The Geothermal Exploration Artificial Intelligence looks to use machine learning to spot geothermal identifiers from land maps. This is done to remotely detect geothermal sites for the purpose of energy uses. Such uses include enhanced geothermal system (EGS) applications, especially regarding finding locations for viable EGS sites. This submission includes the appendices and reports formerly attached to the Geothermal Exploration Artificial Intelligence Quarterly and Final Reports. The appendices below include methodologies, results, and some data regarding what was used to train the Geothermal Exploration AI. The methodology reports explain how specific anomaly detection modes were selected for use with the Geo Exploration AI. This also includes how the detection mode is useful for finding geothermal sites. Some methodology reports also include small amounts of code. Results from these reports explain the accuracy of methods used for the selected sites (Brady Desert Peak and Salton Sea). Data from these detection modes can be found in some of the reports, such as the Mineral Markers Maps, but most of the raw data is included the DOE Database which includes Brady, Desert Peak, and Salton Sea Geothermal Sites.

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.

This data catalog contains information related to the Training Site Analysis for the Geothermica project "DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)." The DEEPEN project aims to reduce exploration risk for geothermal fluids in magmatic systems by developing improved an improved framework for interpretation of exploration data using the Play Fairway Analysis (PFA) methodology. The Training Site Analysis performed for DEEPEN leverages existing datasets to develop a customized PFA approach to exploration for multiple geothermal resource types in magmatic systems (conventional hydrothermal resources, supercritical fluid and superheated steam resources, and superhot EGS resources). This data catalog contains links to publicly available data files related to 8 training sites in the United States. US training sites are: the Cascades/Aleutians PFA project; the Hawaii PFA project, the Oregon Cascades PFA project, the Snake River Plain, Idaho PFA project, the Washington State PFA project, Newberry Volcano, Coso Geothermal Field, and the Geysers Geothermal field. This database contains an overview of these training sites, data sources, and links to publicly available exploration datasets. For the five PFA projects, details on exploration data related to PFA components (heat, fluid, permeability, sometimes seal) are provided, including a summary of data weighting methodologies.

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.

The data is associated to the Fallon FORGE project and includes mudlogs for all wells used to characterize the subsurface, as wells as gravity, magnetotelluric, earthquake seismicity, and temperature data from the Navy GPO and Ormat. Also included are geologic maps from the USGS and Nevada Bureau of Mines and Geology for the Fallon, NV area.

Final Report describing data collection, evaluation, modeling and analysis. Ranking of Cascade and Aleutian volcanic centers for geothermal potential.

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.

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and nonscientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g., temperature, permeability, permitting), a site?s project grade and readiness are evaluated on 12 attributes pertaining to geological, technical, or socio-economic feasibility. In this paper, we present case studies showing how GeoRePORT can be used to compare geological, technical, and socio-economic attributes between geothermal systems. The consistent and objective assessment protocols used in GeoRePORT allow for comparison of project attributes across unique locations and geological settings. GeoRePORT case studies presented here outline the geological, socio-economic, and technical features of four individual geothermal sites: Coso, Chena, Dixie Valley, and White Sands Missile Range. The case studies illustrate the usefulness of GeoRePORT in evaluating project risk and return, identifying gaps in reported data, evaluating R&D impact, and gathering insights on successes and failures as applicable to future projects.

The Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT) was developed with funding from the U.S. Department of Energy Geothermal Technologies Office to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and nonscientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g., temperature, permeability, permitting), a site?s project grade and readiness are evaluated on 12 attributes pertaining to geological, technical, or socio-economic feasibility. In this submission, we present the geological, socio-economic, and technical protocols as well as the spreadsheet template for easy data entry and reporting of the GeoRePORT protocol.

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.

This submission contains raster files associated with several datasets that include earthquake density, Na/K geothermometers, fault density, heat flow, and gravity. Integrated together using spatial modeler tools in ArcGIS, these files can be used for play fairway analysis in regard to geothermal exploration.

