Journal of
Marine Science
and Engineering
Article
A Realistic and Integrated Model for Evaluating Offshore
Oil Development
Rui Qiu 1 , Zhuochao Li 1 , Qin Zhang 1 , Xin Yao 2 , Shuyi Xie 3 , Qi Liao 1 and Bohong Wang 4, *
1
2
3
4
*
Citation: Qiu, R.; Li, Z.; Zhang, Q.;
Yao, X.; Xie, S.; Liao, Q.; Wang, B. A
Realistic and Integrated Model for
Evaluating Offshore Oil Development.
J. Mar. Sci. Eng. 2022, 10, 1155.
https://doi.org/10.3390/
jmse10081155
Academic Editor: Malcolm L.
Spaulding
Beijing Key Laboratory of Urban Oil Distribution Technology, China University of Petroleum-Beijing,
Fuxue Road No. 18, Changping District, Beijing 102200, China
Zhejiang Petroleum & Chemical Co., Ltd., Zhoushan 316200, China
State Key Laboratory for Performance and Structure Safety of Petroleum Tubular Goods and Equipment
Materials, CNPC Tubular Goods Research Institute, Xi0 an 710077, China
National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation
Technology/Zhejiang Key Laboratory of Petrochemical Environmental Pollution Control, School of
Petrochemical Engineering and Environment, Zhejiang Ocean University, No. 1 Haida South Road,
Zhoushan 316022, China
Correspondence: wangbh@zjou.edu.cn
Abstract: With the rising consumption of oil resources, major oil companies around the world have
increasingly engaged in offshore oil exploration and development, and offshore oil resources have
accounted for an increasing proportion. Offshore oil engineering projects are capital intensive, and the
development of offshore oil fields faces a tough battle, especially in a period of low oil prices. Thus,
a comprehensive evaluation model is highly needed to help assess economic benefits and provide
meaningful and valuable information for operators and investors to make sensible decisions. This
study firstly proposed a realistic and integrated evaluation model for offshore oil development based
on actual historical project data. This evaluation model incorporated modules from the underwater
system to the platform system and processes from oil reservoir extraction to oil, gas and water
treatment. The uncertain parameters in the evaluation process are dealt with by sensitivity analysis
and Monte Carlo simulation. The proposed model is applied to a typical offshore oil development
project in Bohai Bay, China. The results reveal that the recovery factor and oil price have the greatest
impact on the economic benefits. In the case of deterministic analysis, the breakeven oil price of the
project is 40.59 USD/bbl. After considering the uncertainty of project parameters, the higher the oil
price, the greater the probability of NPV > 0. When the oil price is higher than 70 USD/bbl, even
with uncertain project parameters, the probability of NPV > 0 can still be as high as 97.39%.
Keywords: offshore oil field; economic evaluation model; monte carlo simulation; uncertainty analysis
Received: 13 July 2022
Accepted: 19 August 2022
Published: 20 August 2022
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1. Introduction
1.1. Background
With the continuous growth of global demand for oil resources and the gradual
reduction of onshore oil resources, more and more countries and oil companies are turning
their attention to the ocean. Numerous offshore oil field projects have been developed in
the Middle East, the North Sea, Brazil, the Gulf of Mexico, the Caspian Sea, etc., and the
production and proven reserves of global offshore oil resources have steadily increased [1].
Since the year 2000, offshore oil fields have produced 30% of the world’s oil production [2].
Taking the offshore oil fields around the Federal Gulf of Mexico as an example, offshore
oil production accounted for approximately 15% of total crude oil production in America
in 2020 [3]. In the New Policies Scenario, the International Energy Agency (IEA) predicts
offshore oil production will grow from 26.4 million bbl/d in the year 2016 to 27.4 million
bbl/d in the year 2040. Tapping huge offshore oil resources will be vital to meeting future
4.0/).
J. Mar. Sci. Eng. 2022, 10, 1155. https://doi.org/10.3390/jmse10081155
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J. Mar. Sci. Eng. 2022, 10, 1155
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energy demand around the world. Therefore, the development and production of offshore
oil resources have gradually become a key research topic in the oil industry [4].
Despite the abundance of offshore oil resources, offshore projects still face many
challenges [5]. The lack of geological data, especially in deep-sea oil fields, is not conducive
to the identification and estimation of oil reserves. Special marine environments, such
as waves and currents, ridges, and slopes, bring more stringent technical requirements
to exploration and construction. The complex characteristics of offshore oil reservoirs,
especially for high-coagulation, high-viscosity, and high-waxy crude oil, bring safety
problems to the processes of oil and gas gathering and transportation. Therefore, the
development and production of offshore oil resources are riskier and more costly projects
than onshore projects [6]. The largest offshore project in Australia called the Gorgon
project, cost more than 57 billion USD, more than double the estimated cost [1]. The cost of
project investment determines whether an offshore field is worth exploiting. The economic
evaluation of the project is a critical subject for the industry to ensure its profitability [7].
In addition, the prospect of offshore oil is also shaken by the shale revolution, the rise
of renewables, and the fall in oil prices [8]. Under the various pressures of the changing
market and policy environment, there is an urgent need to improve the efficiency and
economics of offshore oil projects. How to carry out an effective economic evaluation of
offshore oil development projects under the condition of low oil prices is a top priority,
which is also the research topic of this paper.
1.2. Related Works
Oil and gas field development projects are capital intensive [9]. Mastering the cost
structure of the project and establishing the corresponding economic evaluation model are
the key steps in the development of offshore oil resources. To the best of our knowledge,
there is currently no relevant model integrating various modules for a more comprehensive
economic evaluation of offshore oil projects. A literature search shows that scholars have
only researched economic evaluation or cost optimization on a specific submodule of an
offshore oil project. Many scholars have conducted studies on the optimal design of oil and
gas production systems from the viewpoints of submarine pipeline routes [10], manifold
placement and pipeline layout [11], pressure equipment placement [12], floating and subsea
layouts [13] and gathering system [14]. These studies aim to reduce the investment cost and
risk of the project from the design and layout of the oil and gas gathering pipeline network.
Gao et al. [15] built a multi-period mixed integer nonlinear programming (MINLP) model
to consider both well operation and flow assurance. The optimal objective is to minimize
the total operation cost, including well production state, polymer flooding, flow assurance,
and so on. Allahyarzadeh-Bidgoli et al. [16] took the minimum energy consumption as the
objective function, established an oil treatment process model based on HYSYS, and solved
seven optimal process operating parameters through genetic algorithms. The work of
Veloso et al. [17] and the work of Reis and Gallo [18] both optimized the energy consumption
of heat exchange-related processes on floating production storage and offloading (FPSO)
from the perspective of the Rankine cycle. Kim et al. [19] built an economic evaluation
model of mono ethylene glycol (MEG) injection and regeneration process for oil FPSO
based on NPC (net present cost), which includes capital investment and operating costs. In
the study of Cruz et al. [20], new primary cooling for deepwater offshore platforms was
proposed based on deep seawater intake at 4 ◦ C from depths around 900 m, reducing the
outlet temperature of intercoolers to 12 ◦ C. In the optimal design of oil and gas production
and gathering systems, in addition to improving economic benefits, some studies also focus
on improving the environmental benefits of the system [21,22]. The economic analysis
of the above studies focuses on the individual module level rather than the project level.
Due to this limitation, existing economic models associated with offshore oil development
cannot adequately assess the economic viability of projects.
