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ORIGINAL RESEARCH article

Front. Earth Sci.

Sec. Georeservoirs

Volume 13 - 2025 | doi: 10.3389/feart.2025.1662760

This article is part of the Research TopicAdvances in Accumulation Conditions of Unconventional Oil and Gas Resources in Complicated Structure AreasView all 8 articles

Geological information-driven deep learning for lithology identification from well logs

Provisionally accepted
Luoyuan  ChenLuoyuan Chen1,2Xingjian  WangXingjian Wang1,2*Zhanbo  LiuZhanbo Liu2
  • 1Chengdu University of Technology, Chengdu, China
  • 2Chengdu University of Technology State Key Laboratory of Oil and Gas Reservoir Geology and Exploration, Chengdu, China

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

Lithology identification is crucial for characterizing complex unconventional reservoirs, where thin interlayers significantly influence hydrocarbon accumulation. Although deep learning-based methods utilizing well logs have become prevalent, most approaches treat well logs as generic 1D time series, frequently neglecting the multi-scale geological information inherent in the data. This oversight limits their accuracy and generalizability, especially in geologically complex environments. To overcome this limitation, we propose a novel geology-driven deep learning framework. Our key contribution is the transformation of 1D well logs into 2D multi-scale feature maps through multiresolution wavelet decomposition, a process designed to explicitly represent geological features resembling sedimentary cycles. These feature maps are subsequently processed by a novel Geology-Guided Hybrid Network with channel-spatial attention, which integrates a 2D CNN to capture geological patterns and a Bidirectional LSTM to model sequential dependencies. Evaluated on field data from complex reservoirs, our method achieves an outstanding F1-score of up to 0.966, outperforming four established deep learning benchmarks. Importantly, the approach demonstrates improved accuracy in identifying thin layers and enhanced generalization across wells with differing lithological distributions, attaining an F1-score of 0.885 on a challenging test well exhibiting significant data drift. This study validates the robustness of our geology-informed approach and offers an effective framework for high-precision lithology identification.

Keywords: lithology identification1, deep learning2, wavelet decomposition3, channel-spatialattention mechanism4, geological information-driven5

Received: 15 Jul 2025; Accepted: 03 Sep 2025.

Copyright: © 2025 Chen, Wang and Liu. 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: Xingjian Wang, Chengdu University of Technology State Key Laboratory of Oil and Gas Reservoir Geology and Exploration, Chengdu, China

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