The BISC Initiative: The Stanford-Berkeley Initiative (BISC-SCRF); Intelligent Reservoir Modeling for Optimized Asset Management Decision Making

Masoud Nikravesh1, Jef Caers 2, Andre G. Journel 3, Dr. Bahram Parvin4, and Dr. Fred Aminzadeh5
(Professor Lotfi A. Zadeh)
Berkeley Initiative in Soft Computing and Stanford Center for Reservoir Forecasting

This project describes the need for an initiative in reservoir modeling and management based on two main motivations:

As a result, reservoirs with growing geological and economical challenges are currently being explored and produced. This requires the application of new technologies and intelligent data and knowledge integration methods to achieve necessary efficiency and E&P cost reduction.

While the technological expertise of each component (geology, geophysics, reservoir engineering) becomes increasingly interrelated, there is a need for better integration of different components into a comprehensive reservoir model, adequately capturing the uncertainty on key strategic reservoir parameters. The uncertainty quantification on such key parameters is required in any type of decision analysis.

The initiative will bring an integrated approach to reservoir modeling and reservoir management decision making by integrating the various components and achievements of its team members. As the turnaround time for a reservoir modeling team becomes increasingly shorter, management decisions on new wells or new fields become increasingly complex, given the huge stream of often imprecise and uncertain information.

We intend to combine the reservoir expert knowledge of the Stanford groups with soft computing tools of UC Berkeley groups. Expert knowledge is important in reservoir management, but is becoming increasingly complex, time consuming, and expensive. Moreover, expertise from various disciplines is required and needs to be integrated to provide a global solution.

Therefore, expert knowledge needs to be partially converted into artificial intelligence that can better handle the huge information stream and partially automate the process of modeling and decision-making. Integration of knowledge developed by each group will lead to new systems and work-flows for better, faster, and more efficient reservoir management in terms of decision making.

Decision making under uncertainty can only work if each uncertainty component has been properly accounted for in a global solution to reservoir modeling and management. We believe our current team is unique in the world of reservoir modeling in tackling this problem.

P. Wong, F. Aminzadeh, and M. Nikravesh, "Soft Computing for Reservoir Characterization and Modeling," Studies in Fuzziness and Soft Computing, Physica-Verlag/Springer-Verlag, Vol. 80, 2001.
M. Nikravesh, F. Aminzadeh, and L. A. Zadeh, Soft Computing and Intelligent Data Analysis in Oil Exploration, Elsevier (to appear).
M. Nikravesh and F. Aminzadeh, Reservoir Attributes and Properties for Intelligent Detection (RAPID), Elsevier (to appear).
M. Nikravesh, F. Aminzadeh, and L. A. Zadeh, "Soft Computing and Earth Sciences," Journal of Petroleum Science and Engineering, Special Issue I, Vol. 29, No. 3-4, May 2001 and Special Issue II, December 2001.
P. Wong and M. Nikravesh, "Soft Computing for Reservoir Characterization and Modeling," Special Issue, Journal of Petroleum Geology, December 2001.
2Professor, Stanford University
3Professor, Stanford University
4Lawrence Berkeley National Laboratory
5Fact Inc.

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