In terms of economic feasibility analysis of other similar oil resource development,
many scholars have established models to conduct technical and economic evaluations
J. Mar. Sci. Eng. 2022, 10, 1155
3 of 17
of projects, which can provide stronger support for project decision making. Currently,
the most popular economic evaluation models are the net present value (NPV) model and
the internal rate of return (IRR) model. Based on a great number of actual historical data
in Canada, Rui et al. [23] built an economic evaluation model for oil sands development
with Steam-Assisted Gravity Drainage (SAGD) technology. The model consists of six
submodules and considers the effect of steam oil ratio, flowline length, true vertical depth,
recovery factors and so on. The proposed model helps to analyze the economic benefits
of the oil sands project with SAGD technology in a relatively complete manner. Based on
Jeff’s model [24] and Sawhney’s model [25], Saini et al. [26] built a reservoir scoping model
coupled with economic analysis for heavy oil reservoirs in a pseudo-SAGD mode. This
model helps users predict specific scenarios and rank the impact of different variables on
project viability. For the evaluation of the carbon dioxide enhanced oil recovery (EOR)
project, Wei et al. [27] proposed a hybrid techno-economic method for the evaluation of
storage resources and economic feasibility, which includes a performance model and a
cost model for economic evaluation and sensitivity analysis. In addition, Jiang et al. [28]
developed an integrated technical-economic model for economic potential and greenhouse
gas (GHG) emission analysis based on historical data from about 40 field projects. The NPV
of six scenarios was calculated for different design requirements. Due to the ambiguity of
the knowledge of the oil reservoir and the uncertainty of the development environment, the
reliability of some deterministic evaluations is not high. Therefore, some researchers have
further analyzed the uncertain factors in the project evaluation [29]. Sensitivity analysis,
scenario analysis, and Monte Carlo simulation are often used to analyze the impact of
uncertain factors on project economics [30].
From the above literature, it can be concluded that the existing evaluation models
related to offshore oil development are incomplete, and often only consider specific modules
in the project. These methods can only achieve the purpose of reducing the economic
investment as much as possible, but cannot carry out the overall economic evaluation of
the overall project to achieve the purpose of avoiding investment risks. Although there
have been similar economic evaluation methods for oil development based on the NPV
model and the IRR model, the facilities of offshore oilfields are quite different, such as the
underwater production system module and the platform upper chunk module. In addition,
the input parameter of the evaluation models needs to be verified using actual data. We
need to collect actual project data and analyze the impact of different factors on the project.
These circumstances prevent us from transferring models, data, and conclusions directly
from other project evaluation methods. Therefore, it is of great significance to develop
customized evaluation methods for the quantitative economic evaluation of offshore oil
development projects.
1.3. Contributions
Inspired by the above analysis, this paper puts forward a data-based integrated evaluation model for offshore oil development. The major data sources include the National
Bureau of Statistics of China [31], field practice data, industry statistics, and technical
literature [32–34]. Based on the actual project data of the oil field, the model can conduct
economic evaluations on the development of offshore oil projects and provide data demonstration and theoretical support for decision makers on whether to invest in the project.
The contributions of this work are as follows:
(1) This paper firstly proposed an evaluation model supported by actual data from offshore oil field projects. The actual data will significantly improve the model’s applicability
and reliability because all input parameters have reasonable values and realistic ranges.
(2) This evaluation model incorporated various modules for offshore oil development
from the underwater system to the platform system and from oil reservoir extraction to
oil, gas, and water treatment. Therefore, this model can perform the overall economic
evaluation for an offshore oil development instead of a partial evaluation.
J. Mar. Sci. Eng. 2022, 10, 1155
offshore oil field projects. The actual data will significantly improve the model’s applicability and reliability because all input parameters have reasonable values and realistic
ranges.
(2) This evaluation model incorporated various modules for offshore oil development from the underwater system to the platform system and from oil reservoir extraction
4 of 17
to oil, gas, and water treatment. Therefore, this model can perform the overall economic
evaluation for an offshore oil development instead of a partial evaluation.
(3) A detailed case study is conducted to demonstrate the application of the inte(3) A detailed case study is conducted to demonstrate the application of the integrated
grated model, and the effect of major factors is quantified.
model, and the effect of major factors is quantified.
2. Offshore
Oil Project
Project Description
Description
2.
Offshore Oil
Offshore oil
oil development
development is
is aa huge
huge and
and complicated
complicated engineering
engineering project.
project. In
In recent
recent
Offshore
years, offshore
offshore oil
oil development
development has
has continued
continued to
to advance
advance to
to deep-sea
deep-sea oilfields.
oilfields. This
This kind
kind
years,
of
project
far
away
from
the
mainland
is
a
complex
project
integrating
exploration,
drillof project far away from the mainland is a complex project integrating exploration, drilling,
ing, processing,
storage,
and transportation
processing,
storage,
and transportation
[35]. [35].
2.1. Offshore Oil Project Procedure
oil project
project includes
includes four
four stages:
stages: the exploration
exploration stage,
stage, development
development
The offshore oil
stage, production stage, and disposal stage, as shown in Figure
Figure 1.
1. The exploration
exploration stage
mainly relies on geophysical technology to explore reserves by drilling appraisal
appraisal wells.
wells.
The development phase is mainly about the overall construction of the project to ensure
smooth production. The production stage mainly involves the extraction of crude oil and
its treatment,
refers
to to
thethe
disposal
of
treatment, storage,
storage,and
andtransportation.
transportation.The
Theabandonment
abandonmentstage
stage
refers
disposal
production
facilities
after
all all
production
activities
in the
oilfield
areare
stopped.
of production
facilities
after
production
activities
in the
oilfield
stopped.
Figure 1.
1. Offshore
Offshore oil
oil project
project procedure.
procedure.
Figure
the exploration
explorationstage,
stage,geophysical
geophysicaltechnology
technologyisisused
usedtoto
conduct
exploration
deIn the
conduct
exploration
delineation
andand
oil oil
storage
structure
analysis
in the
target
sea area,
and and
thenthen
the reserves
and
lineation
storage
structure
analysis
in the
target
sea area,
the reserves
the
of oilofreservoirs
can be
in detail
by drilling
exploration
wellswells
and
andproduction
the production
oil reservoirs
canassessed
be assessed
in detail
by drilling
exploration
evaluation
wells.wells.
Thus,Thus,
it canitbecan
preliminary
determined
whether
the oil the
reservoir
can be
and evaluation
be preliminary
determined
whether
oil reservoir
developed.
This stage
mainly
geological
parameters
for offshore
development,
can be developed.
This
stage provides
mainly provides
geological
parameters
foroil
offshore
oil decompletes
formulation
the overallofdevelopment
plan, and prepares
for the
transition
velopment,the
completes
the of
formulation
the overall development
plan, and
prepares
for
to
the
oil
development
stage.
In
terms
of
economic
evaluation,
whether
the
oil
field
will
the transition to the oil development stage. In terms of economic evaluation, whether the
be
or developed
not in the future,
expenses
theexpenses
exploration
stage
already exist,
oil developed
field will be
or not the
in the
future,inthe
in the
exploration
stageand
alit
is regarded
as ita issunk
cost. as
They
are irrelevant
decisions
in the
future
ready
exist, and
regarded
a sunk
cost. They to
areinvestment
irrelevant to
investment
decisions
and
dofuture
not need
benot
considered
when
performing
economic
evaluations.
Therefore,
this
in the
andtodo
need to be
considered
when
performing
economic
evaluations.
paper
doesthis
not paper
consider
thenot
influence
of the
the influence
cost of theofexploration
in the economic
Therefore,
does
consider
the cost of stage
the exploration
stage
evaluation
of offshore
oil development
projects.
in the economic
evaluation
of offshore oil
development projects.
