Database for Assessment Unit-Scale An

(Exclusive of the United States)

Open-File Report 2007-1404

U.S. Department of the Interior

U.S. Geological Survey
Database for Assessment Unit-Scale Analogs 

(Exclusive of the United States) 

By Ronald R. Charpentier, T.R. Klett, and Emil D. Attanasi 

Open-File Report 2007-1404 

U.S. Department of the Interior 

U.S. Geological Survey 

i 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2008

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Charpentier, R.R., Klett, T.R., and Attanasi, E.D., 2008, Database for assessment unit-scale 

analogs (exclusive of the United States): U.S. Geological Survey Open-File Report 2007-1404, 

61 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report. 

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iii 

Table of Contents 

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

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

Sources of Data................................................................................................................................... 2 

 Sizes of Discovered Fields ................................................................................................... 3

 2000 World Petroleum Assessment ................................................................................... 4 

Calculations.......................................................................................................................................... 5 

 Small-Field Extrapolation ..................................................................................................... 5 

 Additional Calculations......................................................................................................... 5 

 Global Distributions ............................................................................................................... 6 

Use of Database .................................................................................................................................. 6 

 Classification Variables........................................................................................................ 7 

 Methods of Searching........................................................................................... 11 

 Utility Variables .................................................................................................................... 14 

 Variables Related to Number of Fields............................................................... 14 

 Variables Related to Field Size Distribution ...................................................... 15 

 Variables Related to Maturity of Exploration.................................................... 15 

 Variables Related to Coproduct Ratios .............................................................. 15 

 Variables Related to Ancillary Data.................................................................... 16 

 Data Analysis Tools............................................................................................................. 16 

 Distributions for Small Analog Sets.................................................................... 16 

 Distributions for Large Analog Sets.................................................................... 18 

 Example of Use with USGS Assessment Methodology................................................ 20 

 Selection of Analogs ............................................................................................. 23 

 Numbers of Fields .................................................................................................. 24 

 Sizes of Fields ......................................................................................................... 26 

 Coproduct Ratios.................................................................................................... 29 

References ......................................................................................................................................... 33

Appendix 1. 1995 Methodology for Small-Field Extrapolation.................................................. 35 

 For the Pareto Distribution................................................................................................. 35

 Relation Between Pareto and Log-Geometric Distributions ....................................... 36 

 Changes to Small-Field Extrapolation Methodology..................................................... 43 

 Sensitivity Tests ................................................................................................................... 48 

 Unresolved Issues ............................................................................................................... 58 

Appendix 2. Database Files ............................................................................................................ 60 
Abstract 

This publication presents a database of geologic analogs useful for the assessment of 

undiscovered oil and gas resources. Particularly in frontier areas, where few oil and gas fields 

have been discovered, assessment methods such as discovery process models may not be usable. 

In such cases, comparison of the assessment area to geologically similar but more maturely 

explored areas may be more appropriate. This analog database consists of 246 assessment units, 

based on the U.S. Geological Survey 2000 World Petroleum Assessment. Besides geologic data 

to facilitate comparisons, the database includes data pertaining to numbers and sizes of oil and 

gas fields and the properties of their produced fluids. 

Introduction 

This publication presents a database of geologic analogs useful for the assessment of 

undiscovered oil and gas resources. Primary use of the database is to compare an immaturely 

explored area to a geologically analogous area that has been more maturely explored. 

Particularly in cases of assessing undiscovered resources in immature areas, sufficient discovery 

history data may not be available for other methodologies, such as discovery process models, to 

be employed. 

Each of the 246 analogs in the database is at the assessment unit (AU) scale and is based 

on the assessments of undiscovered resources in the U.S. Geological Survey (USGS) 2000 

World Petroleum Assessment (U.S. Geological Survey World Energy Assessment Team, 2000, 

referred to as the “WPA 2000”). In that publication, an assessment unit was defined as “A 

mappable volume of rock within the total petroleum system that encompasses fields (discovered 

and undiscovered) which share similar geologic traits and socio-economic factors” (Klett and 

others, 2000). An AU is equivalent to a “play” or a group of related plays. The horizontal and 

vertical sizes of assessment units (their areal extent and the volume of rocks involved) can vary 

from one that covers the entire province to one covering only a small portion (exploration-trend 

scale). Because the WPA 2000 provided estimates only for selected provinces outside the 

United States, the database does not include any analogs from U.S. areas. 

