Your new experience awaits. Try the new design now and help us make it even better

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
Xiongwei  LiXiongwei Li1Pengfei  LiPengfei Li1Huaiyan  QiHuaiyan Qi2Yuan  WangYuan Wang1Tianshu  ZhanTianshu Zhan1Qingchao  LiQingchao Li3*
  • 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

The final, formatted version of the article will be published soon.

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

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.