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    Prediction of hydrocarbon reservoirs permeability using support vector machine

    Access Status
    Open access via publisher
    Authors
    Gholami, Raoof
    Shahraki, A.
    Jamali Paghaleh, M.
    Date
    2012
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Gholami, R. and Shahraki, A. and Jamali Paghaleh, M. 2012. Prediction of hydrocarbon reservoirs permeability using support vector machine. Mathematical Problems in Engineering. 2012.
    Source Title
    Mathematical Problems in Engineering
    DOI
    10.1155/2012/670723
    ISSN
    1024-123X
    School
    Curtin Sarawak
    URI
    http://hdl.handle.net/20.500.11937/24559
    Collection
    • Curtin Research Publications
    Abstract

    Permeability is a key parameter associated with the characterization of any hydrocarbon reservoir. In fact, it is not possible to have accurate solutions to many petroleum engineering problems without having accurate permeability value. The conventional methods for permeability determination are core analysis and well test techniques. These methods are very expensive and time consuming. Therefore, attempts have usually been carried out to use artificial neural network for identification of the relationship between the well log data and core permeability. In this way, recent works on artificial intelligence techniques have led to introduce a robust machine learning methodology called support vector machine. This paper aims to utilize the SVM for predicting the permeability of three gas wells in the Southern Pars field. Obtained results of SVM showed that the correlation coefficient between core and predicted permeability is 0.97 for testing dataset. Comparing the result of SVM with that of a general regression neural network (GRNN) revealed that the SVM approach is faster and more accurate than the GRNN in prediction of hydrocarbon reservoirs permeability. Copyright © 2012 R. Gholami et al.

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