A Supervised Workflow for Predicting Lithofacies in Complex and Heterogeneous Tight Sandstone Reservoirs: A Data-Driven Approach Using Clustering and Classification Models

Muhammad Ali

Abstract


This study introduces a novel supervised workflow for predicting lithofacies in complex, heterogeneous tight sandstone reservoirs with intercalated facies. Using a two-information criteria clustering method, six distinct facies are identified, providing an unbiased, data-driven alternative to manual approaches. Among classification models, Gaussian Process Classification (GPC) outperforms others, including Support Vector Machine (SVM) and Artificial Neural Network (ANN), with Random Forest (RF) performing less effectively. GPC accurately predicts lithofacies in testing data and is assessed for similarity accuracy. Predicted lithofacies are integrated into acoustic impedance versus velocity ratio cross plots, resulting in 2D probability density functions. These, combined with depth data, feed a neural network to forecast synthetic gamma-ray log responses. Results show strong agreement between measured and predicted gamma-ray logs (R2 = 0.978) and nearly identical log trends. Additionally, the predicted lithofacies are classified using inverted impedance and velocity ratio volumes, yielding a facies prediction volume that aligns well with well site lithofacies classification, even without core data

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DOI: http://dx.doi.org/10.17072/psu.geol.22.4.342

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