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Intelligent Integrated Surveillance Tool Improves Field Management Practices

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This article describes solutions developed using a dynamic surveillance tool to automate several workflow processes of the reservoir management, production engineering, and Exploration and Petroleum Engineering Center – Advanced Research Center (EXPEC ARC) at Saudi Aramco. The objective is to provide improved efficiency in field management practices, while enhancing collaboration between reservoir and production engineers; ultimately resulting in improved decision-making processes. The solutions provided include a combination of smart tools and automated workflows designed to improve reservoir management and surveillance processes. A candidate recognition system was developed to identify and flag problem wells that require immediate remediation. As new production and injection data become available, the system that is linked to the corporate database can automatically display this data for fast and rigorous validation. In addition, a formation damage indicator function is also devised using field data and mapped to spot production problem areas and identify damaged wells. A daily surveillance tool, which compares the performance of individual wells to the average performance of a group of wells, is also provided to allow the reservoir and production engineers to easily identify under-performing wells, promptly intervene, and recommend the best completion practices. Benefits include efficient well management and cost avoidance resulting from early intervention and remediation, while avoiding full-scale problem resolution. Another dynamic surveillance tool was designed and views were developed to provide online access to the hydrocarbon phase behavior and petrophysical data for the EXPEC ARC scientists and reservoir engineers. The tool allows integration of the hydrocarbon phase-behavior data and comparison of petrophysical data with historical production/injection data and production well logs, resulting in enhanced analysis, production optimization and data validation. Additional benefits of the smart tools and automated workflow processes include considerable timesavings, with pertinent data being automatically updated, validated and used in the analysis, leading to improved efficiency in field management practices.
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ABSTRACT
This article describes solutions developed using a dynamic
surveillance tool to automate several workflow processes of
the reservoir management, production engineering, and
Exploration and Petroleum Engineering Center – Advanced
Research Center (EXPEC ARC) at Saudi Aramco. The
objective is to provide improved efficiency in field
management practices, while enhancing collaboration between
reservoir and production engineers; ultimately resulting in
improved decision-making processes.
The solutions provided include a combination of smart
tools and automated workflows designed to improve reservoir
management and surveillance processes. A candidate
recognition system was developed to identify and flag problem
wells that require immediate remediation. As new production
and injection data become available, the system that is linked
to the corporate database can automatically display this data
for fast and rigorous validation. In addition, a formation
damage indicator function is also devised using field data and
mapped to spot production problem areas and identify
damaged wells. A daily surveillance tool, which compares the
performance of individual wells to the average performance of
a group of wells, is also provided to allow the reservoir and
production engineers to easily identify under-performing wells,
promptly intervene, and recommend the best completion
practices. Benefits include efficient well management and cost
avoidance resulting from early intervention and remediation,
while avoiding full-scale problem resolution.
Another dynamic surveillance tool was designed and views
were developed to provide online access to the hydrocarbon
phase behavior and petrophysical data for the EXPEC ARC
scientists and reservoir engineers. The tool allows integration
of the hydrocarbon phase-behavior data and comparison of
petrophysical data with historical production/injection data
and production well logs, resulting in enhanced analysis,
production optimization and data validation. Additional
benefits of the smart tools and automated workflow processes
include considerable timesavings, with pertinent data being
automatically updated, validated and used in the analysis,
leading to improved efficiency in field management practices.
INTRODUCTION
It is generally accepted that most of the reservoir and
production engineers’ time is spent in searching, collecting,
checking, and integrating reservoir and production data. Less
time is spent by engineers on analysis and interpretation. Each
engineer uses different tools in gathering this data, resulting in
less collaboration and possible repetition of tasks. One of the
main objectives of the integrated dynamic surveillance tool is
to reduce the spent time on data gathering; letting engineers
focus on data analysis and interpretation rather than data
collection, leading to more efficient use of their time and
increased collaboration between reservoir and production
engineers. In this paper, four sets of tools are provided to
automate reservoir management and surveillance, monitor
production data, provide an integrated online access to
hydrocarbon phase behavior and petrophysical data, and to
manage well test knowledge.
