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Title: Chlorite Dissolution Rates From 25 to 275 degrees and pH 3 to 10

We have calculated a chlorite dissolution rate equation at far from equilibrium conditions by combining new data (20 experiments at high temperature) with previously published data Smith et al. 2013 and Lowson et al. 2007. All rate data (from the 127 experiments) are tabulated in this data submission. More information on the calculation of the rate data can be found in our FY13 Annual support (Carroll LLNL, 2013) which has been submitted to the GDR. The rate equation fills a data gap in geothemal kinetic data base and can be used directly to estimate the impact of chemical alteration on all geothermal processes. It is especially important for understanding the role of chemical alteration in the weakening for shear zones in EGS systems.
Authors:
Publication Date:
Report Number(s):
246
DOE Contract Number:
FY13 AOP 25727
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; chlorite dissolution rate law; geochemistry; geothermal kinetic database; chemical alteration; EGS; chlorite dissolution rate
OSTI Identifier:
1148811
  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).
No associated Collections found.
  1. Spreadsheets provides measured chlorite rate data from 100 to 300C at elevated CO2. Spreadsheet includes derived rate equation.
  2. These files are ambient noise correlation (ANC) functions calculated for 11 days of continuous seismic data recorded by the Lawrence Berkeley network in the Brady geothermal field. These are SAC formatted seismic waveforms. The stations included are BPB04, BPB05, BPB07, BPB08, BPRT1, BPRT2, BPRT3, BPRT5,more » BRB10, BRP01, BRP02, BRP03, BRP04, BPR06, and BRP09 The original data were cut into hour long traces and processed by differentiating, removing the mean, removing the trend, applying a 1% taper, whitening, removing the mean and trend again, and converting to single bit traces. The data were then correlated with those from other stations and stacked. The resulting files were then named according to the convention: STA1.STA2.CHAN1_CHAN2.NHOURS.stacked.sac The days included are from records during 2013 (julian days, 200,220-229). The ANC correlations were calculated on the raw data traces (without instrument corrections applied) to assess the quality of the signal as a function of frequency throughout the network. The data were recorded at 500 Hz. We observe high quality signals 30 Hz on all traces, and measurable signal up to 80 Hz on a subset of the traces. « less
  3. The objective of this suite of experiments was to develop a useful kinetic dissolution expression for illite applicable over an expanded range of solution pH and temperature conditions representative of subsurface conditions in natural and/or engineered geothermal reservoirs. Using our new data, the resulting ratemore » equation is dependent on both pH and temperature and utilizes two specific dissolution mechanisms (a “neutral” and a “basic” mechanism). The form of this rate equation should be easily incorporated into most existing reactive transport codes for to predict rock-water interactions in EGS shear zones. « less
  4. Spreadsheet containing chlorite, illite, and biotite rate data and rate equations that can be used in reactive transport simulations. Submission includes a report on the development of the rate laws.
  5. 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