Chapter 3

Methodology for Calculating Oil Shale 

Resources for the Uinta Basin, Utah 

and Colorado

By Tracey J. Mercier, Michael E. Brownfield, and Ronald C. Johnson

Chapter 3 of 7

Oil Shale Resources of the Uinta Basin, Utah and Colorado 

By U.S. Geological Survey Oil Shale Assessment Team

U.S. Geological Survey Digital Data Series DDS–69–BB

U.S. Department of the Interior

U.S. Geological Survey

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Volume Title Page
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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reproduce any copyrighted materials contained within this report.

Suggested citation:

Mercier, T.J., Brownfield, M.E., and Johnson, R.C., 2010, Methodology for calculating oil shale resources for the Uinta 

Basin, Utah and Colorado: U.S. Geological Survey Digital Data Series DDS–69–BB, chp. 3, 49 p.
iii

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Oil Shale Assessment....................................................................................................................................1

Data Preparation, Capture, and Conversion ....................................................................................1

Spatial Data...................................................................................................................................1

Tabular Fischer Assay Data .......................................................................................................5

Overview of Assessment Methodology ...................................................................................5

In-Place Resource Calculations.........................................................................................................7

Gallons Per Ton ............................................................................................................................7

Barrels Per Acre ..........................................................................................................................8

Missing Intervals........................................................................................................................14

Geospatial Modeling, Analysis, and Presentation ........................................................................14

Oil Shale Zone Thickness Isopachs........................................................................................14

Generating Oil-Yield Models ....................................................................................................15

Summarizing Resource Models ..............................................................................................16

Interpretive Maps ......................................................................................................................16

Conclusions...................................................................................................................................................16

Acknowledgments.......................................................................................................................................16

References Cited..........................................................................................................................................21

Case Study—Oil Shale Assessment ........................................................................................................22

Converting and Loading Legacy Fischer Assay Data ...................................................................23

Converting and Loading Stratigraphic “Tops” Data......................................................................25

Converting and Loading the Barrels Per Acre (BPA) Lookup Table...........................................26

Digitizing Borehole Locations...........................................................................................................27

Building the Access Form..................................................................................................................28

Intersecting Polygon Files to Create Reporting Polygons ...........................................................29

SQL Query to Filter Assays By Zone ................................................................................................30

Calculating Gallons Per Ton Weighted Average............................................................................31

Calculating Barrels Per Acre Oil Yield.............................................................................................32

Calculating the Percentage of Missing Intervals From Each Core Sample..............................33

Storing Calculated Values in a Separate Table .............................................................................34

Filtering Subforms From the Main Form..........................................................................................35

Linking Spatial and Tabular Data......................................................................................................36

Creating GeoStatistical Analyst Models and GRIDs.....................................................................37

Running Barrels Per Acre Zonal Statistics to Calculate Total Barrels of Oil 

Yield Per Township................................................................................................................39

Appendix........................................................................................................................................................40

Digital Files, Entity Relationship Diagram, and Data Dictionaries ..............................................40
iv

Figures

1. Map showing area underlain by oil shale-bearing rocks in the Uinta Basin .....................2

2. Stratigraphic correlation chart of oil shale zones in the Uinta Basin..................................3

3. Two bounding resource polygons depicting Mahogany bed and 

Parachute Creek Member outcrops..........................................................................................4

4. Example of the original Fischer assay laboratory records in ASCII-text format ...............6

5. Overview chart showing the four steps and processes performed on the 

spatial and tabular data for the Uinta Basin oil shale assessment..............................................7

6. Image clip of Microsoft Access 2007 form used to perform oil shale 

resource calculations ..........................................................................................................................8

7. Example of how the Access form can recalculate resource numbers interactively................9

8. Graph of a third-order trendline showing relation between oil yield for Green 

River oil shales and specific gravity................................................................................................14

9. Image clip of Microsoft Access 2007 form showing how missing intervals 

are reported for each core sample..........................................................................................15

10. Image clip showing how data are migrated from the Microsoft Access 2007 

database form and then modeled in the GIS software.................................................................17

11. Eastern part of the Uinta Basin, Utah, showing how the GIS software 

summarizes total barrels of oil yield by township .........................................................................18

12. Examples of interpretive maps generated from resource models: A, oil shale 

zone isopachs; B, average oil yield in gallons per ton; C, oil yield in barrels per acre...........19

13. Examples of interpretive maps generated from resource models: A, oil yield 

in barrels per township; B, percentage of missing intervals from each 

core sample .................................................................................................................................20

14. Image clip of Microsoft Access 2007 form detailing how the user interacts 

with the form’s buttons and controls ...............................................................................................20

15. Image clip of Beside Software Import Wizard dialog window used to import 

legacy Fischer assay data.................................................................................................................23

16. Image clip of Import Wizard model file setup dialog window.....................................................24

17. Structure of the stratigraphic tops table as viewed in Microsoft Access 2007, 

showing the depth, in feet, to top of the A-groove through L-5 oil shale 

zones for boreholes U0020 through U0037A...................................................................................25

18. Structure of the barrels per acre lookup table used for the form 

calculations as viewed in Microsoft Access 2007................................................................26

19. Eastern part of the Uinta Basin, Utah, showing borehole locations after 

digitizing in ArcMap ...........................................................................................................................27

20. Image clip of the main Access form showing how subforms were linked 

by the unique identifier, USGSID, and performed all resource calculations....................28

21. Diagram showing how ESRI ArcToolbox’s Intersect command was used to 

create the assessment’s reporting polygons for the Uinta Basin in 

northeastern Utah.......................................................................................................................29

22. The oil shale assessment’s Microsoft Access 2007 form as viewed in Design mode ............31

23. Image clip of a portion of the Microsoft Access 2007 form showing how 

calculation results performed interactively in the form were stored in a 

table permanently using buttons and macros. ......................................................................34

24. Image clips of the dialog windows used for creating the barrels per acre 

GeoStatistical Analyst model in ArcGIS .........................................................................................38
v

25. Image clip of the dialog window used to generate zonal statistics using 

ESRIs Spatial Analyst.........................................................................................................................39

A1. Entity relationship diagram (ERD) of the tables in the Microsoft Access 

2007 database showing how the tables are linked in the calculations form....................41

Tables

1. Column names and definitions of the Microsoft Access table after 

converting the original Fischer assay laboratory data and adding 

additional columns needed for resource calculations...........................................................6

2. Original volume-weight oil-yield relations based upon Green River 

oil shale from U.S. Bureau of Mines oil shale mine, Rifle, Colorado.........................................10

3. Recalculated volume-weight oil-yield relationships based upon Green 

River oil shale from U.S. Bureau of Mines oil shale mine, Rifle, Colorado........................12

A1. Column names and definitions of the Microsoft Access table CO_Tops_080115............42

A2. Column names and definitions of the Microsoft Access table CO_Assays_INTV ..........46

A3. Column names and definitions of the Microsoft Access table BPA Lookup Table..........46

A4. Column names and definitions of the Microsoft Access table OilShale_Holes_pts.......47
Abstract

Advancements in computer and database technology 

since an assessment of the in-place oil shale resources for the 

Piceance Basin, Colorado was made in 1989, have provided 

the U.S. Geological Survey (USGS) with tools to convert legacy data, store and manipulate new data, perform calculations, 

and quantify, report, and display assessment results. Relational 

database and geographic information systems (GIS) software 

were used seamlessly to streamline the storage and manipulation of data. A deterministic spatial interpolation method, the 

Radial Basis Function (RBF), was used to generate isopach 

and isoresource models with the GIS software, which provided 

a spatial statistics function to summarize the prediction models 

and determine the in-place oil shale resources. 

Introduction

This report presents the methodology used in the 

geology-based assessment of the in-place oil resources in 18 

oil shale zones of the Eocene Green River Formation in the 

Uinta Basin of northeastern Utah (fig. 1). The focus is on the 

methodology used to (1) convert legacy data; (2) analyze the 

data through application of current computer techniques; and 

(3) ultimately quantify the resultant data using spreadsheet, 

database, and geographic information systems (GIS) software. 

After converting, combining, and loading individual 

legacy Fischer assay (American Society for Testing and 

Materials, 1980) data files into a relational database (Dyni, 

1998), custom scripts and queries were written to filter records 

and perform various calculations using a database form. The 

legacy data also contained location information for each oil 

shale borehole that was converted to spatial data and then 

linked to its associated Fischer assay data.

After performing calculations in the database, the resultant data were migrated to a GIS software package and a cellbased modeling technique was used to calculate total barrels 

of oil yield per oil shale zone. Through this process, all data 

were updated to a contemporary relational database format and 

new spatial data models were created for use in GIS software. 

Detailed technical descriptions of the methodology and the 

tools employed in the assessment from a software-centric perspective are presented in a case study following this report. 

Oil Shale Assessment

Data Preparation, Capture, and Conversion

In order to calculate in-place oil shale resources using 

relational database and GIS software, it was necessary to 

collect data points with accompanying oil-yield data, create 

digital outcrop boundary lines to constrain resource calculations, and correlate the 18 oil shale zones within the Green 

River Formation in the Uinta Basin.

Spatial Data

The legacy Fischer assay data files contained header 

information detailing the locations of boreholes, but not all 

of the files contained latitude and longitude coordinates. To 

maintain consistency, the majority of the borehole locations 

were digitized in GIS software based on footage measurements north, south, east, and west of Public Land Survey System (PLSS) section corners contained in a separate database 

(Vanden Berg and others, 2006), or by using the best available 

location information presented in the header, such as the section centerpoint. 

In addition to placing the boreholes in real-world 

coordinates and plotting their locations on maps, 18 oil shale 

zones (Donnell and Blair, 1970; Cashion and Donnell, 1972; 

Donnell, 2008) were defined and correlated between holes in 

the subsurface using oil-yield histograms generated from the 

Fischer assay data files (fig. 2). Previous USGS assessments of 

the Piceance Basin (Pitman and Johnson, 1978; Pitman, 1979; 

Pitman and others, 1989) subdivided the oil shale interval into 

a series of oil-rich and oil-lean zones that were traced across 

most of the basin (Cashion and Donnell, 1972), and each 

zone was assessed separately. For this study, the same set of 

rich and lean zones were used as much as possible in order to 

compare the results of the previous and present assessments. 

Methodology for Calculating Oil Shale Resources for the 

Uinta Basin, Utah and Colorado

By Tracey J. Mercier, Michael E. Brownfield, and Ronald C. Johnson
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SEVIER

UINTAH

SANPETE

CARBON

DUCHESNE

EMERY

GRAND

WASATCH

Price

Roosevelt

Vernal

111°00' 110°30' 110°00' 109°30' 1

40°00'39°30'39°00'

0 6 12 MILES

0 5 10 KILOMETERS

UTAH

UTAH

Area underlain by oil shale rocks

City or townCounty line

EXPLANATION

70

40

191

6

Figure 1. Uinta Basin, northeastern Utah, showing area underlain by oil shale-bearing rocks; cut-off to north is at the 6,000-ft depth contour Mahogany bed of the Parachute Creek Member of the Green River Formation. Outcrops of the Mahogany bed and the Parachute Creek MembGreen River Formation shown in brown.
Oil Shale Assessment 3

Donnell and Blair

(1970), Cashion and

Donnell (1972),

Donnell (2008)

A-groove A-groove

Mahogany zone

B-groove B-groove

Rich oil shale

zone R-6

Rich oil shale

zone R-5

Rich oil shale

zone R-4

Rich oil shale

zone R-3

Rich oil shale

zone R-2

Mahogany

zone

Zone 7

Zone 1

Zone 6

Zone 5

Zone 4

Zone 3

Zone 2

Zone 8

Zone 13

Zone 12

Zone 11

Zone 10

Zone 9

Upper

Middle

Lower

Correlation chart of stratigraphic

nomenclature for oil shale zones

Parachute Creek Member (in part) Garden Gulch Member

Green River Formation (in part)

Rich oil shale

zone R-1

Rich oil shale

zone R-0

Lean oil shale zone L-2

Lean oil shale zone L-1

Lean oil shale zone L-0

Lean oil shale

zone L-4

Lean oil shale zone L-3

Lean oil shale

zone L-5

Dyni (1974)

Parachute Creek Member Garden Gulch Member

Green River Formation Uinta Formation

(in part)

Wasatch

Formation

(in part)

Age Unit Eocene

Bed 76

Bed 44

Figure 2. Stratigraphic columns and nomenclature for oil shale zones in the Eocene 

Green River Formation, Uinta Basin, Utah. The assessments of in-place oil resources 

use the nomenclature (shown in yellow) of Donnell and Blair (1970), Cashion and 

Donnell (1972), and Donnell (2008).