RESERVOIR MANAGEMENT AND SURVEILLANCE TOOLS
One of the reservoir engineer’s main tasks is to manage the
reservoir as efficiently as possible in order to prolong the life
of the reservoir, while maximizing hydrocarbon recovery. Our
aim is to provide the engineer with the necessary software
tools to automate their workflow processes, while integrating
computing processes and data, based on a multidisciplinary
asset team approach. Some of the tools developed to aid in
this task include a remedial well analysis tool, a water
management tool, a heterogeneity index tool, a formation
damage indicator, and an integrated reservoir analysis tool.
Remedial Well Analysys Tool
A candidate recognition system was developed to identify and
flag problem wells that require immediate remediation. The
system consists of various analysis tools that allow
anticipating the onset of problems by leveraging existing
knowledge from nearby wells. Figure 1 shows a typical plot of
the remedial well analysis tool. It summarizes the production
performance of a well by tracking the oil and water
production rates. These rates are then extended to forecast
their future production using decline curve analysis. This
12 SU MME R 2008 SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY
Intelligent Integrated
Surveillance Tool Improves
Field Management Practices
Authors: Dr. Saud M. Al-Fattah, Mohammed M. Dallag, Rami A. Abdulmohsin,
Wael A. Al-Harbi and Dr. Mohammed B. Issaka
allows early detection of mechanical and other problems, such
as high water cut, low productivity or injectivity. The tool also
has the ability to identify the occurrence of production logs,
workover and stimulation jobs. The occurrence of well
pressure buildup and fall-off tests can also be identified. Also
included in the tool is the identification and explanation of
well events that took place. This includes workover and/or
stimulation jobs. Using this tool, the engineer can anticipate
the onset of problems by leveraging existing knowledge from
nearby wells. Benefits include cost avoidance resulting from
early intervention and remediation, while avoiding full-scale
problem resolution.
Water Management Tool
The integrated dynamic surveillance tool was also used to
implement water management strategies for identifying and
controlling high water producing wells and cyclic production
wells. Based on reservoir management specified criteria, this
application allows the reservoir engineer to rapidly screen the
entire field for high water producing wells and recommend the
best reservoir management practices for the candidate wells.
These wells can be recommended for water shut-off, stim-
ulation, rate restriction, and sidetracking. Figures 2 and 3
show a field example of a carbonate reservoir with wells
producing up to 22,000 barrels per day (B/D), 90% water cut,
and oil pay thickness ranging from 20 ft to 160 ft. Wells are
screened by selected criteria based on reservoir performance.
For example, in Figs. 2 and 3, the wells were screened based
on arbitrary cut-offs of 50% water cut, 40 ft oil pay thick-
ness, and 5,000 B/D oil rate. Wells with low water cut (< 50%)
and high net pay thickness (> 40 ft) are considered as
excellent wells. On the other hand, wells with high water cut
(> 50%) and high net pay thickness are prime candidates for
workover, stimulation, and/or rate restriction.
Another water diagnostic technique1was also implemented
to help identify and control high water producing wells.
Figure 4 illustrates the use of this technique that employs both
water/oil ratio and water cut derivatives with respect to
producing time. Using this tool, the source of excessive water
SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY SUMME R 200 8 13
Fig. 1. Remedial well analysis tool showing production history and forecast using decline curve analysis.
1969 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 14
0
5000
10000
15000
20000
D
Daattee
12
SSTTIIMM
WWOO
BBuuiilldduu
FFaallllooffff
RRaatteeFFoorreeccaasstt,,BBCCDD
1. 103: Casing Leak Repair (Prod/OBS) / Complete/Re-Compl in Same RS
2. 103: Fishing/Removing Obstruction / Complete/Re-Compl in Same RSV
Rate Forecast BCD
Date
WO
STIM
Buildup
Fallout
Fig. 2. Scatter plot of water cut and net pay thickness, with corresponding scatter
plot of well locations.
“Net Pay Thickness”
Y-Coordiate
“Water Cut, %” X-Coordinate
Fig. 3. Scatter plot of water cut and oil production rate, with corresponding
scatter plot of well locations.
