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A Data-Driven Approach for Lithology Identification Based on Parameter-Optimized Ensemble Learning

Author

Listed:
  • Zhixue Sun

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Baosheng Jiang

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Xiangling Li

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

  • Jikang Li

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Kang Xiao

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

Abstract

The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algorithm with Gradient Tree Boosting-Differential Evolution (GTB-DE) using the same dataset. The results demonstrated that the average of precision, recall and F1 score of the proposed method are respectively 4.85%, 5.7%, 3.25% greater than GTB-ED. The proposed XGBoost-BO ensemble model can automate the procedure of lithology identification, and it may also be used in the prediction of other reservoir properties.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3903-:d:392375
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    References listed on IDEAS

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    1. Gang Chen & Mian Chen & Guobin Hong & Yunhu Lu & Bo Zhou & Yanfang Gao, 2020. "A New Method of Lithology Classification Based on Convolutional Neural Network Algorithm by Utilizing Drilling String Vibration Data," Energies, MDPI, vol. 13(4), pages 1-24, February.
    2. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    3. 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. 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. 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. Cenk Temizel & Uchenna Odi & Karthik Balaji & Hakki Aydin & Javier E. Santos, 2022. "Classifying Facies in 3D Digital Rock Images Using Supervised and Unsupervised Approaches," Energies, MDPI, vol. 15(20), pages 1-15, October.

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