"Hidden" hydrothermal technical potential & technoeconomics: Revealing permeability & fluids with more data

作者：Whitney Trainor‐Guitton, Karthik Menon, Pavlo Pinchuk, Sophie Min Thomson, Nicole Hart-Wagoner, Chao Lu, Eli Mlawsky, Mark Coolbaugh, Cary R. Lindsey, James E. Faulds · 发表于：Geothermics · 年份：2025 · DOI：10.1016/j.geothermics.2025.103473 · 被引用次数：3 · 研究领域：Reservoir Engineering and Simulation Methods、Global Energy and Sustainability Research、Hydrocarbon exploration and reservoir analysis

• Hydrothermal technical potential estimates are improved by using name plate capacities as evidence that permeability and fluids are naturally present , whereas previous estimates as used temperature with an exponential relationship to arrive at capacity ( Pinchuk et al., 2023 ). • The versatile XGBoost algorithm is used to train a model to predict hydrothermal capacity with 37 geothermal namplate capacities as labels and 48 geologic and geophysical layers as features and proxy information on permeability and fluids. • Geologic domains are used to help balance the 248 0MW sites with the 37 geothermal power plant data ( nameplate capacities). • Distributions of technical capacity and levelized cost of electricity are provided that significantly narrow the ranges from the temperature-alone estimates. Historical hydrothermal estimates have largely relied on temperature or heat flow estimates ignoring the need for natural flowing fluids. More accurate hydrothermal estimates require some indication of permeability and fluids that naturally exist in the subsurface. This paper describes a novel approach that includes proxies of permeability and fluids in hydrothermal estimates by leveraging the relatively data-rich Great Basin. Specifically, nameplate capacities (megawatts) of operating geothermal plants, negative (0 megawatt) locations and 48 geophysical and geologic features are used to used in eXtreme Gradient Boosting (XGBoost) regression to make hydrothermal capacity predictio...