Applications of Experimental Design and Response Surface Method in Probabilistic Geothermal Resource Assessment – Preliminary Results

Abstract

Resource estimates in geothermal green-fields involve significant inherent uncertainty due to poorly constrained subsurface parameters and multiple potential development scenarios. There is also limited published information on probabilistic resource assessments of geothermal prospects. This paper explores the applications of a systematic experimental design (ED) and response surface method (RSM) approach to generate probabilistic resource results. ED and RSM have been successfully used in uncertainty analysis and resource evaluation of petroleum fields. These techniques have also been used in a number of field management strategy assessments of geothermal brown-fields. This work presents the preliminary results of a study to extend the geothermal applications of ED and RSM to geothermal green-field resource assessments. ED and RSM are applied to a simple geothermal process model and used to estimate the amount of electrical generating capacity from this synthetic geothermal system. A response variable (electrical generating capacity) as a function of the main uncertain parameters was derived from the simulation runs. This response function serves as the proxy model in Monte-Carlo probabilistic analysis. For this preliminary study, distributions used for the main uncertain parameters are assumed. The probabilistic results from the proxy model are compared with the probabilistic results from the mass in-place volumetric reserves method. The results provide a preliminary understanding of the potential strengths and weaknesses of the ED and RSM methodologies as applied to geothermal resource assessment. Future work will focus on refining the appropriate workflow, understanding the distribution of uncertain parameters, and exploring the ED and RSM levels of complexity applicable to an actual green-field numerical model.