An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs
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DOI: 10.1016/j.energy.2022.124140
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- 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).
- 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).
- 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).
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
- Li, Hao & Misra, Siddharth, 2021. "Reinforcement learning based automated history matching for improved hydrocarbon production forecast," Applied Energy, Elsevier, vol. 284(C).
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Cited by:
- 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).
- Zhang, Jun, 2023. "Performance of high temperature steam injection in horizontal wells of heavy oil reservoirs," Energy, Elsevier, vol. 282(C).
- 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).
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Keywords
Edge-water heavy oil reservoirs; Deep reinforcement learning model; Numerical simulation; Optimal working system; Enhanced oil recovery; Economic analysis;All these keywords.
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