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Title: Penrose Well Temperatures

Penrose Well Temperatures Geothermal waters have been encountered in several wells near Penrose in Fremont County, Colorado. Most of the wells were drilled for oil and gas exploration and, in a few cases, production. This ESRI point shapefile utilizes data from 95 wells in and around the Penrose area provided by the Colorado Oil and Gas Conservation Commission (COGCC) database at http://cogcc.state.co.us/ . Temperature data from the database were used to calculate a temperature gradient for each well. This information was then used to estimate temperatures at various depths. Projection: UTM Zone 13 NAD27 Extent: West -105.224871 East -105.027633 North 38.486269 South 38.259507 Originators: Colorado Oil and Gas Conservation Commission (COGCC) Karen Christopherson
Authors:
Publication Date:
Report Number(s):
343
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 Office (EE-4G)
Subject:
15 Geothermal Energy; geothermal; Colorado; Fremont County; Temperature gradients; Oil and Gas wells; Temperature Gradient
OSTI Identifier:
1148729
  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. x,y,z data of the 3D temperature model for the West Flank Coso FORGE site. Model grid spacing is 250m. The temperature model for the Coso geothermal field used over 100 geothermal production sized wells and intermediate-depth temperature holes. At the near surface of this model,more » two boundary temperatures were assumed: (1) areas with surface manifestations, including fumaroles along the northeast striking normal faults and northwest striking dextral faults with the hydrothermal field, a temperature of ~104ËšC was applied to datum at +1066 meters above sea level elevation, and (2) a near-surface temperature at about 10 meters depth, of 20ËšC was applied below the diurnal and annual conductive temperature perturbations. These assumptions were based on heat flow studies conducted at the CVF and for the Mojave Desert. On the edges of the hydrothermal system, a 73ËšC/km (4ËšF/100’) temperature gradient contour was established using conductive gradient data from shallow and intermediate-depth temperature holes. This contour was continued to all elevation datums between the 20ËšC surface and -1520 meters below mean sea level. Because the West Flank is outside of the geothermal field footprint, during Phase 1, the three wells inside the FORGE site were incorporated into the preexisting temperature model. To ensure a complete model was built based on all the available data sets, measured bottom-hole temperature gradients in certain wells were downward extrapolated to the next deepest elevation datum (or a maximum of about 25% of the well depth where conductive gradients are evident in the lower portions of the wells). After assuring that the margins of the geothermal field were going to be adequately modelled, the data was contoured using the Kriging method algorithm. Although the extrapolated temperatures and boundary conditions are not rigorous, the calculated temperatures are anticipated to be within ~6ËšC (20ËšF), or one contour interval, of the observed data within the Coso geothermal field. Based on a lack of temperature data west of 74-2TCH, the edges of this model still seem to have an effect on West Flank modeled temperatures. « less
  2. 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
  3. Critically stressed fault segments have a relatively high likelihood of acting as fluid flow conduits (Sibson, 1994). As such, the tendency of a fault segment to slip (slip tendency; Ts; Morris et al., 1996) or to dilate (dilation tendency; Td; Ferrill et al., 1999) providesmore » an indication of which faults or fault segments within a geothermal system are critically stressed and therefore likely to transmit geothermal fluids. The slip tendency of a surface is defined by the ratio of shear stress to normal stress on that surface: Ts = τ / σn (Morris et al., 1996). Dilation tendency is defined by the stress acting normal to a given surface: Td = (σ1-σn) / (σ1-σ3) (Ferrill et al., 1999). Slip and dilation were calculated using 3DStress (Southwest Research Institute). Slip and dilation tendency are both unitless ratios of the resolved stresses applied to the fault plane by ambient stress conditions. Values range from a maximum of 1, a fault plane ideally oriented to slip or dilate under ambient stress conditions to zero, a fault plane with no potential to slip or dilate. Slip and dilation tendency values were calculated for each fault in the focus study areas at, McGinness Hills, Neal Hot Springs, Patua, Salt Wells, San Emidio, and Tuscarora on fault traces. As dip is not well constrained or unknown for many faults mapped in within these we made these calculations using the dip for each fault that would yield the maximum slip tendency or dilation tendency. As such, these results should be viewed as maximum tendency of each fault to slip or dilate. The resulting along-fault and fault-to-fault variation in slip or dilation potential is a proxy for along fault and fault-to-fault variation in fluid flow conduit potential. Stress Magnitudes and directions Stress field variation within each focus area was approximated based on regional published data and the world stress database (Hickman et al., 2000; Hickman et al., 1998 Robertson-Tait et al., 2004; Hickman and Davatzes, 2010; Davatzes and Hickman, 