-asphaltene flow, 29 and it was concluded
that the models proposed by previous researchers were consistent with experimental
data and could be used for asphaltene deposition as well. The Beal model was found
to be the best among all.
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τ p+ =
ρ p d 2p
18μ f
f
.2
2
u avg
γ
(5.3)
Guan et al. [4] developed a one-dimensional transient model to account for crosssectional variance in asphaltene deposition along pipelines and predict deposition
above the bubble point temperature. The EoS model was used to calculate the asphaltene equilibrium concentration i.e. solubility of asphaltene at a particular temperature and pressure while numerically solving the heat, mass, and momentum transport equations. The precipitation and aggregation mechanisms were assumed to be
first-order kinetics while solving the concentration profile.
5.5 Machine Learning Application in Asphaltenes
Precipitation and Deposition Control
The effect of several variables and parameters on asphaltene precipitation was investigated using a Bayesian belief network (BBN) as an artificial intelligence modelling
method. The parameters included pressure, diluents used and dilution ratio. The BBN
model’s projected findings were compared to experimental precipitation data taken
in a high-pressure cell and analysed using image analysis tools. The cell accessories
make in-situ visual monitoring of asphaltene centres growth at high pressures and
temperatures possible. The relative absolute discrepancy between model predictions
and experimental data was found to be less than 4.6% on average. Under varied conditions, the established BBN model was also able to anticipate the start of asphaltene
precipitation or the burst of nucleation. According to a comparison of their predictions, the BBN model anticipates asphaltene precipitation more accurately and covers
a wider range of affected variables/parameters than the alternatives [54]. Figure 5.7
shows the structure of BBN model for asphaltene precipitation.
Sarapardeh et al. [55] offered a reliable and predictive model for predicting asphaltene precipitation, particularly the least-squares support vector machine (LSSVM).
This model was created and evaluated using 157 separate sets of experimental data for
Fig. 5.7 Structure of the
developed BBN for
asphaltene precipitation [54]
5 Machine Learning in Asphaltenes Mitigation
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32 different crude oils from a variety of Iranian oil reserves. Because the data ranges
utilised to construct the model encompass many of Iran’s PVT data, the resulting
model might be trusted to anticipate samples from additional Iranian oil reservoirs.
The model’s adequacy and accuracy were determined by statistical and graphical
error analysis. The findings showed that the developed model was capable of making
accurate predictions based on experimental data. Furthermore, the suggested method
accomplished of reproducing the real physical trend of asphaltene precipitation as a
function of pressure variation. Finally, utilising the statistical Hat matrix, Williams
plot, and residuals of the model results, the Leverage technique was applied to identify the likely outliers. All the experimental data, except for five instances, appeared
to be reliable. As a result, given its applicability range, it was concluded that the
developed model could be useful in forecasting asphaltene precipitation. This model
may be used in any reservoir simulator software and provides adequate accuracy and
performance when compared to existing techniques.
Rostami et al. [56] employed Gaussian Process (GP) regression models to anticipate asphaltene precipitation in tank and live crude oils. To begin, feature selection
was utilised to discover which parts of the training data set were most relevant
for predicting asphaltene precipitation. Consequently, six feature sets were generated, each of which was utilised to develop a Gaussian process model for predicting
asphaltene precipitation. The results of the models developed in this study were then
compared to those of previously published publications. According to the results,
their models outperformed the preceding studies from the literature.
Na’imi et al. [57] used Support vector regression, a unique computer learning
approach, was used to estimate the amount of asphaltene precipitation using experimental titration data. The results of the support vector regression model were
compared to those of the artificial neural network and the scaling equation. The results
indicated satisfactory agreement with experimental data and accurate prediction
when compared to artificial neural networks and scaling equations.
Sarapardeh et al. [58] predicted the amount of asphaltene precipitation during titration testing as a function of easily measured parameters such as temperature, solvent
type, and solvent to oil dilution ratio. Asphaltene precipitation data was obtained
from a variety of sources, and it covered a wide range of thermodynamic conditions
and crude oil types. A least square support vector machine (LSSVM) was utilised for
modelling, and it was modified using a stochastic process known as couples simulated annealing (CSA). The data bank was divided into four groups based on the
kind of solvent and the solvent to oil dilution ratio. Then, for each area, a model was
proposed, and the results showed that all of the proposed models can accurately calculate the amount of asphaltene precipitation. In summary, the proposed CSA-LSSVM
models predicted asphaltene precipitation with an average absolute relative error of
9.46%. The suggested CSA-LSSVM models beat previously published models in
both graphical and statistical assessments. Finally, a mathematical model was used
to identify not only the suggested models’ application area, but also the quality of
the experimental data and the presence of possible outliers. Only 3.3% of the data
indicated possible outliers, indicating that all of the proposed models are statistically
valid.
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Ghorbani et al. [59] used a machine learning technique to predict asphaltene
precipitation under various conditions. The genetic algorithm-support vector regression (GA-SVR) was employed in this research as a new model for predicting the
amount of asphaltene precipitation. To improve the SVR’s generalisation performance, GA was utilised to find the best optimal values of SVR parameters and kernel
parameter at the same time. On the experimental data sets published in the literature, the GA-SVR model was trained and tested. Statistical error measurements and
graphical analysis were used to compare the GASVR model’s performance against
that of two scaling equation models. The findings revealed that the suggested model’s
prediction performance was very dependable.
Wang et al. [60] used coarse-grained molecular dynamics simulations for an
archetypal asphaltene molecule. The phase diagram that mapped self-assembled
morphologies as a function of temperature, pressure, and the n-heptane-toluene
solvent ratio was developed. It was suggested that this information could be utilised
to manage asphaltene aggregation by changing the external processing conditions.
The low-dimensional free energy surfaces governing asphaltene self-assembly were
discovered using simulations, graph matching, and nonlinear manifold learning.
Researchers proposed the first use of many-body diffusion maps to reconstruct a
pseudo-1D free energy landscape for molecular self-assembly, as well as a diffusion
map version that was better suited to data sets with high local density changes.
Increasing pressure had a minor impact on the terrain, destabilising the larger
aggregates. Small cluster sizes and loose bonding configurations were stabilised
by increasing temperature and toluene solvent fraction. The effects of these two
were remarkably similar, even though the underlying molecular mechanisms were
different.
Ristanto [61] used historical bottom hole pressure and flow rate data to develop a
reservoir model using machine learning methodologies without explicitly programming the mechanics of the flow. It was influenced by studying the past pattern of
bottom hole pressure and flow rate or training set. Flow rate could be forecasted
depending on bottom hole pressure using the adopted model. This model could also
be used to aid in the diagnosing process. The actual pressure and flow rate response
differed from what the model predicted if liquid loading, condensate banking, or
wax/asphaltene deposition begin, indicating that the reservoir performance had
changed. Previous studies applied machine learning approaches to single-phase cases
and framed the problem as a linear problem with the nonlinearity highlighted in the
characteristics, but this study employed machine learning and deep learning to solve
multi-well and multiphase problems. The research revealed key characteristics of
such situations as this study looked at the performance of ten different machine and
deep learning algorithms. Python was found to be good for this since it had a wide
variety of machine/deep learning libraries to choose from and is better suited to
machine learning problems. Multiphase flow, reservoir heterogeneity, and data noise
were all considered.
Elkhatib et al. [62] designed the experiments by flowing asphaltene-in-toluene
solutions through capillary polyetheretherketone tubes and measured their crosssectional areas with high-resolution scanning electron microscopy. Denoising, which
5 Machine Learning in Asphaltenes Mitigation
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is an analysing local and global bias and variance and binarization using machinelearning techniques were utilised in a two-step digital image analysis to improve
picture segmentation quality. Polydispersity divided particles on the tube surface
into nanoaggregates (1.54 nm), small clusters (SCs, 410 nm), medium clusters (MCs,
1020 nm), large clusters (LCs, 20,100 nm), and extra-large clusters (XLCs, > 100 nm).
A Langmuir adsorption isotherm was determined in toluene, with an adsorption free
energy of 29 kJ/mol, which corresponded with previous observations. Nanoaggregates and SCs were the predominant elements of the adsorption layer due to their high
mass diffusivity. As the concentration of asphaltene in toluene increased, a conflict
between aggregation and adsorption was observed. Due to better self-assembly in
the bulk phase, the number of adsorbed particles had been progressively decreasing.
Adding n-heptane to toluene at various volume ratios induced the deposition of MCs,
with a peak in particle size, number, and mass density around the start of precipitation.
These clusters were possible fouling precursors because they act as building blocks
of larger particles that amass on the surface over time. Limiting their deposition
could be accomplished by increasing the flow rate or introducing chemical inhibitors
that favour the formation of larger aggregates in the bulk phase under specific flow
conditions. The findings of this study demonstrated that MC-rich petroleum fluids are
more susceptible to flow assurance issues, and that effective flow enhancers enhance
the aggregation of MCs into particles that are too large to deposit.
Gesho et al. [62] developed a fully automated image processing and analysis
method using data analytics, pattern recognition, and machine learning including
deep learning techniques to automate image processing and investigate physical properties and nanoscale deposition of petroleum constituents, asphaltenes, on surfaces
from hundreds of images. In order to accomplish this, many data preparation procedures such as picture filtering and quality assessment methodologies were originally
devised. The retrieved information was then used to identify and quantify required
physical attributes and deposition parameters using denosing and picture segmentation techniques. By comparing the model to experimental results from asphaltene
deposition studies, they were able to validate the proposed technique. The model’s
output was then compared against experimental data published in the literature. This
application revealed the mechanism of asphaltene deposition, allowing for a better
understanding of the process. The construction of fully automated image-processing
model that characterises physical properties of deposited species is novel. Using a
combination of data and descriptive models, researchers were also able to differentiate foreground and background information particles from SEM images, indicating
that the model might be used in future image processing applications. The workflow,
executed processes, and applied methods are shown in the four columns in Fig. 5.8.
Ali et al. [63] employed machine learning methods to develop a realistic predictive model for asphaltene adsorption on MgO nanoparticles. Three machine learning
(ML) methods were used to create the models: gradient boosting machine (GBM),
bagging (BG), and random forest (RF). The models were created utilising 36 data
points, four of which were nanoparticle surface area, nanoparticle dosage, experimental temperature, and asphaltene equilibrium concentration in the supernatant
toluene. Correlation analysis was carried out using a heat map. When the surface area
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A. Qasim and B. Lal
Fig. 5.8 Diagram of the Image Processing and Analysis Model. Yellow, Green, and Orange refer
to deep learning, machine learning, and statistical analysis, respectively [62]
of the nanoparticles was increased, the adsorption of asphaltene on MgO nanoparticles is greatly enhanced, but when the nanoparticle dose was increased, the adsorption of asphaltene on MgO nanoparticles lowered significantly. Temperature and
equilibrium concentration have no effect on the adsorption phenomenon. A new
experimental dataset during the development process was used to validate the models.
5.5.1 Case Study
The case study involves the data analysis from over 100 wells impacted
by asphaltenes of onshore fields in Abu Dhabi by Abu Dhabi National Oil
Company [64].
In order to detect the asphaltene problem earlier, an asphaltene-specific realtime sensor is required as clean-up operations and reactive maintenance rely on
accessibility inspection and may affect the normal operation of the pipeline. In this
study, the chosen sensor design was based on the concept of Electron Paramagnetic
Resonance (EPR), in which free-radicals in asphaltene resonate with an external
magnetic field. The EPR signal is proportional to the amount of asphaltene in the
crude, with a signal reduction indicating probable deposition upstream of the sensor.
From conception through commercialization, the study was based on the research
collaboration between a national oil firm, a university, and a start-up technology
supplier. The outcome is a first-of-its-kind industrial Internet of Things (IoT) realtime monitoring device that can detect asphaltene deposition and optimise chemical
5 Machine Learning in Asphaltenes Mitigation
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programmes by combining surface data into an integrated flow assurance management system. By focusing on the asphaltene free-radical reaction rather than looking
for a broad spectrum of chemicals, it was possible to miniaturise and ruggedize a
device for oilfield applications. It was still required to show that the adjustment had
not resulted in a reduction in resolution. Instead of having the device installed directly
into one well for the first field trial in these onshore fields, the system was tested offline so that daily analysis from 15 distinct wells could be done and device resolution
could be tested. The resolution was found to be superior than 0.1%. Furthermore,
some wells showed nearly 5% variance from one day to the next, while others only
showed 1% variation. The wells with the highest standard deviation have previously
experienced greater asphaltene issues. The results from these 15 wells exceeded
expectations, therefore a second field trial was launched, this time with the device
mounted inline at the wellhead for real-time continuous monitoring. The use of this
method is expected to aid in the planning of clean-up operations before a well is
completely sealed. This should result in significant cost savings as well as reduced
well downtime and output loss.
A sensor was developed to notice the unusual response signature to asphaltene.
Whether the asphaltene is in solution or not, peak-to-peak voltage, Vpp , is proportional to the amount of asphaltene in the crude, according to the laboratory experiments. The signal width, defined as the horizontal distance between two peaks, could
be calculated using automated field matching of this theoretical curve, providing a
reliable method of peak-to-peak voltage analysis. Although two crude samples in
Figs. 5.9 and 5.10 show a correlation with particle size, only preliminary testing
has been done to assess the efficiency of this computation. The first sample has a
peak-to-peak voltage of 2518 mV (about 9.5% asphaltene) and a width of 0.85 (in
normalized units), while the second has a peak-to-peak voltage of 1563 mV (roughly
5.6% asphaltene) and a width of 1.01. Because this is based on laboratory data, one
of the pilot phase’s unknowns will be if we can detect spectral breadth variations and
whether they can be linked to field activity or distinct reservoir zones.
Significant ruggedization of the sensor was performed to prepare it for the onshore
use in Abu Dhabi. The cloud-based approach was used and reviewed by researchers
Fig. 5.9 High-resolution microscope analysis of small asphaltene particles suspended in the oil
(and some water droplets) and a narrow EPR spectral curve [64]
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A. Qasim and B. Lal
Fig. 5.10 High-resolution microscope analysis of large asphaltene particles suspended in the oil
and a broader spectral EPR curve [64]
in MicroSilicon Head Quarter. Cloud systems enabled data access for each hour with
guaranteed backup and storage. It was further proposed that data validation should
be done by the operator by conducting frequent well testing, gauge cutter runs and
asphaltene content measurement through SARA analysis to corroborate the readings
from the sensor. The industrial IoT real-time monitoring device is novel in flow
assurance industry.
5.6 Conclusion
Asphaltenes are complex molecules with the largest molecular weight known in
crude oils, are far more hazardous than typical wax deposition. These are essentially polar molecules. In terms of chemical composition, asphaltenes are made
up of heteroatoms such as carbon, nitrogen, hydrogen, sulphide, and oxygen. The
point at which asphaltenes begin to separate from oil is known as the commencement of asphaltene precipitation which eventually results in their deposition. Once
asphaltenes deposit inside the surface of the pipeline they cause hindrance to normal
flow operation. Mechanical, ultrasonic, thermal, bacterial and chemical treatments
are the main techniques used by flow assurance industry to mitigate asphaltenes
deposition. Various asphaltenes prediction models have been reported in the literature to predict the onset of asphaltenes precipitation and deposition. In this regard,
thermodynamic solubility technique, colloidal based models and asphaltene deposition modelling have been developed and discussed by the preceding researches.
Currently researchers are focussing on machine learning techniques to predict the
onset of asphaltenes precipitation and deposition at laboratory and industrial scale.
The machine and deep learning methods included Bayesian belief network (BBN),
Least-squares support vector machine (LSSVM), Support vector regression and
Genetic algorithm-support vector regression (GA-SVR). It was concluded that the
machine learning and deep learning methods were useful in predicting the temperature and pressure conditions of asphaltenes precipitation onset as the predictions
5 Machine Learning in Asphaltenes Mitigation
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were accurate. In future the use of machine learning techniques can be advanced
further with high accuracy in the field of asphaltenes mitigation.
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Chapter 6
Machine Learning for Scale Deposition
in Oil and Gas Industry
Sirisha Nallakukkala and Bhajan Lal
Abstract This chapter briefly discusses the type of machine learning methods used
for scales precipitation in flow assurance. It also discussed the scale formation
predictive models.
Keywords Machine learning · Flow assurance · Scales · Predictions
6.1 Introduction
In the oil and gas industry, scale precipitation generates a number of issues. Scale
precipitation of inorganic salts like (CaCO3 , CaSO4 , BaSO4 , etc.) is a regular occurrence in the oil and gas industry. The sources and causes of this scale formation are
diverse. The injection of water might be regarded as the key source of sulfate-scale
precipitation resulting due to mixing of water. Scales can also form as a result of
changes in the reservoir’s or well tubulars’ thermodynamic conditions. Precipitation
can result also due to the changes in temperature, pH, pressure, and partial pressure
of carbon dioxide (CO2 )/hydrogen sulphide [1].
6.2 Source of Scaling in Oil and Gas Industry
Scaling in the oil and gas sector is mostly caused by the mixing of incompatible
fluids. When two fluids interact chemically, they precipitate minerals. For instance,
sea water with a high concentration of SO4 –2 and low amounts of Ca2+ , Ba2+ /Sr+2 ,
and formation waters with very low concentrations of SO4 2− but high concentrations
S. Nallakukkala
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
B. Lal (B)
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
e-mail: bhajan.lal@utp.edu.my
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_6
105
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S. Nallakukkala and B. Lal
of Ca2+ , Ba2+ , and Sr2+ are two examples of incompatible waters. As a result of
the mixing of these liquids, CaSO4 , BaSO4 , and/or SrSO4 precipitate. Seawater and
field-produced water (disposal water) can be incompatible. Scale deposition occurs
when waste water is combined with seawater for re-injection [2].