The methodology used in most USGS assessments relies on estimates of numbers and 

sizes of undiscovered accumulations. An accumulation may consist of an entire field or only that 

part of a field that is within a particular AU. (Note: in the WPA 2000, fields were not 

subdivided, except that pools were used in the Western Canada Sedimentary Basin). The term 

“accumulation” is used herein when referring to the USGS methodology and the term “fields” is 

used when referring to the analog database. Thus, the database provides information primarily 

on numbers and sizes of discovered and undiscovered fields and the geologic characteristics by 

which analogs may be chosen. 

The files containing the database are included in two formats. A Microsoft Excel version 

(WorldAnalogs.xls) can be read by users of that software. This version includes several software 

tools, described below, to assist the user in selecting sets of analogs and in creating interpretive 

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charts. An alternate version, for non-users of Microsoft Excel, is presented as eight tabdelimited files (WA_geology.tab, WA_oil.tab, WA_gas.tab, WA_BOE.tab, WA_oilbins.tab, 

WA_gasbins.tab, WA_BOEbins.tab, and WA_ancillary.tab). This version can be used with 

other spreadsheet programs, but does not include any software tools. 

Sources of Data 

The basic data for our analog database are drawn from three sources: (1) sizes and 

numbers of discovered fields are mostly from the IHS Energy, Inc. Exploration and Production 

database (IHS Energy, Inc., 2004); (2) sizes and numbers of Canadian pools are from the NRG 

Associates (1995) Significant Oil and Gas Pools of Canada Data Base; and (3) sizes and numbers 

of undiscovered fields are from the WPA 2000 (U.S. Geological Survey World Energy 

Assessment Team, 2000). 

Only those oil and gas accumulations considered discovered in the WPA 2000 were 

counted as discovered in our analog database. For the most part, those accumulations were 

discovered before January 1, 1996, except for the Central Congo Turbidites AU 72030302, for 

which 24 discoveries from 1996 to 1999 were also included in the WPA 2000. 

In all cases, the basic data are presented as counts or statistical expectations (mean 

estimates) in the standard USGS field-size classes, as listed in table 1. Accumulation sizes are 

based on the reported recoverable quantities (volumes of cumulative production plus reserves) 

according to the primary product (oil in oil accumulations, gas in gas accumulations) rather than 

a total volume of all products. Oil accumulations are differentiated from gas accumulations in 

that they have a gas to oil ratio (based on cumulative production plus reserves) of less than 

twenty thousand cubic feet per barrel. Where the analog database relates sizes in barrels of oil 

equivalent (BOE), an equivalency of one barrel of oil to six thousand cubic feet of natural gas 

was used (1 BOE = 1 barrel of oil = 6,000 cubic feet of gas). 

Table 1. Standard USGS field-size class definitions 

 

 

Size 

Class 

Oil Field Size * 

(Millions of barrels) 

Gas Field Size * 

 (Billions of cubic feet) 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

0.03125 to 0.0625 

0.0625 to 0.125 

0.125 to 0.25 

0.25 to 0.5 

0.5 to 1 

1 to 2 

2 to 4 

4 to 8 

8 to 16 

16 to 32 

0.1875 to 0.375 

0.375 to 0.75 

0.75 to 1.5 

1.5 to 3 

3 to 6 

6 to 12 

12 to 24 

24 to 48 

48 to 96 

96 to 192 

2 
11 32 to 64 192 to 384 

12 64 to 128 384 to 768 

13 128 to 256 768 to 1,536 

14 256 to 512 1,536 to 3,072 

15 512 to 1,024 3,072 to 6,144 

16 1,024 to 2,048 6,144 to 12,288 

17 2,048 to 4,096 12,288 to 24,576 

18 4,096 to 8,192 24,576 to 49,152 

19 8,192 to 16,384 49,152 to 98,304 

20 16, 384 to 32,768 98,304 to 196,608 

21 32, 768 to 65,536 196,608 to 393,216 

22 65,536 to 131,072 393,216 to 786,432 

23 131,072 to 262,144 786,432 to 1,572,864 

* Accumulations of sizes exactly on a class boundary are counted in the higher class. 