2006; Blake and Davatzes 2011; Blake and Davatzes, 2012; Moeck et al., 2010; Moos and Ronne, 2010 and Reinecker et al., 2005) as well as local stress information if applicable. For faults within these focus systems we applied either a normal faulting stress regime where the vertical stress (sv) is larger than the maximum horizontal stress (shmax) which is larger than the minimum horizontal stress (sv>shmax>shmin) or strike-slip faulting stress regime where the maximum horizontal stress (shmax) is larger than the vertical stress (sv) which is larger than the minimum horizontal stress (shmax >sv>shmin) depending on the general tectonic province of the system. Based on visual inspection of the limited stress magnitude data in the Great Basin we used magnitudes such that shmin/shmax = .527 and shmin/sv= .46, which are consistent with complete and partial stress field determinations from Desert Peak, Coso, the Fallon area and Dixie valley (Hickman et al., 2000; Hickman et al., 1998 Robertson-Tait et al., 2004; Hickman and Davatzes, 2011; Davatzes and Hickman, 2006; Blake and Davatzes 2011; Blake and Davatzes, 2012). Slip and dilation tendency for the Salt Wells geothermal field was calculated based on the faults mapped in the Bunejug Mountains quadrangle (Hinz et al., 2011). The Salt Wells area lies in the Basin and Range Province (N. Hinz personal comm.) As such we applied a normal faulting stress regime to the Salt Wells area faults, with a minimum horizontal stress direction oriented 105, based on inspection of local and regional stress determinations. Under these stress conditions north-northeast striking, steeply dipping fault segments have the highest dilation tendency, while north-northeast striking 60° dipping fault segments have the highest tendency to slip. Several such faults intersect in high density in the core of the accommodation zone in the Bunejug Mountains and local to the Salt Wells geothermal . « less
  4. The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The Project chose the Dixie Valley Geothermal System in Nevada as a field laboratory site for methodology calibration purposes because, in the public domain,more » it is a highly characterized geothermal system in the Basin and Range with a considerable amount of geoscience and most importantly, well data. The overall project area is 2500km2 with the Calibration Area (Dixie Valley Geothermal Wellfield) being about 170km2. The project was subdivided into five tasks (1) collect and assess the existing public domain geoscience data; (2) design and populate a GIS database; (3) develop a baseline (existing data) geothermal conceptual model, evaluate geostatistical relationships, and generate baseline, coupled EGS favorability/trust maps from +1km above sea level (asl) to -4km asl for the Calibration Area at 0.5km intervals to identify EGS drilling targets at a scale of 5km x 5km; (4) collect new geophysical and geochemical data, and (5) repeat Task 3 for the enhanced (baseline + new ) data. Favorability maps were based on the integrated assessment of the three critical EGS exploration parameters of interest: rock type, temperature and stress. A complimentary trust map was generated to compliment the favorability maps to graphically illustrate the cumulative confidence in the data used in the favorability mapping. The Final Scientific Report (FSR) is submitted in two parts with Part I describing the results of project Tasks 1 through 3 and Part II covering the results of project Tasks 4 through 5 plus answering nine questions posed in the proposal for the overall project. FSR Part I presents (1) an assessment of the readily available public domain data and some proprietary data provided by Terra-Gen Power, LLC, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region. FSR Part II presents (1) 278 new gravity stations; (2) enhanced gravity-magnetic modeling; (3) 42 new ambient seismic noise survey stations; (4) an integration of the new seismic noise data with a regional seismic network; (5) a new methodology and approach to interpret this data; (5) a novel method to predict rock type and temperature based on the newly interpreted data; (6) 70 new magnetotelluric (MT) stations; (7) an integrated interpretation of the enhanced MT data set; (8) the results of a 308 station soil CO2 gas survey; (9) new conductive thermal modeling in the project area; (10) new convective modeling in the Calibration Area; (11) pseudo-convective modeling in the Calibration Area; (12) enhanced data implications and qualitative geoscience correlations at three scales (a) Regional, (b) Project, and (c) Calibration Area; (13) quantitative geostatistical exploratory data analysis; and (14) responses to nine questions posed in the proposal for this investigation. Enhanced favorability/trust maps were not generated because there was not a sufficient amount of new, fully-vetted (see below) rock type, temperature, and stress data. The enhanced seismic data did generate a new method to infer rock type and temperature. However, in the opinion of the Principal Investigator for this project, this new methodology needs to be tested and evaluated at other sites in the Basin and Range before it is used to generate the referenced maps. As in the baseline conceptual model, the enhanced findings can be applied to both the hydrothermal system and EGS in the Dixie Valley region. « less
  5. 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