According to one of the researchers [3] scales resulting from CaCO3 are very usual
compared with other kinds of inorganic scales because scaling by CaCO3 is formed
either inside the reservoir, within the production tubing, and at the downhole-pump
that results in the decrease in efficiency due to plugging. Furthermore, CaCO3 can
form scales at the wellhead and in the flow lines [2]. At high temperatures, CaCO3
has a very low solubility constant, making it the most stable scale type at reservoir
pressure and temperature [4]. Some of the different forms of scale formations of
CaCO3 are calcite, vaterite, and aragonite, and out of which calcite is the most
prevalent variety found in the reservoir. Temperature, pH, ion content, such as calcium
and bicarbonate, and ionic strength all play a vital role in the development of CaCO3
scale. The conditions that promote the formation of CaCO3 scales include CO2
solubility, pressure and temperature changes, these factors also promote the formation
of iron carbonate scales. The release of CO2 will increase the pH of the solution,
and thus promote the carbonate scale formation by reducing the scales solubility at
higher pH [5].
6.3 Mechanism of Scale Deposition
Scale deposition can occur with water due to supersaturation due to changes in pressure and temperature conditions. When two incompatible waters are mixed and supersaturation is attained then scales can deposit [1]. Supersaturation can be caused by
several factors namely heating/cooling, evaporation of water, mixing and gas/liquid
equilibria. Supersaturation can occur when a salt solution is cooled or when an
inverse solubility salt solution is heated. As water evaporates from solution the solution becomes concentrated making the solution saturated or supersaturated. When
the saturated solution comes in contact with hot surface it leads to direct deposition
of scale on the surface. Sometimes during mixing the solubility limit may exceed
when a soluble salt is added to other salt solution containing similar ion or results in a
sparingly soluble salt. In such situations when ionic product is greater than the solubility constant supersaturation condition is attained. Due to the change of solubility
with temperature, the mixing of saturated or near-saturated solutions may also result
in supersaturation conditions. The equilibrium conditions of CO2 gas dissolved from
atmospheric air in solution might affect the solubility of ions.
6 Machine Learning for Scale Deposition in Oil and Gas Industry
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6.3.1 Types of Scales
The types of scales frequently found in flow assurance are, sulfates of (Ba, Sr, Ca),
oxides/hydroxides of (Fe, Mg), carbonates of (Ca, Mg, Fe), and sulphides (Fe) are
the most prevalent scales found in oil and gas productions. The majority of the scales
are either acid soluble or water soluble however, some are not soluble in both. A
common example of water-soluble scale is sodium chloride. Acid soluble scales
include calcium carbonate, iron sulphide, and iron oxide.
6.3.2 Influence of Impurities on Scale Formation
Impurities adsorb on the site of the crystals, deposit and tend to interfere with the
solid crystalline structures formation which results in decrease in nucleation and
growth followed by change in precipitation. Deposits caused with mixed salts and
impurities have a lower adhesion, and the net scaling rate is reduced due to partial
removal of the deposit by shear force acting on the flow surface [6].
6.3.3 Scale Control Methods
The nature and concentration of scaling species present in the raw feed determine
the severity of a potential scaling challenge in a process. The following are the three
basic techniques to controlling scale formation:
• Removal/decrease of Scale-forming species
• Water treatment is an efficient way to remove scaling issues for instance acidifying
the feed and degassing CO2 generated from carbonates, can help avoid alkaline
scale formation.
• Chemical treatment is best alternative when conventional mechanical removal
methods are ineffective or expensive. This is accomplished by employing acids
or chelating agents, which are used to break apart acid-resistant scale by isolating
and locking up the scale metallic ions within a closed ring-like structure, by
allowing the scale minerals to dissolve by increasing their solubility.
6.4 Effect of Scaling to Equipment Pipelines
In the oilfield water injection, equipment surface, wellbores and pipelines are prone
to scaling resulting in blockage and also increase the corrosion rate of pipeline and
increase the speed of sulphate reducing bacteria. Scaling also reduces the heat transfer
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rate of the equipment resulting in shorter span of the equipment. Scaling also results
in reducing the surface area of the channel by creating blockage and effecting the
permeability of the reservoir.
6.5 Scale Inhibition Placement
6.5.1 Scale Inhibition Placement by the Squeeze Technique
The common approach for injecting the inhibitor into the formation is to inject it
into the water zone at high pressures followed by a brief shut-in time period. Certain
conditions govern which inhibitor should be squeezed: first, the ability of the chemical to inhibit at low/high concentrations; second, the inhibitors ability to be adsorbed
by the surface as well as that surface’s ability to hold the fluid; and third, the fluid’s
ability to slowly release from the rock to maintain the minimum inhibitor concentration required for several months. The isotherm of desirable inhibitor should rise
quickly and then decrease as the inhibitor concentration rises. Sometimes precipitation suggests that calcium salt may precipitate and coat the reservoir rock [7]. As a
result, a large number of calcium ions attach to a single scale inhibitor molecule, as
shown in the reaction below:
INHIBITOR(Scale Inhibition) + Ca2+ ← SI_Ca[the inhibitor complex]
6.5.2 Pumping the Inhibitor with the Stimulation
This approach has the advantage of being cost-effective because by using the same
equipment both stimulation and inhibition can be performed at the same time. Smith
et al. discovered that using too many scale inhibitors can interfere with the corrosion
control agent in the HCl acid system [8].
6.5.3 Pumping the Inhibitor with Fracturing Fluid
The opportunity to place the inhibitor fluid in the entire fracture is the main advantage
to include inhibitor in the fracturing fluid. This method is cost-effective and easy to
implement unless it doesn’t disturb the rheology of the fracturing fluid.
6 Machine Learning for Scale Deposition in Oil and Gas Industry
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6.5.4 Inhibitor Impregnated into Proppant
When the fracture is at risk of deterioration due to the scale, this treatment is used.
The inhibitor adsorbs in the microscopic crevices of the proppant particles and is
slowly released with the produced water.
6.6 Prediction Models Available for Scale Formation
Detection
During exploration of oil and gas from reservoirs, scaling is caused due to presence of
water which affects the oil production [9]. Scaling is very complex issue that results
due to changes in production parameters like the phase change, chemical reactions
in the electrolyte solution, temperature, pressure, rate of dissolution, evaporation
and precipitation adversely affect the scaling [10]. The scaling prediction has been
explored extensively since the 1930s. Over the years, newer and more reliable scaling
prediction models have been developed. Some of the scaling prediction tools or
methods used presently are the.
• Saturation index,
• Solubility product rule,
• Ion association theory.
Water composition, temperature, pressure, mass and energy transfer [11] also affect
the scaling in a reservoir. A new index was developed [12] to predict the carbonate
scaling by saturation index method. Another researcher [13] predicted the carbonate
scale using the thermodynamic solubility method and sulphate solubility product.
The influence of various parameters like temperature, pressure and formation water
composition on scaling are considered but phase change is not considered by these
prediction models. As a result, they cannot accurately depict the inorganic salt scaling
process in the reservoir. Hence its required to develop a model that takes phase change
into consideration under high pressure and high temperature conditions. A multiphase
scaling prediction model based on thermodynamics was proposed by Zheng et al. [14]
that considers phase change, and reaction of inorganics salts scaling under multiphase
equilibrium. The Oddo-Tomson saturation index was a common technique to predict
the scaling in an electrolyte solution.
6.6.1 Saturation Index
The Langelier Saturation Index (LSI) is an equilibrium model derived from the
theoretical idea of saturation that provides indicator of degree of water saturation
with respect to calcium carbonate.
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Langelier saturation index (LSI) is given as.
LSI = pH + TF + CHF + AF – 12.1 (TDS factor).
where TF is the temperature factor.
CHF is calcium hardness factor.
AF is alkalinity factor.
and 12.1 is a constant value for 1000 ppm of total dissolved solids (TDS).
If the LSI < 0: There is no risk of scaling because the water will dissolve the CaCO3 .
If the LSI > 0: Scale can form and result in precipitation of CaCO3 .
If LSI ∼ = 0: Borderline scale potential. The index could be affected by changes
in water quality, temperature, or evaporation.
LSI is purely an equilibrium measure that only considers the thermodynamic
driving force for the formation and growth of CaCO3 scales.
6.6.2 Inhibitor Impregnated into Proppant
The insoluble electrolyte Am Bn (s) exhibits the following chemical equilibrium in
solution at a given temperature and pressure:
Am Bn (s) ⇔ m An+ (aq) + n B m− (aq)
K sp = [An+ ]m · [B m− ]n
where K sp is the thermodynamic solubility product of Am Bn (s)
To analyse the scaling in the case of insoluble electrolyte solution the following
rules can be utilised to analyse the occurrence of dissolution/precipitation.
Rule 1: [An+ ]m · [B m− ]n >K sp : solution is saturated.
Rule 2: [An+ ]m · [B m− ]n <K sp : solution is unsaturated.
Rule 3: [An+ ]m · [B m− ]n =K sp : solution is in equilibrium.
6.6.3 Ion Association Theory
When two ions of different charges are available their columbic force of attraction is
greater than the thermal motion and results in the formation of a new distinct entity
having substantial stability.
A mature scaling prediction approach has been developed after years of scientific
investigation, application, and observation. The Langelier saturation index method
6 Machine Learning for Scale Deposition in Oil and Gas Industry
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[15], Davis stiff saturation index method [16], and Ryznar stability index method [17]
are all used to predict the CaCO3 scaling. Methods like the Oddo-Tomson saturation
index approach [18] and the OLI ScaleChem scaling prediction software [19] are
implemented for mixed scale prediction.
It’s critical to avoid scaling in oil and gas field and the factors that influence the
scaling are numerous [20]. The ability to forecast the scaling trend, volume, and kind
of produced water is critical. It’s also critical to control the water quality index of
produced water, which is critical for scaling management.
Although the oilfield generated scaling prediction technologies have improved
over time, there are still significant limitations Most scaling prediction approaches,
for example, are solely based on thermodynamic parameters and do not take into
consideration the crystallisation kinetics or fluid dynamics. This will lead to a more
comprehensive, targeted, and accurate scaling prediction mechanism.
6.7 Machine Learning for Scale Deposition
Machine learning techniques is a prediction tool while artificial intelligence (AI) is
more related to the process of developing machines with the help of human intelligence. Both machine learning and AI are fascinating technologies that combine
computing power with human intelligence to provide clever and consistent results
to nonlinear and complex problems. Al-Hajri et al. [21] focussed on use of machine
learning techniques such as.
• K-Nearest Neighbour (KNN) algorithms,
• Gradient boosting classifier,
• Decision tree,
• SVM, and
• Gradient boosting.
6.7.1 K-Nearest Neighbor
KNN is a supervised learning algorithm that is used for classification and regression
problems. The input parameter consists of k closest training examples in feature space
environment and the outer parameter rely on whether KNN is used for utilization or
regression.
6.7.2 Gradient Boosting Classifier
It is a boosting algorithm that collectively associates several weak models to create a
strong predictive model to maximise a cost function across function space. Beyond
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regression and classification, this functional-gradient approach of boosting has led to
the development of boosting algorithms in a variety of domains of machine learning
and statistics.
6.7.3 Decision Tree
This method uses decision tree to get observations and reach to conclusions about
the item’s target (from branches to leaves). It is a predictive model which is used in
data mining, statistics, and machine learning.
6.7.4 Support Vector Machines (SVM)
In machine learning SVM is a supervised learning model associated with learning
algorithms used in classification and regression analysis to analyse data. The SVM
training technique creates a model that allocates new samples to one or different
categories, making it a binary linear classifier that is non probabilistic.
6.7.5 Gradient Boosting
It is a machine learning technique for regression and classification problems that
generates a prediction model in the form of a collection of weak prediction models,
usually decision trees. It constructs the model in a staged manner, similar to previous
boosting methods, and it generalises them by allowing optimization of any function.
False Negative (FN) tool is used to predict the formation of scale. Castillo et al.
[22] discussed for function approximation problems. The combination of artificial
neural networks with functional theory is the foundation of FN. It’s a supervised
data-learning technique that’s typically utilised for regression and prediction.
Al-Hajri et al. [21] described stepwise methodology for CaCO3 prediction elsewhere. This can be further extended to include probabilistic framework by which
scale inhibition is designed. The data is divided randomly into 80% training and
20% testing then generate a confusion matrix that describes the performance of
prediction and later define the probabilistic equations used to quantify the data. The
data sets used for prediction include both single data sets associated with single wells
and as well multiple data sets from same well. All techniques resulted good accuracy,
but KNN was chosen further because of its capability to develop classification plots.
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6.8 Applications of Artificial Intelligence in Oil
Desalination Systems
6.8.1 FN Tool to Develop Scale-Formation Correlation
The FN tool, is used to predict the presence of scale formation. An FN technique is
a relatively new AI technique that was first presented for a function-approximation
problem by Castillo et al. [22]. The combination of artificial neural networks with
functional theory is the foundation of FN. It’s a supervised data-learning technique
that’s typically utilised for regression and prediction. A quality check of the data set
is executed before the data is fed into AI model. After removing the outliers 70% of
data is used for model training and 30% for testing the trained models. The training
model selected is the backward elimination method because of its high precision and
short processing time. The fitness criteria adopted is minimum description length.
Minimum description length is given as
δ=
Np
plog N p
+
log π 2
2
2
(6.1)
where N p is number of parameters and π is the root mean square error given as
/
π=
1 Σ 2
∈
N i=1 i
Np
(6.2)
The mathematical model for scale formation is.
Scale formation =
Np
Σ
ωi N a σ 1i Ca σ 2i Mg σ 3i S O 4 σ 4i H C O 3 σ 5i T DS σ 6i p H σ 7i, T σ 8i,
i=1
Where Na, Ca, Mg, SO4 , HCO3 are the molar concentrations of sodium, calcium,
magnesium, sulphate, bicarbonate ions expressed in mol/l and TDS is the total
dissolved solids, pH is the acidity of solution and T is the normalised inspection
time. If the output of the model is 1 then it represents precipitation and if the output
is 2 it means no precipitation. Further applicability of this model and method on
types of scale can be analysed and studied.
The applicability of least square support vector machine (LSSVM) was implemented [23] to estimate the amount of dissolved calcium carbonate in oil field brine is
performed where the LSSVM is coupled with genetic algorithm to determine hyper
parameters (σ 2 ,γ).
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6.8.2 Least Square Support Vector Machine (LSSVM)
SVM is an effective approach and is widely used in pattern recognition, classification and regression [24], As SVM is based on quadratic programming and its time
consuming but is unique unlike other regression techniques such as neural networks
[25]. An alternative to SVM regression, LSSVM was suggested by Suykens et al. [26]
where it needs linear programming which is rather simpler to deal with. The concentration and amount of dissolved CaCO3 in oil field brines is predicted by Ahmadi et al.
[23] using LSSVM model with temperature and pressure as two inputs. A populationbased GA was applied to estimate the hyper parameters along with LSSVM. The
results obtained from vector model followed the real data points with high precision
with a relative deviation span from –4.26% to 4.56% and average absolute deviation
of 1.712%. GA along with LSSVM resulted in consistent output results with R2 value
of 0.9994 and mean square error of 0.0000208 for predicting dissolved CaCO3 in
oil field brines. LSSVM has better characteristics like efficiency, can model highly
non-linear systems in oil diesel system and can be calculated easily.
6.8.3 Scale Thickness Measurement Using Gamma-Ray
Densitometer
Methods like acoustic and non-acoustic methods have been implemented for scale
formation detection in oil wells. Based on the reflection of signal the thickness and
type of scale can be identified through acoustic method. Non acoustic methods are
reliant on monitoring temperature and pressure showing the occurrence of problem
at later stage. Gamma-ray densitometer provides reliant data to accurate density fluctuations in industries. This method also provides real time analysis to determine the
wall thickness in pipes [27]. The gamma-ray densitometer is based on transmission
measurements of gamma beam to determine the density of material. A radiation
detector records the transmitted gamma-ray flux through wall of pipes and inner
fluids. In addition, artificial neural networks is used to interpret the pulse height
distribution obtained by the gamma detector for prediction of scales [28]. Salgado
et al. [29] investigated the response of multiphase system to gamma-ray detection
to predict the scale thickness based on PHD pattern recognition by means of trained
ANN using data generated by Monte Carlo code. The results showed a relative error of
0.60% and 94% of data were predicted with a relative error less than 5%. The results
indicate that this methodology can be used as ANN could predict the scale thickness with acceptable results. To investigate the formation of barium sulphate scales
(BaSO4 ), a measurement approach based on gamma-ray scattering was devised[30]
using three NaI(Tl) detectors and a 137 Cs gamma-ray source to detect the thickness of
scale. The data from the detectors was utilised to train an ANN to predict maximum
scale thickness values regardless of the presence of oil, saltwater, gas, or scale inside
the tube. The Monte Carlo MCNP6 algorithm was used to create a data subset for
6 Machine Learning for Scale Deposition in Oil and Gas Industry
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training and evaluating the ANN generalisation capability. The maximum scale value
was studied at various thicknesses and places and more than 90% of the patterns had
relative errors of less than 10%, according to the findings.
6.8.4 Prediction of CaCO3 Scaling Using MLP and PNN
Artificial intelligence-based numerical analyses such as Multi Layer Perceptron
(MLP) Probabilistic Neural Network (PNN) were used by Paz et al. [31] to predict
CaCO3 scaling using SI method for scaling solutions. The software KNIME Analytic
with an MLP structure based on ANN was used to investigate the feasibility of
producing predictions about the saturation index of CaCO3 scaling difficulties. Both
MLP and PNN approaches produce good results for both types of data, the first
being utilised in the learning process and the second being used to test the ANN
methods. When this method is combined with external data, it is possible to forecast
CaCO3 scaling under circumstances such as temperature, solution pH, and solution
concentration that are often known from similar experimental studies.