Because the original data were from proprietary databases, some appropriate 

modifications were made for the version of the file released to the public. The field sizes were 

binned to obscure their precise sizes, but the sizes of the largest discovered fields were rounded 

to 1.38 times the lower limit of their bin (see Appendix 1). There were nine AUs (10080101, 

10090102, 10160101, 11500301, 38170103, 60220101, 60340102, 60900103, and 80470201) 

that only had one discovered field. For these, ancillary data were not reported, and the totals for 

known oil, gas, natural gas liquids, and total petroleum (Geology worksheet columns W, Y, AA, 

and AC) are marked as “NA” (not available). None of the remaining data can thus be traced 

easily to any specific field. 

Sizes of Discovered Fields 

Sizes of discovered fields (except for Canada) were from the IHS Energy, Inc. 

Exploration and Production database (IHS Energy, Inc., 2004); production and reserve data used 

were as reported at the end of 2003. Sizes of fields were defined as the sum of cumulative 

production plus “2P” reserves (variables OIL_RECOVERABLE_PP_MMBBL, 

GAS_RECOVERABLE_PP_MMSCF, and COND_RECOVERABLE_PP_MMBBL). The 

reported “2P” (proved plus probable) reserves are generally considered estimates with a 50 

percent probability of the actual values being larger. No estimates of reserve-growth potential 

were added, so the “2P” reserve estimates may be conservative. Because of the proprietary 

nature of the database, the raw oil or gas field sizes cannot be released but only summary field or 

pool counts per bin-size class. 

For Canada, the data were from the Significant Oil and Gas Pools of Canada Data Base 

(NRG Associates, 1995). These data are given by oil and gas pool, rather than by field, but were 

considered more complete than the IHS Energy, Inc. data for Canada (IHS Energy, Inc., 2004). 

Otherwise, calculations were performed on the pool-size data for Canadian AUs in the same 

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manner as those done to the field-size data. The Canadian pool-size data are included in our 

database for sake of completeness. It is the option of the user as to whether that data is 

appropriate to use as an analog along with field-size data. 

The discovered oil and gas fields (pools for Canada) were allocated to those 246 AUs of 

the WPA 2000 that were quantitatively assessed. Each oil or gas field was allocated to only one 

AU, thus the counts per size class are all integers. 

2000 World Petroleum Assessment 

The WPA 2000 (U.S. Geological Survey World Energy Assessment Team, 2000) 

assessed oil and gas potential in 128 provinces outside the United States. However, no attempt 

was made in that effort to estimate potential in all areas; it included only those areas that already 

had significant production or that otherwise had some particular interest. Those assessments 

were based on geologic studies of 159 total petroleum systems (TPS), a TPS being defined as “a 

mappable entity encompassing genetically related petroleum that occurs in seeps, shows, and 

accumulations (discovered or undiscovered) that have been generated by a pod or by closely 

related pods of mature source rock, together with the essential mappable geologic elements 

(source, reservoir, seal, and overburden rocks) that controlled fundamental processes of 

generation, migration, entrapment, and preservation of petroleum” (Klett and others, 2000). A 

hierarchical structure was developed consisting of provinces that contained TPSs that, in turn, 

contained AUs. Many of the TPSs were contained within one province, but some crossed 

province boundaries and were assigned to the most appropriate province. Within each TPS, one 

or more AUs were identified, for a total of 270. Of these, 246 AUs (all conventional) in 150 of 

the TPSs were quantitatively assessed, and form the basis of our analog database. 

