GIS II: Raster Analysis and Suitability Modeling

Raster Analysis and Suitability Modeling for Frac Sand Mining in Trempealeau County, West-Central Wisconsin

Introduction

Raster datasets provide an opportunity that is quite unique and can be a very powerful tool for spatial analysis. If properly trained, the functionality of rasters allow the user to reclassify, calculate distance buffers, add multiple raster datasets, and use the product to produce a suitability model. In this exercise, our objective was to develop a number of suitability models, based on a given set of criteria, and combine them in the end to develop a final model for most suitable land. A Python script was then written to assign a weighted value to what I deemed the most prominent variable. The result was a number of suitability maps that showed the most suitable location for frac sand mines in the southern half of Trempealeau County.

Methods

The initial suitability model examined bedrock geology, national land cover data (NLCD), distance from a rail terminal, slope percent, and water table elevation. Most of these were in the form of vector data (points, lines, and polygons). Most of the data was acquired from the Trempealeau County GIS geodatabase (tremplocounty.com), with the exception of the water table data, which was acquired from the Wisconsin Geological and Natural History GIS database, and the NCLD data, which was acquired from the US National map viewer database. The vector data for bedrock geology, NLCD, rail terminal, and surface elevation were clipped to our study area, projected, and converted from vector to raster. The next step was to assign a euclidean distance to the rail terminal. This produced the raster equivalence of a vector buffer, though where the buffer only extends as far as the user input extends, the raster euclidean distance tool extends to the processing extent of the model. This tool produced a new raster of distance from the terminal over the entire study area. The next step was to reclassify all the data and assign it a value of one, two, or three. The most suitable land was given a designation of "3," while the least suitable, or unsuitable land, was given a designation of 1 (Figure 1). In the case of what we will call exclusion rasters, raster used to completely exclude certain areas based upon unsuitable criteria, a value of zero or one was assigned. A value of 0 meant that the area was not useable, such as open water, flooded zones, or developed areas, while a value of 1 meant that the area was suitable for use. An exclusion raster was developed for the NLCD data, in this section, and for areas containing protected wildlife habitat in the next section. 

Figure 1. This figure shows the description of the values used in the reclassified rasters. The (2) variable is missing from Bedrock Geology. I chose not to use it because the bedrock units are either suitable or not suitable, but they need to retain the same weight that the other rasters have. 

After the rasters were all reclassified, they were able to be combined. To do this, the Raster Calculator tool was used to create an equation. The rasters for bedrock geology, NLCD, distance from terminal, percent slope, and water table elevation were added and then multiplied by the NLCD (Excluded) raster. This returned results of either a zero, where the land was unsuitable, or the previous added number where the land was suitable. A data flow model was created in Modelbuilder (Figure 2).

Figure 2. This figure shows the data flow model created in Modelbuilder for analyzing and combining the bedrock geology, NLCD, distance from rail terminal, percent slope, and water table elevation data. The water table data had to be imported, then converted using the Topo to Raster tool before it could be reclassified.

The second portion of the exercise required mostly the same workflow to be used to analyze streams, prime farmland, proximity to residential areas, proximity to schools, and proximity to wildlife areas. The data was clipped, projected, converted to raster, and then reclassified based on a set of determined criteria (Figures 3 and 4). In this dataset, streams, farmland, residential proximity, and school proximity were given a one, two, or three designation, while proximity to wildlife areas was assigned a zero or one value. Only a certain type of streams were selected, to determine the areas of the most prominent flow. Streams of higher stream order are generally larger streams in a watershed and therefore are more likely to be polluted by the mines. Using the Euclidean Distance tool, these streams were given a distance of 500 meters around them before they were given their reclassified designations. This was a number I decided upon to create a buffer zone between potential mines and streams. The data for residential and school proximity was based upon a distance provided to us of 640 meters that were required to be between a residential zone or school and a mine. A distance within 640 meters was least desirable, while a distance of double that, 1280 meters, was the next least desirable, while anything over 1280 meters was desirable. Wildlife Areas within the county was a variable that I wanted to examine. I wanted to exclude these areas from potential mine locations, as I considered them a valuable resource for promoting ecological health. I decided that any distance within 500 meters of a wildlife area was not healthy for the wildlife area, thus it was to be entirely excluded from the search.