6.8.5 Prediction of BaSO4 /CaSO4 Oilfield Scale Using ANN
and SNN
Previous scale formation prediction methods relied mostly on thermodynamics and
limited solubility data, and thus could only predict the potential to scale formation.
However, no research has looked into the impact of kinetic and transport factors.
Statistical Neural Network (SNN) has proven to be effective tool at estimating imprecise and noisy data sets. It’s been discovered to be useful in any field that uses both
qualitative and quantitative data. In another study by Falode et al. [32] used an Artificial Neural Network and a Statistical Neural Network to predict the production
of BaSO4 and CaSO4 oilfield scales while accounting for thermodynamic, transport, and kinetic parameters. This was accomplished by utilising Neural Network
Toolbox in Matlab R2009a to create a neural network model, train, test, and verify
data. From the study it was discovered that the obtained results from the network
were a better match than the needed results, indicating that the network can be used
to predict the amount of BaSO4 precipitation and rate of reaction for CaSO4 at a
given temperature/pressure indicating that the developed ANN and SNN had good
validating results, and high confidence in the prediction.
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6.9 Case Studies on Scaling Measurement Using Machine
Learning
6.9.1 Effective Control of Deposition of Scale Using AI
in Vacuum Pump
The strategy and function of AI at HABER in addressing one of India’s largest
integrated paper mills’ concerns with scale deposition in the vacuum pump. As water
removal is a time-consuming and energy-intensive procedure in the paper industry
the vacuum formed by a liquid ring vacuum pump is employed once dewatering is
completed. Because the cost of removing water is higher in the dryer portion, an
effective vacuum process would mean a lower cost of water removal. It is impossible
to totally avoid mineral salt deposition. However, careful risk analysis and a focused
strategy towards scale prevention can help to reduce costs and impacts. The ELIXA®
technology from HABER delivers closed-loop control for vacuum pump scale control
programmes using real-time artificial intelligence and machine learning.
IoT sensors capture real-time data on many process parameters that affect vacuum
pump performance, and big data analysis takes place on the ELIXA® cloud. The
ELIXA® programme forecasts the need for anti-scalant in the system in real time
based on the process circumstances. This predicted output is set as set point to the
final control element. AI/ML-based automation eliminates bottlenecks in manual
operations such as frequent sampling on a daily basis, resulting in a delay in taking
appropriate decisions. In addition, data points alone were insufficient to provide
insights or useful intelligence. When these issues are resolved, scale deposition can
be controlled precisely, efficiently, and reliably. The impact that was made as a result
of the implementation of AI is.
• The use of anti-scalant chemicals was decreased by 20%, resulting in significant
annual savings.
• Cleaning of vacuum pumps has been reduced by 50%.
6.9.2 Conclusion
It’s critical to avoid scaling in oilfield development, and the factors that influence
the scaling in gas fields are numerous. The ability to forecast the scaling trend,
volume, and kind of produced water is critical. It’s also critical to control the water
quality index of produced water that is of great significance in the scale management
and removal. Although oilfield generated water scaling prediction technologies have
improved over time, there are still significant challenges. The rate constant was
predicted for calcium sulphate at given temperature, flowrate and pressure using
ANN. Machine learning techniques were also implemented to predict the scaling of
calcium carbonate. This study proves that machine learning could correctly predict
6 Machine Learning for Scale Deposition in Oil and Gas Industry
117
the scale thickness in multiphase system. Future research into the applicability of
machine learning to various types of scale development can be examined, as well
as the capacity to forecast the critical value of salt precipitation when it becomes an
issue in oil and gas fields.
References
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Chapter 7
Machine Learning in CO2 Sequestration
Amirun Nissa Rehman and Bhajan Lal
Abstract CO2 capture and sequestration is a prominent field of study with high
research demands. It involves capturing CO2 from various large point sources and
storing it to prevent its emission. Various conventional CO2 sequestration techniques
currently in practice involve CO2 storage in geological formations such as depleted
oil and gas reservoirs, saline aquifers, and enhanced oil recovery (EOR) applications. Another emerging technique is to store CO2 in the hydrate form in marine sediments owing to its large storage capacity. Gas hydrates are crystalline solid structures formed by the physical combination of gas (such as methane, carbon dioxide,
propane, etc.) and water molecules at high-pressure and low-temperature conditions. This chapter briefly describes the conventional CO2 sequestration techniques
with the challenges encountered in their application. Further, the chapter discusses
the use of machine learning in gas hydrate related studies particularly concerning
hydrate-based CO2 capture and sequestration.
Keywords Machine learning · CO2 capture and sequestration · Flow assurance ·
EOR
7.1 Introduction
Due to the rising CO2 emissions causing climate change, carbon dioxide (CO2 )
storage has become a crucial concern. Nearly 55.46% of the total global greenhouse
emissions are attributed to human activity [1–3]. Another 41% of CO2 emissions
comes from power generation [4, 5]. The escalating global temperatures are due to
the increasing amount of greenhouse gases (such as CH4 , CO2 , N2 O, etc.) of which
CO2 is the major contributor (72%) [6–8]. The United Nations has advocated an
A. N. Rehman · B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_7
119
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international action to maintain the global temperature rise below 1.5 °C by 2030
in response to the growing concerns from climate change [9]. In order to support
the current policies and actions, it is important to develop efficient CO2 storage
technologies. Currently, CO2 storage technologies are still an area under exploration
and needs further investigation [10].
The currently existing storage techniques for Carbon Capture and Storage (CCS)
involve the injection of captured CO2 from large sources into deep geological formations, depleted gas reservoirs, deep saline aquifers as recovery agents in enhanced
oil recovery (EOR), and unmineable coal seams [11]. In each case, the trapping efficiency depends upon the migration rate of CO2 , which relies on the rock and fluid
characteristics of the geological site. The efficiency of CO2 storage also depends on
multiple factors, including specific properties of the storage aquifer, such as temperature, pressure, salinity, and heterogeneity. Various modelling/simulation techniques
have been used to predict these parameters. Recently, machine learning techniques
have proved to be useful in predicting these conditions in various applications
including CO2 sequestration. Especially concerning the leakage of CO2 arising from
faults during CO2 injection, machine learning helps in monitoring such leakage by
predicting the uncertainties and avoiding risks involved. It is also used to study the
various kinds of flow behaviour involving gas, water and oil in reservoirs through
predicting the flow.
In this chapter, the various conventional geological CO2 sequestration methods
are elaborated. Also, new approaches employing machine learning techniques in
CO2 sequestration is discussed.
7.2 Conventional CO2 Sequestration Techniques
The various conventional CO2 storage/sequestration methods that are in practice
currently include Enhanced Oil Recovery (EOR), depleted gas reservoirs, unmineable coal seams, and potential basalts [12], as shown in Fig. 7.1. This section briefly
discusses the details of conventional CO2 storage methods.
7.2.1 Enhanced Oil Recovery
Several mitigation strategies considering CO2 emissions are focused in EOR applications where CO2 is replaced with oil while recovery [13–20]. CO2 can be stored
in reservoirs by two mechanisms, either physically or by chemical means [21].
The physical storage method includes structural, stratigraphic, and residual capture
involving underground CO2 injection where there is a high probability of upward
migration due to buoyancy. Whilst chemical storage mechanisms involve mineral
trapping and solubility. In the case of solubility, CO2 dissolves into formation water
and is stored by dissolution, while mineral trapping consists of the formation of solid
7 Machine Learning in CO2 Sequestration
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Fig. 7.1 Geological carbon
dioxide storage techniques
carbonates. But the major drawback of mineral trapping is that it takes a very long
time to occur [22, 23].
In this regard, carbon capture, utilization, and storage (CCUS) technology play an
important role in EOR operations that allow capturing CO2 from anthropogenic emission sources providing the dual advantage of reducing greenhouse emissions while
simultaneously facilitating storage [24]. There are different ways of trapping CO2 ,
residual trapping, hydrodynamic trapping, and aqueous solubility trapping. Due to
the high solubility of CO2 in oil, its fraction in the oil phase is much more compared
to that in the supercritical (hydrodynamic) phase and the aqueous phase [25]. Jia et al.
[13] employed Monte Carlo simulations unified with reduced-order models (RMO)
to evaluate the solubility of CO2 by oil trapping, aqueous trapping, and hydrodynamic
trapping. They developed reduced-order models combined with Polynomial Chaos
Expansion (PCE) technique considering porosity and permeability as the uncertainties. They found that CO2 is carried upward at the beginning of injection due to
buoyancy while after complete injection, dissolved CO2 moves downward because
of an increase in oil and brine density. This technique could store CO2 with an overall
storage efficiency of 10–56% [26].
CO2 -EOR miscible process is another technique that allows CO2 storage with
simultaneous oil production. The CO2 storage capacity in this method is increased by
increasing the cumulative gas injection concurrently with slug length [27]. However,
for successful storage, key factors like reservoir depth, pressure, temperature, and
storage capacity of the reservoir should be properly maintained to avoid huge financial expenses [24]. In addition, CO2 -EOR combined with CCS technology is found
to be unfeasible from a thermodynamics point of view as more energy is consumed
in the process than produced [28]. The highest EOR potential and CO2 storage
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A. N. Rehman and B. Lal
capacity is achieved in low permeability reservoirs; however, heterogeneous reservoirs show poor EOR due to early gas breakthrough arising from high permeability
channels [27, 29]. Model predictions report CO2 storage efficiency within a range
of 70%-100%. However, off-shore application of CO2 -EOR technology has critical
challenges involving commercial and financial risks [30]. According to Ampomah
et al. [31] the trapping mechanism of CO2 involved structural-stratigraphic trapping,
CO2 dissolution in formation water, and residual trapping. The simulation shows
that a huge amount of CO2 can be stored in residual trapping compared to the other
methods.
However, although widespread, EOR techniques face the principal challenge of
high injection cost, making it capital and resource intensive and expensive [32]. It
also has several other constraints, including uncertainty in reservoir properties like
porosity, permeability, caprock integrity, and fluid volume present in the reservoir.
This uncertainty hinders to estimate the nature of trapping CO2 and its distribution
in the reservoir [13]. Further, pressure, temperature, depth of the reservoir, rate of
CO2 injection as well as the effect of temperature on relative permeability of oil–
water systems [33] are crucial parameters that need to be monitored closely for
effective storage of CO2 . Continuous monitoring of reservoir pressure is essential
as elevated pressure beyond the reservoir’s capacity may induce caprock fracture
and CO2 leakage [34]. In addition, the size and location of the reservoir from the
source of CO2 are important parameters that govern the storage capacity in CO2 -EOR
applications. The reservoir must be situated at a certain distance from the source and
should have a minimum storage capacity equivalent to a given number of years of
CO2 emission [24]. Another limitation of CO2 storage via EOR is multiple wells
reaching up to hundreds and thousands that need to be continuously monitored [35].
7.2.2 Depleted Reservoirs
Depleted reservoirs are another form of widely practiced geological sequestration
method for CO2 storage [36]. Storage in depleted reservoirs has been extensively
studied considering various aspects, like geochemical modeling, underground migration simulation, risk assessment, and long-term integrity [36]. In the case of depleted
reservoirs, CO2 is disposed off by the replacement of the underground fluids and gases
generated from the reservoir with supercritical CO2 [37]. However, several factors
govern this whole mechanism. CO2 injection pressure is a major contributing factor
in terms of the original reservoir pressure and fracture pressure of the rock. For safe
sequestration, the pressure of injected CO2 must be lower than the original reservoir
pressure as well as fracture pressure [38]. However, when CO2 is injected, it alters the
interfacial tension between the CO2 /water system causing leakage by volume flow,
and this further affects the gas permeability impacting the storage capacity [39].
Lei et al. [40] investigated the geological storage of CO2 in Huangcaoxia Gas
Reservoir (Eastern Sichuan Basin, China) using numerical simulation. They developed a combined thermal-hydrodynamic model considering faults depending on the
7 Machine Learning in CO2 Sequestration
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geological and hydrogeological conditions in the Huangcaoxia gas field. They found
that the best CO2 storage occurred at a downhole temperature greater than 80 °C at
downhole pressure of 14 MPa. However, uncertainties in terms of reservoir conditions like the thickness of the reservoir, permeability, effect of formation water, and
water–rock-gas interactions still exist that challenge its extensive applicability. It has
been observed that storage capacity largely depends on residual fluids’ pressure and
compressibility in the pores. In 2017, Clarke et al. [41] confirmed that the reservoir
drive mechanism influenced the storage capacity. Agartan et al. [42] improved the
material balance-based correlation that allowed direct estimation of the CO2 storage
volume using existing production data. Depleted condensate gas medium provides
an improved storage potential compared with dry and wet reservoirs [43]. In 2018,
Myshakin et al. [44] studied the CO2 storage efficiency in fractured shale reservoir
employing FRACGEN and NFFLOW simulator. Bender and Akin [45] claimed that
CO2 storage capacity is possible in CO2 flooded oil reservoirs.
The feasible characteristics that make depleted oil and gas reservoirs fit for
CO2 storage applications are already established underground surface infrastructure, including wells and pipelines [39]. In addition, oil and gas reservoirs have been
extensively investigated, which gives an added advantage to use the experience in
CO2 sequestration. However, the risks underlying in applying this technique are far
more serious. Wellbore integrity is one of the top risks where pressure resulting from
production followed by injection weakens the wellbore seal interface causing leakage
[46]. Gas mixing is another critical issue in storing CO2 , because the presence of
CO2 in a natural gas mixture degrades the quality of the natural gas feed. Reactivity
of CO2 with caprock materials also affects the storage mechanism. Apart from this,
reservoir pressure, depth, and distance between the reservoir and production source
are additional factors that largely influence the storage capacity [47].
7.2.3 Deep Saline Aquifers
Deep saline aquifers appear to be a viable technology for CO2 storage representing
the best-salted sink among all geological storage options. Storage efficiency in saline
aquifers depend on several factors, including storage aquifer characteristics like
pressure, temperature, water salinity, porosity, permeability, heterogeneity, and CO2
injection rate [48]. Apart from these, the criteria that favor the proper selection of
storage sites include the capacity of storage, injectivity, and containment. The storage
site should possess sufficient pore volumes allowing more CO2 to be stored in terms
of capacity. In terms of injectivity, permeability should be high to maintain injection
rates as desired [49]. The trapping mechanism of CO2 also highly influences the
storage capacity, containment as well as injectivity. For successful storage in saline
aquifers, the injection of supercritical CO2 is preferred.
The Alberta saline basin’s storage capacity has been estimated as 103 Gt CO2
considering solubility trapping [50]. Junin and Abang [51] evaluated the CO2 storage
potential in deep saline formations in Malaysian sedimentary basins. They performed
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geographic information system analysis integrated with field assessment data to identify potential storage sites within the basin for CO2 storage and found that 27% of
the basin revealed a high potential for storing CO2 . In 2008 Zhou et al. [52] evaluated the storage capacity and expected pressure build-up during storage operations
using an analytical model. Literature studies report the use of various numerical
modeling techniques, including streamline simulation, vertical equilibrium models,
and conventional grid-based numerical models for evaluating CO2 storage [53–55].
Among all streamlines, simulations were the fastest as they reduced the flow equations to a one-dimensional equation. Knopf and Mary [56] estimated the CO2 storage
capacity based on two different methods- regional aquifer based versus trap based
method. They found that the regional aquifer-based method yielded enhanced storage
capacity. Cui et al. [57] employed a comprehensive fluid-rock interaction model to
study CO2 storage behavior. In another study, the CO2 storage capacity in saline
aquifers was assessed by mineral trapping that considered consumption of CO2
through geochemical reactions with rock minerals within the reservoir [58].
The CO2 storage process in saline aquifers is governed by several physical parameters like viscous force, capillary force, and buoyancy arising from density difference
between injected CO2 and aquifer brine. This difference in density leads to upward
migration of CO2 [59] followed by gravity segregation and additional migration
to the surface causing leakage [60]. Furthermore, the storage capacity depends on
numerous factors where CO2 solubility is considered a prime component. It has a
substantial impact on the storage capacity as it determines the amount of gas dissolved
in brine. Neglecting this effect will underestimate the storage capacity of up to 16%
[61]. The density of CO2 at reservoir conditions, interconnected pore volume, and
fluid property are supplementary factors influencing CO2 storage. Another significant
risk underlying in the case of CO2 storage in saline aquifers is the high possibility of
leakage, thereby contaminating drinking water and harming vegetation. This leakage
arises from defective wells, especially in regions containing many wells [62]. Further
estimation of storage capacity in saline aquifers is complicated due to a lack of information and numerous trapping mechanisms operating in different time scales [61].
7.2.4 Unmineable Coal Seams
Methane recovery by CO2 replacement in coal seams is another technique that
accounts for CO2 storage. CO2 has been employed in several enhanced coal bed
methane recovery processes that amplify methane recovery providing simultaneous
CO2 storage [63–65]. In this process, CO2 molecules diffuse through the network of
fractures within the coal bed into the matrix and desorb methane. CO2 can be stored
either as free gas within the pore space, dissolved in the groundwater inside the coal,
or adsorbed on the coal surface. In this case, CO2 binds physically to the coal surface
through adsorption trapping it permanently on the coal, provided adequate pressure
is maintained [66]. Because of the high affinity of CO2 towards the coal matrix than
methane, upon CO2 injection, it immediately replaces methane on the coal surface.
7 Machine Learning in CO2 Sequestration
125
CO2 adsorption affects coal permeability, and data from previous literature reveal that
CO2 exposure to coal results in coal swelling [67, 68]. For successful CO2 injection,
a coal seam must possess sufficient permeability along with pores.
