Abstract
Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Nikravesh, M., F. Aminzadeh, and L. A. Zadeh (2003a), Intelligent Data Analysis for Oil Exploration, Developments in Petroleum Science, 51; ISBN: 0-444-50685-3, Elsevier (March 2003).
Nikravesh, M., L.A. Zadeh and V. Korotkikh (2003b), Fuzzy Partial Differential Equations and Relational Equations: Reservoir Characterization and Modeling, to be published in the Series Studies in Fuzziness and Soft Computing, Physica-Verlag, Springer (Expected, August 2003).
Nikravesh, M., F. Aminzadeh and L.A. Zadeh (2001a), Soft Computing and Earth Sciences (Part 2), Journal of Petroleum Science and Engineering, Volume 31, Issue 2-4, January 2001; Special Issue.
Nikravesh, M., F. Aminzadeh and L.A. Zadeh (2001b), Soft Computing and Earth Sciences, Journal of Petroleum Science and Engineering, Volume 29, Issue 3-4, May 2001; Special Issue, 2001b.
Nikravesh, M., R. D. Adams and R. A. Levey (2001c), Soft computing: tools for intelligent reservoir characterization (IRESC) and optimum well placement (OWP), Journal of Petroleum Science and Engineering, Volume 29, Issues 3-4, May 2001, Pages 239–262.
Nikravesh, M. and F. Aminzadeh (2001), Mining and fusion of petroleum data with fuzzy logic and neural network agents, Journal of Petroleum Science and Engineering, Volume 29, Issues 3-4, May 2001, Pages 221–238.
Wong, P. M., F Aminzadeh, and M. Nikravesh (2001), Soft Computing for Reservoir Characterization, in Studies in Fuzziness, Physica Verlag, Germany
P.M. Wong and M. Nikravesh (2001), A thematic issue on “Field Applications of Intelligent Computing Techniques,” Journal of Petroleum Geology, 24(4), 379–476; Special Issue.
Zadeh, L. A. (1999), From Computing with Numbers to Computing with Words – From Manipulation of Measurements to Manipulation of Perceptions, IEEE Transactions on Circuits and Systems, 45, 105–119, 1999.
Zadeh, L. and Kacprzyk, J. (eds.) (1999a), Computing With Words in Information/Intelligent Systems 1: Foundations, Physica-Verlag, Germany (1999a).
Zadeh, L. and Kacprzyk, J. (eds.) (1999b), Computing With Words in Information/Intelligent Systems 2: Applications, Physica-Verlag, Germany (1999b).
Zadeh, L.A. (1996) Fuzzy Logic = Computing with Words, IEEE Trans. on Fuzzy Systems (1996) 4, 103–111.
Zadeh, L. A. and M. Nikravesh (2002), Perception-Based Intelligent Decision Systems, AINS; ONR Summer 2002 Program Review, 30 July-1 August, UCLA.
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nikravesh, M. (2008). Soft Computing for Intelligent Reservoir Characterization and Decision Analysis. In: Forging New Frontiers: Fuzzy Pioneers II. Studies in Fuzziness and Soft Computing, vol 218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73185-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-73185-6_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73184-9
Online ISBN: 978-3-540-73185-6
eBook Packages: EngineeringEngineering (R0)