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Title: Gravity Data for West-Central Colorado

Modeled Bouger-Corrected Gravity data was extracted from the Pan American Center for Earth and Environmental Studies Gravity Database of the U.S. at http://irpsrvgis08.utep.edu/viewers/Flex/GravityMagnetic/GravityMagnetic_CyberShare/ on 2/29/2012. The downloaded text file was opened in an Excel spreadsheet. This spreadsheet data was then converted into an ESRI point shapefile in UTM Zone 13 NAD27 projection, showing location and gravity (in milligals). This data was then converted to grid and then contoured using ESRI Spatial Analyst. Data from From University of Texas: Pan American Center for Earth and Environmental Studies
Authors:
Publication Date:
Report Number(s):
340
DOE Contract Number:
EE0002828
Product Type:
Dataset
Research Org(s):
DOE Geothermal Data Repository; Flint Geothermal, LLC
Collaborations:
Flint Geothermal, LLC
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Geothermal Technologies Program (EE-2C)
Geolocation:
POLYGON ((-104.8081625 40.975133433831,-104.8081625 36.932552823879,-108.6642625 36.932552823879,-108.6642625 40.975133433831,-104.8081625 40.975133433831))
Subject:
15 Geothermal Energy; geothermal; Gravity; Bouger; Colorado; Shapefile; Point shapefile; Line shapefile; shape file; GIS; ArcGIS; geospatial; contour; west; central; west-central
Related Identifiers:
OSTI Identifier:
1148732
  1. The Geothermal Data Repository (GDR) is the submission point for all data collected from researchers funded by the U.S. Department of Energy's Geothermal Technologies Office (DOE GTO). The DOE GTO is providing access to its geothermal project information through the GDR. The GDR is powered by OpenEI, an energy information portal sponsored by the U.S. Department of Energy and developed by the National Renewable Energy Laboratory (NREL).
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  1. Modeled ground magnetic data was extracted from the Pan American Center for Earth and Environmental Studies database at http://irpsrvgis08.utep.edu/viewers/Flex/GravityMagnetic/GravityMagnetic_CyberShare/ on 2/29/2012. The downloaded text file was then imported into an Excel spreadsheet. This spreadsheet data was converted into an ESRI point shapefile in UTM Zonemore » 13 NAD27 projection, showing location and magnetic field strength in nano-Teslas. This point shapefile was then interpolated to an ESRI grid using an inverse-distance weighting method, using ESRI Spatial Analyst. The grid was used to create a contour map of magnetic field strength. « less
  2. This is a zipped GIS compatible shapefile of gravity data points used in the Milford, Utah FORGE project as of March 21st, 2016. The shapefile is native to ArcGIS, but can be used with many GIS software packages. Additionally, there is a .dbf (dBase) filemore » that contains the dataset which can be read with Microsoft Excel. The Data was downloaded from the PACES (Pan American Center for Earth and Environmental Studies) hosted by University of Texas El Paso (http://research.utep.edu/Default.aspx?alias=research.utep.edu/paces) Explanation:Source: data source code if available LatNAD83: latitude in NAD83 [decimal degrees] LonNAD83: longitude in NAD83 [decimal degrees]zWGS84: elevation in WGS84 (ellipsoidal) [m]OBSless976: observed gravity minus 976000 mGalIZTC: inner zone terrain correction [mGal]OZTC: outer zone terrain correction [mGal]FA: Free Air anomaly value [mGal]CBGA: Complete Bouguer gravity anomaly value [mGal] « less
  3. Over the course of the entire project, field visits were made to 117 geothermal systems in the Great Basin region. Major field excursions, incorporating visits to large groups of systems, were conducted in western Nevada, central Nevada, northwestern Nevada, northeastern Nevada, east‐central Nevada, eastern California,more » southern Oregon, and western Utah. For example, field excursions to the following areas included visits of multiple geothermal systems: - Northwestern Nevada: Baltazor Hot Spring, Blue Mountain, Bog Hot Spring, Dyke Hot Springs, Howard Hot Spring, MacFarlane Hot Spring, McGee Mountain, and Pinto Hot Springs in northwest Nevada. - North‐central to northeastern Nevada: Beowawe, Crescent Valley (Hot Springs Point), Dann Ranch (Hand‐me‐Down Hot Springs), Golconda, and Pumpernickel Valley (Tipton Hot Springs) in north‐central to northeast Nevada. - Eastern Nevada: Ash Springs, Chimney Hot Spring, Duckwater, Hiko Hot Spring, Hot Creek Butte, Iverson Spring, Moon River Hot Spring, Moorman Spring, Railroad Valley, and Williams Hot Spring in eastern Nevada. - Southwestern Nevada‐eastern California: Walley’s Hot Spring, Antelope Valley, Fales Hot Springs, Buckeye Hot Springs, Travertine Hot Springs, Teels Marsh, Rhodes Marsh, Columbus Marsh, Alum‐Silver Peak, Fish Lake Valley, Gabbs Valley, Wild Rose, Rawhide‐ Wedell Hot Springs, Alkali Hot Springs, and Baileys/Hicks/Burrell Hot Springs. - Southern Oregon: Alvord Hot Spring, Antelope Hot Spring‐Hart Mountain, Borax Lake, Crump Geyser, and Mickey Hot Spring in southern Oregon. - Western Utah: Newcastle, Veyo Hot Spring, Dixie Hot Spring, Thermo, Roosevelt, Cove Fort, Red Hill Hot Spring, Joseph Hot Spring, Hatton Hot Spring, and Abraham‐Baker Hot Springs. Structural controls of 426 geothermal systems were analyzed with literature research, air