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An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs

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  • Zhou, Yuhao
  • Wang, Yanwei

Abstract

The development of heavy oil reservoirs with active edge and bottom water is one of the most challenging problems in petroleum engineering. In response to the limited thermal recovery of these reservoirs, a multi-phase and multi-component numerical simulation model for thermal and chemical recovery is proposed. An edge-water assisted chemical flooding (EAC flooding) is proposed, which can improve oil displacement efficiency and sweep efficiency by rational utilization of edge-water energy when compounding multi-component chemical system. Then, a deep reinforcement learning algorithm is proposed to predict dynamic production parameters and determine the optimal working system to maximize the oil recovery according to the above mathematical model. The deep reinforcement learning (DRL) model can predict the dynamic production curves according to given states with optimal strategy. At the same time, the proposed model can determine the best conversion timing from cyclic steam stimulation to EAC flooding. Finally, the DRL model can automatically obtain the optimal working system, effectively improving the oil recovery while considering the economic benefits. Thus, the DRL model can solve traditional numerical simulation's time-consuming and labor-intensive challenges and accurately give the optimal working system for developing heavy oil reservoirs with edge water in the field.

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  • Zhou, Yuhao & Wang, Yanwei, 2022. "An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s036054422201043x
    DOI: 10.1016/j.energy.2022.124140
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    References listed on IDEAS

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    1. Zhang, Lisong & Li, Jing & Sun, Luning & Yang, Feiyue, 2021. "An influence mechanism of shale barrier on heavy oil recovery using SAGD based on theoretical and numerical analysis," Energy, Elsevier, vol. 216(C).
    2. Zhang, Qichen & Liu, Huiqing & Kang, Xiaodong & Liu, Yisheng & Dong, Xiaohu & Wang, Yanwei & Liu, Siyi & Li, Guangbo, 2021. "An investigation of production performance by cyclic steam stimulation using horizontal well in heavy oil reservoirs," Energy, Elsevier, vol. 218(C).
    3. Jiang, Han & Xi, Zhongli & A. Rahman, Anas & Zhang, Xiaoqing, 2020. "Prediction of output power with artificial neural network using extended datasets for Stirling engines," Applied Energy, Elsevier, vol. 271(C).
    4. Xia, Wenjie & Shen, Weijun & Yu, Li & Zheng, Chenggang & Yu, Weichu & Tang, Yongchun, 2016. "Conversion of petroleum to methane by the indigenous methanogenic consortia for oil recovery in heavy oil reservoir," Applied Energy, Elsevier, vol. 171(C), pages 646-655.
    5. Rangriz Shokri, A. & Babadagli, T., 2017. "Feasibility assessment of heavy-oil recovery by CO2 injection after cold production with sands: Lab-to-field scale modeling considering non-equilibrium foamy oil behavior," Applied Energy, Elsevier, vol. 205(C), pages 615-625.
    6. Luo, Erhui & Fan, Zifei & Hu, Yongle & Zhao, Lun & Bo, Bing & Yu, Wei & Liang, Hongwei & Liu, Minghui & Liu, Yunyang & He, Congge & Wang, Jianjun, 2020. "An efficient optimization framework of cyclic steam stimulation with experimental design in extra heavy oil reservoirs," Energy, Elsevier, vol. 192(C).
    7. Phoebe M. R. DeVries & Fernanda Viégas & Martin Wattenberg & Brendan J. Meade, 2018. "Deep learning of aftershock patterns following large earthquakes," Nature, Nature, vol. 560(7720), pages 632-634, August.
    8. Dong, Xiaohu & Liu, Huiqing & Chen, Zhangxin & Wu, Keliu & Lu, Ning & Zhang, Qichen, 2019. "Enhanced oil recovery techniques for heavy oil and oilsands reservoirs after steam injection," Applied Energy, Elsevier, vol. 239(C), pages 1190-1211.
    9. Afsar, Cansu & Akin, Serhat, 2016. "Solar generated steam injection in heavy oil reservoirs: A case study," Renewable Energy, Elsevier, vol. 91(C), pages 83-89.
    10. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    11. Li, Hao & Misra, Siddharth, 2021. "Reinforcement learning based automated history matching for improved hydrocarbon production forecast," Applied Energy, Elsevier, vol. 284(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Zhang, Jun, 2023. "Performance of high temperature steam injection in horizontal wells of heavy oil reservoirs," Energy, Elsevier, vol. 282(C).
    2. Du, Shuyi & Wang, Jiulong & Wang, Meizhu & Yang, Jiaosheng & Zhang, Cong & Zhao, Yang & Song, Hongqing, 2023. "A systematic data-driven approach for production forecasting of coalbed methane incorporating deep learning and ensemble learning adapted to complex production patterns," Energy, Elsevier, vol. 263(PE).
    3. Fathy, Mohammad & Kazemzadeh Haghighi, Foojan & Ahmadi, Mohammad, 2024. "Uncertainty quantification of reservoir performance using machine learning algorithms and structured expert judgment," Energy, Elsevier, vol. 288(C).

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