Probabilistic geothermal resource assessment in Maichen Sag, south China

Abstract

It is crucial for financial providers, investment groups, resource developers, and exploration companies to rate new geothermal projects in terms of resources and reserves. In general, the existing volumetric method is constrained by limited information when projects are at the early stage of development. The main objective of this study is to estimate the probabilistic potential thermal energy of the M research area in the Maichen Sag, a geothermal prospect in South China, through stochastic methodologies. The probabilistic assessment methodology provides a way to embody the uncertainty and risk in geothermal projects and to quantify the power potential in a probable range. In this study, proxy numerical models were built by combining the Experimental Design (ED) and Response Surface Methodology (RSM) with the Monte Carlo Simulation technique. An improved workflow for combined ED-RSM that uses two-level Full Factorial and Box–Behnken designs was proposed. For comparative analysis, the typical volumetric technique was also implemented in this study. The ED-RSM results show that the M area has P10, P50, and P90 reserves of 5.7 × 1014 J, 5.3 × 1014 J, and 5 × 1014 J, respectively, and these numbers from the typical volumetric method are 1.5 × 1015 J, 9 × 1014 J, and 5.1 × 1014 J, respectively. In this study, the operability, applicability, and accessibility of ED-RSM in the assessment of geothermal potential and its ability to provide a reliable output are demonstrated.