“Oil Rate, Mbpd”
Y-Coordiate
“Water Cut, %” X-Coordinate
14 SU MME R 2008 SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY
of HI that is larger than zero. Heterogeneity index values
below zero indicate wells performing below the average. The
heterogeneity index can be calculated for any dynamic variable
such as production rate and water cut. A heterogeneity index
calculated from production rates could be very noisy and hard
to analyze. Therefore, a cumulative HI is introduced to smooth
the production rate HI as follows:
(2)
Figure 5 shows a scatter plot of the HI based on cumulative
production rates and another scatter plot showing the well
locations. Wells falling in the lower-right quadrant of the HI
plot are strong performers, with higher than average oil rates
and lower than average water rates. Analysis of cumulative HI
with time includes not only the relative position of the point
but also the slope of the curve (trend analysis). This HI should
not be confused with the formation heterogeneity.
production can be identified due to either channeling, water
coning, or high oil production, and best reservoir management
practice is recommended.
Heterogeneity Index
The heterogeneity index is a tool that provides a convenient
means of comparing the performance of individual wells to
the average group of wells. This daily surveillance tool allows
the engineer to rapidly identify over and underperforming
wells, and recommend the best completion practices. The
heterogeneity index2, 3 (HI) is defined as
(1)
One is subtracted from the ratio to normalize the hetero-
geneity index to zero, i.e., the average of all the wells is equal
to zero. Wells performing above the average will have a value
Fig. 4. Water diagnostic plots using water/oil ratio and water cut derivatives.
WC Derivative
Water Cut, %
WOR Derivative
WOR, B/B
Elapsed Days (days)
Fig. 5. Scatter plots of heterogeneity index and well locations.
Hctcrogencity Index for ABQQ
Y-Coordinate
HI. Sum Wtr Rate
HI. Sum Oil Rate X-Coordinate
Formation Damage Indicator
The use of the formation damage indicator is to spot production
problem areas and identify damaged wells. Calculation of the
skin factor can easily indicate which wells have formation
damage problems using a steady-state flow equation.
(3)
Although, some of the fields have limited pressure data.
Therefore, by rearranging the equation, we can correlate it
with the formation damage index3(FDI).
(4)
The numerator is the production rate that represents the
capacity of a well to produce. The denominator is the
product of the permeability and the pay zone thickness. It
represents the storage capacity of the formation to deliver. If
a well has formation damage problems, then it will produce
at a low rate even though the formation has a high storage
capacity to deliver. Therefore, the FDI will be a low value.
Figure 6 shows a grid map of the FDI. Areas colored in dark
red have a high FDI, while areas in yellow have low
damage. The green-colored areas have moderate damage.
With this tool the engineer can quickly and easily identify
and quantify all damaged wells of a given field with much
less time and convenience than the traditional way.
Integrated reservoir Analysis Tool
An integrated reservoir analysis tool was also developed,
detailing the workflow process carried out by a reservoir
engineer in analyzing reservoir performance and production
data. Figure 7 shows a field example of several well analyses
tools integrated into a single dynamic surveillance application.
The integrated reservoir analysis tool allows engineers and
geoscientists to view, report, map, and analyze reservoir
performance and production data with ease and convenience.
Among other applications, the integrated analysis tool was
used to analyze reservoir performance through historical
production/injection data, production forecast using decline
curve analysis, single and multiple production well logs,
stratigraphic cross sections, well deviations, and wellbore
schematics of any particular well or a group of wells in a
given field.
The integrated tool can also be used as a useful means of
characterizing the field in a macroscopic scale. The base map
of a field includes the original oil-water contact, gas-oil
contact, faults, fractures, and geological structures of the field.
The integrated reservoir analysis tool was also used in the
waterflood management by monitoring the floodfront
movement, and studying the water encroachment through
mapping the fluid contact movement with time and the
geochemical water analysis. Additional analyses to study the
water encroachment in oil producing wells at the crest of the
field include mapping and displaying of flowing wellhead
pressure, flowing wellhead temperature, cumulative water cut,
and cumulative oil cut.
SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY SUMME R 200 8 15
Fig. 6. A grid map of the formation damage index.
Formation Damage
Index for an
Example Field
Fig. 7. Integrated reservoir analysis tool.
16 SU MME R 2008 SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY
PRODUCTION DATA MONITORING TOOLS
Automated real-time or near real-time production data plays
an important role for monitoring day-to-day reservoir
performance and field operational activities. Through the
integrated dynamic surveillance tool, we developed views to
provide automated real-time access to the daily gas production
data, and to the central production-data-acquisition systems
of oil and gas fields at Saudi Aramco. A brief description of
these tools follows.