Photos of core samples from Lanai Island. During the third phase of the Hawaii Play Fairway project, further exploration involved drilling a groundwater well in Lanai's Palawai Basin and performing more geophysical surveys. The project deepened an existing water well on Lanai. Drilling occurred 24/7 the entire month of June 2019 over which time Lanai Well 10 was deepened from 427 m to 1057 m, with continuous core collected. The roughly linear temperature gradient was an average of 42 degC/km, and a maximum bottom hole temperature, 66 degC. This gradient is more than twice the background for Hawaii and within a range of gradients measured in this depth range for some exploration wells within KERZ. The Hawaii Play Fairway project seeks to explore the geologic structures that exist in the caldera region of Hawaiian volcanoes; how those structures influence groundwater storage and flow; and how the magmatic heat from Hawaiian shield volcanoes cools over time. The Hawaii Groundwater and Geothermal Resources Center (Hawaii Institute of Geophysics and Planetology, University of Hawaii at Manoa) executed the Hawaii Play Fairway project. For more information, go to HGGRC's website that is linked in the resources.

Final Report describing regional signature detection for blind and traditional play fairways as part of Phase I of New Mexico Play Fairway Analysis. This project seeks to reduce exploration risk and identify new prospective targets using available geologic, geochemical, and geophysical data sets. Although this project focuses on southwestern New Mexico, the techniques that were developed during this project are widely applicable elsewhere, particularly in arid regions.

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 report describes all of the work done in Phase I of a geothermal exploration project in the Tularosa Basin, as well as an outline for Phase II work, and more.

KY Coal Exploration Drill Holes

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.

This submission includes composite risk segment models in raster format for permeability, heat of the earth, and MT, as well as the final PFA model of geothermal exploration risk in Southwestern Utah, USA. Additionally, this submission has data regarding hydrothermally altered areas, and opal sinter deposits in the study area. All of this information lends to the understanding and exploration for hidden geothermal systems in the area.

Mineralogical, lithological, and geospatial data of drill cuttings from exploration production wells in Beowawe, Dixie Valley and Roosvelt Hot Springs. These data support whole rock analyses for major, minor and critical elements to assess critical metals in produced fluids from Nevada and Utah geothermal fields. The samples were analyzed by x-ray diffraction (legacy data) and then checked by thin section analysis.

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.

Compilation of boron, lithium, bromine, and silica data from wells and springs throughout New Mexico from a wide variety of sources. The chalcedony geothermometry calculation is included in this file.

For the New Mexico Play fairway Analysis project, gamma ray geophysical well logs from oil wells penetrating the Proterozoic basement in southwestern New Mexico were digitized. Only the portion of the log in the basement was digitized. The gamma ray logs are converted to heat production using the equation (Bucker and Rybach, 1996) : A[microW/m3] = 0.0158 (Gamma Ray [API] - 0.8).

This submission includes three files from two sources. One file is derived from USGS data and includes a series of manipulations to evaluate only shallow wells with high estimated geothermal gradients. Two other files are springs and wells with discharge temperatures above 30 deg C from the NMBGMR Aquifer Mapping database

Research references to literature about the Newberry geothermal area, Oregon.

Combined geochemical and geophysical data, weighted and ranked for geothermal prospect favorability.

Various data sets displayed on a 2km grid for the Play Fairway Analysis CA-NV-OR area. Grids at 2km, updated from 5km.

Various data sets displayed on a 2km grid for the Play Fairway Analysis CA-NV-OR area.

Magnetotelluric (MT) data for Medicine lake with 2km grid.

Git archive containing Python modules and resources used to generate machine-learning models used in the "Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada" project. This software is licensed as free to use, modify, and distribute with attribution. Full license details are included within the archive. See "documentation.zip" for setup instructions and file trees annotated with module descriptions.

