Chapter 3

Methodology for Calculating Oil Shale 

and Nahcolite Resources for the 

Piceance Basin

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

Chapter 3 of 7

Oil Shale and Nahcolite Resources of the Piceance 

Basin, Colorado

By U.S. Geological Survey Oil Shale Assessment Team

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

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 and nahcolite 

resources for the Piceance Basin: U.S. Geological Survey Digital Data Series DDS–69–Y, chp. 3, 61 p.

ISBN: 1–4113–2668–2
iii

Contents

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

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

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

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

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

Tabular Fischer Assay Data .......................................................................................................4

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

In-Place Resource Calculations.........................................................................................................6

Gallons Per Ton ............................................................................................................................6

Barrels Per Acre ..........................................................................................................................7

Missing Intervals..........................................................................................................................8

Geospatial Modeling, Analysis, and Presentation ..........................................................................8

Oil Shale Zone Thickness Isopachs..........................................................................................8

Generating Oil-Yield Models ....................................................................................................12

Summarizing Resource Models ..............................................................................................14

Interpretive Maps ......................................................................................................................14

Nahcolite Assessment................................................................................................................................14

Data Preparation, Capture, and Conversion ..................................................................................14

Spatial Data.................................................................................................................................15

Tabular Nahcolite Data.............................................................................................................15

In-Place Tonnage Resource Calculations ......................................................................................15

Geospatial Modeling, Analysis, and Presentation ........................................................................18

Generating Tonnage Models....................................................................................................18

Summarizing Resource Models ..............................................................................................18

Interpretive Maps ......................................................................................................................21

Conclusions...................................................................................................................................................21

Acknowledgments.......................................................................................................................................21

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

Case Study 1—Oil Shale Assessment .....................................................................................................23

Converting and Loading Legacy Fischer Assay Data ...................................................................24

Converting and Loading Stratigraphic “Tops” Data......................................................................26

Converting and Loading the Barrels Per Acre (BPA) Lookup Table...........................................27

Digitizing Borehole Locations...........................................................................................................28

Building the Access Form..................................................................................................................29

Intersecting Polygon Files to Create Reporting Polygons ...........................................................30

SQL Query to Filter Assays By Zone ................................................................................................31

Calculating Gallons Per Ton Weighted Average............................................................................32

Calculating Barrels Per Acre Oil Yield.............................................................................................34

Calculating the Percentage of Missing Intervals From Each Core Sample..............................35

Storing Calculated Values in a Separate Table .............................................................................36

Filtering Subforms From the Main Form..........................................................................................37

Linking Spatial and Tabular Data......................................................................................................38
iv

Creating GeoStatistical Analyst Models and GRIDs.....................................................................39

Running Zonal Statistics to Calculate Total Barrels of Oil Yield Per Township ........................41

Case Study 2—Nahcolite Assessment ....................................................................................................42

Converting and Loading Nahcolite-Content Data .........................................................................43

Creating Nahcolite-Resource Polygons .........................................................................................44

Calculating Average Weight-Percent Nahcolite...........................................................................45

Reproducing the Nahcolite Formula in Access.............................................................................46

Linking Spatial and Tabular Data......................................................................................................47

Creating GeoStatistical Analyst Models and GRIDs.....................................................................48

Running Zonal Statistics to Calculate Total Tons of Nahcolite Per Township ..........................49

Appendix........................................................................................................................................................50

Digital Files, Entity-Relationship Diagrams, and Data Dictionaries............................................50

Figures

1. Map showing areas within the Piceance Basin underlain by oil shale 

rocks and boundary of nahcolite-bearing zone.......................................................................2

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

3. Map showing three bounding resource polygons depicting Mahogany 

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

4. Chart showing an example of the original Fischer assay records in 

ASCII-text format..........................................................................................................................5

5. Chart showing processes performed on oil shale and nahcolite-resource 

data for the assessments ............................................................................................................7

6. The 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. Chart showing third-order trendline comparing oil yield versus specific gravity ...........12

9. Example of how the Access form is capable of identifying and reporting 

missing intervals from the core sample..................................................................................13

10. Example of how data is migrated from the Access form and then 

modeled in ArcGIS......................................................................................................................14

11. Example of how ESRI ArcGIS Spatial Analyst summarized total barrels 

of oil yield by township ..............................................................................................................15

12. Examples of interpretive maps generated from resource models.....................................16

13. The main Access form with the nahcolite spreadsheet linked and the 

addition of the step-by-step formula used to calculate nahcolite tonnages ............................19

14. Detailed description of the Microsoft Access form used to perform 

resource calculations ................................................................................................................20

15. Import Wizard dialog window to import legacy data as viewed in 

Microsoft Access .......................................................................................................................24

16. Import Wizard model file (.iwm) setup dialog window.........................................................25

17. Structure of the stratigraphic tops table as viewed in Microsoft Access........................26

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

calculations as viewed in Microsoft Access.........................................................................27

19. Borehole locations after digitizing in ArcMap.......................................................................28
v

20. Access form showing how subforms were linked by the unique identifier, 

USGSID, and performed all resource calculations...............................................................29

21. Diagram showing how areas were delineated to report assessment results .................30

22. The main Access form viewed in Design mode ............................................................................33

23. Example showing how calculation results performed interactively were stored 

in a table permanently. .......................................................................................................................36

24. Dialog windows for creating a GeoStatistical Analyst model in ArcGIS ..................................40

25. Dialog window used to generate zonal statistics using ESRIs Spatial Analyst...............41

26. Screenshot of the original nahcolite-content data residing in separate 

worksheets in Microsoft Excel 2007........................................................................................43

27. The difference in areal extent between the nahcolite-bearing zone and the oil shale 

resource polygon ................................................................................................................................44

28. The reproduction of the nahcolite formula modified from Beard and others 

(1974) as viewed in the Access form .......................................................................................46

29. Example of how borehole locations are linked to the calculation results 

stored in the attribute table ...............................................................................................................47

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

2007 database (COPLATOS.mdb) showing how the tables are linked in the 

calculations form (CO Form) ......................................................................................................51

 A2. Entity relationship diagram (ERD) of the tables in the Microsoft Access 

Tables

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

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

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

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

oil shale from U.S. Bureau of Mines oil shale mine, Rifle, Colo ..........................................11

4. Column names and definitions found in the nahcolite-data spreadsheet 

(nahdata.xls) ................................................................................................................................17

 A1. The column names and definitions of the Microsoft Access table 

A2. The column names and definitions of the Microsoft Access table 

CO_Assays_INTV. .......................................................................................................................57

 A3. The column names and definitions of the Microsoft Access table 

A4. The column names and definitions of the Microsoft Access table 

OilShale_Holes_pts ....................................................................................................................58

A5. The column names and definitions of the Microsoft Access table 

C

A6. T

O_Nahc_INTV ...........................................................................................................................60

he column names and definitions of the Microsoft Access table 

Nahcolite_Holes_pts. .................................................................................................................61
Abstract

A detailed description of the methodology employed 

to perform a geology-based assessment of in-place oil shale 

and nahcolite resources in the Piceance Basin of northwestern Colorado is presented here. Considerable advancements 

in computer and database technology since the previous oil 

shale assessment in 1989 provided the U.S. Geological Survey 

(USGS) assessment team with new tools to convert legacy 

data, store and manipulate new data, perform calculations, and 

quantify, report, and display the assessment results. Relational 

database and geographic information systems (GIS) software 

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

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

and isoresource models in the GIS software, which provided a 

spatial statistics function to summarize the prediction models 

and determine the in-place oil shale and nahcolite resource 

totals. 