Ibrahim and Nasr-El-Din [69] studied the effect of salinity of coal seam water,
the composition of the injected gas, and formation pressure on CO2 sequestration in
bitumen coal by core flooding experiments using coal cores. They measured variation
in permeability arising from CO2 adsorption and found that permeability reduction
was significant with high salinity. Another study conducted by Reeves et al. [70]
show similar results of CO2 adsorption that prompt swelling of the coal matrix,
ultimately decreasing permeability and injectivity. Zhiming and Xiaochun [71] evaluated the effect of coal swelling under stress conditions by adsorption of carbon
dioxide, nitrogen, and methane, respectively. The effect of CO2 injection on coal
porosity and permeability were studied theoretically by Mazumdar and Wolf [72].
They further measured coal swelling in CO2 -ECBM experiments. Romanov [73]
used CO2 adsorption to study the difference in swelling between block samples and
powdered samples. Romnav et al. [74] further measured the adsorption capacity by
examining the impact of CO2 adsorption induced coal swelling.
CO2 storage in coal seams is an attractive approach providing the additional benefit
of methane recovery in enhanced coal bed methane (ECBM) process. CO2 preferentially absorbs to coal surface, replacing methane, thereby facilitating enhanced
methane recovery. This technique also serves as a potential resource for large volumes
of CO2 storage in the U.S. Further, it helps to reduce greenhouse emissions alleviating
the effect of global warming.
However, there are several challenges associated with this technique. Storage of
CO2 in any type of coal bed is not possible as the stored CO2 makes the coal unfit for
future energy purposes. Thus, according to the U.S. Congress Act, only unmineable
coal may be considered for CO2 storage [75]. With increasing depth, the permeability declines exponentially, which affects successful CO2 injection. Therefore,
high permeability is desired for high CO2 injection. CO2 adsorption further reduces
permeability due to coal swelling. At greater depths (approximately 1000 m), CO2 is
found in a supercritical state that diffuses into and plasticizes coal further lessening
coal porosity and permeability [76]. In the enhanced coal bed methane (ECBM) technique, higher CO2 injection for enhanced methane recovery may cause coal to shrink,
ultimately affecting permeability. Also, the sorption capacity of coal is reduced from
applied stress [77]. In addition, CO2 is highly soluble in water as compared to
methane, which may lead to pore water acidification, mineral dissolution, eventually
causing coal failure [78].
7.2.5 Conventional/Prediction Models for CO2 Sequestration
Numerous modelling studies in literature report geological CO2 sequestration by
predicting the favourable conditions of CO2 storage under conditions mimicking
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a reservoir environment. Such kind of prediction analysis facilitate the understanding of the feasibility of CO2 sequestration process without having to perform
lengthy experiments in the lab. In this regard, this section briefly describes the
prediction studies carried out on CO2 sequestration in sediments.
Kinetic growth model of CO2 hydrate in porous media with promoter has been
proposed by Palodkar et al. [79]. Herein, the model was based on four assumptions
and the chemical potential difference between the liquid and hydrate phase was
considered as the driving force.
Further, the kinetic model was developed considering CO2 consumption rate
dependence on the chemical potential difference of the liquid and hydrate phase
and the reaction rate taking place on the surface of sediment particles.
Based on the modifications, the final form of the equation proposed by Palodkar
et al. [79] representing the kinetic growth model for CO2 hydrate formation in porous
sediment is shown below
⎧
[
)
]⎫
(
)
n C O2
α
−ΔE a β ( H
L
1 − ex p −n H K 0 ex p
A μw − μw t
(7.1)
=
n H2 O,T
nH
RT
RT
They further estimated the driving force by adopting Holder et al. [80] approach
through estimating the chemical potential at operating temperature and pressure and
also estimated effective surface area of the porous media. Furthermore, they evaluated
the proposed kinetic model by comparing with two other models- the Avrami [81]
and Lee et al. [82] models. They selected data sets for four different porous media
systems including, cellulose foam, silica gel, silica sand and polyurethane foam in
a fixed bed reactor with varying gas feed involving pure CO2 and mixtures of CO2 H2 and CO2 -H2 -C3 H8 . The findings reveal improved performance of the proposed
kinetic model in predicting the experimental data as compared to the existing models
of Avrami [81] and Lee et al. [82].
Prediction studies have been carried out regarding CO2 sequestration in saline
aquifers through reservoir simulation. Generally, CO2 has been successfully
sequestered in aquifers at shallow water depth as supercritical CO2 . However, a
hydrate stability zone (HSZ) exists at water depth larger than 630 m in tropical
regions, where it extends below the seafloor. In this zone CO2 and water can exist as
solid CO2 hydrate. Kai Zhang et al. [83] investigated the feasibility of storing CO2
inside the hydrate stable zone (HSZ) by reservoir pressure management by maintaining the CO2 hydrate formation pressure using water producers and CO2 injectors.
They conducted numerical simulations to evaluate CO2 storage in three aquifers in
tropical waters and then compared the three confined aquifers. CMG STARS simulation software was used to build the three aquifer models. The porosity, permeability and thickness of the considered aquifer varied from 30%, 3000 md and 50 m,
respectively.
The first aquifer is considered a shallow one without a HSZ with a water depth
of 800 m and buried depth of 830 mbsf. For the second aquifer, it is considered
at a water depth of 800 m and buried depth of 70 mbsf with 60% outside and 40%
inside the HSZ, while the third and last aquifer resides inside HSZ with a water depth
7 Machine Learning in CO2 Sequestration
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of 800 m and buried depth of 30 mbsf. Four corner wells acting as CO2 injectors
or water producers manage the reservoir pressure during the simulation. For the
first aquifer, the results showed that by reducing the reservoir pressure to below the
fracture pressure through water production, it is possible to store up to 6% PV or 61
Mt of CO2 . CO2 breakthrough in water producers and the amount of water produced
affects the amount of CO2 stored.
However, in case of second and third aquifer the results revealed that by reducing
the reservoir pressure to below the fracture pressure through water production, up
to 12% PV or 164 Mt and 21% PV and 13 Mt of CO2 can be stored, respectively.
Finally, the findings show that during CO2 injection, CO2 hydrate formation slows
CO2 breakthrough and moderates reservoir pressure due to volume shrinkage thus
allowing more CO2 to be stored. Another benefit is the solid form of CO2 storage
as hydrate which minimizes the probability of CO2 migration and leakage post CO2
injection through the caprock.
CO2 injection into porous reservoir has been studied by Singh et al. [84] to predict
geological CO2 sequestration. They also reported an additional simulation of CO2
injection in a depleted methane hydrate reservoir. In their study they mainly focused
on four parameters to analyse the response of the reservoir to storage of CO2 . These
parameters include reservoir porosity, initial water saturation and reservoir temperature and injection pressure. In addition, the main quantities of interest were the pattern
of hydrate formation and the cumulative mass of CO2 sequestration in the reservoir
as a function of time. They considered a transient, non-isothermal, and multiphase
model which involves three phases- gaseous, aqueous and CO2 -hydrate and three
components- water, CO2, and CO2 -hydrate. Water and CO2 forms the mobile phase
while hydrate is the immobile phase.
Results obtained from the numerical simulation reveal that the initial water saturation is a significant parameter affecting both CO2 gas migration and hydrate formation. However, the results from isothermal simulation are almost similar to nonisothermal model. Overall, the simulation results conclude slow reaction kinetics
with less amount of CO2 stored as hydrate, however, with time the capacity increases.
7.3 Machine Learning in CO2 Sequestration
Machine learning in CCUS (CO2 capture, utilization, and storage) technologies is
expanding knowledge through advancement. Especially in CO2 sequestration, the
application of machine learning is growing in areas such as predicting leakage during
CO2 injection and monitoring the flow of CO2 within the reservoir where there exists
a large number of variables that needs to be considered. These applications are helpful
in reducing the experimental time that would otherwise take long to achieve. The
successful application of machine learning in predicting the reservoir conditions for
CO2 sequestration could benefit from such applications through reducing time as
well as cost from experiments. The following section briefly describes the machine
learning based CO2 sequestration techniques.
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7.3.1 Machine Learning in Enhanced Oil Recovery (EOR)
EOR assisted CO2 sequestration via machine learning is developing rapidly in sectors
of oil and gas production. Recent studies demonstrate a lot of prediction analysis that
could estimate the flow of gas, CO2 leakage and trapping efficiency of CO2 during
injection process. Table 7.1 shows a brief summary of the EOR-CO2 sequestration
studies in the literature.
• Case Study I
To ensure a good performance of both project profit and CO2 storage, optimization
of EOR project with multiple objectives are required. You et al. [85] estimated the
CO2 storage and production performance through optimizing field scale numerical
Table 7.1 Summary of machine-learning based EOR-CO2 sequestration studies
Author
Algorithm
Brief description
You et al. [85]
Hybridized machine learning
A new framework to co-optimize field
performance and CO2 storage by
merging hybridized machine learning
algorithms for reduced order modeling
and optimization techniques
You et al. [86]
Artificial Neural Network
(ANN)/ MOPSO
In this study a stable computational
framework combining artificial neural
network (ANN) and multi-objective
optimizers was proposed to optimize
CO2 -EOR processes
VoThanh et al. [87]
Artificial Neural Network
(ANN)
This study predicted the oil recovery
and CO2 storage capacity in Residual
Oil Zones (ROZs) using ANN
application
You et al. [88]
Artificial Neural Network
(ANN) and Particle Swarm
Optimization
This work reports a computational
workflow using machine learning to
optimize a CO2 -WAG project that
considers both hydrocarbon recovery
and CO2 sequestration efficacies
Balch et al. [89]
Deep learning
Developing and validating
technologies for safely and
permanently storing CO2 and ensuring
storage permanence. Further, improve
reservoir storage efficiency and
support predictability of CO2 storage
capacity in geologic formations
Kashkooli et al. [90]
Coupled optimization
CCS-EOR problem was tackled by
utilizing dynamic well flow settings as
optimization variables and improve the
efficiency through a sensitivity
analysis
7 Machine Learning in CO2 Sequestration
129
compositional simulations in the Pennsylvanian Upper Morrow sandstone reservoir
in the Farnsworth Unit (FWU), Ochiltree County, Texas. In this project, they developed an improved framework combining hybridized machine learning algorithms
for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage. The model’s framework incorporates geological, geophysical, and engineering data. The model was calibrated with the performance history of
an active CO2 flood data to attain a successful history matched model. Further, uncertain parameters such as reservoir rock properties and relative permeability exponents
were adjusted to incorporate potential changes in wettability in the history matched
model.
Furthermore, a proxy model was generated with hybridized multilayer and radial
basis function (RBF) neural network methods. Then a series of training and calibration runs were conducted in the proxy model to make it reliable and robust until it
reached the specified criteria for validation. After the proxy model validation, hybrid
evolutionary and machine learning optimization algorithms were used to obtain an
optimum solution. The uncertain variables considered in the modelling study for the
optimization process includes gas oil ratio, production rates, water alternating cycle,
bottom hole pressure of producer and injectors. Overall, 160 optimization experiments were performed with the PSO evolutionary algorithm to obtain the optimum
combinations of control variables [85].
On comparison, it was observed that the optimal case showed a slightly smaller
project NPV and almost the same cumulative oil production as compared to the
baseline case. However, the CO2 storage of optimal case was 18.08% higher than
that of baseline case, which finally resulted in a 9% higher overall objective function
for optimal case. This study confirms the possibility of developing robust and reliable machine learning optimization algorithm that could optimize a developmental
strategy to maximize oil production and storage of CO2 within the FWU. As a result,
it will serve as a baseline for future field-scale compositional modeling and reactive
transport for CO2 -EOR initiatives all over the world.
CO2 -EOR combined processes involve oil recovery with simultaneous storage of
CO2 . In such processes, to optimise the oil recovery to maximum with efficient CO2
storage volume and also optimise the cost simultaneously, a stable computational
framework was designed by You et al. [86]. This framework operates by combining
artificial neural network (ANN) with multi-objective optimizers to optimise the entire
process. For this, expert ANN systems were trained and employed as surrogate
models of the high-fidelity compositional simulator during the optimization. Further,
a synthetic injection pattern-base case study was conducted to confirm the robustness
of the development optimization protocol. Then, a field implementation in MorrowB formation to optimize the tertiary recovery stage of the field is presented [86].
Furthermore, a comparison is made of the optimum solution and the optimization
findings provide valuable insight into the decision-making process of CO2 -EOR
project when multiple objective functions are considered. They show that there are
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potential multi-solutions satisfying certain criteria in a CO2 -EOR project designing,
which otherwise cannot be found using traditional weighted sum method.
In this study, Thanh et al. [87] predicted the oil recovery and CO2 storage capacity
through artificial neural network during CO2 injection process. The training database
was created by incorporating uncertainty parameters, such as geological factors and
well operations. The cumulative oil production, cumulative CO2 storage, and cumulative CO2 retained simulations were then developed using a total of 351 numerical
samples. When compared to objective values, the findings showed that the developed
ANN model had an exceptional prediction ability with a high correlation coefficient
(R2) of over 0.98 and a total root mean square error of less than 2%. Five real ROZs
in the Permian Basin were used to test the accuracy and stability of ANN models.
It was concluded in the results that the ANN model could accurately predict the
CO2 storage and oil recovery, and that it could be used as a reliable tool to examine
the viability of CCUS in ROZs at an early stage. Finally, CO2 -EOR storage projects
were used to evaluate the potential use of the proposed ANN model.
• Case Study II
This study is on an ongoing CO2 -EOR project in Farnsworth Unit (Texas, US) [88].
Here, a CO2 -WAG project is optimized using a machine learning assisted computational approach that considers both hydrocarbon recovery and CO2 sequestration
efficacies. The fluid flow dynamics of the on-going CO2 - EOR project is investigated using a compositional field-scale numerical simulation model. Time-series
project responses such as hydrocarbon production, CO2 storage and reservoir pressure data are predicted using artificial-neural-network (ANN) based proxy models.
The proxy model outputs are used to not only evaluate the objective function but
also to restrict the optimization processes physically and economically. To optimize
the required objective function, the proposed optimization workflow combines the
Particle Swarm Optimization (PSO) algorithm with the ANN proxies. The findings
show that the proposed workflow is a more reliable approach for co-optimizing
CO2 -EOR projects.
According to the findings, the optimised case could store roughly 94% of the
purchased CO2 within Farnsworth Unit. When compared to the baseline scenario,
the observed optimal case has a 21.69% increase in CO2 storage and 8.74% increase
in oil output. The developed co-optimization approach for CO2 sequestration and oil
recovery was highly successful providing 8.74% greater project NPV and 19.79%
higher overall objective function value.
Further studies related to CO2 -EOR projects are in the form of coupled optimization that offers simultaneous path for both CO2 sequestration and oil production to
solve the time consuming issue associated with carbon capture and storage-enhanced
oil recovery (CCS-EOR) applications. The objective of such studies is to be able to
conduct the entire process of oil recovery and CO2 storage within a stipulated time
frame. Dynamic well flow parameters were used to solve the CCS-EOR issue. The
optimization variables in this case were the bottom-hole pressure and the flow rate
were with a weighted objective function [90].
7 Machine Learning in CO2 Sequestration
131
An optimization model referred as EC was used for the case study. The model is in
the form of a dynamic partial differential equation (PDE) restricted problem having
a nonlinear, nonconvex, and non-smooth nature. In order to simplify the analysis, the
solution method was divided into two stages- predicting the production and updating
the BHPs correspondingly. For the prediction analysis a compositional commercial
numerical flow simulator (ECLIPSE 300, version 2018) is utilised while for the BHPs
a serial optimization algorithm is used. Due to the nonlinear nature of the problem,
a combination series of genetic algorithms (GA) and pattern search algorithm (PS)
were used to avoid trapping in local minima.
• Case Study III
In this case study the validity of the EC model is tested on real 3-D with three
phase heterogeneous carbonate oil reservoir. This reservoir consists of 32 × 28 ×
12 anisotropic grid blocks with inhomogeneous sizes. The reservoir is under CO2
injection process and is located in the south side of Iran and has three producing and
two injecting wells in the reservoir. A compositional simulator is used to model oil
production. The initial reservoir model parameters include initial reservoir pressure,
bubble point pressure, reservoir temperature, oil viscosity, formation water viscosity,
average porosity, average permeability, maximum water cut and maximum GOR.
Herein EOR and CO2 storage is designed for a time frame of 20 years with low flow
rate profiles. For unbiased comparison initially a base scenario is defined, and its
objective value is compared with those of the optimized scenarios [90].
The optimized case was shown to improve the objective value and gas storage
efficiency by 2.86% and 12.1%, respectively, in the optimal scenario. The findings can
be utilized to investigate a number of operational matters such as the CO2 distribution
map in the reservoir, and the well settings.
7.3.2 Machine Learning in Saline Aquifers
Machine learning based prediction for CO2 sequestration in saline aquifers is helpful
in ensuring safe deposition of CO2 for an extended period. There are recent studies
that report the application of machine learning algorithms to determine the feasibility
of CO2 sequestration process in saline aquifers. Table 7.2 presents the summary of the
reported studies in literature for CO2 sequestration in saline aquifers using machine
learning.
