Images from a 3D seismic reflection survey across a geothermal area are shown. Images of coherency and acoustic impedance are shown. Well tracks and MEQ location are overlain.

Agricultural Land Classification Grade and Job number for post-1988 ALC surveys for England Only. Polygons showing 5 classes of agricultural land plus classifications for urban and non-agricultural land. Grade one is best quality and grade five is poorest quality. A number of consistent criteria were used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness). Full metadata can be viewed on data.gov.uk.

Agricultural Land Classification Grade and Job number for post-1988 ALC surveys for England Only. Polygons showing 5 classes of agricultural land plus classifications for urban and non-agricultural land. Grade one is best quality and grade five is poorest quality. A number of consistent criteria were used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness). Full metadata can be viewed on data.gov.uk.

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.

We conducted a comprehensive analysis of the structural controls of geothermal systems within the Great Basin and adjacent regions. Our main objectives were to: 1) Produce a catalogue of favorable structural environments and models for geothermal systems. 2) Improve site-specific targeting of geothermal resources through detailed studies of representative sites, which included innovative techniques of slip tendency analysis of faults and 3D modeling. 3) Compare and contrast the structural controls and models in different tectonic settings. 4) Synthesize data and develop methodologies for enhancement of exploration strategies for conventional and EGS systems, reduction in the risk of drilling non-productive wells, and selecting the best EGS sites. Phase I (Year 1) involved a broad inventory of structural settings of geothermal systems in the Great Basin, Walker Lane, and southern Cascades, with the aim of developing conceptual structural models and a structural catalogue of the most favorable structural environments. This overview permitted selection of 5-6 representative sites for more detailed studies in Years 2 and 3. Sites were selected on the basis of quality of exposure, potential for development, availability of subsurface data, and type of system, so that major types of systems can be evaluated and compared. The detailed investigations included geologic mapping, kinematic analysis, stress determinations, gravity surveys, integration of available geophysical data, slip tendency analysis, and for some areas 3D modeling. In Year 3, the detailed studies were completed and data synthesized to a) compare structural controls in various tectonic settings, b) complete the structural catalogue, and c) apply knowledge to exploration strategies and selection of drilling sites.

Earthquake Faults and Folds in the USAThis feature layer, utilizing data from the U.S. Geological Survey's (USGS) Earthquake Hazards Program (EHP), displays known faults and folds in the U.S. This layer, per USGS, "contains information on faults and associated folds in the United States that demonstrate geological evidence of coseismic surface deformation in large earthquakes during the past 1.6 million years (Myr)."Earthquake Faults and FoldsData currency: This cached Esri service is checked monthly for updates from its federal source (Faults)Data modification: noneFor more information: Earthquake HazardsFor feedback please contact: ArcGIScomNationalMaps@esri.comNote: the map is designed to be displayed at a "States scale", in order to showcase the contents more efficiently.U.S. Geological SurveyPer USGS, "The USGS provides science about the natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change."

An ArcGIS Online Story Map that reviews the geothermal potential exploration and modeling of Newberry Volcano. The Story Map focuses on the subsurface model built by an NETL team for the DOE 4D EGS Geothermal Monitoring project.

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.

Online web mapping tool for visualization and simple analysis of Earth-energy data files from public and DOE related sources. Geocube allows users to upload and visualize their own datasets but also comes preloaded with individual spatial datasets as well as spatial data collections that align to topical themes.

To prepare for its third phase, the Hawaii Play Fairway project conducted groundwater sampling and analyses in ten locations in the Hawaiian islands, magnetotelluric (MT) and gravity surveys, as well as calculations of 3D subsurface stress due to the weight of the rock underlying the topography of the volcano. The subsurface stresses were used to evaluate the potential for fracture-induced permeability. Inversions of the MT and gravity data produce 3D models of resistivity and density, respectively, on Lanai, across Haleakala's SW rift (Maui), and surrounding Mauna Kea (Hawaii Island). The project developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The project then incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. These results were used to identify target sites for exploratory drilling. Spreadsheet information: Each sheet contains data for a particular depth in kilometers. Positive depths are above sea level, and negative below. For more information, go to the Hawaii Groundwater and Geothermal Resources Center website linked in the resources.