Introduction

This report presents the results of a comprehensive, 

geology-based assessment of the in-place oil and nahcolite 

resources in 17 oil shale zones of the Eocene Green River Formation in the Piceance Basin of northwestern Colorado (fig. 1; 

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

2008). The focus is on the methodology used to: (1) convert 

legacy and new data; (2) analyze the data through application 

of updated 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 relational database software (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 core hole that was converted to spatial data and then 

linked to its associated Fischer assay data.

After performing calculations in the database, the data 

were migrated to a GIS and a cell-based modeling technique 

was employed to calculate total barrels of oil yield per oil 

shale zone. Through this process original and new data were 

updated to a contemporary 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 the two case studies 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 17 oil shale zones within the Piceance Basin.

Spatial Data

The legacy Fischer assay files contained header information detailing the locations of core holes, but not all of the files 

contained latitude and longitude coordinates. To maintain consistency, the majority of the core hole locations were digitized 

in GIS software based on footage measurements north, south, 

east, and west of Public Land Survey System (PLSS) section 

corners or by using the best available location information 

present in the header, such as the section centerpoint. 

In addition to placing the core holes in real-world coordinates and plotting their locations on maps, 17 oil shale zones 

(Donnell and Blair, 1970; Cashion and Donnell, 1972; Donnell, 2008) were correlated between holes in the subsurface 

using oil-yield histograms generated from the Fischer assay 

data files (fig. 2). The previous USGS assessment (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 could be traced across most of the basin 

Methodology for Calculating Oil Shale and Nahcolite 

Resources for the Piceance Basin

By Tracey J. Mercier, Michael E. Brownfield, and Ronald C. Johnson
2 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

108° 30' 108°00'

40°00'

39° 30'

T. 2 N.

T. 1 N.

T. 1 S.

T. 2 S.

T. 3 S.

T. 4 S.

T. 5 S.

T. 8 S.

T. 7 S.

T. 6 S.

R. 101 W. R. 100 W. R. 99 W.

R 99 W

R. 98 W.

R 98 W

R. 97 W.

R 97 W

R. 96 W.

R 96 W R 95 W

R. 102 W. R. 95 W. R. 94 W.

Colorado

Index map

0 12 MILES

70

Piceance

White River

White River

64

64

13

13

139

139

Meeker

Rangely

Rifle

Rio Blanco

DeBeque

Parachute

City or town

Area underlain by

 oil shale rocks

EXPLANATION

County line

Colorado River

Creek

Creek

Par achute Creek

MESA

GARFIELD

RIO BLANCO

Roan

Boundary of nahcolite

 zone 

Figure 1. Map of Piceance Basin, northwestern Colorado, showing inferred boundaries of the 

nahcolite-bearing zone (Brownfield and others, chapter 2 this CD-ROM), and generalized outcrop of oil 

shale-bearing rocks in Parachute Creek Member of the Green River Formation shown in yellow.
Oil Shale Assessment 3

Donnell and Blair

(1970), Cashion and

Donnell (1972),

Donnell (2008)

A-groove

Bed 44

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

Figure 2. Stratigraphic column and correlation chart of stratigraphic nomenclature for oil shale zones in 

the Eocene Green River Formation, Piceance Basin, Colorado. The assessments of in-place oil and nahcolite 

resources use the nomenclature of Donnell and Blair (1970), Cashion and Donnell (1972), and Donnel (2008) 

shown in yellow.

(Cashion and Donnell, 1972), and each zone was assessed 

separately. For the present study, the same set of rich and lean 

zones was used as much as possible in order to make the two 

assessments comparable. Only a computer printout of part 

of the files listing the tops for these zones in the drill holes 

that were used in the previous assessment was recovered, and 

this information was incorporated into the new digital file. In 

addition, a considerable amount of core hole information was 

collected subsequent to the previous assessment, as tops had to 

be picked for these additional holes. A series of stratigraphic 

cross sections was constructed to aid in this effort, and these 

are published separately (Self and others, chapter 5, this CDROM). Although different workers in the past used slightly 

different picks for the tops of some of the rich and lean zones, 

an attempt was made to use the same zonal contacts that were 

used in the previous USGS assessment to also make the two 

assessments as comparable as possible. In some isolated cases, 

it was necessary to correct minor errors in the tops file used in 

the previous assessment. The tops and bases of the 17 identified zones were entered in a spreadsheet and then 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 Piceance Basin by digitizing the top 

of the Mahogany zone and the base of the Parachute Creek 

Member of the Green River Formation from two published 

1:100,000-scale geologic maps (Hail and Smith, 1994, 1997). 

These lines served as bounding polygons for in-place resource 

calculations. Due to the steepness of the vertical cliffs formed 
4 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

by these strata throughout the Piceance Basin, three resource 

outcrops. The Mahogany zone outcrop was used to delineate 

upper three zones were eroded. The Mahogany zone outcrop 

the B-groove and R-6 zones, but these zones are below the 

Mahogany zone and were not eroded. A third resource polygon 

 3) by expanding the Mahogany zone polygon with the 

areal extent of the Parachute Creek Member in the northern 

half of the Piceance Basin.

-

tially with a Public Land Survey System (PLSS) land grid 

for the Piceance Basin to allow resource calculation on a 

per township basis. Resource calculations were then directly 

comparable to legacy reports that estimated in-place oil shale 

resources by township. 

Tabular Fischer Assay Data

Source ASCII text data were obtained from published 

Fischer assays (Dyni, 1998) and from previously unpublished 

borehole that contained the header-location information and 

the column-delimited Fischer assay records. Each borehole 

was assigned a unique four-digit number preceded by “C” for 

Colorado by USGS. In order to expedite the querying of the 

used to convert and combine all of the Fischer assay records 

into one relational database table allowing detailed queries on 

one large table (some 300,000 records) instead of hundreds of 

Some of the original Fischer assay records were 

incomplete, especially those associated with rotary drill holes, 

and those records were not used in this assessment—that is, 

(USGSID). Almost all drill holes contain missing intervals 

that represent samples that were not recovered during the 

labeled as “0.0B” or “0.00B” in all columns except for the top 

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

to remove the “B” in order for those values to be imported 

interval in the assays table. This step allowed us to perform 

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

in the Import Wizard conversion process by using the original 

thickness of the sampled interval, was calculated in Access 

Mahogany zone outcrop

Parachute Creek Member outcrop

Areas excluded from the three

upper zones due to erosion within the basin

Three resource polygons were necessary to limit resource

calculations within the Piceance Basin:

1) Areas within the Mahogany zone outcrop, minus eroded areas

 (3 upper zones),

2) Areas within the Mahogany zone outcrop, including areas

 below the eroded areas (2 middle zones), and

3) Areas within the Parachute Creek Member and the 

 Mahogany zone outcrop combined (12 lower zones).

Figure 3. Map of northeastern part of the Piceance Basin, Colorado, showing the three resource polygons used to limit 

in-place oil shale resource calculations: base of the Parachute Creek Member of the Green River Formation, top of the 

Mahogany zone, and small outcrop areas excluded from resource calculations due to erosion.
Oil Shale Assessment 5

Column 1 2 3 4 5 6 7 8 9 10 11

Figure 4. Image clip of a portion of the original Fischer assay data (Dyni, 1998) for the Wolf Ridge Minerals Corporation, 

Dunn 20–1 borehole showing header data (first two rows). Header data includes operator name, borehole name, location, and 

elevation. 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, SPCFCAV; 11, COKETEND. [FEL, feet east of section line; FNL, feet 

north of section line; GL, ground level in feet; 0.0B or 0.00B, not analyzed].

