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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 Carlodoi:10.18154/rwth-2020-06608 fatcat:qisfuhauj5g2nprngr6off4jm4