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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Geothermal-integrated thermally anisotropic building envelope for energy and peak-demand reduction

Buildings consume large amounts of energy for heating and cooling, while peak electricity demand places significant stress on the power grid. This paper presents a reduced-order co-simulation framework and load-oriented supervisory control strategy for a geothermal-integrated thermally anisotropic building envelope with a ground loop (TABE+GL). In TABE+GL, a hydronic loop embedded in the building envelope is directly coupled with a geothermal ground loop, allowing for bidirectional heat exchange between the envelope, the ground, and the indoor environment. A hybrid co-simulation framework was established by coupling a reduced-order resistor–capacitor (RC) thermal network model with EnergyPlus augmented with GHEDesigner modules. The RC model generated feasible heat flux options under three operating modes, and EnergyPlus predicted sensible loads, energy use, and pump energy demand. At each simulation step, a supervisory control algorithm selected the optimal loop configuration and duty factor that maximizes useful TABE geothermal utilization without exceeding the predicted sensible load, thereby avoiding overheating or cooling. Case studies were conducted for Los Angeles, California, Charleston, South Carolina, and Denver, Colorado. Results showed that the proposed framework reduced HVAC electricity consumption by 43%–67%, natural gas use for space heating by 11%–38%, and peak electricity demand by 43%–88%. These results highlight the potential of combining reduced-order envelope modeling, direct geothermal coupling, and load-oriented supervisory control to improve whole building energy performance and reduce peak demand across diverse weather conditions.

Howard, Daniel [Southern Adventist University]↗
DOE OSTI2021

REopt Lite Geothermal Heat Pump Design Requirements

This document describes the design requirements for the geothermal heat pump (GHP) module being added to the existing REopt Lite web tool. This document describes the purpose, users, and functional requirements to which the modified web tool shall conform. This document will be revised, as required, throughout the development phase with consensus between the Department of Energy (DOE) Geothermal Technologies Office (GTO) and NREL. The GHP module will expand existing REopt Lite capabilities to include techno-economic optimization of GHP systems, either stand alone, or integrated with the other existing technology types, namely solar photovoltaics (PV), wind power, battery energy storage, thermal energy storage, combined hear and power (CHP), and absorption chiller. Included in this submission are links to the REopt Lite web tool and open-source Git-Hub page. The REopt Lite tool can also be accessed directly via the NREL Developer Network. A link is also provided for the REopt report that overviews the module's capabilities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Three-dimensional cooperative inversion of airborne magnetic and gravity gradient data using deep-learning techniques

Using multiple geophysical methods has become a prevailing approach in numerous geophysical applications to investigate subsurface structures and parameters. These multimethod-based exploration strategies have the potential to greatly diminish uncertainties and ambiguities encountered during geophysical data analysis and interpretation. One of the applications is the cooperative inversion of airborne magnetic and gravity gradient data for the interpretation of data obtained in mineral, oil and gas, and geothermal explorations. In this paper, a unified cooperative inversion framework is designed by combining the standard separate inversions with a deep neural network (DNN), which serves as the link between different types of data. A well-trained DNN takes the separately inverted susceptibility and density models as the inputs and provides improved models that will be used as the initial models of deterministic inversions. A two-round iteration strategy is adopted to guarantee the reasonability of the recovered models and overall efficiency of the inversion. In addition, this deep-learning (DL)-based framework demonstrates excellent generalization abilities when tested on models that are entirely distinct from the training data sets. The framework can easily incorporate multiphysics without necessitating any structural changes to the network. Synthetic experiments validate that our DL-based method outperforms conventional separate inversions and cross-gradient-based joint inversion in view of the accuracy of the recovered models and inversion efficiency. Successful application to field data further verifies the effectiveness of our DL-based method.

Geochemistry & Geophysics↗
DOE OSTI2023

Resource Characterization to Estimate Potential for Electricity Co-Production at Blackburn Oil Field, Nevada

The U.S. Department of Energy estimates that an annual average of 25 billion barrels of hot water are produced from oil and gas wells within the United States. The thermal energy available in the co-produced water stream is usually discarded, as the produced waters are considered an inconvenience by the operators and are disposed of using injection wells. However, utilizing organic Rankine cycle (ORC) generators, a vast amount of thermal energy can be captured and converted into electricity (albeit at relatively low efficiency due to the low temperatures). The National Renewable Energy Laboratory (NREL), in collaboration with Transitional Energy and Grant Canyon Oil & Gas, evaluated the feasibility of geothermal co-production of electricity by utilizing existing oil wells in Blackburn oil field in Nevada. The once prolific Blackburn oil field is located in Pine Valley, approximately 45 miles east-southeast of Elko, Nevada. Currently, the wells targeting the highly fractured Devonian Nevada dolomite reservoir are operating at a water cut ratio of more than 99%, with individual fluid (oil and water) production rates reaching 7.4 L/s (4,021 BBL/day). Analysis of publicly available data showed that the combination of the suitable wells' maximum historical production rates reached 22.90 L/s. The production from these wells occurs naturally and the wells are choked (and even shut down) by the operator to mitigate excessive water production, indicating a strong reservoir recharge and future opportunity to increase the water production for geothermal electricity generation. The main goal of this study was to evaluate the productivity of the existing wells, the performance of the reservoir, the surface network, and the operational constraints in order to achieve 1 MWe of electricity production from the field's water production. Utilizing the GEOPHIRES tool, we have determined that a twofold to threefold increase in the total fluid production, compared to the historical production under artificial restraint (choke), is required to reach a 1-MWe net target output for a low-temperature ORC system with air-cooled condensers. Lower flow rates would be required when utilizing water-based condensers instead of air-cooled condensers. However, that would require a constant supply of cold water, which may be challenging given the arid environment of the project site.

