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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Appalachian Basin Play Fairway Analysis: Thermal Quality Analysis in Low-Temperature Geothermal Play Fairway Analysis (GPFA-AB)

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS). This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations. Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015. Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted. UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below)

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Python Codebase and Jupyter Notebooks - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

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.

Brown, Stephen↗
DOE OSTI2016

Summary of Phase 1 and Plans for Phase 2 of the Washington State Geothermal Play-Fairway Analysis

A geothermal play fairway is constituted by the collocation of heat, permeability, and saturated porosity in sufficient volume that provide adequate heat exchange at depths accessible by modern drilling technologies. The focus of Phase 1 of the Washington State play-fairway analysis (PFA) was to develop and apply a methodology to integrate detailed models of heat and permeability potential for each play (study area). The goal is to minimize risk associated with investments in geothermal exploration of greenfield projects by rigorously analyzing available data, applying favorability modeling concepts that have been successfully used at known geothermal systems, and addressing data quality and model sensitivity. The integrated results indicate locations where commercial geothermal potential is highest, based on available data. Here, these results also reveal where new data are needed to increase resolution and reduce uncertainty to minimize risk (work conducted in Phase 2), and thus successfully identify sites for exploration wells (in Phase 3).

geothermal favorability↗
DOE OSTI2015

Utilization Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)

This submission of Utilization Analysis data to the Geothermal Data Repository (GDR) node of the National Geothermal Data System (NGDS) is in support of Phase 1 Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The submission includes data pertinent to the methods and results of an analysis of the Surface Levelized Cost of Heat (SLCOH) for US Census Bureau Places within the study area. This was calculated using a modification of a program called GEOPHIRES, available at http://koenraadbeckers.net/geophires/index.php. The MATLAB modules used in conjunction with GEOPHIRES, the MATLAB data input file, the GEOPHIRES output data file, and an explanation of the software components have been provided. Results of the SLCOH analysis appear on 4 .png image files as mapped risk of heat utilization. For each of the 4 image (.png) files, there is an accompanying georeferenced TIF (.tif) file by the same name. In addition to calculating SLCOH, this Task 4 also identified many sites that may be prospects for use of a geothermal district heating system, based on their size and industry, rather than on the SLCOH. An industry sorted listing of the sites (.xlsx) and a map of these sites plotted as a layer onto different iterations of maps combining the three geological risk factors (Thermal Quality, Natural Reservoir Quality, and Risk of Seismicity) has been provided. In addition to the 6 image (.png) files of the maps in this series, a shape (.shp) file and 7 associated files are included as well. Finally, supporting files (.pdf) describing the utilization analysis methodology and summarizing the anticipated permitting for a deep district heating system are supplied. UPDATE: Newer version of the Utilization Analysis has been added here: https://gdr.openei.org/submissions/878

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Natural Reservoir Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin

The files included in this submission contain all data pertinent to the methods and results of this task's output, which is a cohesive multi-state map of all known potential geothermal reservoirs in our region, ranked by their potential favorability. Favorability is quantified using a new metric, Reservoir Productivity Index, as explained in the Reservoirs Methodology Memo (included in zip file). Shapefile and images of the Reservoir Productivity and Reservoir Uncertainty are included as well (hover over file display names to see actual file names in bottom-left corner of screen).

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Geothermal Play Fairway Analysis of Low-Temperature Resources for Sedimentary Basin Geothermal Play Types: An Example in the Denver Basin

This project is part of a nationwide effort to highlight the advantages of incorporating low-temperature geothermal resource evaluation into the implementation of combined heat and power (CHP), and geothermal direct use (GDU) technologies (e.g., space heating and/or cooling). The initiative aims to hasten the nation's decarbonization process by exploring the potential for using low-temperature geothermal resources (< 150 Degrees Celsius) in selected sedimentary basins that have several population centers. The Play Fairway Analysis (PFA) techniques were modified from earlier studies of sedimentary basin geothermal play types (SBGPTs) that assessed the viability of low-temperature resources. The decision-making process for leveraging low-temperature geothermal resources for GDU and CHP applications is complex and considers a variety of factors, including geological, economic, and risk criteria. This study covers workflows, relevant datasets, python code, and both common and composite maps used to create low-temperature geothermal resource favorability maps for the Denver Basin, which extends across Colorado, Nebraska, and Wyoming. The replication of these methodologies in other SBGPTs can evaluate potential for low-temperature resources. The proposed geothermal PFA approach for low-temperature geothermal resources includes: (1) identifying available relevant data and grouping data sets into PFA criteria (e.g., geological, economic, and risk criteria); (2) analyzing data gaps enable future focalized exploration; (3) performing uncertainty quantification; (4) weighting relevant data; (5) developing favorability and common risk maps for low-temperature geothermal resources to identify potential locations for more focused data collection. This project will facilitate future deployment of CHP and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources in sedimentary basins.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Improvements in 2016 to Natural Reservoir Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin

*These files add to and replace same-named files found within Submission 559 (hover over file display names to see actual file names in bottom-left corner of screen)* The files included in this submission contain all data pertinent to the methods and results of a cohesive multi-state analysis of all known potential geothermal reservoirs in sedimentary rocks in the Appalachian Basin region, ranked by their potential favorability. Favorability is quantified using three metrics: Reservoir Productivity Index for water; Reservoir Productivity Index; Reservoir Flow Capacity. The metrics are explained in the Reservoirs Methodology Memo (included in zip file). The product represents a minimum spatial extent of potential sedimentary rock geothermal reservoirs. Only natural porosity and permeability were analyzed. Shapefile and images of the spatial distributions of these reservoir quality metrics and of the uncertainty on these metrics are included as well. UPDATE: Accompanying geologic reservoirs data may be found at: https://gdr.openei.org/submissions/881 (linked below).

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Low Temperature Geothermal Play Fairway Analysis For The Appalachian Basin: Phase 1 Revised Report November 18, 2016

Geothermal energy is an attractive sustainable energy source. Yet project developers need confirmation of the resource base to warrant their time and financial resources. The Geothermal Play Fairway Analysis of the Appalachian Basin evaluated risk metrics that communicate the favorability of potential low-temperature geothermal energy resources in reservoirs more than 1000 m below the surface. This analysis is focused on the direct use of the heat, rather than on electricity production. Four risk factors of concern for direct-use geothermal plays in the Appalachian Basin portions of New York, Pennsylvania, and West Virginia are examined individually, and then in combination: 1) thermal resource quality, 2) natural reservoir quality, 3) induced seismicity, and 4) utilization opportunities. Uncertainty in the risk estimation is 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 °C), direct use geothermal resource, risk, and uncertainty assessment. Three methods with which to combine the four risk factors were used. Among these, the averaging of the individual risk factors indicates the most favorable counties within the study area are the West Virginia counties of Monongalia, Harrison, Lewis (dubbed the Morgantown–Clarksburg play fairway), Putnam, and Kanawha (Charleston play fairway), the New York counties of Chemung and Steuben plus adjacent Bradford county in Pennsylvania (Corning–Ithaca play fairway), and the Pennsylvania counties of Mercer, Crawford, Erie, and Warren, and adjacent Chautauqua county in New York (together, the Meadville–Jamestown play fairway). These higher priority regions are surrounded by broader medium priority zones. Also worthy of additional exploration is a broad region near Pittsburgh Pennsylvania, for which the available geological data are insufficient to fully analyze the geological risks but yet the population is high. First, to assess the spatial variation in the depth to which one would need to drill to obtain geothermal temperatures that are useful to a future project, the project used bottom-hole temperature data from Appalachian Basin oil and gas exploration. These bottom hole temperature data are abundant but of low quality. Second, the project examined the potential for sufficient water flow rates through rocks to harvest heat from a geothermal well field, considering only natural reservoirs. This analysis provides a very incomplete picture of spatial variability of natural reservoirs because the oil and gas reservoir data lack key properties and are spatially biased toward those locations with profitable amounts of hydrocarbons in the rock pore spaces. Third, in light of the fact that earthquake activity has been induced in several states by subsurface work related to the oil and gas industry, this project examined the potential for similar activity in the Appalachian Basin. Acknowledging that data for such a task are insufficient, we utilized what was available: records of seismic activity, regional estimates of the orientations of stress in the rocks, and locations and orientations of zones of lateral change in rock properties at depths down to several kilometers below Earth’s surface. With these data, we created a first approximation of spatially variable risks for induced earthquakes. Because no data existed with which to test the reliability of these methods, the results have a high degree of uncertainty. Fourth, we examined the spatial variability of the above-the-ground factors that contribute to the economical viability of projects to tap low-temperature geothermal resources for direct-use. We worked principally with population density as a regionally known variable that would impact the cost of district heating. The resulting maps omit the costs of producing the hot water from the ground, because the below-ground costs are directly coupled to the thermal resource risk factor and natural reservoir risk factor – later analyses of those costs will be needed. The result of the district heating analysis is highly skewed: few census locations yielded a low estimated surface cost. The team also identified more than 165 prospects for high value direct-use geothermal energy opportunities such as industrial sites, university campuses, and federal facilities, among others. At the closure of this regional analysis, the most significant technical uncertainties are 1) reservoir distribution and capacities; 2) validity of thermal resource maps, and 3) the holistic estimation of Levelized Cost of Heat for favorable geological situations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Risk Factor Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB)

