A dual attention-based deep learning model for lithology identification while drilling

Lithology identification while drilling technology can obtain rock information in real-time. However, traditional lithology identification models often face limitations in feature extraction and adaptability to complex geological conditions, limiting their accuracy in challenging environments. To address these challenges, a deep learning model for lithology identification while drilling is proposed. The proposed model introduces a dual attention mechanism in the long short-term memory (LSTM) network, effectively enhancing the ability to capture spatial and channel dimension information. Subsequently, the crayfish optimization algorithm (COA) is applied to optimize the model network structure, thereby enhancing its lithology identification capability. Laboratory test results demonstrate that the proposed model achieves 97.15% accuracy on the testing set, significantly outperforming the traditional support vector machine (SVM) method (81.77%). Field tests under actual drilling conditions demonstrate an average accuracy of 91.96% for the proposed model, representing a 14.31% improvement over the LSTM model alone. The proposed model demonstrates robust adaptability and generalization ability across diverse operational scenarios. This research offers reliable technical support for lithology identification while drilling.