SPE-194594-MS
Anish Gupta, Puveneshwari Narayanan, Kukuh Trjangganung, Suzanna Juyanty Mohd Jeffry, Boon Choon Tan, M
Rais Saufuan Awang, Khaled Badawy, and Pui Mun Yip, PETRONAS
Copyright 2019, Society of Petroleum Engineers
This paper was prepared for presentation at the SPE Oil and Gas India Conference and Exhibition held in Mumbai, India, 9-11 April 2019.
This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents
of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect
any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written
consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may
not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.
Abstract
A matrix stimulation candidate screening workflow was developed with the objective to reduce the time
and effort in identifying under-performing wells. The workflow was initially tested manually for few fields
followed by inclusion in Integrated Operation for an automated screening of wells with suspected formation
damage. Analysis done in three fields for stimulation candidate selection will be displayed with actual
statistics.
The main aim of the work was to digitalize the selection of non-performing candidates rather than
manually looking into performance of each well. A concept of Formation Damage Indicator (FDI) was
combined with Heterogeneity Index (HI) of the formations to screen out the candidates. Separate database
sets of Reservoir engineering, Petrophysicist and Production was integrated with suitable programming
algorithms to come up with first set of screened wells evaluating well production performances, FDI and
HI trends up to over the last 30 years. The shortlisted candidates were further screened on the basis of
practical approach such as gas lift optimization, production trending, OWC-GOC contacts, well integrity
and well history to come up with second round of screened candidates. The final candidates were analyzed
further using nodal analysis models for skin evaluation and expected gain to come up with type of formation
damage and expected remedial solution.
For fields A and D with a total of 210 strings each, the initial FDI and HI screening resulted in 70 and
120 strings being shortlisted, respectively. This was followed by a second round of screening with 25 and
35 strings being further shortlisted as stimulation candidates, respectively. Nodal analysis models indicated
presence of high skin in 90% of the selected wells indicating a very good efficiency and function-test of
the workflow. In addition to selection of the candidates, the identification of formation damage type was
compiled on an asset-wise basis rather than field basis which helped in more efficient planning of remedial
treatments using a multiple well campaign approach to optimize huge amount of cost. The entire screening
process was done in one month which was earlier a herculean task of almost one year and much more
man-hours. With effective manual testing of the workflow in two major fields, workflow was included in
Integrated Operations for future automation to conduct the same task in minutes rather than months.
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Digitalization of Formation Damage Candidate Screening Workflow
Improves Process Efficiency
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SPE-194594-MS
With this digitalized unique workflow, the selection of under-performing wells due to formation damage
is now a one click exercise and a dynamic data. This workflow can be easily operated by any engineer to
increase their operational efficiency for flow assurance issues saving tons of cost and time.
Formation Damage can occur during various phases of oil and gas production including drilling, production,
workover operations or stimulation / chemical treatments. When a well is producing below its optimum
productivity (can also be termed as "under-performing well"), it is highly important to identify the source due
to which this under-performance has occurred. Puthalath et al1 had classified formation damage mechanism
into various categories:
1. Damage during drilling – drilling mud damage, pore plugging and low permeability filter cake
2. Damage during completion and workover – perforation design2 (Ref 2), workover fluids and sand
control design
3. Damage during acidization / production enhancement – Fines migration, Reaction and precipitation,
sludge formation, water blockage and hydraulic fracturing (proppant pack damage)
This category only concentrated on inflow issues and it can be further expanded for under-performing
wells by including vertical lift issues as follows:
1. Sub-optimal artificial lift system such as gas lift issues3 or ESP efficiency
2. Deposition of scales into the tubing or flow path leading to restricted well flow4
3. Wax or asphaltene build-ups in tubing or production system
The determination of the mechanisms responsible for the loss of flow efficiency (productivity or
injectivity) requires a number of studies. Yeager et al5 recommended a three-stage approach consisting of:
1. Quantifying the degree of existing damage
2. Diagnosing the existing downhole damage mechanisms
3. Performing laboratory studies to increase knowledge about specific mechanisms
The workflow developed in this paper caters to #1 and #2 above i.e. the quantification and diagnosis of
downhole damage mechanism, but solely by the use of existing production and reservoir data and a defined
workflow.
From the above description, it is quite apparent that identifying the formation damage mechanism and
thereby treating the well with appropriate solution holds the key on revival of any under-performing well.