Blackburn↗
DOE OSTI2021

"Hybrid fracture/matrix modeling for well completion options evaluation"

Orientation and completion for well pairs that have been subjected to multi-zonal stimulation play a critical role in the long-term performance of an Enhanced Geothermal Reservoir. Here we present the development of a methodology to rapidly and efficiently numerically simulate mixed fracture-matrix flow systems for evaluation of well design and completion options. The methodology is based on a loose coupling framework, allowing the fracture and matrix systems to be meshed separately. The fracture system includes the integration of fracture growth and aperture data from well stimulation simulations of stochastically generated fracture networks. Automatic mesh refinement is used in the matrix simulation to resolve heat transfer near the fracture network. This simulation framework is used to efficiently determine optimal production and injection well placement using adaptive sampling.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data: Preprint

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDRs success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

accessibility↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDR's success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

access↗
DOE OSTI2021

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady geothermal field, Nevada, USA

In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a throughgoing network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density.

58 GEOSCIENCES↗
DOE OSTI2021

3-D Geologic Controls of Hydrothermal Fluid Flow at Brady Geothermal Field, Nevada using PCA

In many hydrothermal systems, fracture permeability along faults provides pathways for groundwater to transport heat from depth. Faulting generates a range of deformation styles that cross-cut heterogeneous geology, resulting in complex patterns of permeability, porosity, and hydraulic conductivity. Vertical connectivity (a through going network of permeable areas that allows advection of heat from depth to the shallow subsurface) is rare and is confined to relatively small volumes that have highly variable spatial distribution. This local compartmentalization of connectivity represents a significant challenge to understanding hydrothermal circulation and for exploring, developing, and managing hydrothermal resources. Here, we present an evaluation of the geologic characteristics that control this compartmentalization in hydrothermal systems through 3-D analysis of the Brady geothermal field in western Nevada. A published 3-D geologic map of the Brady area is used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity. The 3-D distribution of these variables is compared to the distribution of productive and non-productive fluid flow intervals along production wells and non-productive wells via principal component analysis (PCA). This comparison elucidates which geologic and structural variables are most closely associated with productive fluid flow intervals. Results indicate that production intervals at Brady are located: (1) within or near to known and stress-loaded macro-scale faults, and (2) in areas of high fault and fracture density. This submission includes the published journal article detailing this work, the published 3-D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3-D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using existing R programs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior (Final Technical Report)

Enhanced Geothermal Systems (EGS) produce clean energy by circulating fluid through hot rock deep underground and bringing that heat to the surface to generate electricity. For this process to work reliably, fluids must be able to move efficiently through networks of natural or engineered fractures in the rock. Enhancing and maintaining subsurface permeability over time is essential for sustainable energy production. However, fluid injection changes the underground temperature, pressure, rock stress, and chemistry, which can alter permeability and sometimes trigger earthquakes. Predicting these interconnected processes remains a key challenge. To address this, we combined high-temperature laboratory experiments with high-fidelity simulations to better understand how fractures in geothermal reservoirs evolve over time. Our experiments measured how fractures respond to stress, slip, slip rate, and chemical reactions under geothermal conditions. These data were integrated into coupled thermal-hydrological-mechanical-chemical and earthquake (THMC+E) models tailored to the Utah FORGE site. The validated modeling framework improves predictions of reservoir performance and seismic response and helps guide operational decisions. This work reduces technical risk and strengthens the scientific foundation needed to make geothermal energy a reliable and scalable clean energy resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Cooperative Research and Development Agreement between National Energy Technology Laboratory and West Virginia University Research Corporation (Abstract)

The National Energy Technology Laboratory (NETL) and West Virginia University Research Corporation (Participant) will collaborate on the testing of a 3D printed sparger in a temperature and pressure chamber, for the purpose of supporting the “American-Made Geothermal Prize” competition, designed to catalyze manufacturing innovation in the American geothermal industry by harnessing the rapid advances additive manufacturing can provide in improving design, fabrication, and functionality, which is accomplished through a series of prize competitions and the development of a diverse and powerful support network that leverages national laboratories, energy incubators, and other resources from across the United States. The intent is to connect competitor teams with national laboratories that can help accelerate the development of innovative solutions and products. Teams who have won the Set!, Go!, Make! Contests are eligible to utilize vouchers at national laboratories to advance their ideas.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Geothermal district energy systems coupled with seasonal underground thermal energy storage: a U.S. techno-economic screening by climate and geology

