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Title: Value of Information spreadsheet

This spreadsheet represents the information posteriors derived from synthetic data of magnetotellurics (MT). These were used to calculate value of information of MT for geothermal exploration. Information posteriors describe how well MT was able to locate the "throat" of clay caps, which are indicative of hidden geothermal resources. This data is full explained in the peer-reviewed publication: Trainor-Guitton, W., Hoversten, G. M., Ramirez, A., Roberts, J., Júlíusson, E., Key, K., Mellors, R. (Sept-Oct. 2014) The value of spatial information for determining well placement: a geothermal example, Geophysics.
Authors:
Publication Date:
Report Number(s):
426
DOE Contract Number:
FY14 AOP 3.3.1.2
Product Type:
Dataset
Research Org(s):
DOE Geothermal Data Repository; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Collaborations:
Lawrence Livermore National Laboratory
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Geothermal Technologies Office (EE-4G)
Subject:
15 Geothermal Energy; geothermal; magnetotellurics; information posterior
OSTI Identifier:
1148896
  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. 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
  2. To help guide its future data collection efforts, The DOE GTO funded a data gap analysis in FY2012 to identify high potential hydrothermal areas where critical data are needed. This analysis was updated in FY2013 and the resulting datasets are represented by this metadata. Themore » original process was published in FY 2012 and is available here: https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2013/Esposito.pdf Though there are many types of data that can be used for hydrothermal exploration, five types of exploration data were targeted for this analysis. These data types were selected for their regional reconnaissance potential, and include many of the primary exploration techniques currently used by the geothermal industry. The data types include: 1. well data 2. geologic maps 3. fault maps 4. geochemistry data 5. geophysical data To determine data coverage, metadata for exploration data (including data type, data status, and coverage information) were collected and catalogued from nodes on the National Geothermal Data System (NGDS). It is the intention of this analysis that the data be updated from this source in a semi-automated fashion as new datasets are added to the NGDS nodes. In addition to this upload, an online tool was developed to allow all geothermal data providers to access this assessment and to directly add metadata themselves and view the results of the analysis via maps of data coverage in Geothermal Prospector (http://maps.nrel.gov/gt_prospector). A grid of the contiguous U.S. was created with 88,000 10-km by 10-km grid cells, and each cell was populated with the status of data availability corresponding to the five data types. Using these five data coverage maps and the USGS Resource Potential Map, sites were identified for future data collection efforts. These sites signify both that the USGS has indicated high favorability of occurrence of geothermal resources and that data gaps exist. The uploaded data are contained in two data files for each data category. The first file contains the grid and is in the SHP file format (shape file.) Each populated grid cell represents a 10k area within which data is known to exist. The second file is a CSV (comma separated value) file that contains all of the individual layers that intersected with the grid. This CSV can be joined with the map to retrieve a list of datasets that are available at any given site. The attributes in the CSV include: 1. grid_id : The id of the grid cell that the data intersects with 2. title: This represents the name of the WFS service that intersected with this grid cell 3. abstract: This represents the description of the WFS service that intersected with this grid cell 4. gap_type: This represents the category of data availability that these data fall within. As the current processing is pulling data from NGDS, this category universally represents data that are available in the NGDS and are ready for acquisition for analytic purposes. 5. proprietary_type: Whether the data are considered proprietary 6. service_type: The type of service 7. base_url: The service URL « less
  3. The US Geological Survey (USGS) resource assessment (Williams et al., 2009) outlined a mean 30GWe of undiscovered hydrothermal resource in the western US. One goal of the Geothermal Technologies Office (GTO) is to accelerate the development of this undiscovered resource. The Geothermal Technologies Program (GTP)more » Blue Ribbon Panel (GTO, 2011) recommended that DOE focus efforts on helping industry identify hidden geothermal resources to increase geothermal capacity in the near term. Increased exploration activity will produce more prospects, more discoveries, and more readily developable resources. Detailed exploration case studies akin to those found in oil and gas (e.g. Beaumont, et al, 1990) will give operators a single point of information to gather clean, unbiased information on which to build geothermal drilling prospects. To support this effort, the National Renewable Energy laboratory (NREL) has been working with the Department of Energy (DOE) to develop a template for geothermal case studies on the Geothermal Gateway on OpenEI. In fiscal year 2013, the template was developed and tested with two case studies: Raft River Geothermal Area (http://en.openei.org/wiki/Raft_River_Geothermal_Area) and Coso Geothermal Area (http://en.openei.org/wiki/Coso_Geothermal_Area). In fiscal year 2014, ten additional case studies were completed, and additional features were added to the template to allow for more data and the direct citations of data. The template allows for: Data - a variety of data can be collected for each area, including power production information, well field information, geologic information, reservoir information, and geochemistry information. Narratives ? general (e.g. area overview, history and infrastructure), technical (e.g. exploration history, well field description, R&D activities) and geologic narratives (e.g. area geology, hydrothermal system, heat source, geochemistry.) Exploration Activity Catalog - catalog of exploration activities conducted in the area (with dates and references.) NEPA Analysis ? a query of NEPA analyses conducted in the area (that have been catalogued in the OpenEI NEPA database.) In fiscal year 2015, NREL is working with universities to populate additional case studies on OpenEI. The goal is to provide a large enough dataset to start conducting analyses of exploration programs to identify correlations between successful exploration plans for areas with similar geologic occurrence models. « less
  4. This layer contains favorable geochemistry for high-temperature geothermal systems, as interpreted by Richard "Rick" Zehner. The data is compiled from the data obtained from the USGS. The original data set combines 15,622 samples collected in the State of Colorado from several sources including 1) themore » original Geotherm geochemical database, 2) USGS NWIS (National Water Information System), 3) Colorado Geological Survey geothermal sample data, and 4) original samples collected by R. Zehner at various sites during the 2011 field season. These samples are also available in a separate shapefile FlintWaterSamples.shp. Data from all samples were reportedly collected using standard water sampling protocols (filtering through 0.45 micron filter, etc.) Sample information was standardized to ppm (micrograms/liter) in spreadsheet columns. Commonly-used cation and silica geothermometer temperature estimates are included. « less
  5. Geologic map data in shapefile format that includes faults, unit contacts, unit polygons, attitudes of strata and faults, and surficial geothermal features. 5 cross‐sections in Adobe Illustrator format. Comprehensive catalogue of drill‐hole data in spreadsheet, shapefile, and Geosoft database formats. Includes XYZ locations of wellmore » heads, year drilled, type of well, operator, total depths, well path data (deviations), lithology logs, and temperature data. 3D model constructed with EarthVision using geologic map data, cross‐sections, drill‐hole data, and geophysics. « less