Figure 3. This figure shows the initial portion of the data flow model. An iterator tool was used to automate workflow for all included variables. In this case, it was used to clip all of the feature classes to the study area. Another data flow model was created and another iterator was used to project all of the feature classes. No more than one iterator can be used in a data flow model.

Figure 4. This figure shows the description of the values used in the reclassified rasters. The reclassified raster for wildlife areas was determined to be an exclusionary raster, as was the viewshed visibility raster. 

The next objective was to determine a viewshed for a certain feature in Trempealeau County deemed a prime recreational area. I chose to work with a Mississippi River recreational trail. The Viewshed tool takes an input feature, a digital elevation model, and determines any areas visible from that location. Any area that was visible from the trail was considered to detract from the recreational potential and was to be omitted from the search. For this reason, a value of zero or one was assigned to the areas of the viewshed analysis. If any land was visible from the trail it was to be omitted.

All of this data was able to be combined using the raster calculator tool. First, the streams, farmland, residential proximity, and school proximity were added and multiplied by the wildlife areas to omit any area that did not meet the criteria of being suitable, but within the 500 meter zone near the wildlife areas. This raster was then multiplied by the viewshed raster to determine a final model for this section. A data flow model was created to illustrate this workflow (Figure 5).

Figure 5. This figure shows the data flow model created in Modelbuilder for analyzing and combining the streams, farmland, residential proximity, school proximity, wildlife areas, and viewshed.

The raster that showed suitability for land necessity and rail terminal proximity was then combined with the raster that showed suitability for environmental and community impact (Figure 6).

Figure 6. This model generated the final suitability model that combined the other two suitability rasters.

A python script was written to assign a weighted value to a raster that I felt was required more value than the other rasters. I chose to add a weight to the residential zones, as I felt they were the most likely to be affected by having a mine in close proximity to them (Figure 7). 

Figure 7. This figure shows the python script required to add a weighted value to a certain raster. I chose to assign weight to the residential raster, as it was the most likely to be affected by the proximity of a mine.

Results

It should be noted that all of the data in the following section is hypothetical and in no way calculates usable results other than those determined for the sole purpose of this exercise. The results gathered were a number of maps showing suitability. Figure 8 shows the suitability raster for land necessity and rail terminal proximity. Figure 9 shows the suitability raster for environmental and community risks. Figure 10 shows the viewshed visibility raster. Figure 11 shows the combined raster for the viewshed and the environmental and community risks. Figure 12 shows the combined raster for land necessity and rail terminal proximity and environmental, community, and viewshed suitability. Finally, Figure 13 shows the result of the weighted model created from the Python script. 

Figure 8. This map shows the suitability results for the bedrock geology, NLCD, distance from rail terminal, percent slope, and water table elevation data. The most suitable location in this map is in the west central section of the study area. This is the location of the one rail terminal in the study area.

Figure 9. This map shows the suitability results for the streams, farmland, residential proximity, school proximity, and wildlife areas. The most suitable location in this map is dispersed throughout the study area. The area with the highest suitability appears to be in the southern portion of the map, in a small dark green cluster, however it is well dispersed throughout the map.

Figure 10. This map shows the viewshed suitability raster. Suitability was determined for a recreational trail along the Mississippi River in the southwestern portion of the study area. Black signifies areas that are not visible from the trail, and therefore areas suitable for a frac sand mine. The light gray signifies areas visible from the trail, therefore not suitable for a frac sand mine.

FIgure 11. This map shows the suitability results for streams, farmland, residential proximity, school proximity, wildlife areas, and viewshed. Much of the area in the southwestern portion is red when the viewshed is taken into account.

Figure 12. This map shows the suitability of the rasters from Figure 8 and Figure 11. The most suitable area is in the west-central portion, where there is the highest concentration of dark green, signifying the most suitable area.

Figure 13. This map shows a comparison between the environmental and community suitability before and after the weight was applied to the residential zones. The left map shows the suitability raster before the weight was applied. The right map shows the suitability raster after the weight was applied.

