ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Economic Geology
Volume 13 - 2025 | doi: 10.3389/feart.2025.1595574
A Combined Approach to Lithology Identification Using Reinforcement Learning and Transformer Algorithms
Provisionally accepted- 1China National Logging Corporation, Xi’an, China
- 2Institute of Geology, No. 3 oil production plant, Changqing Oil Field, Yinchuan, China
- 3Henan Polytechnic University, Jiaozuo, Henan Province, China
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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.
Keywords: Well logging interpretation, Lithology identification, reinforcement learning, transformer algorithm, time series relationship
Received: 20 Mar 2025; Accepted: 02 Oct 2025.
Copyright: © 2025 Li, Li, Qi, Wang, Zhan and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qingchao Li, liqingchao2020@hpu.edu.cn
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