CO2 sequestration in geological sediments is associated with several uncertain
parameters that call for risk management approaches to monitor the safety of the
stored CO2 in marine sediments. However, prior proceeding with any such risk assessment approach, there are certain factors that needs to be considered like the location
of the monitoring well and the type of input parameters to be measured considering
the uncertainties at the geologic sequestration sites. In view of this, a filtering-based
data assimilation procedure was developed by Chen et al. [91] to effectively monitor
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Table 7.2 Summary of machine-learning for CO2 sequestration in saline aquifers
Author
Algorithm
Brief description
Chen et al. [91]
Multivariate adaptive regression
splines
Designing effective monitoring
systems for CO2 leakage using a
filtering-based data assimilation
procedure. Machine learning was
employed to lower the computing
cost of the filtering-based data
assimilation process
Song et al. [92]
Artificial Neural Network (ANN)
The ANN model was effectively
used to solve and predict trapping
mechanisms as well as optimize
interactions between physical
parameters such as formation and
storage efficiency
Tang et al. [93]
Deep learning
This study reports
a deep-learning-based surrogate
model to treat coupled flow and
geomechanics in carbon storage
operations
Thanh and Lee [94]
Gaussian process regression (GPR),
Support vector machine (SVM), and
Random forest (RF)
This study predicts CO2 trapping
efficiency in saline aquifers using
3 machine learning models
De-Gald et al. [95]
RBF Neural Network and CMG
STARS software
This work models CO2 storage as
hydrate in saline aquifers using
STARS software
the process with minimum cost using Multivariate Adaptive Regression Splines algorithm. The algorithm employs physics based simulation results of CO2 injection and
subsequent multiphase flow in saline aquifers to generate efficient reduced order
models. For the validation of the workflow it was compared to both a simple validation case as well as a complex case involving multiple monitoring wells. The
findings revealed successful application of the proposed method in building effective monitoring strategies considering uncertainties. Further, when various forms of
monitoring signals are used simultaneously, the outcome is observed to be better
with reduced uncertainty levels as compared single signal type.
Song et al. [92] used a machine learning approach to predict the efficiency of CO2
sequestration in sediments. They further tested the practical application of geological
CO2 sequestration through several case studies using artificial neural network (ANN)
involving geologic, petrophysical, and reservoir characteristics. The ANN based CO2
sequestration model was constructed for saline aquifers. The input data for the testing
of the model was developed from a synthetic aquifer using residual and solubility
trapping data. Machine learning algorithm Python coupled with Keras was used for
the training and testing of the model. The estimated coefficient of determination
(R2) and root mean square error (RMSE) values were used to determine the best
iteration and regression. The model is made up of 8 hidden layers, each of which
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has 64 nodes and has an R2 of 0.9847 and an RMSE of 0.0082. In order to test for
practical applications, the model was validated using a saline aquifer field model
in Korea. The model was found to predict with high accuracy resulting in an R2 of
0.9933 and an RMSE of 0.0197 for RTI and an R2 of 0.9442 and an RMSE of 0.0113
for STI. The model was highly successful in predicting CO2 trapping efficiency in a
huge number of case studies, and optimize correlations between physical formation
parameters and storage efficiency. Hence, this model could be a viable alternative,
where conventional simulations fail to provide successful solutions.
Tang et al. [93] developed a deep-learning-based surrogate model capable of
predicting flow and geomechanical responses in CO2 storage operations. The 3D
recurrent R-U-Net model combines deep convolutional and recurrent neural networks
to capture the spatial distribution and temporal evolution of saturation, pressure and
surface displacement fields. The method is trained using high-fidelity simulation
results for 2000 storage-aquifer realizations characterized by multi-Gaussian porosity
and log-permeability fields. These numerical solutions are expensive because the
domain that must be considered for the coupled problem includes not only the
storage aquifer but also a surrounding region, overburden and bedrock. The surrogate model is trained to predict the 3D CO2 saturation and pressure fields in the
storage aquifer, and 2D displacement maps at the Earth’s surface. Detailed comparisons between surrogate model and full-order simulation results for new (test-case)
storage-aquifer realizations show that the saturation, pressure and surface displacement fields provided by the surrogate model display a high degree of accuracy, both
for individual test-case realizations and for ensemble statistics. Finally, the recurrent
R-U-Net surrogate model is applied with a rejection sampling procedure for data
assimilation. Although the observations consist of only a small number of surface
displacement measurements, significant uncertainty reduction in pressure buildup at
the top of the storage aquifer (caprock) is achieved.
Geological carbon storage (GCS) has been practiced in deep saline formations
for decades by trapping CO2 within the aquifers. In view of this, it is significant to
evaluate the efficiency of CO2 trapping during the process of CO2 storage. Hence
the need arises to develop efficient means that could estimate CO2 trapping efficiency accurately to achieve high carbon storage capacity in saline aquifers. Machine
learning has proved meaningful in this area and has been used by Thanh and Lee
[94] to predict CO2 trapping efficiency in saline formations. They employed three
machine learning algorithms namely, Gaussian process regression (GPR), support
vector machine (SVM), and random forest (RF) for the prediction. For this initially,
they created a training dataset by incorporating data for various uncertainty variables
such as the reservoir petrophysical properties, physical characteristics, and geological
parameters and performed about 101 reservoir simulations. Trapping efficiency was
then evaluated by considering the residual trapping, solubility trapping, and cumulative CO2 injection data obtained from the simulation runs. Their findings revealed
that the three mentioned algorithms (GPR, SVM and RF) proved highly successful
in predicting CO2 trapping efficiency in deep saline aquifers with the GPR model
obtaining the highest correlation factor (R2 = 0.992) and lowest root mean square
error (RMSE = 0.00491) among the three models. Further, the accuracy of the GPR
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A. N. Rehman and B. Lal
models was tested for a real reservoir in offshore Vietnam and the results indicated
a good agreement between the two. This suggested the success of GPR machine
learning models in predicting the CO2 trapping efficiency in saline aquifers.
However, most of the studies related to machine learning mainly focus on
the detection of leakage while CO2 injection and its flow behavior within the reservoir. Hydrate related studies using machine learning are quite rare when it comes to
CO2 hydrate storage in sediments. De-Gald et al. [95] evaluated the storage capacity
of CO2 in saline aquifers in the hydrate form using machine learning algorithms.
The hydrate form of storage is advantageous over other CO2 storage methods since
it involves storing CO2 in the solid form. Hence the possibility of CO2 leakage
through migration is less. However, it is highly important to ensure the feasibility
of the hydrate method for practical applicability. Several studies focus on the experimental approach through lab scale testing to test the feasibility which certainly
provides meaningful insights but is quite time consuming involving lengthy experiments. Machine learning plays an important role where multiple simulation runs
can be conducted through different algorithms within a short span of time. Such
a study employing machine learning algorithms and reservoir simulation software
were conducted to evaluate the sensitivity of reservoir parameters to facilitate the
understanding of the specific reservoir characteristics impacting the stability of CO2
hydrate storage [95]. Here, Hydra Flash software was used to calculate the hydrate
formation curve considering it as a reference. Further, CO2 hydrate storage in aquifers
was modelled using CMG STARS simulation software. They considered fluid flow,
formation and decomposition reactions and heat transfer including water, gaseous
CO2 , and solid CO2 hydrate to simulate the developed reservoir model. The simulations were assumed to be on an isotropic and homogeneous infinite aquifer. The
formation and decomposition reaction equations considered for the simulation model
are shown below
7.7H2 O + C O2 = C O2 (H yd)
(7.2)
H2 O + C O2 (H yd) = 7.7H2 O + C O2
(7.3)
They applied Sobol Analysis and Effect Estimates in their model due to their high
prediction accuracy. The Radial Basis Function (RBF) Neural Network was used as a
model. The simplest form of the RBF network is a three-layered feedforward neural
network. All the simulations were performed using CMG CMOST to estimate the
factors affecting the hydrate formation over a period of 10 years. The factors considered include the effects of porosity, sea depth, permeability, thermal conductivity,
and heat capacity. The findings suggested that within the studied range the rate
of CO2 hydrate formation varied linearly or quadratically with respect to porosity,
permeability, injection temperature, the thermal conductivity of the rocks, volumetric
heat capacity and reference pressure or sea depth. Injection temperature and rock
conductivity showed negligible effect on hydrate formation while a moderate effect
was observed in the case of permeability considering the range studied. The rate of
7 Machine Learning in CO2 Sequestration
135
hydrate formation was highly sensitive to varying porosity, heat capacity and reference pressure. They finally concluded that CO2 storage as hydrate in saline aquifers
for an extended period largely depends on several reservoir characteristics that need
special consideration before its practical implementation.
7.3.3 Machine Learning in Depleted Reservoirs
The use of machine learning techniques in the area of depleted reservoirs is still new
and there is not much literature available on it. However, few studies report the use
of machine learning based applications in CO2 sequestration in these reservoirs. The
major advantage of using machine learning is the ability to perform a huge number
of simulations within a short time. Thus, time management is very essential as such
analysis involves thousands of runs. Machine learning and artificial intelligence was
employed by Amini and Mohaghegh [96] through developing a proxy fluid model
to test for a real case CO2 sequestration project. The primary objective of the model
was to evaluate the uncertain reservoir parameters during the CO2 injection process.
These parameters include saturation, pressure, and CO2 mole fraction in the reservoir.
The major advantage of the data-driven model compared to the numerical reservoir
simulation model lies in its ability to generate saturation, pressure, and CO2 mole
fraction data through the entire reservoir within a short time with significantly less
computational effort. In their study, they successfully managed to fasten the process
for a specific case by about 20 times using this proxy model which took around fifteen
minutes for a single simulation run and about 45 s to apply for all monthly time steps.
This shows that an artificial intelligence based proxy model could be used to model a
significantly non-linear system. The ability of neural networks to recognize patterns
allows the model to capture the interaction between numerous system parameters
without any kind of interference.
Machine learning in carbon sequestration finds its best use in detecting leakage
during carbon capture and storage applications. Automatic leakage detection during
CO2 injection in reservoirs was studied using machine learning models by Sinha et al.
[97]. In the model they used simple harmonic pulse testing (HPT) to measure data for
the pressure and the rate of CO2 injection. Harmonic pulse testing data from a field
study in the Cranfield oil field, Mississippi, USA was used to validate the feasibility of
the machine learning model by incorporating baseline parameters and an artificially
introduced leak in the model. The reservoir leakage problem is detected by assuming
it is an anomaly detection problem with deviations from the predicted behavior
indicating reservoir leaks. The results indicate that different machine learning algorithms used in this study such as multi-layer feed-forward network, Long Short-Term
Memory, and convolutional neural network prove successful in identifying leakages
which could prevent great loss during CO2 storage applications.
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7.4 Conclusion
Machine learning techniques are useful in multiple ways including leak detection,
migration and monitoring flow behavior in the reservoir during CO2 injection. These
types of predictions are beneficial to know the feasibility of the entire process and
also the risk associated with it. Thus, it helps to reduce the time for thousands
of simulation runs and also makes complex problems easy and quick. The future
application of machine learning in areas that involve large and complex simulations
will help generate quick responses within short time.
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Chapter 8
Machine Learning in Wax Deposition
Ihtisham Ul Haq and Bhajan Lal
Abstract One of the biggest sectors providing energy demands is the oil and gas
industry. Ensuring a continuous fuel supply necessitates flow assurance. Flow assurance is challenging and crucial for subsea pipelines since the seawater temperature,
as well as the encircling temperature, is typically much colder than the exterior
atmospheric temperature. Wax deposition takes place when the inner pipe temperature drops below the crude oil cloud temperature. Such challenges emerge when
the paraffins in crude oil crystallise and accumulate on the cold upstream wall.
The primary issue affecting flow assurance or decreasing the efficiency of oil and
gas transportation is wax deposition. Consequently, this chapter provides an insight
into methodologies for reducing and monitoring wax accumulation. In addition, the
numerical models for wax deposition are highlighted. In the final section, the role of
artificial intelligence (machine learning) in the efficacious prediction of wax or the
enhancement of various numerical models is affirmed.
Keywords Machine learning · Wax accumulation · Flow assurance · Numerical
models · Artificial intelligence
8.1 Introduction
Flow assurance is very significant for the profitable and valid manufacturing of the
oil and gas industry in the Deepwater atmosphere. It may be summed up as “the
process to manage the secure delivery of fluid flow from the reservoirs to the focal
point effectively [1]”. Fossil fuel is a great energy resource currently and will be
continued in forthcoming years. The vast quantities of oil and gas found in the deep
ocean have piqued the interest of those interested in utilising and utilising offshore oil
I. U. Haq · B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_8
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resources. Because of high production costs and a hostile environment, it is difficult
to obtain oil and gas reserves in deep water and arctic regions [2]. The major concern
of flow assurance during the manufacturing of oil and gas, especially in deep water
projects, is the managing of various solid accumulations, for instance, gas hydrates,
waxing, asphaltenes, corrosion, erosion, and slugging [3]. That may cause capital
losses in terms of production facilities, which lead to pipeline blockages as the solid
deposits can interact with each other and therefore in flow assurance failure [4].
Flow assurance is not only concerned with the hydrocarbon flow path, although
it is applied to every segment of production involving, selection, inspection, and
repair of process issues. As the number off wells expanding in deep water sea, it is
supposed to be essential to make less the severe threats due to solid deposition and
its difficult recovery [5]. To accomplish the safe and economical process, the only
possible solution is to understand the causes of tragedies, primary flow assurance
challenges, and the development of reduction plaining, which plays a vital role and
gives a victorious perfection in term of, operation of wells [6]. Flow assurance does
play an important role, both technically and economically. The challenges become
more sever and the expenditure goes up as moving to the deep-water activities, as
the seabed temperature is normally between 273.15 k–1094 k and at 1500–3000
dept which is favourable temperature for gas hydrate formation, wax deposition, and
chances of slugging [7–10]. The rock-hard deposited in the fossil fuel manufacturing
industry may be mitigated through mechanical, chemical, and heating processes or a
combination of them [11]. If solid deposits is not mitigated early, it will be a massive
impediment in the transportation of hydrocarbons because a deposit layer will grow
up swiftly and impede the follow as a result, severe blockage in pipeline [5, 12].
The reduction techniques are the most important industrially, as the development
of oil and gas operations are carried on at a hug deepness and worst atmosphere,
which leads to enormous issues faced by industry [13]. To minimise the serious
threat which leading the oil and gas company to spend billions of dollars every year,
it is imperative to handle the critical situation before it causes a big catastrophe and
financial loss from the production interruption or asset damage. There are various
techniques that are used to control these challenges, which are faced by the oil and gas
industry during manufacturing and delivering of hydrocarbons in the worst weather
conditions, where high pressure and low temperature are involved. these methods are
to be applied through mechanically, thermodynamically, and chemically, but in fact,
the most feasible way among all the given techniques suggested for the reduction of
existence and unseen problems is to use the chemical inhibitors. however, it is not
the permanent solution but somehow it can show better performance.
The most critical flow assurance issues, which are essential to be contemplated
for the manufacturing of multiple flows, are slugging, waxing, corrosion, erosion,
emulsion, asphaltenes, naphthenates, scale, and lastly, gas hydrates. Multiphase flow
means the combined flow of gas, oil and water in pipelines elevated in the offshore
and onshore oil and gas industry.
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8.1.1 Wax Deposition in Oil and Gas Industry
Wax is a class of hydrocarbon that are natural components of any crude oil and most
gas condensate. Waxy deposition in oil industry can create problems during production and transportation, and it is observed one of the most severe flow assurances
issues in oil and gas field all around the world, mainly within the manufacturing of
onshore and offshore field. The enough solid Wax depositions can be taken place, by
the time the waxy oil constituents in crude oil can be deposited approximately 4–6
wt.% in the pipelines. Usually, two sorts of wax are encountered, when it comes to
categorizing of wax: (1) microcrystalline wax, that may add to bottom tank sludge,
and (2) macrocrystalline that may elicit as massive flow assurance challenges during
manufacturing and conveying of crude oil. Paraffine wax would be initially solidify
or crystalize or encourage out of raw petroleum before the accumulation of paraffine
wax taken place. The deposition of paraffine wax happens by two phases, as known
as nucleation and crustal growth [1]. Most of the time, the raw petroleum viscosity
may augment as the soon as the deposition of wax take place. Despite of the fact that
the sticky wax is not happened to wall of pipe [14].
The formation of the wax deposited layer will be enlarged during the hydrocarbon
production and with passage of time it can block the flow of pipeline completely,
as can be seen in Fig. 8.1, which indicates discontinuation of fluid flow in term of
production, it can also exhibit the expelling of wax plug [2]. In addition, the wax
formation directs to some substantive failure such as decrease in permeability as well
as formation damage closed the wellbore, modifications of source fluid arrangement,
its rheology because of the phase separation since wax solid precipitates, and also
constraining effect on functioning ability of the whole framework [15].
Fig. 8.1 Expelling of wax by pigging technique [4]
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There are few elements which affected Wax participation for instance, stream rate,
induced temperature drops. Temperature of tubular side wall, unrefined petroleum
temperature, sharing stress and crude oil or raw petroleum design or composition. As
wax deposition is renowned for massive flow assurance defiance within the production system of the oil and gas industry. It may be diminished or reverted either by
chemical, mechanical or heating technique or through a collection of few of them but
the chemical techniques is the foremost better among. In case the given techniques
are not effective meant that they cannot be able to hindrances wax deposition, subsequently a layer of wax will be developed. To avoid the accumulation of wax there
are some methodologies are being employed.
8.2 Wax Deposition Mitigation Techniques
Wax formation in undersea pipes is a remarkable financial concern in oil and gas
fields. The formation of wax occurs when the crude oil is passed by a deep-sea
transmission line where the temperature of crude oil drops below its cloud point
temperature, or “wax appearance temperature)” As the wax accumulates on the wall
of the cold pipe, it causes a severe flow assurance [5]. Under the worst scenario,
production must have been halted and the clogged pipeline which need to be replaced.
The repair and interruption expenditures are anticipated to be $30 million per event.
Wax deposits were so extensive and frequent in one case that a North Sea offshore
platform had to be abandoned at a cost of approximately $100,000,000. Therefore,
the need for remediation approaches has been considered an important aspect of the
flow assurance study [6].
Pigging is one of the conventional mechanical approaches for wax deposition
removal in which an observation gauge, or “pig,” is inserted into the piping system to
scrape off the wax. Although, if the deposition of wax develops quickly and solidifies,
the use of pig may get stuck in the pipe, as it has happened in a pipeline in the Gulf
of Mexico [7]. Another strategy is the insertion of chemicals (an exothermic reaction
with regulated temperature to avoid heat emission and take advantage of fusion
chemical processes). There are some common approaches that have been utilized
for wax deposition removal, such as mechanical, heating, chemical inhibitors, and
microbiological, which are detailed in the following segments [9].