When
the
exploration
stage
is
completed,
When the exploration stage is completed, it
it will
will be
be transferred
transferred to
to the
the development
development
stage
according
to
the
evaluation
results
of
the
exploration
stage.
This
mainly
stage according to the evaluation results of the exploration stage. This stagestage
mainly
comcompletes the project construction and commissioning work, including jacket platform
installation, submarine pipeline layout, cable laying, FPSO facility connection and so on.
After system commissioning, if there is no fault, the production well can be drilled, the
well completion operation can be carried out and trial production can be started. Due
to the relatively small output and short duration of the trial production, the cost of the
trial production stage is included in the production stage when performing the economic
evaluation. The investment at this stage mainly includes investment in engineering facilities
and drilling and completion costs. The investment in engineering facilities can be divided
into underwater production facility investment, platform construction investment, and
upper block facility investment.
J. Mar. Sci. Eng. 2022, 10, 1155
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After the completion of the development stage, it will enter the production stage. At
this stage, crude oil is extracted from the reservoir through the built engineering facilities
and transported to the target platform through the subsea gathering pipeline network
and riser system for further production and treatment. After treatment, the qualified oil
is exported for sale through shuttle cruise ships or submarine pipelines. In this stage, to
ensure stable and safe production, it is necessary to invest in corresponding workover costs,
equipment maintenance costs, chemical purchase and injection costs, energy consumption
costs, and personnel welfare costs. The expenses incurred in the production process can
be included in the operating expenses in the economic evaluation and are regarded as
annual costs.
With continuous development, the output of the oil field decreases year by year, and
it enters the middle and late stages of development and, finally, enters the disposal stage,
which cannot generate economic benefits [36,37]. When the oil field reaches its end of life,
all production facilities need to be dismantled and disposed of following relevant laws
and regulations, which is also a reflection of the company’s environmental awareness [38].
The disposal process generally includes the following parts: facility shutdown, well abandonment, submarine pipeline abandonment, platform shutdown, debris cleaning and so
on. At present, there are three conventional disposal methods: local abolition, off-site
abolition, and conversion to other uses. Since disposal costs account for only a minor
proportion of the total investment, they are ignored in the economic evaluation in this
paper for simplification.
2.2. Offshore Oil Production Process
The production process of offshore oil is mainly divided into four steps: drilling and
completion, gathering and transportation, treatment, storage, and transportation. After
reservoir exploration, drilling and completion are performed. Formation crude oil is extracted from the reservoir through production wells, and the extracted oil is gathered and
transported to the FPSO through the subsea oil gathering pipeline network and risers. The
treatment on the platform includes oil, gas, and water separation, crude oil dehydration,
natural gas dehydration, carbon dioxide and hydrogen sulfide removal for natural gas,
oil and suspended solid removal for produced water and so on. Qualified oil is stored
in storage tanks and is regularly exported by shuttle cruise ships or submarine pipelines.
Natural gas is burned or reinjected or exported according to production requirements. Produced water is reinjected or discharged according to production requirements. A complete
economic evaluation of an offshore oil project must include all production processes and
take into account all investment and operating costs involved.
2.3. Offshore Oil Project Investment
Offshore oil project investment can be divided into drilling and completion investment,
underwater production system investment, platform substructure investment, and platform
upper block investment according to the different development and production processes,
as shown in Figure 2.
Drilling and completion costs are an important part of project investment, including
drilling costs and completion costs. Offshore oil drilling and completion operations are
generally carried out on drilling platforms or drilling ships, and the operation sites are
located in the ocean and face relatively harsh natural environments. Due to the limited
working space of the platform, the technical content of drilling and completion engineering
in offshore oilfields is relatively high. At the same time, with the increasing awareness of
marine environmental protection, offshore operations are also subject to relevant laws and
regulations. Therefore, compared with onshore oil drilling and completion engineering, offshore engineering has higher technical requirements, more complex investment structures,
and higher investment costs.
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Sci.
Eng.
FOR PEER REVIEW
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6 of 1
Figure2.2.Investment
Investment
composition
an offshore
oil project.
Figure
composition
of anofoffshore
oil project.
ADrilling
typical underwater
production
system
consists of part
underwater
production
equipand completion
costs are
an important
of project
investment,
including
ment and control facilities. According to the function, it can be divided into a wellhead
drilling costs and completion costs. Offshore oil drilling and completion operations ar
and Christmas tree system, manifold system and connection system, underwater control
generally carried out on drilling platforms or drilling ships, and the operation sites ar
and umbilical cable system. Oil in the reservoir is collected into manifolds through underlocated
in the ocean
and face trees”,
relatively
natural
environments.
Due
to the limited
water
wellheads
and “Christmas
then harsh
collected
and transported
through
terminal
working space
of the platform,
technical
content oftodrilling
completion
equipment
on underwater
pipelinesthe
and
finally transported
surface and
facilities
by risers.engineer
ing in
offshore
oilfields
is relatively
high. At
sameoilfields,
time, with
increasing
awarenes
Due
to the unique
operating
environment
of the
offshore
it is the
necessary
to build
aofplatform
on
the
sea
to
place
production
facilities.
A
floating
platform
hull
refers
to
the
marine environmental protection, offshore operations are also subject to relevant law
architectural
structure
of the platform,
whichwith
is used
to support
and place
thecompletion
base structureengineer
and regulations.
Therefore,
compared
onshore
oil drilling
and
of
the
well
and
upper
block.
Fixing
the
platform
on
the
sea
faces
difficulties,
such as wind
ing, offshore engineering has higher technical requirements, more complex
investmen
and waves, ocean currents, water depth, and load-bearing. It is an indispensable part of
structures, and higher investment costs.
the economic evaluation of offshore oil projects.
A typical underwater production system consists of underwater production equip
The platform upper block is divided into two functional modules according to the
ment
and
control and
facilities.
According
the function,
can be divided
into a wellhead
needs of
production
life. The
productiontomodule
is mainlyit equipped
with equipment
and Christmas
manifold
system
and connection
related
to oil, gas,tree
and system,
water treatment,
and
has functions
such as oil,system,
gas, andunderwater
water sepa- contro
and
umbilical
cable
system.
Oil
in
the
reservoir
is
collected
into
manifolds
through under
ration and metering. The life module is mainly used to ensure the daily life of employees
on
the platform
andand
is also
the production
command
center. and transported through termina
water
wellheads
“Christmas
trees”,
then collected
equipment on underwater pipelines and finally transported to surface facilities by risers
Due to the unique operating environment of offshore oilfields, it is necessary to build
Based onon
thethe
above
paper established
a realistic
and integrated
a platform
seaanalysis,
to placethis
production
facilities.
A floating
platformevaluation
hull refers to th
method for offshore oil development based on actual historical project data, shown in
architectural structure of the platform, which is used to support and place the base struc
Figure 3. Firstly, the engineering parameters/factors that affect the investment in offshore
ture ofprojects
the well
upper block.
Fixing by
thereferring
platform
on literature
the sea faces
difficulties, such a
oilfield
areand
summarized
and screened
to the
and investigating
wind engineering
and waves,cases.
oceanNext,
currents,
water
depth,
and
is an
indispensabl
actual
historical
project
data
areload-bearing.
collected fromItthe
National
part
of
the
economic
evaluation
of
offshore
oil
projects.
Bureau of Statistics of China, field practice data, industry statistics, and technical literature
The platform
upper
block is
into two
functional
modulesregression
according to th
for statistical
analysis.
According
to divided
the collected
historical
data, multiple
analysis
used to fit the
calculationmodule
submodel.