Vanden Berg and others (2006) published a file listing tops 

for oil shale zones in boreholes in the Uinta Basin. Tops were 

not picked for all of the boreholes used in this assessment and 

many of the boreholes did not have tops listed for all of the 

oil shale zones assessed. The tops file of Vanden Berg and 

others (2006) formed the basis of this assessment with new 

boreholes and additional tops added. In some isolated cases, it 

was necessary to correct minor errors in the tops file published 

by Vanden Berg and others (2006). The tops and bases of the 

18 identified zones were entered in a spreadsheet and imported 

into the database where the tops and bases were linked with 

their associated Fischer assay records to create subsets of data 

on which calculations were performed. 

Two updated boundary files (fig. 3) were created for the 

oil shale deposits of the Uinta Basin by digitizing mapped outlines of the top of the Mahogany bed and the base of the Parachute Creek Member of the Green River Formation from four 

published 1:250,000-scale geologic maps—Bryant (1992), 

Cashion (1973), Rowley and others (1985), and Witkind 

(1995). These mapped horizons served as bounding polygons 

for in-place resource calculations. Due to the steepness of 

the vertical cliffs formed by these strata throughout the Uinta 

Basin, only two resource-bounding polygons were needed for 

the 18 zones. The top of the Mahogany bed outcrop was used 

to delineate a resource-polygon file for the five uppermost 

zones—B-groove, Mahogany zone, A-groove, Bed 44, and 
Mahogany bed outcrop Parachute Creek Member outcrop Areas in which all 18 zones were assessed Areas in which only the 13 lower zones were assesse

Due to steepness of cliffs, two resource polygons were used to lresource calculations within the Uinta Basin: 109°37'30" 109°30' 109

39°45' 39°37'30"

Figure 3. Central part of the Uinta Basin, Utah, showing the two resource polygons used to limit in-place oil shale resource calculations; basCreek Member of the Green River Formation and top of the Mahogany bed. Small outcrop areas excluded from resource calculations due to e
Oil Shale Assessment 5

Bed 76 (figs. 2, 3). This polygon file excluded areas within 

the basin where the upper five zones were eroded. The second 

resource-polygon file was created for all oil shale zones below 

the B-groove (fig. 3) by expanding the Mahogany bed polygon 

to include the areal extent of the Parachute Creek Member in 

the eastern half of the basin.

The two resource-polygon files were intersected spatially 

with a Public Land Survey System (PLSS) land grid for the 

Uinta Basin to allow resource calculation on a per township basis. Some township polygons were deleted from the 

intersected resource files owing to a lack of control points, and 

only township polygons were used to define a resource area 

for the L-2 zone. Ten different resource polygon files were 

needed to perform resource calculations for the 18 oil shale 

zones.

Tabular Fischer Assay Data

Source ASCII text data were obtained from published 

Fischer assays (Dyni, 1998) and from previously unpublished 

Fischer assay data stored by the USGS. These files were 

stored as individual ASCII-formatted text files (fig. 4) for each 

borehole that contained the header location information and 

the column-delimited Fischer assay records. Each borehole 

was assigned a unique four-digit number, with the prefix “U” 

for Utah. To expedite the querying of the Fischer assay data, 

a file-conversion software package was used to convert and 

combine all of the Fischer assay records into one relational 

database table that allowed detailed queries on one large table 

(some 100,000 records) instead of hundreds of different files.

Some legacy Fischer assay records were incomplete, 

especially those associated with rotary drill cuttings instead of 

core—those boreholes have the character “R” in their USGS 

identifier (USGSID). Almost all boreholes contain missing 

intervals that represent samples that were not recovered during 

the drilling/coring process. Missing records in the original data 

files were labeled as “0.0B” or “0.00B” in all columns except 

for the top and base of the interval fields (Dyni, 1998). During 

the initial data import and conversion process (table 1), it was 

necessary to remove the “B” for those values to be imported 

as a numeric field, which were then considered to be missing 

intervals in the assay table. This step allowed calculations to 

be made on any given field, as those values were converted 

from characters to numeric values. Four other fields were 

added to the master Fischer assays table (1) the “USGSID” 

field, the unique borehole identifier, or primary key, was 

added to each record in the Import Wizard conversion process 

by using the original text filename; (2) the “INTVL” field, 

an abbreviation for thickness of the sampled interval, was 

calculated in Access by using an update query (base of the 

sampled interval minus the top of the sampled interval); (3) 

the “INTXOIL” field (thickness of interval times shale oil in 

gallons per short ton of rock) was also calculated by using an 

update query, which was necessary to perform weightedaverage calculations; and (4) the “ROCKTYPE” field was 

added to denote beds of halite (“NH”) and (or) nahcolite, 

and sandstone (“NO”). Although such beds were commonly 

assayed, the assay results typically produced zero oil and thus 

are listed as containing zero oil in the assay tables. However, 

these legitimate zero-oil-yield values then needed to be distinguished from actual missing intervals that are also listed as 

zero oil yield in the assay tables in order to correctly calculate 

an average gallon per ton (GPT) for the zone in which they 

were contained. To distinguish the two, a minimum oil-yield 

value of 0.5 GPT was assigned to these zero-value sandstone 

beds. 

Overview of Assessment Methodology

The column-delimited ASCII text Fischer assay records 

were converted using Import Wizard 9 (Beside Software, 

2006) and then stored in a Microsoft Access (Microsoft 

Corporation, 2006) table (fig. 5). Additionally, the oil shale 

zone “tops” file (CO_Tops_080115.xlsx) with correlation 

data for Colorado and Utah was converted from a Microsoft Excel 2007 spreadsheet and also stored in the Access 

database (CO_Tops_080115). A one-to-many relation was 

established between formation tops and the Fischer assay data 

(CO_Assays_INTV), providing access to many assay records 

for each borehole. (Note: The Utah data were combined with 

the Colorado data because of similar stratigraphy, and also 

to create a plateau-wide database, thus the “CO” filenames.) 

By establishing this link, Structured Query Language (SQL) 

queries and Visual Basic formulas were developed to calculate 

resource estimates for each borehole by oil shale zone. Derivative maps were then constructed, including (1) oil shale zone 

thicknesses, (2) average oil yield in gallons per ton (GPT), (3) 

oil yield in barrels per acre (BPA), (4) barrels of oil yield per 

township, and (5) percentage of missing intervals determined 

from the core sample.

Footages measured from north, south, east, and west of 

PLSS section corners were used to digitize the spatial location for each borehole using GIS software (Vanden Berg and 

others, 2006). The point location and its unique USGSID were 

stored in a point feature class in a GIS geodatabase. Each 

borehole was also assigned a unique USGSID that was used 

in correlation data and resource-estimate tabular relationships. 

Geostatistical modeling software was then used to model 

the resource data for each zone using a RBF method. After 

comparing and testing several modeling techniques, it was 

determined the RBF-Multiquadric function produced the most 

geologically reasonable models. 

Once satisfied with a particular model, raster datasets 

were generated for further analysis, including the ability to 

generate summary statistics based on the BPA models. Zonal 

statistics functions were used to quantify resources using the 

defined resource polygons intersected with townships as limiting zones to count each raster cell’s estimated BPA value. As 

the analysis cell size was one acre, no mathematical conversions were necessary as the software simply counted each 

cell’s BPA value contained within each individual polygon 

of each resource-reporting file (the outcrop-polygon file 
6 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Figure 4. Image clip of a portion of the original Fischer assay data (Dyni, 1998) for the Western 

Oil Shale Corp. EX-1 borehole showing header data (first row). Header data include operator 

name, borehole name, and location. Columns (see table 1 for explanation of abbreviations): 

1, LABNO; 2, TOPFT; 3, BOTFT; 4, SHLOILPCT; 5, WATERPCT; 6, SHLRSDPCT; 7, GASPLSPCT; 8, 

OILGPT; 9, WATERGPT; 10, SPCFGRAV; 11, COKETEND.

Table 1. Column names and definitions of the Microsoft Access table after converting the original Fischer assay 

laboratory data and adding additional columns needed for resource calculations.

Column name Column definition

OBJECTID Software-calculated identifier

LABNO Six-digit USBM Laramie laboratory number

TOPFT Depth, in ft, measured from the surface datum to the top of the sampled interval

BOTFT Depth, in ft, measured from the surface datum to the base of the sampled interval

SHLOILPCT Amount of shale oil, in weight percent

WATERPCT Amount of water, in weight percent

SHLRSDPCT Amount of shale residue, in weight percent

GASPLSPCT Amount of “gas plus loss,” in weight percent

OILGPT Shale oil, in U.S. gallons per short ton of rock

WATERGPT Water, in U.S. gallons per short ton of rock

SPCFGRAV Specific gravity of the shale oil

COKETEND Tendency for spent shale to coke

USGSID Unique ID assigned by staff geologist

INTVL Thickness of interval, in ft (BOTFT-TOPFT)

INTXOIL Column used for weighted-average gallons per ton calculation (INTVL * OILGPT)

ROCKTYPE Column added to denote intervals that were edited to distinguish between missing records and 

records found in core descriptions to be sandstone (“NO”).
Oil Shale Assessment 7

Tabular data

Oil Shale Resource Methodology Overview

Spatial data

Convert legacy Fischer assay data 

and load into relational database 

software (Microsoft Access).

Digitize point locations of boreholes and 

attribute by unique id (USGSID).

Merge boundary polygons with 

townships to create reporting 

polygons.

Create GeoStatistical Analyst model using 

boreholes and the resultant calculation 

results stored in the ArcGIS table.

Export models to ESRIs GRID format.

Run Zonal Statistics on the resource GRIDs 

(barrels per acre oil yield) using the 

reporting polygons as the zone datasets 

and export summary resource totals.

Link summary tables to reporting polygons 

and generate interpretive resource maps.

Generate interpretive maps for zone 

thickness intervals, averages, and so 

forth.

Digitize geology outcrops (boundary 

polygons).

Digitize Public Land Survey System (PLSS) 

township polygons.

Convert stratigraphic data (tops file) 

and load into Access.

Convert lookup table for barrels per 

acre (BPA) calculations and load 

into Access. 

Build Access form and link tables by 

unique ID (USGSID).

Link BPA lookup table to Access form 

by interactively calculated GPT 

average by zone.

Build separate table (ArcGIS table) to 

store the form’s calculation results 

permanently.

Export calculation results to the ArcGIS 

table and link to borehole point 

locations in ArcMap.

Store summary resource tables from 

ArcGIS in .dbf or Access format.

Build text box controls in the form to 

filter subsets of data by zones and 

perform necessary calculations.

Adjust tops stratigraphic data as 

needed and update ArcGIS table 

with new calculated results.

First step Second step Third step Final step

Numerous iterations can be 

necessary after adjusting 

stratigraphic data and 

generating new spatial 

models (error-checking 

phase).

Figure 5. Overview chart showing the four steps and processes performed on the spatial and tabular data for the Uinta Basin oil 

shale assessment.

intersected with the township file). Various summary calculations and presentation-quality tables were then generated using 

the resource estimates for total barrels of oil yield per township. A more detailed and technical description of the zonal 

statistics function is presented in a case study following this 

report.

In-Place Resource Calculations

Gallons Per Ton

For each borehole, resource calculations were performed 

for each of the oil shale zones listed in a Microsoft Access 

form (fig. 6). An Access form “is a database object that you 

can use to enter, edit, or display data from a table or a query” 

(Microsoft Corporation, 2006), and to view many records from 

several linked tables in an easier and less cluttered manner. 