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 weighted-average 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 needed to be distinguished from the missing intervals 

that are also listed as zero oil yield in the assay tables in order 

to correctly calculate an average gallon per ton for the zone 

in which they were contained. In order to distinguish the two, 

a minimum oil-yield value of 0.1 GPT was assigned to these 

nahcolite and 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 with correlation data was converted from 

a Microsoft Excel 2007 spreadsheet and then 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 core hole. By establishing this link, 

Structured Query Language (SQL) queries and Visual Basic 

formulas were developed to calculate resource estimates for 

each core hole by oil shale zone. After calculating resource 

estimates for each core hole, derivative maps were then 
6 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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

additional columns needed for 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 filter out halite intervals ("NH") and to denote intervals that were edited to distinguish between 

missing records and records found in core descriptions to be either nahcolite or sandstone ("NO").

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.

The header information from the legacy borehole files 

was used to digitize the spatial location for each hole using 

GIS software. The point location as well as its unique identifier (USGSID) were stored in a point feature class in a 

personal geodatabase. Each borehole was assigned a unique 

USGSID that was used in correlation-data and resourceestimate 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 

established 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 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.

In-Place Resource Calculations

Gallons Per Ton

Resource calculations were performed for each core hole 

in each zone 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 assay table. Although the 

results are calculated within the form interactively, we stored 

the results permanently in a separate database table that we 

were then able to directly link 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. Diagrams describing the table relationships in the database are in the Appendix following this 

report.

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

core hole, 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, 
Oil Shale Assessment 7

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

Basin oil shale and nahcolite assessments.

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 entire 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 * gallongs per ton)/thickness of interval in feet.

Barrels Per Acre

The determination of oil-in-place resource numbers in 

BPA were generated from the derived GPT weighted-average 

values calculated in the database form. Stanfield and others 

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

from nearly 20,500 U.S. Bureau of Mines (USBM) oilyield 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, as well 

as specific gravity, and were only 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 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 value for the GPT column. 

A third-order trendline was then 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 then calculated using the 

new values for oil yield and specific gravity. A final lookup 

table was then created containing records for oil yield (GPT), 

specific gravity, and oil yield per unit volume from 1.0 to 

Tabular data

Oil Shale and Nahcolite 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 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 and 

nahcolite tons per acre) 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. 

Convert Nahcolite content table 

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).
8 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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.

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 barrels per acre (BPA) for each core hole, 

the previously calculated weighted-average value for GPT 

was related to the lookup table (table 3) in order to retrieve 

the associated value for oil yield per unit volume (gallons per 

cubic foot); 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 to be input into the BPA formula. For example, 

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

10.0 for a specific core hole 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 

then subtracting any beds denoted as halite in the ROCKTYPE 

column–“NH.”

Missing Intervals

In addition to resource calculations, statistics describing 

missing intervals in percent, maximum thickness of missing 

intervals, and the number of missing intervals for each core 

sample were generated for each borehole and zone (fig. 9). In 

general, the larger the proportion of a given core that constitutes missing interval, the greater the imprecision of the 

resource calculation. 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, a series of custom-scripting functions would 

count and perform calculations on the missing records in the 

Fischer assay table. These values are then permanently stored 

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

maps can be produced by linking the missing-interval statistics 

to the borehole locations in the GIS. The missing-intervals 

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. This was accomplished by subtracting a pick for any given zone if there was 

also a pick in the spreadsheet for the immediately underlying 

zone. Formulas were created to automate this function, but 
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 core hole C0034 (see fig. 13 for location data).
10 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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

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

mine, Rifle, Colo. 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
12 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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

gravity. 

actual values were used in converting the spreadsheet to a 

database table. When a zone top pick 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 on-the-fly (fig. 7).

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

thickness values for each zone were used to generate spatialdata models using a RBF-Multiquadric modeling method. By 

generating and analyzing a spatial model for each zone, errors 

were located in the tops file and changes were made to the correlations as needed. Upon completion of the database, a final 

model 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 fewer 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 

Specific gravity versus oil yield, Green River oil shale

0.000

0.500

1.000

1.500

2.000

2.500

3.000

SPECIFIC GRAVITY 

OIL YIELD IN GALLON PER TON (GPT)

0 2010 30 40 50 60 70 80 90 100

EXPLANATION

Specific gravity

Trendline

y = -1E-06x

3

 + 0.0003x

2

 - 0.0265x + 2.7656

R

2

 = 0.9998
Oil Shale Assessment 13

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 coarse-cell spacing, so we 

were 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 gallons per ton (GPT) and barrels per 

acre (BPA) were migrated to a separate database table and 

linked to the core hole locations file. RBF models were then 

generated using the resultant core hole 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 

due to the number of core holes 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 (208.7 ft (63.615 

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

were reported for each core sample. Example shown is for the A-groove oil shale zone 

in core hole C0034 (see fig. 13 for location data).
14 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

m) 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 

Case Study 1 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 that the software required to summarize the 

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

zones) were townships or portions thereof that were cropped 

by the outcrop lines. As the analysis cell size was one acre 

and 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, or 

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 sections, in order 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 17 oil shale zones, including isopachs of 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 (fig. 12). 

Nahcolite Assessment

Data Preparation, Capture, and Conversion

Calculation of in-place nahcolite resources in the 

north-central part of the Piceance Basin applied the same 

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 core hole C0034 (see fig. 13 for location data).
Nahcolite Assessment 15

Figure 11. Map of northeastern part of the Piceance Basin, Colorado, showing how the GIS software summarized 

total barrels of oil yield by township.

ESRI ArcGIS Spatial Analyst’s Zonal Statistics Function

Each one of these one-acre cells in the data model

contains an estimated value for barrels of oil yield.

The software considers this polygon

as one “zone” (a surficial areal extent

not to be confused with the subsurface

oil shale zone) and counts all of the

underlying data model’s cell values

if their centerpoints fall within its boundary. 

The result for this polygon “zone” is

321,637,000 total barrels in T. 2 N. R. 97 W.

of the R-5 oil shale zone.

One township

(6 miles)

Outcrop

This polygon was created by intersecting the outcrop

file with the Public Land Survey System township file. 

methodology as was used in the assessment of in-place oil in 

oil shale zones with a few variations: (1) an additional table 

containing nahcolite-content data was linked to the main 

database form, (2) spatial data delineating the areal extent of 

the nahcolite-bearing zone in the Parachute Creek Member of 

the Green River Formation was created and combined with 

the outcrop data, and (3) a formula in the database form was 

regenerated to calculate in-place nahcolite resources (Beard 

and others, 1974). 

Spatial Data

The same core hole point-locations file as the oil shale 

assessment was used for all nahcolite mapping and spatial 

modeling tasks. In order to constrain resource calculations, a 

new nahcolite-bearing polygon file was digitized using 

nahcolite-bearing and non-nahcolite-bearing (zero points) core 

holes and was intersected with the Mahogany zone outcrop 

file. The resultant file was then intersected with the PLSS grid 

to create the surface zones required for zonal-statistics functions used to calculate tonnage per township. 

Tabular Nahcolite Data

Multiple tables containing nahcolite weight-percent data 

derived from the original USBM analyses performed at the 

Laramie, Wyo. Oil Shale Laboratory (since shutdown) and 

USGS nahcolite analyses were combined into one spreadsheet 

using a public domain macro (VBAX, 2008). The resultant 

table was then linked to the database form by the unique 

core hole identifier (USGSID). 

Estimates of nahcolite content in oil shale samples from 

the lower part of the Parachute Creek Member of the Green 

River Formation were compiled in a database containing 58 

core holes by oil shale zone (fig. 2; table 4). This database 

was compiled from USBM and USGS sample data that were 

analyzed at the Laramie, Wyo. facility and contains data on 

sample interval, Al2

O3, Na, and nahcolite values in weight 

percent, specific gravity of the sample, and oil yields. Table 4 

defines the column names in the nahcolite-data spreadsheet.