The integrated
needs ofisproduction
andinvestment
life. The production
is mainly
equippedeconomic
with equipmen
evaluation
model
considers
various
components
of
the
deep-sea
oil
project
and
includes
related to oil, gas, and water treatment, and has functions such as oil, gas, and water sep
seven
submodels:
the geological
estimation
model,
andthe
completion
aration
and metering.
The lifereserve
module
is mainly
useddrilling
to ensure
daily lifemodel,
of employee
underwater production system model, platform substructure model, platform upper chunk
on the platform and is also the production command center.
3. Methodology
model (new FPSO), operation cost model and NPV model. Finally, the proposed economic
evaluation method is applied to the evaluation and analysis of actual engineering cases,
3. Methodology
and
uncertainty analysis is carried out through Monte Carlo simulation.
Based on the above analysis, this paper established a realistic and integrated evalua
tion method for offshore oil development based on actual historical project data, shown
in Figure 3. Firstly, the engineering parameters/factors that affect the investment in off
shore oilfield projects are summarized and screened by referring to the literature and in
vestigating actual engineering cases. Next, historical project data are collected from th
National Bureau of Statistics of China, field practice data, industry statistics, and technica
literature for statistical analysis. According to the collected historical data, multiple re
gression analysis is used to fit the investment calculation submodel. The integrated eco
J. Mar. Sci. Eng. 2022, 10, 1155
nomic evaluation model considers various components of the deep-sea oil project and
cludes seven submodels: the geological reserve estimation model, drilling and comple
model, underwater production system model, platform substructure model, platform
per chunk model (new FPSO), operation cost model and NPV model. Finally, the p
7 of 17
posed economic evaluation method is applied to the evaluation and analysis of actual
gineering cases, and uncertainty analysis is carried out through Monte Carlo simulati
Figure
Diagramand
of the
realistic evaluation
and integrated
evaluation method.
Figure 3. Diagram
of the3.realistic
integrated
method.
3.1. Regression Analysis
3.1. Regression Analysis
Investment in Investment
each part ofinan
offshore
project
is influenced
a variety by
of a variet
each
part ofoilfield
an offshore
oilfield
project isbyinfluenced
factors. Screening
the main
factorsthe
and
quantifying
the quantifying
impact of each
onof
investment
factors.
Screening
main
factors and
thefactor
impact
each factor on inv
is a key step inment
this isresearch.
historical
project
data, this
research
a key stepBased
in thison
research.
Based
on historical
project
data, introduces
this research introdu
multiple
to fit the
investment
calculation
submodel.
this way,
multiple regression
to fitregression
the investment
calculation
submodel.
In this
way, theInstructure
ofthe struc
of the
submodel can
be determined,
including
model
variable
(main factors)
the submodel can
be determined,
including
model variable
types
(main
factors)types
and model
model coefficients
(influence
degree).from
The data
originate Bureau
from theofNational
Bureau of
coefficients (influence
degree). The
data originate
the National
Statistics
tisticspractice
of China
[31],industry
field practice
data, and
industry
statistics,
and technical
[32–
of China [31], field
data,
statistics,
technical
literature
[32–34].literature
The
Theregression
specific multiple
regression
is shownA.inThe
Appendix
A. The
regression anal
specific multiple
method
is shownmethod
in Appendix
regression
analysis
results
areintroduction
shown in theofintroduction
of each
submodel
results are shown
in the
each submodel
in Section
3.2.in Section 3.2.
3.2. Integrated Economic
Evaluation
Models
3.2. Integrated
Economic
Evaluation Models
3.2.1. Geological
Reserve
Estimation
Model
3.2.1. Geological Reserve Estimation Model
The geological The
reserve
model is
the basis
of economic
evaluation.
It isevaluation.
used to evaluate
geological
reserve
model
is the basis
of economic
It is used to ev
the original oil storage
of
the
target
reservoir
and
can
help
calculate
the
recoverable
reserves
ate the original oil storage of the target reservoir and can help calculate the recover
and the production
of associated
gas and of
produced
water.
Theproduced
calculation
result
the
reserves
and the production
associated
gas and
water.
Theiscalculation
re
foundation forisdetermining
the
number
of
wells,
FPSO
capacity,
field
life
and
so
on.
the foundation for determining the number of wells, FPSO capacity, fieldIn
life and so
this paper, the In
volume
method
is used method
to estimate
theto
original
oilthe
storage
of oil
thestorage
target of the ta
this paper,
the volume
is used
estimate
original
reservoir [39]. reservoir
It is the most
commonly
used
static
method.
It
is
measured
by
reservoir
[39]. It is the most commonly used static method. It is measured by reser
volume, oil saturation,
rock porosity.
geological
reserve
model is
shownmodel
in
volume,and
oil saturation,
andThe
rockspecific
porosity.
The specific
geological
reserve
is sho
Equations (1)–(4):
in Equations (1)–(4):
AhφS
N=
(1)
Ahφ S
B
N=
Poil = N × RF
(2)
B
Pgas = Poil × GOR
(3)
Pwater = Poil × RW
(4)
Poil = N × RF
where N is the original oil storage [m3 ], A is the drainage
area
[m2 ], h is the net pay thickPgas = P
oil × GOR
ness [m], φ is the porosity, fraction, S is the oil saturation, fraction, B is the formation volume
factor, Poil is the recoverable reserves [m3 ], RF is recovery factor, Pgas is the associated gas
Pwater water
= Poil ×[mRW
3 ] and RW is water cut.
[m3 ], GOR is the gas and oil ratio, Pwater is the produced
3.2.2. Drilling and Completion Model
Based on the project survey and literature review, three factors are identified as the
main drivers affecting the cost of drilling and completion: well depth, water depth, and
drilling cycle. The greater the well depth, the more difficult it is to drill. The greater the
water depth of the target reservoir, the greater the difficulty of operation. These lead to
higher requirements for drilling and completion technology and equipment, and the corresponding higher costs. Additionally, longer drilling cycles result in longer operation times
J. Mar. Sci. Eng. 2022, 10, 1155
8 of 17
and higher drilling costs. To describe the relationship between drilling and completion
costs and the above factors, the project parameters of six wells were collected for regression
analysis, as seen in Table 1. The expression of the drilling and completion model is shown
in Equation (5):
Cwell = β 0 + β 1 Dewater + β 2 Dewell + β 3 Tdrilling
(5)
where Cwell is the cost of drilling and completion [million USD], Dewater is the water depth
[m], Dewell is the well depth [m] and Tdrilling is the drilling cycle. Estimates for each
parameter are derived from historical project data and the regression model: β1 = 0.0111,
β2 = 0.0006, β3 = 0.1069.
Table 1. Project parameters of six wells.
Parameters
Average
Minimum
Maximum
Drilling and completion cost (million USD)
Well depth (m)
Water depth (m)
Drilling cycle (day)
10.00
2223
60.83
30.35
3.71
1400
16.00
16.10
8.85
3100
116.00
75.00
3.2.3. Underwater Production System Model
The investment composition of the underwater production system is extremely complex, but in general, all costs are related to the number of underwater wells, pipeline length,
and pipeline diameter. Thus, the number of underwater wells, pipeline length, and pipeline
diameter are selected as independent variables to develop the multiple regression model,
as shown in Equation (6). In total, 24 sets of underwater production data are collected.
Statistics show that the cost ranges from 491.40 million USD to 1407.10 million USD with
an average of 809.30 million USD. The number of wells is between 5 and 15. The pipeline
diameter is between 2 inches and 16 inches, and the pipeline length ranges from 10 km to
30 km with an average of 20 km.
