Volume 44 Issue 1
Jan.  2025
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LIU Yuejiao,LAI Fuqiang,XU Hao,et al. Intelligent identification methods for shale lithology based on the coupling deeply of logging curves[J]. Bulletin of Geological Science and Technology,2025,44(1):308-320 doi: 10.19509/j.cnki.dzkq.tb20230361
Citation: LIU Yuejiao,LAI Fuqiang,XU Hao,et al. Intelligent identification methods for shale lithology based on the coupling deeply of logging curves[J]. Bulletin of Geological Science and Technology,2025,44(1):308-320 doi: 10.19509/j.cnki.dzkq.tb20230361

Intelligent identification methods for shale lithology based on the coupling deeply of logging curves

doi: 10.19509/j.cnki.dzkq.tb20230361
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  • Author Bio:

    E-mail:liu-yuejiao@qq.com

  • Corresponding author: E-mail:laifq1982@163.com
  • Received Date: 26 Jun 2023
  • Accepted Date: 23 Nov 2023
  • Rev Recd Date: 03 Aug 2023
  • Available Online: 17 Dec 2023
  • Objective

    The Wufeng-Longmaxi formations in the Yuxi Block of the Sichuan Basin, China are typical shale gas reservoirs. The strong heterogeneities of these formations leads to both information redundancy and complex coupling relationships of logging curves, which is challenging and inaccurate for traditional lithofacies identification.

    Methods

    This study developed an intelligent lithofacies identification method that integrated with both principal component analysis (PCA) and the random forest algorithm based on lithofacies classification and analysis.

    Results

    Research findings were given as follows: First, PCA optimization can strengthen the coupling of logging curves, reducing the impact of lithofacies identification such as logging curve information redundancy and complex relationships . Second, data augmentation was achieved by including minor changes to the original data without impacting lithofacies, improving model generalization and stability during handling small or imbalanced datasets. Finally, lithofacies identification accuracy based on PCA with the random forest algorithm achievedabove 83%, with a high precision and a strong applicability.

    Conclusion

    This method not only overcomes the difficulty of lithofacies identification in the study area, but also greatly improves the efficiency of lithofacies identification, which is of great significance for promoting the economic and efficient development of shale gas in the study area.

     

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