In-Place Tonnage Resource Calculations

The following data are required from the nahcolite table 

and the Fischer assay table to estimate nahcolite resources: 

(1) weight-percent nahcolite for each sample or zone, (2) oil 

yield in GPT determined by Fischer assay, (3) sample or zone 

depth and thickness, and (4) depth to the top and bottom of the 

nahcolite intervals. This procedure compensates for the wide 

variation in oil shale specific gravity with changes in oil yield 
EXPLANATION Top of Mahogany Bed outcrop Base of Parachute Creek Member Core hole—average oil yield in gallons per ton 10 Mahogany zone interval—average oil yield in gallons per ton < 7.5 7.6–15 15.1–22.5 22.6–30 30.1–37.5 R 96 W R 95 W R 101 W R 100 W R 99 W R 98 W R 97 W R 94 W T8ST7S R 93 W T6S T5ST4ST3S T9S R 94 W R 101 W R 100 W R 99 W R 98 W R 97 W T2S T1ST1N R 96 W R 95 W T2N 108°30' 108°00' 40°00' 39°30' 0 3 6 12 MILES COLORADO 0 5 10 2.5 KILOMETERS R 96 W 

R 101 W R 100 W R 99 W R 98 W R 97 W 

R 101 W R 100 W R 99 W R 98 W R 97 W 

R 96 W R 95 W T2N 108°30' 

40°00' 39°30' 0 3 6 12 MILES EXPLANATION Top of Mahogany Bed outcrop Base of Parachute Creek Member Core hole—in thousands of barrels per acre 10 Mahogany zone—in barrels p

< 100,000 100,001– 200,000 200,001– 300,000 

0 5 10 2.5 KILOMETERS R 96 W R 95 W R 101 W R 100 W R 99 W R 98 W R 97 W R 94 W T8ST7S R 93 W T6S T5ST4ST3S T9S R 94 W R 101 W R 100 W R 99 W R 98 W R 97 W T2S T1ST1N R 96 W R 95 W T2N 108°30' 108°00' 40°00' 39°30' 0 3 6 12 MILES COLORADO 0 5 10 2.5 KILOMETERS EXPLANATION Top of Mahogany Bed outcrop Base of Parachute Creek Member Core hole—thicknes of oil shale interval in feet 10 Mahogany zone interval—thickness in feet < 100 101–150 151–200 201–250

R 96 W 

R 101 W R 100 W R 99 W R 98 W R 97 W 

R 101 W R 100 W R 99 W R 98 W R 97 W

R 96 W R 95 W

T2

N

108°30' 10

40°0'

39°30'

EXPLANATION

Mahogany Ledge outcropBase of Parachute Creek Member

Mahogany Zone m

< 2

2.1–55.1–1515.1–20> 20

2

6.5 Thickest missing interval—shown in blue

Number of missing records—shown in red

0 3 6 12 MILES0 5 10 2.5 KILOMETERS

R 96 W R 95 W

R 101 W R 100 W R 99 W R 98 W R 97 W R 94 W

T8

S

T7

S

R 93 W

T6

S

T5

S

T4

S

T3

S

T9

S

R 94 W

R 101 W R 100 W R 99 W R 98 W R 97 W

T2

S

T1

S

T1

N

R 96 W R 95 W

T2

N

108°30' 108°00'

40°00'39°30'

0 3 6 12 MILES

EXPLANATION

Mahogany zone shale oil, total in-place resource—in millions of barrels

< 330

330–1,000

1,000–2,600

2,600–5,3005,300–8,800

Top of Mahogany zoneBase of Parachute Creek MemberMahogany zone shale oil in-place resource—by township, in barrels 228,800,000

COLORADO

0 5 10 2.5 KILOMETERS

D

A B C

E

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

oil yield in barrels per acre; D, oil yield in barrels per township; E, percentage of missing intervals from each core sample. See Johnson and ochapter 1, this CD-ROM, for original figures.
Nahcolite Assessment 17

Table 4. Column names and definitions found in the nahcolite-data spreadsheet 

(nahdata.xls).

Column name Column definition

OBSNO Sample number

SAMPLEID U.S. Bureau of Mines, Laramie, Wyo. laboratory number 

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

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

SAMPDEN Density of oil shale core sample

AL2O3_PCT Al2O3 content, in weight percent

NA_PCT Na content, in weight percent

NAPLOT Na content, numeric data

NAHCO3_PCT Nahcolite content, in weight percent

NAHPLOT Nahcolite content, numeric data

OILWONAH Oil yield without nahcolite, in gallons per ton

OILWOPLOT Oil yield without nahcolite, numeric data in gallons per ton

OILWNAH Oil yield with nahcolite, in gallons per ton

OILWPLOT Oil yield with nahcolite, numeric data in gallons per ton

OILGPTWNAH Oil yield with nahcolite, in gallons per ton, 0.00B = not analyzed 

OSZONE Oil shale zone (Cashion and Donnell, 1972)

LONGITUDE Longitude, in decimal degrees

LATITUDE Latitude, in decimal degrees

USGSID Unique drill-hole number assigned by the USGS

REMARKS Comment field

(Smith, 1956). The procedure, modified from Beard and others (1974), for determining the amount of nahcolite in each well by 

zone is as follows:

1. Calculate the weight fraction of nahcolite and oil shale in a sample or zone:

Weight fraction nahcolite Weight nahcolite = %

100

Weight fraction oil shale Weight fraction nahcolite = −1

2. Calculate oil yield of the oil shale weight fraction using Fischer assay oil yield and weight fraction oil shale (step 1);

Oil yield,weight fraction, GPT Oil yield,Fischer assay,GPT

Weigh = t fraction oil shale

3. Calculate specific gravity of the oil shale weight fraction using quadratic equation that relates oil yield to shale specific 

gravity (Smith, 1956) using the oil yield, weight fraction (step 2):

Specific gravity of oil shale fraction =

205 998 205 998 4 31 563 326 624 2 . ( . ) . ( . − − × × − Oil yield,weight fraction, GPT)

× .2 31 563

4. Calculate volume of 1 gram (g) of sample using nahcolite specific gravity as 2.18 and the specific gravity of the oil shale 

fraction (step 3):

Volume g nahcolite sample,cc Weight fraction nahcolite We , . 1

2 18 = + ight fraction oil shale

Specific gravity oil shale
18 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

5. Calculate specific gravity of whole sample using the volume of 1 g of nahcolite (step 4):

Sample specific gravity = 1

Volume of 1 g nahcolite sample

6. Calculate the weight of 1 ft3 of sample from the sample specific gravity (step 5) using 62.3 lb/ft3 as the weight of water at 

room temperature in air (Stanfield and others, 1960):

Nahcolite sample weight, lb/ft = Samplespecific gravity 62.3 3

7. Calculate the weight in pounds of 1 ft2 of sample as thick as the sample interval or zone thickness in the well using nahcolite 

sample weight (step 6) and interval or zone thickness from the nahcolite database:

Weight, interval or zone, lb/ft = Sample weight, lb/ft S 2 3 ample or zone thickness, ft

8. Calculate the weight of nahcolite in the nahcolite-bearing interval or zone in the well using the weight fraction of nahcolite 

(step 1) and the weight of 1 ft2 of the interval or zone calculated in step 7:

Weight, nahcolite in well or zone, lb/ft =Nahcolite weight fractio 2 n Weight, interval or zone

9. Calculate tons of nahcolite per mile by multiply the weight of nahcolite in a well or zone (step 8) by a given number of ft2

(1 mi = 27,878,400 ft2) and divide by 2,000 lbs/ton:

Nahcolite, short tons/mi = 2 Weight of nahcolite, lb/ft 27,878,4 2 00 ft /mi

2,000 lbs/ton

2

The methodology for calculating in-place nahcolite 

resources (Beard and others, 1974), shown above, was regenerated in the Microsoft Access form using several controls and 

Visual Basic scripting (figs. 13, 14). The formulas and their 

associated control boxes in the database form were able to filter data from the nahcolite and Fischer assay tables in order to 

perform calculations. An average nahcolite weight percent was 

first calculated for each oil shale zone (Cashion and Donnell, 

1972). Another critical value needed for the resource methodology was an average per zone for GPT oil yield extracted 

from the Fischer assay table. It is important to note that the 

form controls performed calculations using only those records 

that were common to both the nahcolite table and Fischer 

assay table. That is, the average GPT value was calculated 

from the Fischer assay table using only that part of a particular 

zone containing nahcolite. For this reason, a separate database 

was created that contained only those Fischer assay records 

that correlated with the nahcolite intervals. This allowed 

calculations of the average GPT to be dependent on the assays 

common to the nahcolite intervals, yielding values for each 

zone in each hole in terms of tons of nahcolite per square mile. 