Cunderwater = β 0 + β 1 D pipeline + β 2 L pipeline + β 3 Nwell
(6)
where Cunderwater is the cost of the underwater production system [million USD], Lpipeline
is the pipeline length [km], Dpipeline is the pipeline diameter [in] and Nwell is the number
of wells. Estimates for each parameter are derived from historical project data and the
regression model: β1 = 17.484, β2 = 2.912, β3 = 56.312.
3.2.4. Platform Substructure Model
The platform substructure mainly carries oil processing facilities and is not equipped
with operators. Its investment cost is mainly affected by the water depth of the sea area
and the weight and volume of treatment facilities on the upper block, while the weight and
volume of treatment facilities depend on the treatment capacity. Therefore, in this module,
this paper chooses water depth, oil storage capacity, and gas and water treatment volume
as the independent variables for regression analysis, as shown in Equation (7). The cost
ranges from 100 million USD to 1800 million USD with an average of 446.36 million USD.
The oil storage capacity is between 88.25 million bbl and 2800 million bbl with an average
of 1180.05 million bbl. The production of gas is between 4 mmscfd and 950 mmscf with
an average of 147.49 mmscf, and the production of water is between 12 million bbl and
391 million bbl with an average of 96.25 million bbl. The water depth ranges from 26.82 m
to 2200.05 m with an average of 592.03 m.
C plat f orm substructure =
β 0 + β 1 Soil + β 2 Pgas,day + β 3 Pwater,day + β 4 Dewater
(7)
where Cplatform substructure is the cost of platform substructure [million USD], Soil is the oil
storage capacity [million bbl], Pgas,day is the daily production of gas [mmscf], Pwater,day is
J. Mar. Sci. Eng. 2022, 10, 1155
9 of 17
the daily production of water [million bbl] and Dewater is the water depth [m]. Estimates
for each parameter are derived from historical project data and the regression model:
β1 = 0.1130, β2 = 0.3290, β3 = 2.6630, β4 = 0.0929.
3.2.5. Platform Upper Chunk Model
The platform upper chunk integrates production and life and is equipped with oil,
gas, and water treatment facilities and public living facilities for employees. The principle
of equipment configuration is to meet the demand for oil, gas, and water treatment. Thus,
oil production and gas production are selected as the independent variables of regression
analysis for the platform upper chunk model, as shown in Equation (8). Statistics show
that the cost ranges from 782.32 million USD to 1813.92 million USD, with an average of
1265.53 million USD. The oil production is between 50 million bbl and 350 million bbl
with an average of 202.78 million bbl, and the gas production is between 27 mmscf and
1850 mmscf with an average of 677.25 mmscf.
C plat f orm upper = β 0 + β 1 Poil,day + β 2 Pgas,day
(8)
where Cplatform upper is the cost of platform upper chunk [million USD], Poil,day is the daily
production of oil [million bbl] and Pgas,day is the daily production of gas [mmscf]. Estimates
for each parameter are derived from historical project data and the regression model:
β1 = 1.757, β2 = 0.490.
3.2.6. Operation Cost Model
Operation costs are the daily cost of maintaining normal production, which is complicated in composition, including fuel consumption, equipment maintenance, communication, insurance, environmental protection and so on. Operation costs are based on
production demand and operation environment. Therefore, oil production, water depth,
and distance from shore are selected as independent variables for regression analysis, as
shown in Equation (9). Statistics show that the operation cost ranges from 4.63 USD/bbl to
7.93 USD/bbl with an average of 6.02 USD/bbl. The oil production is between 100 million
bbl and 200 million bbl, with an average of 150 million bbl. The water depth ranges from
1500 m to 2500 m with an average of 2000 m. The distance from shore ranges from 100 m to
300 m with an average of 200 m.
Coperation = β 0 + β 1 Poil,day + β 2 Dewater + β 3 Dis
(9)
where Coperation is the operation cost [USD/bbl], Dewater is the water depth [m], Poil,day is
the daily production of oil [bbl] and Dis is the distance from shore [m]. Estimates for each
parameter are derived from historical project data and the regression model: β1 = −0.029,
β2 = 0.00034, β3 = 0.0016.
3.2.7. NPV Model
Based on the six submodels established above, the overall economic evaluation model
of offshore oil development projects, namely the NPV model, is established. Referring
to the research work of Rui et al. [23], project revenue comes from the sale of oil and gas.
Project expenditures include project construction investment, operation costs, oil and gas
royalty fee, and income tax. The NPV model is as follows in Equations (10)–(15):
GRt = Coil Poil,t + Cgas Pgas,t
(10)
ROt = GRt × Rroyalty
(11)
NRt = ( GRt − Cinvest,t − Coperation,t − ROt )
(12)
ITt = NRt × TRt
(13)
NCFt = NRt − ITt
(14)
J. Mar. Sci. Eng. 2022, 10, 1155
10 of 17
n
NPV =
∑
t =1
NCFt
(15)
(1 + r ) t
where GRt is annual oil and gas sales revenue [million USD/year], Coil is the oil price
[USD/bbl], Cgas is the gas price [USD/m3 ], Poil,t is annual oil production [million bbl/year],
Pgas,t is annual gas production [million m3 ], ROt is oil and gas royalty fee [million USD/year],
Rroyalty is royalty rate [%], NRt is annual operation profit [million USD/year], Cinvest,t is
annual investment cost [million USD/year], Coperation,t is annual operation cost [million
USD/year], TRt is the income tax rate [%], NCFt is annual net cash flow [million USD/year],
NPV is the net present value [million USD] and r is the discounted rate [%].
4. Case Studies
An oil field development project in Bohai Bay, China is taken as a case for economic
evaluation. The project is far from the mainland and is located in the deep sea. The new
FPSO all-sea type is adopted as the development mode.
4.1. Basic Data
The distribution of the input parameters for the integrated model is given in Table 2.
Input parameters include geological parameters, environmental parameters, technical
parameters, and economic parameters. The base, minimum, and maximum of the input
parameters are collected to reflect the uncertainty realistically. Monte Carlo simulation
is used for uncertainty analysis. It is assumed that each uncertain parameter follows a
triangular distribution.
Table 2. Input parameters of the model.
Parameters
(km2 )
Drainage area
Net pay thickness (m)
Water depth (m)
Porosity (%)
Oil saturation (%)
Formation volume factor
Recovery factor (%)
Well depth (m)
Gas and oil ratio (m3 /m3 )
Drilling cycle (day)
Pipeline length (km)
Pipeline diameter (in)
Pipeline wall thickness (in)
Oil price (USD/bbl)
Royalty rate (%)
Discounted rate (%)
Income tax rate (%)
Base
Minimum
Maximum
10
149.4
1200
21
71.37
1.23
33
3415
87.60
44
20
10
0.43
45
10
10
11
10
110.9
50
14
69.27
1.04
11
3100
35.74
15
5.39
4
0.43
30
10
10
11
10
187.9
2000
35
73.46
1.23
64
3491
66.33
78
25
18
0.68
70
10
10
11
4.2. Results and Discussion
4.2.1. Economic Benefit Analysis
Based on the oil price of 45 USD/bbl, the economic performance of the case is calculated using the proposed integrated economic evaluation model. It can be seen from Table 2
that the total investment in the offshore oil development project is 3066.62 million USD. The
revenue is about 14,732.39 million USD, which can create a royalty fee of 1473.24 million
USD and a tax of 882.74 million USD. The NPV of the project is 369.09 million USD, which
means that the project can create economic benefits.
Considering the uncertainty of input parameters, it is found through Monte Carlo
simulation that the economic evaluation results of the project will fluctuate within a relatively large range, as shown in Table 3. In the worst case, the NPV is only −1880.67 million
USD, which is much less than 0, but in the best case, the NPV value can be as high as
J. Mar. Sci. Eng. 2022, 10, 1155
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7839.33 million USD, showing great economic potential. Therefore, it is very necessary to
conduct uncertainty analysis and sensitivity analysis on the input parameters of the model
to provide theoretical support for decision makers to effectively avoid risks.
