Nikravesh M., Aminzadeh F., Zadeh L.A. (Eds.) Soft Computing and Intelligent Data Analysis in Oil Exploration

Elsevier Science, Amsterdam, 2003, 744 pages. — ISBN: 0444506853, 9780444506856,
9780080541327. — (Developments in Petroleum Science, Volume 51).This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects. The book 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.Introduction: fundamentals of soft computing
Soft computing for intelligent reservoir characterization and modeling
Fuzzy logic
Introduction to using genetic algorithms
Heuristic approaches to combinatorial optimization
Introduction to geostatistics
Geostatistics: from pattern recognition to pattern reproductionGeophysical analysis and interpretation
Mining and fusion of petroleum data with fuzzy logic and neural network agents
Time lapse seismic as a complementary tool for in-fill drilling
Improving seismic chimney detection using directional attributes
Modeling a fluvial reservoir with multipoint statistics and principal componentsComputational geology
The role of fuzzy logic in sedimentology and stratigraphic models
Spatial contiguity analysis. A method for describing spatial structures of seismic data
Litho-seismic data handling for hydrocarbon reservoir estimate: fuzzy system modeling approach
Neural vector quantization for geobody detection and static multivariate upscaling
High resolution reservoir heterogeneity characterization using recognition technology
Extending the use of linguistic petrographical descriptions to characterise core porosityReservoir and production engineering
Using genetic algorithms for reservoir characterisation
Applying soft computing methods to improve the computational tractability of a subsurface simulation-optimization problem
Neural network prediction of permeability in the el garia formation, ashtart oilfield, offshore tunisia
Enhancing gas storage wells deuverabhjty using intelligent systemsIntegrated field studies
Soft computing: tools for intelligent reservoir characterization and optimum well placement
Combining geological information with seismic and production data
Interpreting biostratigraphical data using fuzzy logic: the identification of regional mudstones within the fleming field, UK north sea
Geostatistical characterization of the carpinteria field, California
Integrated fractured reservoir characterization using neural networks and fuzzy logic: three case studiesGeneral applications
Virtual magnetic resonance logs, a low cost reservoir description tool
Artificial neural networks linked to gis
Intelligent computing techniques for complex systems
Multivariate statistical techniques including pca and rule based systems for well log correlation