This process was repeated for the average nahcolite value over 

the total thickness of the nahcolite-bearing interval, resulting 

in values in tons of nahcolite per square mile for the entire 

nahcolite-bearing interval within each borehole. These values 

were migrated from the Access form to a separate ESRI shapefile (Nahc_Master_pts.shp) to keep the oil shale and nahcolite 

final values in separate tables.

Geospatial Modeling, Analysis, and 

Presentation

Generating Tonnage Models

The resultant nahcolite shapefile contains the point locations of nahcolite core holes as well as their final calculatedresource values. As the assessment-analysis cell size was 

one acre, all values for tons per square mile were converted 

to tons per acre. Nahcolite-resource models were then created using ArcGIS’s GeoStatistical Analyst extension. The 

RBF in GeoStatistical Analyst was used to interpolate (and 

extrapolate) surfaces using the same Multiquadric method as 

the oil shale assessment. The final resource models were created using a sampling method containing 10 moving window 

sectors with 15 neighbors in each sector. After numerous tests, 

these parameters were observed to yield the most geologically 

reasonable nahcolite-resource models due to the number of 

core holes and the extent of the dataset. 

Summarizing Resource Models

Once the models were created and converted to ArcGIS’s 

GRID format, the Spatial Analyst extension’s Zonal Statistics 

function was applied to determine total tonnages of nahcolite as well as tonnages per township. We also summarized 
Figure 13. Image clip of Microsoft Access 2007 form used to perform nahcolite-resource calculations containing: (1) tops table under the headinand Intervals, (2) Fischer assays table, (3) Calculations section containing controls that performed the assessment calculations, (4) nahcolite resostep formula and calculations reproduced under the Nahcolite heading, and (5) ArcGIS table used to store the calculation values permanently. 
All of this data is stored in the “tops” table (CO_Tops_080115) All of this da

assays table 

Calculations done interactively as the user scrolls through borehole records

Calculatitable to l

These buttons use Visual Basic scripting to populate the fields in the ArcGIS table

These buttons filter the assays table by the desired oil-shale zone

All of this data is stored in the nahcolite table (CO_Nahc_INTV)

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 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

Beside Software, 2006, Import Wizard ver. 9.

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 65.

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.

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 

Investigations Report SIR 2008-5237, 25 p.

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.

Hail, W.J., Jr., and Smith, M.C., 1994, Geologic map of the 

northern part of the Piceance Creek basin, northwestern 

Colorado: U.S. Geological Survey Miscellaneous 

Investigations Series Map I-2400, scale 1:100,000. 

Hail, W.J., Jr., and Smith, M.C., 1997, Geologic map of the 

southern part of the Piceance Creek basin, northwestern 

Colorado: U.S. Geological Survey Miscellaneous 

Investigations Series Map I-2529, scale 1:100,000. 

Microsoft Corporation, 2006, Microsoft Access and Excel 

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. 

nahcolite resources by oil shale zone and total in-place nahcolite within the inferred extent of the nahcolite-bearing interval. 

In addition to modeling resources, we generated a 

nahcolite total-thickness interval model that contained thicknesses for each nahcolite-bearing interval for each core hole 

being assessed. For this, we also used ArcGIS’s GeoStatistical 

Analyst (GA) extension to model the interval thickness values 

using a RBF-Multiquadric modeling method. 

Interpretive Maps

-

tion of nahcolite resources, a series of interpretive maps were 

generated for eight of the 17 oil shale zones for publication, 

including total nahcolite-bearing interval isopachs, average weight-percent nahcolite for the entire nahcolite-bearing 

interval, total in-place nahcolite resource in tons per acre, total 

in-place nahcolite resource by township, tons per acre by zone, 

and tons per township by zone.

Conclusions

For the 17 oil shale zones in the Piceance Basin, an 

updated and reproducible method was created to calculate 

in-place oil and nahcolite resources using modern relational 

database 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 thank Gregory Gunther, Christopher Skinner, and Willia m Keefer of the U.S. Geological Survey Central 

Energy Resources Science Center for performing thorough 

technical reviews of this report. Their comments and suggestions led to improvements and clarity to many of the discussed 

topics. 

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. 

Beard, T.N., Tait, D.B., and Smith, J.H., 1974, Nahcolite 

and dawsonite resources in the Green River Formation, 

Piceance Basin, Colorado: Rocky Mountain Association of 

Geologists 1974 Guidebook, p. 101–109. 
22 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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.

Rusu, C., and Rusu, V., 2006, Artificial Intelligence in Theory 

and Practice, in Bramer, M., ed., IFIP International Federation for Information Processing, v. 217, 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.

Stanfield, K.E., Smith, I.W., and Trudell, L.G., 1960, Oil 

yields of sections of Green River oil shale in Colorado, 

1957–1963: U.S. Bureau of Mines Report of Investigation 

RI–5614, 186 p.

VBAX (VBAExpress.com), 2008, A macro containing Visual 

Basic code to combine multiple spreadsheets into one worksheet. Available at URL: http://www.vbaexpress.com/kb/

getarticle.php?kb_id=151 (last accessed 14 January 2009).
Case Study 1—Oil Shale Assessment 
Converting and Loading Legacy Fischer Assay Data We loaded legacy Fischer assay data files (Dyni, 1998) into a Microsoft Access (Access) database table (fig. 15) using Import Wizard ve2006). It was necessary, initially, to create an Import Wizard model file (A_FISCHER_DEPTHS.iwm; fig. 16) that defined the column names ers in the original ASCII text files (Dyni, 1998). After the model was created, we imported more than 700 Fischer assay files into one Access fields according to the character spacing in the original ASCII files. Once the table was created and populated in Access, columns were added sampled 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 to import legacy Fischer assay data (Dyni, 1998). A portion of the original for the Wolf Ridge Minerals Corp. Dunn 20-1 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 blue area denotes the header informatioduring the import process. The red area denotes the selected field (LABNO, laboratory number) and the width of the column. The yellow area denofields to be imported.
Figure 17. Structure of the stratigraphic tops table (CO_Tops_080115) as viewed in Microsoft Access 2007 showing the depth, in ft, to the top of tthrough L-5 oil shale zones for core holes C0001 through C0012. Tops of oil shale zones are listed stratigraphically from left to right: AGROOV, A- GrMAHOGZN, Mahogany Zone; TPMAHOG, Mahogany Bed; MAHOG, Mahogany Bed; BGROOV, B- Groove; R6 (rich-zone 6); L5 (lean-zone 5). Tops pJohn Donnell (USGS) or Janet Pitman (USGS); O, authors of this report.

Converting and Loading Stratigraphic “Tops” Data

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

were created in Microsoft Excel to calculate the interval thicknesses and the results were converted to cell values in order to transfer all the nThe Excel spreadsheet was then imported to an Access table (CO Tops_080115). 
Figure 18. Structure of the barrels per acre lookup table (BPA Lookup Table) used for the form calculations as viewed in Microsoft Access 2007, showing how values for gallons per cubic foot oil yield were filled in for every 0.1 gallon using a linear trend fill series function.

