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Study on multi-factor casing damage prediction method based on machine learning

Author

Listed:
  • Li, Fuli
  • Yan, Wei
  • Kong, Xianyong
  • Li, Juan
  • Zhang, Wei
  • Kang, Zeze
  • Yang, Tao
  • Tang, Qing
  • Wang, Kongyang
  • Tan, Chaodong

Abstract

Casing damage is one of the common problems encountered in reservoir development, which seriously affects the normal production of the oil field. In this study, through the analysis of oil field data, a casing damage model under the coupling effects of mudstone hydration-corrosion and sand production-corrosion was established. Thirty-four influencing factors of casing damage were determined in four categories: geology, engineering, development, and corrosion. Six machine learning methods were used to predict the probability of casing damage under the coupling effects of multiple factors. The generalization performance of the model was evaluated using the recall rate of casing damage wells and accuracy. The results show that the random forest and LightGBM models show the best generalization performance. The prediction accuracy rates of the two models on the test set were 84.2% and 85.9%, respectively, and the random forest model showed an excellent performance of 92.3% on the recall rate of casing damage wells. Finally, the optimized model was used to perform sensitivity analysis on each influencing factor, and the main controlling factors of casing damage were obtained. Engineering measures to prevent casing damage are proposed. This study has made outstanding contributions to improving the economic benefits of the oilfield.

Suggested Citation

  • Li, Fuli & Yan, Wei & Kong, Xianyong & Li, Juan & Zhang, Wei & Kang, Zeze & Yang, Tao & Tang, Qing & Wang, Kongyang & Tan, Chaodong, 2024. "Study on multi-factor casing damage prediction method based on machine learning," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224008168
    DOI: 10.1016/j.energy.2024.131044
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    References listed on IDEAS

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    1. Golsanami, Naser & Jayasuriya, Madusanka N. & Yan, Weichao & Fernando, Shanilka G. & Liu, Xuefeng & Cui, Likai & Zhang, Xuepeng & Yasin, Qamar & Dong, Huaimin & Dong, Xu, 2022. "Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images," Energy, Elsevier, vol. 240(C).
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    3. Choi, Yosoon & Nguyen, Hoang & Bui, Xuan-Nam & Nguyen-Thoi, Trung, 2022. "Optimization of haulage-truck system performance for ore production in open-pit mines using big data and machine learning-based methods," Resources Policy, Elsevier, vol. 75(C).
    4. Kang, Yili & Ma, Chenglin & Xu, Chengyuan & You, Lijun & You, Zhenjiang, 2023. "Prediction of drilling fluid lost-circulation zone based on deep learning," Energy, Elsevier, vol. 276(C).
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