Discussion

Examining the various criteria created a number of very interesting results. Perhaps the most interesting to me was examining the results derived from the community and environmental risk factors, as I feel this did the best job of denoting areas of unsuitability based upon their impact on desirable areas for community or environmental health. The original suitability raster developed shows the most suitable location based upon the criteria necessary for an economically productive frac sand mine. The correct geologic formations, proper land classification, gentle slopes, and a short distance to transport produced the most suitable location for a mine. In fact, it seems like the variable with the greatest effect on the data was that of the distance from the railroad terminal located in the west-central portion of the study area. In examining the final model before the weighted value was applied, it seems apparent that the area in the central portion of the study area, nearest the rail terminal, is the most suitable location for a rail terminal.

Conclusion

The functionality inherent in raster analysis tools opens a wide array of advanced analytical functions. Not only are we able to reclassify data based upon desired criteria, but we are also able to use the Raster Calculator function to compare and add rasters together. When determining suitability, these tools provide a toolset that vector analysis cannot provide.

Works Cited

Trempealeau County Land Records. 2014. [cited 13 December 2014]. Available from http://www.tremplocounty.com/landrecords/.

Wisconsin Geological and Natural History Survey. 2014. [cited 13 December 2014]. Available from http://wgnhs.uwex.edu/maps-data/gis-data/.

GIS II: Network Analysis, Routing, and Assessment

Network Analysis and Routing for Frac Sand Mines and Railroad Terminals

Introduction

The process of network analysis is a vital tool for analyzing most effective routing and  for potential cost assessment for a variety of different levels from city to state. When coupled with python scripting and data flow modeling, the possibilities for customization of search criteria and the end usability of the resulting data far surpasses the time required to initially develop the models. In this exercise, we started by developing scripts to eliminate extraneous data from our datasets. We then learned about the functions of the Network Analysis toolbox in ArcMap. Our final step was to develop a data flow model that automated the processes after our python scripting. The resulting output provided the comprehensive length of routes for each county and the cost of travel for trucks on those roads.

Methods

Python Scripting

A python script was written in the first half of this exercise that selected active mines that were not rail loading stations. To account for any mines that may have been rail loading stations as well, a 1.5 kilometer zone around the railways was eliminated from the search criteria. More details concerning the scripting process and a screenshot of the script can be seen under the “Python Scripts” tab found at the top of this page.

Network Analyst

Network Analyst is a toolbox within the ArcGIS Suite that allows for advanced route modeling, among other things. Using Network Analyst, we were able to assess the fastest routing available for sand trucks traveling from the sand mines to the rail loading terminals. The network dataset used for the roadway mapping was acquired from ESRI Street Map data for the United States.  The mine location data was acquired from the Wisconsin DNR. The rail terminal data was acquired from the Department of Transportation website. In order to determine travel distance and cost of travel we first had to determine a closest facility that each truck would be traveling to where they could offload their sand. These routing options were tested by manually inputting the facilities (rail terminals) and the incidents (mines). The objective of this lab was to fully automate the process where a useable model could be easily used again by implementing new base layers and rerunning the data flow model.

Data Flow Model

A data flow model was created that automated the work required for replicating the results for new datasets, if needed. Figure 1 shows the complete data flow model for this exercise. A majority of the data flow model was quite simple to compile. In order to create the routing for the sand trucks from the mines to the rail terminals a closest facility was determined for each mine, locations were added that determined the input functions for where the trucks were being routed to, and the data was solved, all using simple tools within the Network Analyst toolbox. Following this, the data was selected, the features were copied, and an output feature class was created that saved the temporary feature class created by the previous solve function.  Finally, the data was projected in a Wisconsin (meter) projection that allowed it to be compatible with the rest of the data. However, when following the model, after the green circle called “Results_Prj,” the methods involved were somewhat more difficult.

Figure 1. This data flow model shows the process needed to complete the entire process described in the methods section.

A number of steps were required in order to convert the route data to a useable feature on the county level, which allowed us to calculate comprehensive distance traveled and cost of travel for each county. There were a number of different methods to accomplish this, but I started mine using the intersect tool. Intersecting allowed me to split the route line feature class by the polygon boundary. After this, I used a spatial join to combine the feature class for Wisconsin counties by the intersected route data. This prepared the data to be summarized by county, rather than just overall route length. When summarizing the comprehensive route length I used the Summarize Statistics tool, summarizing the length by county designation. After this, I was able to use the add field tool to add a field showing the distance in meters converted to distance in miles. I used the Calculate Field tool to create an SQL statement to calculate the comprehensive lengths multiplied by the conversion factor for one meter to one mile (0.00062137). Finally, another field was added and a value was calculated for the cost of travel for a trip by a truck to and from a rail terminal, estimating the cost of fuel per mile at $2.20.