Additionally, by creating a custom form with Visual Basic, 

SQL, and several macros, we were able to apply filters and 

perform calculations on many subsets of the master Fischer 

assays table. Although results are calculated within the form 

interactively, the results were stored permanently in a separate 

database table that was directly linked to GIS software. By 

using this method, we could continually revise the database 

(fig. 7) and concurrently generate numerous iterations of 

spatial models on corrected figures using ArcGIS’s GeoStatistical Analyst (ESRI, 2006) extension. A diagram describing 

the table relationships in the database is in the accompanying 

Appendix.

To calculate average oil yield per zone (in GPT) for each 

borehole, missing records were first removed from each zone 

so they would not affect the weighted-average calculation. 

This was accomplished by writing queries that filtered out 

those records where the INTXOIL (thickness of interval times 

oil yield in GPT) field had a value of 0. As stated previously, 
8 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Figure 6. Image clip of Microsoft Access 2007 form used to perform oil shale resource calculations containing (1) tops table 

under the headings Cores, Tops, and Intervals; (2) Fischer Assays table; (3) Calculations section containing controls that 

performed assessment calculations; and (4) ArcGIS Table used to store calculation values permanently.

missing records were identified and removed from the computation if either the OILGPT or INTXOIL fields contained 0 

values. It should be noted that the weighted-average calculation used only valid values for GPT for a particular zone (no 

zero values), but used the total thickness of all the sampled 

intervals within that zone in the formula. The formula used to 

calculate the weighted average of GPT for each zone was: 

Sum of (thickness of interval in feet * gallons per ton) / thickness of interval in feet.

Barrels Per Acre

The determination of oil-in-place resource values in BPA 

were generated from the derived GPT weighted-averages 

calculated in the database form (fig. 6). Stanfield and others 

(1954) reported data on volume-weight oil-yield relationships 

from nearly 20,500 U.S. Bureau of Mines oil-yield analyses 

(table 2). Smith (1956) reported that oil-yield values were 

related to the specific gravity of the oil shale. Table 2 contains 

original values for oil yield in GPT and associated specificgravity values and were reported to the nearest 1 GPT. Values 

for weight of oil shale, volume of oil shale, and oil yield per 

unit volume were updated using currently accepted conversion 

factors (table 3). A third-order trendline with a R2

 value (the 

measure of the reliability of the linear relationship) of 0.9998 

was generated to compare oil yield with specific gravity 

(fig. 8). As the original table contained only integer values for 

GPT, new records were inserted to fill in values to one decimal 

place (for example, 0.1 GPT). A linear-trend series-fill function was then used to calculate specific gravity values for each 

0.1 GPT value. A third-order trendline was regenerated comparing the new oil yield versus specific-gravity data yielding a 

R2

 value of 0.9997. Values for weight of oil shale, volume of 

oil shale, and oil yield per unit volume were recalculated using 

the new values for oil yield and specific gravity. The updated 

lookup table was created containing records for oil yield 

(GPT), specific gravity, and oil yield per unit volume from 1.0 

to 80.0 GPT, at 0.1 GPT intervals that were then related to the 

database form by using the values for oil yield in GPT.

To calculate BPA for each borehole location, the previously calculated weighted-average value for GPT was related 

to the lookup table (table 3) to retrieve the associated value for 

oil yield per unit volume (gallons per cubic foot, gal/ft3

); this 

value was needed to perform the BPA calculation:

Interval thickness in feet (without halite beds) * 43,560 (ft2

/

acre) * oil yield per unit volume (gal/ft3

) / 42 (gals/barrel of oil). 

In short, the calculated value for GPT was used as 

another unique identifier to link to the lookup table (table 3) in 

order to use the associated value for oil yield per unit volume 

in that record as input into the BPA formula. For example, 

if the Microsoft Access form (as in fig. 6) listed GPT to be 
Oil Shale Assessment 9

Figure 7. Image clip showing how changing an interpretation of a tops pick in the tops table and re-filtering the 

Fischer assays table immediately affects the Microsoft Access 2007 form’s resource calculations, but not the ArcGIS 

Table’s records. Example shown is for the Mahogany oil shale zone in borehole U0043 (see fig. 14 for location data).
10 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Table 2. Original volume-weight oil-yield relations based upon Green River oil shale from U.S. Bureau of Mines oil shale 

mine, Rifle, Colorado from Stanfield and others (1954).

[GPT, gallons per ton; lbs/ft3, pounds per cubic foot; ft3/ton, cubic feet per ton; gal/ft3, gallons per cubic foot]

Oil yield by assay 

(GPT)

Specific gravity of 

oil shale

Weight of oil shale 

(lbs/ft3

)

Volume of oil shale 

(ft3/ton)

Oil yield, per unit 

volume (gal/ft3)

1 2.740 170.98 11.70 0.085

2 2.715 169.42 11.80 0.169

3 2.690 167.86 11.91 0.252

4 2.655 166.30 12.03 0.333

5 2.640 164.74 12.14 0.412

6 2.618 163.36 12.24 0.490

7 2.596 161.98 12.35 0.567

8 2.574 160.61 12.45 0.642

9 2.552 159.24 12.56 0.716

10 2.530 157.87 12.67 0.789

11 2.508 156.49 12.78 0.860

12 2.486 155.12 12.89 0.930

13 2.464 153.75 13.01 0.999

14 2.442 152.38 13.13 1.067

15 2.420 151.01 13.24 1.133

16 2.400 149.76 13.35 1.198

17 2.380 148.51 13.47 1.262

18 2.360 147.26 13.58 1.325

19 2.340 146.02 13.70 1.387

20 2.320 144.77 13.80 1.448

21 2.302 143.64 13.92 1.508

22 2.284 142.52 14.03 1.567

23 2.266 141.40 14.14 1.625

24 2.248 140.78 14.26 1.683

25 2.230 139.15 14.37 1.740

26 2.216 138.28 14.46 1.797

27 2.202 137.40 14.56 1.854

28 2.188 136.53 14.65 1.910

29 2.174 135.66 14.74 1.966

30 2.160 134.78 14.83 2.022

31 2.147 133.97 14.92 2.077

32 2.134 133.16 15.02 2.131

33 2.121 132.35 15.11 2.184

34 2.108 131.54 15.20 2.236

35 2.093 130.73 15.30 2.288

36 2.082 129.92 15.44 2.339

37 2.069 129.11 15.49 2.389

38 2.056 128.29 15.59 2.438

39 2.043 127.48 15.69 2.486

40 2.030 126.67 15.79 2.534

41 2.018 125.92 15.88 2.581

42 2.006 125.17 15.98 2.628
Oil Shale Assessment 11

Table 2. Original volume-weight oil-yield relations based upon Green River oil shale from U.S. Bureau of Mines oil shale 

mine, Rifle, Colorado from Stanfield and others (1954).—Continued

[GPT, gallons per ton; lbs/ft3, pounds per cubic foot; ft3/ton, cubic feet per ton; gal/ft3, gallons per cubic foot]

Oil yield by assay 

(GPT)

Specific gravity of oil 

shale

Weight of oil shale 

(lbs/ft3

)

Volume of oil shale 

(ft3/ton)

Oil yield, per unit 

volume (gal/ft3)

43 1.994 124.43 16.07 2.674

44 1.982 123.68 16.17 2.720

45 1.970 122.93 16.27 2.766

46 1.959 122.24 16.36 2.811

47 1.948 121.56 16.45 2.856

48 1.937 120.87 16.55 2.901

49 1.926 120.18 16.64 2.945

50 1.915 119.50 16.74 2.938

51 1.904 118.81 16.83 3.030

52 1.893 118.12 16.93 3.071

53 1.882 117.44 17.03 3.112

54 1.871 116.79 17.12 3.152

55 1.860 116.06 17.23 3.192

56 1.849 115.38 17.33 3.231

57 1.838 114.69 17.44 3.269

58 1.827 114.00 17.54 3.306

59 1.816 113.32 17.65 3.343

60 1.805 112.63 17.76 3.379

61 1.794 111.95 17.87 3.414

62 1.783 111.26 17.98 3.449

63 1.772 110.57 18.09 3.483

64 1.761 109.89 18.20 3.516

65 1.750 109.20 18.32 3.549

66 1.740 108.58 18.42 3.582

67 1.730 107.95 18.53 3.615

68 1.720 107.33 18.63 3.648

69 1.710 106.70 18.74 3.681

70 1.700 106.08 18.85 3.713

71 1.690 105.46 18.96 3.744

72 1.680 104.83 19.08 3.774

73 1.670 104.21 19.19 3.804

74 1.660 103.58 19.31 3.833

75 1.650 102.96 19.43 3.861

76 1.640 102.34 19.54 3.889

77 1.630 101.71 19.66 3.916

78 1.620 101.09 19.78 3.943

79 1.610 100.46 19.91 3.969

80 1.600 99.84 20.03 3.994

90 1.500 93.75 21.33 4.219
12 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Table 3. Recalculated volume-weight oil-yield relationships based upon Green River oil shale from U.S. Bureau of Mines oil shale 

mine, Rifle, Colorado from Stanfield and others (1954).

[GPT, gallons per ton; lbs/ft3, pounds per cubic foot; ft3/ton, cubic feet per ton; gal/ft3, gallons per cubic foot]

Oil yield by assay 

(GPT)

Specific gravity of 

oil shale

Weight of oil shale 

(lbs/ft3

)

Volume of oil shale 

(ft3/ton)

Oil yield, per unit 

volume (gal/ft3)

1 2.740 171.06 11.69 0.086

2 2.715 169.50 11.80 0.169

3 2.690 167.94 11.91 0.252

4 2.655 165.75 12.07 0.332

5 2.640 164.82 12.13 0.412

6 2.618 163.44 12.24 0.490

7 2.596 162.07 12.34 0.567

8 2.574 160.69 12.45 0.643

9 2.552 159.32 12.55 0.717

10 2.530 157.95 12.66 0.790

11 2.508 156.57 12.77 0.861

12 2.486 155.20 12.89 0.931

13 2.464 153.83 13.00 1.000

14 2.442 152.45 13.12 1.067

15 2.420 151.08 13.24 1.133

16 2.400 149.83 13.35 1.199

17 2.380 148.58 13.46 1.263

18 2.360 147.33 13.57 1.326

19 2.340 146.09 13.69 1.388

20 2.320 144.84 13.81 1.448

21 2.302 143.71 13.92 1.509

22 2.284 142.59 14.03 1.568

23 2.266 141.47 14.14 1.627

24 2.248 140.34 14.25 1.684

25 2.230 139.22 14.37 1.740

26 2.216 138.34 14.46 1.798

27 2.202 137.47 14.55 1.856

28 2.188 136.60 14.64 1.912

29 2.174 135.72 14.74 1.968

30 2.160 134.85 14.83 2.023

31 2.147 134.04 14.92 2.078

32 2.134 133.23 15.01 2.132

33 2.121 132.41 15.10 2.185

34 2.108 131.60 15.20 2.237

35 2.093 130.67 15.31 2.287

36 2.082 129.98 15.39 2.340

37 2.069 129.17 15.48 2.390

38 2.056 128.36 15.58 2.439

39 2.043 127.54 15.68 2.487

40 2.030 126.73 15.78 2.535

41 2.018 125.98 15.88 2.583

42 2.006 125.23 15.97 2.630
Oil Shale Assessment 13

Table 3. Recalculated volume-weight-oil yielded relationships based upon Green River oil shale from U.S. Bureau of Mines oil shale 

mine, Rifle, Colorado from Stanfield and others (1954).—Continued

[GPT, gallons per ton; lbs/ft3, pounds per cubic foot; ft3/ton, cubic feet per ton; gal/ft3, gallons per cubic foot]

Oil yield by assay, 

(GPT)

Specific gravity of 

oil shale

Weight of oil shale 

(lbs/ft3

)

Volume of oil shale 

(ft3/ton)

Oil yield, per unit volume 

(gal/ft3)

43 1.994 124.49 16.07 2.676

44 1.982 123.74 16.16 2.722

45 1.970 122.99 16.26 2.767

46 1.959 122.30 16.35 2.813

47 1.948 121.61 16.45 2.858

48 1.937 120.93 16.54 2.902

49 1.926 120.24 16.63 2.946

50 1.915 119.55 16.73 2.989

51 1.904 118.87 16.83 3.031

52 1.893 118.18 16.92 3.073

53 1.882 117.49 17.02 3.114

54 1.871 116.81 17.12 3.154

55 1.860 116.12 17.22 3.193

56 1.849 115.43 17.33 3.232

57 1.838 114.75 17.43 3.270

58 1.827 114.06 17.53 3.308

59 1.816 113.37 17.64 3.344

60 1.805 112.69 17.75 3.381

61 1.794 112.00 17.86 3.416

62 1.783 111.31 17.97 3.451

63 1.772 110.63 18.08 3.485

64 1.761 109.94 18.19 3.518

65 1.750 109.25 18.31 3.551

66 1.740 108.63 18.41 3.585

67 1.730 108.00 18.52 3.618

68 1.720 107.38 18.63 3.651

69 1.710 106.76 18.73 3.683

70 1.700 106.13 18.84 3.715

71 1.690 105.51 18.96 3.745

72 1.680 104.88 19.07 3.776

73 1.670 104.26 19.18 3.805

74 1.660 103.63 19.30 3.834

75 1.650 103.01 19.42 3.863

76 1.640 102.39 19.53 3.891

77 1.630 101.76 19.65 3.918

78 1.620 101.14 19.78 3.944

79 1.610 100.51 19.90 3.970

80 1.600 99.89 20.02 3.996

90 1.500 93.65 21.36 4.214
14 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Figure 8. Graph of a third-order trendline showing relation between oil yield for Green 

River oil shales and specific gravity.

