Enhanced Lithofacies Classi?cation of Tight Sandstone Reservoirs Using a Hybrid CNN-GRU Model with BSMOTE and Heat Kernel Imputation
Li Pan, Meng Jia-bing, Li Jun*, Chen Qi-jing
1. Institute of Disaster Prevention, Hebei 101601, China.
2. Hebei Province University Smart Emergency Application Technology Research and Development Center, Hebei 065201, China.
Abstract 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.
Fund: This research was supported by the Langfang Science and Technology Program with self-raised funds under the project “Application of Deep Learning-Based Joint Well-Seismic Analysis in Lithology Prediction” (Project No. 2024011013), and by the Science and Technology Innovation Program for Postgraduate students in IDP subsidized by Fundamental Research Funds for the Central Universities, under the project “Research on CNN Algorithm Enhanced by Physical Information for Lithofacies Prediction in Tight Sandstone Reservoirs” (Project No. ZY20250328).
About author: Li Pan, Ph.D., Associate Professor at the Institute of Disaster Prevention. Awarded a Ph.D. from Jilin University in 2009. Dedicated to research in oil and gas exploration, reservoir prediction, and related fields
Cite this article:
. Enhanced Lithofacies Classi?cation of Tight Sandstone Reservoirs Using a Hybrid CNN-GRU Model with BSMOTE and Heat Kernel Imputation[J]. APPLIED GEOPHYSICS, 2025, 22(4): 1141-1157.
2025, Vol. 22 
