Stratigraphic Sequence Correlation of Well Logs Using KANFormer Enhanced With OMP-Based Data Augmentation

The stratigraphic sequence correlation of well logs is a crucial step for reservoir investigation. The manual interpretation is a commonly used method, and the experience of geophysicists influences it. Recently, deep learning (DL) has been introduced for automated stratigraphic correlation of well logs. However, building a complete training dataset with high-quality labels is challenging and time-consuming. In this study, we propose an automated stratigraphic sequence correlation method called OMP-KANFormer, which integrates the Kolmogorov-Arnold network with the transformer (KANFormer) model with orthogonal matching pursuit (OMP)-based data augmentation. To solve the problem of training dataset generation, we first propose a data augmentation algorithm based on OMP to simulate waveform features and generate a large number of synthetic well logs as training data. We suggest combining the KANFormer to accurately segment the stratigraphic. The KAN effectively models complex nonlinear relationships, while the transformer excels at capturing long-range dependencies in the features of well logs. Finally, we apply the proposed data augmentation method and KANFormer to a well-log dataset, demonstrating their validity and effectiveness via a comprehensive ablation study and comparisons with widely used SegNet and U-Net.