10.0 for a specific borehole and zone, this would correspond 

to a value of 0.790 gal/ft3 in table 3, which would then be the 

input into the BPA formula as the oil yield per unit volume 

multiplier. The interval thickness value in the formula was 

calculated by summing all of the intervals within a zone and 

subtracting any beds denoted as sandstone in the ROCKTYPE 

column–“NO.”

Missing Intervals

In addition to resource calculations, statistics describing 

missing core intervals in percent, maximum thickness of 

missing intervals, and the number of missing intervals for 

each borehole and zone were generated (fig. 9). In general, the 

larger the proportion of a given core that constitutes a missing 

interval, the greater the imprecision of the resource calculation. Therefore, an accurate accounting of the number and 

thickness of missing intervals is especially important, because 

a few thick, missing intervals could potentially have a greater 

impact on the precision of a resource calculation than a large 

number of thin intervals. Once a specific borehole was filtered 

by zone in the database form, custom-scripting functions 

counted and performed calculations on the missing records in 

the Fischer assays table. These values were then permanently 

stored in the table used for GIS functions and a series of 

derivative maps were produced by linking the missing-interval 

statistics to the borehole locations in the GIS. The missingintervals derivative maps are a valuable aid in assessing the 

uncertainty of the resource estimates.

Geospatial Modeling, Analysis, and 

Presentation

Oil Shale Zone Thickness Isopachs

Oil shale zone thickness values were calculated using 

the zonal contacts as identified in boreholes and listed in a 

spreadsheet, by subtracting a pick of the top for any given 

zone from that of the immediately underlying zone. Formulas 

were created to automate this function, but actual values were 

used in converting the spreadsheet to a database table. When 

the pick of a zone top was revised in the database form, the 

thickness of the zone interval was recalculated. Using this 

method, values for all resource calculations were continuously 

and immediately updated, as the new tops and interval values 

affected all formulas contained in the form and were recalculated interactively (fig. 7).

Once a set of tops for a given zone was finalized, 

the thickness values for each zone were used to generate 

Specific gravity versus oil yield, Green River oil shale

y = –1E-06x3+ 0.0003x2 – 0.0265x + 2.7656

R2 = 0.9998

0

0.500

1.000

1.500

2.000

2.500

3.000

SPECIFIC GRAVITY 

OIL YIELD, IN GALLON PER TON (GPT)

0 10 20 30 40 50 60 70 80 90 100

EXPLANATION

Specific gravity

Trendline
Oil Shale Assessment 15

Figure 9. Image clip of Microsoft Access 2007 form showing how missing intervals are reported for each 

core sample. Example shown is for the Mahogany oil shale zone in borehole U0043 (see fig. 14 for location 

data).

spatial-data models using a RBF-Multiquadric modeling 

method (ESRI, 2006). By generating and analyzing a spatial 

model for each zone, errors were located in the tops table 

and changes were made to the correlations as needed. Upon 

completion of the database, a final model for each oil shale 

zone was converted to a fixed raster dataset and a series of oil 

shale zone thickness isopach maps were generated.

Generating Oil-Yield Models

In a previous oil shale assessment, Pitman and others 

(1989) used geostatistical interpolation by kriging to generate 

resource maps and numbers. They reported that kriging gave 

good results in areas with large numbers of control points, but 

that the calculations gave unreliable resource numbers with 

large error limits in areas with few control points; consequently, they resorted to hand-contouring and hand-calculating 

resources in these areas. In the present assessment, three 

modeling methods were evaluated for spatial interpolation and 

extrapolation purposes (1) the RBF method, (2) the Inverse 

Distance Weighted (IDW) method in ArcGIS (ESRI, 2006), 

and (3) the minimum-tension gridding technique in EarthVision (Dynamic Graphics, Inc., 2004). The three methods 

gave remarkably similar results, and RBF was ultimately 

chosen. One of the determining factors was that the RBF 

method did not limit us to a coarse-cell spacing, so we were 
16 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

able to model and report resources using a one-acre cell size. 

Although not as robust as kriging or other geostatistical spatial 

modeling methods, it has been demonstrated that the RBF 

method can give comparable results (Rusu and Rusu, 2006). 

RBF is an exact interpolator; it will honor all data points 

and not introduce any error at those locations (ESRI, 2006). 

Although it is important for the modeling method to honor 

the measured values, RBF can also extrapolate values above 

or below the actual values outside the data point locations. 

Extrapolation of values beyond the dataset boundaries was 

appropriate in this geology-based assessment, as each zone’s 

oil yield varies in a predictable manner throughout the basin.

After the database revisions were completed, the resultant 

calculated values for GPT and BPA were written to a separate 

database table and related to the borehole-locations file. RBF 

models were then generated using the resultant borehole data 

containing oil-yield values (fig. 10). The values for GPT and 

BPA were modeled using the RBF-Multiquadric method. The 

final resource models were created using a sampling method 

containing eight moving window sectors with eight neighbors 

in each sector; after numerous tests, these parameters yielded 

the most geologically reasonable oil shale resource models 

based on the number of boreholes and the extent of the dataset. 

After all of the models were finalized, they were exported 

to a fixed raster format with a one-acre cell size (63.615 m 

(208.7 ft) per side) along with a mean error, root mean-squared 

error (RMS), and cross-validation table for each BPA model. A 

more detailed description of the error tables is presented in the 

case study at the end of this report. A series of derivative maps 

were then created using ArcGIS (ESRI, 2006).

Summarizing Resource Models

A zonal-statistics function was used on the finalized BPA 

model to calculate resources per township; this step being 

critical inasmuch as the software was able to count each cell’s 

BPA value within a specified zone (fig. 11). In this case, the 

surface zones required as input for the software to summarize 

the raster cells (not to be confused with the subsurface oil 

shale zones) included townships or portions thereof (those 

that were cropped by the outcrop lines). As the analysis cell 

size of one acre and the BPA were modeled, a straightforward 

summary of total barrels of oil yield per township was performed as the software simply counted all of the values for 

BPA for each cell (acre) contained within each resource township. The summary statistics were then linked to each township and another series of derivative maps detailing the total 

barrels of oil yield per township were generated. Although 

a polygon file delineating township boundaries within the 

resource zones was used, in the future a user could easily run 

statistics using a different zone boundary, such as for a 640-

acre (1 mi2

) section to obtain a summary of barrels of oil yield 

per section. 

Interpretive Maps

Once the spatial analysis and quantification of resources 

were completed, a series of interpretive maps were generated 

for each of the 18 oil shale zones, including thickness isopachs 

of individual oil shale zones, average oil yield in GPT, oil 

yield in BPA, oil yield in barrels per township, and the percentage of missing intervals in each core sample (figs. 12, 13). 

Conclusions

For the 18 oil shale zones in the Uinta Basin, an updated 

and reproducible method was created to calculate in-place 

oil resources using current relational database management 

(fig. 14) and GIS software. The process involved the conversion of legacy data and generation of new data. The results are 

presented in digital formats that can be used by other investigators to develop their own interpretations and generate their 

own data models using other spatial-modeling techniques. 

Acknowledgments

The authors thankWilli am Keefer and Michael Pantea of 

the U.S. Geological Survey for performing thorough technical 

reviews of this report. Their comments and suggestions led to 

improvements and clarity to many of the discussed topics
Acknowledgments 17

Figure 10. Image clip showing how data are migrated from the Microsoft Access 2007 database form and then modeled in 

the GIS software. Values labeled in model are in thousands of barrels. Example shown is for the Mahogany oil shale zone in 

borehole U0043 (see fig. 14 for location data).
18 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Figure 11. Eastern part of the Uinta Basin, Utah, showing how the GIS software summarizes total barrels 

of oil yield by township.
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0 6 12 MILES0 5 10 KILOMETERS

EXPLANATION

Top of Mahogany bedDepth to Mahogany bed, 6,000-ft contourBase of Parachute Creek MemberDefined boundary for assessment

Mahogany zone interval, average oil yield

 in gallons per ton

24.1–3018.1–2412.1–186.1–122.1–6Core hole (in gallons per ton) 12 !

Rotary hole (in gallons per ton) 3.9 !

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16

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R 22 E R 23 E

R 19 E

R 23 E R 24 E

R 23 E

R 22 E

T

17

S

R 19 E R 20 E R 21 E

T

18

S

T

19

S

T

15

S

R 17 E

R 18 E

R 16 E

T

16

S

T

17

S

T

18

S

T

15

S

T

14

S

R 15 E

R 14 E

R 2 E

T

13

S

T

12

S

T

11

S

R 13 E R 10 E R 11 E R 12 E

T6

S

R 8 E

R 6 E R 7 E

T

10

S

T9

S

T8

S

T7

S

T3

S

T4

S

T5

S

T6

S

3 4

8 8

10 11

6

18

13

8

9

15

9

17

9

10

10

10

84

105

51

73

97

56

65

54

82

53

129

99

142

146 104 74

70

27

148

66

222

42

46

66 58

90

33

94

107

55

128

196

95

86

112

77

77

26

114

226

72

60

98

61

164

12

45

40

64

156

41

68

198

154

72 

139

99

55

95

54

73

42

62

79

84

55

74 73

105

74

100 78

121 177

169

161

140

176

141

189

1

176

122

217

83

10

111

68

65 74

111

10

90 105

128

94

187

145 156

108

184

153

17

11

25

56

34

130

129

209

121

139

121

121

135

113

109

114

113

120

121

118

118

175

165

189

141

171

111

195

180

172

182 

1

216

229

142

116

124

11

11

123

115

117

124

75

145

107

115

28

59

39

0 6 12 MILES0 5 10 KILOMETERS

EXPLANATION

Top of Mahogany bedDepth to Mahogany bed, 6,000-ft contourBase of Parachute Creek MemberDefined boundary for assessment

Mahogany zone interval, barrels per acre

200,001–250,000150,001–200,000100,001–150,00050,001–100,0002,131–50,000Core hole (in thousands of barrels) 12 !

Rotary hole (in thousands of barrels) 72 !

