Accurate net pay classification is essential in hydrocarbon resource volumetric calculation. However, there is no universal methodology developed for its evaluation hence the existence of many incongruent views on its application since it is data-driven and differs for each reservoir. This research incorporates machine learning and data analytics in predicting net pay, intending to reduce uncertainties associated with the net-pay classification. Log analysis was performed to determine the cut-offs for sonic, neutron, density, and gamma-ray using unsupervised learning and data analytics. The log cut-offs were calculated with petrophysical properties; shale volume, water saturation, permeability, and porosity. A Bayesian Optimised Extreme Gradient Boosting (Bayes Opt-XGBoost) model was applied to predict the petrophysical properties using five wireline logs. The model's performance and a computational function in classifying net reservoir resulted in an accuracy of 0.93, a combined precision of 0.94, a combined recall of 0.92, and a combined F1-score of 0.93. The model and methodology were deployed on a new well for validation. The classification of net reservoir zones via the proposed data analytics method, Bayes Opt-XGBoost predicted petrophysical properties, and computational function code matched mobility drawdown test data for the well. These results indicate that the developed methodology and machine learning model can work for other reservoirs since the additional computational function code can be manipulated for any data-driven estimated cut-offs. This developed approach can determine net reservoir and net pay zones in any sandstone reservoir.
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