An Interpretable Deep Transfer Learning Approach for Drilling Operation State Identification

Accurate identification of drilling operation states is essential for improving drilling efficiency and operational safety. However, existing methods often suffer from limited temporal feature extraction capability, weak cross-well generalization, and insufficient model interpretability. To address these issues, this study proposes a drilling-state recognition framework based on MultiHead-BiLSTM and low-rank adaptation (LoRA) transfer learning. The MultiHead-BiLSTM model combines multi-head attention with bidirectional long short-term memory to capture both critical temporal segments and global sequential dependencies in drilling time series data. To improve cross-well adaptability while reducing training computational cost, a parameter-efficient LoRA fine-tuning strategy is introduced within the transfer learning framework. In addition, SHAP-based feature attribution and attention visualization are employed to enhance model interpretability. Experimental results show that the proposed method achieves an accuracy of 95.11% and an F1-score of 94.00%, outperforming LSTM, GRU, BiLSTM, and Transformer baselines. The LoRA-based transfer strategy reduces the cross-well error rate to 1.91%, compared with 8.79% for direct transfer and 4.48–5.39% for partial-layer freezing methods. Interpretability analysis qualitatively suggests that bit depth, weight on bit, and block position contribute strongly to drilling-state discrimination, while attention visualization qualitatively suggests that the model tends to focus on operational transition periods. The proposed framework provides an effective and computationally efficient solution for intelligent drilling-state recognition and cross-well deployment.