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Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs

Author

Listed:
  • Wenchao Liu

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Yuejie Yang

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Chengcheng Qiao

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Chen Liu

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Boyu Lian

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qingwang Yuan

    (Bob L. Herd Department of Petroleum Engineering, Texas Tech University, 2500 Broadway, Lubbock, TX 79409, USA)

Abstract

With the continuous development of conventional oil and gas resources, the strategic transformation of energy structure is imminent. Shale condensate gas reservoir has high development value because of its abundant reserves. However, due to the multi-scale flow of shale gas, adsorption and desorption, the strong stress sensitivity of matrix and fractures, the abnormal condensation phase transition mechanism, high-speed non-Darcy seepage in artificial fractures, and heterogeneity of reservoir and multiphase flows, the multi-scale nonlinear seepage mechanisms are extremely complicated in shale condensate gas reservoirs. A certain theoretical basis for the engineering development can be provided by mastering the percolation law of shale condensate gas reservoirs, such as improvement of productivity prediction and recovery efficiency. The productivity evaluation method of shale condensate gas wells based on empirical method is simple in calculation but poor in reliability. The characteristic curve analysis method has strong reliability but a great dependence on the selection of the seepage model. The artificial intelligence method can deal with complex data and has a high prediction accuracy. Establishing an efficient shale condensate gas reservoir development simulation technology and accurately predicting the production performance of production wells will help to rationally formulate a stable and high-yield mining scheme, so as to obtain better economic benefits.

Suggested Citation

  • Wenchao Liu & Yuejie Yang & Chengcheng Qiao & Chen Liu & Boyu Lian & Qingwang Yuan, 2023. "Progress of Seepage Law and Development Technologies for Shale Condensate Gas Reservoirs," Energies, MDPI, vol. 16(5), pages 1-30, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2446-:d:1087471
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    References listed on IDEAS

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    1. Fengshuang Du & Bahareh Nojabaei, 2019. "A Review of Gas Injection in Shale Reservoirs: Enhanced Oil/Gas Recovery Approaches and Greenhouse Gas Control," Energies, MDPI, vol. 12(12), pages 1-33, June.
    2. Hongming Zhan & Feifei Fang & Xizhe Li & Zhiming Hu & Jie Zhang, 2022. "Shale Reservoir Heterogeneity: A Case Study of Organic-Rich Longmaxi Shale in Southern Sichuan, China," Energies, MDPI, vol. 15(3), pages 1-14, January.
    3. Tian, Weibing & Wu, Keliu & Chen, Zhangxin & Gao, Yanling & Li, Jing & Wang, Muyuan, 2022. "A relative permeability model considering nanoconfinement and dynamic contact angle effects for tight reservoirs," Energy, Elsevier, vol. 258(C).
    4. Wang, Hui & Chen, Li & Qu, Zhiguo & Yin, Ying & Kang, Qinjun & Yu, Bo & Tao, Wen-Quan, 2020. "Modeling of multi-scale transport phenomena in shale gas production — A critical review," Applied Energy, Elsevier, vol. 262(C).
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