The Hall plot technique4, 5 was also implemented and integrated
in the dynamic surveillance tool for analyzing injection wells with
the assumption of a series of steady-state injection conditions. A
Hall plot, as shown in Fig. 8, is a plot that displays the cumulative
water injection on the X-axis and the Hall coefficient on the Y-axis.
The Hall coefficient is a running sum of the injection pressures for a
water injection well. This technique was deployed and used in
waterflood studies resulting in better reservoir understanding, and
improved efficiency in monitoring and control of injection
performance, and management of waterflood project.
Fig. 8. A typical example of Hall plot.
Fig. 9. Past and current workflow processes of daily gas production data.
Real Time
Production Data
Corporate
Database
Temporary
Database
Daily
Production
Database
Data in
Excel
Sheet
Excel
Sheet
Excel
Sheet
Excel
Sheet
Excel
Sheet
Excel
Sheet
Real Time
Production Data
Gas Prod.
Engineering
Gas Res.
Management
Gas Production Eng.
Prod. & Facilities
Development
Excel
sheets
Temporary
Database
Daily
Production
Database
End users
Corporate
Database
Gas Res.
Management
Gas Plant
Pi server
Gas Plant
Pi server
Dynamic
Surveilllance
Tool
Previous Data Flow New Data Flow
Example Hall PlotTypical Hall Plot for various conditions
Cumulative Water Injection - BBL Cum Wrj
583 MLB
A - stable or normal injector
after fill-up
B - negative skin / injecting
above parting pressure
C- Water channeling / out of
zone injection
D- positive skin / poor
water quality
Cumulative Pressure - PSI X Days/BBL
Hall Coeficient
A
D
C
Gas Fill-UP
b
a
Daily Gas Production Data
Having access to gas production data on a daily basis is critical
for monitoring the performance of gas wells. We automated the
updating of the daily gas production data, making it available
online to gas reservoir management and gas production
engineering through an integrated dynamic surveillance tool. The
engineers now have access to all historical gas production data,
up to present for all gas wells, which is crucial to the efficient
management of gas fields. It provides reservoir and production
engineers an online access to gas production data on a daily
basis, enabling close monitoring of the production performance
of gas wells, predicting instantaneous gas production rates,
detecting production problems, and tracking the compliance of
the well production to the reservoir management guidelines.
Figure 9 shows a comparison of the past and current workflow
processes of the daily gas production data.
Real Time Data Coupling To And Scada Systems
One solution that we plan to provide and integrate in this tool
is a system for real timedata retrival, visualization, and
anallysis of reservior dynamic surveilllance tool with Plant
Information (PI) and Supervisory Control and Data
Acqusition (SCADA) systems. Figure 10 shows a schematic
diagram of the planned integrated workflow process of
realtime data coupling to the PI and SCADA systems. Withe
this solution, data such as daily fluid fluid rates, condensate
rates, wellhead pressures and temperatures can be provided in
realtime at the engineer’s desktop. These real time data are
critical for the day-to-day monitoring of well performance,
troubleshooting, and optimazation, leading to improved
efficiency of fluid management practices.
ROCK AND FLUID DATA ANALYSIS SYSTEMS
Hydrocarbon phase-behavior data and petrophysical core
sample data are among the most important information
required by research scientists, reservoir engineers, and
geoscientists for reservoir characterization and description.
The following section presents two systems that were
developed aiming at analyzing the hydrocarbon phase
behavior and petrophysical data through an integrated
dynamic surveillance tool.
Hydrocarbon Phase Behavior Analysis System
This system was developed using the integrated dynamic
surveillance tool as a front-end, enabling the research
scientists and engineers to retrieve, report, visualize, map, and
analyze pressure-volume-temperature (PVT) property data
with ease. It provides the user with an online access to several
SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY SUMME R 200 8 17
Fig. 10. Workflow of coupling integrated dynamic surveillance tool to PI and SCADA systems.
Real-Time
Production
Database
PI SCADA
Integrated Dynamic
Surveillance Tool
Plants
End Users
PI SCADA
Real-Time
Production
Database
18 SU MME R 2008 SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY
Petrophysical Data Analysis Tool
Customized templates were also developed to help research
scientists retrieve petrophysical data from the corporate
database and display them for analysis and interpretation.
