Tier 3 data for Appalachian Basin sectors of New York, Pennsylvania and West Virginia used in a Geothermal Play Fairway Analysis of opportunities for low-temperature direct-use applications of heat. It accompanies data and materials submitted as Geothermal Data Repository Submission "Natural Reservoir Analysis 2016 GPFA-AB" (linked below). Reservoir information are derived from oil and gas exploration and production data sets, or derived from those data based on further analysis. Data reported here encompass locations (horizontal and depth), geologic formation names, lithology, reservoir volume, porosity and permeability, and derived approximations of the quality of the reservoir. These differ from the linked 2015 data submission in that this file presents data for New York that are comparable to those in the other two states. In contrast, the 2015 data available measured differing attributes across the state boundaries.

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.

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.

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).

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).

*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).

*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).

This submission contains information used to compute the combined risk factors for deep geothermal energy opportunities in the Appalachian Basin, in the context of a the Play Fairway Analysis project. The risk factors are sedimentary rock reservoir quality, thermal resource quality, potential for induced seismicity, and utilization for direct-use heating of neighborhoods. The methods used to combine the risk factors included taking the average, the geometric mean, and the minimum of the four risk factors. Combined risk maps are provided for three different sedimentary rock reservoir metrics. Combined risk maps are also provided for the three geologic risk factors alone (thermal, reservoir, and seismic), and for the three risk factors that exclude reservoir quality (utilization, seismicity, and thermal qualities). The 2015 data submission should be visited to obtain associated shapefiles, which include: 1) definition of the High and Medium priority play fairways (Inner_Fairway, and Outer_Fairway), 2) definition of the US Census Places (usCensusPlaces), 3) places (cities) of interest in the region (Places_of_Interest) identified as geothermal play fairways, 4) the point centers of the raster cells (Raster_Center_Locations), and 5) locations of industries and special-use communities (e.g., colleges and military bases) identified as low temperature heat users (Industries). The 2015 submission also includes: 1) a methodology memo that explains how the risk factors were combined (GPFA-AB_combining_risk_factors.pdf), 2) the earthquake-based seismic risk map, and 3) supporting information with details of the calculations or processing used in generating these data files. More details on each file are given in the spreadsheet "list_of_contents.xlsx" in the folder "Supporting_Information". Code used to calculate values is available at https://github.com/calvinwhealton/geothermal_pfa under the folder "combining_metrics". Note that the 2016 code is currently under the branch named "combining_metrics_2016" in the folder called "combining_metrics". This branch may be merged with the master branch in the future. Many files contained within this submission update and replace the indicated files contained in: Cornell University. (2015). Risk Factor Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) [data set]. Retrieved from https://gdr.openei.org/submissions/622. doi:10.15121/1261942

This submission contains information used to compute the combined risk factors for deep geothermal energy opportunities in the Appalachian Basin, in the context of a the Play Fairway Analysis project. The risk factors are sedimentary rock reservoir quality, thermal resource quality, potential for induced seismicity, and utilization for direct-use heating of neighborhoods. The methods used to combine the risk factors included taking the average, the geometric mean, and the minimum of the four risk factors. Combined risk maps are provided for three different sedimentary rock reservoir metrics. Combined risk maps are also provided for the three geologic risk factors alone (thermal, reservoir, and seismic), and for the three risk factors that exclude reservoir quality (utilization, seismicity, and thermal qualities). The 2015 data submission should be visited to obtain associated shapefiles, which include: 1) definition of the High and Medium priority play fairways (Inner_Fairway, and Outer_Fairway), 2) definition of the US Census Places (usCensusPlaces), 3) places (cities) of interest in the region (Places_of_Interest) identified as geothermal play fairways, 4) the point centers of the raster cells (Raster_Center_Locations), and 5) locations of industries and special-use communities (e.g., colleges and military bases) identified as low temperature heat users (Industries). The 2015 submission also includes: 1) a methodology memo that explains how the risk factors were combined (GPFA-AB_combining_risk_factors.pdf), 2) the earthquake-based seismic risk map, and 3) supporting information with details of the calculations or processing used in generating these data files. More details on each file are given in the spreadsheet "list_of_contents.xlsx" in the folder "Supporting_Information". Code used to calculate values is available at https://github.com/calvinwhealton/geothermal_pfa under the folder "combining_metrics". Note that the 2016 code is currently under the branch named "combining_metrics_2016" in the folder called "combining_metrics". This branch may be merged with the master branch in the future. Many files contained within this submission update and replace the indicated files contained in: Cornell University. (2015). Risk Factor Analysis in Low-Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) [data set]. Retrieved from https://gdr.openei.org/submissions/622. doi:10.15121/1261942

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