UTAH

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R 103 W

R 3 W R 2 W R 12 W R 11 W R 10 W R 9 W R 8 W R 7 W R 6 W R 5 W R 4 W R 1 W R 1 E R 24 E R 25 E R 6 E

R 20 E R 21 E

R 25 E

T

16

S

R 22 E R 23 E

R 19 E

R 23 E R 24 E

R 23 E

R 22 E

T

17

S

R 19 E R 20 E R 21 E

T

18

S

T

19

S

T

14

S

T

15

S

R 17 E

R 18 E

R 16 E

T

16

S

T

17

S

T

18

S

T

15

S

T

14

S

R 15 E

R 14 E

R 2 E

T

13

S

T

12

S

T

11

S

R 13 E R 10 E R 11 E R 12 E

T6

S

R 8 E

R 6 E R 7 E

T

10

S

T9

S

T8

S

T7

S

T3

S

T4

S

T5

S

T

13

S

T7

S

T2

N

T1

N

T1

S

T2

S

T3

S

T6

S

2 3

6 6

6 7

3

13

6

5

5

8

5

8

6

5

5

5

123.8

143.7

90

100

150

210

90

100

140

120

150

190

180

120

160

180

140 130 110

70

110

90

130

130

140

130

150

170

140

130

140 150

140

120

125.1

170

80

120

150

150

220

130

120

180

80

115

140

85

150

130

100

100

110

120

120

150

160

120

120

80

120

160

110

70 140 120

110

80

100

185

165

50

60

100

100

90

120 80

75

100

100 110

120 110

120

120

110

130

100

100

130

150

130

80

120

121.2

64

68

106.1

74 94

122

69

63

65

97

93

88

80

58 64

66 60

95

78

76

70

62

76

140

104

106

90 85

77

101

95

15

9

31.6

70

31

70

96

106

96

75

119

123

114

110.9

80.7

99.3

72.2

83.9

78

107.6

115.4

103.7

102.8

107.1

107.6

107.2

101

96

99

102

110

95 99

108

109

73

90

109.4

99

95

105.5 104 110107.8

133

130

92

74.3

77.4

100 111.5

75.9

85.7

101.4

96.5

68.9

63.8

121.5

130.2

140.8

109.8

127.3

120.2

127.6

115.6

126.3

22.9

37.6

26.1

111°00' 110°30' 110°00' 109°30' 109°00'

40°00'

39°30'

39°00'

UTAH

COLORADO

0 6 12 MILES0 5 10 KILOMETERS

Top of Mahogany bedDepth to Mahogany bed, 6,000-ft contourBase of Parachute Creek MemberDefined boundary for assessment

EXPLANATION

Mahogany zone interval (thickness in feet)

151–200101–15051–1000–50Core hole (interval thickness in feet) 3.1 !

Rotary hole (interval thickness in feet) 200 !

UTAH

111°00' 110°30' 110°00' 109°30' 

40°00'

39°30'

39°00'

111°00' 110°30' 110°00' 109°30' 

40°00'

39°30'

39°00'

B

C

A

Figure 12. Examples of interpretive maps generated from resource models: A, oil shale zone isopachs; B, average oil yield in gallons 

per ton; C, oil yield in barrels per acre. See Johnson and others, Chapter 1, this CD–ROM, for original figures.
20 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

R 103 W

R 3 W R 2 W R 12 W R 11 W R 10 W R 9 W R 8 W R 7 W R 6 W R 5 W R 4 W R 1 W R 1 E R 24 E R 25 E R 6 E

R 20 E R 21 E

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R 22 E R 23 E

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R 23 E R 24 E

R 23 E

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R 13 E R 10 E R 11 E R 12 E

T

6

S

R 8 E

R 6 E R 7 E

T

10

S

T

9

S

T

8

S

T

7

S

T

3

S

T

4

S

T

5

S

T

13

S

T

7

S

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2

N

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1

N

T

1

S

T

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S

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3

S

T

6

S

111°0' 110°30' 110°0' 109°30' 109°0'

40°0'

39°30'

39°0'

UTAH

COLORADO

0 6 12 MILES

0 5 10 KILOMETERS

EXPLANATION

Missing core in the Mahogany zone (in percent)

41–50

31–40

21–30

11–20

0–10

Top of Mahogany bed

Depth to Mahogany bed, 6,000-ft contour

Base of Parachute Creek Member

Defined boundary for assessment

UTAH

R 103 W

R 3 W R 2 W R 12 W R 11 W R 10 W R 9 W R 8 W R 7 W R 6 W R 5 W R 4 W R 1 W R 1 E R 24 E R 25 E R 6 E

R 20 E R 21 E

R 25 E

T

16

S

R 22 E R 23 E

R 19 E

R 23 E R 24 E

R 23 E

R 22 E

T

17

S

R 19 E R 20 E R 21 E

T

18

S

T

19

S

T

14

S

T

15

S

R 17 E

R 18 E

R 16 E

T

16

S

T

17

S

T

18

S

T

15

S

T

14

S

R 15 E

R 14 E

R 2 E

T

13

S

T

12

S

T

11

S

R 13 E R 10 E R 11 E R 12 E

T

6

S

R 8 E

R 6 E R 7 E

T

10

S

T

9

S

T

8

S

T

7

S

T

3

S

T

4

S

T

5

S

T

13

S

T

7

S

T

2

N

T

1

N

T

1

S

T

2

S

T

3

S

T

6

S

3,007 3,456 4,044 4,063 4,521 3,943 1,932 2,225 2,538 2,240 1,

038

4,289 3,572 2,711 2,724 2,646 2,404

645 1,013 1,161 161

119 130 149 371 374

385 1,346 934 2,397 3,058 3,450

228

199 1,010 1,283 1,230 1,970 2,099 687

251 802

1,253 1,577 1,791 1,647 1,940 2,326

1,

365

174 660

1,284 1,610 1,955 1,307 1,365 1,403 1,436

576

1,395 2,047 1,325 1,559

454 802 844 926 1,166 1,120 1,214 1,635 1,567 1,502 1,604

789384

224

849 535

42

47 26

145 81 170 159

13

88

241 269

258 307 132

17 5 218 315 455 369 193

35

86

194

269 247

1,077 510

288 624 385 91

105

126 800

1,267 2,115 1,047 1,797 1,238

971

205

931 767

318

332 1,057 2,640 2,612 2,121 2,601 2,887 2,516 1,138

734 213

275

2,202 1,992 2,085 1,653 2,639 3,075 2,985 1,610 1,390 1,735 529 932 474 294 355

1,

908

3,081 3,634 3,952 4,028 2,969 2,001 2,238 2,180 2,238 1,354 146

111°0' 110°30' 110°0' 109°30' 109°0'

40°0'

39°30'

39°0'

UTAH

COLORADO

0 6 12 MILES

0 5 10 KILOMETERS

EXPLANATION

Top of Mahogany bed

Depth to Mahogany bed, 6,000-ft contour

Base of Parachute Creek Member

Defined boundary for assessment

Mahogany zone interval, oil yield, in millions of barrels 

2,900–4,520

1,810–2,890

1,130–1,800

475–1,120

0.285–474

UTAH

A B

Figure 13. Examples of interpretive maps generated from resource models: A, oil yield in barrels per township; B, percentage 

of missing intervals from each core sample. See Johnson and others, Chapter 1, this CD-ROM, for original figures.

Figure 14. Image clip of Microsoft Access 2007 form detailing how the user interacts with the form’s buttons and controls. 

Red text and linework details the visual representation of the database’s tables within the form, as well as buttons and controls 

added to utilize Visual Basic scripting and macros.
References Cited 21

References Cited

American Society for Testing and Materials, 1980, Standard 

method of test for oil from oil shale: Annual Book of ASTM 

Standards, Part 25, Designation D 3904–80, p. 513–515.

Bryant, B.H., 1992, Geologic and structure maps of the Salt 

Lake City 1 degree by 2 degrees quadrangle, Utah and 

Wyoming: U.S. Geological Survey Miscellaneous Investigation Map I-1997, scale 1:250,000, http://pubs.er.usgs.gov/

usgspubs/i/i1997 (Last accessed September 9, 2009).

Beside Software, 2006, Import Wizard ver. 9.

Cashion, W.B., 1973, Geologic and structure map of the Grand 

Junction quadrangle, Colorado and Utah: U.S. Geological Survey Miscellaneous Investigation Map I-736, scale 

1:250,000, http://pubs.er.usgs.gov/usgspubs/i/i736 (Last 

accessed September 9, 2009).

Cashion, W.B., and Donnell, J.R., 1972, Chart showing correlation of selected key units in the organic-rich sequence 

of the Green River Formation, Piceance Creek Basin, 

Colorado, and Uinta Basin, Utah: U.S. Geological Survey 

Oil and Gas Investigation Chart OC-65, http://pubs.er.usgs.

gov/usgspubs/oc/oc65 (Last accessed September 9, 2009).

Donnell, J.R., 2008, Intertonguing of the lower part of the 

Uinta Formation with the upper part of the Green River Formation in the Piceance Creek Basin during the late stages 

of Lake Uinta: U.S. Geological Survey Scientific Investigations Report SIR 2008-5237, 25 p.

Donnell, J.R., and Blair, R.W., Jr., 1970, Resource appraisal 

of three rich oil shale zones in the Green River Formation, 

Piceance Creek Basin, Colorado: Colorado School of Mines 

Quarterly, v. 65, no. 4, p. 73–87.

Dynamic Graphics, Inc., 2004, EarthVision, ver. 7.5.

Dyni, J.R., 1974, Stratigraphy and nahcolite resources of the 

saline facies of the Green River Formation, Rio Blanco 

County, Colorado: Rocky Mountain Association of Geologists, 1974 Guidebook, p. 111–122.

Dyni, J.R., 1998, Fischer assays of oil shale drill cores and 

rotary cuttings from the Piceance Creek Basin: U.S. 

Geological Survey Open-File Report 98–483, CD-ROM.

ESRI (Environmental Systems Research Institute, Inc.), 2006, 

ArcGIS, ver. 9.2.

Microsoft Corporation, 2006, Microsoft Access and Excel 

(part of Microsoft Office 2007).

Pitman, J.K., 1979, Isopach, structure contour, and resource 

maps of the R-6 oil shale zone, Green River Formation, 

Piceance Creek Basin, Colorado: U.S. Geological Survey 

Miscellaneous Field Investigations Map MF-1069, 

scale 1:126,720.

Pitman, J.K., and Johnson, R.C., 1978, Isopach, structure 

contour, and resource maps of the Mahogany oil-shale zone, 

Green River Formation, Piceance Creek Basin, Colorado: 

U.S. Geological Survey Miscellaneous Field Investigations 

Map MF-958, scale 1:126,720.

Pitman, J.K., Wahl Pierce, Frances, and Grundy, W.D., 1989, 

Thickness, oil-yield, and kriged resource estimates for the 

Eocene Green River Formation, Piceance Creek Basin, 

Colorado: U.S. Geological Survey Oil and Gas Investigations Chart OC-132.

Rowley, P.D., Hansen, W.R., Tweto, Ogden, and Carrara, P.E.,

1985, Geologic map of the Vernal 1 degree by 2 degrees 

quadrangle, Colorado, Utah, and Wyoming: U.S. Geological Survey Miscellaneous Investigation Map I-1526, scale 

1:250,000, http://pubs.er.usgs.gov/usgspubs/i/i1526 (Last 

accessed September 9, 2009).

Rusu, C., and Rusu, V., 2006, Radial basis functions versus 

geostatistics in spatial interpolations, in Bramer, M., ed., 

IFIP International Federation for Information Processing, 

v. 217, [Artificial Intelligence in Theory and Practice], 

(Boston: Springer), p. 119–128.

Smith, J.W., 1956, Specific gravity-oil yield relationships of 

two Colorado oil shale cores: Industrial and Engineering 

Chemistry, v. 48, no. 3, p. 441–444.

Stanfield, K.E., Rose, C.K., McAuley, W.S., and Tesch, W.J., 

Jr., 1954, Oil yields of sections of Green River oil shale in 

Colorado, Utah, and Wyoming, 1945–1952: U.S. Bureau of 

Mines Report of Investigations RI–5081, 153 p.

Vanden Berg, M.D., Dyni, J.R., and Tabet, D.E., 2006, Utah 

oil shale database: Utah Geological Survey Open-File 

Report 469, CD–ROM.

