Enhanced Geothermal Systems (EGS) present a significant and long-term opportunity for widespread renewable power production. The EGS approach makes it possible to utilize otherwise inaccessible geothermal resources. It is estimated that within the U.S. alone the electricity production potential of EGS is in excess of 100 GWe. Hence, the efforts to accurately model and predict the performance of EGS reservoirs under various reservoir conditions (e.g., formation permeability, reservoir temperature, existing fracture/fault connectivity, geothermal tracers, and the in-situ stress distribution) are vital. We present a site-scale and regional-scale modeling study based on field data (e.g., geochemical tracers, groundwater velocity, initial temperatures from the wells) from New Mexico. The modeling includes coupled fluid flow, energy transport, and tracer modeling by using PFLOTRAN, a massively parallel multi-physics subsurface simulator. The subsurface material properties are varied to generate multiple realizations of simulation datasets. Based on the generated simulation data, we investigate the geothermal potential and the impact of associated subsurface uncertainties (e.g, permeability, water table depth, heat flow, discharge temperature, water chemistry, flow velocity) that can influence the discovery and development of hidden geothermal resources. The datasets and simulation results generated in this study provide the foundation for ongoing supervised and unsupervised machine learning analyses. The work presented in this paper is a part of machine learning for geothermal energy, which is being supported by the US Department of Energy – Geothermal Technologies Office.

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