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<p>Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies. Traditional core-based interpretation introduces subjectivity, while conventional deep learning models often fail to capture stratigraphic sequences effectively. To address these limitations, we propose a hybrid CNN–GRU framework that integrates spatial feature extraction and sequential modeling. Heat Kernel Imputation is applied to reconstruct missing log data, and Borderline SMOTE (BSMOTE) improves class balance by augmenting boundary-case minority samples. The CNN component extracts localized petrophysical features, and the GRU component captures depth-wise lithological transitions, to enable spatial-sequential feature fusion. Experiments on real-well datasets from tight sandstone reservoirs show that the proposed model achieves an average accuracy of 93.3% and a Macro F1-score of 0.934. It outperforms baseline models, including RF (87.8%), GBDT (81.8%), CNN-only (87.5%), and GRU-only (86.1%). Leave-one-well-out validation further confirms strong generalization ability. These results demonstrate that the proposed approach effectively addresses data imbalance and enhances classification robustness, offering a scalable and automated solution for lithofacies interpretation under complex geological conditions.</p>

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Enhanced Lithofacies Classification of Tight Sandstone Reservoirs Using a Hybrid CNN-GRU Model with BSMOTE and Heat Kernel Imputation

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Pan Li,
  • Jia-bing Meng,
  • Jun Li,
  • Qi-jing Chen

摘要

Accurate lithofacies classification in low-permeability sandstone reservoirs remains challenging due to class imbalance in well-log data and the difficulty of the modeling vertical lithological dependencies. Traditional core-based interpretation introduces subjectivity, while conventional deep learning models often fail to capture stratigraphic sequences effectively. To address these limitations, we propose a hybrid CNN–GRU framework that integrates spatial feature extraction and sequential modeling. Heat Kernel Imputation is applied to reconstruct missing log data, and Borderline SMOTE (BSMOTE) improves class balance by augmenting boundary-case minority samples. The CNN component extracts localized petrophysical features, and the GRU component captures depth-wise lithological transitions, to enable spatial-sequential feature fusion. Experiments on real-well datasets from tight sandstone reservoirs show that the proposed model achieves an average accuracy of 93.3% and a Macro F1-score of 0.934. It outperforms baseline models, including RF (87.8%), GBDT (81.8%), CNN-only (87.5%), and GRU-only (86.1%). Leave-one-well-out validation further confirms strong generalization ability. These results demonstrate that the proposed approach effectively addresses data imbalance and enhances classification robustness, offering a scalable and automated solution for lithofacies interpretation under complex geological conditions.