New Mexico has a rich legacy of petroleum and mineral exploration and production, most of which has involved subsurface investigations. Hundreds of thousands of holes have been drilled into the subsurface, some to depths of 20,000 feet or more. Considerable data have been collected from these wells in the form of electrical or geophysical logs, cuttings, and rock cores. These materials contain a rich lode of information about the kinds of rocks that lie below the surface, how porous and permeable they are, and even what types of fluids they contain. Part of the Mission of New Mexico Bureau of Geology and Mineral Resources is to serve as a repository for these kinds of data. Data in our collections have been acquired from wells drilled throughout the state over the last 90 years. We currently have more than 15 million pieces of unique subsurface data in our collections, much of it stored in seven steel storage buildings on campus. Core - 20,000+ boxes (oil & gas and mining) from 4,000+ wells Cuttings - 51,0000+ boxes from 16,600+ wells Geophysical Logs (some with mudlogs)- 50,000+ wells Porosity and Permeability Analyses Petroleum Source Rock Analyses Well records - 100,000+ wells Drillers logs - 17,000+ wells Sample descriptions and sample logs - 4,300+ wells Historic petroleum exploration maps with well locations in 26 counties Pool maps with locations of producing oil and gas pools by stratigraphic unit Historic production data - Pre-2002

Dataset containing 646 petrophysical well log interpretations spanning multiple geologic domains (as defined by Subsurface Trend Analysis™) in the Gulf of Mexico. Well logs were manually interpreted by geologic researchers at NETL and were derived from BOEM Sands and IHS data raster logs. This interpretation dataset underpins the Offshore CO2 Saline Storage Calculator.

Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

Provisional Agricultural Land Classification Grade. Agricultural land classified into five grades. Grade one is best quality and grade five is poorest quality. A number of consistent criteria used for assessment which include climate (temperature, rainfall, aspect, exposure, frost risk), site (gradient, micro-relief, flood risk) and soil (depth, structure, texture, chemicals, stoniness) for England only. Digitised from the published 1:250,000 map which was in turn compiled from the 1 inch to the mile maps.More information about the Agricultural Land Classification can be found at the following links:http://webarchive.nationalarchives.gov.uk/20130402200910/http://archive.defra.gov.uk/foodfarm/landmanage/land-use/documents/alc-guidelines-1988.pdfhttp://publications.naturalengland.org.uk/publication/35012.Full metadata can be viewed on data.gov.uk.

Earthquake Faults and Folds in the USAThis feature layer, utilizing data from the U.S. Geological Survey's (USGS) Earthquake Hazards Program (EHP), displays known faults and folds in the U.S. This layer, per USGS, "contains information on faults and associated folds in the United States that demonstrate geological evidence of coseismic surface deformation in large earthquakes during the past 1.6 million years (Myr)."Earthquake Faults and FoldsData currency: This cached Esri service is checked monthly for updates from its federal source (Faults)Data modification: noneFor more information: Earthquake HazardsFor feedback please contact: ArcGIScomNationalMaps@esri.comNote: the map is designed to be displayed at a "States scale", in order to showcase the contents more efficiently.U.S. Geological SurveyPer USGS, "The USGS provides science about the natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change."

SIMPA (Spatially Integrated Multivariate Probabilistic Assessment) is a Python-based fuzzy logic tool designed to help assess the likelihood of fluid and/or gas migration pathways throughout the subsurface. The SIMPA tool helps users develop and apply fuzzy logic to various datasets to construct knowledge-based inferential rules that reduce uncertainty and results in a visual representation depicting the likelihood of potential fluid and/or gas migration pathways. SIMPA results can offers an understanding of the magnitude and extent of natural and anthropogenic subsurface pathways, offering insight to subsurface hazards to improve storage assessments and critical information for improving industry decisions related to the use of various CCS methods and technologies. In addition, SIMPA can offer new insights for areas with little to no data to help inform site selection and production, evaluate potential risks and hazards, and support various decision making and risk management needs.

