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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

Author

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  • Junlong Zhang

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • Youbin He

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • Yuan Zhang

    (Research Institute of Petroleum Exploration and Development, SINOPEC Jianghan Oilfield Company, Wuhan 430223, China)

  • Weifeng Li

    (School of Geosciences, Yangtze University, Wuhan 430100, China)

  • Junjie Zhang

    (Global Research, RBC Capital Markets, Toronto, ON M5J 2J5, Canada)

Abstract

The identification of underground formation lithology is fundamental in reservoir characterization during petroleum exploration. With the increasing availability and diversity of well-logging data, automated interpretation of well-logging data is in great demand for more efficient and reliable decision making for geologists and geophysicists. This study benchmarked the performances of an array of machine learning models, from linear and nonlinear individual classifiers to ensemble methods, on the task of lithology identification. Cross-validation and Bayesian optimization were utilized to optimize the hyperparameters of different models and performances were evaluated based on the metrics of accuracy—the area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. The dataset of the study consists of well-logging data acquired from the Baikouquan formation in the Mahu Sag of the Junggar Basin, China, including 4156 labeled data points with 9 well-logging variables. Results exhibit that ensemble methods (XGBoost and RF) outperform the other two categories of machine learning methods by a material margin. Within the ensemble methods, XGBoost has the best performance, achieving an overall accuracy of 0.882 and AUC of 0.947 in classifying mudstone, sandstone, and sandy conglomerate. Among the three lithology classes, sandy conglomerate, as in the potential reservoirs in the study area, can be best distinguished with accuracy of 97%, precision of 0.888, and recall of 0.969, suggesting the XGBoost model as a strong candidate machine learning model for more efficient and accurate lithology identification and reservoir quantification for geologists.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3675-:d:817721
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    References listed on IDEAS

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    1. Yunxin Xie & Chenyang Zhu & Yue Lu & Zhengwei Zhu, 2019. "Towards Optimization of Boosting Models for Formation Lithology Identification," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-13, August.
    2. 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.
    3. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, June.
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    5. 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.
    6. Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
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