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Geothermal resource assessment using Experimental Design and Response Surface Methods: The Ngatamariki geothermal field, New Zealand

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  • Quinao, Jaime Jose D.
  • Zarrouk, Sadiq J.

Abstract

Numerical reservoir simulation is becoming prevalent in geothermal resource assessment. However, the uncertainties in geothermal reservoir model inputs and resource assessment outputs are not fully captured in these evaluations. On one hand, sensitivity analysis or one-factor-at-a-time (OFAT) scenario evaluations would not test the model enough to describe the uncertainty but on the other hand, thousands of scenario evaluations to approximate a probabilistic resource assessment will require prohibitively large computing capability. To solve this, the Experimental Design and Response Surface Methods (ED and RSM) workflow is applied. The workflow results in a probabilistic geothermal resource assessment using a response surface derived from the minimum required number of designed reservoir simulation runs. The numerical reservoir model of the Ngatamariki geothermal field, New Zealand was used as a case study. The workflow was used to (1) provide a systematic way of building multiple versions of the Ngatamariki reservoir model through designed experiments, (2) assess the effects of uncertain parameters and scenarios to the resource assessment, and (3) use the results from the designed experiment simulation runs to fit a response surface or proxy model. The proxy model (polynomial) is used as the mathematical model in the Monte Carlo simulation to generate the probabilistic geothermal resource capacity.

Suggested Citation

  • Quinao, Jaime Jose D. & Zarrouk, Sadiq J., 2018. "Geothermal resource assessment using Experimental Design and Response Surface Methods: The Ngatamariki geothermal field, New Zealand," Renewable Energy, Elsevier, vol. 116(PA), pages 324-334.
    • Handle: RePEc:eee:renene:v:116:y:2018:i:pa:p:324-334
    DOI: 10.1016/j.renene.2017.09.084
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    References listed on IDEAS

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    1. Rivera Diaz, Alexandre & Kaya, Eylem & Zarrouk, Sadiq J., 2016. "Reinjection in geothermal fields − A worldwide review update," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 105-162.
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    Cited by:

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    2. Pan, Shu-Yuan & Gao, Mengyao & Shah, Kinjal J. & Zheng, Jianming & Pei, Si-Lu & Chiang, Pen-Chi, 2019. "Establishment of enhanced geothermal energy utilization plans: Barriers and strategies," Renewable Energy, Elsevier, vol. 132(C), pages 19-32.
    3. Wang, Jiacheng & Tan, Xianfeng & Zhao, Zhihong & Chen, Jinfan & He, Jie & Shi, Qipeng, 2024. "Coupled thermo-hydro-mechanical modeling on geothermal doublet subject to seasonal exploitation and storage," Energy, Elsevier, vol. 293(C).
    4. 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.
    5. Gascuel, Violaine & Rivard, Christine & Raymond, Jasmin, 2024. "Deep geothermal doublets versus deep borehole heat exchangers: A comparative study for cold sedimentary basins," Applied Energy, Elsevier, vol. 361(C).
    6. Ciriaco, Anthony E. & Zarrouk, Sadiq J. & Zakeri, Golbon, 2020. "Geothermal resource and reserve assessment methodology: Overview, analysis and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
    7. Li, Shengtao & Wen, Dongguang & Feng, Bo & Li, Fengyu & Yue, Dongdong & Zhang, Qiuxia & Wang, Junzhao & Feng, Zhaolong, 2023. "Numerical optimization of geothermal energy extraction from deep karst reservoir in North China," Renewable Energy, Elsevier, vol. 202(C), pages 1071-1085.

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