The Great Basin region (GBR) of the Basin and Range province in the western USA is an active extensional to transtensional setting that contains a wide variety of geothermal plays, including 1) conventional hydrothermal systems largely controlled by favorable settings along Quaternary fault zones, 2) sparse magmatic systems concentrated along its western and eastern margins, 3) hot sedimentary aquifers, 4) conductive heat envelopes encompassing conventional systems and conducive for EGS development, and 5) enhanced geothermal systems (EGS) independent of conventional hydrothermal and resulting from high regional geothermal gradients. For exploration strategies and regional geothermal assessments, these different play types are not mutually exclusive. Conductive heat envelopes surround convective hydrothermal systems, and most EGS development to date has been located in these conductive zones. Some deeper basins likely contain hot sedimentary aquifers, which may locally be enhanced by convective flow, further accentuating geothermal capacity in parts of the GBR. However, both hot sedimentary aquifers and conductive heat envelopes around hydrothermal systems are poorly constrained by available drilling, which is generally sparse outside the permeable nuclei of convective hydrothermal systems. Nonetheless, many basins contain multiple favorable structural settings that host several independent hydrothermal systems. Considering the abundance and variety of play types, high resource potential may be semi-continuous across broad portions of the GBR. This suggests that regional assessments have underestimated the geothermal potential of the GBR. Optimal exploration of the full spectrum of interrelated play types requires 1) greater coverage of detailed geological and geophysical datasets, which would facilitate improved characterization and modeling of geothermal resources, and 2) application of statistical analyses and machine learning techniques to refine workflows to utilize and appropriately weight multiple datasets indicative of geothermal activity. Combining supervised and unsupervised machine learning methods appears to yield the most robust results for assessing conventional resources but have yet to be fully applied in evaluating next generation play types.
James Faulds is a Professor with the Nevada Bureau of Mines and Geology (NBMG) at the University of Nevada, Reno (UNR). NBMG is a research and public service unit of UNR and is the state geological survey of Nevada. Faulds is a structural geologist with more than 35 years of experience. He has been with UNR and NBMG since 1997, first as Professor and also serving as NBMG Director/Nevada State Geologist from 2012-2024. Prior to coming to UNR, he was an Assistant Professor at the University of Iowa from 1992 to 1997. He earned his B.S. at the University of Montana, M.S. at the University of Arizona, and Ph.D. at the University of New Mexico. His research has focused on how fault systems initiate and evolve through time, as well as the structural settings and exploration strategies for geothermal systems. His geothermal journeys have included short-term appointments with the BRGM in France, both GNS Science and the University of Canterbury in New Zealand, and University of Bern in Switzerland while on sabbaticals. He has published over 100 papers and dozens of geologic maps, taught a variety of courses in the geosciences, and served as advisor for more than 30 graduate students. Service contributions include stints as President of the Nevada Petroleum and Geothermal Society and President of the Association of American State Geologists.
