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A Comparison of Machine Learning Algorithms in Predicting Lithofacies: Case Studies from Norway and Kazakhstan

Author

Listed:
  • Timur Merembayev

    (Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan)

  • Darkhan Kurmangaliyev

    (KazMunayGas Engineering LLP, Nur-Sultan 010000, Kazakhstan)

  • Bakhbergen Bekbauov

    (KazMunayGas Engineering LLP, Nur-Sultan 010000, Kazakhstan)

  • Yerlan Amanbek

    (Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan)

Abstract

Defining distinctive areas of the physical properties of rocks plays an important role in reservoir evaluation and hydrocarbon production as core data are challenging to obtain from all wells. In this work, we study the evaluation of lithofacies values using the machine learning algorithms in the determination of classification from various well log data of Kazakhstan and Norway. We also use the wavelet-transformed data in machine learning algorithms to identify geological properties from the well log data. Numerical results are presented for the multiple oil and gas reservoir data which contain more than 90 released wells from Norway and 10 wells from the Kazakhstan field. We have compared the the machine learning algorithms including KNN, Decision Tree, Random Forest, XGBoost, and LightGBM. The evaluation of the model score is conducted by using metrics such as accuracy, Hamming loss, and penalty matrix. In addition, the influence of the dataset features on the prediction is investigated using the machine learning algorithms. The result of research shows that the Random Forest model has the best score among considered algorithms. In addition, the results are consistent with outcome of the SHapley Additive exPlanations (SHAP) framework.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1896-:d:526352
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    References listed on IDEAS

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    1. Zhixue Sun & Baosheng Jiang & Xiangling Li & Jikang Li & Kang Xiao, 2020. "A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning," Energies, MDPI, vol. 13(15), pages 1-15, July.
    2. Chuanbo Shen & Solomon Asante-Okyere & Yao Yevenyo Ziggah & Liang Wang & Xiangfeng Zhu, 2019. "Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques," Energies, MDPI, vol. 12(8), pages 1-16, April.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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