Results

It should be noted that all of the data in the following section is hypothetical and in no way calculates usable results other than those determined for the sole purpose of this lab. Figure 2 shows the resulting map created after running the data flow model. 

Figure 2. This map shows the results of the data flow model after the routes have been created.

Figure 3 shows the resulting attribute table created after running the data flow model. Figures 4 and 5 are choropleth maps that denote the counties with the highest travel distance and counties with the highest cost associated with travel. It makes sense that Chippewa County, the county that travels the greatest distance (approximately 205.77 miles) would pay the most per year in fuel ($45,269.70), as this would constitute a direct relationship. As distance traveled increases the cost will increase in a direct fashion. Burnett travels the least of the sixteen counties included (1.28 miles), and therefore pays the least ($280.56).

Figure 3. This table shows the data after the final statistics have been calculated.

Figure 4. This map shows the total distance per year by county for frac sand transport routes.

Figure 5. This map shows the total cost per year by county for frac sand transport routes.

Figure 6 is a graph showing the total cost compared to the total distance traveled. Figure 7 is a small subset of Figure 6, shows the congested data that exists below one thousand miles traveled.

Figure 6. This graph shows the total cost per year vs. the total distance per year for each county included in the analysis.

Figure 7. This graph is the same as the graph in Figure 6, however it only examines the total distance under fifty miles. This shows a number of the counties in a way that is readable.

Discussion

There are much wider implications for county level analysis of the data determined in this exercise. Accelerated deterioration of the roadways caused by greater usage of the roads raises interesting questions asking who should be taking on the brunt of road repair costs. Should mines have to contribute a greater amount of money to county or city funded roads because they use them to a greater extent then a normal citizen? From personal experience I know that logging companies will pay for the repairing of roads because they use them to a substantial degree and the loggers need them maintained to a certain degree to ensure that transportation is streamlined. Should frac sand mines be called upon to support the repair of roadways and railways that are stressed as a result of a transportation-heavy industry? Is there a better way to transport these resources from place to place? Currently, I do not believe there is. When transporting extracted resources such as coal, iron ore, timber, and sand there are really no other logistically feasible ways to transport the raw materials, other than simply by truck and train. Should the railway be expanded with more nodes closer to the location of the sand mines, or even directly on site? That could be helpful, as it would reduce the amount of stress placed on the roads, but it would most likely have an adverse affect on the surrounding environment, as large swaths of trees would have to be created to make this efficient and connect all the railways.

Conclusion

The logistics behind transporting frac sand from the extraction sites to the distribution rail terminals poses a number of questions that must be further assessed before determining a final result. In the counties where frac sand companies place a large amount of stress on the railways, an agreement must be reached between the company and the county on what will be done to properly ensure the quality and safety of the roads and rails for all users, whether that be the mining industry or civilians. Network analysis and routing can assist in determining the most efficient way of accomplishing this, while allowing for usage parameters to be set. Creating a data flow model allows the workflow to be replicated as new data becomes available and current data is updated. Using these techniques, the ability to assess cost associated with frac sand transportation is much more accessible to a wider audience.

GIS II: Normalizing Data and Geocoding

Exercise 6: Data Normalization, Geocoding, and Error Assessment

Goal: 

The purpose of this lab was to become familiar with normalizing data so that it could be processed, geocoding addresses, and assessing errors in acquired data. We were provided data for all of the current frac sand mines in the state of Wisconsin. This data was provided by the Wisconsin DNR and had not been normalized or altered so that it could be successfully geocoded. Once we normalized the data, it was geocoded to begin to spatially analyze where the frac sand mines were located. We also had to assess errors within the data, as our classmates geocoding may have differed slightly from ours, or they may have normalized the data in a different manner.

Methods:

The first step was to normalize the acquired data. Many of the addresses for the mines did not provide street addresses. A number of the mines were listed in the Public Land Survey System (PLSS), and had no addresses at all. The addresses of the mines were also not divided up the same way, by address, city, and county. In an attempt to normalize the data, the web GIS servers for each county were opened and the parcel information was gathered. If not address was available for the parcel being located, a proximal parcel was chosen if it provided an access road to the desired parcel. Figures 1 and 2 show a comparison between the original data and the data after normalization. Once all of the data were normalized, they were ready to be geocoded.

