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    Intelligent approaches for the synthesis of petrophysical logs

    Access Status
    Fulltext not available
    Authors
    Rezaee, M. Reza
    Kadkhodaie-Ilkhchi, A.
    Alizadeh, P.
    Date
    2008
    Type
    Journal Article
    
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    Citation
    Rezaee, M. Reza and Kadkhodaie-Ilkhchi, Ali and Alizadeh, Pooya Mohammad. 2008. Intelligent approaches for the synthesis of petrophysical logs. Journal of Geophysics and Engineering 5 (1): 12-26.
    Source Title
    Journal of Geophysics and Engineering
    DOI
    10.1088/1742-2132/5/1/002
    Faculty
    Division of Resources and Environment
    Department of Petroleum Engineering
    URI
    http://hdl.handle.net/20.500.11937/20726
    Collection
    • Curtin Research Publications
    Abstract

    Log data are of prime importance in acquiring petrophysical data from hydrocarbon reservoirs. Reliable log analysis in a hydrocarbon reservoir requires a complete set of logs. For many reasons, such as incomplete logging in old wells, destruction of logs due to inappropriate data storage and measurement errors due to problems with logging apparatus or hole conditions, log suites are either incomplete or unreliable. In this study, fuzzy logic and artificial neural networks were used as intelligent tools to synthesize petrophysical logs including neutron, density, sonic and deep resistivity. The petrophysical data from two wells were used for constructing intelligent models in the Fahlian limestone reservoir, Southern Iran. A third well from the field was used to evaluate the reliability of the models. The results showed that fuzzy logic and artificial neural networks were successful in synthesizing wireline logs. The combination of the results obtained from fuzzy logic and neural networks in a simpleaveraging committee machine (CM) showed a significant improvement in the accuracy of theestimations. This committee machine performed better than fuzzy logic or the neural network model in the problem of estimating petrophysical properties from well logs.

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