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Computational Intelligence for Geosciences and Oil Exploration

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Forging New Frontiers: Fuzzy Pioneers I

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 217))

Abstract

In this overview paper, we highlight role of Soft Computing techniques for intelligent reservoir characterization and exploration, seismic data processing and characterization, well logging, reservoir mapping and engineering. 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.

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  1. Computer Sciences Division, EECS Department and Imaging and Informatics Group-LBNL, BISC Program, University of California, Berkeley, CA 94720, USA

    Masoud Nikravesh

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  1. Department of Electrical Engineering and Computer Science – EECS, University of California Berkeley, 94720 Berkeley, USA

    Masoud Nikravesh

  2. PAN Warszawa Systems Research Institute, Newelska 6, 01-447 Warszawa, Poland

    Janusz Kacprzyk

  3. Department of Electrical Engineering and Computer Science(EECS), University of California Berkeley, Soda Hall 729, 94720-1776 Berkeley, USA

    Lofti A. Zadeh

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© 2007 Springer-Verlag Berlin Heidelberg

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Nikravesh, M. (2007). Computational Intelligence for Geosciences and Oil Exploration. In: Nikravesh, M., Kacprzyk, J., Zadeh, L.A. (eds) Forging New Frontiers: Fuzzy Pioneers I. Studies in Fuzziness and Soft Computing, vol 217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73182-5_14

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  • DOI: https://doi.org/10.1007/978-3-540-73182-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73181-8

  • Online ISBN: 978-3-540-73182-5

  • eBook Packages: EngineeringEngineering (R0)

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