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    Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs

    Kohona Walawwe KOS 2025 Public.pdf (10.36Mb)
    Access Status
    Open access
    Authors
    Kohona Walawwe, Kushan Oshadi Sandunil
    Date
    2025
    Supervisor
    Ziad Bennour
    Saaveethya Sivakumar
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Curtin Malaysia
    School
    Curtin Malaysia
    URI
    http://hdl.handle.net/20.500.11937/97704
    Collection
    • Curtin Theses
    Abstract

    This research investigated the applicability of bagging and boosting ensemble machine learning algorithms in predicting petrophysical properties, namely porosity, permeability and water saturation which is a vital aspect in reservoir characterization. Further, an in-depth analysis was done on the effects of different hyperparameter optimization algorithms. The study successfully proposed stacking-based novel ensemble models to predict petrophysical properties of sandstone reservoirs where some models performed up to 97% in prediction accuracy.

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