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Machine Learning Algorithms for Lithofacies Classification of the Gulong Shale from the Songliao Basin, China

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  • Mingqiu Hou

    (Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
    College of Geosciences, China University of Petroleum, Beijing 102249, China)

  • Yuxiang Xiao

    (Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Zhengdong Lei

    (Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China
    College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China)

  • Zhi Yang

    (Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China)

  • Yihuai Lou

    (Center for Hypergravity Experimental and Interdisciplinary Research, Zhejiang University, Hangzhou 310058, China
    MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Yuming Liu

    (College of Geosciences, China University of Petroleum, Beijing 102249, China
    State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China)

Abstract

Lithofacies identification and classification are critical for characterizing the hydrocarbon potential of unconventional resources. Although extensive applications of machine learning models in predicting lithofacies have been applied to conventional reservoir systems, the effectiveness of machine learning models in predicting clay-rich, lacustrine shale lithofacies has yet to be tackled. Here, we apply machine learning models to conventional well log data to automatically identify the shale lithofacies of Gulong Shale in the Songliao Basin. The shale lithofacies were classified into six types based on total organic carbon and mineral composition data from core analysis and geochemical logs. We compared the accuracy of Multilayer Perceptron (MLP), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest models. We mitigated the bias of imbalanced data by applying oversampling algorithms. Our results show that ensemble methods (XGBoost and Random Forest) have a better performance in shale lithofacies identification than the other models do, with accuracies of 0.868 and 0.884, respectively. The organic siliceous shale proposed to have the best hydrocarbon potential in Gulong Shale can be identified with F1 scores of 0.853 by XGBoost and 0.877 by Random Forest. Our study suggests that ensemble machine learning models can effectively identify the lithofacies of clay-rich shale from conventional well logs, providing insight into the sweet spot prediction of unconventional reservoirs. Further improvements in model performances can be achieved by adding domain knowledge and employing advanced well log data.

Suggested Citation

  • Mingqiu Hou & Yuxiang Xiao & Zhengdong Lei & Zhi Yang & Yihuai Lou & Yuming Liu, 2023. "Machine Learning Algorithms for Lithofacies Classification of the Gulong Shale from the Songliao Basin, China," Energies, MDPI, vol. 16(6), pages 1-19, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2581-:d:1092051
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    References listed on IDEAS

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    1. Timur Merembayev & Darkhan Kurmangaliyev & Bakhbergen Bekbauov & Yerlan Amanbek, 2021. "A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan," Energies, MDPI, vol. 14(7), pages 1-16, March.
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    Cited by:

    1. Fawz Naim & Ann E. Cook & Joachim Moortgat, 2023. "Estimating Compressional Velocity and Bulk Density Logs in Marine Gas Hydrates Using Machine Learning," Energies, MDPI, vol. 16(23), pages 1-22, November.
    2. Chao Wang & Chunjing Yan & Zhengjun Zhu & Shaohua Li & Duanchuan Lv & Xixin Wang & Dawang Liu, 2023. "Interpretation of Sand Body Architecture in Complex Fault Block Area of Craton Basin: Case Study of TIII in Sangtamu Area, Tarim Basin," Energies, MDPI, vol. 16(8), pages 1-15, April.
    3. Radulescu, Magdalena & Dalal, Surjeet & Lilhore, Umesh Kumar & Saimiya, Sarita, 2024. "Optimizing mineral identification for sustainable resource extraction through hybrid deep learning enabled FinTech model," Resources Policy, Elsevier, vol. 89(C).

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