Table 3. The outcome of the integrated economic evaluation model.
Outcome (Million USD)
Base
Average
Low
High
Drilling and completion cost
774.96
726.60
221.05
1210.59
Underwater production system cost
1145.84
1153.83
999.23
1299.18
Platform substructure cost
481.94
514.59
250.33
1055.76
Platform upper chunk cost
663.88
681.54
588.79
906.92
Total investment
3066.62
3077.96
2173.56
4077.04
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW
12 of 18
Operation cost
2883.17
3261.14
742.34
6521.98
Revenue
14,732.39
17,766.71
3495.91
51,365.27
Operation profit
6426.63
8539.93
−541.53
34,735.32
Income tax
882.74
1150.47
−4.02
3844.56
In total, Royalty
50.80% fee
of the NPV variation
is caused 1771.49
by the uncertainty
recovery
1473.24
361.83 of the5447.36
NPV
369.09
−1880.67
7839.33
factor, and 24.00%
of the NPV variation is
caused by 1029.21
the uncertainty
of oil price.
The re-
maining 25.2% is due to porosity, net pay thickness, formation volume factor, water depth,
and pipeline
diameter.
These
show
that oil production and oil price are important factors
4.2.2.
Sensitivity
Analysis
of All
Parameters
in determining
the project
viable. The
output
ofimpact
the project
is
The impactwhether
of uncertain
factors is
in economically
project development
has a
greater
on the
constrained
by
both
internal
and
external
factors.
Among
them,
reservoir
parameters
are
economic evaluation, and may even cause the economic evaluation results to be opposite.
internal factors
such as porosity,
thickness,
and formation
reTherefore,
it is necessary
to extractnet
andpay
analyze
the uncertain
factors volume
involvedfactor.
in the The
project
covery
factor
is
an
external
factor,
determined
by
the
mining
technology.
The
oil
price
is
development process to improve the robustness of economic evaluation. In this study, the
determined
by
the
market
and
is
a
factor
beyond
the
control
of
policymakers.
To
avoid
Monte Carlo simulation method is used for sensitivity analysis to analyze the influence
decision-making
as muchon
as NPV.
possible,
is necessary
to further
analyze
of
uncertain inputrisks
parameters
Theitresults
are shown
in Figure
4. Inthe
thespecific
figure,
impact
of
the
oil
price
and
the
impact
of
other
important
parameters
based
on
possible
oil
the importance of all input parameters is ranked according to the contribution variance,
prices
on
the
economic
evaluation.
followed by recovery factor, oil price, porosity, net pay thickness, formation volume factor,
water depth, and pipeline diameter.
Figure 4. Sensitivity analysis of all parameters.
total, 50.80%
of the
variation is caused by the uncertainty of the recovery
4.2.3.InSensitivity
Analysis
ofNPV
Oil Price
factor,
and
24.00%
of
the
NPV
variation
caused
byimportant
the uncertainty
offactor
oil price.
The
Sensitivity analysis shows that the oilisprice
is an
external
affecting
remaining
25.2%
is due
to porosity,
net pay thickness,
formation
volume
factor, water
the economic
benefit
of offshore
oil development
projects.
Especially
in the context
of the
depth, and pipeline diameter. These show that oil production and oil price are important
low oil price, the level of oil price determines the profitability of oilfield projects to a large
factors in determining whether the project is economically viable. The output of the project
extent. This paper chooses NPV as the key performance indicator for project economic
is constrained by both internal and external factors. Among them, reservoir parameters
evaluation. NPV = 0 is the key node of economic evaluation. If the NPV is positive, it means
are internal factors such as porosity, net pay thickness, and formation volume factor. The
that the project is profitable, and investment in the project will be considered. Otherwise,
recovery factor is an external factor, determined by the mining technology. The oil price
the project cannot create economic value, and investment in the project will not be conis determined by the market and is a factor beyond the control of policymakers. To avoid
sidered. The relationship between the NPV and oil prices is shown in Figure 5. When oil
decision-making risks as much as possible, it is necessary to further analyze the specific
prices rise from 30 USD/barrel to 70 USD/barrel, the NPV increases from −886.48 million
impact of the oil price and the impact of other important parameters based on possible oil
USD to 2461.70 million USD. For this offshore oilfield project, the oil price of 40.59
prices on the economic evaluation.
USD/bbl is identified as the breakeven oil price, which is the oil price when NPV = 0. This
means that for this project, when the oil price is greater than 40.59 USD/bbl, the project
can create economic benefits and investment can be considered.
The above analysis of the oil price is carried out using the base of other parameters
and does not take into account the uncertainty of other parameters. Then, considering the
J. Mar. Sci. Eng. 2022, 10, 1155
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4.2.3. Sensitivity Analysis of Oil Price
Sensitivity analysis shows that the oil price is an important external factor affecting the
economic benefit of offshore oil development projects. Especially in the context of the low
oil price, the level of oil price determines the profitability of oilfield projects to a large extent.
This paper chooses NPV as the key performance indicator for project economic evaluation.
NPV = 0 is the key node of economic evaluation. If the NPV is positive, it means that
the project is profitable, and investment in the project will be considered. Otherwise, the
project cannot create economic value, and investment in the project will not be considered.
The relationship between the NPV and oil prices is shown in Figure 5. When oil prices
rise from 30 USD/barrel to 70 USD/barrel, the NPV increases from −886.48 million USD
to 2461.70 million USD. For this offshore oilfield project, the oil price of 40.59 USD/bbl
J. Mar. Sci. Eng. 2022, 10, x FOR PEER is
REVIEW
identified as the breakeven oil price, which is the oil price when NPV = 0. This means 13 of 18
that for this project, when the oil price is greater than 40.59 USD/bbl, the project can create
economic benefits and investment can be considered.
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW
13 of 18
Figure 5.
5. NPV
NPV under
oiloil
prices
(without
considering
uncertainty).
Figure
underdifferent
different
prices
(without
considering
uncertainty).
The above analysis of the oil price is carried out using the base of other parameters
and does not take into account the uncertainty of other parameters. Then, considering
the uncertainty of other parameters, Monte Carlo simulation is applied to analyze the
probability of NPV > 0 under different oil prices (Figure 6). As can be seen from the
figure, the probability of NPV > 0 increases as oil prices rise. When the oil price is above
70 USD/bbl, the probability of a positive NPV is 97.39%, and when the oil price is below
30 USD/bbl, the probability of a positive NPV is 23.98%. The results indicate that when
the oil price is higher than 70 USD/bbl, this offshore oilfield project will be economically
feasible, but when the oil price is lower than 30 USD/bbl, the project has a great risk of
being 5.
unprofitable,
investors
need(without
to make considering
a prudent decision.
Figure
NPV under and
different
oil prices
uncertainty).
Figure 6. Probability of positive NPV under different oil prices (considering uncertainty).
4.2.4. Sensitivity Analysis of Parameters under Low Oil Price
In recent years, affected by political, economic, market, and other factors, international oil prices have continued to decline. Especially under the influence of the current
COVID-19 pandemic, the international oil price situation is not optimistic. This requires
decision makers to fully understand the factors affecting project development in the context of low oil prices to guide decision making. Based on the low oil price of 40 USD/bbl,
this paper uses Monte Carlo simulation to analyze the impact of the uncertainty of the
reservoir
parameters,
environmental
parameters,
and
technical
parameters
on the ecoFigure6.6.Probability
Probability
of
NPV
under
different
oil prices
(considering
uncertainty).