The WPA 2000 assessment methodology included an estimation of numbers and sizes of 

undiscovered accumulations for each AU (Schmoker, and Klett, 2000). The methodology is 

defined in terms of an accumulation, which can include either an entire field or only those 

reservoirs in a field that lie within a certain stratigraphic interval. For the WPA 2000, entire 

fields were used (pools in western Canada). The uncertainty about the number of undiscovered 

fields was expressed as a triangular distribution; for our database, the mean value of that 

distribution was used as the total number of undiscovered fields. Because that is a statistical 

expectation, it is not necessarily an integer. 

The size distribution of undiscovered fields was approximated by a shifted truncated 

lognormal distribution. By integration of this distribution, the proportions of undiscovered fields 

in each field-size class were determined (Attanasi and Charpentier, 2007). When those 

proportions were multiplied by the mean number of undiscovered fields, the result was the 

expected number of undiscovered fields per class. Again, this is a statistical expectation, and 

thus not necessarily an integer. 

4 
Calculations 

The field-size distributions presented in the database are those of the entire natural 

population—that is, they include both discovered and undiscovered subpopulations. The basic 

counts of fields per size-class bin are recorded separately for discovered versus undiscovered 

fields in the database. Besides the basic counts of fields per size-class bin, a number of 

calculated parameters of the distributions are also given. Some parameters, such as the median 

size or the number of fields per 1,000 km2

, are dependent on the minimum size assessed. For 

convenience, these were calculated for both a minimum of 5 million barrels of oil equivalent 

(MMBOE) and for a minimum of 50 MMBOE. 

The actual Visual Basic for Applications macros that were used in compiling the database 

are preserved as part of the WorldAnalogs.xls workbook. They are included in module 4, which 

can be accessed within Microsoft Excel by the Visual Basic editor. Modules 1 through 3 include 

the code for the tools (Analog Search, Extend Selection, Analog Plot, and Analog Histogram) 

programmed for use with the database. 

Small-Field Extrapolation 

In WPA 2000, estimates were made of numbers and sizes of undiscovered fields greater 

than a selected minimum field size that varied from AU to AU. This minimum field size ranged 

from 1 to 20 MMBOE, except for pool sizes within the Western Canada Sedimentary Basin, 

where minimum pool size was set at 0.5 MMBOE. Because some additional analyses require 

estimates of smaller field sizes, an extrapolation of the size distribution into smaller field sizes is 

helpful. 

A similar analysis of small field sizes was performed for the 1995 National Assessment 

of U.S. oil and gas potential (Gautier and others, 1995). An estimate of “large” undiscovered 

fields (defined as 1 MMBOE or larger) was performed by an earlier version of the methodology 

used in the WPA 2000. An estimate of number and sizes of undiscovered fields between 31,250 

BOE and 1 MMBOE was performed by a separate analysis (Root and Attanasi, 1993). This 

analog database used a methodology for small-field extrapolation similar to that used in 1995 

(see Appendix 1). 

Additional Calculations 

The analog database was designed to be used as part of the USGS assessment procedures 

for conventional oil and gas resources (Schmoker and Klett, 2000). That methodology estimates 

numbers and sizes of undiscovered accumulations. The standard assessment input form is also 

5 
documented in Schmoker and Klett (2000). Probability distributions are required for the 

numbers of undiscovered accumulations, the sizes of undiscovered accumulations, and for 

coproduct ratios (such as the gas-to-oil ratio in oil accumulations). Oil and gas accumulations 

are estimated separately. 

The total number of fields and several parameters of the field-size distribution are 

dependent on the size of the smallest field considered for the assessment. Values dependent on 

the minimum field size are the median and maximum field sizes and the numbers of fields per 

1,000 km2

. For purposes of future assessments, these values were calculated for minimum field 

sizes of 5 and 50 MMBOE. The variables calculated for minima of 5 and 50 MMBOE are 

marked in the analog dataset with names containing “> 5” and “> 50”, respectively, even on 

those pages devoted to gas data in billions of cubic feet of gas (BCFG). Values for other 

minimum field sizes can be estimated based on the original binned data in the analog database. 

Global Distributions 

Except for the Geology worksheet, most columns of numerical variables have summary 

information at the bottom of the column. These data describe the “global” distribution of that 

variable; global, in this sense, refers to the entire set of 246 AUs in the analog database. Because 

the database is a biased set, preferentially containing the most successful provinces, this 

distribution should be characterized by larger field sizes and higher field densities than a true 

worldwide distribution that would also include non-productive and minimally productive areas. 