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 BPA calculations was converted from a Mic

spreadsheet to an Access table (BPA Lookup Table). After the gallons per ton weighted average is calculated for a particular zone interactivel“GALFT3NEW” value associated with that gallons per ton average is required as a multiplier in the formula. For example, if a zone’s weightton 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 fill in values ton in Excel before importing the spreadsheet to Access (fig. 18). 
Digitizing Borehole Locations Locations of boreholes (fig. 19) were digitized in ESRIs ArcMap based on footage measurements from section corners recorded in the oASCII text file’s header information. A custom tool was developed that combined the distance and sketch tools in ArcMap to digitize points. Ewith its unique borehole identifier, USGSID, which allowed the linking of spatial and tabular data. C2018X C0719R C

C0610RB

C0381

C0529

C0498

C0382

C0380

C0379 C0378 C0377

C0376

C0375

C0374

C0373

C0372

C0370

C0275

C0274

C0247

C0246

C0245

C0244

C0212

C0211

C0210

C0209

C0198

C0196

C0192

C0191

C0190

C0189

C0188

C0187

C0186

C0185

C0171

C0157

C0129

C0128

C0127

C0121

C0034

C0004

C0762

C0761

C0718R

C0502

C0501

C0325

Figure 19. Map of the northeastern part of the Piceance Basin showing borehole locations after digitizing in ArcMap.
Figure 20. Image clip of the main Access form showing how subforms were linked by the unique identifier, USGSID, and performed all resource c

Building the Access Form

We built a custom Access Form (CO Form) (fig. 20) that allowed the linking of tables by a unique identifier (USGSID) used in all tables 

Lookup Table. The form was based on the stratigraphic tops table (CO_Tops_080115) and the linked tables are considered subforms. 
Intersecting Polygon Files to Create Reporting Polygons Initially, outcrop lines were digitized in ArcMap for the top of the Mahogany ledge and the base of the Parachute Creek Member of the Gbased on 1:100,000-scale geologic maps. These polygons served as bounding resource polygons for resource assessments by oil shale zone. Wship lines in ESRI shapefile format from the Bureau of Land Management’s (BLM) geocommunicator website (http://www.geocommunicator

township polygons with the outcrop boundary resource polygons in ArcGIS’s ArcToolbox to create “reporting” polygons (fig. 21). The report

areal extent used to quantify barrels per acre oil yield for only that part of each township that is underlain by a particular oil shale zone. 

Township polygons Outcrop polygons (resource polygons) Report

Figure 21. Diagram showing how ESRI ArcToolbox’s Intersect command was used to create the assessment’s reporting polygons for the Piceance northwestern Colorado.
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, we w

Language (SQL) queries to filter out subsets of assay records for each oil shale zone. For example, the query (QuerR4toL3) to select only thobetween 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 BETWEE

SELECT CO_Assays_INTV.TOPFT, CO_Assays_INTV.BOTFT, CO_Assays_INTV.OILGPT, CO_Assays_INTV.USGSID, COCO_Assays_INTV.INTXOIL, CO_Assays_INTV.ROCKTYPEFROM 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 would return assay records from the top of the R-4 zone to the top of the L-3 zone, but only if: (1) the value 

record (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 (JDL3), and (3) all assay records in between as long as those records contained the same USGSID as the currently selected core hole in the fo
Calculating Gallons Per Ton Weighted Average Formula: Sum of (thickness of interval * gallons per ton)/thickness of interval Within the assessed zones, any OILGPT value equal to 0 or thus, any INTXOIL (thickness of interval times OILGPT) value equal to 0 wweighted-average calculations; those records were considered missing or erroneous. In the Fischer assays subform (fig. 22), we built a text bothe 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, Access would: (1) sum all of the INTXOIL values and the INTVL values, (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 missing or 
Figure 22. The oil shale assessment’s Microsoft Access 2007 form as viewed in Design mode. Some text box control calculations are “hidden” in the Fischer assays subform footer.
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 halite beds. 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 w

ing a text box control containing the following statement: =DLookUp(“[GALFT3NEW]”,”[BPA Lookup Table]”,”[GALPERTON]=” & Forms![CO Form]!Text151) where Text151 is the text-box control that calculated the weighted-average value for gallons per ton oil yield. In effect, this statement “looks 

value in the BPA Lookup Table that is associated with the calculated GPT average. For example, for the R-4 zone in core hole C0034, a GPT returned. The associated value in the BPA Lookup table for 33.2 is 2.195. Using the statements above, the form also returned a value of 143.7 the R-4 zone. Using the formula to calculate barrels per acre oil yield, the following statement was entered into another text box control in the 

=Round([Text335]*43560*[Text239]/42,0) where Text335 is the sum of all intervals minus halite and Text239 is the va

Lookup Table, with the result rounded to 0 decimal places. In this case, for the R-4 zone in borehole C0034, the form barrels per acre oil yield (143.7 * 43560 * 2.195 / 42).
Calculating the Percentage of Missing Intervals From Each Core Sample

Text-box controls were added to the form footer of the assays subform to report the percentage of missing intervals from each core samp

ness of missing intervals, and the number of intervals missing from each core sample. The following statements were added to three separate To 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 transfelated values from the text-box controls to a separate table that could then be linked to in ArcGIS. We stored the results permanently in anothe(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 mthe main form triggered a macro to run using a SetValue action. The SetValue action would populate the appropriate field in the ArcGIS table calculated 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 tastatements: Item: [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 Esimply refers to the value calculated in the main form—in this case, the Text341 text-box control that contained the statement to calculate the missing intervals. 

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 for the R-4 oil shale zone in borehole C0034.
Filtering Subforms From the Main Form

A method to filter the assay records by a particular oil shale zone was established by using SQL BETWEEN statements. Eighteen button

form to 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, unmethod was also needed to restrict the display of fields in the ArcGIS Table subform by the zone being filtered. Each of the 17 oil shale zones for gallons per ton (AVGPT), barrels per acre (BPA), percentage of missing intervals (AVMSIN), maximum interval missing (MXMISS), and missing (CNTMISS). For example, the R-4 zone contained the fields R4AVGPT, R4BPA, R4AVMSIN, R4MXMISS, and R4CNTMISS. We fields being displayed in the ArcGIS Table subform by applying ControlSource statements to each zone button in addition to the SQL BETWwould the button filter the assay records by a particular zone, it would also change the fields to be attributed in the OilShale_Holes_pts subforplished by using the 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 linkin ArcGIS. Although several different interpretive maps could thereby be generated, this case study focuses on the barrels per acre oil-yield msummary task. Our borehole locations file was stored in a point feature class (OilShale_Holes_pts) contained in an ESRI ArcGIS personal geodatabase calculation values were stored in a table (OilShale_Holes_pts) in a separate Access database (COPLATOS.mdb). In ArcMap ver. 9.2, the attribute table was joined to the point feature class and, through several definition queries, 17 separate point layers were created c

shale zone. It is important to note that no rotary holes were included in any of the assessments—that is, those boreholes containing an “R” in 
Creating GeoStatistical Analyst Models and GRIDs

Once layers were defined for each oil shale zone in ArcMap, we generated models using ESRI ArcGIS’s GeoStatistical Analyst (GA) ex

Radial Basis Function-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 

the extent of the model to the rectangular extent of a separate polygon file. For example, for the R-4 BPA model we changed the extent of the 

gular extent of a Mahogany zone outcrop file we had buffered at a distance of two kilometers. We used the extent of this file for all models to for extrapolation purposes. 

