Oil & Natural Gas Projects
Exploration and Production Technologies
Development of a Virtual Intelligence Technique for the Upstream Oil Industry
The project was selected under the Preferred Upstream Management Practices
(PUMP) solicitation DE-PS26-01BC15304 issued in the fall of 2000. PUMP is
aimed at pairing "best practices' and solutions coming from new technologies
to an active campaign of dissemination information to domestic producers.
PUMP goals are to slow the decline of domestic oil fields and to maintain
the infrastructure to continue to produce oil as a vital part of National
The goal of the project was to investigate applicability of soft computing
techniques such as fuzzy logic, pattern recognition, and genetic algorithm
to identification of the most influential parameter impacting production rate
and ultimate recovery and development of an optimized approach using conventional
(crisp as opposed to fuzzy) data analyses. The ultimate goal was increased
production by achieving maintainable production rate and increased ultimate
recovery through optimization of the upstream oilfield practices.
Gas Technology Institute
Des Plaines, IL
West Virginia University
The project was implemented in three fields in the Golden Trend of Oklahoma.
Application of soft computing techniques pointed to hydraulic fracturing as
the most influential operation relative to production rate and ultimate recovery.
A set of guidelines and recommendations for optimization of hydraulic fracturing
was developed. A virtual intelligence software, specially developed for these
fields, was developed and delivered to the participating producers. In the
meantime, production data analyses pointed to wells that although in close
vicinity of one another, had different production history. Advanced seismic
data processing using wavelet based spectral decomposition techniques revealed
the presence of severe faulting between these wells. Results were presented
to these companies for their consideration in their in-fill drilling projects.
The fundamental benefits from this research and development project was the
proof that soft computing has the capability of analyzing the heterogeneous
and often incomplete data from producing fields time- and cost-effectively.
Results from these virtual intelligence techniques would then point to specific
area/operation for detailed geoscientific and engineering studies. The net
benefits would thus be increased production efficiency and increased production.
The U.S. Department of Energy has been supporting research and development projects
aimed at increased domestic production in terms of ultimate recovery as well
as production rate, through optimization of the upstream practices. Optimization
of any process involves characterization of the processes and improvements upon
them and characterization entails analysis of the existing data. However, in
the case of oil field data, specifically regarding fields with long production
history, the data is inaccurate, imprecise, and incomplete; and as such, accurate
operations characterization is next to impossible.
Soft computing techniques, collectively referred to as Virtual Intelligence
techniques, mimic the human brain in that it can decipher incomplete data,
recognize patterns, and identify the net result of the cause-and-effect phenomena
without resolving the intrinsic makeup of the phenomena. As such, application
of the virtual intelligence techniques would identify the areas where elaborate
conventional engineering and geoscientific analyses would resolve the cause-and-effect
process. At that point, the operations parameter would be re-designed (optimized)
to produce the most desirable result. This approach was used on data from
three producing fields in Oklahoma.
The project was implemented in three fields operated by three independent
operators in the Golden Trend in Oklahoma. Detail stimulation and production
data from more than 230 wells were collected and analyzed using the virtual
intelligence methodology. These analyses revealed that the most influential
controllable parameter effecting the production rate and ultimate recovery
was fracture stimulation, and as such, the bulk of soft computing efforts
were focused on determination of optimized hydraulic fracturing procedure.
Results of these studies were presented to the participating producers as
lists of re-completion candidates and the recompletion characteristics.
Production data analysis showed that in parts of the area wells that were
in close vicinity of one another had vastly different production. Elaborate
seismic data processing using wavelet based spectral decomposition techniques
revealed the presence of severe faulting between these wells. Results were
presented to the participating producing companies for their consideration
in their in-fill drilling projects. Figures 1 is the wideband seismic section
and figures 2, and 3 show results of spectral decomposition on data from the
Technology transfer was achieved through several in-house workshops carried
out in the offices of the participating producers. In these workshops, the
computer model developed specifically for the golden trend was explained in
a series of hands-on tutorial sessions.
The three Oklahoma producers that participated in the project are using the
results and one company has indicated their interest in participating in any
follow-up work that would expand the technique beyond the Golden Trend. Since
the publication of the final report we have received several enquiries from
the US and foreign producers.
Final report, DE-FC26-01BC15271: "Development of a Virtual Intelligence
Technique for the Upstream Oil Industry"; Salehi, I. Gas Technology Institute,
Mohaghegh, S.D., Intelligent Solutions Inc., Ameri, S, West Virginia University,
September 19, 2004, 177 pages.
Mohaghegh, S.D. et. al.; "Analysis of Best Hydraulic Fracturing Practices
in the Golden Trend Fields of Oklahoma," SPE 95942, SPE Annual Conference,
Dallas, October 2005.
Salehi, I., Walker, G.; "PUMP Project: Quantifying Best Practice Analysis
to Cut Costs and Boost Output", GasTIPS, April 2004, p 25-30.
Project Start: August 20, 2001
Project End: June 19, 2004
Anticipated DOE Contribution: $571,014
Performer Contribution: $614,863 (52 % of total)
NETL - Jerry Casteel (firstname.lastname@example.org
GTI - Iraj Salehi (email@example.com