These petrophysical data include core porosity, permeability,
fluid saturation, hydraulic flow unit, reservoir quality index
(RQI), normalized porosity index (NPI), and flow zonation
indicator (FZI). Petrophysical data are extensively used
in interpretation, calculation, and completion of other
technical and research studies to support the development
of oil and gas reservoirs.
The main advantages of linking the petrophysical data
to the integrated surveillance tool through the corporate
database are:
Reducing the time required for retrieving core plug data
and allowing the engineer and scientist to use additional
derived functions to manipulate data or adding more
properties to core plugs, and
Integration with complementary well data, such
as production and well test data, that may be used
for better interpretation and correlation of reservoir
parameters.
Figures 11 to 13 show examples of analysis plots of
petrophysical data used by geoscientists and reservior
engineers in conducting research studies. The hydraulic
unit is a statistical representation of the reservoir zonation
and reservoir quality. The functions of the RQI and NPI
are to quantify the flow character of the reservoir and
provide an association between petrophysical properties
and the micro and macro level properties of the tested core
samples. The FZI is commonly used in conjunction with
cluster analysis or probability plots to differentiate
between the flow zones.
PVT property data of black oil and gas condensate fluids.
Among the black oil data are the PVT sample, hydrocarbon
analysis, differential liberation process, thermal expansion,
compressibility, bubblepoint, and volume-density-viscosity
data. The gas condensate data include depletion process
analysis, retrograde condensation, fluid composition, dew
point, and pressure-volume relations. The system can also
be utilized by reservoir simulation engineers in building and
preparing the PVT input data for the simulation models,
and by reservoir management engineers in conducting PVT
dependent field studies. The system has resulted in a significant
savings of time and effort of scientists and engineers in
conducting quality control, experimental optimization, and
research field studies.
Fig. 11. Flow zone indicator (FZI) correlated with adjusted core and drilling depth.
Fig. 12. Normalized porosity index (NPI) vs. reservoir quality index (RQI).
Fig. 13. Permeability-porosity transform.
Depth, ft
FZI
RQI
NPI
Permeability, md
Log of Permeability
Porosity, %
WELL TEST KNOWLEDGE MANAGEMENT SYSTEM
A knowledge management system was developed to provide
well test analysis information in one environment. Figure 14
shows a schematic diagram of the well test knowledge
management system. The system is designed to display pressure
derivative signatures as thumbnails at the well locations on a
base map of any chosen field. A mouse click on each thumbnail
enlarges it for detailed viewing and interpretation. The system
also allows the engineer to launch the well test analysis
application directly, for further analysis and interpretation of
any well test. This will help reduce the time it takes to analyze a
well test, by looking for patterns in interpretation and
comparing analysis results from nearby wells.
SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY S UMM ER 200 8 19
Fig. 14. Well tests knowledge management system.
Pressure derivative signature can be
compared with other wells.
Well test package can be launched directly
from within the base map for additional interpretation.
Additional well information
can also be viewed.
Wells with well
test interpretation
can be located
on the base map.
20 SU MME R 2008 SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY
CONCLUSION
In this article, we have presented several software tools aimed
at automating the daily workflow processes of reservoir and
production engineers, as well as research scientists. These
automated tools proved to increase user productivity, reduce
time and effort, optimize operational activities, enhance
reservoir analysis, increase collaborative efforts between
reservoir and production engineers, and minimize human
errors, leading to improved efficiency of field management
practices. The integrated solutions are also dynamic, fully
automated, and capable of providing true performance
monitoring, with reliable real-time production and injection
data validation. Through these integrated dynamic tools,
engineers can efficiently manage oil and gas fields throughout
the E&P lifecycle, make better-informed and faster decisions
based on up-to-date production data, manage more wells in less
time, and detect production problems early in the well’s life.
NOMENCLATURE
h= formation thickness, ft
k= permeability, md
q= oil rate, B/D
p= pressure drop, psi
re= outer reservoir radius, ft
rw= wellbore radius, ft
s= skin, dimensionless
ACKNOWLEDGEMENT
We would like to thank Saudi Aramco management for their
permission to publish this article.
REFERENCE
1. Chan, K.S.: “Water Control Diagnostic Plots,” SPE paper
30775 presented at the 1995 SPE Annual Technical Con-
ference and Exhibition, Dallas, Texas, October 22-25, 1995.
