A combined approach to lithology identification using reinforcement learning and transformer algorithms - Li, Xiongwei; Li, Pengfei; Qi, Huaiyan; Wang, Yuan; Zhan, Tianshu; Li, Qingchao (Frontiers in Earth Science, 2025)
Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification methods predominantly depend on manual interpretation of formation physical property data, which is inherently subjective and susceptible to inconsistency. To overcome these limitations, this study proposes a novel lithology identification framework that synergistically combines reinforcement learning (RL) for automated hyperparameter optimization and feature selection with a Transformer-based model capable of capturing complex temporal dependencies within large-scale well logging data. The RL agent systematically explores the hyperparameter and feature space to enhance model performance, while the Transformer encoder extracts meaningful sequential patterns essential for accurate lithology classification. Empirical evaluation on a dataset exceeding two million samples demonstrates that the proposed method achieves a prediction accuracy of 94.89%, evidencing its effectiveness and robustness. The results indicate that this approach can provide rapid, objective, and reliable lithology recognition in drilling environments, thereby facilitating improved operational efficiency and reduced costs.