This is a final report summarizing a one-year (2014-15) DOE funded Geothermal Play Fairway Analysis of the Low-Temperature resources of the Appalachian Basin of New York, Pennsylvania and West Virginia. Collaborators included Cornell University, Southern Methodist University, and West Virginia University. As a result of the research, 'play fairways' were identified for further study, based on four 'risk' criteria: 1) the Thermal Resource Quality, 2) the Natural Reservoir Quality, 3) the Risk of Seismic Activity, and the 4) Utilization Viability. In addition to the final report document, this submission includes project 'memos' referred to throughout the report. Many of these same memos are also provided in the submission with the detailed data products accompanying the relevant risk factor (thermal, reservoir, seismicity, and utilization). A portion of the executive overview follows: Geothermal energy is an attractive sustainable energy source. Project developers need confirmation of the resource base to warrant their time and financial resources. The hydrocarbon industry has addressed exploration and development complexities through use of a technique referred to as Play Fairway Analysis (PFA). The PFA technique assigns risk metrics that communicate the favorability of potential hydrocarbon bearing reservoirs in order to enable prudent allocation of exploration and development resources. The purpose of this Department of Energy funded effort is to apply the PFA approach to geothermal exploration and development, thus providing a technique for Geothermal Play Fairway Analysis (GPFA). This project focuses on four risk factors of concern for direct-use geothermal plays in the Appalachian Basin (AB) portions of New York, Pennsylvania, and West Virginia (Figure 1). These risk factors are 1) thermal resource quality, 2) natural reservoir quality, 3) induced seismicity, and 4) utilization opportunities (Figure 2). This research expands upon and updates methodologies used in previous assessments of the potential for geothermal fields and utilization in the Appalachian Basin, and also introduces novel approaches and metrics for quantification of geothermal reservoir productivity in sedimentary basins. Unique to this project are several methodologies for combining the risk factors into a single commensurate objective that communicates the estimated overall favorability of geothermal development. Uncertainty in the risk estimation is also quantified. Based on these metrics, geothermal plays in the Appalachian Basin were identified as potentially viable for a variety of direct-use-heat applications. The methodologies developed in this project may be applied in other sedimentary basins as a foundation for low temperature (50-150 degC), direct use geothermal resource, risk, and uncertainty assessment. Through our identification of plays, this project reveals the potential for widespread assessment of low-temperature geothermal energy from sedimentary basins as an alternative to current heating sources that are unsustainable. There is an important distinction in this Geothermal Play Fairway Analysis project as compared to hydrothermal projects: this Appalachian Basin analysis is focused on the direct use of the heat, rather than on electrical production. Lindal (1973) illuminated numerous industrial and other low-temperature applications of geothermal energy for which this analysis can be useful. The major relationship to electricity is that direct-use applications reduce the electricity requirements for a region. Even though all of the geothermal resources in the Appalachian Basin are low grade, the high population and high heating demand across New York, Pennsylvania, and West Virginia translate into economic advantages if geothermal direct-use heating replaces electricity-based heating. The advantage is derived from the high efficiency of extracting heat from geothermal fluids rather than converting the fluids to electricity (Tester et al., 2015).

Within this submission are multiple .tif images with accompanying metadata of magnetotelluric conductor occurrence, fault critical stress composite risk segment (CRS), permeability CRS, Quaternary mafic extrusions, Quaternary fault density, and Quaternary rhyolite maps. Each of these contributed to a final play fairway analysis (PFA) for the SE Great Basin study area.

Rock formation top picks from oil wells from southwestern New Mexico from scout cards and other sources. There are differing formation tops interpretations for some wells, so for those wells duplicate formation top data are presented in this file.

This report details all of the work done in Phase 2 of a geothermal exploration project in Tularosa Basin, New Mexico. Data acquired as part of Phase 2 includes field geology (geological reconnaissance and mapping), gravity surveys, shallow temperature surveys, well water sampling and geothermometry, temperature logging, and magnetotelluric (MT) surveys. The new data is incorporated into new PFA models and Phase 2 plays were subsequently developed, ranked, and prioritized. This report also presents an overview of recommendations and costs for the next phase.

