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Abstract We investigated the techno-economic feasibility and power supply potential of enhanced geothermal systems (EGS) across the contiguous United States using a new subsurface temperature model and detailed simulations of EGS project life cycle. Under business-as-usual scenarios and across depths of 1–7 kilometers, we estimated 82,945 GW and 0.65 GW of EGS supply capacity with lower levelized cost of electricity than conventional hydrothermal and solar photovoltaic projects, respectively. Considering the scenario of flexible geothermal dispatch via wellhead throttling and power plant bypass, these estimates climbed up to 184,112 GW and 44.66 GW, respectively. The majority of EGS supply potential was found in the Western and Southwestern regions of the United States, where California, Oregon, Nevada, Montana, and Texas had the greatest EGS capacity potential. With advanced drilling rates based on state-of-the-art implementations of recent EGS projects, we estimated an average improvement of 25.1% in the levelized cost of electricity. These findings underscored the pivotal role of flexible operations in enhancing the competitiveness and scalability of EGS as a dispatchable renewable energy source.

Abstract This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of $$18 \ km^2$$ 18 k m 2 per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of $$0-7 \ km$$ 0 - 7 k m at an interval of $$1 \ km$$ 1 k m . Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of $$6.4^\circ C$$ 6 . 4 ∘ C , $$6.9 \ mW/m^2$$ 6.9 m W / m 2 and $$0.04 \ W/m-K$$ 0.04 W / m - K , respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu , feature layers on ArcGIS at https://arcg.is/nLzzT0 , and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592 .

Insufficient understanding of the microbial communities and associated microbial processes in geological reservoirs hinders the utilization of this rich data source for improved resource management. In this study, along with four interwell tracer tests at a 1478-m deep fractured crystalline-rock aquifer, we analyzed the microbial communities in the injected and produced water by high-throughput sequencing. The microbial community similarities across boreholes during an interwell flow scenario frequently encountered in reservoir development was explored. Despite the significant tracer recoveries (~30%) in all tracer tests and the cumulatively >100,000 L of exogenous water (carrying exogenous microbes) injected into the 10-m-scale reservoir, the overall structure of produced-fluid microbiome did not increasingly resemble that of the injectate. However, producers with better connectivity with the injector (based on tracer test results) did have more amplicon sequence variants (ASVs) that overlapped with those in the injectate. We identified possible drivers behind our observations and verified the practicality of repeated microbial sampling in the context of reservoir characterization and long-term monitoring. We highlight that injector-producer microbial profiling could provide insights on the relative connectivities across different producers with a given injector, and that the associated logistical needs may be comparable or even less than that of classic tracer tests.