Figure 1. This figure shows the excel data before it has been normalized. Many of the PLSS designations are available when there is no address applied to the parcel. Also, many of the addresses listed the street address, city, and state in the same line. ArcMap is not able to separate these, so we must set up our excel file so that everything is separated by a single attribute.

Figure 2. This figure shows the excel data after it has been normalized. The address has been split up and every entry was able to be assigned an entry, based on referencing of access roads in proximal properties.

After normalizing the data, the excel file was ready to import into ArcMap to be referenced for the geocoding process. In this instance, all of the data were normalized in a manner that made geocoding possible, allowing for a match for all data points. To attempt to compare our results with those of our classmates, all of the other geocoded data had to be merged. Figure 3 shows a map of west-central Wisconsin and the results of my geocoding and the geocoding of the rest of the class.

Figure 3. This map shows the location of my geocoded mines and the mines geocoded by the rest of the class. There are noticeable differences between the data. For example, the mine in western Wood County has a mine to its southeast. This mine is the same as the western mine, though the normalization process was performed differently and the geocoded results were not the same.

A query was developed to select all of the mines geocoded by our classmates with the same Mine UNIQUE ID field as our own. After these were selected a new feature class was created to make comparing the selected mines of our classmates to our own. This allowed us to utilize the Point Distance tool in the ArcMap toolbox to calculate the distance from out point to the surrounding mines. After projecting the geocoded results, this tool was run and a table was produced. If the Distance field listed a distance of 0 meters, there was a perfect match and that was the same mine. If there was a difference then it meant that there was a difference in where the same mine was geocoded based on normalization differences, or the distance was measured to another mine. Figure 4 shows the table with feature ID classes listed and the distances between the input and near feature ID.

Figure 4. This table shows the distance between our mines (INPUT_ID) and the distance to the other geocoded mines (NEAR_FID). A distance of 0 meters indicates that the mine is an exact match. Other mines needed to be checked to ensure that we were examining the correct mine. 

Discussion:

There are a number of possible explanations for the reasons behind the variation in distances from geocoding. These error types are listed in an assigned class reading Lo (2003). I did not notice any gross errors in this data (errors caused by blunders or mistakes). If the entire class had been trained on standardization prior to the normalization of the data, an argument would be correct that there would be gross errors. However, because the purpose of the exercise was to learn proper methods of how standardizing should be conducted, no standardization method was set, and therefore there was not really any "gross errors." There were definitely systematic errors in the data caused by human bias when determining where the mine should be placed, if a manual placement had to be assigned.

Inherent errors in the data are also a main source of the error in the geocoded data. According to Lo (2003), there is inherent error in digitizing and attribute data input. Both of these were conducted in this lab and both had inherent error within them. Each dataset was normalized by a different student, which meant that there were as many different methods as there were students for normalizing the data. This lead to different ways that things were divided, recorded, and listed. Operation errors were also very much a part of this lab, for many of the same reasons as listed previously. There is error that will come with user bias and different methods that nothing will alter but prestandardization of the normalization process. Even after standardizing, it is hard to remove all the error possibilities.

In order to determine which points are correct and which points are off, we would need a perfect dataset. We would need addresses for every mine in a format that leaves little to no room for error in normalization of the dataset. To determine the most accurate points on our map without a perfect dataset, we would need to come together as a class and decide which points should be counted as the correct location. Then we would be able to determine the distance of our points from the point that was deemed correct.

Conclusion:

Data normalization is something that needs to be determined before the start of a project. In order to ensure that data melds in a manner that makes it usable by all, strict guidelines must be put in place prior to the data management process. If this is not done, the chances that a dataset will be managed differently from another are much higher. 

References Cited:

Lo, C., & Yeung, A. (2003). Data Quality and Data Standards. In Concepts and Techniques in Geographic Information Systems (pp. 104-132). Pearson Prentice Hall.

