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AAPG Bulletin

Abstract

AAPG Bulletin, V. 87, No. 7 (July 2003),

P. 1223-1240.

Copyright copy2003. The American Association of Petroleum Geologists. All rights reserved.

A fuzzy logic approach for the estimation of facies from wire-line logs

M. M. Saggaf,1 Ed L. Nebrija2

1Saudi Arabian Oil Company, Saudi Arabia; email: [email protected]
2Saudi Arabian Oil Company, Saudi Arabia; email: [email protected]

AUTHORS

Muhammad Saggaf has a B.S. degree from King Fahd University of Petroleum and Minerals and an M.S. degree and a Ph.D. from the Massachusetts Institute of Technology. He joined Saudi Aramco in 1989 and has worked in exploration, reservoir characterization, and research and development. Along the way, he was awarded the Technology Achievement Award (1997), Creative Contribution Award (2001), Society of Exploration Geophysicists J. Clarence Karcher Award (2001), and a patent for Fractal Deconvolution (2002). He is a member of the Society of Exploration Geophysicists and AAPG, an elected member of the European Academy of Sciences, and president of the Dhahran Geoscience Society.

Ed Nebrija has a B.S. degree in physics from the University of the Philippines and a Ph.D. in geophysics from the University of Wisconsin, Madison. From 1979 to 1992, he worked for Shell Oil (United States) in various capacities as marine seismic party chief, explorationist, and reservoir geophysicist. He is currently a consultant geophysicist at Saudi Aramco, specializing in the reservoir characterization of offshore and onshore oil and gas fields. He is a member of the Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers.

ACKNOWLEDGMENTS

We would like to thank Saudi Arabian Oil (Saudi Aramco) for supporting this research and for granting us permission for its publication.

ABSTRACT

A method based on fuzzy logic inference can be used to identify lithological and depositional facies from wire-line logs. Fuzzy logic is inherently well suited to characterizing vague and imperfectly defined knowledge, a situation encountered in most geological data. It can thus yield models that are simpler and more robust than those based on crisp logic. The method is simple, easy to comprehend, and robust. It also generates several confidence measures that can be used to assess the quality of the analysis. Several enhancements, including static and dynamic constraints, are discussed. The technique is tested here by applying it to predict the depositional facies of a cored well in a marine carbonate environment and comparing the output with the facies derived from core analysis. The two show considerable agreement, which indicates that this method can be an effective means of predicting the facies of uncored wells from their logs. The method has advantages when contrasted with other techniques that rely on multivariate statistics and neural networks. Compared to those techniques, this method is simpler, easier to retrain, more reproducible, noniterative, and more computer efficient.

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