Witkind, I. J., 1995, Geologic map of the Price 1 degree by 

2 degrees quadrangle, Utah: U.S. Geological Survey 

Miscellaneous Investigation Map I-2462, scale 1:250,000, 

http://pubs.er.usgs.gov/usgspubs/i/i2462 (Last accessed 

September 9, 2009).
Case Study—Oil Shale Assessment 
Converting and Loading Legacy Fischer Assay Data

We loaded legacy Fischer assay data files (Dyni, 1998) into a Microsoft Access database table (fig. 15) using Import Wizard ver. 9 (Besi

was necessary, initially, to create an Import Wizard model file (A_FISCHER_DEPTHS.iwm; fig. 16) that defined the column names accordinoriginal ASCII text files (Dyni, 1998). After the model was created, more than 300 Fischer assay files were imported into one Access table. Waccording to the character spacing in the original ASCII files. Once the table was created and populated using Access, columns were added fosampled interval (INTVL), the thickness of the interval times the oil yield in gallons per ton (INTXOIL), and a field to denote halite and recogists (ROCKTYPE). We populated the INTVL and INTXOIL fields by using update queries in Access.

Figure 15. Image clip of Beside Software Import Wizard dialog window used to import legacy Fischer assay data (Dyni, 1998). A portion of the o

assay data for the Western Oil Shale Corp. EX-1 borehole is shown to the right (see fig. 4, table 1).
Figure 16. Image clip of Import Wizard model file (A_FISCHER_DEPTHS.iwm) setup dialog window. The different colors denote (1) blue—header was ignored during the import process; (2) red—selected field (LABNO, laboratory number) and width of column; and (3) yellow—remainder of thimported.
Converting and Loading Stratigraphic “Tops” Data

The stratigraphic “tops” table (fig. 17) contains depths (in ft) from the surface to each oil shale zone, from which the thickness of each o

mined. Formulas were created in Microsoft Excel to calculate the interval thicknesses and the results were converted to cell values in order to data to Access. The Excel spreadsheet was then imported to an Access table (CO Tops_080115). (Note: The Utah data were combined with thdatabase, as the Piceance and Uinta Basins share the same stratigraphy, thus the “CO” filenames.)

Figure 17. Structure of the stratigraphic tops table (CO_Tops_080115) as viewed in Microsoft Access 2007, showing the depth, in ft, to top of the A-groove through L-5 oil shale zones for boreholes U0020 through U0037A. Tops of oil shale zones are listed stratigraphically from left to right: AGROOV, A-groove; MAHOGZN, Mahogany Zone; TPMAHOG, Mahogany Bed; MAHOG, Mahogany Bed; BGROOV, B-groove; R6 (rich-zone 6); L5 (lzone 5). Tops picked by: JD, John Donnell (USGS); O, authors of this report.
Converting and Loading the Barrels Per Acre (BPA) Lookup Table The Lookup Table containing the updated values for gallons per cubic foot necessary for the barrels per acre (BPA) calculations was converte2007 spreadsheet to an Access table (BPA Lookup Table). After the gallons per ton (GPT) weighted average is calculated for a particular zone Access form, the “GALFT3NEW” value associated with that GPT average is required as a multiplier in the formula. For example, if a zone’s gallons per ton oil yield is 1.4, the multiplier is 0.119 for the calculation. We used a linear trend based on Excel 2007’s fill series function to figallon per ton in Excel before importing the spreadsheet to Access (fig. 18). 

Figure 18. Structure of the barrels per acre lookup table (BPA Lookup Table) usfor the form calculations as viewed in Microsoft Access 2007, showing how valufor gallons per cubic foot (GALFT3NEW) oil yield were filled in for every 0.1 gallon using a linear trend fill series function. GALPERTON, gallons per ton.
Digitizing Borehole LocationsLocations of boreholes (fig. 19) were digitized in ESRIs ArcMap based on footage measurements from section corners recorded in a separate and others, 2006). A custom tool was developed that combined the distance and sketch tools in ArcMap to digitize points. Each point was attrborehole identifier, USGSID, which allowed the linking of spatial and tabular data.

Figure 19. Eastern part of the Uinta Basin, Utah, showing borehole locations after digitizing in ArcMap. Parachute Creek Member outcrop areas shown between green and brown lines. Mahogany bed outcrops shown by brown lines.
Building the Access Form A custom Access Form (CO Form) (fig. 20) was built that allowed the linking of tables by a unique identifier, or primary key (USGSID), that except for the BPA Lookup Table. The form was based on the stratigraphic tops table (CO_Tops_080115), and the linked tables are displayed 

Figure 20. Image clip of the main Access form showing how subforms were linked by the unique identifier, USGSID, and performed all resource 
Intersecting Polygon Files to Create Reporting PolygonsInitially, outcrop lines were digitized in ArcMap for the top of the Mahogany bed and the base of the Parachute Creek Member of the Green Ron 1:250,000-scale published geologic maps—Bryant (1992), Cashion (1973), Rowley and others (1985), and Witkind (1995). These polygonresource polygons for resource assessments by oil shale zone. Township lines in ESRI shapefile format were downloaded from the Bureau of (BLM) GeoCommunicator website (http://www.geocommunicator.gov), and the township polygons were intersected with the outcrop boundaArcGIS’s ArcToolbox to create “reporting” polygons (fig. 21). The reporting polygons provided the areal extents used to quantify barrels per part of each township underlain by a particular oil shale zone. 

Figure 21. Diagram showing how ESRI ArcToolbox’s Intersect command was used to create the assessment’s reporting polygons for the Uinta B

Township polygons Outcrop polygons (resource polygons) Reporting polygons
SQL Query to Filter Assays By Zone Following linkage of the Fischer assays table (CO_Assays_INTV) with the tops table (CO_Tops_080115) by USGSID in the form, Structured queries were written to filter out subsets of assay records for each oil shale zone. For example, the query (QuerR4toL3) to select only those asbetween the R-4 zone top and the L-3 zone top picks in the tops table, and to display those records in the assays subform, utilized a BETWEESELECT CO_Assays_INTV.TOPFT, CO_Assays_INTV.BOTFT, CO_Assays_INTV.OILGPT, CO_Assays_INTV.USGSID, CO_Assays_INTINTV.INTXOIL, CO_Assays_INTV.ROCKTYPE FROM CO_Assays_INTV WHERE (((CO_Assays_INTV.TOPFT) Between [Forms]![CO Form]![JDR4] And [Forms]![CO Form]![JDL3]) AND ((CO_Assays_INTV.BOTFT) Between [Forms]![CO Form]![JDR4] And [Forms]![CO Form]![JDL3]) AND (([Forms]![CO Form]![JDR4])>0) AND (([Forms]![CO Form]![JDL3])>0)) ORDER BY CO_Assays_INTV.TOPFT; In effect, the R-4 zone query returned assay records from the top of the R-4 zone to the top of the L-3 zone, but only if (1) the value for the to(TOPFT) was equal to or greater than the R-4 tops pick (JDR4), (2) the base of an assay record (BOTFT) was equal to or less than the top of and (3) all assay records in between as long as those records contained the same USGSID as the currently selected borehole in the form. 
Calculating Gallons Per Ton Weighted AverageFormula: Sum of (thickness of interval * gallons per ton) / thickness of intervalWithin the assessed zones, any OILGPT value equal to 0.0 or thus, any INTXOIL (thickness of interval times OILGPT) value equal to 0.0 waweighted-average calculations; those records were considered missing or erroneous. In the Fischer Assays subform (fig. 22), we built a text bthe weighted average for each oil shale zone. We accomplished this by creating a text-box control in the subform’s footer and placed the follocontrol:=Sum(IIf([INTXOIL]>0,[INTXOIL],0))/Sum(IIf([INTXOIL]>0,[INTVL],0))After filtering the assays subform using the SQL zone query, the Access form would (1) sum all of the INTXOIL values and the INTVL valueon those sums, and (3) return the result in the box as long as each record met the criteria of INTXOIL being greater than 0 (that is, not missin

Figure 22. The oil shale assessment’s Microsoft Ac

2007 form as viewed in Design mode. Some text box calculations are “hidden” in the Fischer assays subf
Calculating Barrels Per Acre Oil Yield Formula: Interval thickness * 43,560 (sq. ft/acre) * oil yield per unit volume / 42 (gals/barrel of oil) Following determination of a weighted-average value for gallons per ton oil yield, it was necessary to calculate the total thickness of the oil shalite beds, if present. (Note: While no halite beds were present in Utah, it was necessary to retain the following statements for the form, baseColorado Assessment, to function correctly). We added text-box controls to the subform that contained the following statements: To sum all intervals for the subset of assays: =Sum([INTVL]) To sum all halite intervals for the subset of assays: =Sum(IIf([ROCKTYPE]=”NH”,[INTVL],0)) To subtract halite intervals from the total and round to one decimal place: =Round(([Text218]-[Text333]),1) where Text218 is the sum of all intervals and Text333 is the sum of halite intervals. Next, we retrieved our multiplier for oil yield per unit volume from the Lookup Table based on the GPT calculation in the form, which was actext box control containing the following statement: =DLookUp(“[GALFT3NEW]”,”[BPA Lookup Table]”,”[GALPERTON]=” & Forms![CO Form]!Text151) where Text151 is the text-box control that caaverage value for gallons per ton oil yield. In effect, this statement “looks up” the GALFT3NEW value in the BPA Lookup Table that is assocGPT average. For example, for the R-4 zone in borehole U0043, a GPT average of 9.5 was returned. The associated value in the BPA Lookup Using the statements above, the form also returned a value of 87.0 for the total thickness of the R-4 zone. Using the formula to calculate barrefollowing statement was entered into another text box control in the form: =Round([Text335]*43560*[Text239]/42,0) where Text335 is the sum of all intervals minus halite and Text239 is the value returned from the Loorounded to 0 decimal places. In this case, for the R-4 zone in borehole U0043, the form calculated 68,034 barrels per acre oil yield (87.0 * 43
Calculating the Percentage of Missing Intervals From Each Core SampleText-box controls were added to the form footer of the assays subform to report the percentage of missing intervals from each core sample, thmissing intervals, and the number of intervals missing from each core sample. The following statements were added to three separate controlTo calculate the percentage missing:=Sum(IIf([INTXOIL]=0,[INTVL],0))/Sum([INTVL])To calculate the maximum thickness of missing intervals:=Max(IIf([INTXOIL]=0,[INTVL],0))To count the number of records missing:=Sum(IIf([OILGPT]=0,1,0))
Storing Calculated Values in a Separate Table To expedite attributing in Access and to help avoid data entry errors, buttons and macros (fig. 23) were added to the main form to transfer temvalues from the text-box controls to a separate table that could then be linked to in ArcGIS. We stored the results permanently in another table (OilShale_Holes_pts). Four buttons were created to transfer values for gallons per ton, barrels per acre, maximum interval missing, and the number of records missimain form triggered a macro to run using a SetValue action. The SetValue action would populate the appropriate field in the ArcGIS table witcalculated interactively in the form. For example, the macro to store the value for the maximum interval missing from a core sample for a particular zone in the permanent table rItem: [Forms]![CO form]![OilShale_Holes_pts subform].[Form]![Text21] Expression: [Forms]![CO form]![Text341] The Item statement contains the field we wanted to set. Text21 refers to the fieldname for MXMISS in the ArcGIS Table subform. The Expresrefers to the value calculated in the main form—in this case, the Text341 text-box control that contained the statement to calculate the maximintervals. 

