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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

GBCGE Subsurface Database Explorer and APIs

This submission defines a DOI for the Great Basin Center for Geothermal Energy's (GBCGE) Subsurface Database Explorer web application and underlying data services, and acknowledges the INGENIOUS project as a major source of funding for data compilation and quality assurance. The GBCGE Subsurface Database Explorer is an interactive web mapping application that provides public access to the GBCGE Subsurface Database, and its collection of datasets pertinent to geothermal exploration, oil and gas exploration, critical mineral exploration, and other subsurface characterization for the Great Basin Region, western US. This is a living database, and will be continuously updated with new data and datasets as funding and motivations allow. The underlying database views that populate the web application are on an automated refresh schedule. Data sources and acknowledgements: We thank our partners with the Nevada Division of Minerals (NDOM), the Southern Methodist University (SMU), and Great Basin State Geological Surveys for their active efforts in data curation, schema design, and quality assurance. We also thank contributors among the USGS, Oregon Institute of Technology, State Divisions of Water Resources, State Divisions of Oil, Gas, and Minerals, and State Geological Surveys for open data availability and direct contributions made under the National Geothermal Data System (NGDS).

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
DOE OSTI2020

Environmental Concerns and Mitigation Associated with Geothermal Resource Confirmation Drilling Activities

In 2017-2018, the Bureau of Land Management's National Renewable Energy Coordination Office, through its Geothermal Program, funded the National Renewable Energy Laboratory (NREL) to analyze technical and environmental considerations related to geothermal resource confirmation drilling. NREL solicited input from a group of technical and environmental experts in the geothermal industry, along with analyzing National Environmental Policy Act of 1969 (NEPA) documentation for previously approved projects. The collected data and analysis will be used by the Bureau of Land Management to examine the possibility of developing a new classification of wells and/or expediting the NEPA compliance process, which could potentially reduce permitting and regulatory compliance timelines when compared to the current process for obtaining a geothermal drilling permit for resource confirmation drilling activities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Derisking Superhot Geothermal Plays with Value of Information: Utilizing Play Fairway Analysis, Geophysics and Technoeconomics

This paper describes a methodology for evaluating how the play fairways analysis (e.g., favorability) can improve our chances of making geothermal development decisions. We make statistical resource assessments and couple them with technoeconomic analysis utilizing previous favorability work performed for the Newberry Volcano. We demonstrate how the favorability can be used in a decision analysis framework because the Newberry favorability also estimated an associated uncertainty. The specific decision considered is how large of a power plant to build, which is difficult given the uncertainty about the resource size. Our results focus on two resource types, hydrothermal and enhanced geothermal systems, and they demonstrate how estimates of the minimum, most likely, and maximum estimates of the geothermal resource (denoted as the P10, P50, and P90, respectively) can be used in a decision analysis framework. Lastly, the value of information results explore using favorability with and without the magnetotelluric and gravity data from the Newberry Volcano. As expected, the favorability is more reliable, according to our methodology, at indicating the resource size when it includes the two geophysical models.

enhanced geothermal system↗
DOE OSTI2024

Assessing Suitable Geologic Carbon Storage Sites Across Utah

Utah has a wealth of potential geological reservoirs for carbon dioxide storage (CS) and a long history of geologic research resulting in an abundance of available subsurface data to evaluate CS potential. Reservoirs may include sandstone, carbonate, and basalt; these rock types are plentiful in Utah’s subsurface and the complex Phanerozoic history throughout the state requires evaluating each geologic region individually for promising reservoir-seal pairs for CO2 storage. Classifying Utah by geologic provinces (or “geo-regions”) allows for customized thinking about suitable CS reservoir and seal distribution, CO2 point sources, land use, and existing infrastructure. Preliminary results from this study highlight the geologic CS potential across 15 geo-regions. Four regions stand out as having high CS potential: the Uinta Basin, San Rafael Swell, Paradox Basin, and the southern Basin and Range Province. The Uinta Basin and San Rafael Swell geo-regions are well suited for CS and have several projects ongoing to evaluate Cretaceous Frontier and Naturita Formations, Jurassic Navajo Sandstone and Entrada Sandstone, and Permian Weber Sandstone reservoir units that lie beneath robust sealing units like the ~5000-ft-thick Mancos Shale and Carmel Formation. Reservoirs such as the Navajo and Weber Sandstones have been demonstrated to be suitable reservoirs through a long history of oil and gas exploration in Utah. New areas of interest include the southern Basin and Range in southwest Utah, where the Jurassic Navajo Sandstone is overlain by the sealing Carmel Formation at suitable depths (>3000 ft), and has good porosities based on outcrop analogue data. Just to the north (e.g., central Basin and Range), legacy wells and 2D seismic data show possible salt and subsurface basalt flows that may provide additional possible CS reservoirs and seals. This geo-region also has the advantage of being coupled with geothermal energy resources that may be used to power burgeoning direct air capture technologies. In the Paradox Basin of southeastern Utah, the Leadville Limestone is a potential storage reservoir beneath the thick (4000–5000 ft), salt-bearing Pennsylvanian Paradox Formation. Although the northern and western parts of Utah offer CS potential, these areas typically contain less infrastructure and subsurface penetrations, creating geologic uncertainty associated with subsurface seals and reservoirs due to a lack of data. Overall, this statewide assessment and ranking is the first step to aid in evaluating CS potential across Utah and provides a foundation for future research in the most favorable locations.

