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Analytical solutions for predicting and optimizing geothermal energy extraction from an enhanced geothermal system with a multiple hydraulically fractured horizontal-well doublet

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  • Akdas, Satuk Bugra
  • Onur, Mustafa

Abstract

In this study, novel analytical solutions are presented for predicting and optimizing energy (heat and power) extraction from an idealized single hot-dry rock (HDR) or hot-wet rock (HWR) through a multistage hydraulically fractured horizontal well doublet (MHFWD). The solutions are based on a two-dimensional analytical model that accounts for heat transfer mechanisms through an MHFWD completed in a homogeneous nearly impermeable matrix of the HDR/HWR system. The method of Laplace transformation is used to obtain the solutions. The solutions are inverted numerically and analytically to compute the fracture water outlet temperature and the temperature inside the matrix in the real-time domain. The analytical solutions presented in the study have not been presented elsewhere. The fluid outlet and matrix temperature distributions computed from the analytical model were validated against the results of a commercial simulator. The derived analytical solutions were integrated to convert thermal energy to electric power, and a sensitivity study was performed to investigate the effect of certain parameters, such as fracture height, number, half spacing, injection rate, and injection temperature. In addition, the study provides insights and guidelines to predict, design, and optimize heat recovery and electricity production from an EGS system stimulated by an MHFWD.

Suggested Citation

  • Akdas, Satuk Bugra & Onur, Mustafa, 2022. "Analytical solutions for predicting and optimizing geothermal energy extraction from an enhanced geothermal system with a multiple hydraulically fractured horizontal-well doublet," Renewable Energy, Elsevier, vol. 181(C), pages 567-580.
    • Handle: RePEc:eee:renene:v:181:y:2022:i:c:p:567-580
    DOI: 10.1016/j.renene.2021.09.051
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    References listed on IDEAS

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    Cited by:

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    3. Ashok Bhansali & Namala Narasimhulu & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Dayanand Lal Narayan, 2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models," Energies, MDPI, vol. 16(17), pages 1-18, August.
    4. 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).
    5. Zhang, Bo & Guo, Tiankui & Qu, Zhanqing & Wang, Jiwei & Chen, Ming & Liu, Xiaoqiang, 2023. "Numerical simulation of fracture propagation and production performance in a fractured geothermal reservoir using a 2D FEM-based THMD coupling model," Energy, Elsevier, vol. 273(C).
    6. Zheng, Jun & Li, Peng & Dou, Bin & Fan, Tao & Tian, Hong & Lai, Xiaotian, 2022. "Impact research of well layout schemes and fracture parameters on heat production performance of enhanced geothermal system considering water cooling effect," Energy, Elsevier, vol. 255(C).
    7. 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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