This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area.

Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency.

For additional project information, see the INGENIOUS project site linked in the submission.

Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.

This submission contains links to Geothermal Areas on OpenEI that were completed as part of an effort to gather clean, unbiased information on which to build geothermal drilling prospects. The specific areas that were part of this focused effort, or case studies, are linked individually, and also available for download as a table in CSV format.

The Geothermal Areas exist live on OpenEI and are constantly evolving with updated information. Snapshots of both the specific case study areas and of all geothermal areas on OpenEI, from the time of submission, have been included in CSV format. For the most up-to-date information, please use the provided All Geothermal Areas link to view these on OpenEI.

This dataset also includes a link to the Geothermal Exploration Overview page on OpenEI, which provides exhaustive detail on the activities cataloged for these case studies and their references, as well as the technologies employed in each geothermal area.

To better understand the heat production, electricity generation performance, and economic viability of closed-loop geothermal systems in hot-dry rock, the Closed-Loop Geothermal Working Group -- a consortium of several national labs and academic institutions has tabulated time-dependent numerical solutions and levelized cost results of two popular closed-loop heat exchanger designs (u-tube and co-axial). The heat exchanger designs were evaluated for two working fluids (water and supercritical CO2) while varying seven continuous independent parameters of interest (mass flow rate, vertical depth, horizontal extent, borehole diameter, formation gradient, formation conductivity, and injection temperature). The corresponding numerical solutions (approximately 1.2 million per heat exchanger design) are stored as multi-dimensional HDF5 datasets and can be queried at off-grid points using multi-dimensional linear interpolation. A Python script was developed to query this database and estimate time-dependent electricity generation using an organic Rankine cycle (for water) or direct turbine expansion cycle (for CO2) and perform a cost assessment. This document aims to give an overview of the HDF5 database file and highlights how to read, visualize, and query quantities of interest (e.g., levelized cost of electricity, levelized cost of heat) using the accompanying Python scripts. Details regarding the capital, operation, and maintenance and levelized cost calculation using the techno-economic analysis script are provided.

This data submission will contain results from the Closed Loop Geothermal Working Group study that are within the public domain, including publications, simulation results, databases, and computer codes.

GeoCLUSTER is a Python-based web application created using Dash, an open-source framework built on top of Flask that streamlines the building of data dashboards. GeoCLUSTER provides users with a collection of interactive methods for streamlining the exploration and visualization of an HDF5 dataset. The GeoCluster app and database are contained in the compressed file geocluster_vx.zip, where the "x" refers to the version number. For example, geocluster_v1.zip is Version 1 of the app. This zip file also contains installation instructions.

**To use the GeoCLUSTER app in the cloud, click the link to "GeoCLUSTER on AWS" in the Resources section below. To use the GeoCLUSTER app locally, download the geocluster_vx.zip to your computer and uncompress this file. When uncompressed this file comprises two directories and the geocluster_installation.pdf file. The geo-data app contains the HDF5 database in condensed format, and the GeoCLUSTER directory contains the GeoCLUSTER app in the subdirectory dash_app, as app.py. The geocluster_installation.pdf file provides instructions on installing Python, the needed Python modules, and then executing the app.