58 GEOSCIENCES↗
DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Utah FORGE 3-2535: Joint EM-Seismic-InSAR Imaging of Fracture Properties - Workshop Presentation

This is a presentation on the Joint Electromagnetic/Seismic/InSAR Imaging of Spatial-Temporal Fracture Growth and Estimation of Physical Fracture Properties During EGS Resource Development project by Lawrence Berkeley National Laboratory, presented by Dr. David Alumbaugh, Staff Scientist. The project's objective was to develop a set of technologies and workflow to image induced fracture generation and growth for an Enhanced Geothermal System (EGS). The project anticipates imaging of the fracture generation and growth at FORGE using a combination of passive seismic, active source borehole EM, and INSAR technology. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 8, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

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↗
DOE OSTI2024

Revealing complex subsurface dynamics with continuous seismic monitoring: Observations using distributed acoustic sensing and surface orbital vibrators during hydraulic fracturing

Understanding hydraulic fracturing is crucial to improving the stimulation of unconventional reservoirs and increasing fluid production. This study develops a novel seismic monitoring technology using distributed acoustic sensing (DAS) and surface orbital vibrators (SOV) to capture fracture seismic response and mechanical properties at high temporal intervals. We analyze continuous time-lapse vertical seismic profiling (VSP) data acquired every hour during the first nine days of treatment of an unconventional reservoir in the Austin Chalk/Eagle Field Laboratory. The VSP data contain clear seismic signals scattered from the activated fractures. The spatiotemporal changes of the fracture reflectivity revealed by the SOV/DAS data correlate well with the observations of fracture locations inferred from low-frequency DAS data. These results capture the fracture opening and closure processes, as well as highlight potential prestage activations of the fractures due to hydraulic connectivity with preexisting fracture systems. Therefore, analysis of the presented data set provides a unique opportunity to understand fracture initiation and subsequent evolution, not only in the context of unconventional resources but also in enhanced geothermal systems.

Correa, Julia↗
DOE OSTI2020

Magnetotelluric Data from Mountain Home, ID

This dataset includes magnetotelluric transfer functions in the form of EDI files for 16 stations collected by the USGS and 40 stations collected by Quantec Geoscience for Lawerence Berkeley National Lab around the Mountain Home area in Idaho. A 3D electrical resistivity model is included that images resistive and conductive bodies in the subsurface that maybe important for geothermal characterization. The model was created using ModEM using the high performance computer Yeti at the USGS.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Foam Fracturing Fluid Half-life Experimental Data

Foam thermal stability was studies at Temple University in collaboration with Oak Ridge National Lab (ORNL). The goal of this project is to explore thermally stable foams as hydrofracking fluid media for potential applications in enhanced geothermal system (EGS). Data generated from this project will allow researchers to explore foam as potential fracturing fluid. More than 800 data points on the half-life of foams are recorded in Excel files in the included archive resource (Half-life of Foams with Different Surfactants and Stabilizing Agents). The Excel file within each surfactant folder contains half-life data of the respective surfactant with different stabilizing agents, pressure, and temperature. The respective folders also contains Word files describing the details of the data included in the respective Excel sheet.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Hybrid geothermal-wind integration in the US energy grid

Here, we combine US wind generation—a cheap yet intermittent source of electricity—with the latest geothermal resource estimates to understand the technoeconomic possibilities of pairing enhanced geothermal systems (EGSs) with existing wind farms to develop a hybrid energy system. Using observed generation data from 583 wind farms, generation gaps are quantified and geographically paired with the latest EGS estimates. Results demonstrate that EGS potential within a 1 km 2 footprint can supplement wind generation at 56% of onshore wind farms. Each wind farm can be supplemented by EGSs when 10% of its surface-occupying footprint is available. The cost of EGSs at wind farms is lowest in the western US and southern Texas border and highest in the central US. While further experiments are warranted, a wind + EGS hybrid system offers an opportunity to increase power output from the same land footprint while maximizing the use of existing electrical infrastructure.

EGS↗
DOE OSTI2024

Assessing the Viability of Geothermal Microgrid Deployment: A Geospatial Analysis Across the United States

Geothermal microgrids hold a potential of supplying clean and dependable power to communities throughout the United States (US), all while sidestepping the expenses associated with connecting to strained or isolated power grids. Nonetheless, their implementation is still in its early stages in the country. The objective of this analysis is to leverage available data to pinpoint regions across the US that exhibit favorable conditions for the development of geothermal microgrids. Drawing from a variety of sources, including estimates of geothermal resources, the costs associated with geothermal energy generation and electricity transmission, existing microgrid locations, and subsidy programs, we aim to identify promising areas for further exploration. By mapping out the contiguous US, Alaska, and Hawaii, we delineate regions with high relative favorability for geothermal microgrid deployment. Our findings reveal the presence of highly favorable regions across the Western states of the contiguous US, as well as isolated areas in Alaska and Hawaii. Furthermore, we delve into a discussion on state policies and incentive programs, considering their role in fostering favorable conditions or posing barriers to geothermal microgrid development.