photos, google‐Earth imagery, and/or field reviews (Figures 1 and 2). Of the systems analyzed, we were able to determine the structural settings of more than 240 sites. However, we found that many “systems” consisted of little more than a warm or hot well in the central part of a basin. Such “systems” were difficult to evaluate in terms of structural setting in areas lacking in geophysical data. Developed database for structural catalogue in a master spreadsheet. Data components include structural setting, primary fault orientation, presence or absence of Quaternary faulting, reservoir lithology, geothermometry, presence or absence of recent magmatism, and distinguishing blind systems from those that have surface expressions. Reviewed site locations for all 426 geothermal systems– Confirmed and/or relocated spring and geothermal sites based on imagery, maps, and other information for master database. Many systems were mislocated in the original database. In addition, some systems that included several separate springs spread over large areas were divided into two or more distinct systems. Further, all hot wells were assigned names based on their location to facilitate subsequent analyses. We catalogued systems into the following eight major groups, based on the dominant pattern of faulting (Figure 1): - Major normal fault segments (i.e., near displacement maxima). - Fault bends. - Fault terminations or tips. - Step‐overs or relay ramps in normal faults. - Fault intersections. - Accommodation zones (i.e., belts of intermeshing oppositely dipping normal faults), - Displacement transfer zones whereby strike‐slip faults terminate in arrays of normal faults. - Transtensional pull‐aparts. These settings form a hierarchal pattern with respect to fault complexity. - Major normal faults and fault bends are the simplest. - Fault terminations are typically more complex than mid‐segments, as faults commonly break up into multiple strands or horsetail near their ends. - A fault intersection is generally more complex, as it generally contains both multiple fault strands and can include discrete di... « less
  4. AASG Wells Data for the EGS Test Site Planning and Analysis Task Temperature measurement data obtained from boreholes for the Association of American State Geologists (AASG) geothermal data project. Typically bottomhole temperatures are recorded from log headers, and this information is provided through a boreholemore » temperature observation service for each state. Service includes header records, well logs, temperature measurements, and other information for each borehole. Information presented in Geothermal Prospector was derived from data aggregated from the borehole temperature observations for all states. For each observation, the given well location was recorded and the best available well identified (name), temperature and depth were chosen. The “Well Name Source,” “Temp. Type” and “Depth Type” attributes indicate the field used from the original service. This data was then cleaned and converted to consistent units. The accuracy of the observation’s location, name, temperature or depth was note assessed beyond that originally provided by the service. - AASG bottom hole temperature datasets were downloaded from repository.usgin.org between the dates of May 16th and May 24th, 2013. - Datasets were cleaned to remove “null” and non-real entries, and data converted into consistent units across all datasets - Methodology for selecting ”best” temperature and depth attributes from column headers in AASG BHT Data sets: • Temperature: • CorrectedTemperature – best • MeasuredTemperature – next best • Depth: • DepthOfMeasurement – best • TrueVerticalDepth – next best • DrillerTotalDepth – last option • Well Name/Identifier • APINo – best • WellName – next best • ObservationURI - last option. The column headers are as follows: • gid = internal unique ID • src_state = the state from which the well was downloaded (note: the low temperature wells in Idaho are coded as “ID_LowTemp”, while all other wells are simply the two character state abbreviation) • source_url = the url for the source WFS service or Excel file • temp_c = “best” temperature in Celsius • temp_type = indicates whether temp_c comes from the corrected or measured temperature header column in the source document • depth_m = “best” depth in meters • depth_type = indicates whether depth_m comes from the measured, true vertical, or driller total depth header column in the source document • well_name = “best” well name or ID • name_src = indicates whether well_name came from apino, wellname, or observationuri header column in the source document • lat_wgs84 = latitude in wgs84 • lon_wgs84 = longitude in wgs84 • state = state in which the point is located • county = county in which the point is located « less
  5. This submission includes data collected from experiments on the performance of rare earth adsorption by immobilized bacteria that accompany the FY18 Q2 and Q3 quarter reports. Relevant information from these reports is included in a resource below. The spreadsheet below includes data from the followingmore » three experiments: REE Bioadsorption from buffer solution by Caulobacter biofilms. REE Bioadsorption from mock geothermal fluids by Caulobacter biofilms. REE biosorption capacity and its temperature dependence with Mutag Biochips. « less