Figure
ofpositive
positive
NPV
under
different
oil prices
(considering
uncertainty).
nomic benefits of the project, as shown in Figure 7. It can be seen from the figure that the
recovery
factor is
still theofmost
important,
accounting
for 65.30%, followed by reservoir
4.2.4.
Sensitivity
Analysis
Parameters
under
Low Oil Price
geological parameters (porosity, net pay thickness, and formation volume factor). It is
In recent years, affected by political, economic, market, and other factors, internaworth noting that the water depth also has a great impact on the economic benefits of the
tional oil prices have continued to decline. Especially under the influence of the current
project. Technical parameters, such as pipeline diameter, also have a certain influence.
J. Mar. Sci. Eng. 2022, 10, 1155
13 of 17
4.2.4. Sensitivity Analysis of Parameters under Low Oil Price
In recent years, affected by political, economic, market, and other factors, international
oil prices have continued to decline. Especially under the influence of the current COVID-19
pandemic, the international oil price situation is not optimistic. This requires decision
makers to fully understand the factors affecting project development in the context of
low oil prices to guide decision making. Based on the low oil price of 40 USD/bbl, this
paper uses Monte Carlo simulation to analyze the impact of the uncertainty of the reservoir
parameters, environmental parameters, and technical parameters on the economic benefits
of the project, as shown in Figure 7. It can be seen from the figure that the recovery factor is
still the most important, accounting for 65.30%, followed by reservoir geological parameters
(porosity, net pay thickness, and formation volume factor). It is worth noting that the water
depth also has a great impact on the economic benefits of the project. Technical parameters,
such as pipeline diameter, also have a certain influence. This shows that in the context
of low oil prices, how to improve reservoir recovery is still the key to ensuring economic
benefits. In the face of a low oil price market, increasing production is still the most effective
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW
14 of 18
strategy to ensure the economic viability of the project. In addition, low oil prices will lead
to more factors, such as pipeline diameter affecting the economics of the project. Therefore,
decision makers also need to reduce project construction investment as much as possible to
reduce investment risks.
Figure7.7.Sensitivity
Sensitivityanalysis
analysisof
ofparameters
parametersunder
underlow
lowoil
oilprice.
price.
Figure
Thefollowing
followingtakes
takesthe
thetop
topthree
threefactors
factors(recovery
(recoveryfactor,
factor,porosity
porosityand
andnet
netpay
paythickthickThe
ness)
with
the
greatest
influence
as
the
research
object,
and
analyzes
their
key
profit
curves
ness) with the greatest influence as the research object, and analyzes their key profit curves
under different oil price ranges from 30 USD/bbl to 70 USD/bbl. Changes in the above
under different oil price ranges from 30 USD/bbl to 70 USD/bbl. Changes in the above
three parameters are controlled while maintaining the uncertainty of other parameters.
three parameters are controlled while maintaining the uncertainty of other parameters.
The goal is to find the value of each parameter corresponding to different oil prices when
The goal is to find the value of each parameter corresponding to different oil prices when
the NPV average is equal to 0. The simulation results of key profit curves are shown in
the NPV average is equal to 0. The simulation results of key profit curves are shown in
Figure 8. In the figure, taking NPV average = 0 as the dividing line, the left side of the curve
Figure 8. In the figure, taking NPV average = 0 as the dividing line, the left side of the
represents that the NPV average is less than 0, and the right side of the curve represents
curve represents that the NPV average is less than 0, and the right side of the curve reprethat the NPV average is greater than 0. For the recovery factor, lower oil prices bring higher
sents that the NPV average is greater than 0. For the recovery factor, lower oil prices bring
requirements for recovery. When the oil price is as low as 40 USD/bbl, the recovery factor
higher requirements for recovery. When the oil price is as low as 40 USD/bbl, the recovery
is at least 29.65% to guarantee an average NPV of 0. This means that the recovery factor
factor is at least 29.65% to guarantee an average NPV of 0. This means that the recovery
must be at least 29.65% to have a greater probability of obtaining considerable economic
factor must be at least 29.65% to have a greater probability of obtaining considerable ecovalue. As the oil price rises, the expected economic benefits can be achieved even with a
nomic
As factor.
the oil When
price rises,
expected
economic
benefits can
achieved
lower value.
recovery
the oilthe
price
is as high
as USD70/bbl,
andbe
the
recoveryeven
rate
with
a
lower
recovery
factor.
When
the
oil
price
is
as
high
as
USD70/bbl,
and
the recovery
is greater than 14.32%, the probability of obtaining economic returns is greater.
Similarly,
rate
is greater
thanis14.32%,
the40probability
of obtaining
economic
greater.
Simiwhen
the oil price
as low as
USD/bbl, the
porosity needs
to bereturns
greater is
than
19.32%
and
larly,
when
the
oil
price
is
as
low
as
40
USD/bbl,
the
porosity
needs
to
be
greater
than
the net pay thickness needs to be greater than 123 m to ensure that the project has a greater
19.32%
and of
theobtaining
net pay thickness
needs
to beWhen
greaterthe
than
m is
to as
ensure
project
possibility
economic
benefits.
oil123
price
high that
as 70the
USD/bbl,
has
greater possibility
obtaining
economic
benefits.
oilthickness
price is asonly
high needs
as 70
theaporosity
only needsofto
be greater
than 9.34%
and When
the netthe
pay
USD/bbl,
the
porosity
only
needs
to
be
greater
than
9.34%
and
the
net
pay
thickness
only
to be greater than 59.7 m to ensure that the project has a greater possibility of obtaining
needs
to
be
greater
than
59.7
m
to
ensure
that
the
project
has
a
greater
possibility
of
obeconomic benefits.
taining economic benefits.
J. Mar. Sci. Eng. 2022, 10, 1155
rate is greater than 14.32%, the probability of obtaining economic returns is greater. Similarly, when the oil price is as low as 40 USD/bbl, the porosity needs to be greater than
19.32% and the net pay thickness needs to be greater than 123 m to ensure that the project
has a greater possibility of obtaining economic benefits. When the oil price is as high as 70
USD/bbl, the porosity only needs to be greater than 9.34% and the net pay thickness only
of 17
needs to be greater than 59.7 m to ensure that the project has a greater possibility of14obtaining economic benefits.
J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW
15 of 18
Figure
Figure 8.
8. Key
Keyprofit
profitcurves
curvesof
ofreservoir
reservoirparameters.
parameters.
4.2.5.Influence
InfluenceofofParameter
ParameterUncertainty
Uncertaintyunder
underDifferent
DifferentOil
OilPrices
Prices
4.2.5.
Tofurther
furtheranalyze
analyzethe
theinfluence
influenceofofparameter
parameteruncertainty
uncertaintyon
onproject
projecteconomics,
economics,we
we
To
adjust
the
fluctuation
range
of
parameters.
The
wider
the
fluctuation
range
of
the
parameter,
adjust the fluctuation range of parameters. The wider the fluctuation range of the paramthe greater
the uncertainty
of the
parameter.
By Monte
Carlo
Simulation,
the the
probability
eter,
the greater
the uncertainty
of the
parameter.
By Monte
Carlo
Simulation,
probaof
positive
NPV
as
oil
prices
change
under
different
fluctuation
ranges
of
parameters
bility of positive NPV as oil prices change under different fluctuation ranges of parameters
calculated.The
The
influence
parameter
uncertainty
on NPV
is shown
in Figure
9. In
isiscalculated.
influence
of of
parameter
uncertainty
on NPV
is shown
in Figure
9. In FigFigure
9, the
ordinate
is the
probability
that
project’s
NPV
greaterthan
than0.0.Different
Different
ure
9, the
ordinate
is the
probability
that
thethe
project’s
NPV
is is
greater
legends
reflect
the
fluctuation
range
of
uncertain
parameters;
10%
means
that
the
uncertain
legends reflect the fluctuation range of uncertain parameters; 10% means that the uncerparameter
changes
within
10%
of the
reference
value.
