Analysis of Geothermal Gradient in Southern Idaho

USING ARCGIS PRO TO MODEL THE GEOTHERMAL GRADIENT OF SOUTHERN IDAHO

With current technology, Enhanced Geothermal Systems (EGS) or geothermal systems harvested for power have the potential to power an estimated 40 million American homes and businesses (Energy.gov). With a capacity factor of over 90%, geothermal electricity generation could offset coal, natural gas, or nuclear power as a baseload supply in the electricity market as well as provide needed solutions in the search for clean, affordable, and renewable energy.

Evaluation of geothermal systems for energy generation hinge on three key elements: Heat, permeability (meaning water can move freely through the rock), and fluid (Energy.gov). The Yellowstone Hot Spot generates significant heat in the region; however, the Snake Plain aquifer interferes with the heat due to its high fluid flow.

In this project, I focused on the development of a three-dimensional model of the geothermal temperature profile in Southern Idaho. I created this model using data from geothermal gradient and geothermal well temperature data (obtained from the Idaho Department of Water Resources and Idaho Geological Survey). Utilizing pattern analysis and visualization tools in ArcGIS Pro, the aim of this study is to model how the Snake Plain aquifer interacts with the geothermal gradient in Southern Idaho.

Empirical Bayesian Kriging 3D (EBK 3D)

To model the geothermal gradient of Southern Idaho, I used the Empirical Bayesian Kriging 3D (EKB 3D) tool and the Voxel Visualization capabilities of ArcGIS Pro.

The EBK 3D tool has certain data requirements that must be met to produce a useable interpolation:

It must have x, y, and z data, as well as a value to be interpolated, and,
The data must be in a projected coordinate system.

The geothermal well data is given in decimal degrees, with depths that I used for the z-values, and I interpolated temperatures which were also included in the data. The data was also given in the projected coordinate system of WGS 1984 web mercator (auxiliary sphere). The tool utilizes local models in order to predict data in locations where it is not measured, so I needed to perform some analysis of my data before running the tool.

Figure 1. Histogram of Minimum Well Temperature and statistics with no transformation applied. The 739 data points are not normally distributed, which makes the EBK tool a better choice than Ordinary Kriging not only because it can interpolate data in three dimensions but also because it uses local models, and so the data can have trends, like the draw-down of the geothermal gradient produced by the Snake River Aquifer.

Figure 2. QQ plot of minimum measured water temperature to normal distribution. The data is close to the trend line but does not follow it exactly.

After my analysis of the data, I ran the EBK 3D tool with the Power semivariogram model.

Figure 3. Temperature contour map of Idaho surface temperatures, produced using Empirical Bayesian Kriging 3D tool.

Figure 4. Temperature contour map of Idaho at 5,000 feet depth, produced using Empirical Bayesian Kriging 3D tool.

Figure 5. Temperature contour map of Idaho at 10,000 feet depth, produced using Empirical Bayesian Kriging 3D tool.

QQ Plot for interpolated temperature contours, showing the interpolated model fits the trend line of a normal distribution fairly well.

Summary of statistics for interpolated model, showing how much confidence you can have in the predictive model. Both the Mean and Root-Mean-Square values should be as close to zero as possible; and the Root-Mean-Square Standardized should be close to one. My data fits this model well; however the Root-Mean Square value is very high, showing the data has a trend.

With the 3D temperature contour map complete, I then converted the resulting interpolation into a NetCDF file, which can be used to create a 3D visualization using "voxels."

Visualizing Three Dimensional Data with Voxels

A voxel is a three-dimensional pixel, representing a value or attribute in a volumetric space. It is commonly used in computer graphics and scientific visualization to represent data in a three-dimensional grid. Each voxel contains information about a specific point in space, such as color, density, or material properties. In this case, I used the voxels to visualize the temperature gradient of Southern Idaho.

In the 3D environment hosted by ArcGIS Pro (a "local scene"), I imported the NetCDF file I had created as a multi-dimensional voxel layer. By adjusting the data shown and how it was displayed, I was able to create a three-dimensional contour map showing the drawdown of the geothermal gradient due to the aquifer.

Figure 11. Three dimensional voxel model of temperature contours from the surface to 10,000 feet depth

The three-dimensional model shows a clear bathymetric profile due to the aquifer.

Sources

DeGrey-Ellis, L., Link, P., and Pearson, D. Snake River Plain Aquifer: Idaho State University: Digital Geology of Idaho, https://www.isu.edu/digitalgeologyidaho/srp-aquifer/ (accessed November 2023).

Enhanced Geothermal Systems Energy.gov, https://www.energy.gov/eere/geothermal/enhanced-geothermal-systems (accessed November 2023).

Garrity, C. TopoView: USGS: USGS Topoview, https://ngmdb.usgs.gov/topoview (accessed November 2023).

Geothermal Data Repository (GDR) GDR, https://gdr.openei.org/home (accessed November 2023).

Geothermal Research NREL.gov, https://www.nrel.gov/geothermal (accessed November 2023).

Geothermal Resources Idaho Geological Survey, https://www.idahogeology.org/geologic-resources/geothermal-resources (accessed November 2023).

Geothermal Resources, 2023, Energy & Minerals, https://geology.utah.gov/energy-minerals/geothermal/ (accessed November 2023).

Idaho, S. of, 2023, Idaho Department of Water Resources GIS Data: Idaho Department of Water Resources, https://data-idwr.hub.arcgis.com/ (accessed November 2023).

Nielson, D.L., and Shervais, J.W., 2014, Conceptual Model for Snake River Plain Geothermal Systems: PROCEEDINGS, Thirty-Ninth Workshop on Geothermal Reservoir Engineering.

What is a voxel layer? ESRI, ArcGIS Pro | Documentation, https://pro.arcgis.com/en/pro-app/latest/help/mapping/layer-properties/what-is-a-voxel-layer-.htm (accessed November 2023).

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