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An integrated multi-objective optimization method to improve the performance of multilateral-well geothermal system

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  • Song, Guofeng
  • Song, Xianzhi
  • Li, Gensheng
  • Shi, Yu
  • Wang, Gaosheng
  • Ji, Jiayan
  • Xu, Fuqiang
  • Song, Zihao

Abstract

Operational parameters optimization is of great significance to improve overall heat extraction performance from the hydrothermal or enhanced geothermal system. Injection flowrate, injection temperature, and production pressure are several of the easily human-controlled parameters to make the best of limited geothermal resources during the planned life. The net heat power and flow impedance are two contradictory production indexes to be optimized for efficient exploitation of long-term geothermal production. In this study, an integrated approach of finite element, multiple regression, non-dominated sorting genetic algorithm, and the technique for order preference by similarity to ideal solution is proposed and applied to the multilateral-well system to realize the optimization of geothermal development. Firstly, parametric cases coupling with thermal and hydraulic models are analyzed. Then, multiple regression is employed to obtain the net heat power and flow impedance functions considering human-controlled operational parameters and reservoir physical properties. Afterward, the multi-objective optimization algorithm is used to gain the Pareto solution set of injection and production parameters. Finally, the technique for order preference by similarity to ideal solution is employed to select the optimal combination of operational parameters for geothermal extraction. It is concluded that water loss and thermal drawdown are necessarily considered in the optimization process. The proposed approach represents global optimization. Operational parameters for the optimal case are (Qin, Tin, pout) which equal (49.98 °C, 62.21 kg/s, 27.44 MPa) under the conditions of this study. From the comparison between the base case and optimal case, it is observed that horizontal spread length, 2D swept area and 3D swept volume of the low-temperature scope is reduced by 77.9 m, 10 × 104 m2, and 16 × 106 m3. Besides, the thermal breakthrough has been delayed by 1.1 years. The water loss decreases by 36.23%. The optimal case demonstrates a great improvement in production performance. Sustainable exploitation is achieved through multi-objective optimization for operational parameters. The proposed method reflects superiority, efficiency, and intelligence in geothermal development.

Suggested Citation

  • Song, Guofeng & Song, Xianzhi & Li, Gensheng & Shi, Yu & Wang, Gaosheng & Ji, Jiayan & Xu, Fuqiang & Song, Zihao, 2021. "An integrated multi-objective optimization method to improve the performance of multilateral-well geothermal system," Renewable Energy, Elsevier, vol. 172(C), pages 1233-1249.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:1233-1249
    DOI: 10.1016/j.renene.2021.03.073
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    2. Zhai, Haizhen & Jin, Guangrong & Liu, Lihua & Su, Zheng & Zeng, Yuchao & Liu, Jie & Li, Guangyu & Feng, Chuangji & Wu, Nengyou, 2023. "Parametric study of the geothermal exploitation performance from a HDR reservoir through multilateral horizontal wells: The Qiabuqia geothermal area, Gonghe Basin," Energy, Elsevier, vol. 275(C).
    3. Wang, Zhipeng & Ning, Zhengfu & Guo, Wenting & Zhan, Jie & Zhang, Yuanxin, 2024. "Study of fracture monitoring and heat extraction evaluation in geothermal reservoir modified by abandoned well pattern: Numerical models and case studies," Energy, Elsevier, vol. 296(C).
    4. Esmaeilpour, Morteza & Gholami Korzani, Maziar & Kohl, Thomas, 2023. "Stochastic performance assessment on long-term behavior of multilateral closed deep geothermal systems," Renewable Energy, Elsevier, vol. 208(C), pages 26-35.
    5. Chen, Guodong & Jiao, Jiu Jimmy & Jiang, Chuanyin & Luo, Xin, 2024. "Surrogate-assisted level-based learning evolutionary search for geothermal heat extraction optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Xie, Jingxuan & Wang, Jiansheng, 2022. "Compatibility investigation and techno-economic performance optimization of whole geothermal power generation system," Applied Energy, Elsevier, vol. 328(C).
    7. Guo, Tiankui & Hao, Tong & Chen, Ming & Zhang, Yuelong & Qu, Zhanqing & Jia, Xuliang & Zhang, Wei & Yu, Haiyang, 2023. "Numerical simulation on Geothermal extraction by radial well assisted hydraulic fracturing," Renewable Energy, Elsevier, vol. 210(C), pages 440-450.
    8. Xu, Fuqiang & Song, Xianzhi & Song, Guofeng & Ji, Jiayan & Song, Zihao & Shi, Yu & Lv, Zehao, 2023. "Numerical studies on heat extraction evaluation and multi-objective optimization of abandoned oil well patterns in intermittent operation mode," Energy, Elsevier, vol. 269(C).
    9. Li, Shijie & Liu, Jie & Huang, Wanying & Zhang, Chenghang, 2024. "Numerical simulation of the thermo-hydro-chemical coupling in enhanced geothermal systems: Impact of SiO2 dissolution/precipitation in matrix and fractures," Energy, Elsevier, vol. 290(C).
    10. Xu, Fuqiang & Shi, Yu & Song, Xianzhi & Wu, Wei & Song, Guofeng & Li, Shuang, 2024. "Experimental characterization of damage during geothermal production of hot dry rocks: Comprehensive effects of the damage-elastic deformation on conductivity evolution," Energy, Elsevier, vol. 294(C).
    11. Wang, Jiacheng & Zhao, Zhihong & Liu, Guihong & Xu, Haoran, 2022. "A robust optimization approach of well placement for doublet in heterogeneous geothermal reservoirs using random forest technique and genetic algorithm," Energy, Elsevier, vol. 254(PC).
    12. Wang, Zhipeng & Ning, Zhengfu & Guo, Wenting & Zhan, Jie & Chen, Zhangxin, 2024. "DC electric field assisted heat extraction evaluation via water circulation in abandoned production well patterns: Semi-analytical and numerical models," Renewable Energy, Elsevier, vol. 228(C).

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