Combining Probabilistic Volumetric and Numerical Simulation Approaches to Improve Estimates of Geothermal Resource Capacity

Abstract

Confidence in estimates of geothermal resource electricity generation capacity can be improved by a probabilistic approach that combines numerical reservoir simulation with the classic volumetric estimation based on fluid mass in place. A simplified reservoir simulation model is used to examine the sensitivity of resource performance to changes in the assumptions concerning key reservoir parameters. In particular, this process establishes the expected variability of the volumetric recovery factor with respect to the likely variations in resource thermodynamic and hydraulic characteristics. Recovery factor is defined as the fraction of fluid in place that can be produced as usable steam over the life of the project. In typical volumetric estimates, the recovery factor is treated as an independent variable and is chosen somewhat arbitrarily based on experience with other geothermal fields. The important innovation in this method is a more systematic approach establishing the dependence of recovery factor on certain key reservoir parameters. Probability distributions are assigned to account for the uncertainty in reservoir parameters that affect recovery factor and the volumetric estimation of fluid mass in place. Monte Carlo simulation is then used to assess the probability of occurrence of a given electricity generating capacity based on steam recovery and mass in place. This approach treats recovery factor as a dependent variable. The outcome of this capacity estimation is therefore explicitly linked to the key thermodynamic and hydraulic characteristics of the resource and to their uncertainty level, rather than to volumetric parameters alone. This method is particularly suited to capacity estimates in the early exploration stages of a geothermal prospect, when only a few deep wells have been drilled and numerical reservoir simulation is not well constrained. It provides a more coherent approach to the estimation of both proven reserves and upside potential, with both being defined in terms of the required confidence level needed for making investment decisions.