Stochastic workflows for the evaluation of Enhanced Geothermal System (EGS) potential in geothermal greenfields with sparse data: the case study of Acoculco, Mexico

This paper presents a workflow for resource characterization and assessment of exploration geothermal fields with minimum data. Our approach utilizes stochastic methods to estimate the temperature distribution at potential target depths by focusing on the impact of uncertain input parameters such as thermal conductivity and porosity. We first perform stochastic forward simulations to determine the initial steady-state thermal field and subsequently quantify the uncertainty via a Monte Carlo approach known as Sequential Gaussian Simulation (SGSim). Next, we analyze the in-field likelihood of success for Enhanced Geothermal Systems by simulating hypothetical energy production scenarios based on existing geothermal installations. This approach is applied to the case study of a Hot Dry Rock geothermal field with two exploration wells, located in Acoculco, Mexico. Data scarcity in this field necessitates the use of stochastic methods for plausible prediction of reservoir temperature used to determine the accessible thermal power. Once reliable temperature estimates are obtained at potential target depths, we simulate production scenarios by assuming a prior successful stimulation process in the existing wells. In addition to providing preliminary estimates of thermal power for different injection/production rates, stimulated volumes and created permeability, we present the long-term impact of production on the temperature and pressure fields.