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    A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field

    Access Status
    Fulltext not available
    Authors
    Kadkhodaie Ilkhchi, A.
    Rezaee, M. Reza
    Moallemi, A.
    Date
    2006
    Type
    Journal Article
    
    Metadata
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    Citation
    Kadkhodaie Ilkhchi, Ali and Rezaee, Mohammadreza and Moallemi, Ali. 2006. A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field. Journal of Geophysics and Engineering. 3: pp. 356-369.
    Source Title
    Journal of Geophysics and Engineering
    DOI
    10.1088/1742-2132/3/4/007
    ISSN
    17422132
    Faculty
    School of Chemical and Petroleum Engineering
    Department of Petroleum Engineering
    Faculty of Science and Engineering
    Remarks

    The link to the journal’s home page is: http://www.iop.org/EJ/jge

    Copyright © 2006 Institute of Physics and IOP Publishing Limited

    URI
    http://hdl.handle.net/20.500.11937/33386
    Collection
    • Curtin Research Publications
    Abstract

    Permeability and rock type are the most important rock properties which can be used as input parameters to build 3D petrophysical models of hydrocarbon reservoirs. These parameters are derived from core samples which may not be available for all boreholes, whereas, almost all boreholes have well log data. In this study, the importance of the fuzzy logic approach for prediction of rock type from well log responses was shown by using an example of the Vp to Vs ratio for lithology determination from crisp and fuzzy logic approaches. A fuzzy c-means clustering technique was used for rock type classification using porosity and permeability data. Then, based on the fuzzy possibility concept, an algorithm was prepared to estimate clustering derived rock types from well log data. Permeability was modelled and predicted using a Takagi-Sugeno fuzzy inference system. Then a back propagation neural network was applied to verify fuzzy results for permeability modelling. For this purpose, three wells of the Iran offshore gas field were chosen for the construction of intelligent models of the reservoir, and a forth well was used as a test well to evaluate the reliability of the models. The results of this study show that fuzzy logic approach was successful for the prediction of permeability and rock types in the Iran offshore gas field.

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