Soft computing and intelligent data analysis in oil exploration

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, petr...

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Bibliographic Details
Other Authors Nikravesh, Masoud, 1959-, Aminzadeh, Fred, Zadeh, Lotfi A.
Format Electronic eBook
LanguageEnglish
Published Amsterdam ; Boston : Elsevier, 2003.
Edition1st ed.
SeriesDevelopments in petroleum science ; 51.
Subjects
Petroleum > Prospecting > Data processing.
Hydrocarbon reservoirs > Computer simulation.
Soft computing.
elektronické knihy
electronic books
Online AccessFull text
ISBN9780444506856
9780080541327
ISSN0376-7361 ;
Physical Description1 online zdroj (xxix, 724 pages) : illustrations (some color) : digital, HTML and PDF files.

Cover

Table of Contents:
  • Cover
  • Contents
  • Foreword
  • Preface
  • About the Editors
  • List of Contributors
  • Part 1: Introduction: Fundamentals of Soft Computing
  • CHAPTER 1. SOFT COMPUTING FOR INTELLIGENT RESERVOIR CHARACTERIZATION AND MODELING
  • Abstract
  • 1. Introduction
  • 2. The role of soft computing techniques for intelligent reservoir characterization and exploration
  • 3. Artificial neural network and geoscience applications of artificial neural networks for exploration
  • 4. Fuzzy logic
  • 5. Genetics algorithms
  • 6. Principal component analysis and wavelet
  • 7. Intelligent reservoir characterization
  • 8. Fractured reservoir characterization
  • 9. Future trends and conclusions
  • Appendix A.A basic primer on neural network and fuzzy logic terminology
  • Appendix B. Neural networks
  • Appendix C. Modified Levenberge-Marquardt technique
  • Appendix D. Neuro-fuzzy models
  • Appendix E. K-means clustering
  • Appendix F. Fuzzy c-means clustering
  • Appendix G. Neural network clustering
  • -References
  • CHAPTER 2. FUZZY LOGIC
  • Abstract
  • CHAPTER 3. INTRODUCTION TO USING GENETIC ALGORITHMS
  • 1. Introduction
  • 2. Background to Genetic Algorithms
  • 3. Design of a Genetic Algorithm
  • 4. Conclusions
  • References
  • CHAPTER 4. HEURISTIC APPROACHES TO COMBINATORIAL OPTIMIZATION
  • 1. Introduction
  • 2. Decision variables
  • 3. Properties of the objective function
  • 4. Heuristic techniques
  • References
  • CHAPTER 5. INTRODUCTION TO GEOSTATISTICS
  • 1. Introduction
  • 2. Random variables
  • 3. Covariance and spatial variability
  • 4. Kriging
  • 5. Stochastic simulations
  • References
  • CHAPTER 6. GEOSTATISTICS: FROM PATTERN RECOGNITION TO PATTERN REPRODUCTION
  • 1. Introduction
  • 2. The decision of stationarity
  • 3. The multi-Gaussian approach to spatial estimation and simulation
  • 4. Spatial interpolation with kriging
  • 5. Beyond two-point models: multiple-point geostatistics
  • 6. Conclusions
  • 7. Glossary
  • References
  • -Part 2: Geophysical Analysis and Interpretation
  • CHAPTER 7. MINING AND FUSION OF PETROLEUM DATA WITH FUZZY LOGIC AND NEURAL NETWORK AGENTS
  • Abstract
  • 1. Introduction
  • 2. Neural network and nonlinear mapping
  • 3. Neuro-fuzzy model for rule extraction
  • 4. Conclusion
  • Appendix A. Basic primer on neural network and fuzzy logic terminology
  • Appendix B. Neural networks
  • Appendix C. Modified Levenberge-Marquardt technique
  • Appendix D. Neuro-fuzzy models
  • Appendix E. K-means clustering
  • References
  • CHAPTER 8. TIME LAPSE SEISMIC AS A COMPLEMENTARY TOOL FOR IN-FILL DRILLING
  • Abstract
  • 1. Introduction
  • 2. Feasibility study
  • 3. 3D seismic data sets
  • 4. 4D seismic analysis approach
  • 5. Seismic modeling of various flow scenarios
  • 6. 4D seismic for detecting fluid movement
  • 7. 4D seismic for detecting pore pressure changes
  • 8. 4D seismic and interaction with the drilling program
  • 9. Conclusions
  • Acknowledgements
  • References
  • CHAPTER.

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