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Open Access

Lithological Facies Classification Using Attention-Based Gated Recurrent Unit

College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang 262700, China
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China
Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan 250022, China
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Lithological facies classification is a pivotal task in petroleum geology, underpinning reservoir characterization and influencing decision-making in exploration and production operations. Traditional classification methods, such as support vector machines and Gaussian process classifiers, often struggle with the complexity and nonlinearity of geological data, leading to suboptimal performance. Moreover, numerous prevalent approaches fail to adequately consider the inherent dependencies in the sequence of measurements from adjacent depths in a well. A novel approach leveraging an attention-based gated recurrent unit (AGRU) model is introduced in this paper to address these challenges. The AGRU model excels by exploiting the sequential nature of well-log data and capturing long-range dependencies through an attention mechanism. This model enables a flexible and context-dependent weighting of different parts of the sequence, enhancing the discernment of key features for classification. The proposed method was validated on two publicly available datasets. Results demonstrate a considerably improvement over traditional methods. Specifically, the AGRU model achieved superior performance metrics considering precision, recall, and F1-score.



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Tsinghua Science and Technology
Pages 1206-1218
Cite this article:
Liu Y, Zhang Y, Mao X, et al. Lithological Facies Classification Using Attention-Based Gated Recurrent Unit. Tsinghua Science and Technology, 2024, 29(4): 1206-1218.








Web of Science






Received: 20 June 2023
Revised: 07 July 2023
Accepted: 22 July 2024
Published: 09 February 2024
© The Author(s) 2024.

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