In the United States, cooling-dominated commercial building loads can cause geothermal heat pump-based district energy systems to accumulate a long-term subsurface thermal imbalance, motivating the incorporation of seasonal underground thermal energy storage. We developed a transferable workflow to evaluate geothermal district systems that pair ground heat exchangers with seasonal underground thermal energy storage. Using standardized hourly loads for seven commercial buildings and a uniform cost framework, we simulated ten U.S. cities with a physics-based ground heat exchanger model, subsurface storage simulations, and economic assessment to isolate the roles of climate and hydrogeology. In cooling-dominated cities, underground thermal energy storage supplied the majority of annual cooling, cutting electricity use and summer peaks substantially while achieving levelized costs comparable to or below conventional chiller-boiler plants. In cooler climates, the storage share shrunk, required borefield size and costs rose, and levelized cost of energy increased nearly linearly with declining underground thermal energy storage fraction, indicating storage fraction as the primary economic lever. Sensitivity analysis showed capital risk dominated by borefield drilling and surface heating, ventilation, and air-conditioning and piping, with underground thermal energy storage costs secondary. This workflow provides a transparent foundation for site-specific design and screening of next-generation geothermal district energy systems.

25 ENERGY STORAGE↗
DOE OSTI2023

A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations.

Yazbeck, Joe (ORCID:0000000302235260)↗
DOE OSTI2025

The value of integrating a geothermal district heating system into a microgrid

As electrical grids increasingly rely on variable renewable energy, maintaining reliability and cost efficiency becomes more complex. To address these challenges, this study analyzed the integration of geothermal district heating as a grid-responsive thermal resource within a microgrid in Tuttle, Oklahoma. Building energy modeling using EnergyPlus estimated annual district heating demand at 2.9 GWh, with a peak load of 2.8 MW th . Techno-economic analyses were conducted to meet the heating demand under three geothermal scenarios, varying by production depth, flow rate, and thermal output, each supplemented by natural gas peaking boilers. In parallel, equivalent electrical load profiles were developed using typical coefficients of performance (COPs) for air-source heat pumps and electric boilers to establish an electrified baseline scenario. A complete end-use electrical load profile was also developed for the microgrid using Cambium dataset. The modeling results demonstrated reliable and economic operation of the geothermal systems over 30 years, with COPs ranging from 2.6 to 8.9 and the lowest levelized heating cost at $\$$54.6/MWh. Geothermal integration reduced electricity consumption by up to 94.7 % compared to the non-geothermal base case, yielding annual energy savings of up to $\$$803 k. Avoided grid costs ranged from $\$$65 k–$\$$147 k per year, with individual events avoiding up to $\$$4,863 per hour. Grid-responsive operation further reduced wholesale energy costs by 53–56 %. These findings demonstrate geothermal heating, traditionally treated as a non-grid-responsive thermal resource, can be reconfigured to support dynamic grid services, offering a scalable pathway to enhance reliability and reduce costs in renewable-rich microgrids and district heating networks.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

GOOML Big Kahuna Forecast Modeling and Genetic Optimization Files

This submission includes example files associated with the Geothermal Operational Optimization using Machine Learning (GOOML) Big Kahuna fictional power plant, which uses synthetic data to model a fictional power plant. A forecast was produced using the GOOML data model framework and fictional input data, and a genetic optimization is included which determines optimal flash plant parameters. The inputs and outputs associated with the forecast and genetic optimization are included. The input and output files consist of data, configuration files, and plots. A link to the Physics-Guided Neural Networks (phygnn) GitHub repository is also included, which augments a traditional neural network loss function with a generic loss term that can be used to guide the neural network to learn physical or theoretical constraints. phygnn is used by the GOOML framework to help integrate its machine learning models into the relevant physics and engineering applications. Note that the data included in this submission are intended to provide a demonstration of GOOML's capabilities. Additional files that have not been released to the public are needed for users to run these models and reproduce these results. Units can be found in the readme data resource.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs

This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geophones and distributed acoustic sensing (DAS) systems. These technologies were deployed across several locations, including wells 16A, 16B, and Delano-1, with sensor arrays at multiple depths to capture microseismic activity during the stimulations. The catalogs provide both real-time and manually checked seismic event locations, with detailed parameters such as trigger conditions, velocity models, and data acquisition settings. The dataset includes information on the stimulation stages, event rates, and hydraulic injection conditions for each well, with a report detailing the data acquisition configuration and seismic event location methodologies. Users will need to reference the included report for a complete understanding of the sensor network, data processing techniques, and accuracy considerations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

U.S. Geothermal District Heating Systems and Funding Data

This dataset contains detailed information on geothermal district heating (GDH) systems across the United States, including technical specifications, costs, energy usage, and funding sources. Data includes system details such as state, site, location, operational years, temperature, flow rate, capacity, energy use, load factor, costs (original, updated, and operational), funding sources, employment impact, and project status. Additionally included are contributions to GDH systems, including federal, state, and local grants, as well as loans from agencies such as USDA and USDOE. This dataset serves as a resource for analyzing the economic and technical landscape of geothermal district heating in the United States.

15 GEOTHERMAL ENERGY↗