A simple step wise procedure and timeline can help to understand this challenge more practically.
In general, a cycle of identifying under-performing wells until implementing the solution constitutes of
following steps:
1. Use of Reservoir, production and petrophysical data at one place
2. Multi-disciplinary teams to decide whether the wells come under category of formation damage or
otherwise
3. Nodal analysis, production decline and water cut history to understand the type of formation damage
4. Calibrated models to identify skin and quantification on gain post stimulation
Assuming a practical conventional case for an operator with all data intact and wells being monitored
from beginning of production, the above cycle for one well might take 2 to 10 man-days depending on data
availability and quality. Taking an average of 6 man-days, with an inventory of 200 wells, this process could
require up to 1200 man-days. This effort can be challenging due to various reasons for many operators. It is
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Introduction
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to be noted that the man-hours calculations conducted in this paper are only an indication of the amount of
time and work in relative terms for better representation & understanding of paper. The man-days mentioned
can be very subjective and can vary for other fields, regions or organizations. In this case study, the Operator
has faced the following challenges prior initiation of the workflow:
All the above was a cumbersome task involving lot of man-hours. To overcome the challenge, it was
very important to come up with a practical solution in providing positive results within the boundary of
data acquisition and cost limitations. A focused group was set-up specially to look into various practical
solutions like, how to use the production data, the available kh values, and other formation parameters to
come up with a workflow. A workflow which could be easily converted into simple programming steps1
for the achievement of following objectives.
i.
ii.
iii.
iv.
Standardization of the process and procedures
Utilization of simple production, reservoir and petrophysical data in one platform
Reduction of man-days in identifying under-performing wells
Ability to perform integration with other online workflows
Formation Damage Identification Workflow
A workflow was developed by the multi-disciplinary team to shortlist stimulation candidates through the
following standardized process:
a. Well and formation data gathering in terms of Reservoir Units
b. Initial screening process through the Analysis of Formation Damage Indicator (FDI) and
Heterogeneity Index (HI)
c. Elimination of zonal related issues–Water Cut or Zone change
d. Elimination on basis of Outflow issues: Categorization of under-performing wells on basis of VLP
issues (scale in tubing, artificial lift, etc.)
e. Production decline analysis, Nodal analysis and skin quantification to identify Formation Damage
mechanism
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1. Large well inventory – With one field producing approximately 200 strings and assuming the study
being done in two such fields in domestic operations, the identification of formation damage in each
and every well can be a herculean task. The 1200 man-days can be increased up to 2400 man-days
with this challenge.
2. Matured field and multiple operators - Producing fields are normally operated through a petroleum
contract. In some cases, there are multiple operators managing the field throughout the field life
especially for matured fields. The changes through multiple operators resulted in incomplete of loss
of data throughout the handover process. A 1-2 man-days of data collection at one place can easily
increase by four folds or more. Hence, the estimation of 2400 man-days in #1 above, can now be
easily 4,800 man-days (twice the time in extracting data)
3. Lack of continuous data base – Even if data is available to a certain extent, a lack of continuous
monitoring or regular data base (like daily or weekly) again leads to no control in establishing the
trend. This again leads to inconclusive well behavior analysis
4. Limitations in gathering appropriate pressure survey data – Calculating the skin values from pressure
survey data for the wells and then selecting a proper candidate would take weeks especially in the
offshore environment. For instance, planning a PBU for a 5-10 well campaign in a platform might take
about 1-2 months in a best case scenario. Assuming gathering of such data requires a conventional
intervention in offshore environment, the number of man-days can be four folds. In such a scenario,
the 2400 man-days of work can lead to 10,000 man-days.
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f.
Generic treatment selection and handover to Operations team with estimated gains (his step was an
additional step on initially developed workflow
Before proceeding with the detailed application of workflow, it is worthwhile to explain the two important
parameters used in this workflow which relates to the two questions below:
i.
ii.
How effective is the well performing with respect to its counter-parts in the same reservoir?
In a reservoir with similar capacity (kh), how efficiently a well is performing?
The two questions above were resolved using two functionalities, which are Heterogeneity Index and
Formation Damage Indicator as explained below. Use of these parameters is not new in the identification
of formation damage and has been used before6 as well.
a. Heterogeneity Index (HI): This parameter for a particular well can be defined as the normalized ratio of
difference of water / oil rates of the current well with the current average oil/water rates in the same reservoir.
