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Intelligent decisions to stop or mitigate lost circulation based on machine learning

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  • Abbas, Ahmed K.
  • Bashikh, Ali A.
  • Abbas, Hayder
  • Mohammed, Haider Q.

Abstract

Lost circulation is one of the frequent challenges encountered during the drilling of oil and gas wells. It is detrimental because it can not only increase non-productive time and operational cost but also lead to other safety hazards such as wellbore instability, pipe sticking, and blow out. However, selecting the most effective treatment may still be regarded as an ill-structured issue since it does not have a unique solution. Therefore, the objective of this study is to develop an expert system that can screen drilling operation parameters and drilling fluid characteristics required to diagnose the lost circulation problem correctly and suggest the most appropriate solution for the issue at hand.

Suggested Citation

  • Abbas, Ahmed K. & Bashikh, Ali A. & Abbas, Hayder & Mohammed, Haider Q., 2019. "Intelligent decisions to stop or mitigate lost circulation based on machine learning," Energy, Elsevier, vol. 183(C), pages 1104-1113.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:1104-1113
    DOI: 10.1016/j.energy.2019.07.020
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    References listed on IDEAS

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    1. Zhang, Zheng & Xiong, Youming & Gao, Yun & Liu, Liming & Wang, Menghao & Peng, Geng, 2018. "Wellbore temperature distribution during circulation stage when well-kick occurs in a continuous formation from the bottom-hole," Energy, Elsevier, vol. 164(C), pages 964-977.
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    4. Xu, Chengyuan & Yan, Xiaopeng & Kang, Yili & You, Lijun & You, Zhenjiang & Zhang, Hao & Zhang, Jingyi, 2019. "Friction coefficient: A significant parameter for lost circulation control and material selection in naturally fractured reservoir," Energy, Elsevier, vol. 174(C), pages 1012-1025.
    5. Li, Liang & Yuan, Zhiming & Gao, Yan, 2018. "Maximization of energy absorption for a wave energy converter using the deep machine learning," Energy, Elsevier, vol. 165(PA), pages 340-349.
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    Cited by:

    1. Xu, Chengyuan & Xie, Zhichao & Kang, Yili & Yu, Guoyi & You, Zhenjiang & You, Lijun & Zhang, Jingyi & Yan, Xiaopeng, 2020. "A novel material evaluation method for lost circulation control and formation damage prevention in deep fractured tight reservoir," Energy, Elsevier, vol. 210(C).
    2. Wang, Lian & Yao, Yuedong & Wang, Kongjie & Adenutsi, Caspar Daniel & Zhao, Guoxiang & Lai, Fengpeng, 2022. "Hybrid application of unsupervised and supervised learning in forecasting absolute open flow potential for shale gas reservoirs," Energy, Elsevier, vol. 243(C).
    3. Yang, Xianyu & Xie, Jingyu & Ye, Xiaoping & Chen, Shuya & Jiang, Guosheng & Cai, Jihua & Shi, Yanping & Yue, Ye & Xue, Man & Dai, Zhaokai & Fang, Changliang, 2023. "Sealing characteristics and discrete element fluid dynamics analysis of nanofiber in nanoscale shale pores: Modeling and prediction," Energy, Elsevier, vol. 273(C).
    4. Zhang, Zheng & Wei, Yongqi & Xiong, Youming & Peng, Geng & Wang, Guorong & Lu, Jingsheng & Zhong, Lin & Wang, Jingpeng, 2022. "Influence of the location of drilling fluid loss on wellbore temperature distribution during drilling," Energy, Elsevier, vol. 244(PB).
    5. 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).
    6. Mikhail Dvoynikov & Dmitry Sidorov & Evgeniy Kambulov & Frederick Rose & Rustem Ahiyarov, 2022. "Salt Deposits and Brine Blowout: Development of a Cross-Linking Composition for Blocking Formations and Methodology for Its Testing," Energies, MDPI, vol. 15(19), pages 1-20, October.

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