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<p>This study explores the application of machine learning for facies classification in complex sandstone formations with overlapping petrophysical features. Three boosting ensemble models, Random Forest, XGBoost and CatBoost, were compared with one non-ensemble supervised model, Support Vector Machine, and one unsupervised model, K Means. Synthetic data were generated using Latin Hypercube Sampling to expand the feature space, and feature selection was performed using Random Forest importance, Recursive Feature Elimination and Lasso regression. Principal Component Analysis was used to examine feature relationships and reduce dimensional complexity. Model performance was evaluated using classification accuracy, precision recall analysis, ROC curves and confusion matrices to assess both overall and facies-level prediction reliability. CatBoost achieved the highest cross validation accuracy at 95.4%, followed by XGBoost at 93.7%, Random Forest at 89.5% and Support Vector Machine at 85.6%. K-Means showed the lowest performance with 49.7% accuracy. The results show that ensemble and supervised models provided higher consistent and accurate classifications, especially for distinguishing subtle differences between similar facies. The results also highlight the role of synthetic data in improving model generalization and the value of combining multiple feature selection methods. These findings support the integration of machine learning and data augmentation as a practical and scalable workflow for improving facies prediction and reservoir characterization in geologically variable and data-limited settings.</p>

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Comparative machine learning facies prediction using ensemble boosting models and support vector machine versus unsupervised clustering

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月14日

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  • Muhammad Risha,
  • Paul Liu

摘要

This study explores the application of machine learning for facies classification in complex sandstone formations with overlapping petrophysical features. Three boosting ensemble models, Random Forest, XGBoost and CatBoost, were compared with one non-ensemble supervised model, Support Vector Machine, and one unsupervised model, K Means. Synthetic data were generated using Latin Hypercube Sampling to expand the feature space, and feature selection was performed using Random Forest importance, Recursive Feature Elimination and Lasso regression. Principal Component Analysis was used to examine feature relationships and reduce dimensional complexity. Model performance was evaluated using classification accuracy, precision recall analysis, ROC curves and confusion matrices to assess both overall and facies-level prediction reliability. CatBoost achieved the highest cross validation accuracy at 95.4%, followed by XGBoost at 93.7%, Random Forest at 89.5% and Support Vector Machine at 85.6%. K-Means showed the lowest performance with 49.7% accuracy. The results show that ensemble and supervised models provided higher consistent and accurate classifications, especially for distinguishing subtle differences between similar facies. The results also highlight the role of synthetic data in improving model generalization and the value of combining multiple feature selection methods. These findings support the integration of machine learning and data augmentation as a practical and scalable workflow for improving facies prediction and reservoir characterization in geologically variable and data-limited settings.