However, the amount of bias may be less for coproduct ratios and ancillary data, which may 

approximate more closely a true worldwide distribution. The global distribution is described by 

minimum, median, maximum, and mean values, as well as the number of AUs for which there is 

a value. 

Use of Database 

The analog database is meant to provide a source for analogs used in assessment of 

conventional oil and gas fields. Particularly in the case of frontier areas with few or no 

discovered fields, assessments must rely on comparisons to geologically similar areas that have 

been more maturely explored. In such frontier areas, other assessment methodologies, such as 

discovery process models, are unusable. Even in areas of moderately mature exploration, 

comparison to analogs can also be a useful check on estimates made by other methods. 

Although constructed with the USGS methodology in mind, the database can be used with other 

methodologies. 

The foregoing is not to imply that resource assessment is the only use for our analog 

database. More than 95 percent of the world’s discovered oil and gas resources as of the end of 

1995 (excluding the United States) are in the 246 AUs included in the database, thus it offers 

additional opportunities for analysis of patterns of oil and gas occurrence. 

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7 

The examples presented below are based on constructing sets of analogs, but analyses 

may be based on any number from one to all 246 AUs. Although some may prefer to search for 

a single “best” analog, the construction of a set of analogs better characterizes the range of 

uncertainty. 

For a particular assessment, more than one set of analogs may be necessary. Numbers 

and sizes of fields are commonly constrained by the available trapping configurations, which in 

turn are related to the tectonic and sedimentologic characteristics of the area. Coproduct ratios, 

such as the gas-to-oil ratio in oil fields, relate more closely to source rock characteristics and 

thermal history. A different set of analogs may thus be appropriate for assessing the coproduct 

ratios. 

Classification Variables 

The first worksheet (Geology) of the analog database workbook includes 34 variables 

(referred to as classification variables) that allow the user to classify the AUs and thus select 

appropriate analogs (table 2). (Note: For those using the tab-delimited files, this is file 

“geology.tab.”) The first six variables identify the assessment unit and place it into the 

hierarchical structure of province and total petroleum system. The other variables relate either to 

(1) province-scale geology, such as the tectonic regime or basin type; or (2) assessment-unitscale geology, such as the source, reservoir, and trap characteristics. This geologic information 

was compiled as part of the WPA 2000 (U.S. Geological Survey World Energy Assessment 

Team, 2000), mostly from the text summaries describing each assessment unit. 
Table 2. Classification variables in the world analog database and the allowed values for those variables with a limite

[AU, assessment unit; TPS, total petroleum system; BBO, billion barrels of oil; TCFG, trillion cubic feet of gas; BBNGL, billion barreBBOE, billion barrels of oil equivalent] 

Variable Description Values 

AU_Code USGS code for this assessment unit AU_Name official USGS name for this assessment unit 

TPS_Code USGS code for this total petroleum system 

TPS_Name official USGS name for this total petroleum system Province Code USGS code for this province Province Name official USGS name for this province Structural Setting Compressional 

Extensional Passive 

Unknown 

Crustal System Continental 

Oceanic Transitional 

Uncertain 

Architecture Backarc 

Craton interior Deltas Foreland Platform Rift, sag 

Rifted passive margi

Small ocean basin 

Strike-slip systems 

Trap System (Major) Basement-involved b

Compressional anticDiapir-related structu

Extensional grabens 

related to norma

Gravity-induced growLocal uplifts of uncer

8 
Paleogeomorphic 

Salt-induced structur

Stratigraphic undefor

Transtensional and t

Depositional System Carbonate shelf 

Carbonate shelf marg

Continental clastics Deepwater carbonate

Paralic clastics 

Slope, clinoforms, tu

Source Rock Depositional Environment Coaly 

Deep marine Lacustrine 

Shallow marine 

Kerogen Type No source rocks 

Type II Type III 

Types I and II Types II and III Types I, II, and III 

Source Type Anoxic/disoxic 

Carbonaceous muds

Disoxic/oxic Lacustrine anoxic/dio

Marine anoxic Marine disoxic 

Terrestrial 

Source Rock Qualifier Assumed 

Known 

Probable 

Status Hypothetical 

Established 

Frontier 

Specific Reservoir Rock Age various General Reservoir Rock Age various Reservoir Rock Lithology Carbonates 

