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    Fracture density estimation from petrophysical log data using Adaptive Neuro-Fuzzy Inference System

    Access Status
    Fulltext not available
    Authors
    Kadkhodaie, Ali
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Ja’fari, A. and Kadkhodaie, A. and Shargi, Y. and Ghanavati, K. 2011. Fracture density estimation from petrophysical log data using Adaptive Neuro-Fuzzy Inference System. Journal of Geophysics and Engineering. 9 (2012): pp. 105-114.
    Source Title
    Journal of Geophysics and Engineering
    DOI
    10.1088/1742-2132/9/1/013
    School
    Department of Petroleum Engineering
    URI
    http://hdl.handle.net/20.500.11937/41064
    Collection
    • Curtin Research Publications
    Abstract

    Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.

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