Alaska↗
DOE OSTI2024

Evolution of a Geological Model for Co-Producing Electricity at the Blackburn Oil Field, Nevada: Preprint

Oil fields characterized by significant proportions of hot water in the reservoir offer a unique opportunity for geothermal energy co-production and utilization. Development of an accurate geological model is essential in understanding the subsurface. However, this is a challenge at the preliminary stages of resource assessment when data on the subsurface parameters and subterranean fault network is scarce. Our focus in this paper is to explore a methodology for developing an accurate conceptual geological model of the complexly faulted reservoir at the Blackburn Field, Nevada, to create a numerical simulation that will eventually be used to assess the potential of co-producing electricity. There are four steps to the evolution of this model: (1) collection of public data and research on the geological setting of the system; (2) digitization of seismic time-interpretation and cross-sections to create the most representative model of the subsurface; and (3) creation of a natural-state numerical model to simulate the thermal gradient through the subsurface. Utilizing publicly available data of the geological system and Leapfrog conceptual modeling software, we refined a preliminary model to be more representative of the subsurface geology of an active geothermal exploration stage project. The conceptual geological model developed has been imported into VOLSUNG reservoir modeling software to create a natural-state model, further characterizing the Blackburn Field reservoir's thermal gradient. In the future, new seismic data will be utilized to further evolve the conceptual geological model for numerical simulations.

Basin and Range province↗
DOE OSTI2023

Utah FORGE: Fault Reactivation Through Fluid Injection Induced Seismicity Laboratory Experiments

Included are results from shear reactivation experiments on laboratory faults pre-loaded close to failure and reactivated by the injection of fluid into the fault. The sample comprises a single-inclined-fracture (SIF) transecting a cylindrical sample of Westerly granite. All experiments are conducted at ambient temperature and follow a similar protocol: (i) application of confining stresses (3MPa) on the fault fully saturated with DI water, (ii) shear-mobilization through the increase of axial loading at a constant displacement rate until a post-peak steady-state condition is reached, (iii) reduction of axial loading and related shear stress to a prescribed fraction of the peak steady-state frictional strength (typically 60% to 90%, representing intermediate to high magnitudes) and (iv), fault reactivation triggered by a stepwise increase of pore pressure on the fault in 0.1 MPa increments held constant for 1-5 minutes. Mechanical data from three ISCO pumps connected to a Temco pressure vessel measure axial, confining, and fault-related parameters, including fluid pressure (kPa), fluid flow rate (mL/min), and axial displacement (mm). See included code for initial data analysis and visualization for select experiments. Resource names represent experiment numbers found in the "Read Me" file, which describes each experimental setup and parameters.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Empowering Geothermal Research: The Geothermal Data Repository's New AI Research Assistant

The Department of Energy's (DOE) Geothermal Data Repository (GDR) team has integrated a Large Language Model (LLM) with the metadata and supporting documents associated with GDR datasets to create an Artificially Intelligent (AI) research assistant. By leveraging work done to make GDR metadata machine-readable and an open-source LLM integration model called the Energy Language Model, developed by the National Renewable Energy Laboratory, AskGDR serves as a virtual research assistant to GDR users. It provides answers to a variety of user-provided questions using natural language processing and generative machine learning. Users can get answers to questions about specific datasets, including inquiries about the equipment, assumptions and methodologies used in the origination of the data; or more abstract questions, such as the applicability of data to specific research fields. AskGDR improves the discoverability of geothermal data by helping guide users to datasets beyond simple keyword searches. It enables users to find data based on properties of the data, discover information contained within supporting documents, and explore data from projects related to their research objectives. This paper will outline the development, integration, output, and efficacy of the AskGDR LLM, including adherence to scientific rigor through improvements designed to increase the accuracy of generated answers, avoid speculation, and provide proper references for all resources used.

access↗
DOE OSTI2023

Utah FORGE 2-2446: Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity - Workshop Presentation

This is a presentation on the Closing the Loop Between In-situ Stress Complexity and EGS Fracture Complexity project by Lawrence Livermore National Laboratory, presented by Dr. Matteo Cusini. The project's objective was to employ a combination of high-fidelity simulations and true-triaxial block fracturing tests at high temperature to explore the intricate relationship between in-situ stress and hydraulic fracture patterns and better characterize the in-situ stress at Utah FORGE. This presentation was featured in the Utah FORGE R&D Annual Workshop on September 7, 2023. The workshop provided a valuable opportunity to explore the progress made in each of the 17 Research and Development projects funded under Solicitation 2020-1 which aim to enhance our understanding of the crucial factors influencing the development of Enhanced Geothermal Systems (EGS) reservoirs and resources.

15 GEOTHERMAL ENERGY↗