DOI: 10.5061/dryad.3bk3j9kfh
title: Supporting information and data for "Chapter 1: Constraining Hydraulic Permeability at Great Depth by Using Magnetotellurics" in Pepin JD (2019) New Approaches to Geothermal Resource Exploration and Characterization (PhD dissertation)

description: This supporting information includes additional text, figures, and tables
 regarding the newly derived sodium-chloride fluid resistivity model and
 the magnetotelluric (MT) inversion methodology. It also includes
 additional simulated electrical resistivity results that are not
 explicitly presented in the main text; the three simulations featured in
 the main text are selected to represent this larger set of simulations.
 Six supporting datasets are also described in this document. The first is
 a compilation of previously published laboratory-measured electrical
 resistivity data taken at various temperatures and salinities for
 sodium-chloride fluids. While all of this data is considered reliable, the
 measurement accuracy is variable, since the data were compiled from work
 that was published over a time period ranging from 1907 to 2009. These
 data are used to derive a thin-plate spline model that permits estimation
 of sodium-chloride fluid resistivity over an extensive range of salinities
 (6 to 321,420 mg/L) and temperatures (0 to 309°C). A table of fluid
 resistivity, as estimated by the spline model, that covers salinities from
 0.001 to 5.5 mol/L over a temperature range of 0 to 300°C is also
 provided. A zip folder containing an R script and the necessary input
 files to use this spline model independently is included as well. Tables
 containing the hydrologic modeling results are provided for the three
 simulations featured in the main text. Lastly, the MT forward responses
 and calculated inversion fits to those responses are enclosed. The forward
 responses include both noise-free MT curves and those with 2% Gaussian
 noise added; the noisy curves were used exclusively for the inverse
 analysis.
description: Dataset S1. Compilation of published
 sodium-chloride resistivity data measured at numerous temperatures (0 to
 309°C) and salinities (6 to 321,420 mg/L). These data are used for the
 fitting of the spline fluid resistivity surface derived in this study.
 Measurements reported at non-unique temperature and salinity are averaged;
 these data are flagged in the “notes” column of the dataset. Literature
 sources for the data include <i>Noyes</i> [1907];
 <i>National Research Council</i> [1930]; <i>Quist and
 Marshall</i> [1968]; <i>Ucok et al.</i> [1980];
 <i>Zimmerman et al.</i> [1995]; <i>Ho et al.</i>
 [2000]; and <i>Lide and Haynes</i> [2009]. This dataset
 includes the following fields: <i>ID:</i>
 Unique data identification number.
 <i>Conc_mol_L:</i> Fluid concentration, in moles per
 liter. <i>Temp_C:</i> Fluid temperature, in
 degrees Celsius. <i>Resistivity_ohm_m:</i>
 Fluid resistivity, in ohm-meters.
 <i>Source:</i> Data reference.
 <i>Conc_g_L:</i> Fluid concentration, in grams per
 liter. <i>Conc_mg_L:</i> Fluid
 concentration, in milligrams per liter.
 <i>Notes:</i> Comments regarding the data.
 Dataset S2. Table presenting sodium-chloride
 fluid resistivity as a function of temperature (0 to 300°C) and salinity
 (0.001 to 5.5 mol/L). This table is produced by using the thin-plate
 spline model that is derived as part of this study. Temperatures increase
 by column while salinities increase by row. Dataset
 S3. A .zip folder including an R script and associated
 data files required to estimate resistivity as a function of temperature
 and salinity by using the thin-plate spline model derived in this study.
 This model is developed using R version 3.4.1. [<i>R Core
 Team</i>, 2017] and RStudio version 1.0.153. Reference the “READ
 ME.txt” file that is included in the .zip folder for instructions on using
 the script. The subfolders and files included in the .zip are as
 follows:
 <i>NaCl_Fluid_Resistivity_Model:</i> Master folder
 that contains all other files and folders.
 <i>input:</i> Subfolder containing files that are
 read in by the model, including calibration data
 (Literature_Resistivity_Data.csv), an example of input data
 (Input_Example.csv), and an input data template (Input_Template.csv). The
 calibration data is identical to Dataset S1 described above. The
 Input_Example.csv includes fields for a unique identification number (ID),
 spatial location (X and Y), fluid temperature (temp_C), and fluid
 concentration (conc_mol_L). The Input_Template.csv file only includes
 fields for fluid temperature (temp_C) and fluid concentration
 (conc_mol_L). Additional columns can be added to the Input_Template.csv as
 needed; model results will be appended after the last column of the input
 data.