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10 

Siliclastics 

Siliciclastics and Car

Siliciclastics, Carbon

Reservoir Rock Depositional 

Environment Deep marine 

Nonmarine 

Nonmarine to Marine 

Shallow Marine 

Shallow to Deep Mar

Seal Rock Lithology Evaporites 

Evaporites and Shale Shale 

Trap Type general trap type Stratigraphic 

Structural 

Structural and Stratig

Known Oil (BBO) cumulative production plus reserves of oil Undiscovered Oil (Mean, BBO) mean USGS estimate of oil in undiscovered fields Known Gas (TCFG) cumulative production plus reserves of natural gas Undiscovered Gas (Mean, TCFG) mean USGS estimate of natural gas in undiscovered fields Known NGL (BBNGL) cumulative production plus reserves of NGL Undiscovered NGL (Mean, BBNGL) mean USGS estimate of NGL in undiscovered fields Known Total Petroleum (BBOE) cumulative production plus reserves of petroleum in BOE Undiscovered Total Petroleum (Mean, 

BBOE) 

mean USGS estimate of petroleum in 

undiscovered fields (in BOE) 

Specific Source Rock Age various General Source Rock Age various Age of Maturation various Migration Distance Short 

Short to long 
Methods of Searching 

For those using the Microsoft Excel version of the database, tools have been developed 

for easier use. These are located under the Analog menu in the menu bar. Two of these, Analog 

Search and Extend Selection, are aids to searching and building analog sets for further analysis. 

Analog Search, found under the Analog menu in the menu bar, is a tool for simple 

searches on several of the most important geologic variables. An initial form asks whether to 

search within the current selection. This allows multi-level (Boolean AND) searches. Next, the 

tool provides a pop-up menu of the most important classification variables (fig. 1). Following 

selection of one of the variables, another pop-up menu provides a list of the possible values for 

that variable (fig. 2). The results of the search are highlighted on all the worksheets of the 

database. 

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Figure 1. Select variables form for the Analog Search tool.

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13 

Figure 2. Select values form for the Analog Search tool. 
Microsoft Excel users can use the other search capabilities built into the program, such as 

the Find or Filter commands or manually selecting with the mouse. The Extend Selection

command can be used in conjunction with these search methods. Extend Selection finds all 

selected cells in the Geology worksheet and extends the selection such that the entire row is 

selected for each row with a selected cell. The selection is further extended to all the other 

worksheets in the database. As an example, one could manually select all the cells in column 

Specific Source Rock Age (column AE) on the Geology worksheet that had value “Devonian”. 

Running Extend Selection would thus select the entire row for each of those chosen AUs on all 

the worksheets. Microsoft Excel users who create selections on the Geology worksheet other 

than by Analog Search and wish to use the additional graphing tools Analog Plot and Analog 

Histogram should run Extend Selection before using the graphing tools. 

For users of other spreadsheet programs, searching for a set of analogs can be 

accomplished by use of tools specific to the user’s spreadsheet program. The tools Analog 

Search and Extend Selection are not available outside the Microsoft Excel version of the analog 

database. 

Utility Variables 

Utility variables are so named because they can be used for analysis once an analog set 

has been selected. Most of the major utility variables are on the Oil, Gas, and BOE worksheets. 

Additional utility variables are on the Oil Bins, Gas Bins, BOE Bins, and Ancillary worksheets. 

Variables Related to Number of Fields 

The Oil Bins, Gas Bins, and BOE Bins worksheets contain the basic numbers of 

discovered and undiscovered fields in each size class. Most users will not want to use such basic 

data, but instead will use some of the summary data variables. These worksheets include totals 

of “Number of Discovered,” “Number of Undiscovered,” as well as “Number > 5,” and “Number 

> 50.” The latter two variables give the number of fields greater than the minima of 5 and 50 

MMBOE respectively. 

