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    Petrophysical data prediction from seismic attributes using committee fuzzy inference system

    133054_petrophysical.pdf (495.2Kb)
    Access Status
    Open access
    Authors
    Kadkhodaie Ilkhchi, A.
    Rezaee, M. Reza
    Rahimpour-Bonab, H.
    Chehrazi, A.
    Date
    2009
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Kadkhodaie Ilkhchi, Ali and Rezaee, M. Reza and Rahimpour-Bonab, Hossain and Chehrazi, Ali. 2009. Petrophysical data prediction from seismic attributes using committee fuzzy inference system. Computers & Geosciences. 35 (12): pp. 2314-2330.
    Source Title
    Computers & Geosciences
    DOI
    10.1016/j.cageo.2009.04.010
    ISSN
    00983004
    Faculty
    Department of Petroleum Engineering
    School of Engineering
    Faculty of Science and Engineering
    Remarks

    The link to the journal’s home page is:http://www.elsevier.com/wps/find/journaldescription.cws_home/622273/description#description. Copyright © 2009 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/45044
    Collection
    • Curtin Research Publications
    Abstract

    This study presents an intelligent model based on fuzzy systems for making aquantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various Fuzzy Inference Systems (FIS), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a Committee Fuzzy Inference System (CFIS) is constructed using a hybrid Genetic Algorithms-Pattern Search (GA-PS) technique. The inputs of the CFIS model are the output averages of theFIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a Probabilistic Neural Network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.

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