 <i>NaCl_Fluid_Resistivity_Model.Rproj:</i> RStudio
 project file that is used to set the working directory for the
 model. <i>output: </i>Subfolder containing
 an example of model output data (Resistivity_Results_Input_Example.csv).
 The fields for this example data are the same as Input_Example.csv, except
 the model’s resistivity results have been appended to the input example
 data (fluid_resis_ohm_m). The model will automatically generate model
 output to this folder with a naming convention as follows:
 Resistivity_Results_&lt;Input Data Filename&gt;
 <i>READ_ME.txt:</i> Text file containing instructions
 for using the model. <i>scripts:</i>
 Subfolder containing the R script used for running for the model
 (NaCl_Fluid_Resistivity_Model.R). Dataset
 S4. A .zip folder including tables that present the
 hydrologic modeling results from the final timestep of the three
 simulations (Damköhler number = 0.2, 1, and 99) featured in main text. The
 fields in these files are identical to each other and include:
 <i>x_m:</i> Horizontal distance for model node
 location, in meters. <i>elevation_m:</i>
 Elevation for model node location, in meters.
 <i>conc_kgsolute_kg_solution:</i> Fluid
 concentration, in kilograms of solute per kilogram of solution.
 <i>density_g_L:</i> Fluid density, in grams per
 liter. <i>viscosity_Pa_s:</i> Fluid
 viscosity, in Pascals per second.
 <i>temp_C:</i> Fluid temperature, in degrees
 Celsius. <i>conc_mol_L:</i> Fluid
 concentration, in moles per liter.
 <i>conc_mg_L:</i> Fluid concentration, in milligrams
 per liter. <i>fluid_resis_ohm_m:</i> Fluid
 resistivity, in ohm-meters.
 <i>archie_ohm_m:</i> Effective resistivity from
 Archie’s law, in ohm-meters.
 <i>glover_ohm_m:</i> Effective resistivity from the
 Glover equation, in ohm-meters. Dataset S5.
 A .zip folder containing MT forward model responses at synthetic
 stations for the three simulations that are featured in the main text.
 These files are in the industry-standard EDI file format as generated by
 WinGLink. EDI files for noise-free forward modeled responses are included
 in addition to that with 2% Gaussian noise added. Gaussian noise is not
 added to the tipper data, as the tipper is not utilized in the inversions
 in order to be more conservative. These files are organized by the
 Damköhler number associated with each simulation (see Figure 5 and Table
 S1). Horizontal distance and elevation of the stations are indicated in
 the “Synthetic_Station_Locations.csv” file. The subfolders and files
 included in this .zip folder are:
 <i>Folder_Model_EDI_Files: </i>Master folder that
 contains all other files and folder.
 <i>No_noise_EDI_Files:</i> Noise-free EDI files for
 all synthetic stations (stations 1 through 43). These files are organized
 in subfolders that are named after the Damköhler number of the
 corresponding simulation. The fields in these files are discussed in the
 WinGLink documentation and software.
 <i>Noisy_EDI_Files:</i> Noisy (2% Gaussian noise) EDI
 files for all synthetic stations (stations 1 through 43). These files are
 organized in subfolders that are named after the Damköhler number of the
 corresponding simulation. The fields in these files are discussed in the
 WinGLink documentation and software.
 <i>Synthetic_Station_Locations.csv: </i>Synthetic
 station locations within the model domain. Fields include a station
 identification number (ID), horizontal distance location in meters
 (X_meters), and an elevation in meters (Z_meters) for each
 station. Dataset S6. A .pdf
 presenting two-percent Gaussian noise forward modeled MT responses
 (Original) with inversion fits (Calculated) for each synthetic station.
 The apparent resistivity and phase of the transverse electric (TE) and
 transverse magnetic (TM) modes of the MT response are shown. Inversion
 root-mean-squared error (rms) is shown for each station. The three
 simulations featured in the main text (Damköhler number = 0.2, 1, and 99)
 are all represented.
description: All related notes are included in the methods section above or in
 above referenced readme files included in the individual code/dataset
 files in the collection.