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An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty

Author

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  • Chen, Mingjie
  • Tompson, Andrew F.B.
  • Mellors, Robert J.
  • Abdalla, Osman

Abstract

This study applies an efficient optimization technique based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of a potential geothermal production operation at a prospect near Superstition Mountain in Southern California, USA. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models within the intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is initially developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the prospect. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled total order sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. At the Southern California prospect, this parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analyzed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as hydrocarbon production or CO2 sequestration, under uncertain reservoir conditions.

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  • Chen, Mingjie & Tompson, Andrew F.B. & Mellors, Robert J. & Abdalla, Osman, 2015. "An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty," Applied Energy, Elsevier, vol. 137(C), pages 352-363.
    • Handle: RePEc:eee:appene:v:137:y:2015:i:c:p:352-363
    DOI: 10.1016/j.apenergy.2014.10.036
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    References listed on IDEAS

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    1. Regis, Rommel G. & Shoemaker, Christine A., 2007. "Parallel radial basis function methods for the global optimization of expensive functions," European Journal of Operational Research, Elsevier, vol. 182(2), pages 514-535, October.
    2. Rommel G. Regis & Christine A. Shoemaker, 2007. "A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions," INFORMS Journal on Computing, INFORMS, vol. 19(4), pages 497-509, November.
    3. Chen, Mingjie & Tompson, Andrew F.B. & Mellors, Robert J. & Ramirez, Abelardo L. & Dyer, Kathleen M. & Yang, Xianjin & Wagoner, Jeffrey L., 2014. "An efficient Bayesian inversion of a geothermal prospect using a multivariate adaptive regression spline method," Applied Energy, Elsevier, vol. 136(C), pages 619-627.
    4. Procesi, M. & Cantucci, B. & Buttinelli, M. & Armezzani, G. & Quattrocchi, F. & Boschi, E., 2013. "Strategic use of the underground in an energy mix plan: Synergies among CO2, CH4 geological storage and geothermal energy. Latium Region case study (Central Italy)," Applied Energy, Elsevier, vol. 110(C), pages 104-131.
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    2. Samin, Maleaha Y. & Faramarzi, Asaad & Jefferson, Ian & Harireche, Ouahid, 2019. "A hybrid optimisation approach to improve long-term performance of enhanced geothermal system (EGS) reservoirs," Renewable Energy, Elsevier, vol. 134(C), pages 379-389.
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    4. 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).
    5. Babaei, Masoud & Nick, Hamidreza M., 2019. "Performance of low-enthalpy geothermal systems: Interplay of spatially correlated heterogeneity and well-doublet spacings," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Wang, Yang & Voskov, Denis & Khait, Mark & Saeid, Sanaz & Bruhn, David, 2021. "Influential factors on the development of a low-enthalpy geothermal reservoir: A sensitivity study of a realistic field," Renewable Energy, Elsevier, vol. 179(C), pages 641-651.
    7. Anna Wachowicz-Pyzik & Anna Sowiżdżał & Leszek Pająk & Paweł Ziółkowski & Janusz Badur, 2020. "Assessment of the Effective Variants Leading to Higher Efficiency for the Geothermal Doublet, Using Numerical Analysis‒Case Study from Poland (Szczecin Trough)," Energies, MDPI, vol. 13(9), pages 1-20, May.
    8. Liu, Guihong & Wang, Guiling & Zhao, Zhihong & Ma, Feng, 2020. "A new well pattern of cluster-layout for deep geothermal reservoirs: Case study from the Dezhou geothermal field, China," Renewable Energy, Elsevier, vol. 155(C), pages 484-499.
    9. Daniilidis, Alexandros & Scholten, Tjardo & Hooghiem, Joram & De Persis, Claudio & Herber, Rien, 2017. "Geochemical implications of production and storage control by coupling a direct-use geothermal system with heat networks," Applied Energy, Elsevier, vol. 204(C), pages 254-270.
    10. Wang, Nanzhe & Chang, Haibin & Kong, Xiang-Zhao & Zhang, Dongxiao, 2023. "Deep learning based closed-loop well control optimization of geothermal reservoir with uncertain permeability," Renewable Energy, Elsevier, vol. 211(C), pages 379-394.
    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).

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