Machine learning-based seismic attribute analysis for porosity prediction and lithofacies classification in clastic hydrocarbon reservoirs

Abstract

Accurate prediction of reservoir properties such as porosity, permeability, and lithofacies distribution is essential for reliable hydrocarbon reserve estimation, well placement, and field development planning. Conventional deterministic methods for relating seismic response to reservoir properties are limited by the nonlinear and multivariate nature of the seismic–petrophysical relationship, and single-attribute correlations frequently fail to capture the complexity of clastic reservoir systems. This study presents a machine learning-assisted workflow that integrates multi-attribute seismic analysis with wireline log data to predict porosity and classify lithofacies within a clastic hydrocarbon reservoir. A suite of seismic attributes including root-mean-square (RMS) amplitude, sweetness, instantaneous frequency, coherence, envelope amplitude, and acoustic impedance derived from post-stack inversion was extracted and calibrated against log measurements at control wells. Feature selection based on correlation ranking and recursive feature elimination identified the most informative attributes, and three supervised learning models random forest (RF), support vector regression (SVR), and a feed-forward artificial neural network (ANN) were trained for porosity prediction. A separate classification stage using random forest and gradient boosting was applied for lithofacies discrimination. The results demonstrate that the ANN and RF models substantially outperform single- and multi-attribute linear regression, achieving a coefficient of determination (R²) of 0.86–0.88 for porosity prediction at blind-test wells, while the ensemble classifier reached an overall lithofacies classification accuracy of 88%. Integrating machine learning with multi-attribute seismic analysis reduces prediction uncertainty and produces spatially continuous reservoir property volumes that support more reliable reservoir characterization and lower-risk exploration decision-making.