Wednesday, March 6, 2019 3:36 pm

This dataset contains Schlumberger dip meter and neutron density logs for Lu Lu State #1 wild cat well which is located in the Beaver Basin, Beaver County, Utah. There is also a text file with well location coordinates. This archive contains Schlumberger dip meter and neutron density logs for Lu Lu State #1 wild cat well which is located in the Beaver Basin, Beaver County, Utah. Logs are included in PNG and TIFF formats. There is also a text file with well location coordinates.

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Wednesday, March 6, 2019 3:36 pm

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. Methods and results of a cohesive multi-state analysis of all known potential geothermal reservoirs in sedimentary rocks in our region, ranked by their potential favorability. Favorability is quantified using three metrics: Reservoir Productivity Index for water as the heat-transport working fluid; Reservoir Productivity Index for supercritical carbon dioxide as the heat-transport working fluid; Reservoir Flow Capacity. The first two metrics include engineering inputs to predict production-stage performance of the reservoirs. The third metric includes only geologic properties. The metrics are explained in the Reservoirs Methodology Memo (included in zip file). Because the data sources are well data from oil and gas exploration and production and these data are not inclusive of all locations in the Appalachian Basin study area, 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.

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Wednesday, March 6, 2019 3:36 pm

The geocellular model of the St. Peter Sandstone was constructed for the University of Illinois at Urbana-Champaign DDU feasibility study. Starting with the initial area of review (18.0 km by 18.1 km [11.2 miles by 11.3 miles]) the boundaries of the model were trimmed down to 9.7 km by 9.7 km (6 miles by 6 miles) to ensure that the model enclosed a large enough volume so that the cones of depression of both the production and injection wells would not interact with each other, while at the same time minimizing the number of cells to model to reduce computational time. The grid-cell size was set to 61.0 m by 61.0 m (200 feet by 200 feet) for 160 nodes in the X and Y directions.

The top surface of the St. Peter Sandstone was provided by geologists working on the project, and the average thickness of the formation was taken from the geologic prospectus they provided. An average thickness of 68.6 m (225 feet) was used for the St. Peter Sandstone, resulting in 45 layers for the model. Petrophysical data was taken from available rotary sidewall core data (Morrow et al., 2017). As geothermal properties (thermal conductivity, specific heat capacity) are closely related to mineralogy, specifically the percentage of quartz, available mineralogical data was assembled and used with published data of geothermal values to determine these properties (Waples and Waples, 2004; Robertson, 1988). The St. Peter Sandstone was divided into facies according to similar geothermal and petrophysical properties, and distributed according to available geophysical log data and prevailing interpretations of the depositional/diagenetic history (Will et al. 2014). Petrophysical and geothermal properties were distributed through geostatistical means according to the associated distributions for each lithofacies. The formation temperature was calculated, based on data from continuous temperature geophysical log from a deep well drilled into the Precambrian basement at the nearby Illinois Basin Decatur Project (IBDP) where CO2 is currently being sequestered (Schlumberger, 2012). Salinity values used in the model were taken from regional studies of brine chemistry in the St. Peter Sandstone, including for the IBDP (e.g., Panno et al. 2018). After being reviewed by the project's geologists, the model was then passed onto the geological engineers to begin simulations of the geothermal reservoir and wellbores.
Geocellular model of St. Peter Sandstone for University of Illinois at Urbana-Champaign DDU feasibility study in ASCII format.

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Wednesday, March 6, 2019 3:36 pm

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 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 early in the analysis (2015, this report) to combine the risk factors included taking the product, sum, and minimum of the four risk factors. The risk combination methods were revised as the analysis matured (2016).

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Wednesday, March 6, 2019 3:35 pm

Using an ultra-light aircraft, a high-resolution aeromagnetic survey was carried out over Ormat Nevada's Glass Buttes project area in Oregon. Survey operations were completed on May 25, 2010.

Average terrain clearance was 223 meters from the sensor. A total of 1,352 line-miles of aeromagnetic data were acquired. Processed survey data includes a total magnetic intensity map, reduced to pole (TMI) map, horizontal gradient (RTP) map, tilt derivative (RTP) map, and a horizontal gradient map of the tilt derivative grid. Airborne Magnetic Survey, Glass Buttes, Oregon, Tilt Derivative Reduced to Pole Magnetics

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Wednesday, March 6, 2019 3:35 pm

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. See the readme.txt file for complete discussion of the data included in this file

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Wednesday, March 6, 2019 3:35 pm

This submission contains a number of data files with vertices of meshed/interpolated surfaces used in the Phase 2B earth model. Examples include land surface (based on 10-meter DEM), the granitoid-basin fill contact, several faults, and also interpolated temperature isosurfaces for 175 and 225 degrees C.