Because neither 5 nor 50 MMBOE are exact bin limits, the numbers of fields greater than 

these minimum sizes had to be estimated. For number of fields of at least 5 MMBOE, the counts 

from bin sizes 8 MMBOE and larger are added to two-thirds the number in the 4 to 8 MMBOE 

size class. For number of fields of at least 50 MMBOE, the counts from bin sizes 64 MMBOE 

and larger are added to one-third the number in the 32 to 64 MMBOE size class. 

Perhaps more useful for calculating numbers of undiscovered fields in an area to be 

assessed are several variables listed on the Oil, Gas, and BOE worksheets. Here the variables 

“Number / 1000 km2

 for > 5,” and “Number / 1000 km2 for > 50” give field densities per 1,000 

km2

 rather than raw numbers of fields. 

14 
Variables Related to Field Size Distributions 

The raw field-size bin counts for each analog AU are given in the Oil Bins, Gas Bins, and 

BOE Bins worksheets. More useful to the assessor are several variables in the Oil, Gas, and BOE 

worksheets: “Median of > 5,” “Median of > 50,” “Maximum of > 5,” and “Maximum of > 50”, 

which give the medians and maxima of the size distributions of those fields greater than the 

minima of 5 and 50 MMBOE, respectively. 

Median sizes are approximated from the bin count data. The bin counts were used to 

identify the bin that included the median field size, which was calculated within that bin by 

interpolation. 

The variables “Maximum of > 5” and “Maximum of > 50” are defined as the maximum 

size of discovered fields (column BB in the bins worksheets) or the expected (mean) size of the 

largest undiscovered field (column BC in the bins worksheets), whichever is larger. In order to 

preserve the proprietary field-size data, the maximum size of discovered fields is rounded to 1.38 

times the lower limit of the bin in which it resides. The variables “Maximum of > 5” and 

“Maximum of > 50” are different only in the case where the maximum size is between 5 and 50 

MMBOE. In that case there would be a value for “Maximum of > 5” but not for “Maximum of > 

50.” 

Variables Related to Maturity of Exploration 

Four utility variables are provided as indices of exploration maturity. These are 

“Discovered % by Number for > 5,” “Discovered % by Number for > 50,” “Discovered % by 

Volume for > 5,” and “Discovered % by Volume for > 50,” all of which are listed on the Oil, 

Gas, and BOE worksheets. These maturity measures are used by the Analog Plot tool described 

below. 

Variables Related to Coproduct Ratios 

The methodology used in the WPA 2000 treated fields as being either oil or gas 

(Schmoker and Klett, 2000). Oil fields are differentiated from gas fields in that they have a gasto-oil ratio (based on cumulative production plus reserves) of less than 20 thousand cubic feet 

per barrel. Field sizes are expressed as volumes of oil in oil fields and volumes of gas in gas 

fields, a procedure that was followed in the analog database. Other petroleum products are 

handled by the use of coproduct ratios. In oil fields, associated and dissolved gas volumes are 

calculated using a gas-to-oil ratio and natural gas liquids (NGL) volumes are calculated using a 

NGL-to-gas ratio. In gas fields, total liquids are calculated using a liquids-to-gas ratio. These 

three coproduct ratios are provided in the analog database—the two coproduct ratios for oil fields 

are on the Oil worksheet, the coproduct ratio for gas fields is on the Gas worksheet, and all three 

coproduct ratios are on the BOE worksheet. 

Data provided for coproduct ratios in the analog database is based on production and 

reserves data from discovered fields drawn from the IHS Energy, Inc.(2004), and NRG 

Associates (1995) databases. Coproduct ratios for individual fields were calculated from the 

15 
16 

cumulative production plus reserves of each product. Each field was allocated to a particular 

AU. The analog database contains five variables describing the distribution of each coproduct 

ratio—minimum, median, maximum, and mean, as well as the number of fields that had a 

calculated coproduct ratio of that type. The number of fields gives an indication of the 

confidence in the other measures. A small number of fields indicates poorer sampling, and the 

distribution is thus known with less confidence. 