Figure 23. Image clip of a portion of the Microsoft Access 2007 form showing how calculation results performed interactively in the form were stored in a table permanently using buttons and macros. Example shown is for the Bed 44 oil shale zone in borehole U0043.
Filtering Subforms From the Main FormA method to filter the assay records by a particular oil shale zone was established by using SQL BETWEEN statements. Eighteen buttons werto filter assays by each oil shale zone as well as an “All” button that would display all assay records for a particular borehole (that is, unfilterewas also needed to restrict the display of fields in the ArcGIS Table subform by the zone being filtered. Each of the 18 oil shale zones containgallons per ton (AVGPT), barrels per acre (BPA), percentage of missing intervals (AVMSIN), maximum interval missing (MXMISS), and the ing (CNTMISS). For example, the R-4 zone contained the fields R4AVGPT, R4BPA, R4AVMSIN, R4MXMISS, and R4CNTMISS. We were being displayed in the ArcGIS Table subform by applying ControlSource statements to each zone button in addition to the SQL BETWEEN qbutton filter the assay records by a particular zone, it would also change the fields to be attributed in the OilShale_Holes_pts subform. This wthe following code:Private Sub Command260_Click()

Me.CO_Assays_subform.Form.RecordSource = “QuerR4toL3”

OilShale_Holes_pts_subform.Form.[MXMISS].ControlSource = “R4MXMISS”End Sub
Linking Spatial and Tabular Data Upon completing the calculations in Access and populating all of the fields in the ArcGIS table for each oil shale zone, the data were linked to ArcGIS. Although several different interpretive maps could thereby be generated, this case study focuses on the barrels per acre oil-yield mapmary task. Our borehole locations file was stored in a point feature class (OilShale_Holes_pts) contained in an ESRI ArcGIS personal geodatabase (COPcalculation values were stored in a table (OilShale_Holes_pts) in a separate Access database (COPLATOS.mdb). In ArcMap ver.9.2, the attribthe point feature class and, through several definition queries, 18 separate point layers were created corresponding to each oil shale zone. It is boreholes having only rotary cuttings analyzed were included in the assessments—that is, those boreholes containing an “R” in their USGSID 
Creating GeoStatistical Analyst Models and GRIDsOnce layers were defined for each oil shale zone in ArcMap, we generated models using ESRI ArcGIS’s GeoStatistical Analyst (GA) extensioBasis Function(RBF)-Multiquadric method was used to model BPA values. The searching neighborhood parameters used were the standard eeight neighbors in each sector. Although not as robust as kriging or other geostatistical methods in assessing error, the RBF-Multiquadric metand Root Mean Squared (RMS) error for each model generated. We assessed these errors and judged them to be acceptable using the chosen pexported the cross validation tables containing predictions and errors at each data point for each BPA model. To obtain the difference between the measured value, the RBF method predicts a value at a given control point from the nearest control points without knowing the actual valupoint. That predicted value is then compared with the measured value, and the difference between the two is calculated.A GA model will only interpolate and extrapolate values within the rectangular extent of the input point layer. We extrapolated values to outcextent of the model to the rectangular extent of a separate polygon file. For example, for the R-4 zone BPA model we changed the extent of th

gular extent of an Uinta Basin township file that contained the R-4 resource area within its boundaries. We used the extent of this file for all mcoverage for extrapolation purposes. After all revisions were made, in some cases after there were numerous iterations between the Access form and GeoStatistical Analyst, the fin

exported to an ESRI GRID format at a one-acre cell size (63.615 m (208.7 ft) per side) using one point for each block (acre) interpolation. Oning beyond our dataset boundary was that a model may have contained negative values. To sum all of the values in one-acre cells, all negative from the GRIDs by using a CON statement. In ESRI ArcGIS’s Spatial Analyst extension the Raster Calculator was used to remove the negatistatement:CON(([GRIDNAME] < 0), 0, [GRIDNAME]) where GRIDNAME is the name of the GRID, such as r4_bFor all cells in the final BPA GRID, the CON statement set the negative values to 0. If the values were greater than or equal to 0, the values re
A B

C

Figure 24. Image clips of the dialog windows used for creating the barrels per acre (BPA) GeoStatistical Analyst model in ArcGIS.
Running Barrels Per Acre Zonal Statistics to Calculate Total Barrels of Oil Yield Per TownshipThe final BPA GRID model was used as the value raster for counting cell values using ESRIs Spatial Analyst extension. The Zonal Statistics to count all of the estimated values contained within each one-acre cell of our model, as long as their centerpoints fell within a specified zone the R-4 zone, the zone dataset (UTr6_to_r4_res) was a polygon feature class stored in a geodatabase created by intersecting the resource polyR-4 with the township polygons. The Zonal Statistics function used the “label” attribute as the zone field from the polygon feature class to covalues (figs. 11, 25). That is, for all polygons in the zone dataset file with the same value in the “label” field, the Zonal Statistics function coucells in the BPA GRID and provided the sum total for each “label,” or township. It is important to note that our Spatial Analyst analysis-cell sBPA GRID cell size: 63.615 m (208.7 ft) per side.The resultant statistics were then exported to a .dbf table named by zone, such as r4_sum_twsp.dbf for the R-4 zone. We linked this table to thfeature class by the “label” field, thus we were able to provide an interpretive map that quantified the total barrels of oil yield from oil shale in each township in the Uinta Basin. 

Figure 25. Image clip of the dialog window used to generate zonal statistics using ESRIs Spatial Analyst.
Appendix Digital Files, Entity Relationship Diagram, and Data Dictionaries Digital file—COPLATOS.mdb (Microsoft Access database) Entity relationship diagram (fig. A1) Data dictionaries (tables A1–A4)
Table: CO_Tops_080115

USGSID Unique ID assigned by staff geologistNAMEJ Name of the boreholeSECJ SectionTWPJ TownshipRANGEJ Range

Table: CO_Assays_INTV

USGSID Unique ID assigned by staff geologistLABNO Six-digit USBM Laramie laboratory numberSHLOILPCT Amount of shale oil, in weight percentOILGPT Shale oil, in U.S. gallons per short ton of rockINTVL Thickness of interval, in ft

Table: OilShale_Holes_pts

USGSID Unique ID assigned by staff BED44AVGPT Bed 44 average gallons per BED44BPA Bed 44 barrels per acre oil AGVAVGPT A-groove average gallons pAGVBPA A-groove barrels per acre o

Table: BPA Lookup Table

ID Software-calculated identifGALPERTON Shale oil, in U.S. gallons peGALFT3NEW Gallons per cubic foot

(1:M) One-to-many relationship(1:1) One-to-one relationship

Relationship established to database f

1 11 M 1 1

Figure A1. Entity relationship diagram (ERD) of the tables in the Microsoft Access 2007 database (COPLATOS.mdb) showing how the tables are lcalculations form (CO Form). The figure contains a partial listing of column names for illustrative purposes only.
42 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Table A1. Column names and definitions of the Microsoft Access table CO_Tops_080115. 

Column name Column definition

(Note: all column names ending in "J" originated from J. Dyni's original tops file)

ID Software-calculated identifier

USGSIDJ Unique ID assigned by staff geologist

CMPNYPROJJ Name of the company or agency that drilled the borehole

NAMEJ Name of the borehole assigned by the company or agency that drilled it

EASTWESTJ Distance, in ft, measured east or west from section line

NORSOUJ Distance, in ft, measured north or south from section line

QQJ Quarter-quarter section

RANGEJ Range

SECJ Section

TWPJ Township

LATDDJ Latitude, in decimal degrees, North American Datum 1927, original record

LONGDDJ Longitude, in decimal degrees, North American Datum 1927, original record

LOCSRCJ Source of the borehole location, usually from the Fischer assay file, geophysical log, lithologic log, or 

survey

COUNTYJ Name of county in Utah or Colorado 

ELEVFTJ A borehole-reference elevation, such as ground surface, rotary bushing, or rotary table, from which 

down hole depths were measured

COREDINTVJ Depths, in ft, of the sequence that was cored in the borehole

ELEVSRCJ Source of elevation, usually from the Fischer assay file, geophysical log, lithologic log, topo or survey

TOTDEPTFTJ Total depth of the borehole, in ft

QUADJ Name of 7.5-minute USGS topographic map, borehole may or may not be shown on map

ARCHIVEDJ Core archived, yes or no

YRDRILLEDJ Year that the borehole was drilled

LOCCOREJ Physical location of the core from core hole, for example, USGS Core Research Center

SHOWNMAPJ Indicates whether the actual borehole location is shown on the topographic map

LITHFTJ Top and bottom borehole depths, in ft, of sequence of core or rotary cuttings for which a lithologic log 

was prepared

PHOTOFTJ Top and bottom borehole depths, in ft, of photographic record of drill core

ELECFTJ Top and bottom depths, in ft, of electric log of borehole

GAMMAFTJ Top and bottom depths, in ft, of gamma ray log of borehole

DENSFTJ Top and bottom depths, in ft, of density log of borehole

SONICFTJ Top and bottom depths, in ft, of sonic log of borehole

NEUTRONFTJ Top and bottom depths, in ft, of neutron log of borehole

CALIPERFTJ Top and bottom depths, in ft, of caliper log of borehole

RQDFTJ Top and bottom borehole depths, in ft, of rock quality data log

TEMPFTJ Top and bottom depths, in ft, of temperature log of borehole

OTHERLOGSJ Top and bottom depths, in ft, of other geophysical logs

FISCHASSYJ Top and bottom depths, in ft, of sequence analyzed by Fischer assays

LABJ Name of laboratory where Fischer assays were performed

ALUMINAFTJ Top and bottom depths, in ft, of sequence analyzed for alumina

NUMBASSYSJ Number of Fischer assays that were made
Appendix 43

Table A1. Column names and definitions of the Microsoft Access table CO_Tops_080115.—Continued

Column name Column definition

USBMNUMBRJ A number assigned to the report of Fischer assays made by the former U.S. Bureau of Mines

LOCATNOTEJ Additional information, commonly used where there is a problem with the location

NAHCOLTFTJ Top and bottom depths, in ft, of sequence analyzed for nahcolite

XRDFTJ Top and bottom depths, in ft, of X-ray diffraction analyses made on samples from the borehole

ELEVNOTEJ Additional information, commonly used where there is a problem with the elevation

MISCNOTEJ Miscellaneous information, such as publications related to the borehole, and other data

REVISDATEJ Date of last revision for original borehole data

NAME Name of the borehole assigned by the company or agency that drilled it

TRSEC Township, range, and section

EASTWEST Distance, in ft, measured east or west from section line

NORSOU Distance, in ft, measured north or south from section line

LATDD Latitude, in decimal degrees, North American Datum 1927, software-calculated, this report

LONGDD Longitude, in decimal degrees, North American Datum 1927, software-calculated, this report

ELEVFT A borehole reference elevation, such as ground surface, rotary bushing, or rotary table, from which 

down hole depths were measured

CHDH Core hole or drill hole

ELEVDATUM Elevation, in ft, for various reference surfaces including Kelly bushing, ground level, topographic map, 

and rotary table

USGSID Unique ID assigned by staff geologist

JD76 Depth to top of Bed 76, in feet

JD74 Depth to top of Bed 74, in ft

JD72 Depth to top of Bed 72, in ft

JDTPPORCTF Depth to top of Porcupine Creek tuff, in ft

JDBSPORCTF Depth to base of Porcupine Creek tuff, in ft

JD70 Depth to top of Bed 70, in ft

JD68 Depth to top of Bed 68, in ft

JD67 Depth to top of Bed 67, in ft

JD66 Depth to top of Bed 66, in ft

JD64 Depth to top of Bed 64, in ft

JD62 Depth to top of Bed 62, in ft

JD60 Depth to top of Bed 60, in ft

JD58 Depth to top of Bed 58, in ft

JD56 Depth to top of Bed 56, in ft

JD54 Depth to top of Bed 54, in ft

JD52 Depth to top of Bed 52, in ft

JD50 Depth to top of Bed 50, in ft

JD48 Depth to top of Bed 48, in ft

JD46 Depth to top of Bed 46, in ft

JD44BIG3 Depth to top of Bed 44 of Big 3 oil shale beds, in ft

JD42BIG3 Depth to top of Bed 42 of Big 3 oil shale beds, in ft
44 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Table A1. Column names and definitions of the Microsoft Access table CO_Tops_080115.—Continued

