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    A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran

    132615_132615.pdf (563.4Kb)
    Access Status
    Open access
    Authors
    Kadkhodaie Ilkhchi, A.
    Rahimpour-Bonab, H.
    Rezaee, M. Reza
    Date
    2009
    Type
    Journal Article
    
    Metadata
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    Citation
    Kadkhodaie Ilkhchi, Ali and Rahimpour-Bonab, Hossain and Rezaee, M. Reza. 2009. A Committee Machine with Intelligent Systems for Estimation of Total Organic Carbon Content from Petrophysical Data: an Example from Kangan and Dalan Reservoirs in South Pars Gas Field, Iran. Computers and Geosciences. 35 (3): pp. 459-474.
    Source Title
    Computers and Geosciences
    DOI
    10.1016/j.cageo.2007.12.007
    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/398/description#description. Copyright © 2009 Elsevier B.V. All rights reserved

    URI
    http://hdl.handle.net/20.500.11937/24000
    Collection
    • Curtin Research Publications
    Abstract

    Total Organic Carbon (TOC) content present in reservoir rocks is one of the important parameters which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon bearing units. In general, organic rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher gamma-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into eighty-seven training sets to build the CMIS model and thirty-seven testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.

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