2. Reese, R.D.: “Completion Ranking Using Production
Heterogeneity Indexing,” SPE paper 36604 presented at
the 1996 SPE Annual Technical Conference and Exhibition,
Denver, Colorado, October 6-9, 1996.
3. “Reservoir Optimization Workshop,” Schlumberger-DCS,
Houston, Texas, June 2004.
4. Hall, H.N.: “How to Analyze Waterflood Injection Well
Performance,” World Oil, Oct. 1963, pp. 128-130.
5. Earlougher, R.C.: Advances in Well Test Analysis. SPE
Monograph Series, Vol. 5, 1977.
6. “Special Core Analysis Study,” Report L-3309, Saudi
Aramco, June 2002.
BIOGRAPHIES
Dr. Saud M. Al-Fattah is currently
working in the Reserves Assessment &
Development Studies Division,
Production Facilities & Development
Department of Saudi Aramco. Prior to
that, he was acting group leader in the
Reservoir Engineering Systems Division,
Petroleum Engineering Applications Services Dept. (PEASD).
He previously worked as a senior reservoir engineer in
‘Udhailiyah Reservoir Management Division (2002-2003)
and in the Abqaiq Reservoir Management Division (1995-
1997). Saud also worked as a petroleum engineering systems
analyst in PEASD’s Simulation Systems Division. His areas of
specialty include reservoir engineering, artificial intelligence,
operations research, economic evaluation and energy
forecasting. Saud earned his Ph.D. from Texas A&M
University, College Station, TX and his M.S. and B.S. degrees
with honors from King Fahd University of Petroleum and
Minerals, (KFUPM) Saudi Arabia, all in the field of
Petroleum Engineering.
Saud is a technical editor of the SPE Reservoir
Evaluation and Engineering Journal. He has been also a
technical editor of the e-Journal of Reservoir Engineering
and the e-Journal of Petroleum Management and
Economics since 2005. Saud has published several technical
papers and made numerous presentations at various
international conferences and symposia.
Saud has a U.S. patent Pending. He was awarded the
2006 SPE Saudi Arabia Technical Symposium’s Best Paper
of the Year award (first place), and best Ph.D. paper of the
student paper contest at Texas A&M University in 1998
and in 1999. Saud is an active member of SPE, a mentor in
the SPE e-Mentoring program since 2005, vice chairman of
the 2006 SPE Saudi Arabia Annual Technical Symposium,
and chairman of the 2007 SPE Saudi Arabia Annual
Technical Symposium.
Mohammed M. Dallag is a Senior
Reservoir and Production Engineer
working for Schlumberger since 1997.
With more than 10 years of experience
in upstream oil and gas projects, Dallag
has contributed to Schlumberger
Solution projects worldwide and has led
the development and deployment of technologies for
quickly and cost-effectively analyzing reservoirs using SIS
software. He received a B.S. degree in 1993 and a M.S.
degree in 1997, in Petroleum Engineering with the first
honor, both from King Fahd University of Petroleum and
Minerals (KFUPM), Saudi Arabia.
Rami A. Abdulmohsin joined Saudi
Aramco in June 2001 as a Petroleum
Engineering Systems Analyst in the
Petroleum Engineering Application
Services Department. He is currently
the main support for the Oil Field
Manager application. Rami holds a B.S.
degree in Computer Engineering from King Fahd
University of Petroleum and Minerals (KFUPM), and he is
currently pursuing his M.S. degree in Petroleum
Engineering from KFUPM.
Wael A. Al-Harbi is a PE Systems Analyst
in the Petroleum Engineering Application
Services Department since 2004 when he
joined Saudi Aramco. He supports
reservoir management and formation
evaluation applications. Wael graduated
with a B.S. degree in Petroleum
Engineering from Louisiana State University, Baton Rouge,
LA in 2004. He was an educational editor of the PE&D
Emerging Leaders newsletter. Wael is a member of the Society
of Petroleum Engineers (SPE).
Dr. Mohammed B. Issaka is a Petroleum
Engineering Systems Specialist with the
Petroleum Engineering Application
Services Department of Saudi Aramco.