After all revisions were made, sometimes after numerous iterations between the Access form and GeoStatistical Analyst, the final GA m

ESRI GRID format at a one-acre cell size (208.7 ft (63.615 m) per side) using one point for each block (acre) interpolation. One drawback to our dataset boundary was that a model sometimes contained negative values. To sum all of the values in our one-acre cells, all negative value

GRIDs by using a CON statement. In ESRI ArcGIS’s Spatial Analyst extension the Raster Calculator was used to remove the negative values 

CON(([GRIDNAME] < 0), 0, [GRIDNAME]) where GRIDNAME is the name of the GRID, such as R4BPA_g

For 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 valu
Figure 24. Image clips of the dialog windows used for creating the barrels per acre (BPA) GeoStatistical Analyst model in ArcGIS. 
Running Zonal Statistics to Calculate Total Barrels of Oil Yield Per Township

The final BPA GRID model was used as the value raster for counting cell values using ESRIs Spatial Analyst extension. The Zonal Stati

was used to count all of the estimated values contained within each one-acre cell of our model, as long as their centerpoints fell within a speccase, for the R-4 zone, the zone dataset (low_12_zones_rept_pol) was a polygon feature class stored in a geodatabase created by intersecting all zones below the R-6 zone with the township polygons. The Zonal Statistics function used the TWNRNG attribute as the zone field from thto count the GRID’s BPA values (figs. 11, 25). That is, for all polygons in the zone dataset file with the same value in the TWNRNG field, the 

counted all of the underlying cells in the BPA GRID and provided the sum total for each TWNRNG, or township. It is important to note that osis cell size was the same as our BPA GRID cell size: 208.7 ft (63.615 m) 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 

feature class by the TWNRNG field, thus were able to provide an interpretive map that quantified the total barrels of oil yield from oil shale ieach township in the Piceance Basin. 

Figure 25. Image clip of the dialog window used to generate zonal statistics using ESRIs Spatial Analyst.
Case Study 2—Nahcolite Assessment
Converting and Loading Nahcolite-Content Data

We converted the nahcolite content spreadsheet from Excel 2007 to an Access table (CO_Nahc_INTV). The original spreadsheet had a s

26) for each borehole, so we combined them using a macro (VBAX, 2008) that combined all of the separate worksheets into one before impospreadsheet to Access. The macro can be found here: http://www.vbaexpress.com/kb/getarticle.php?kb_id=151.Figure 26. Image clip showing the original nahcolite-content data residing in separate worksheets in Microsoft Excel 2007.
Figure 27. Map showing the difference in areal extent between the nahcolite-bearing zone (shaded) and the oil shale resource polygon outline in the Piceance Basin, northwestern Colorado.Creating Nahcolite-Resource Polygons

A new polygon (fig. 27) was digitized in ArcGIS to delineate nahcolite-bearing areas using the areal extent of nahcolite-bearing and non

(zero points) boreholes and then intersected with the Mahogany ledge outcrop in the northeastern part of the basin. The same procedure was fshale assessment—that is, intersecting the resource polygon with townships to create nahcolite-reporting polygons in order to determine total township. 
Calculating Average Weight-Percent Nahcolite

Upon linking the nahcolite content table (CO_Nahc_INTV) to our Access form by the unique ID (USGSID) and creating the nahcolite ta

average for weight-percent nahcolite was calculated for each zone containing nahcolite beds. Two new columns were added to the CO_Nahc_and INTXNAHC, and each column populated via update queries. The INVTL column was updated by subtracting the TOPFT column from ththe INTXNAHC column was updated by multiplying the NAHPLOT column by the INTVL column. Because queries to filter the assays table already established, the same queries were applied to the nahcolite table to filter the nahcolite records by each zone. Text-box controls were thtable subform in Access to calculate, by zone, the sum of nahcolite intervals and the weighted average for weight-percent nahcolite. To calculate the sum of nahcolite intervals:

=Sum(IIf([INTXNAHC]>0,[INTVL],0))

To calculate the weighted average for weight percent nahcolite:

=Sum(IIf([INTXNAHC]>0,[INTXNAHC],0))/Sum(IIf([INTXNAHC]>0,[INTVL],0))
Reproducing the Nahcolite Formula in Access After linking all the necessary tables and creating the subforms in the main Access form, we reproduced the formula modified from Beardescribed in detail in the chapter text) as a series of text-box controls in the form (fig. 28). One important multiplier in the formula was oil yie(GPT) determined by the Fischer assays subform. As it was necessary to calculate only the weighted average GPT for the nahcolite-bearing inzone, we deleted all assay records from the assays table (CO_Assays_INTV) above and below the nahcolite-bearing intervals as recorded in tNahc_INTV). In order to maintain the overall functionality of the main Access form, a new Access database (COPLAT_NAHC.mdb) was crebetween the oil shale database (COPLATOS.mdb) and the nahcolite database (COPLAT_NAHC.mdb) is the absence of assay records above ovals in boreholes containing nahcolite beds. 

Figure 28. Image clip of the Microsoft Access 2007 form showing the reproduction of the nahcolite formula modified from Beard and others (1974) as viewed in the Access form for borehole C0002 in the R-4 oil shale zone.
Figure 29. Image clip from ArcMap showing how borehole locations are linked to the calculation results stored in the attribute table. The table lists total nahcolite tonnages for zones R-5 through R-2 for borehole C0171.Linking Spatial and Tabular Data

As the number of boreholes containing nahcolite beds was much smaller (58 total) than our oil shale point feature class, the point feature 

an ESRI shapefile (Piceance_Nahcolite_Holes.shp) and all calculations from the Access form were stored permanently in the shapefile’s attrib9.2, we created definition queries and generated layers based on the nahcolite-point shapefile for eight zones (L-5, R-5, L-4, R-4, L-3, R-3, L-layer to display data for all nahcolite zones combined (fig. 29). The .dbf file stored the values for each zone’s average weight-percent nahcolitby zone, tons per acre by zone, total tons per square mile, total tons per acre, and the total nahcolite-bearing interval thickness for each borehgeologists. 
Creating GeoStatistical Analyst Models and GRIDs After layers for each of the eight zones, as well as a layer which combined all zones in ArcMap to model nahcolite were defined, models ESRI ArcGIS’s GeoStatistical Analyst (GA) extension. The Radial Basis Function-Multiquadric method was used to model tons per acre valuborhood parameters used were a standard 10 sectors containing 15 neighbors in each sector. To maintain consistency with our oil shale methodology, we extrapolated nahcolite values to the nahcolite-bearing area by changing the rectangular extent of a separate polygon file. For example, for the R-4 nahcolite-tonnage model, the extent of the GA model was changed to tthe nahcolite-resource polygon file buffered at a distance of 1 km. We used the extent of this file for all models to ensure adequate coverage f

After all data corrections were made, commonly involving numerous iterations between the Access form and GeoStatistical Analyst, the 

exported to an ESRI GRID format at a one-acre cell size (208.7 ft (63.615 m) per side). As in the oil shale modeling, one drawback to extrapoboundary was that a model, in some cases, contained negative values. As we wanted to sum all of the values in our one-acre cells, all negative from the GRIDs by using a CON statement. In ESRI ArcGIS’s Spatial Analyst extension, we again used the Raster Calculator to remove the nstatement: CON(([GRIDNAME] < 0), 0, [GRIDNAME]) where GRIDNAME is the name of the GRID, such as r4_n For all cells in the final nahcolite-tonnage GRID, the CON statement set the negative values to 0. If the values were greater than or equaunchanged. 
Running Zonal Statistics to Calculate Total Tons of Nahcolite Per Township

The final nahcolite-tonnage GRID model was used as the value raster for counting cell values using ArcGIS’s Spatial Analyst. The Zona

used to count all of the estimated values contained within each one-acre cell of our model, as long as the centerpoints fell within a specified zfor the R-4 zone, the zone dataset was a polygon feature class stored in our geodatabase that was created by intersecting the nahcolite-resourclite zones with the township polygons. The Zonal Statistics function used the TWNRNG attribute as the zone field to count the GRID’s tonnapolygons in the zone dataset file having the same value in the TWNRNG field, the Zonal Statistics function counted all of the underlying cellGRID and provided the sum total for each TWNRNG, or township. It is important to note that our Spatial Analyst analysis cell size was the stonnage GRID cell size: 208.7 ft (63.615 m) per side.