This project is a component of a broader effort focused on geothermal heating and cooling (GHC) with the aim of illustrating the numerous benefits of incorporating GHC and geothermal heat exchange (GHX) into community energy planning and national decarbonization strategies. To better assist private sector investment, it is currently necessary to define and assess the potential of low-temperature geothermal resources. For shallow GHC/GHX fields, there is no formal compilation of subsurface characteristics shared among industry practitioners that can improve system design and operations. Alaska is specifically noted in this work, because heretofore, it has not received a similar focus in geothermal potential evaluations as the contiguous United States. The methodology consists of leveraging relevant data to generate a baseline geospatial dataset of low-temperature resources (less than 150 degrees C) to compare and analyze information accessible to anyone trying to understand the potential of GHC/GHX and small-scale low-temperature geothermal power in Alaska (e.g., energy modelers, communities, planners, and policymakers). Importantly, this project identifies data related to (1) the evaluation of GHC/GHX in the shallow subsurface, and (2) the evaluation of low-temperature geothermal resource availability. Additionally, data is being compiled to assess repurposing of oil and gas wells to contribute co-produced fluids toward the geothermal direct use and heating and cooling resource potential. In this work we identified new data from three different datasets of isolated geothermal systems in Alaska and bottom-hole temperature data from oil and gas wells that can be leveraged for evaluation of low-temperature geothermal resource potential. The goal of this project is to facilitate future deployment of GHC/GHX analysis and community-led programs and update the low-temperature geothermal resources assessment of Alaska. A better understanding of shallow potential for GHX will improve design and operations of highly efficient GHC systems. The deployment and impact that can be achieved for low-temperature geothermal resources will contribute to decarbonization goals and facilitate widespread electrification by shaving and shifting grid loads.

Most of the data uses WGS84 coordinate system. However, each dataset come from different sources and has a metadata file with the original coordinate system.

During the 2013 fiscal year, the National Renewable Energy Laboratory (NREL) developed the Geothermal National Environmental Policy Act (NEPA) Database with funding provided by the U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO). The information in the database was collected in an effort to conduct analyses on NEPA timelines (Young et al., 2014). The database was then made available to the public on OpenEI in an effort to share the data collection effort with others. OpenEI allows information related to geothermal NEPA documents from all federal agencies to be accessed and maintained in a single location so that others can utilize the data for their own analyses and so that the structure and content can be expanded for other uses.

This submission includes links to the NEPA Database on OpenEI and in the Regulatory and Permitting Information Desktop (RAPID) Toolkit. Also included are a paper and poster by Young et. al presenting the NEPA Database to the Geothermal Resources Council (GRC).

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

The Department of Energy (DOE), Geothermal Technologies Office (GTO) launched a multi-phase funding program to advance technologies for extraction of lithium from geothermal brines. This initiative, known as American-Made Geothermal Lithium Extraction Prize (GLEP), had objectives of advancing technology for direct lithium extraction (DLE) from geothermal brines and make it as cost competitive as the conventional lithium extraction methods. To support these GLEP projects, Idaho National Laboratory (INL) formulated a Synthetic Li Prize Brine (SLPB) and provided it to all Phase 3 finalists to test with their technologies. The SLPB was used as a baseline lithium extraction feed brine for testing the efficacy of direct lithium extraction (DLE) technology developed by finalists. Included here are details on the synthesis of the SLPB.

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

An updated database of geothermal direct-use systems in the U.S. has been compiled and analyzed, building upon the Oregon Institute of Technology (OIT) Geo-Heat Center direct-use database. Types of direct-use applications examined include hot springs resorts and pools, aquaculture farms, greenhouses, and district heating systems, among others; power-generating facilities and ground-source heat pumps were excluded. Where possible, the current operation status, open and close dates, well data, and other technical data were obtained for each entry. The database contains 545 installations, of which 407 are open, 108 are closed, and 30 have an unknown status. A report is also included which details and analyzes current geothermal direct-use installations and barriers to further implementation.

NREL, as part of the Play Fairway Analysis Retrospective, compiled and mapped publicly available geologic and geophysical data in relation to the 2008 USGS geothermal potential analysis. Included in this submission are maps displaying the publicly available data for LIDAR coverage, aeromagnetic coverage, gravity station locations, and geologic map coverage over the Western United States.

The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.*
The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV.

Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm

Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

GEOTHERM is a comprehensive system of public databases and software used to store, locate, and evaluate information on the geology, geochemistry, and hydrology of geothermal systems. Three main databases address the general characteristics of geothermal wells and fields, and the chemical properties of geothermal fluids; the last database is currently the most active. System tasks are divided into four areas:

1. data acquisition and entry, involving data entry via word processors and magnetic tape;
2. quality assurance, including the criteria and standards handbook and front-end data-screening programs;
3. operation, involving database backups and information extraction; and
4. user assistance, preparation of such items as application programs, and a quarterly newsletter.