GIS II: Data Acquisition

Exercise 5: Data Downloading, Interoperability, and Working with Projections in Python

Goal: 

The purpose of this lab was to become familiar with a number of different things. We were to become familiar with the acquisition of data from a multitude of sources. In this lab we acquired data from the USGS, NRCS, USDA, and Trempealeau County. After all of our data was acquired we had to know how to join the necessary tables so that the data was more usable. Then we had to understand how to write a Python code that allow us to project, clip, and load all of the data into the Trempealeau County geodatabase. All of this will continue to build upon the groundwork laid in Exercise 1, as we now have much of the data necessary to begin our analysis on Frac Sand Mining in Trempealeau County in West-Central Wisconsin.

Methods:

Our first step was to acquire the data necessary to begin our analysis. We downloaded data from the US Department of Transportation for railroad lines, from the USGS National Map Viewer for national land cover data and elevation data, from the USDA Geospatial Gateway for cropland data for the state of Wisconsin, from Trempealeau County for their most current working county geodatabase, and finally from the NRCS to download SSURGO data that had the county soil data. The SSURGO data needed to be joined with a number of other tables to ensure that it had all the data necessary to be beneficial and understandable for our project. In order to accomplish this, a SSURGO database was first brought into Microsoft Access A component table was imported from the downloaded soils geodatabase. The data was assigned the necessary parameters and then it was brought into ArcMap. In ArcMap we are able to conduct the necessary joins that bring this data together into one usable feature. Drainage Index and Productivity Index tables were acquired from the SSURGO website and brought into the soils feature as well. The last step for this first section of the project was to bring in the railroad feature class and clip it to the Trempealeau County boundary.

This next portion of the project was write a script to project, clip, and load all of the data acquired previously into the Trempealeau County Geodatabase. The workflow for this script has been provided in my blog "GIS II: Python Scripting." Figures 1, 2, and 3 are maps derived from the scripting.

Figure 1: This map shows the Digital Elevation Model (DEM) clipped to fit the Trempealeau County boundary.

Figure 2: This map shows the National Land Cover Data (NLCD) Land Use Designations clipped to fit the Trempealeau County boundary.

Figure 3: This map shows the National Agricultural Statistics Survey (NASS) Cropland Designation clipped to fit the Trempealeau County boundary.

A large component of this lab was to better understand interoperability and data accuracy. Below is a table (Table 1) that highlights what was able to be determined to assist in explaining the accuracy of the data. Some of the numbers were determined based on scale, however the rest was searched for in the provided metadata. If nothing was found for the particular cell then the designation "none" was used.

Table 1: This table shows what data was able to be acquire or derived from pre-existing metadata.

Conclusion:

This was a very frustrating lab. However, that I feel was a large objective of this exercise. Unfortunately, data is not available in a perfect, usable format every time. Data from different organizations, with different parameters, and with missing information are very common to come across when attempting to acquire data. The ability to understand the layout of this data, the integrity of the data, and the ways to make the data fit the needs of the project are very necessary skills.

Sites for Data Acquisition:

The data for this exercise was acquired from the following websites:

United States Department of Transportation: http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

USGS National Map Viewer: http://nationalmap.gov/viewers.html

USGS National Agricultural Statistics Survey: http://datagateway.nrcs.usda.gov/

Trempealeau County: http://www.tremplocounty.com/landrecords/
NRCS Soil Survey: http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

GIS II: Sand Mining in Western Wisconsin

Background on the Practice of Frac Sand Mining in Western Wisconsin

Introduction

     The practice of frac sand mining has been the cause of much discussion over the last few years, in western Wisconsin. However, to be able to speak on the topic, one must ensure that they are informed and understand what frac sand mining and hydraulic fracturing really is. In order to do that, there are a number of sources and sites that one can seek out to educate themselves on the topic, a number of which I have listed at the end of this post. In this post, I will begin from a regional scale, in this case the western portion of Wisconsin, and move to a national view, to attempt to highlight both frac sand mining and hydraulic "fracking," and in doing so, bridge the gap between what happens in our own backyard and what happens once the sand leaves it.

What is frac sand mining?

Figure 1: Size of the individual frac sand grains in relation to
the size of a penny (Marcotty 2012).