(1)
In simple terms, the parameter above provides a comparison of oil rates and water rates of any well
flowing through a particular formation to the average performance of the entire formation. Figure-2 provides
a description of various quadrants to the output of the results of above equations for any field or cluster
of wells.
•
•
•
•
Quadrant I (Higher productivity wells) : More oil and more water production
Quadrant II (Poor performance wells) : Less oil and more water (Water shut off)
Quadrant III (Lower productivity wells) : Less oil and less water production (stimulation
candidates)
Quadrant IV (Good performance wells) : More oil and less water production
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Figure 1—Workflow chart
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b. Formation Damage Indicator (FDI): This parameter for a particular well can be defined as the ratio of
liquid rate of the well to the capacity of the formation i.e. kh.
(2)
A plot of liquid rate vs kh and the slope of the line is considered as the FDI. Any well data lying to the
left of the line is a low FDI, i.e. for same kh values, these wells are producing less than their couterparts
in same reservoir. The denominator of the term represents storage capacity of the formation. As apparent
from the figure below, the best fit line is plotted representing the Formation Damage Indicator (FDI).
Figure 3—Formation Damage Indicator Plot
The above two parameters hold the key in the identification and development of the workflow. The
complete procedure of various screening steps, final determination of the expected gain and candidate
identification has been discussed.
Manual Application of Workflow
Two fields, namely Field-A and Field-D located within Malaysia were identified. Field A, mainly a
sandstone reservoir commenced production in early 2000s. Field A has wells completed with multiple
reservoirs and started witnessing sand production and scale issues since the last decade. Out of the 210
strings in Field-A, approximately 110 were producing and many of them were shut-in due to under-
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Figure 2—Heterogeneity Index Indicative diagram
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SPE-194594-MS
Data Gathering
In this case, the Company had a reservoir database which not only had the production data but had
information of all production allocation parameters entered since the initial completion of the wells i.e. even
four decades old data. This was a combination of Reservoir and Production disciplines. This has helped in
the efficient generation of FDI. In addition, an effective petrophysical parameter database was established
since last ten years which facilitated a smooth calculation of kh. The challenging part here is that in few
zones, this database was not available or complete and the team took few weeks to obtain the required data.
However, there was still a gap since the entire data was not available at one place and spreadsheets were
initially used to gather all data at one place. The information gathered can be summarized as below:
i.
ii.
iii.
Reservoir parameters – Zonal allocation of production data, reservoir pressure decline trends, etc.
Petrophysical parameters – Latest OWC & GOC, log derived calibrated permeability, formation
thickness, etc.
Production parameters – Well production test data, calibrated nodal analysis models, if available, etc.
There was separate database system for each of the three disciplines above. Figure 1-4 shows the database
used during data gathering.
Figure 4—Clockwise from top left, representation of three different databases – Production
database (Company's internal portal), Reservoir data base (generated from a commercial
application) and Petrophysical data base (generated from commercial application)
Screening of Wells using HI
Heterogeneity Index plot was generated for each wells in the reservoir unit. For Field A, 88 out of 422
producing zones were identified as low HI and 489 out of 722 producing zones were identified in Field D.
The major challenge was to confirm if the wells that fall under this quadrant is truly under-performing wells
or it was misguiding due to data capture error. The problem was resolved by listing down all the wells that
falls in Quadrant III and then cross-checking with the current monthly production rate for those wells. This
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performance, high water cut and various other reasons. For the completion of study and demonstration of the
effectiveness of the workflow, even shut-in wells were included as part of formation damage identification.
Field-D, also producing from the same geological basin had similar number of wells but more challenging
for production due to H2S, CO2 and sand production issues, simultaneously. Furthermore, most of the wells
have been completed with dual string and multiple reservoirs. Field D started production since 1980s. Both
fields A and D have most of the oil wells producing via gas lift system.