This submission contains information used to compute the risk factors for the GPFA-AB project. The risk factors are natural reservoir quality, thermal resource quality, potential for induced seismicity, and utilization. The methods used to combine the risk factors included taking the product, sum, and minimum of the four risk factors. The files are divided into images, rasters, shapefiles, and supporting information. The image files show what the raster and shapefiles should look like. The raster files contain the input risk factors, calculation of the scaled risk factors, and calculation of the combined risk factors. The shapefiles include definition of the fairways, definition of the US Census Places, the center of the raster cells, and locations of industries. Supporting information contains details of the calculations or processing used in generating the files. An image of the raster will have the same name except *.png as the file ending instead of *.tif. Images with 'fairways' or 'industries' added to the name are composed of a raster with the relevant shapefile added. The file About_GPFA-AB_Phase1RiskAnalysisTask5DataUpload.pdf contains information the citation, special use considerations, authorship, etc. ***See 'GPFA-AB.zip' at bottom for compressed and organized version of the files associated with this submission*** **More details (including location) on each file are given in the spreadsheet 'list_of_contents.csv' in the folder 'SupportingInfo'** Code used to calculate values is available: https://github.com/calvinwhealton/geothermal_pfa under the folder 'combining_metrics' - *See link below*

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Geologic Reservoir Content Model from Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin

This dataset contains the known hydrocarbon reservoirs within the study area of the Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) as part of Phase 1, Natural Reservoirs Quality Analysis. The final values for Reservoir Productivity Index (RPI) and uncertainty (in terms of coefficient of variation, CV) are included. RPI is in units of liters per MegaPascal-second (L/MPa-s), quantified using permeability, thickness of formation, and depth. A higher RPI is more optimal. Coefficient of Variation (CV) is the ratio of the standard deviation to the mean RPI for each reservoir. A lower CV is more optimal. Details on these metrics can be found in the Reservoirs_Methodology_Memo.pdf uploaded in the associated "Natural Reservoir Analysis" dataset linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Geothermal Play Fairway Analysis, Part 2: GIS methodology

Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results. A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

New Mexico Geothermal Play Fairway Analysis from LANL

This submission contains geospatial (GIS) data on water table gradient and depth, subcrop gravity and magnetic, propsectivity, heat flow, physiographic, boron and BHT for the Southwest New Mexico Geothermal Play Fairway Analysis by LANL Earth & Environmental Sciences. GIS data is in ArcGIS map package format.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Low-Temperature Geothermal Play Fairway Analysis for the Denver Basin

This project is part of a national initiative to showcase the benefits of incorporating low-temperature geothermal resource assessment into the deployment of geothermal heating, combined heat and power (CHP), and geothermal direct-use (GDU) technologies. The initiative was established to accelerate the country's decarbonization efforts by identifying potential for low-temperature geothermal resource utilization (<150 degrees C, e.g., CHP and GDU) in selected sedimentary basins with numerous population centers. The play fairway analysis (PFA) methodologies in this study were adapted from previous PFA investigations of sedimentary basin geothermal play types (SBGPTs) that evaluated the potential for low-temperature resources (<150 degrees C). Workflows, relevant datasets, a new Python library, and common and composite geological criteria maps are utilized to develop low-temperature geothermal resource favorability maps for the Denver Basin, a sedimentary basin spanning Colorado, Nebraska, and Wyoming. The replication of these methodologies in other SBGPTs can evaluate potential for low-temperature resources. To facilitate future assessment of low-temperature geothermal resources in SBGPTs, this project provides PFA workflows, data, tools, and favorability maps that will ultimately support the utilization of low-temperature geothermal resources in sedimentary basins.

combined heat and power↗
DOE OSTI2024

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Low-Temperature Geothermal Play Fairway Analysis for the Denver Basin: Preprint

This project is part of a national initiative to showcase the benefits of incorporating low-temperature geothermal resource assessment into the deployment of geothermal heating and cooling (GHC), combined heat and power (CHP), and geothermal direct use (GDU) technologies. The initiative was established to accelerate the country's decarbonization efforts by identifying potential for low-temperature geothermal resource utilization (< 150 Degrees Celsius, i.e., GHC, CHP, and GDU) in selected sedimentary basins with numerous population centers. The Play Fairway Analysis (PFA) methodologies in this study were adapted from previous PFA investigations of sedimentary basin geothermal play types (SBGPTs) that evaluated the potential for low-temperature resources (< 150 Degrees Celsius). Workflows, relevant datasets, python code, common and composite geological criteria maps are utilized to develop low-temperature geothermal resource favorability maps for the Denver Basin, a sedimentary basin spanning Colorado, Nebraska, and Wyoming. The replication of these methodologies in other SBGPTs can evaluate potential for low-temperature resources. To facilitate future assessment of low-temperature geothermal resources in SBGPTs, this project provides PFA workflows, data, tools, and favorability maps that will ultimately support the utilization of low-temperature geothermal resources in sedimentary basins.

Denver Basin↗