8.2.1 Mechanical Techniques
The mechanical method is the conventional way of being applied by industry for
wax accumulation elimination. In this technique, scrappers are employed in the
tube as well as a “pig” utilization inside the pipeline. The mechanical method is the
conventional way of being applied by industry for wax accumulation elimination [8].
In this technique, scrappers are employed in the tube as well as a “pig” utilization
8 Machine Learning in Wax Deposition
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Fig. 8.2 The pigging process for wax deposition removal
inside the pipeline. The walls of the tube are scrapped by scrappers, and the wax is
removed as the well is operating [11]. The transmission line check-up gauge “pig”
instrument is one of the most conventional techniques, having been in operation by
the industry for almost a century as shown in Fig. 8.2.
These two wax treatment strategies are under repair and maintenance, but they
have drawbacks such as perforation clogging inside the well during scraping and the
pigging device being trapped inside due to wax. In comparison to other ways, all
mechanical methods are cost-effective [8].
8.2.2 Heating
Heating approaches are mainly applied to keep the temperature of crude oil constant
to reduce wax formation. The most prevalent ways are surface coating, insulating the
pipeline, and warm watering, etc. The hot water or hot oil at 65–105 °C is supplied
into the transmission line by a pump which melts the accumulated wax [12]. The
wax is inhibited from forming against the wall by adding solid resin particles with
a melting point greater than WAT in the oil to facilitate slurry flow. The wax is
inhibited by using a surface layer of thermal insulation material such as plastic to
hide a reduction in crude oil temperature. All these ways are applicable, but they are
highly exorbitant [9, 13].
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8.2.3 Chemical Inhibitors
Chemical approaches entail the addition of chemical inhibitors to the oil to suppress
wax development. Dispersants, detergents, and wax crystal modifiers are the three
types of chemical wax inhibitors. The dispersants and detergents are surface activators that keep the wax particles dispersed and suspended, inhibiting them from
sticking to each other, tube walls, or other solid surfaces. The surface activation chemicals improve the pipe’s solid surface, decreasing shear and wax particle contact on
the transmission line’s wall. Various kinds of surfactant alter solubility by dissolving
the nuclei and preventing wax particle agglomeration [16].
Wax crystal enhancers are commonly referred to as “pour point depressors”
since they let oil stream at low temperature while maintaining its density and under
certain scenarios [17]. Wax crystal stabilizers possess the identical geometry as wax
molecules and coprecipitate by scooping the wax particle’s crystalline structure and
producing hydrocarbon linkages [1]. In this way, they obstruct the formation of
wax crystals by restricting the potential of wax crystals to create three dimensional
structures. Each of the wax inhibitors is efficient. Although they need to be employed
once the bulk temperature of the hydrocarbon exceeds it wax appearance temperature
(WAT) [6, 18].
8.2.4 Microbiological techniques
The use of microbes to cope with wax is uncommon, although it has been proven to
be efficient in a number of field studies. The activity of enteric bacteria involves the
production of bioactive compounds that are considered useful as wax suppressors.
When treating crude oil samples, bacterial strains including Actinomyces species
have demonstrated destruction of ligand-binding hydrocarbon components (from
C15 to C20 ) [8]. The bacterial treatment has also been observed to enhance crude oil,
reducing the wax appearance temperature of crude oil. Consequently, the crude oil
is lower vulnerable to wax deposition. The adoption of a microbial technology is
novel, but it might be employed in wells to maintain a static culture.
The wax mitigation techniques encompassing mechanical, heating, chemical
inhibitors, and microbial processes are discussed prior to being applied to eliminate and treat deposited wax within the transportation line [11]. At the moment, both
computing and intelligent systems are required to determine the wax formation and
its preventative management.
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8.3 Prediction’s Models in Wax Deposition
The computational modelling technique that is commonly used to track and predict
wax development through direct numerical approximation or by software with a backend mathematical model is Some of the most essential wax deposition models from
open literature are addressed in the chapter alongside their corresponding equations.
The development of wax deposition models is considered on the basis of assumptions
and parameter choices. In this section, the discussion is based on four different
models. The equilibrium model (EM), the Matzain’s model (MM), the film mass
transfer model (FMTM), and Venkatesan’s model (VM) are all described [19].
The EM is based on thermodynamic equilibrium and takes into account the
concentration gradient in the model. MM is an adapted version of EM that incorporates the diffusion equation as an empirical relationship along with shear stripping
and trapped oil components to make it more effective. The FMTM is established on
mass and heat transfer The ANIFS model contains five layers, as seen in Fig. 8.5,
and the equations describing the model’s output are listed below. The if/then rule is
used to apply the first fuzzy logic [20–22].
The coefficients are primarily considered in the development of the VM, as is
the measurement of mass flux in the model. Due to its agreement with experimental
results, the applicability of MM has been proven to be the most self-sufficient of the
four models. In addition, it is owing to the model’s consideration of oil capture and
the linkage of the shear stripping influence.
8.4 Machine Learning in Wax Deposition
The oil and natural gas industry’s research and development is moving to toward the
use of artificial intelligence (AI) algorithms and machine learning ideas. Depending
on the operating scenarios, equipping systems with AI can improve their decisionmaking capabilities. The algorithms that are generally utilized include evolutionary
algorithms (EA), artificial neural networks (ANN), swarm intelligence (SI), and fuzzy
logic (FL) [20]. If required, the use of one or more artificial intelligent algorithms can
be applicable. The Adaptive Neural Fuzzy Inference System (ANFIS) gave insights
about the finest condensation proportions utilizing ANN, and also the NFL evaluation
assisted in the prolonged enhancement of the wax deposition model. The application
of the statistical model took into account the wax accumulation dependent and independent variables among the recent research [6, 23]. The viscosity of the crude oil
is the dependent factor, and pressure is the independent factor. The linear boundary
limits could be maintained by plotting a pressure/viscosity versus pressure plot (as
can be seen in Fig. 8.3), and if the actual plot exceeds the specified limit, it indicates
a high possibility of wax deposition. The ANN model was created using a backpropagation neural network (BNN). BNN employs two loops, one forward and one
reverse. The forward loop facilitates the processing of data from inputs to outputs,
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considering that the backward loop reverses the process from output to input. The
reverse loop processes the data and applies weight error checking to it before passing
it to the forward loop.
The backpropagation algorithm is trained in this manner through the ongoing
operation of forward-to-backward and backward-to-forward loops. Because of the
adjusting weights contained within the neural network, BNN is, however, referred
to as a training algorithm. The most concise modelling can be seen in the work of
[17], which used an ANN mathematical model to predict the probability of wax
accumulation. Upon noticing experimentally that the deposition rate of wax was
nonlinear, the Kolmogorov theorem was adapted; it virtually emulates a nonlinear
function into a linear function by employing a two-layer ANN with a predefined error
Fig. 8.3 The potential prediction of wax development [23]
8 Machine Learning in Wax Deposition
149
limit. Figure 8.4 depicts the mapping framework for making predictions on the wax
deposition rate. Wax accumulation proportion is the output parameter of the input
parameters (viscosity, shear stress, temperature gradient, and concentration gradient).
When the results were compared to the calculated set, the linear regression model
had a correlation of 0.78, while the ANN model had a correlation of 0.97. The ANFIS
model was applied to the prediction of the accumulated layer of wax in the presence of
single-phase turbulent fluid [24]. A five-layered ANFIS model with input parameters
such as wax concentration (%), Reynolds number, time, temperature-driven force,
and temperatures (outside, inside, and pipe wall) was considered (the ratio of the
gradient temperature wall and the outside temperature to the bulk temperature of the
hydrocarbons). The ANIFS model contains five layers, as seen in Fig. 8.5, and the
equations describing the model’s output are listed below. The if/then rule is used to
apply the first fuzzy logic.
The ANIFS model contains five layers, as seen in Fig. 8.5, and the equations
describing the model’s output are listed below. The if/then rule is used to apply the
first fuzzy logic. This model’s prediction of wax accumulated thickness was found
to be very close to experimental values. The mean square error values in comparison
to experimental data were 3-digit reliability (0.00077034) and a higher predictive
Fig. 8.4 The ANN model’s structure for predicting the rate of wax accumulation [17]
Fig. 8.5 The ANFIS model’s structure for predicting the rate of wax accumulation [17]
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value (0.9858). On the other hand, Amar et al. [25] has used robust machine learning
methods very recently to predict wax accumulation under large production circumstances. to accomplish this, 88 experiments measurements were applied to train a
multilayer perceptron (MLP) optimised with the Levenberg–Marquardt algorithm
(MLP-LMA) and the Bayesian Regularization algorithm (MLP-BR). The findings
demonstrates that MLP-LMA performed much better, with an as a whole root mean
square error of 0.2198 and a regression coefficient (R2) of 0.9971. MLP-LMA outperforms preceding techniques in the literature, according to the comparison of results.
Yu et al. [26], have employed Radial Basis Function and support vector machines
for wax development rate in the transmission line. The outcomes indicate that the
errors of the two techniques are within the acceptable range. Because support vector
machines can determine and model finite samples, it has been discovered that their
accuracy is higher. To conclude that, Different researchers have implied different
techniques, but the best way or approach to predict the wax deposition is to use the
machine learning.
8.5 Wax Deposition Case Studies
The oil is transported from offshore platform to the onshore via a 30- inch, 203-km
transmission line for in India’s offshore field to the west of India. As the crude oil is
transported, it has a high wax content and a 30 °C pour point. Typically, the seabed
temperature is 22 °C. A pour point depressant (PPD) is infused. Notably, 300 ppm
to 350 ppm PPD is added to achieve a pour point of 18 °C or 21 °C and thus keep
the oil flowing. The Deepwater transmission lines are pigged on a regular basis
[27]. The PPD infusion on its own ends up costing $15 million annually. While the
consumption of PPD has managed to keep the oil flowing, an increment in upstream
pressure has been noticed over time. It thus identifies the existence of wax deposits
in the pipeline’s Deepwater. A technological survey reported that approximately
20,000 m3 of wax has been accumulated in the pipeline (as it has a volume of
90,226 m3 ). To reduce wax formation, hence the frequency of pigging, a combination
of PPD and dispersant was taken into account [28].
Another case study is the Gannet oil field, which is situated roughly 180 kms
east of Aberdeen in the United Kingdom at a depth of around 90 m. The Gannet
field can be found 16 kms northeast of the Gannet Alpha platform. Risers 31 and
32 transport Gannet oil. This oil’s wax appearance temperature is generally 35.5 °C.
Wax accumulation in Gannet’s subsea pipeline has been approximated to be about
21 m3 of wax, developed over an 8 km distance. A suitable substance was chosen
and transfused at a speed of 0.6 m/min. An extra 480 m3 /day of hydrocarbons can
be managed to recover after chemical inoculation. In addition, the pigging technique
has been used frequently in conjunction with inhibitors to prevent wax deposition in
the Gannet oil field [27–29]. Production began in 2008 at the Power Play oil field,
which is situated in 706 m of water in the Garden Banks block 28 of the Gulf of
Mexico. It is a Deepwater tieback to a network facility that includes a Deepwater Sea
8 Machine Learning in Wax Deposition
151
flow loop for oil circulation and pigging. Two 10 km 4.5'' (pipe in piping system)
PIP pipeline pigging loop systems with enclosed steel catenary risers comprise the
subsea architecture. The oil’s wax appearance temperature is 41.1 °C, and the pour
point varies from −5 °C to 19 °C. Wax build-up is conceivable due to the high WAT
[27]. To reduce or eliminate the risk, a PIP platform was being used to dissipate heat
from the oil. An exponential increase in the flowing downhole pressure and a fall in
the oil supply rate, as noticed in the northern and southern pipeline systems during oil
production, mentioned the possibility of temporary plugging due to wax formation
in the piping system. To inhibit blockage, xylene substances have been inoculated
into the transmission lines at rates ranging from 0.04 to 0.32m3 /min. This, coupled
with an increase in pressure, was inadequate to discard the wax build-up [30].
On the contrary, Russian oil production is almost entirely onshore and plagued by
wax formation, as evidenced by the Mukhanovskoe oil field, the Padovskoe oil field,
the Petrukhnovskoe oil field, and the Urmanskoe oilfield. To obtain the maximum
effectiveness in attempting to control wax deposition in transmission lines, technological approaches including thermal coating and pigging were used in accordance with chemical inhibitors. Surfactants (nanylphenol, synthol), aromatic solvents
(benzene, xylene, toluene), surfactants (nanylphenol, synthanol), and aliphatic alcohols (butano, pentanol, hexanol) have been utilised as chemical inhibitors to mitigate
wax formation [31].
The incidence of wax deposition problems takes place in high-temperature environments. For instance, the Kirkuk-Ceyhan crude oil channel between Iraq and
Turkey is an example of an onshore pipe system with wax formation during the
cold season. The aromatic solvents and pigging approach were utilised to lower wax
deposition in the tubing. The typical low temperature in winter between northern Iraq
and Turkey is about 2 °C, which is sufficient to produce and deposit wax within the
pipes. The Kirkuk-Ceyhan crude oil tube has a diameter of 40 inches and a distance
of 1049 kms, 986 km of which cross through the Turkish region, with a volume of
700,000 barrels per day. The wax mole fraction moves up with a value of 0.193 about
300 miles from the nearest inlet and reaches 0.2225 at the pipeline’s end [32].
8.6 Conclusion
In summary, this chapter examines the application of various industrially pertinent
techniques and studies to monitor and predict wax accumulation. furthermore, the
discussion has made on the process and models used for wax deposition, as a theoretical basis. According to recent literature, the role of machine learning in accurate
and suitable prediction of wax deposition is mentioned. Therefore, the application of
machine learning can ensure the prediction of wax deposition and help the wax deposition models to integrate oil sample knowledge on a consistent premise to confirm
that wax forecasting is credible.
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Chapter 9
Machine Learning Application in Gas
Hydrates
Ali Qasim and Bhajan Lal
Abstract The issue of gas hydrates is one of the major concerns in flow assurance
industry. In order to study the gas hydrates theroretically, reserachers have developed
different thermodynamic and kinetic models to predict the hydrate formation parameters. The use of machine learning in gas hydrate inhibition prediction and analysis has
become a well-established field of study as computational capability has increased
in recent years. In the literature, both supervised and unsupervised learning methods
have been applied to predict the gas hydrate parameters. This chapter has discussed
the conventional modeling approaches in gas hydrate parameters prediction and the
use of machine learning techniques in this field of study. Four different case studies
involving the use of machine learning in gas hydrates prediction have also been
presented.
Keywords Machine learning · Flow assurance · Hydrates · Modelling
9.1 Introduction to Gas Hydrates
In 1810, Sir Humphry Davy discovered gas hydrates or also known as clathrate
hydrates. Gas hydrates are solid crystalline inclusion compounds made up of polyhedral water holes (the host) and small gas molecules (the guest) physically trapped
within them [1–4]. Gas hydrates form when water and non-polar low molecular
weight gases or volatile liquids are exposed to low temperatures ranging from
275 to 285 K and high pressures ranging from 2.0 to 10.0 Mpa [5–10]. Encaged
guest molecules use Van der Waals interactions to stabilise hydrogen-bonded water
molecules in gas hydrate forms. Methane (CH4 ), ethane (C2 H6 ), carbon dioxide
(CO2 ), propane (C3 H8 ), hydrogen sulphide (H2 S), iso-butane (i-C4 H10 ), and n-butane
A. Qasim · B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_9
155
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A. Qasim and B. Lal
Fig. 9.1 a Structure I, b Structure II, c Structure H
(n-C4 H10 ) are all hydrate forming gases [11–13]. The formation of hydrates in natural
gas field deep-water pipes poses a serious environmental risk and may threaten
employees in the exploration and transmission sectors [8, 14–17]. As a result, risk
management for gas hydrates is becoming increasingly crucial. Gas hydrates are not
entirely harmful in and of themselves because they are useful in a range of applications such as fuel delivery, storage, and separation techniques. Massive amounts of
natural gas hydrates can be discovered at marine continental margins and permafrost
zones, making gas hydrates a potential future fuel resource [1, 18, 19]. However, gas
hydrates have so far presented more problems than commercially viable solutions.
Natural gas hydrates can be divided into three categories depending on their crystal
structures: cubic structure I (sI), cubic structure II (sII), and hexagonal structure (sH)
[20–23]. Guest molecules with diameters ranging from 4.2 to 6, such as CH4 , C2 H6 ,
CO2 , and H2 S, are defined as belonging to Structure 1 [24, 25]. The diameter of
Structure II hydrate is estimated to be less than 4.2. Structure II is made up of N2 ,
H2 , C3 H8 , and C4 H10 as single guests [26, 27]. Figure 9.1a–c represent the hydrate
structure categories I, II, and H, respectively.
According to the literature, hydrate equilibrium and structure play a significant
role in cavity occupancy of guest molecules. Because the equilibrium conditions are
determined by the guest molecule, structure I and II hydrates require just one guest
molecule, whereas structure H requires at least two different guest molecules. It
should be noted that both CH4 and CO2 produce sI hydrates under the high pressure
and low temperature conditions found in oil and gas transmission lines [28–30].
9.2 Introduction to Gas Hydrates
Gas hydrates found their application in varying fields at industrial or environmental level and can be utililized according to different research interests. They
may include methane recovery, energy storage, desalination, greenhouse gas capture
and flow assurance. The details of these categories are provided and discussed in the
subsequent sections.
