IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2581-d1092051.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2581/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2581/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
    2. Mohamed Zul Fadhli Khairuddin & Puat Lu Hui & Khairunnisa Hasikin & Nasrul Anuar Abd Razak & Khin Wee Lai & Ahmad Shakir Mohd Saudi & Siti Salwa Ibrahim, 2022. "Occupational Injury Risk Mitigation: Machine Learning Approach and Feature Optimization for Smart Workplace Surveillance," IJERPH, MDPI, vol. 19(21), pages 1-19, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2581-:d:1092051. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.