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4. Using history matching with tracers and available production data, the model should be tuned to represent the subsurface reservoir as accurately as possible 5. A large number of simulations is run using the history-matched reservoir model. Each simulation assumes a different wellbore flow rate allocation across the injection and production wells, where the individual selected flow rates do not violate the practical constraints for the corresponding wells. 6. ML models are trained using the simulation data. The code in our GitHub repository demonstrates how these models can be trained and evaluated. 7. The trained ML models can be used to evaluate a large set of candidate flow allocations with the goal of selecting the most optimal allocations, i.e., producing the largest amounts of thermal energy over the modeled period of time. The referenced paper provides more details about this optimization process

Information on the layers and subsurface of the Appalachian Basin. Includes cross section PDFs.

Topographic maps for the northern Appalachian Basin area, subsurface readings, and seismic data in the forms of PDFs and tables.

A series of oscillatory pumping tests were performed at the BHRS. The data collected from these wells will be used to tomographically image the shallow subsurface. This excel file only contains well coordinates for all wells at the Boise site.

This is 2D and 3D seismic reflection data from Utah FORGE reprocessed during Phase 2c. The readme file containing an explanation of the data including data formats, software that can be used, processing, and projection and datum used. The Reprocessing document gives the rationale for reprocessing and shows examples of the improvements that were obtained. For all 3D and 2D data the following datasets were created and output in SEG-Y format: - Unmigrated Time - Prestack Time Migration (PSTM), Unenhanced (UnEnh) and Enhanced (Enh) - Prestack Depth Migration (PSDM), Unenhanced (UnEnh) and Enhanced (Enh) - Velocity Model used for Migration

This is a compilation of logs and data from Well 14-2 in the Roosevelt Hot Springs area in Utah. This well is also in the Utah FORGE study area. Data includes: flowmeter survey (1989), geochemistry (1977-1978, 1977-1983), injection test data (1979, 1982), and spinner surveys (1989, 1985-1986). Logs include: borehole compensated sonic and gamma ray (600'-6112'), borehole geometry and gamma ray (50'-4829'), caliper (0'-1720'), compensated neutron formation density (600'-6121'), induction electric (650'-6118'), mud log (79'-6100'), steam injection survey (50'-1175'), subsurface pressure surveys (0'-6087'), and subsurface temperature surveys (0'-6106'). The file is in a compressed .zip format and there is a data inventory table (Excel spreadsheet) in the root folder that is a guide to the data that is accessible in subfolders.

This is a compilation of logs and data from Well 52-21 in the Roosevelt Hot Springs area in Utah. This well is also in the Utah FORGE study area. The file is in a compressed .zip format and there is a data inventory table (Excel spreadsheet) in the root folder that is a guide to the data that is accessible in subfolders.

This is a compilation of logs and data from Well 9-1 in the Roosevelt Hot Springs area in Utah. This well is also in the Utah FORGE study area. The file is in a compressed .zip format and there is a data inventory table (Excel spreadsheet) in the root folder that is a guide to the data that is accessible in subfolders.

From the site: "Major geologic units in the West Virginia region including metamorphic provinces, major faults, calderas, impact structures, and the limits of continental glaciation. The data depict the geology of the bedrock that lies at or near the land surface, but not the distribution of surficial materials such as soils, alluvium, and glacial deposits. Generalized geology shapefiles were downloaded from the National Atlas of the United States web site and clipped to an extent covering -76 to -84 degrees west longitude, 35 degrees 30 minutes to 42 degrees north latitude using a shape file that is included in the zip archive. Published April 2002. The data are generalized from a compilation prepared for use in the Geologic Map of North America, to be published in hard copy by the Geological Society of America and released as a digital file by the U.S. Geological Survey."

Earthquake Faults and Folds in the USAThis feature layer, utilizing data from the U.S. Geological Survey's (USGS) Earthquake Hazards Program (EHP), displays known faults and folds in the U.S. This layer, per USGS, "contains information on faults and associated folds in the United States that demonstrate geological evidence of coseismic surface deformation in large earthquakes during the past 1.6 million years (Myr)."Earthquake Faults and FoldsData currency: This cached Esri service is checked monthly for updates from its federal source (Faults)Data modification: noneFor more information: Earthquake HazardsFor feedback please contact: ArcGIScomNationalMaps@esri.comNote: the map is designed to be displayed at a "States scale", in order to showcase the contents more efficiently.U.S. Geological SurveyPer USGS, "The USGS provides science about the natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change."