     Frac Sand Mining is the process of extracting silica sand, or silicon dioxide (quartz) from the ground (WDNR 2012). Generally this is accomplished through the excavation of open pit mines, procuring the sandstone, crushing it, washing it, sorting it, and storing it until it can be shipped. While this highlights the process, it does not necessarily describe what the mining company hopes to obtain and what they are looking for in the first place. To understand this, one must look at the product, itself. Not any kind of sandstone will do, either, as a specific kind of sand is required for fracking. This sand is generally found in the Cambrian aged Jordan, Wonewoc, and Mt. Simon formations, or in the Ordovician aged St. Peter formation (WDNR 2012). These formations are comprised dominantly of a very pure form of quartz. These quartz grains have to fit a number of criteria to meet the specific fracking job. This generally means that the grains need to be uniformly shaped, 0.2-0.8 mm in diameter (Figure 1), well rounded, and high in strength (Syverson 2012). This will ensure that the next step of the process, the actual fracturing process, will be successful.

     The mines can be range in varying degrees of size, from small operations of 100 acres to much larger operations of 1,500 acres. Much goes into the excavation and processing of the sand in order to change it into a usable product to mine shale gas, the natural gas being obtained from hydraulic fracking. A very useful site to better understand the process of frac sand mining is found on the Wisconsin Geological and Natural History Survey (http://wgnhs.uwex.edu/wisconsin-geology/frac-sand-mining/frac-sand-mining-process/). This site gives a ten-step breakdown of the process and provides aerial imagery and ground imagery to help in the description.

Figure 2: Map showing the growth of frac sand mines and
processing plants in Western Wisconsin (Prengaman 2012)

Frac Sand Mining in Wisconsin

     Frac sand mining in Wisconsin has taken on considerably over the last few years. Figure 2 illustrates the extent of the development of frac sand mining in western Wisconsin. It is quite apparent how much growth has occurred, as the number of "permitted, developing" sites is much greater, by visual analysis, than that of any other except "operating facilities." The graph in Figure 3 also helps to quantitatively illustrate the number of proposed, in-development, and current sand mines in operation, the most of which happen to be in Trempealeau County, the area of interest for our future studies in this course.

Figure 3: This line graph shows the data from the map, though allows for quantitative understanding. The number of proposed and in development sites are quite staggering compared to the number of sites already operational. (Prengaman 2012)

Figure 4: This map helps to show the location

of the railways in the state in relation to the

location of the frac sand mining and processing

facilities (Golden 2014).

     According to Kate Prengaman of the Wisconsin Center for Investigative Journalism, if the current trend continues, Wisconsin is on track to sell roughly 50,000,000 tons of sand per year (as estimated by the Department of Transportation). This equates to about 9,000 truckloads per day of sand moving to processing facilities. Where does all this sand go? It goes to railways, mostly, which then ship the sand to the necessary locations for the purchaser to begin the process of hydraulic fracturing. Many of these sand mines are located very near to railroad tracks in order to cut costs and reduce the overall expense on gas from transport trucks. Figure 4 helps to show the location of the railways in relation to the frac sand mines.

     There are a large number of different regions that will receive the sand once it is shipped for use in hydraulic fracturing. Figure 5 and Figure 6 help to show the many different regions that use the sand for fracking purposes, as well as where Wisconsin send its sand.

Figure 5: This map shows the locations of the various regions underlain by shale that is desirable for hydraulic fracturing. A booming region currently is the Marcellus Shale region, located in the Appalachian Basin. This is one of the largest shale gas deposits in the country. (EIA 2011).

Figure 6: This map shows the destination of Wisconsin frac sand in the United States. The Bakken Formation in North Dakota, Denver Basin Formation in Colorado, Eagle Ford and Barnett Formations in Texas, and Marcellus Formation in the Appalachian Basin are all included. (Chase and Golden 2014)

Hydraulic Fracturing: A Brief Overview

     The process of hydraulic fracturing is a very resource and work intensive process. For the purpose of providing an insight into the process in a general unit, we will examine the Marcellus Formation

Figure 7: Cross-Section of the approximate orientation of

the Marcellus and Utica Shale formations in Pennsylvania

and Ohio (King 2014).