AsAs
can
bebe
seen
tain
parameter
changes
within
10%
of the
reference
value.
can
seenfrom
fromthe
thefigure,
figure,in
the
case
of
low
oil
prices,
parameter
uncertainty
has
a
favorable
impact
on
the
economic
in the case of low oil prices, parameter uncertainty has a favorable impact on the economic
evaluationofofthe
theproject.
project.Greater
Greaterparameter
parameteruncertainty
uncertaintybrings
bringsa agreater
greaterprobability
probabilityofof
evaluation
positive
NPV.
As
oil
prices
rise,
the
influence
of
parameter
uncertainty
gradually
changes.
positive NPV. As oil prices rise, the influence of parameter uncertainty gradually changes.
In
the
case
of
higher
oil
prices,
the
increase
in
parameter
uncertainty
will
reduce
the
In the case of higher oil prices, the increase in parameter uncertainty will reduce the probprobability of positive NPV. This is because low oil prices will reduce the probability of
ability of positive NPV. This is because low oil prices will reduce the probability of posipositive NPV, while the uncertainty of other parameters may increase NPV in this case.
tive NPV, while the uncertainty of other parameters may increase NPV in this case. When
When the oil price rises to 40 USD/bbl, the oil price began to show economic potential,
the oil price rises to 40 USD/bbl, the oil price began to show economic potential, and the
and the uncertainty of other parameters began to show more adverse effects. In addition,
uncertainty of other parameters began to show more adverse effects. In addition, it is
it is found that even if the project is affected by the uncertainty of other parameters, the
found that even if the project is affected by the uncertainty of other parameters, the impact
impact of oil prices on project economics is still crucial. As oil prices rise, the impact of
of oil prices on project economics is still crucial. As oil prices rise, the impact of parameter
parameter uncertainty gradually decreases. When the oil price is higher than 65 USD/bbl,
uncertainty gradually decreases. When the oil price is higher than 65 USD/bbl, the influthe influence of parameter uncertainty slows down with the change in oil price and the
ence of parameter uncertainty slows down with the change in oil price and the probability
probability of positive NPV even approaches 100% with the fluctuation range of 10%, 20%,
of positive NPV even approaches 100% with the fluctuation range of 10%, 20%, and 30%.
and 30%.
Figure9.9.Probability
Probabilityofofpositive
positiveNPV
NPVunder
underdifferent
differentoil
oilprices
prices(considering
(consideringfluctuation
fluctuationrange).
range).
Figure
5. Conclusions
Based on actual historical project data, this paper develops a realistic and integrated
economic evaluation model for offshore oil development projects, including drilling and
J. Mar. Sci. Eng. 2022, 10, 1155
15 of 17
5. Conclusions
Based on actual historical project data, this paper develops a realistic and integrated
economic evaluation model for offshore oil development projects, including drilling and
completion module, underwater production system module, platform substructure module
and platform upper chunk module. Monte Carlo simulation is used to analyze the impact
of oil prices, reservoir parameters, environmental parameters, and technical parameters on
the economic benefits of offshore oil projects, providing support for investors to avoid risks.
(1) Through regression analysis, the quantitative relationship between the main parameters and costs in each submodule is given, and the integrated evaluation model of
offshore oil development projects is established to guide investment decisions.
(2) In the project evaluation of the case, the four most influential factors are the recovery
factor, oil price, porosity, and net pay thickness, and their uncertainty brings about 95.9%
of NPV variation. The recovery factor and oil price are the most important factors affecting
project economics. In the case of low oil prices, investors should focus on how to enhance
oil recovery and minimize construction investment.
(3) Based on sensitivity analysis, this paper focuses on the impact of oil prices. In the
case of deterministic analysis, the breakeven oil price of the project is 40.59 USD/bbl. When
the oil price is as low as 30 USD/bbl, the probability of NPV > 0 is extremely low, about
23.98%, but when the oil price is higher than 70 USD/bbl, the probability of NPV > 0 is as
high as 97.39%, showing great economic potential. In addition, when the oil price is higher
than 70 USD/bbl, the economic benefit of the project will be less affected by parameter
uncertainty, and it will show a very high probability of profitability. These data can provide
decision support for the preliminary investment evaluation of the project.
In summary, this integrated model can provide decision makers with realistic and
confident results for investing in offshore oil development projects to reduce investment
risk. The proposed model is general, but the quantity and quality of historical project data
greatly affect the accuracy and reliability of the project’s economic evaluation. For different
types of offshore oil fields, the model submodules should be updated according to their
characteristics, and the cost calculation models of the submodules with higher applicability
should be fitted based on specific historical data.
Author Contributions: Conceptualization, R.Q.; methodology, R.Q. and Q.Z.; validation, Z.L. and
X.Y., and S.X.; investigation, R.Q., Q.Z., and B.W.; writing—original draft preparation, R.Q. and Q.Z.;
writing—review and editing, Q.L. and B.W.; visualization, Q.L.; funding acquisition, B.W. All authors
have read and agreed to the published version of the manuscript.
Funding: This work was funded by the Science Foundation of Zhejiang Ocean University [11025092122].
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: The authors are grateful to all study participants.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A. Regression Analysis
Regression analysis is a method of evaluating the relationship between two or more
variables [40]. It can represent the important relationship between the dependent variable
and the independent variable and the degree of influence of multiple independent variables
on the dependent variable. In this study, reservoir parameters, environmental parameters
and technical parameters of offshore oil projects are used as candidate independent variables, and project investment is used as dependent variables. Multiple linear regression
analysis is applied for data analysis.
J. Mar. Sci. Eng. 2022, 10, 1155
16 of 17
The basic model of multiple linear regression is as follows:
yi = β 0 + β 1 xi1 + β 2 xi2 + . . . . . . + β P xip + ε i
(A1)
where yi is the dependent variable, xi is the independent variable, βi is the coefficient of the
independent variable and εi is the estimation error, which is required to meet the following
assumptions: the error term εi obeys a normal distribution; the expected value of the error
term εi is zero, that is E(εi ) = 0; the error term εi is not related to any observation value of
the independent variable xi , namely COV(xi , εi ) = 0. Additionally, it is required to meet the
Gauss–Markov assumption. The specific expression is as follows:


ε1
E(ε i , ε i 0 ) = E . . . (ε 1 . . . ε n )
εn

  2
var(ε 1 )
. . . cov(ε 1 , ε n )
σ
 =  ...
=
...
...
cov(ε n , ε 1 ) . . .
var(ε n )
0


0
. . .  = σ2 I
σ2
...
...
(A2)
The matrix expression of the above regression model is as follows:




y1
β0
Y =  . . . , β =  . . . , X = 
yn
βp

1 x11 . . . x1p
... ...
... ...
1 xn1 . . . xnp



ε1
, ε =  . . . 
εn
(A3)
Based on n sets of observations, the estimated values of the parameters in the regression model are calculated according to the principle of least squares. Therefore, the problem
turns into finding a set of parameter estimates that minimize the residual sum of squares.
The overall investment of the project is classified according to its composition and
divided into submodules. Through the above multiple linear regression method, based
on the historical investment data, the cost calculation model of the submodule is fitted for
subsequent economic analysis.
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