Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs
Access Status
Open access
Date
2025Supervisor
Ziad Bennour
Saaveethya Sivakumar
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Curtin Malaysia
School
Curtin Malaysia
Collection
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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