Final results of a 3D finite difference thermal model of Newberry Volcano, Oregon. Model data are formatted as a text file with four data columns (X, Y, Z, T). X and Y coordinates are in UTM (NAD83 Zone 10N), Z is elevation from mean sea level (meters), T is temperature in deg C. Model is 40km X 40km X 12.5 km, grid node spacing is 100m in X, Y, and Z directions. A symmetric cylinder shaped magmatic heat source centered on the present day caldera is the modeled heat source. The center of the modeled body is a -1700 m (elevation) and is 600m thick with a radius of 8700m. This is the best fit results from 2D modeling of the west flank of the volcano. The model accounts for temperature dependent thermal properties and latent heat of crystallization. For additional details, assumptions made, data used, and a discussion of the validity of the model see Frone, 2015 (Link below).

This submission includes raster datasets for each layer of evidence used for weights of evidence analysis as well as the deterministic play fairway analysis (PFA). Data representative of heat, permeability and groundwater comprises some of the raster datasets. Additionally, the final deterministic PFA model is provided along with a certainty model. All of these datasets are best used with an ArcGIS software package, specifically Spatial Data Modeler.

This is a hydrothermal alteration map of the Tularosa Basin area, New Mexico and Texas that was created using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data band ratios based upon diagnostic features of clay, calcite, silica, gypsum, ferric iron, and ferrous iron. Mesoproterozoic granite in the San Andreas Range often appeared altered, but this may be from clays produced by weathering or, locally, by hydrothermal alteration. However, no field checking was done.

These images show the comprehensive methodology used for creation of a Play Fairway Analysis to explore the geothermal resource potential of the Tularosa Basin, New Mexico. The deterministic methodology was originated by the petroleum industry, but was custom-modified to function as a knowledge-based geothermal exploration tool. The stochastic PFA flow chart uses weights of evidence, and is data-driven.

A DEM of the Tularosa Basin was divided into twelve zones, each of which a ZR ratio was calculated for. This submission has a TIFF image of the zoning designations, along with a table with respective ZR ratio calculations in the metadata. The primary results are in the table below, and high ZR ratio values indicate relatively high strain rates. Zone ZR ratio 1 1.2852479 2 1.17442846 3 0.89700274 4 0.74546427 5 0.99841793 6 0.86434253 7 0.83016287 8 1.91696538 9 1.13691977 10 1.68062953 11 1.23044486 12 1.13160887

This submission contains multiple excel spreadsheets and associated written reports. The datasets area are representative of shallow temperature, geochemistry, and other well logging observations made across WSMR (white sands missile range); located to the west of the Tularosa Basin but still within the study area. Written reports accompany some of the datasets, and they provide ample description of the methodology and results obtained from these studies. Gravity data is also included, as point data in a shapefile, along with a written report describing that particular study.

These models are related to weights of evidence play fairway anlaysis of the Tularosa Basin, New Mexico and Texas. They were created through Spatial Data Modeler: ArcMAP 9.3 geoprocessing tools for spatial data modeling using weights of evidence, logistic regression, fuzzy logic and neural networks. It used to identify high values for potential geothermal plays and low values. The results are relative not only within the Tularosa Basin, but also throughout New Mexico, Utah, Nevada, and other places where high to moderate enthalpy geothermal systems are present (training sites).

From the site: "A cells polygon feature class was created by the U.S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the United States. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. The well information was initially retrieved from IHS Inc.'s PI/Dwights PLUS Well Data on CD-ROM, which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current through 10/1/2005."

From the site: "A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Illinois. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Data were retrieved from the Illinois State Geological Survey (ISGS) oil and gas wells database. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006."

From the site: "A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Indiana. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. The well information was acquired from the Indiana Geological Survey, Petroleum Database Management System (PDMS). Using the table viewer at http://igs.indiana.edu/pdms/getdata.cfm, oil and gas well events and locations tables were downloaded for the entire state of Indiana. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006."

From the site: "A cells polygon feature class was created by the U.S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Kentucky. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Data were retrieved from the Kentucky Oil and Gas Well Records database and saved as a shapefile of oil and gas well locations for Kentucky. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2005."

