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    Facies classification with different machine learning algorithm – An efficient artificial intelligence technique for improved classification

    Access Status
    Open access via publisher
    Authors
    Mandal, Partha Pratim
    Rezaee, Reza
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Mandal, P.P. and Rezaee, R. 2019. Facies classification with different machine learning algorithm – An efficient artificial intelligence technique for improved classification. Exploration Geophysics. 2019 (1).
    Source Title
    Exploration Geophysics
    DOI
    10.1080/22020586.2019.12072918
    ISSN
    0812-3985
    Faculty
    Faculty of Science and Engineering
    School
    WASM: Minerals, Energy and Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/89581
    Collection
    • Curtin Research Publications
    Abstract

    Accurate and precise identification of lithological facies is vital to understand geological variation in a proven reservoir. Four specific different machine learning (ML) classification algorithms are implemented to predict facies on an open dataset in the Panoma gas field in southwest Kansas, USA. The objective is improvement of facies classification accuracy with robust application of ML technique compared to previous published work on the same dataset. A total of 4,149 data samples are available for analysis with known facies from the core data where each sample point contains four or five measured properties (wire-line logs), and two derived geological properties (geological constraining variables). Facies classification is addressed with four well-known classification algorithm which are artificial neural network (ANN), support vector machine (SVM), decision trees and gaussian process classifier (GPC). High dimensionality, non-linear correlation and overlapping feature space of facies classes make the non-parametric method more suitable to handle complex datasets. Among the presented classifiers, ANN perform better relative to others on validation dataset. It is observed that our present approach of adding more input features, increasing number of training dataset and efficient implementation of algorithm have improved facies prediction accuracy significantly on a blind well.

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