9 Machine Learning Application in Gas Hydrates
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9.2.1 Methane Recovery
Natural gas hydrates can be found beneath Arctic permafrost as well as beneath the
ocean floor. Because there are substantial deposits of hydrates available, gas hydrates
could be a potential source of natural gas in the near future. When a cubic metre of
gas hydrate is lifted to the earth’s surface, it creates 164 cubic metres of natural gas,
the majority of which is CH4 . The organic carbon content contained in the world’s
gas hydrates, according to the US Geological Survey, exceeds that of coal, oil, and
other types of natural gas combined. The naturally occurring gas hydrate resource is
estimated to be worth 10,000 trillion cubic feet to more than 100,000 trillion cubic
feet [31, 32].
Currently, identified gas hydrate reserves in permafrost areas around the world
are expected to be more than twice as vast as all other known hydrocarbon reserves
combined. Natural gas hydrate resources are estimated to be more than 1.5 × 1016 m3 ,
and even if only 15–17 mol percent of methane is gathered from these sources, it can
supply energy for at least 200 years [1, 33, 34]. To increase methane recovery from
natural gas hydrate sources, techniques including depressurization and heat stimulation with thermodynamic and kinetic inhibitors are commonly used [35, 36].
9.2.2 Energy Storage
Natural gas can be found in a variety of locations around the world. As a result, the
transportation of natural gas from one location to another is typically accomplished
by long-distance gas transmission lines. Liquefied natural gas (LNG) carrier ships,
which demand massive capital and operating costs, are typically utilised for longdistance transportation. Gas hydrates, which are more stable than LNG, can be created
and stored at greater temperatures [37].
Furthermore, because hydrates have a self-preservation proclivity that permits
them to maintain stability even at atmospheric pressures, the utilisation of hydrate
technology for gas storage and transportation may be investigated. Numerous studies
suggest that storing gases in hydrate structures has significant potential as a storage
medium for a wide range of gases [18, 38]. Gas hydrates could be used for hydrogen
or natural gas storage and transportation, as well as excellent cooling storage media
in air conditioning systems [39, 40]. When compared to cryogenic applications,
keeping gases in hydrate form is far safer [41].
9.2.3 Desalination
A desalination system based on gas hydrates has been offered as a potential alternative to reverse osmosis and typical multi-flash distillation procedures. The hydrate
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production method removes all salts, ions, and other impurities from the hydrate crystalline structure since the hydrate structure consists only of guest gases and hosting
water molecules. According to recent studies, hydrate-based desalination has the
potential to produce cheaper desalinated water than current technologies [42, 43].
9.2.4 Greenhouse Gas Capture
One of the most difficult challenges in the gas industry is the separation of acidic
pollutants such as carbon dioxide (CO2 ) and hydrogen sulphide (H2 S) [44, 45]. To
solve this, the gas industry frequently employs amine-based absorption procedures.
Commercial solvent costs and regeneration have an impact on the industry’s operating
expenses (Opex). Because gas hydrate formation is mostly solvent-free, the concept
of acidic gas removal via gas hydrates is extremely appealing. favourable conditions
of low temperature and high pressure. To accelerate the formation of hydrates, a trace
amount of chemicals such as hydrate promoters can be introduced to water [46–49].
The hydrate-based separation is based on HLVE, which states that different gases
have different demands for equilibrium [12, 50]. CO2 and H2 S are both captureable
in hydrate form and require low pressure to make gas hydrates. Furthermore, gas
hydrate is used in the CO2 gas capture in the flue gas stream [7].
9.3 Conventional Gas Hydrate Mitigation Method
Gas hydrate development in the gas transmission line can be avoided using a range of
common processes, including depressurization, heating, dehydration, and chemical
additives, each with its own set of benefits and drawbacks [2, 51]. Depressurization
is a corrective method that reduces pressure on one side of the transmission line and
is typically used after hydrate plug growth has commenced. In the case of long and
high-pressure gas transmission pipes, however, the line-depressurization technique
is impracticable [13]. However, it is one of the most expensive preventative methods,
costing over $1 million USD per kilometre of pipeline length [52]. Dehydration
or water removal as a hydrate preventive approach is recommended as a long-term
remedy [53, 54]. The problem of hydrate formation will disappear if there is no water
in the stream, however due to human footprint limits, this is an economically unviable
option. This approach, on the other hand, is extremely effective for gas processing
facilities and other downstream applications [55].
Hydrate mitigation can also be achieved by pumping chemicals into the system
to keep operating temperatures and pressures outside of the hydrate formation zone,
so maintaining a hydrate-free region. These molecules either delay or change the
phase behaviour of the newly formed mixture [5]. Chemical inhibition is the most
appropriate preventive method among those mentioned above, and as such, it is
often utilised in oil and gas pipelines. Chemical inhibitors are non-disruptive and
9 Machine Learning Application in Gas Hydrates
159
do not obstruct pipeline flow, resulting in a consistent, smooth stream flow [56–58].
According to prior research, the hydrate inhibition technique of adding inhibitors is
the sole viable option in the vast majority of cases [12]. The next section delves more
into the various types and qualities of gas hydrate inhibitors.
9.4 Chemical Inhibition of Gas Hydrates
Chemical inhibitors used to prevent hydrate formation are divided into two types
based on dosage: thermodynamic hydrate inhibitors (THIs) and low dose hydrate
inhibitors (LDHIs) (LDHIs).
9.4.1 Thermodynamic Hydrate Inhibitors (THIs)
The THIs are used in large concentrations in the gas–water stream to prevent hydrate
formation in gas production systems. THIs shift the hydrate formation equilibrium
curve to the left, shifting the site of hydrate formation to a higher pressure and lower
temperature, maintaining it outside the hydrate formation area [4, 49, 59]. Methanol
(MeOH) and monoethylene glycol (MEG) are two more commercial THIs that are
commonly used [12]. MeOH and MEG’s inhibitory efficacy is due to the interaction
of the inhibitor oxygen atoms with the surrounding water molecules [60]. The lone
electron of the hydrogen atom migrates to the other covalently bonded atom, leaving
the positively charged hydrogen proton exposed. The proton is attracted to the negatively charged ends of other molecules and establishes an electrostatic bond with one
of the other electrons [60]. The selection of thermodynamic inhibitors necessitates
a comparison of several parameters such as operational cost, safety, corrosion inhibition, physical qualities, and gas dehydration ability [61, 62]. Furthermore, waste
chemical recovery, regeneration, and reinjection are key considerations that should
be considered during the selection process. THIs are employed in concentrations
ranging from 1 to 50 weight percent to suppress gas hydrate formation [12, 63].
The precise concentration value employed for the application is determined by the
operational situation and the temperature of the surrounding environment.
Cha et al. [43] examined the thermodynamic inhibition performance of monoethylene glycol (MEG) for pure CH4 at concentrations of 10, 30, and 50% wt. Ng and
Robinson [64] studied the capacity of methanol to inhibit methane hydrate formation. Following that, Anderson et al. [65] studied the effect of methanol on methane
hydrate generation, and their findings agreed with those of Ng and Robinson.
The temperature and pressure profile of a 50-mile-long natural gas pipeline from
the well head to the production platform of a deep-water field in the Gulf of Mexico
is seen in Fig. 9.2 [18]. In this case, thermodynamic inhibitors such as methanol are
injected into the pipeline in varying mass amounts to ensure that the operating state
is set beyond the hydrate formation area. Methanol’s inhibitory impact is described
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A. Qasim and B. Lal
Fig. 9.2 The temperature
and pressure profile for Gulf
of Mexico pipeline in the
presence of conventional
THI (methanol) [18]
by the degree to which the hydrate equilibrium curve moves from hydrate generating
regions to hydrate free zones.
Despite the fact that commercial THIs can significantly reduce the likelihood of
hydrate formation in petroleum pipelines, their extremely volatile and environmentally hazardous nature limits their use [46, 47]. The quest for new THIs is important
to ensuring long-term environmental and economic sustainability.
9.4.2 Low Dosage Hydrate Inhibitors (LDHIs)
During the exploration of advanced hydrate mitigation technologies, the emphasis
changed from hydrate removal to hydrate management employing LDHIs. LDHIs
are used in small amounts, ranging from 0.5 to 2.0 wt% of water reductions [16,
58]. They are non-volatile and largely non-hazardous to the environment; they are
further classed as Kinetic Hydrate Inhibitors (KHIs) and Anti-Agglomerates (AAs)
[66, 67]. KHIs are water-soluble polymers like polyvinylpyrrolidone (PVP) and
polyvinylcaprolactam (PVCap), whereas AAs are typically surfactant molecules [68,
69].
• Kinetic Hydrate Inhibitors (KHIs)
KHIs prevent hydrate blockage by delaying formation and avoiding nucleation for
a longer period of time than the free water phase’s residence time in the flow line
[16]. The hydrate nucleation time is another term for the induction time. It is the
amount of time that elapsed between the equilibrium condition and the production of
the first hydrate crystal nucleus with the necessary stable properties [70, 71]. KHIs
are used at modest doses (0.5–2 wt%) and their effectiveness is time-dependent, as
contrast to thermodynamic inhibitors [72, 73]. The deeper a system goes into the
hydrate area, the more likely it is that hydrate production will need the use of kinetic
hydrate inhibitors as a viable option [74, 75]. KHIs prevent hydrates by restricting
crystal nucleation and growth at the crystal’s surface by adsorption [76, 77]. Because
they are hydrocarbon phase sensitive, they can be successfully used to a wide range
9 Machine Learning Application in Gas Hydrates
161
of hydrocarbon systems and have seen widespread commercial application over the
last two decades.
The most commonly used KHI inhibitors in flow assurance are water-based polymers such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap) [78,
79]. In addition to water-soluble polymers, ionic liquids and biomolecules have been
used as KHIs. Commercial KHIs (polymers) such as PVP and PVCap are composed
of polyethylene strands joined by N atom and C=O group chemical rings that are
both polar and spherical. KHI polymers work by adsorbing on the hydrate surface,
with the polymer group linked inside the growing hydrate cage at the crystal surface
[80].
• Anti-Agglomerates (AAs)
Anti-Agglomerate (AA) chemicals keep hydrate crystals in smaller formations
within the hydrocarbon stream, preventing bigger crystals from forming. They act
by adhering to the surface of small hydration crystals and making their surface
hydrophobic, limiting further hydrate crystal formation [2, 81]. Anti-agglomerate
hydrate inhibitors can function effectively at higher subcooling temperatures. At high
water cuts, they appear inefficient, and they frequently cause emulsion formation in
water and hydrocarbon systems.
Anti-agglomerants are frequently longer-chained compounds, such as surfactants. When utilised in a pipeline, the hydrophilic (anionic) end of the compound
forms dipole-dipole interactions with the water molecule’s hydrogen ion, while the
hydrophobic (cationic sections, including the long chain) head dissolves in the oil
or gas phase [12]. As early as 1993, Shell Oil Company patented their results for
quaternary ammonium surfactants containing two or three n-butyl, n-pentyl, and
isopentyl groups that displayed good anti-agglomeration capabilities. Twin-tailed
quaternary AAs, such as tetraalkylammonium salts and dicocoyldibutylammonium
bromide, were also developed, with the latter being employed in field testing by
the Dutch Shell team [82]. Huo et al. [67] used a variety of commercial surfactants
in their study, including Span 20, Span 40, Span 60, and Span 80. Their findings
showed that the surfactants tested can keep hydrate particles floating at temperatures
of 277 K and pressures of 8.20 MPa. Their findings were also compared to dodecyl2-(2-caprolactamyl) ethanamide’s (synthesized AA). The synthesised molecule, on
the other hand, displayed improved dispersion under higher water cut conditions,
namely 0.75 wt%. The effort to develop low-cost LDHIs for the oil industry motivated
Kelland et al [83] to widen their AA study to include polypropoxylates. Polyamine
polypropoxylates and other branching polypropoxylates dispersed gas hydrates in a
hydrocarbon fluid as long as the fluid was well agitated.
9.5 Flow Assurance Challenge
In 1934, Hammerschmidt reported the first industrial research effort on gas hydrates
[84]. Since then, significant research on gas hydrate mitigation has been done. Among
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the flow assurance challenges, the development of gas hydrates in oil and gas pipelines
continues to be a major impediment. Every year, industry spends millions of dollars
on various techniques to avoid hydrate development. Because this work only works
with CH4 and CO2 gases, just a single hydrate structure is formed, and the behaviour
of these two gases is examined in depth [5].
In 1888, Villard found methane hydrates as well as other hydrocarbons like ethane,
ethylene, and acetylene. Methane hydrate typically forms sI structure as the simplest
and smallest guest molecule under suitable pressure and temperature conditions, with
both big and microscopic cages of sI hydrate. CH4 hydrates always occupy smaller
cavities sI more than larger cavities due to their smaller diameter. The chemical
formula of CH4 hydrate for total hydrate cavity occupancy is CH4 .5.75H2 O for
small cages and CH4 .7.67H2 O for large cages [27].
Carbon dioxide hydrates were discovered in 1882, and Villard et al. described
the phase equilibrium behaviour of CO2 hydrate in the temperature range 267 K
to 283 K later that year [85]. Under appropriate temperature and pressure settings,
CO2 hydrate forms sI hydrate structure and occupies both large and small cages of
structure I. For full hydrate cavity occupancy, the chemical formula of CO2 hydrate
is CO2.5.75H2 O [27].
9.6 Machine Learning in Gas Hydrates
The flow assurance industry rquires to resolve the problem of gas hydrate development by introducing appropriate preventive and predictive strategies [86]. The use
of Machine Learning (ML) is an innovative risk control technique that is currently
being applied. It is basically a numerical representation of a phenomenon, given
with a certain significance and based on a certain environment, aimed at performing
the desired work [87]. Deployment of ML models would allow us to do predictive
analysis of gas hydrate growth because the value of determining hydrate formation is
also that the solid crystalline structure which forms like ice will plug the oil and gas
pipes as hydrate forms, either during gas production or transmission. This presents a
potentially expensive situation for the organisation as it often takes additional time
and cost to conduct a corrective intervention. Laboratory experiments are required to
determine at which conditions for hydrate to perform. The best practise, therefore, is
to predict the formation of hydrates by using the ML model to minimise gas hydrate
growth.
Artificial neural network (ANN) and Least-squares support-vector machine
(LSSVM) are commonly used machine learning techniques in gas hydrate inhibition. ANN is a measurement tool based on the way measurements are carried out
by the brain [88]. It has been developed for a wide range of topics, such as classification, approximation of functions and prediction. Inside and between the layers,
there are several similarities showing the strength and weights between neurons.
Output of the ANN assuming a linear output neuron j, a single hidden layer with h
sigmoid hidden nodes and the output variable (xt) is given by. LSSVM development
9 Machine Learning Application in Gas Hydrates
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is aimed at reducing the complexity of the Support Vector Machine (SVM) and to
eliminate complexity in the optimization process, linear formulas are deciphered by
linear encoding rather than solving quadratic encoding equations to improve their
speed. LSSVM algorithm’s output can be overcome by fixed linear equality rather
than inequality constraints.
Machine learning has also been used to simulate gas hydrate equilibria, enabling
for the construction of models that are better able to anticipate the highly nonlinear,
multimodal phenomena of gas hydrate equilibria than those based on statistical
thermodynamics. A wide range of machine learning models have been published.
Temperature, pressure, and any means of accounting for fluid composition are all
frequent input features for developed models. A regression model’s output is either
the equilibrium temperature or pressure. It should be emphasized that the models
studied were focused on multicomponent hydrocarbons and natural gases. Heydari
et al. [89] estimated equilibrium temperature from pressure and specific gravity inputs
using neural network models. The relative error (RE) was explored in a comparison
of ANN performance analysis, and the maximum error was found to be 3.035%, with
an R2 value of 0.9941. To be sure, the results of the ANN were compared to those
of the Sloan model and ANN model was found to be a better predicting tool. As a
result, it was concluded that ANN is a useful technique for predicting the temperature at which hydrates form. Zeinali et al. [90] created neural network and ANFIS
models for temperature and gas composition inputs using a dataset of over 700 points.
Ghavipour et al. [91] used leave-one-out cross-validation to develop a model using
130 data points for pressure and gas composition inputs up to n-butane. To forecast
hydrate equilibrium conditions in the presence of thermodynamic inhibitors and electrolytes, models have been developed. Elgibaly and Elkamel [92] developed models
with up to 16 inputs, such as gas composition, hydrogen sulphide, and inhibitor and
electrolyte concentration, which were expanded on by Elgibaly and Elkamel [93],
who took inhibitor costs into consideration. While many studies cover a wide range
of circumstances, they also include substantial amounts of data for both pure and
binary components. Because there is a scarcity of reliable publically available multicomponent data, constructing multicomponent models is difficult. Soroush et al. [94]
built a two-hidden-layer neural network from just under 300 equilibrium points from
the literature, achieving a holdout validation R2 of 0.998 and, despite a small dataset,
restricting the methane + ethane molar gas-phase composition to a minimum of
66.38%, increasing model relevance to natural gas. Support vector machine models
are continuously being developed, despite the prominence of neural networks.
While neural network models have delivered promising results across a wide range
of experimental data, many models’ outcomes lack corresponding error, which can
be evaluated via cross-validation processes. Ward et al. [95] emphasised the significance of completely accounting for hydrate metastability in equilibrium experimental results. Because data used to build models is often culled from the literature,
confidence in some dated data sources may be strengthened by outdated methods
and apparatus. The cross-validated neural network used in their study predicted the
hydrate equilibrium pressure for a given temperature and gas mixture. Because large
amounts of experimental data from many sources are used to create models, Sloan
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and Koh proposed that equilibrium be defined as the point at which complete dissociation of generated hydrates occurs. Overall, a strong, flexible alternative to the thermodynamic model is required. Chapoy et al. [96] developed a feed-forward neural
network with one hidden layer to calculate hydrate dissociation pressures of natural
gases in the presence or absence of inhibitor aqueous solutions. The model was built
with more recent hydrate dissociation data from the literature, and its predictions
were compared against some independent experimental data. The predictions and
experimental findings of this method were reasonably consistent, demonstrating the
predictability of the predictive tool developed in this work. The research found that
experimental hydrate dissociation data at high inhibitor concentrations is required.
