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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

Geothermal Case Studies

The US Geological Survey (USGS) resource assessment (Williams et al., 2009) outlined a mean 30GWe of undiscovered hydrothermal resource in the western US. One goal of the Geothermal Technologies Office (GTO) is to accelerate the development of this undiscovered resource. The Geothermal Technologies Program (GTP) Blue Ribbon Panel (GTO, 2011) recommended that DOE focus efforts on helping industry identify hidden geothermal resources to increase geothermal capacity in the near term. Increased exploration activity will produce more prospects, more discoveries, and more readily developable resources. Detailed exploration case studies akin to those found in oil and gas (e.g. Beaumont, et al, 1990) will give operators a single point of information to gather clean, unbiased information on which to build geothermal drilling prospects. To support this effort, the National Renewable Energy laboratory (NREL) has been working with the Department of Energy (DOE) to develop a template for geothermal case studies on the Geothermal Gateway on OpenEI. In fiscal year 2013, the template was developed and tested with two case studies: Raft River Geothermal Area (http://en.openei.org/wiki/Raft_River_Geothermal_Area) and Coso Geothermal Area (http://en.openei.org/wiki/Coso_Geothermal_Area). In fiscal year 2014, ten additional case studies were completed, and additional features were added to the template to allow for more data and the direct citations of data. The template allows for: Data - a variety of data can be collected for each area, including power production information, well field information, geologic information, reservoir information, and geochemistry information. Narratives ? general (e.g. area overview, history and infrastructure), technical (e.g. exploration history, well field description, R&D activities) and geologic narratives (e.g. area geology, hydrothermal system, heat source, geochemistry.) Exploration Activity Catalog - catalog of exploration activities conducted in the area (with dates and references.) NEPA Analysis ? a query of NEPA analyses conducted in the area (that have been catalogued in the OpenEI NEPA database.) In fiscal year 2015, NREL is working with universities to populate additional case studies on OpenEI. The goal is to provide a large enough dataset to start conducting analyses of exploration programs to identify correlations between successful exploration plans for areas with similar geologic occurrence models.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Innovative computational tools for reducing exploration risk through integration of water-rock interactions and magnetotelluric surveys

Mapping permeability distributions in geothermal reservoirs is essential for reducing the cost of geothermal development. To avoid the cost and sampling bias of measuring permeability directly through drilling, we require remote methods of imaging permeability such as geophysics. Electrical resistivity (or its inverse, conductivity) is one of the most sensitive geophysical properties known to reflect long range fluid interconnection and thus the likelihood of permeability. Perhaps the most widely applied geophysical methods for imaging subsurface resistivity is magnetotellurics (MT) due to its relatively great penetration depths. A primary goal of this project is to confirm through ground truthing at existing geothermal systems that MT resistivity structure interpreted integratively is capable of revealing permeable fluid pathways into geothermal systems.

15 GEOTHERMAL ENERGY↗