Column name Column definition

JD40BIG3 Depth to top of Bed 40 of Big 3 oil shale beds, in ft

JD39 Depth to top of Bed 39, in ft

JD38STLWTR Depth to top of Stillwater zone, in ft

JD37 Depth to top of Bed 37, in ft

JD36A Depth to top of Bed 36A, in ft

JD36 Depth to top of Bed 36, in ft

JD34 Depth to top of Bed 34, in ft

JD32FRSENR Depth to top of Bed 32 of the Four Senators, in ft

JD30 Depth to top of Bed 30 of the Four Senators, in ft

JD28FRSENR Depth to top of Bed 28 of the Four Senators, in ft

JD26 Depth to top of Bed 26 of the Four Senators, in ft

JD25 Depth to top of Bed 25, in ft

JD24 Depth to top of Bed 24, in ft

JD22 Depth to top of Bed 22, in ft

JD21 Depth to top of Bed 21, in ft

JD20 Depth to top of Bed 20, in ft

JD18 Depth to top of Bed 18, in ft

JD16 Depth to top of Bed 16, in ft

JD14 Depth to top of Bed 14, in ft

OTPUPWAVY Depth to top of Upper Wavy, in ft

OBSUPWAVY Depth to base of Upper Wavy, in ft

JD12 Depth to top of Bed 12, in ft

JD10 Depth to top of Bed 10, in ft

JD08 Depth to top of Bed 8, in ft

JD06 Depth to top of Bed 6, in ft

JD04 Depth to top of Bed 4, in ft

JD02 Depth to top of Bed 2, in ft

JDAGROOV Depth to top of A-groove, in ft

JDMAHOGZN Depth to top of Mahogany zone, in ft

OMAHOGZN Depth to top of Mahogany zone, in ft (space holder, not used in this assessment)

JDTPMAHOG Depth to top of Mahogany bed (Donnell, USGS), in ft

OMAHOG Depth to top of Mahogany bed (Johnson, USGS), in ft

JDBGROOV Depth to top of B-groove, in ft

JDR6 Depth to top of R-6 zone, in ft

JDL5 Depth to top of L-5 zone, in ft

JDR5 Depth to top of R-5 zone, in ft

JDL4 Depth to top of L-4 zone, in ft

JDR4 Depth to top of R-4 zone, in ft

JDL3 Depth to top of L-3 zone, in ft

JDR3 Depth to top of R-3 zone, in ft

JDL2 Depth to top of L-2 zone, in ft

R2 Depth to top of R-2 zone, in ft

L1 Depth to top of L-1 zone, in ft
Appendix 45

Table A1. Column names and definitions of the Microsoft Access table CO_Tops_080115.—Continued

Column name Column definition

R1 Depth to top of R-1 zone, in ft

CLASTCR1TP Depth to top of clastic wedge in R-1 zone, in ft

CLASTCR1BS Depth to base of clastic wedge in R-1 zone, in ft

L0 Depth to top of L-0 zone, in ft

R0 Depth to top of R-0 zone, in ft

LPBS Depth to base of Long Point, in ft

BMARKRTP Depth to top of B Marker, in ft

BMARKRBS Depth to base of B Marker, in ft (space holder, not used in this assessment)

CMARKRTP Depth to top of C Marker, in ft

CMARKRBS Depth to base of C Marker, in ft (space holder, not used in this assessment)

DMARKRTP Depth to top of D Marker, in ft

DMARKRBS Depth to base of D Marker, in ft (space holder, not used in this assessment)

FMARKR Depth to top of F Marker, in ft

IMARKR Depth to top of I Marker, in ft

TOPILES Depth to top of Iles, in ft

TOPKMV Depth to top of Kmv, in ft

TOPRLNS Depth to top of Rollins, in ft

TOPCSGT Depth to top of Castlegate, in ft

MHG2BGRV Thickness of interval from the Mahogany bed to B-groove, in ft

MHGZN2BGRV Thickness of interval from the Mahogany zone to B-groove, in ft

BGRV2R6 Thickness of interval from B-groove to the R-6 zone, in ft

R62L5 Thickness of interval from R-6 to the L-5 zone, in ft

L52R5 Thickness of interval from L-5 to the R-5 zone, in ft

R52L4 Thickness of interval from R-5 to the L-4 zone, in ft

L42R4 Thickness of interval from L-4 to the R-4 zone, in ft

R42L3 Thickness of interval from R-4 to the L-3 zone, in ft

L32R3 Thickness of interval from L-3 to the R-3 zone, in ft

R32L2 Thickness of interval from R-3 to the L-2 zone, in ft

L22R2 Thickness of interval from L-2 to the R-2 zone, in ft

R22L1 Thickness of interval from R-2 to the L-1 zone, in ft

L12R1 Thickness of interval from L-1 to the R-1 zone, in ft

R12L0 Thickness of interval from R-1 to the L-0 zone, in ft

L02R0 Thickness of interval from L-0 to the R-0 zone, in ft

R02LPBS Thickness of interval from R-0 to the Long Point base, in ft

AGRV2MHGZN Thickness of interval from A-groove to the Mahogany zone, in ft

B442AGRV Thickness of interval from Bed 44 to A-groove, in ft

MGBS2R5 Thickness of interval from the Mahogany zone base to the R-5 zone, in ft

R52LPBS Thickness of interval from R-5 to the Long Point base, in ft

JD762JD44 Thickness of interval from bed 76 to bed 44, in ft
46 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Table A2. Column names and definitions of the Microsoft Access table CO_Assays_INTV.

Column name Column definition

OBJECTID Software-calculated identifier

LABNO Six-digit U.S. Bureau of Mines (USBM) Laramie laboratory number

TOPFT Depth, in ft, measured from the surface datum to the top of the sampled interval

BOTFT Depth, in ft, measured from the surface datum to the base of the sampled interval

SHLOILPCT Amount of shale oil, in weight percent

WATERPCT Amount of water, in weight percent

SHLRSDPCT Amount of shale residue, in weight percent

GASPLSPCT Amount of "gas plus loss," in weight percent

OILGPT Shale oil, in U.S. gallons per short ton of rock

WATERGPT Water, in U.S. gallons per short ton of rock

SPCFGRAV Specific gravity of the shale oil

COKETEND Tendency for spent shale to coke

USGSID Unique ID assigned by staff geologist

INTVL Thickness of interval, in ft (BOTFT-TOPFT)

INTXOIL Column used for weighted-average gallons per ton calculation (INTVL * OILGPT)

ROCKTYPE Column added to denote intervals that were edited to distinguish between missing records and records 

found in core descriptions to be sandstone ("NO").

Table A3. Column names and definitions of the Microsoft Access table BPA Lookup Table.

Column name Column definition

ID Software-calculated identifier

GALPERTON Shale oil, in U.S. gallons per short ton of rock

GALFT3NEW Oil yield, per unit volume, gallons per cubic foot
Appendix 47

Table A4. Column names and definitions of the Microsoft Access table OilShale_Holes_pts. 

Column name Column definition

OBJECTID Software-calculated identifier

USGSID Unique ID assigned by staff geologist

LATDD Latitude, in decimal degrees, North American Datum 1927, software-calculated, this report

LONGDD Longitude, in decimal degrees, North American Datum 1927, software-calculated, this report

BED76AVGPT Bed 76 average gallons per ton oil yield

BED76AVMSIN Bed 76 percent missing intervals from core, represented as a floating point value

BED76BPA Bed 76 barrels per acre oil yield

BED44AVGPT Bed 44 average gallons per ton oil yield

BED44AVMSIN Bed 44 percent missing intervals from core, represented as a floating point value

BED44BPA Bed 44 barrels per acre oil yield

AGVAVGPT A-groove average gallons per ton oil yield

AGVAVMSIN A-groove percent missing intervals from core, represented as a floating point value

AGVBPA A-groove barrels per acre oil yield

MGZNAVGPT Mahogany zone average gallons per ton oil yield

MGZNAVMSIN Mahogany zone percent missing intervals from core, represented as a floating point value

MGZNBPA Mahogany zone barrels per acre oil yield

BGVAVGPT B-groove average gallons per ton oil yield

BGVAVMSIN B-groove percent missing intervals from core, represented as a floating point value

BGVBPA B-groove barrels per acre oil yield

R6AVGPT R-6 average gallons per ton oil yield

R6AVMSIN R-6 percent missing intervals from core, represented as a floating point value

R6BPA R-6 barrels per acre oil yield

L5AVGPT L-5 average gallons per ton oil yield

L5AVMSIN L-5 percent missing intervals from core, represented as a floating point value

L5BPA L-5 barrels per acre oil yield

R5AVGPT R-5 average gallons per ton oil yield

R5AVMSIN R-5 percent missing intervals from core, represented as a floating point value

R5BPA R-5 barrels per acre oil yield

L4AVGPT L-4 average gallons per ton oil yield

L4AVMSIN L-4 percent missing intervals from core, represented as a floating point value

L4BPA L-4 barrels per acre oil yield

R4AVGPT R-4 average gallons per ton oil yield

R4AVMSIN R-4 percent missing intervals from core, represented as a floating point value

R4BPA R-4 barrels per acre oil yield

L3AVGPT L-3 average gallons per ton oil yield

L3AVMSIN L-3 percent missing intervals from core, represented as a floating point value

L3BPA L-3 barrels per acre oil yield

R3AVGPT R-3 average gallons per ton oil yield

R3AVMSIN R-3 percent missing intervals from core, represented as a floating point value

R3BPA R-3 barrels per acre oil yield

L2AVGPT L-2 average gallons per ton oil yield

L2AVMSIN L-2 percent missing intervals from core, represented as a floating point value

L2BPA L-2 barrels per acre oil yield

R2AVGPT R-2 average gallons per ton oil yield

R2AVMSIN R-2 percent missing intervals from core, represented as a floating point value
48 Methodology for calculating oil shale resources for the Uinta Basin, Utah and Colorado

Table A4. Column names and definitions of the Microsoft Access table OilShale_Holes_pts.—Continued

Column name Column definition

R2BPA R-2 barrels per acre oil yield

L1AVGPT L-1 average gallons per ton oil yield

L1AVMSIN L-1 percent missing intervals from core, represented as a floating point value

L1BPA L-1 barrels per acre oil yield

R1AVGPT R-1 average gallons per ton oil yield

R1AVMSIN R-1 percent missing intervals from core, represented as a floating point value

R1BPA R-1 barrels per acre oil yield

L0AVGPT L-0 average gallons per ton oil yield

L0AVMSIN L-0 percent missing intervals from core, represented as a floating point value

L0BPA L-0 barrels per acre oil yield

R0AVGPT R-0 average gallons per ton oil yield

R0AVMSIN R-0 percent missing intervals from core, represented as a floating point value

R0BPA R-0 barrels per acre oil yield

BD76MXMISS Bed 76 maximum thickness of records missing from core

BD76CNTMISS Bed 76 number of records missing from core

BD44MXMISS Bed 44 maximum thickness of records missing from core

BD44CNTMISS Bed 44 number of records missing from core

AGVMXMISS A-groove maximum thickness of records missing from core

AGVCNTMISS A-groove number of records missing from core

MGZNMXMISS Mahogany zone maximum thickness of records missing from core

MGZNCNTMISS Mahogany zone number of records missing from core

BGVMXMISS B-groove maximum thickness of records missing from core

BGVCNTMISS B-groove number of records missing from core

R6MXMISS R-6 maximum thickness of records missing from core

R6CNTMISS R-6 number of records missing from core

L5MXMISS L-5 maximum thickness of records missing from core

L5CNTMISS L-5 number of records missing from core

R5MXMISS R-5 maximum thickness of records missing from core

R5CNTMISS R-5 number of records missing from core

L4MXMISS L-4 maximum thickness of records missing from core

L4CNTMISS L-4 number of records missing from core

R4MXMISS R-4 maximum thickness of records missing from core

R4CNTMISS R-4 number of records missing from core

L3MXMISS L-3 maximum thickness of records missing from core

L3CNTMISS L-3 number of records missing from core

R3MXMISS R-3 maximum thickness of records missing from core

R3CNTMISS R-3 number of records missing from core

L2MXMISS L-2 maximum thickness of records missing from core

L2CNTMISS L-2 number of records missing from core

R2MXMISS R-2 maximum thickness of records missing from core

R2CNTMISS R-2 number of records missing from core

L1MXMISS L-1 maximum thickness of records missing from core

L1CNTMISS L-1 number of records missing from core
Appendix 49

Table A4. Column names and definitions of the Microsoft Access table OilShale_Holes_pts.—Continued

Column name Column definition

R1MXMISS R-1 maximum thickness of records missing from core

R1CNTMISS R-1 number of records missing from core

L0MXMISS L-0 maximum thickness of records missing from core

L0CNTMISS L-0 number of records missing from core

R0MXMISS R-0 maximum thickness of records missing from core

R0CNTMISS R-0 number of records missing from core

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