SPE-194594-MS
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Figure 5—Heterogeneity Index Indicative diagram for Field A
Identification of Under Performing Wells using FDI
The under-performing wells in Fields A and D were then screened based on Formation Damage Indicator
(FDI). Generating FDI plots require permeability and net pay values for each reservoir layer at each well
location. This was a challenge since the information was not available in reservoir database. Hence, the
help of Petrophysics were sought to sort out this issue. For FDI evaluation purpose, geometric average
permeability was used. A dataset of netpay thickness and permeability at all well locations for each reservoir/
zones were prepared to generate the FDI plots. The challenge in getting these data for Field A and D was the
difference in the naming and classifications of the reservoirs/zones. The issue was resolved by identifying
the reservoir grouping system for the fields and re-classifying them in FDI plotting database. For Field A,
22 out of 422 producing formations were identified as low FDI while for Field D 135 out of 722 producing
formations were identified.
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has allowed the engineers to eliminate the wells that were initially identified under HI but were performing
better after stimulation jobs that were not captured in the reservoir database.
Another challenge was generating HI plots for large number of wells and reservoirs/zones in Field D.
Field D had a total of 722 zones producing i.e. almost 200+ wells with various multiple reservoirs. Plotting
all the well data in a single HI plot did not give a good indication as there were too many wells in each
Quadrant. Also, due to data load of 722 different condition, the simulator response was too slow. Hence,
the datasets were divided into reservoir groups/zones and were analyzed by those grouping system.
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SPE-194594-MS
A total of 67 strings in Field A and 91 strings in Field D falls under the initial screening exercise.
Screening out candidates due to Other factors
HI and FDI factors above helped in the first screening of wells, but, all the wells identified in the list above
were not necessarily under-performing. There were other factors due to which the above FDI & HI logic
screened in these wells. The other factors apart from formation damage or VLP issues which could get the
wells under non-performance categories were as follows:
i.
ii.
iii.
iv.
Wells with water cut more than 70% were excluded from the list – The objective was to identify
candidates for stimulation. For well more than 70% practically and economically do not qualify
under stimulation/ chemical stimulation category. Petrophysical data was referred to understand the
OWC and most of the wells with high water cut were confirmed to be on that range due to revised
OWC values.
Wells with zone change – As mentioned earlier, with very old wells, the data was not very consistent.
Many zones or formations were already watered out or abandoned, but due to discontinuity of
reporting from one operator to other, they were still being considered in the centralized database.
This manual activity helped the team in gathering info on many such producing reservoirs.
Wells with high GOR: Similar to OWC issues, many wells with high GOR were also part of the
initial list. But by the use of GOC data, the wells with excessively high GOR which recorded to lie
at the GOC or in that proximity were eliminated.
Well integrity: With more detailed work, it was observed that cases like fish-in hole, leak tubing
or failed SSSV were also part of the initial screening list. The wells were later eliminated from the
formation damage list and set aside under well integrity issues.
It is worth highlighting here that many wells which had under-performance issue due to factors other than
VLP/IPR also shortlisted under this category. This was beneficial as all under-performing wells irrespective
of the cause can be shortlisted under one platform.
Further analysis on the current water cut, liquid rate and sand production data was recorded for each of
the wells. The wells were screened for water cut exceeding 70%, liquid rate less than 300 bpd and sand or
gas lift optimization issues as shown in Figure 7 & 8. 11 strings in Field A and 31 strings in Field D were
identified as potential stimulation candidate after this pass.
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Figure 6—Formation Damage Indicator diagram for one of the Reservoir in Field A
SPE-194594-MS
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Figure 8—Second pass screening table for Field A
Nodal analysis, Skin Evaluation and Formation Damage Mechanism Identification
The shortlisted wells were then analyzed for actual skin and then the identification of formation damage
mechanism. Since PBU or FGS data for the wells are limited, the nodal analysis application was used to
determine the skin trend. With the help of nodal analysis and gas lift optimization models, the wells were
further demarcated into various categories as follows:
i.
ii.
iii.
Wells with gas lift issues – Even the wells with gas lift issues apparently became part of shortlisted
candidates. This indicated a robustness of the workflow concept that wells non-performing due to
any reasons can be easily categorized
Wells with scaling issues – Few of the wells under this category when later referred to, were screened
out in having under performance due to scale build up issues
Wells with formation damage – The wells which really displayed significant skin due to fines, which
in turn causing screen plugging or perforation plugging, etc.
Figure-9 represents a typical workflow adopted for most of the wells for nodal analysis.
a.
b.
c.
d.