more deeply. Shale is the targeted formation as it has a low permeability (King 2014). This low permeability coupled with the porosity of shale allows for natural gases derived from decayed organic material to become trapped in the pore space, within the rock (Energy.gov n.d). The issue is reaching the necessary depths to begin to mine the shale, which can be well over a mile under the surface (Figure 7). An exposure of Marcellus shale can be seen in Figure 8 as the black layer, a key indicator in the field of the formations presence. A number of methods exist to begin to mine the shale. The two methods include horizontal drilling and vertical drilling, though they differ because the horizontal drilling drills down vertically, then is curved at the end until it is horizontally situated. Vertical drilling does not curve the core at the end. Cement is then injected into the core in an attempt to better stabilize the surrounding area. Next, small explosives are detonated to crack the surrounding shale. The mined frac sand, called a proppant, is mixed with water and chemical additives and blasted through the detonated cracks. The well rounded shape of the sand grains and their overall strength allows them to prop open the fractures and allow the natural gas within to escape. The gas is then collected for use (EPA 2014). A large quantity of water is used in this process and is contained, generally on site, in waste water retention ponds.

Figure 8: This photograph shows an exposure of Marcellus Formation shale (the black layer) found in the Appalachian Basin (USGS NY). Estimates place the amount of accessible natural gas in the Marcellus Formation between 50-500 trillion cubic feet (PA DCNR).

     The video below is provided by the Oklahoma Energy Resources Board and is a very useful animation showing the process of hydraulic fracturing.

Conclusion

     The process of frac sand mining and hydraulic fracturing is indeed a very work intensive, resource intensive process. In many areas of the country this is a very polarizing issue. The debate as to whether or not this should go on examines more material than is feasible to discuss. On purpose, the intent of this blog was not to pick a faction to back, but rather to inform of the process and allow the reader to decide. One should find a number of informative sources under the "Useful Links" tab, located at the top of this page. The hope is that these sources will provide even more information about the process from credible sources, that assist the reader in forming their own opinion on the matter.

Works Cited 

• Chase, T., & Golden, K. (2014, July 13). As rail moves frac sand across Wisconsin landscape, new conflicts emerge. Retrieved October 1, 2014.
• EIA. (2011, May 9). Lower 48 States Shale Plays. Retrieved October 1, 2014, from http://www.eia.gov/oil_gas/rpd/shale_gas.pdf 
• Energy.gov. (n.d.)What is Shale Gas? Retrieved October 1, 2014, from http://energy.gov/sites/prod/files/2013/04/f0/what_is_shale_gas.pdf
• EPA. (2014, August 11). The Process of Hydraulic Fracturing. Retrieved October 1, 2014, from http://www2.epa.gov/hydraulicfracturing/process-hydraulic-fracturing
• Frac sand: How is it mined? (n.d.). Retrieved October 1, 2014, from http://wgnhs.uwex.edu/wisconsin-geology/frac-sand-mining/frac-sand-mining-process/
• King, H. (2014). Marcellus Shale - Appalachian Basin Natural Gas Play. Retrieved October 1, 2014, from http://geology.com/articles/marcellus-shale.shtml
• Marcotty, J. (2012, January 3). Frac sand mining erupts in Winona County. Retrieved October 1, 2014, from http://www.startribune.com/local/blogs/136612183.html 
• NY USGS. (n.d.). Marcellus Shale Image. Retrieved October 1, 2014, from http://ny.cf.er.usgs.gov/nyprojectsearch/projects/images/shalebig.jpg
• PA DCNR. (2008). Marcellus Shale. Retrieved October 1, 2014, from http://www.dcnr.state.pa.us/topogeo/econresource/oilandgas/marcellus/marcellus_faq/marcellus_shale/index.htm
• Parsen, M., & Zambito, J. (2014). Mining: Frac Sand. Retrieved October 1, 2014, from http://wgnhs.uwex.edu/wisconsin-geology/frac-sand-mining/ 
• Phillis, M. (2013, July 28). Frac sand mining splits Wisconsin communities. Retrieved October 1, 2014. 
• Prengaman, K. (2012, July 22). Wisconsin frac sand sites double. Retrieved October 1, 2014, from http://wisconsinwatch.org/2012/07/sand-sites-double/ 
• Syverson, K. (2012, February 17). The Frac Sand Mining Boom in Wisconsin: Geology, Impacts, and Politics. Retrieved October 1, 2014, from http://www.uwec.edu/geology/index.htm 
• WDNR. (2012). Silica Sand Mining in Wisconsin. Retrieved October 1, 2014. 
• Well Frac Animation. (2011). Retrieved October 1, 2014, from http://www.oerb.com/Default.aspx?tabid=245