From the site: "A cells polygon feature class was created by the U.S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Ohio. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. The well information was acquired from the Ohio Department of Natural Resources, Division of Geological Survey in a Geographic Information System (GIS) data layer that contains all of the locatable oil and gas wells in Ohio. Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2004."

From the site: "A cells polygon feature class was created by the U. S. Geological Survey (USGS) to illustrate the degree of exploration, type of production, and distribution of production in the State of Pennsylvania. Each cell represents a quarter-mile square of the land surface, and the cells are coded to represent whether the wells included within the cell are predominantly oil-producing, gas-producing, both oil and gas-producing, or the type of production of the wells located within the cell is unknown or dry. Data were retrieved from the Pennsylvania Internet Record Imaging System (PA*IRIS). Cells were developed as a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. The data are current as of 2006."

This package contains USGS data contributions to the DOE-funded Nevada Geothermal Machine Learning Project, with the objective of developing a machine learning approach to identifying new geothermal systems in the Great Basin. This package contains three major data products (geophysics, heat flow, and fault dilation and slip tendencies) that cover a large portion of northern Nevada. The geophysics data include map surfaces related to gravity and magnetic data, and line and point data derived from those surfaces. Heat flow data include an interpolated map of heat flow in mW/m^2, an error surface, and well data used to construct them. The dilation and slip tendency information exist as attributes assigned to each line segment of mapped faults and geophysical lineaments. GDR submission contains link to official USGS data release. Additional metadata available on source DOI page.

Well 58-32 (previously labeled MU-ESW1) was drilled near Milford Utah during Phase 2B of the FORGE Project to confirm geothermal reservoir characteristics met requirements for the final FORGE site. Well Accord-1 was drilled decades ago for geothermal exploration purposes. While the conditions encountered in the well were not suitable for developing a conventional hydrothermal system, the information obtained suggested the region may be suitable for an enhanced geothermal system. Geophysical well logs were collected in both wells to obtain useful information regarding there nature of the subsurface materials. For the recent testing of 58-32, the Utah FORGE Project contracted with the well services company Schlumberger to collect the well logs.

An investment of $0.7M from the Geothermal Technology Office for Phase 2 of Play Fairway Analysis in Washington State improved existing favorability models and increased model confidence. New 1:24,000-scale geological mapping, 15 detailed geophysical surveys, 2 passive seismic surveys, and geochronology collected during this phase were coupled with updated and detailed structural modeling and have significantly improved the conceptual models of three potential blind geothermal systems/plays in Washington State, the St. Helens Shear Zone, Mount Baker, and Wind River Valley. Results of this analysis reveal the presence of commercially viable undiscovered geothermal resources in all three study areas. The analysis additionally provides a clear definition of the geothermal prospects in terms of the essential elements of a functioning geothermal system, the confidence in these assessments, and associated potential and risk of development. This report also includes a proposal to validate the modeling results in highly favorable areas for two main reasons: (1) to develop confidence in the modeling approach that will encourage future development of geothermal resources in Washington State inside and outside of the Phase 2 study areas, and (2) to provide actionable results to the DOE, existing industry partners, newly identified developers, and other renewable-energy stakeholders. The proposed validation activities aim to collect new data that will further the understanding of geothermal resource potential in Washington, as well as substantiate the favorability, confidence, and risk models developed in Phases 1 and 2.

This submission contains Downhole geophysical logs associated with Wister, CA Wells 12-27 and 85-20. The logs include Spontaneous Potential (SP), HILT Caliper (HCAL), Gamma Ray (GR), Array Induction (AIT), and Neutron Porosity (NPOR) data. Also included are a well log, Injection Test, Pressure Temperature Spinner log, shut in temperature survey, a final well schematic, and files about the well's location and drilling history. This submission also contains data from a three-dimensional (3D) multi-component (3C) seismic reflection survey on the Wister Geothermal prospect area in the northern portion of the Imperial Valley, California. The Wister seismic survey area was 13.2 square miles. (Resistivity image logs (Schlumberger FMI) in 85-20 indicate that maximum horizontal stress (Shmax) is oriented NNE but that open fractures are oriented suboptimally).