Stratigraphic Correlation of Well Logs Using Geology-Informed Deep Learning Networks

Stratigraphic correlation plays a crucial role in reservoir characterization. However, it is often time-consuming and heavily dependent on geological expertise. To address this issue, we propose a novel method called CMT-enhanced Hiformer, which integrates convolutional neural networks meet vision transformers (CMT) and hierarchical multi-scale representations using transformers (Hiformer). First, the architecture of CMT-enhanced Hiformer fuses the advantages of convolutional neural networks and transformers, effectively extracting complex features from well logs and capturing both local and global dependencies via a well-designed attention mechanism. Next, a geological constraint with regularization parameters is incorporated into the loss function. The new loss function promotes the accuracy of stratigraphic boundaries. The proposed method was validated using data from the Shuanghe oil field in central China. Specifically, the model achieved a maximum F1 score of 0.8857 and a precision of 0.8865 on the blind test dataset, demonstrating its robustness and high classification accuracy. Moreover, we conducted ablation studies and performed a detailed comparison with state-of-the-art deep learning models. The results demonstrate that the proposed method significantly improves the accuracy and efficiency of stratigraphic correlation.