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

Jaime Jose D. Quinao and Sadiq J. Zarrouk

Renewable Energy, 2018, vol. 116, issue PA, 324-334

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.

Keywords: Probabilistic resource assessment; Numerical model; Experimental design; Response surface; Proxy model; Ngatamariki geothermal field; Uncertainty analysis (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148117309527
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:116:y:2018:i:pa:p:324-334

DOI: 10.1016/j.renene.2017.09.084

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().