Well production history and understanding decline rate and trend of well production since beginning
VLP-IPR matching with existing well data and ensuring that the model is well calibrated
For wells with Gas Lift, also ensuring that the gas lift injection models are matched
Once nodal analysis model is matched with some known values, skin trend is generated to understand
the mechanism of well under-performance e.g. a high skin since beginning may indicate drilling fluid
damage
e. Finally, the latest skin value is reduced by 30-40% to estimate the gain for further economic analysis
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Figure 7—First pass screening table for Field A
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SPE-194594-MS
Field A apparently had no wax or asphaltene issue. Furthermore, wells with scale effects were already
separated. A closer look indicated that most of the wells under skin damage category had sand control
or were producing sand. Further analysis on the mineralogy indicated presence of fines of about 10-15%.
In addition, the presence of calcite in the mineralogy was quite apparent that the main formation damage
mechanism reasons were expected as follows:
a. Apparent plugging of screens due to fines migration
b. Fines migration within the reservoir
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Figure 9—Nodal Analysis modelling Steps for a typical well to identify skin trend and predict approximate gain
SPE-194594-MS
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The above issue of fines migration and plugging due to fines can be resolved by use of retarded acid
system mixed with fine stabilization system7 or chelatants8. Three well strings were selected with a minimum
estimated gain of ~650 bopd to proceed with further operational planning.
The workflow was followed for Field-D, but at the stage of screening wells with integrity issues, the
workflow ended since most of the wells were under-performing due to integrity issue. Though some wells
were finally identified, but another limitation of having no firm candidate for Field-D due to allocation of
production rates for commingled production as explained in next section.
Figure-11 summarizes the entire string work conducted for Fields A and D which took around 6 months
of the work.
Figure 11—Screening stages and no of candidates at each stage for Field A and Field D
In summary, if going through a conventional way of evaluating skin, collecting data and non-integration
of various disciplines, the above screening of ~400 strings would had taken 1-2 years or almost ~10,000
man-days as mentioned in Section-1. But with the above utilization of data base, collaboration between
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Figure 10—Summary table for A field on final candidates
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SPE-194594-MS
various disciplines and a coordinated way of structured workflow, the work was conducted within 6 months.
Assuming again a team of four dedicated engineers with only 5-6 hours/day resulted in about 1000 mandays for two fields. The key was developing a methodology which can significantly reduce the time
required to perform the candidate screening from about two years to six months.
There were many challenges faced by the team during the workflow execution, which are listed as follows:
a. Integrated data base
It is very important to highlight that a robust database system is very important to have such a
system or workflow in place. A data base with inclusion of data related to all disciplines in a single
platform is key to success.
b. Identification of Best Fit Line for Formation Damage Indicator
It was mentioned in Section 2 that a best fit line had to be generated for the Formation Damage
Index which would decide the under-performance of the well. With only one point of time value, it
was seen that it was difficult to get the exact trend line. Example in Figure 12 shows that there is
a possibility of generating 3 different trend lines which gives the best fit line with the data. But, by
having a proper animated display of entire data from the start until the current production, the trend
of the particular reservoir can be observed and the best fit could be accurately fitted. Figure 13 shows
the production trend prior fitting the best fit line.
Figure 12—Scatter plot indicating 3 best fit lines that could possibly be fitted
based on production data within the last 2 years of production (2017-2018)
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Challenges during the Workflow Execution
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c. Multi-disciplinary team
Though the main lead discipline for this work was Production Technologist team, still the entire
effort would had been incomplete without proper collaboration with other disciplines. For example,
reservoir engineering evaluations of allocation of production zone, injectivity and water flooding
issues in the wells and pressure of each zone with proper decline rates were important aspects in
deriving the performance of the reservoir. Similarly, petrophysical information such as the GOC,
OWC, mean permeability (geometric, harmonic and arithmetic) and the minerology input were
very important. Such collaboration is very important for the successful deliverable of such tasks. It
was a dedicated work of six different disciplines (Production Technology, Reservoir, Petrophysicist,
Production Chemist, Production Surveillance and Well Intervention) for almost six months to
manually implement the workflow for almost 1000 different producing formations.
d. Additional issues in the well apart from formation damage
As observed in both Fields A and D, a lot of candidates shortlisted in the initial stages were found
to not having formation damage, but under-performing due to various other reasons listed as follows:
i.
ii.
iii.
iv.
