Soft Computing and Intelligent Data Analysis in Oil Exploration - 1st Edition - ISBN: 9780444506856, 9780080541327

Soft Computing and Intelligent Data Analysis in Oil Exploration, Volume 51

1st Edition

Editors: M. Nikravesh L.A. Zadeh Fred Aminzadeh
eBook ISBN: 9780080541327
Hardcover ISBN: 9780444506856
Imprint: Elsevier Science
Published Date: 22nd April 2003
Page Count: 754
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Table of Contents

Foreword. Preface. About the Editors. List of Contributors. Part 1. Introduction: Fundamentals of Soft Computing. 1. Soft computing for intelligent reservoir characterization and modeling (M. Nikravesh, F. Aminzadeh). 2. Fuzzy logic (G.J.Klir). 3. Introduction to using genetic algorithms (J.N. Carter). 4. Heuristic approaches to combinatorial optimazation (V.M. Johnson). 5. Introduction to geostatistics (R.J. Pawar). 6. Geostatistics: From pattern recognition to pattern reproduction (J. Caers). Part 2. Geophysical Analysis and Interpretation. 7. Mining and fusion of petroleum date with fuzzy logic and neural network agents (M. Nikravesh, F.Aminzadeh). 8. Time lapse seismic as a complementary tool for in-fill drilling (M. Landrø, L.K. Strønen et al.). 9. Improving seismic chimney detection using directional attributes (K.M. Tingdahl). 10. Modeling a fluvial reservoir with multipoint statistics and principal components (P.M.Wong, S.A.R. Shibli). Part 3. Computational Geology. 11. The role of fuzzy logic in sedimentology and stratigraphic models (R.V. Demicco, G.J.Klir, R. Belohlavek). 12. Spatial contiguity analysis. A method for describing spatial structures of seismic data (A. Faraj, F. Cailly). 13. Litho-seismic data handling for hydrocarbon reservoir estimate: Fuzzy system modeling approach (E.A. Shyllon). 14. Neural vector quantization for geobody detection and static multivariate upscaling (A. Chawathé, M. Ye). 15. High resolution reservoir heterogeneity characterization using recognition technology (M. Hassibi, I. Ershaghi, F. Aminzadeh). 16. Extending the use of linguistic petrographical descriptions to characterise core porosity (T.D. Gedeon, P.M. Wong et al.). Part 4. Reservoir and Production Engineering. 17. Using genetic algorithms for reservoir characterisation (C. Romero, J.N. Carter). 18. Applying soft computing methods to improve the computational tractability of a subsurface simulation-optimization problem (V.M. Johnson, L.L. Rogers). 19. Neural network prediction of permeability in the El Garia formation, Ashtart oilfield, offshore Tunisia (J.H. Ligtenberg, A.G. Wansink). 20. Using RBF network to model the reservoir fluid behavior of black oil systems (A.M. Elsharkawy). 21. Enhancing gas storage wells deliverability using intelligent systems (S.D. Mohaghegh). Part 5. Integrated field studies. 22. Soft computing: Tools for intelligent reservoir characterization and optimum well placement (M. Nikravesh, R.D. Adams, R.A. Levey). 23. Combining geological information with seismic and production data (J. Caers, S. Srinivasan). 24. Interpreting biostratigraphical data using fuzzy logic: The identification of regional mudstones within the Fleming field, UK North Sea (M.I. Wakefield, R.J. Cook et al.). 25. Geostatistical characterization of the Carpinteria field, California (R.J. Pawar, E.B. Edwards, E.M. Whitney). 26. Integrated fractured reservoir characterization using neural networks and fuzzy logic: Three case studies (A.M. Zellou, A. Quenes). Part 6. General Applications. 27. Virtual magnetic resonance logs, a low cost reservoir description tool (S.D. Mohaghegh). 28. Artificial neural networks linked to GIS (Y. Yang, M.S. Rosenbaum). 29. Intelligent computing techniques for complex systems (M. Nikravesh). 30. Multivariate statistical techniques including PCA and rule based systems for well log correlation (J.-S Lim). Author Index. Subject Index.


This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects.

It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering. It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties (porosity, sand thickness, lithology, fluid), seismic processing, seismic and bio stratigraphy, time lapse seismic and core analysis.

There is a good balance between introducing soft computing and geostatistics methodologies that are not routinely used in the petroleum industry and various applications areas. The book can be used by many practitioners such as processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and technology application professionals. It will also be of interest to academics to assess the importance of, and contribute to, R&D efforts in relevant areas.


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@from:P.H.H. Nelson @qu:...I heartily congratulate them on the outstanding job they have done in putting this book together. ...of great value to all those in the petroleum industry who would advance oil- and gas-finding, and production, into the twenty-first century. @source:Petroleum Geoscience

About the Editors

M. Nikravesh Editor

Affiliations and Expertise

BISC Program, Electrical Engineering and Computer Sciences Department, University of California, Berkeley, CA, USA

L.A. Zadeh Editor

Affiliations and Expertise

BISC Program, Electrical Engineering and Computer Sciences Department, University of California, Berkeley, CA, USA

Fred Aminzadeh Editor

Affiliations and Expertise

Professor of Petroleum and Electrical Engineering, University of Southern California, Los Angeles