The principal task of GEOTHERM is to provide information and research support for the conduct of national geothermal-resource assessments. The principal users of GEOTHERM are those involved with the Geothermal Research Program of the U.S. Geological Survey.

This data package includes exploration material from the Basin & Range Investigation for Developing Geothermal Energy [in Hidden Systems] project (BRIDGE), which is part of a broader initiative to advance the exploration of hidden geothermal resources in the Basin & Range Province of the western U.S.

Data modalities include a helicopter-borne time-domain electromagnetic survey, magnetotellurics, 2-meter temperature measurements, ground-based gravity and legacy aeromagnetic surveys, geochemistry, geologic mapping, LiDAR analysis, 3D models, associated geospatial data, and a bibliography of existing data and references utilized in prospect characterization and conceptual modeling. Key files are in CSV, Geosoft, and Geotools formats. Please refer to READMEs for dataset-specific information. Where applicable, acquisition data and inversion models for a particular prospect or area of interest are organized separately.

This BRIDGE data package is the product of a collaboration led by Sandia National Laboratories with partners from Geologica Geothermal Group, Inc., the U.S. Navy Geothermal Program Office, and consultants Steven Sewell (Australis Geoscience Ltd) and William Cumming (Cumming Geoscience). The project's areas of interest (AOIs) are based off priority areas of interest in the southwestern portion of the Nevada Play Fairway map, distribution across tectonic provinces, accessibility, and the project team's extensive experience in the region. AOIs cover about a dozen basins that include unexplored prospects, partially explored prospects, and some developed analogue resources that provide validation cases. Many unexplored and partially explored prospects are on U.S. Department of Defense (DoD) land, though adjacent lands are included as well.

This reports includes a video description of recent upgrades and changes to the National Geothermal Data System (geothermaldata.org) and a text report of its relevant security upgrades. Improvements include a new operating system, implementation of HTTPS, implementation of a standard firewall, PostgreSQL upgrades, an ESRI ArcGIS server, new registration policies, and a non-public API.

This project focused on defining geothermal play fairways and development of a detailed geothermal potential map of a large transect across the Great Basin region (96,000 km2), with the primary objective of facilitating discovery of commercial-grade, blind geothermal fields (i.e. systems with no surface hot springs or fumaroles) and thereby accelerating geothermal development in this promising region. Data included in this submission consists of: structural settings (target areas, recency of faulting, slip and dilation potential, slip rates, quality), regional-scale strain rates, earthquake density and magnitude, gravity data, temperature at 3 km depth, permeability models, favorability models, degree of exploration and exploration opportunities, data from springs and wells, transmission lines and wilderness areas, and published maps and theses for the Nevada Play Fairway area.

Geochemical data for cold groundwaters and produced geothermal fluids around the Utah FORGE site. The data is compiled into four tables in the attached Excel File. Table 1 is a compilation of compositions (anions, cations, weak acids, oxygen, hydrogen, and carbon isotopes) for cold groundwaters and produced geothermal waters in the Milford valley, Utah. Table 2 is a compilation of noble gas (He, Ne, Ar) and He and Ne isotopic compositions for cold groundwaters and produced geothermal waters in the Milford valley, Utah. Table 3 provides values for calculated advective and diffusive fluxes of helium. Table 4 provides values of calculated subsurface stored heat between the Opal Mound fault and the Utah FORGE site, which are related to volumes of recently solidified magmatic heat sources.

Geothermal exploration and production are challenging, expensive and risky. The GeoThermalCloud uses Machine Learning to predict the location of hidden geothermal resources. This submission includes a training dataset for the GeoThermalCloud neural network. Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources.

Mineral, Temperature, Gravity, and Fault Density maps in the Coso Geothermal Field in California.

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Desert Peak Geothermal Field.