Scale issues deposited inside tubing were identified for few wells in Field A
Integrity issue such as production packer leak across commingle production was also surfaced
out during the activity
Gas lift optimization was studied in few wells
High water cut wells
Different teams within the organization were working to resolve the above issues in those wells and
later confirmed that the wells were actually under-performing. Having the wells as part of shortlisted
wells was encouraging for this workflow. It meant that both VLP and IPR aspects were covered under
this workflow and this could be the main candidate screening workflow for all the under-performing
wells irrespective of the reason of under-performance.
e. Production Allocation issues in multiple reservoir completion
As mentioned in previous section, Field – D was not identified with any firm candidate in the
list for formation damage. But having a conclusion of an old field without any formation damage
candidates appeared anomalous. With a closer analysis, it was apparent that due to completion strategy
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Figure 13—Animated scatter plot showing the trend of production pattern within
the reservoir from the start of production in 1991 till the current production in 2018.
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Moving Towards Digitalization
While the above activity was being conducted manually, a separate team was in process to convert the entire
database and procedure into a workflow to be integrated with proper database and interactive system to get
the results in one click. A summary of workflow is provided below and Appendix –I shows the screenshots of
the automated workflow. Technical paper from. Jeffry et al9 explains the development and overall execution
of Stimulation Workflow in much more detail. Major steps in the digitalized workflow were as follows:
i.
ii.
iii.
iv.
List of all the strings within the field including well test data, reservoir parameters, production data,
etc. at one place
Daily update of Heterogeneity Index and formation Damage Indicator with Artificial Intelligence
to best fit the FDI line
Historical trending of the well production with tools to estimate the rate of decline over a certain
period with inclusion of deferment of wells due to shut-down, etc.
A One-Click generation of under-performing wells with filtering efficiency in terms of GLR, gas
lift optimization, well integrity, etc.
It is quite apparent that a work which took earlier six months can now be done in a single click saving
significant number of man-hours and effective time saving for the engineers. If calculated for all the major
fields within the Company and assuming that the workflow is well established in all the fields, the work of
14,000 man-hours is now reduced to barely few man-hours for the rest of the life of field. With the workflow
established, engineers can now concentrate more on the issue of resolving the mechanism due to which
the wells are under-performing rather than quantifying the under-performance. Digitalized workflow also
helped the team in including more diagnostic features.
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of dual string completion with commingled reservoirs in each string, this challenge had surfaced out.
Due to a complex completion design along with limited production logging data and above all small
pools of reservoirs, the production allocation into each formation was difficult. This allocation issue
uncertainty led to a scenario with no wells being identified as firm formation damage candidates as
well. Steps are being taken, as we site this paper to reduce the uncertainty and select firm candidates
for Field D.
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Future Work using Workflow
With the initial phase of digitalization of the workflow done, it has opened a lot of scope to optimize in
future:
1. To digitize the entire company's data base in production and Nodal analysis into this data base
2. Trending of skin and related formation damage mechanism to establish the estimated skin for each
completion types. This will ensure completion optimization for future wells
3. Use of trends and data science to predict skin. This will help to reduce the surveillance plan for skin
evaluation and also a continuous understanding of well behavior
4. Adding features such as expected solutions, percentage decrease in skin and the expected production
increment in near future can be included in this portal
5. It is planned to combine this workflow with other workflows such as gas lift optimization, sand
management10 and production allocation to build an integrated Production Management portal. This
will ensure an efficient operating and production envelope for all the producing strings and process
efficiency improvement
Acknowledgement
Authors are highly acknowledged to Petronas Carigali management for providing the permission to publish
this paper.
Abbreviations
OWC
GOC
FDI
HI
ESP
VLP
IPR
k
Oil Water Contact
Gas Oil Contact
Formation Damage Indicator
Heterogeneity Index
Electrical Submersible Pump
Vertical Lift Performance
Inflow Performance Relationship
Permeability, md
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Figure 14—Comparison between different screening methods and the
average man-days required to screen one field (assuming it has 200 strings).
16
SPE-194594-MS
Net pay thickness, ft
barrels per day
barrels of oil per day
Productivity Index, bbl/d/psi
Pressure Build Up
Flowing Gradient Survey
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h
bbl/d
bopd
PI
PBU
FGS
3. Heterogeneity Index
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2. Reservoir Overview
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Appendix-I
Snapshots of the Automated Workflow
1. Listing of wells from all related data base
SPE-194594-MS
18
5. Historical Trending
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4. Formation Damage Index
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