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Optimization of Well Control during Gas Flooding Using the Deep-LSTM-Based Proxy Model: A Case Study in the Baoshaceng Reservoir, Tarim, China

Author

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  • Qihong Feng

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

  • Kuankuan Wu

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

  • Jiyuan Zhang

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

  • Sen Wang

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

  • Xianmin Zhang

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

  • Daiyu Zhou

    (Tarim Oilfield Company, China National Petroleum Corporation, Korla 841000, China)

  • An Zhao

    (Tarim Oilfield Company, China National Petroleum Corporation, Korla 841000, China)

Abstract

Gas flooding has proven to be a promising method of enhanced oil recovery (EOR) for mature water-flooding reservoirs. The determination of optimal well control parameters is an essential step for proper and economic development of underground hydrocarbon resources using gas injection. Generally, the optimization of well control parameters in gas flooding requires the use of compositional numerical simulation for forecasting the production dynamics, which is computationally expensive and time-consuming. This paper proposes the use of a deep long-short-term memory neural network (Deep-LSTM) as a proxy model for a compositional numerical simulator in order to accelerate the optimization speed. The Deep-LSTM model was integrated with the classical covariance matrix adaptive evolutionary (CMA-ES) algorithm to conduct well injection and production optimization in gas flooding. The proposed method was applied in the Baoshaceng reservoir of the Tarim oilfield, and shows comparable accuracy (with an error of less than 3%) but significantly improved efficiency (reduced computational duration of ~90%) against the conventional numerical simulation method.

Suggested Citation

  • Qihong Feng & Kuankuan Wu & Jiyuan Zhang & Sen Wang & Xianmin Zhang & Daiyu Zhou & An Zhao, 2022. "Optimization of Well Control during Gas Flooding Using the Deep-LSTM-Based Proxy Model: A Case Study in the Baoshaceng Reservoir, Tarim, China," Energies, MDPI, vol. 15(7), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2398-:d:779046
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    References listed on IDEAS

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