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

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.