All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88. This file contains vertices of meshed/interpolated surfaces of the land surface (based on 10-meter DEM) used in the Phase 2B earth model. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

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Wednesday, March 6, 2019 3:35 pm

This submission contains a number of maps and shapefiles related to the Utah FORGE site. Examples include geologic maps (several variations) and GIS data for the Utah FORGE site outline.

All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88. This file contains a geologic map of the Utah FORGE site with aerial imagery. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

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Wednesday, March 6, 2019 3:35 pm

Included in this dataset is a spreadsheet with the primary fault information used in the basin model, a spreadsheet with earthquake magnitude estimates, and a figure showing location of faults and microseismicity in the Portland Basin This file is a figure of the Portland and Tualatin Basins with faults included in this study as well as microseismicity.

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Wednesday, March 6, 2019 3:35 pm

This submission contains pressure and flow time series data from the reservoir testing of Well 58-32. These activities were part of the Utah FORGE Phase 2B site suitability confirmatory testing. This file contains pressure time series data from the reservoir testing of Well 58-32.

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Wednesday, March 6, 2019 3:35 pm

Groundwater Chemistry data for the Portland Basin was compiled from published literature, as well as state and federal groundwater quality reports. Mineralogies were identified based on previous literature, as well as XRD and SEM analysis conducted at Portland State University. Uploaded data sets include the compiled data on Portland and Tualatin Basin hydrogeology, and corresponding hydrogeochemical analyses. Compiled hydrogeochemistry for the Portland and Tualatin Basins

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Wednesday, March 6, 2019 3:34 pm

Paper authored by Stumpf et al. for the 2018 Geothermal Resources Council Annual Meeting held in Reno, NV USA. Included with the paper is the Microsoft PowerPoint presentation made at the GRC meeting and data tables associated with some of the figures. Link to a separate GDR submission containing the files associated with the geocellular model of the Mt. Simon Sandstone

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Wednesday, March 6, 2019 3:34 pm

This submission is a follow-up to Distributed Temperature Sensing (DTS) measurements made in Brady observation well 56-1 during the PoroTomo field experiment conducted in March, 2016. The measurements in this data set were made on August 24, 2018 over an approximately 20 hour period. The fiber-optic cable extends to the bottom of the well at 367 m below the wellhead. Measurements were made with a Silixa XT DTS interrogator configured to continuously record in each file a sixty-second average of stokes and anti-stokes readings on a single channel with a bottom hole U-bend. The 2016 data were collected using a Silixia Ultima with 12.5 cm spatial sampling, whereas the XT spatial sampling interval is 25 cm with a temperature resolution of 0.03 °C. Raw, uncalibrated data were converted to a single .MAT file using code provided by Oregon State University's CTEMPs https://ctemps.org/data-processing. The binary Matlab file containing processed Silixa XT data is read using the Matlab statement "load('Brady_25Aug2018_ch1.mat')", which contains the arrays below. Arrays with 2361 rows represent the channels and arrays with 1210 columns represent the one-minute samples. Link to a previous GDR submission with data from the DTS measurements made in Brady observation well 56-1 during the PoroTomo field experiment conducted in March, 2016.

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Wednesday, March 6, 2019 3:34 pm

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. Data exported from the site earth model

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Wednesday, March 6, 2019 3:34 pm

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 *A list of all files contained within this submission is included in the file titled
list_of_contents.csv*

This folder contains information used to compute the risk factors for the GPFA-AB project. The file types in this upload consist of images, rasters, and supporting information. The image files show what the raster files should look like. An image of the raster will have the same name except *.png as the file ending instead of *.tif. The raster files contain the scaled risk factor data (on [0, 5] and [0, 3]) and the combined risk factor data.

The file About_GPFA-AB_Phase1RiskAnalysisTask5DataUpload.pdf contains information inclusive of references utilized and consulted, special use considerations, authorship, etc.

Note that some of the *.tif files were updated with the 2016 data submission, along with their *.png file. The 2015 data file names that these files update are listed in a column in list_of_contents.xlsx. Also note that the updated 2016 files had 3 different reservoir objectives. These are the Reservoir Productivity Index for water as the working fluid (RPIw), the Reservoir Productivity Index for gas (supercritical CO2) as the working fluid (RPIg), and the Reservoir Flow Capacity (RFC) for water as the working fluid. Results are presented with these abbreviated names in the file name.

Note that the thresholds that define the scaled values for the thermal risk factor and the reservoir risk factor changed from the 2015 to the 2016 data submission. The method for calculating the uncertainty of the seismic stress-based risk has also been updated.

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