The resultant statistics were then exported to a .dbf table, which was linked to the reporting polygons feature class by the TWNRNG fiel

pretive map that quantified the total tons of nahcolite in each of the eight zones.

The same methodology was employed to create the nahcolite series of interpretive maps, including total nahcolite-bearing interval isopa

percent nahcolite for entire nahcolite-bearing interval, total in-place nahcolite resource in tons per acre, total in-place nahcolite resource by tozone, and tons per township by zone.
50 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

Appendix

Digital Files, Entity-Relationship Diagrams, and 

Data Dictionaries

Digital file – COPLATOS.mdb (Microsoft Access database)

Digital file – COPLAT_NAHC.mdb (Microsoft Access 

database)

Entity relationship diagrams (figs. A1, A2) 

Data dictionaries (tables A1–A6)
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 yAGVAVGPT 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 per GALFT3NEW 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 lin

the calculations form (CO Form). The figure contains a partial listing of column names for illustrative purposes only.
Table: CO_Tops_080115 USGSID Unique ID assigned by staff geologist NAMEJ Name of the borehole SECJ Section TWPJ Township RANGEJ Range Table: CO_Assays_INTV USGSID Unique ID assigned by staff geologist LABNO Six-digit USBM Laramie laboratory number SHLOILPCT Amount of shale oil in weight percent OILGPT Shale oil in U.S. gallons per short ton of rock INTVL Thickness of interval, in ft Table: OilShale_Holes_pts USGSID Unique ID assigned by sta

R5NAHC R-5 zone average weight-pR5NAHCTON R-5 zone nahcolite conten

Table: CO_Nahc_INTV USGSID Unique ID assigned by sta

NAHCO3_PCT Nahcolite content, in weigINTXNAHC Thickness of interval times 

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

Figure A2. Entity relationship diagram (ERD) of the tables in the Microsoft Access 2007 database (COPLAT_NAHC.mdb) showing how the tables acalculations form (CO Form). The figure contains a partial listing of column names for illustrative purposes only.
Appendix 53

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

(Note: all column names ending in “J” originated from J. Dyni’s original tops file)

Column name Column definition

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 Colorado

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

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
54 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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

(Note: all column names ending in “J” originated from J. Dyni’s original tops file)

Column name Column definition

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 (2009)

LONGDD Longitude, in decimal degrees, North American Datum 1927, software-calculated, this report (2009)

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 ft

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 67, 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

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
Appendix 55

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

(Note: all column names ending in “J” originated from J. Dyni’s original tops file)

Column name Column definition

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 (space holder, not used in this assessment)

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

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
56 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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

(Note: all column names ending in “J” originated from J. Dyni’s original tops file)

Column name Column definition

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
Appendix 57

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

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 filter out halite intervals ("NH") and to denote intervals that were edited to distinguish between 

missing records and records found in core descriptions to be either nahcolite or sandstone ("NO").

Table A3. The 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
58 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

Table A4. The 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 (2009)

LONGDD Longitude, in decimal degrees, North American Datum 1927, software-calculated, this report (2009)

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

R2BPA R-2 barrels per acre oil yield

L1AVGPT L-1 average gallons per ton oil yield
Appendix 59

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

Column name Column definition

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

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

R1MXMISS R-1 maximum thickness of records missing from core

R1CNTMISS R-1 number of records missing from core
60 Methodology for Calculating Oil Shale and Nahcolite Resources for the Piceance Basin

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

Column name Column definition

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

Table A5. The column names and definitions of the Microsoft Access table CO_Nahc_INTV.

Column name Column definition

OBJECTID Software-calculated identifier

OBSNO Sample number

SAMPLEID U.S. Bureau of Mines, Laramie, Wyo. laboratory number

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

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

SAMPDEN Density of oil shale core sample

AL2O3_PCT Al2

O3 content, in weight percent

NA_PCT Na content, in weight percent

NAPLOT Na content, numeric data

NAHCO3_PCT Nahcolite content, in weight percent

NAHPLOT Nahcolite content, numeric data

OILWONAH Oil yield without nahcolite, in gallons per ton

OILWOPLOT Oil yield without nahcolite, numeric data in gallons per ton

OILWNAH Oil yield without nahcolite, in gallons per ton

OILWPLOT Oil yield with nahcolite, numeric data in gallons per ton

OILGPTWNAH Oil yield with nahcolite in gallons per ton, 0.00B = not analyzed

OSZONE Oil shale zone (Cashion and Donnell, 1972)

LONGITUDE Longitude, in decimal degrees

LATITUDE Latitude, in decimal degrees

USGSID Unique drill-hole number assigned by the USGS

REMARKS Comment field

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

INTXNAHC Column used for weighted-average nahcolite content within nahcolite-bearing interval only (INTVL * NAHPLOT)
Appendix 61

Table A6. The column names and definitions of the Microsoft Access table Nahcolite_Holes_pts.

Column name Column definition

ID Software-calculated identifier

USGSID Unique ID assigned by staff geologist

NAZNTHK Total thickness, in ft, of nahcolite-bearing interval

ALLNAHC Average weight-percent nahcolite of all zones

ALLNAHCTON Tons per square mile of nahcolite for the entire nahcolite-bearing interval

ALLNAHTNAC Tons per acre of nahcolite for the entire nahcolite-bearing interval

L5NAHC Average weight-percent nahcolite, L-5 oil shale zone

L5NAHCTON Tons per square mile of nahcolite, L-5 oil shale zone

R5NAHC Average weight-percent nahcolite, R-5 oil shale zone

R5NAHCTON Tons per square mile of nahcolite, R-5 oil shale zone

L4NAHC Average weight-percent nahcolite, L-4 oil shale zone

L4NAHCTON Tons per square mile of nahcolite, L-4 oil shale zone

R4NAHC Average weight-percent nahcolite, R-4 oil shale zone

R4NAHCTON Tons per square mile of nahcolite, R-4 oil shale zone

L3NAHC Average weight-percent nahcolite, L-3 oil shale zone

L3NAHCTON Tons per square mile of nahcolite, L-3 oil shale zone

R3NAHC Average weight-percent nahcolite, R-3 oil shale zone

R3NAHCTON Tons per square mile of nahcolite, R-3 oil shale zone

L2NAHC Average weight-percent nahcolite, L-2 oil shale zone

L2NAHCTON Tons per square mile of nahcolite, L-2 oil shale zone

R2NAHC Average weight-percent nahcolite, R-2 oil shale zone

R2NAHCTON Tons per square mile of nahcolite, R-2 oil shale zone

L5NATNAC Tons per acre of nahcolite, L-5 oil shale zone

R5NATNAC Tons per acre of nahcolite, R-5 oil shale zone

L4NATNAC Tons per acre of nahcolite, L-4 oil shale zone

R4NATNAC Tons per acre of nahcolite, R-4 oil shale zone

L3NATNAC Tons per acre of nahcolite, L-3 oil shale zone

R3NATNAC Tons per acre of nahcolite, R-3 oil shale zone

L2NATNAC Tons per acre of nahcolite, L-2 oil shale zone

R2NATNAC Tons per acre of nahcolite, R-2 oil shale zone

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