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System and multi-physics coupling model of liquid-CO2 injection on CO2 storage with enhanced gas recovery (CSEGR) framework

Author

Listed:
  • Gao, Xinyuan
  • Yang, Shenglai
  • Tian, Lerao
  • Shen, Bin
  • Bi, Lufei
  • Zhang, Yiqi
  • Wang, Mengyu
  • Rui, Zhenhua

Abstract

Injecting CO2 into gas reservoirs can achieve CO2 Storage with enhanced gas recovery (CSEGR). The development of liquid-CO2 injection has the characteristics of high injectability, high mobility ratio, and low diffusion coefficient. Therefore, this paper established a wellbore-reservoir-thermo-hydro-mechanical-diffusion (WR-THMD) multi-physics fully coupled model of the wellbore-reservoir system, and verified the model based on field and experimental data. The mass transfer, heat transfer, and gas physical property changes in the wellbore and reservoir during the injection of liquid-CO2 were studied. The impact of different engineering parameters on improving CH4 recovery and CO2 storage is also discussed. The results show that the impact of the wellbore on the physical properties and phase state of CO2 is crucial. The injection of liquid-CO2 is beneficial to the storage of CO2 and the displacement of CH4. Lowering the injection temperature will slightly improve the injectability of liquid-CO2 and increase the CO2 storage rate by 5.13%. Using a high injection mass flow rate will effectively raise CH4 recovery rate by 15.60%, but it will weaken the injectability of CO2 and cause CO2 to break through in the production well prematurely. The research results provide important suggestions and theoretical support for the application of liquid-CO2 injection on CSEGR.

Suggested Citation

  • Gao, Xinyuan & Yang, Shenglai & Tian, Lerao & Shen, Bin & Bi, Lufei & Zhang, Yiqi & Wang, Mengyu & Rui, Zhenhua, 2024. "System and multi-physics coupling model of liquid-CO2 injection on CO2 storage with enhanced gas recovery (CSEGR) framework," Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:energy:v:294:y:2024:i:c:s0360544224007230
    DOI: 10.1016/j.energy.2024.130951
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    6. Shen, Bin & Yang, Shenglai & Hu, Jiangtao & Zhang, Yiqi & Zhang, Lingfeng & Ye, Shanlin & Yang, Zhengze & Yu, Jiayi & Gao, Xinyuan & Zhao, Ermeng, 2024. "Interpretable causal-based temporal graph convolutional network framework in complex spatio-temporal systems for CCUS-EOR," Energy, Elsevier, vol. 309(C).

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