Zahedi et al. [88] determined hydrate formation temperature (HFT) using two
distinct approaches i.e. statistical and machine learning. In the first strategy, two
generic HFT correlations were proposed. In one correlation, there were 11 parameters, while in the other, there were 18. In order to derive constants for the indicated
equations, 203 experimental data points were acquired from the literature. The Engineering Equation Solver (EES) and Statistical Package for the Social Sciences (SPSS)
software were used to statistically analyse the data. In the second method, HFT is
calculated using an artificial neural network (ANN). Different architectures were
explored in this scenario, with 70% of the experimental data used for ANN training.
Among the numerous architectures, the multilayer perceptron (MLP) network with
the trainlm training algorithm was shown to be the best. The ANN model’s good estimation performance is proven when the resulting ANN model outputs are compared
to 30% of unseen data. Traditional methods, including our two proposed correlations
for HFT estimation, have been shown to be less accurate than ANN. Mohammadi
et al. [97] presented a feed forward artificial neural network (ANN) algorithm for
estimating dissociation pressures of binary hydrates of tert-butylamine + methane
and tert-butylamine + hydrogen as a function of temperature and tert-butylamine
concentration in aqueous solution as a function of temperature and tert-butylamine
concentration in aqueous solution as a function of temperature and tert-butylamine
concentration in This algorithm was built using experimental data from the literature on the dissociation pressures of the aforementioned binary hydrates. This ANN
technique proved to be capable of representing these hydrate systems.
Yarveicy et al. [98] determined the freezing point depression (FPD) of different
electrolyte solutions using a robust kind of machine learning called least squares
support vector machine, which is based on statistical learning theory (LSSVM).
It was studied as a part in the investigation of the gas hydrate inhibition performance of electrolytic salts such as NaCl, KCl and CaCl2 . The predictions of the
developed model were compared to the results of cubic-plus-association (CPA) EoS
combined with the Debyee Huckel electrostatic term and they were found to be
better. Babakhani et al. [99] used a feed-forward multi-layer artificial neural network
to forecast hydrate formation pressure of binary mixtures including at least one hydrocarbon. For this study, 895 experimental data from various studies published in the
literature were gathered, covering a wide range of temperatures and compositions.
The absolute average relative deviation percent (AARD), mean square error (MSE),
and regression coefficient (R2 ) were used to compare different ANN types in order
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to find the best configuration. An R-squared value of 0.9938 was determined which
showed good fit. The chosen ANN model demonstrated a high level of agreement
with the experimental findings.
Baghban et al. [100] devloped SupportVector Machine (SVM) and a SupportVector Machine with Genetic Algorithm (GASVM) to predict semi-clathrate hydrate
pressure of CO2 , CH4 , N2 , H2 , Ar, Xe, and H2 S in the presence of TBAB ionic liquid
based on the critical temperature (Tc ), critical pressure (Pc ), and acentric factor (ω)
of the above-mentioned gases over a wide range of published data. For SVM model,
an R-squared value of 0.9776 was obtained and for GAVSM model, it was found to
be 0.9994. In the continuation of the use of LSSVM model, Baghban et al. [101]
used Katz [102] k-chart data points to estimate natural gas hydrate formation temperature as a function of pressure and gas gravity using the least square support vector
machine (LSSVM) technique. In addition, the LSSVM’s hyper parameters were
optimised using a genetic method. Furthermore, the present model was compared
to five commonly used correlations, with the LSSVM technique having less deviations. The model showed the R-squared value of 0.9973. Recently, Yarveicy et al.
[103] modeled gas hydrate phase equilibria using Extremely Randomized Trees and
LSSVM approaches. The formation and dissociation conditions of various gases in
the presence of pure water, water-based salts, and alcohol solutions were determined in this novel study. The support vector machine’s least square form was
also used to create a smart model (LSSVM). For modelling purposes, a large databank containing up to 1840 experimental data on equilibrium conditions of clathrate
hydrate formation/dissociation were acquired from the literature (1940–2016). The
collected databank covered a wide variety of pressure–temperature conditions for
liquid water-hydrate-vapor or ice-hydrate-vapor equilibria. According to the error
analysis, all of the suggested models revealed an R-squared value of greater than
0.96. The projections given by the Extra Trees and LSSVM models were found to
be in good agreement with the experimental data. In another study, Yarveicy et al.
[86] developed new methods for calculating the hydrate formation limits of isenthalpic Joule–Thomson (JT) expansions. First, a new empirical correlation is shown
for determining the required minimum initial temperature of a natural gas travelling
through a JT expansion without the development of hydrates. The adaptive neurofuzzy inference system (ANFIS), least squares support vector machine (LSSVM),
and radial basis function artificial neural network were used to present the models
capable of anticipating the gas beginning temperature (RBF-ANN). The methods
produced excellent estimations, according to the findings of the performed error
analysis. The value of R2 was found to be greater than 0.99 in the proposed models.
The RBF ANN model, on the other hand, provided the most accurate estimates.
The research by Rebai et al. [104] proposed a two-pronged technique for
improving the accuracy with which hydrate formation conditions predicted, particularly for gas mixture systems, by combining thermodynamics with artificial neural
network modelling. In this scenario, the Van der Waals-Platteeuw thermodynamic
model fails badly. To improve the accuracy of its predictions, an additional correction
term was approximated using a trained network of artificial neurons. The total relative
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error on pressure was reduced from 23.75% to 3.15% after this method was validated using a database of 4660 data points. Hosseinzadeh and others [105] predicted
the dissociation pressure of semiclathrate hydrates of CO2 + N2 , CH4 + N2 , CO2
+ CH4 , CO2 , CH4 , and N2 systems in the presence of different concentrations of
Tetra n-butyl ammonium bromide (TBAB), using a supervised learning technique
known as multi-layer perceptron neural network (MLP-NN). The hydrate dissociation pressure was estimated using the molar concentrations of the gases, the weight
percent of TBAB, and the temperature of the system. A total of 349 data points were
collected from the open literature and divided into three categories: training, validation, and testing, with 70%, 15%, and 15% ratios for each. Landgrebe et al. [106]
worked on a supervised neural network multilayer perceptron that was trained with
backpropagation to successfully model gas hydrate equilibrium with an emphasis on
multicomponent gases. The reserachers employed deep learning method. It yielded
an average cross-validation-adjusted coefficient of determination of 0.9867, a standard deviation of 0.0030, and a holdout validated R-adjusted squared of 0.9926.
The input variables of temperature and pressure were used for machine learning
and output variable of equilibrium pressure. Acharya et al. [107] utilised machine
learning (ML) to train and evaluate the performance of three algorithms to predict
hydrate dissociation temperatures as a function of the constituent hydrate precursors
and inhibitors on an experimental database (>1800 data points). In addition, unlike
most previous research, we use thermodynamic properties as input features in the
prediction algorithms, such as the activity-based contribution due to electrolytes,
partial pressure of specific gases, and specific gravity of the total mixture. Random
Forest (RF), Extra Trees (ET), and Extreme Gradient Boosting (XGBoost), three
machine learning approaches, all showed high accuracy in estimating hydrate equilibrium conditions. For all of the ML models, the cumulative coefficient of determination (R2 ) percentage is greater than 97%. XGBoost surpasses RF and ET in terms
of overall coefficient of determination (R2 ) and average absolute relative deviation
(AARD), with 99.56% and 0.086%, respectively. Predicting the formation conditions
of gas hydrates in salt water is crucial for hydrate management in activities such as
flow assurance, deep-sea drilling, and hydrate-based technology development. In this
regard, Xu et al. [108] recently employed five machine learning methods to develop
prediction tools for calculating the temperature of methane hydrate formation in
the presence of salt water in this study. Machine learning methods included Multiple
Linear Regression, k-Nearest Neighbor, Support Vector Regression, Random Forest,
and Gradient Boosting Regression. A total of 702 experimental data points from the
literature were collected for modelling purposes between 1951 and 2020.
The literature clearly shows that the use of machine learning techniques in gas
hydrate studies is pertinent and different parameters including but not limited to
hydrate formation temperature and pressure can successfuly be predicted. In the
upcoming section, the case studies related to machine learning application in gas
hydrates are presented and discussed.
9 Machine Learning Application in Gas Hydrates
167
9.7 Case Study
This work involves the use of the data from the flowloop tests performed across a
wide variety of experimental conditions at two pilot-scale flowloop facilities (FL1
and FL2), and field data from a dry tree facility in the Gulf of Mexico [109].
Srivastava et al. [109] determined regression, classification, and feature learning
are used to analyse data sets from: (1) hydrate tests conducted at pilot-scale flowloop
facilities (4,500 data points) and (2) transient operation field data using an algorithm
like support vector machine (SVM) and neural networks (NN). Several independent
input variables based on flowloop test data are used to calculate the hydrate fraction
and probability of hydrate plugging in the pipeline, including water cut, gas-oil
ratio, hydrate particle cohesive force, fluid velocity, oil viscosity, specific gravity,
interfacial tension, and time in the hydrate stable zone. Using field data as input,
including water cut, shut-down period (where applicable), and gas-oil ratio, the semisupervised learning model was utilised to determine the level of hydrate resistance
to flow during restart or dead oil displacement following production shut-down. The
flowloop-based machine learning model demonstrated high prediction accuracies in
test and validation processes, and it was used to evaluate hydrate risks in a real field.
The ability to construct field risk maps was demonstrated by a machine learning
model based on field data. The hydrate management technique could be used to
estimate the risk of hydrate development in subsea oil/gas pipelines using machine
learning. As a companion to more complex transient multiphase flow simulations,
this machine learning approach can aid in the development of sophisticated hydrate
management strategies.
In classification predictive modelling, approximating a mapping function (f) from
input variables (X) to a discrete output variable is a data mining task (y). Some of
the process variables (features) detected in the flowloop tests in this work are water
cut, GOR, surface fluid velocity, oil specific gravity (SG), interfacial tension (IFT),
viscosity, hydrate volume fraction (HVF), and hydrate inter-particle cohesive force
(Fa). A value between 0 and 1 is assigned as a binary classification output and rounded
up to the nearest integer. It designates probability of failure. The value of 0 indicates
no plug formation while 1 means complete plug formation. This categorization model
is depicted in Fig. 9.3. The machine learning algorithms/frameworks used in the
model are the support vector classifier (SVC) and artificial neural networks (ANN).
Figure 9.4 compares the HVF predicted by the neural network regression to the
HVF recorded during the procedure to highlight the test and validation results. For the
913 test data points, the mean absolute error (MAE) was 0.018, the mean square error
(MSE) was 0.0009, and the coefficient of determination (R2) was greater than 0.92.
The results of the tests show that neural network regression is good at predicting
hydrate formation and improves the applicability of the risk classification model
indicated above.
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A. Qasim and B. Lal
Fig. 9.3 A simple binary classification model that employs flowloop testing to predict the likelihood
of hydrate clogging [109]
Fig. 9.4 Comparison of
predicted and measured
values of Hydrate volume
fraction in the pipe using
ANN model [109]
9.8 Conclusion
Machine learning application in gas hydrate inhibition prediction and analysis is
now well-established field of study in recent years as the power of computing has
strengthened. In the flow assurance application regarding gas hydrate inhibition,
most frequently, machine learning has been used to simulate gas hydrate equilibria, allowing models to be created that are better able to anticipate the highly
nonflinear, multimodal phenomena of gas hydrate equilibria than statistical thermodynamics models. Various machine learning models have been published. Temperature, pressure, gas hydrate saturation and fluid composition prediction are all frequent
input features for developed models. A regression model determines the equilibrium
temperature, pressure or the desired output value. In the literature, both supervised
and unsupervised learning methods have been employed for the application. As
an instance in a previous study, a deep learning based model was used to predict
9 Machine Learning Application in Gas Hydrates
169
the hydrate equilibrium pressure. The higher R-squared value indicated a good fit.
Among the different case studies, it was observed that the prediction accuracy of
machine learning is strongly dependent on the amount of data input as large number
of data points are suitable for the accurate prediction Apart from hydrate equilibrium temperature or pressure, different machine learning algorithms such as Ridge
Regression, Decision Tree, Random Forest, AdaBoost and Neural Network have been
employed to predict gas hydrate saturation values for number of well log combinations. In this case the results of neural network algorithm provided higher accuracy.
The success of the machine learning model was due to ample availibity of required
data points. Hydrate plugging risk can also be predicted through machine learning.
In future, the use of machine learning can be enhaced further as the prediction algorithms get better and more reliable. The use of machine learning algorithms in flow
loop can provide real time data to help improve the smooth flow of oil in the pipeline
at commercial scale.
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Chapter 10
Machine Learning Application
Guidelines in Flow Assurance
Cornelius Borecho Bavoh and Bhajan Lal
Abstract In this chapter guidelines for conducting an effective machine learning
based prediction models in flow assurance areas is presented with much emphasis of
data availability, data representation and model selection.
Keywords Machine learning · Flow assurance · Big data · Models
10.1 Introduction
Generally, the availability and processing of data is the most critical guidelines for any
machine learning applications. This also involves the use of data that well represents
the model of the choice. In this chapter we briefly discuss some useful guidelines for
use machine learning in flow assurance [1, 2].
10.2 Data Selection
Data is the currency needed to successfully conduct a machine learning prediction.
This is the initial and important information needed to train or teach the model. The
developed machine’s performance highly depends on the strength or weakness of
the data. In the area of flow assurance, data might be experimental thermodynamic
or kinetic parameters, images, signals, temperatures, pressures, inhibitors concentrations, additives molecular structures and properties, first-principles calculations,
or complex simulation models. The use of big data is highly recommended in flow
assurance applications, since this could be readily available from company’s actives
C. B. Bavoh · B. Lal (B)
Chemical Engineering Department, Universiti Teknologi PETRONAS, 32610 Bandar Seri
Iskandar, Perak Darul Ridzuan, Malaysia
e-mail: bhajan.lal@utp.edu.my
Research Centre for CO2 Capture (RCCO2C), Universiti Teknologi PETRONAS, 32610 Bandar
Seri Iskandar, Perak, Malaysia
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
B. Lal et al. (eds.), Machine Learning and Flow Assurance in Oil and Gas Production,
https://doi.org/10.1007/978-3-031-24231-1_10
175
176
C. B. Bavoh and B. Lal
log data. For some systems such as thermodynamics of most flow assurance aspects,
the use of existing simulations (such as PVTSIM, OLGA, etc.) could be the fasters
source of huge database. Due to limitations on kinetics data and the high cost of
experimentations, the use of published data in patents and articles could be relevant
for such simulations. It must be stated that the kinetics of most flow assurance systems
are very stochastics and system dependant. This further narrows the challenges of
acquiring large flow assurance kinetics related data from literature. Data curation
is an important aspect data preparation for the machine learning methods in flow
assurance. Issues like data quality which range from the generations to the storage
of data must be done in a manner to prevent biases. It is worth noting that, the data
size and type is also dependant on the machine learning models. For instant, the data
requirement for deep learning systems are different from the traditional modelling.
The data used for ANNs are both used for learning and training. That is why they
require huge amount of data. On the other hand, the data is mostly splintered into
training, validations, and testing. Typically, most author widely use about 70% of
the data for training and validation while the remaining 30% is used for testing. The
training and validation are done at the same phase because the validation is used to
provide an unbiased evaluation of the model fit during the training phase. However,
the test data are unseen and is used to finally fit the model. This is mostly known as
the main indicator of the model quality [1, 2].
10.3 Data Representation
Aside from the just getting data, the next very important guideline needed to ensure
a quality model is the representation of the data. In flow assurance systems the data
could either be numerical and in image format. The ability to accurately define the
right input model variables or feature is very important to yield an accurate model
prediction. This process is known as feature selection and has been the topic of
several studies. The feature selection must be done to target the actual variables that
affect the output variable to be predicted. Also, the number of variables needed for the
prediction must be well considered and properly determined. Limiting the number of
selected features may reduce the computational cost of both training and executing
the model, while improving the overall accuracy. This feature-selection process is of
lesser importance in so-called deep learning methods, which are assumed to internally select those features that are considered to be important. Then, an input layer
that consists of basic process parameters in flow assurance (e.g., pressure, temperature, gas gravity, moles consumed, rate of formation, induction time, etc.), feed
characterizations (e.g., equilibrium, feed compositions), or hydrocarbon or inhibitor
properties (e.g., gravity, concentration, molecular weight, surface tension, activity
coefficient etc.) are often sufficient. It is mostly challenging to represent data set
that are not numerical, especially when dealing with molecules and reaction in flow
assurance systems. In addition, the stochastic nature of most flow assurance areas
10 Machine Learning Application Guidelines …
177
makes it difficult to accurately represent their data set in a machine learning model
[1, 2].
10.4 Model Development
The type of model for the selected data is the final step in machine learning modelling.
Since there are several machine learning models, it is very difficulty to choose a
models. This mostly require fundamental knowledge on the machine learning models
coupled with vested command over the respective flow assurance modelling area.
Currently in flow assurance modelling authors mostly use random models for predictions and sometimes compare their efficiency amongst themselves and traditional
models. There are classification and regression models, however, the use of regression
model is mostly used in flow assurance issues. The models could also be unsupervised, supervised, active, or transfer learning [3–5]. Generally, supervised learning
is used in flow assurance issues, with few studies based on unsupervised learning.
Common techniques such as k-means algorithm, decision trees [5], random forests
[6], support vector machines [5], and ANNs [7] can be used for machine learning
in flow assurance. Also, the use of hybrid models can be adopted with effective and
suitable transfer functions.
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