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Study on heat extraction considering the number and orientation of multilateral wells in a complex fractured geothermal reservoir

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  • Gao, Xuefeng
  • Zhang, Yanjun
  • Huang, Yibin
  • Ma, Yongjie
  • Zhao, Yi
  • Liu, Qiangbin

Abstract

Understanding the flow and heat transfer in fractured geothermal reservoir is critical for the development of enhanced geothermal system (EGS) in the future. Under the framework of discrete fracture network, a thermal-hydraulic coupled model is proposed to evaluate the heat extraction performance of multilateral wells in fractured reservoirs. Five cases with varying numbers of branch wells considering well orientation are compared. In addition, we analyze the heat extraction performance response of multilateral wells in fractured reservoirs to the operating parameters. Results demonstrate the minimal effect of the increasing number of branch wells on the production temperature, while impacts on the well pressure are much greater. The injection pressure is observed to decrease significantly as the number of branch wells increases, resulting in a reduced reservoir flow resistance and increased net thermal energy by reducing pump energy consumption. For the same case, increasing the number of intersections between wells and fractures reduces the production temperature and injection pressure. Parameter sensitivity analysis reveals heat production to increases with the mass flow rate, well length and well spacing, and decreases with increasing fracture permeability. This study is expected to provide a view for the application of multilateral wells in fractured geothermal reservoirs.

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  • Gao, Xuefeng & Zhang, Yanjun & Huang, Yibin & Ma, Yongjie & Zhao, Yi & Liu, Qiangbin, 2021. "Study on heat extraction considering the number and orientation of multilateral wells in a complex fractured geothermal reservoir," Renewable Energy, Elsevier, vol. 177(C), pages 833-852.
  • Handle: RePEc:eee:renene:v:177:y:2021:i:c:p:833-852
    DOI: 10.1016/j.renene.2021.06.024
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    Cited by:

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    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. Gao, Xuefeng & Zhang, Yanjun & Cheng, Yuxiang & Huang, Yibin & Deng, Hao & Ma, Yongjie, 2022. "A novel strategy utilizing local fracture networks to enhance CBHE heat extraction performance: A case study of the Songyuan geothermal field in China," Energy, Elsevier, vol. 255(C).
    6. Qiao, Mingzheng & Jing, Zefeng & Feng, Chenchen & Li, Minghui & Chen, Cheng & Zou, Xupeng & Zhou, Yujuan, 2024. "Review on heat extraction systems of hot dry rock: Classifications, benefits, limitations, research status and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 196(C).
    7. Xie, Jingxuan & Wang, Jiansheng, 2022. "Compatibility investigation and techno-economic performance optimization of whole geothermal power generation system," Applied Energy, Elsevier, vol. 328(C).
    8. 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).
    9. Liang, Xu & Xu, Tianfu & Chen, Jingyi & Jiang, Zhenjiao, 2023. "A deep-learning based model for fracture network characterization constrained by induced micro-seismicity and tracer test data in enhanced geothermal system," Renewable Energy, Elsevier, vol. 216(C).
    10. 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).

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