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    Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, anexample from a North American Shale Gas Reservoir

    Access Status
    Fulltext not available
    Authors
    Rezaee, M. Reza
    Slatt, R.
    Sigal, R.
    Date
    2007
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Rezaee, M. Reza and Slatt, Roger and Sigal, Richard. 2007. Shale Gas Rock Properties Prediction using Artificial Neural Network Technique and Multi Regression Analysis, an example from a North American Shale Gas Reservoir. ASEG Extended Abstracts 2007 1: 1-4.
    Source Title
    ASEG Extended Abstracts
    Additional URLs
    http://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm.
    ISSN
    08123985
    Faculty
    Department of Petroleum Engineering
    Faculty of Science & Engineering
    Remarks

    The definitive version of this article can be found at http://www.publish.csiro.au/nid/267/paper/ASEG2007ab120.htm.

    This article was orginally published by CSIRO Publishing

    URI
    http://hdl.handle.net/20.500.11937/11257
    Collection
    • Curtin Research Publications
    Abstract

    Estimation of reservoir parameters has always been a challenge for shale gas reservoirs. This study has concentrated on neural network technique and multiple regression analysis to predict reservoir properties including porosity, permeability, fluid saturation and total organic carbon content from conventional wireline log data for a large North American shale gas reservoir. More than 262 core analysis data from 3 wells were used as "target" and "response" for neural network and multiple regression analysis. Common log data available in three wells including GR, SP, RHOB, NPHI, DT and deep resistivity were used as "input" and "predictor".This study shows that reservoir parameters could be better estimated using the neural network technique than through multiple regression. The neural network method had a correlation coefficient greater than 80% for most of the parameters. Although providing a set of algorithms, multiple regression analysis was less successful for predicting reservoir parameters.

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