He has been involved in providing
software application support to both
reservoir and production engineers here
in Saudi Aramco. Mohammed previously worked for Fekete
Associates Inc., in Calgary, Canada, providing consultation
on well test interpretation and developing a well test analysis
software package. He holds a Ph.D. degree in Petroleum
Engineering from the University of Alberta in Canada.
Mohammed has research interests in well testing of
horizontal and multilateral wells, as well as the integration of
systems for real-time management of petroleum reservoirs.
SAU DI AR AMCO J OUR NAL OF T ECH NOLO GY S UMM ER 200 8 21
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Article
This paper describes an analysis method for production data referred to as heterogeneity indexing which quantifies well performance anomalies for the purpose of assessing completion efficiency. It has been proven useful for determining the most successful completion practices in a given area and formation as well as a surveillance tool for primary and secondary recovery operations. Utilizing computer aided normalization methods, heterogeneity signatures and indexes may be extracted from time-based production data. The paper illustrates how to calculate these indexes, integrate this information with petrophysical and completion data, and then interpret it for the purpose of assessing the effectiveness of various types of completion methods. In developing this method, fluid simulation modeling of various types of reservoir heterogeneities were used to develop a series of type curves. The signatures in these type curves exhibit characteristics which have been shown to relate to reservoir properties and completion efficiency. Introduction When subjected to appropriate analysis methods, oil and gas production data exhibit well performance anomalies useful to the assessment of completion efficiency. This information can be used to rank the effectiveness of different completion methods being used with a particular producing formation in a given geographical region. Completion efficiencies are also useful for identifying specific wells with poor or superior performing completions. An analysis method referred to as heterogeneity indexing may be used for this purpose. It involves the computer analysis of historical production data which is then integrated with available completion and rock data to develop performance ranking criteria and assessment tools. Production Heterogeneity Indexes Anomalies are observable in oil and gas well production data that are attributable to one or more of the following factors: Time well is on production Reservoir pressure Completion method and efficiency Reservoir quality and tank size With proper analysis methods, the effect of the first two factors can be normalized out so that assessment of completion efficiency and/or reservoir quality becomes possible. This assessment is accomplished with heterogeneity indexing which may be generally defined as follows: where HI Fluid is the heterogeneity index for any type of fluid production ratio. Fluid may be oil, gas, barrels of oil equivalent, total liquid, gas-oil ratio or water cut and may consist of either "rate" or "cumulative" numbers. A well with no heterogeneity (an average well for the field or formation) has an HI equal to 1.0. When HI is examined over n periods of time, a heterogeneity signature is obtained. An example is shown in Figure 1. The shape of this signature, relative to the benchmark of HI = 1.0, can be indicative of completion or reservoir anomalies. P. 303
Article
A new technique to determine excessive water and gas production mechanisms as seen in petroleum production wells has been developed and verified. Based on systematic numerical simulation studies on reservoir water coning and channeling, it was discovered that log-log plots of WOR (Water/Oil Ratio) vs time or GOR (Gas/Oil Ratio) vs time show different characteristic trends for different mechanisms. The time derivatives of WOR and GOR were found to be capable of differentiating whether the well is experiencing water and gas coning, high-permeability, layer breakthrough or near wellbore channeling. This technique was applied on wells in several fields in Texas, California, the Gulf coast and Alaska Plots using the actual production history data determined the production problem mechanisms. Together with well tests and logs, the technique was used to select well treatment candidates and to optimize treatments to enhance the return of investment. INTRODUCTION Over the last 30 years, technical efforts for water control were mainly on the development and implementation of gels to create flow barriers for suppressing water production. various types of gels were applied in different types of formations and to solve different types of problems.1,2 Quite often, excessive water production mechanisms were not clearly understood or confirmed. Although many successful treatments were reported, the overall treatment success ratio remains low.³ Through these field trials, the art of treatment job execution was progressively improved. Good practices in the process of candidate selection, job design, gel mixing and pumping and job quality control were recognized and adapted. More effectlve tools and placement techniques were also used. The desire to define different types of excessive water production problems began to surface.
SPE paper 30775 presented at the 1995 SPE Annual Technical Conference and Exhibition
  • K S Chan
Chan, K.S.: "Water Control Diagnostic Plots," SPE paper 30775 presented at the 1995 SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 22-25, 1995.
Reservoir Optimization Workshop
"Reservoir Optimization Workshop," Schlumberger-DCS, Houston, Texas, June 2004.