Variables Related to Ancillary Data 

Ancillary data are not used to calculate volumes of undiscovered resources, but rather 

give information affecting the technical and economic recoverability of the resource. Some 

ancillary variables describe the quality of the oil and gas fluids: oil viscosity (in centipoises and 

in centistokes), API gravity of the oil, sulfur content of the oil in percent, and non-hydrocarbon 

fractions of the gas (percentages of hydrogen sulfide, carbon dioxide, and nitrogen). The 

ancillary data also include drilling depths and, for the offshore, water depths (in feet). As with 

the coproduct ratios, the data in the analog database comes from discovered fields only. The 

ancillary data are presented on worksheet Ancillary. 

The ancillary data are presented in a manner similar to that of the coproduct data. There 

are five variables describing the distribution of each ancillary variable—minimum, median, 

maximum, and mean, as well as the number of fields that had a ancillary value of that type. The 

number of fields gives an indication of the confidence in the other measures. A small number of 

fields indicates poorer sampling, and the distribution is thus known with less confidence. 

Data Analysis Tools 

Several tools added to the Microsoft Excel version of the analog database can be accessed 

through the menu bar under Analog; to use, the analog set should first be selected on the 

Geology worksheet. If the selection is made manually, the program Extend Selection (under the 

Analog menu in the menu bar) should be run before using the data analysis tools. 

Distributions for Small Analog Sets 

For small analog sets (those with fewer than about 20 AUs), an additional graphing tool 

has been added to the Microsoft Excel version of the database. This tool, Analog Plot, can be 

found under the Analog menu in the menu bar. The resulting graph shows the distribution of a 

selected utility variable for the analog set selected in the Geology worksheet. 

Each analog AU is represented on the graph by a vertical bar placed at the value of the 

utility variable (fig. 3). The length of the shaded portion of the bar is a maturity measure 

showing the percent of resource already discovered. The user may choose either percent by 

volume (suggested) or percent by number of fields to show maturity. Values of the utility 

variable are more credible for those AUs with higher maturity. Each bar is labeled by the AU 

name.
Rift-Sag

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 

Total Number of Fields per 1000 km2 >= 50 MMBOE

Carboniferous-Lower Permian Clastics

Devonian Synrift

South Mangyshlak (Entire)

Mesozoic Sandstone Reservoirs

Jurassic-Tertiary

Upper Paleozoic Carbonates

Upper Jurassic-Cretaceous Sandstones

Pre-UpperJurassic

Northern West Siberian Onshore Gas South Kara Sea Offshore

Ce

Southeast Sirte Clastics ntral Sirte Carbonates

OffshoreSirte Hypothetical

SoutheastSirteHypothetical

Tertiary Lacustrine

Pre-Tertiary Buried Hills Subtle Traps Anticlinal

Structural Traps

South Malay Lacustrine

North Malay Lacustrine

South Malay Coaly

Gippsland

Moray Firth

Central Graben

Greater Hungarian Plain Basins

Zala-Drava-Sava Basins Danube Basin Transcarpathian Basin

Hungarian Paleogene Basin

Transylvanian Neogene Suprasalt Gas

Number of values Minimum Median Maximum Mean 

Figure 3. Example of chart generated by the Analog Plot tool. MMBOE, million barrels of oil equivalent. 

17 
A large number of analogs with similar values of the utility variable in question can lead 

to an unreadable chart. In such a case, one could remove AUs with extreme values from the 

analog set, thus changing the X-axis scale. Alternatively, one could use the Analog Histogram

tool. 

In constructing the plot (as in fig. 3), data are copied to worksheet Chart Data. Column A 

contains the values of the utility variable in ascending sorted order. Column B contains the 

maturity measures for the data in column A. The distribution of the utility variable data in 

column A is summarized in column K, where the number of values, minimum, median, 

maximum, and mean are given. These data are replaced when a new chart is constructed either 

by Analog Plot or by Analog Histogram. 

Distributions for Large Analog Sets 

For large analog sets (those with more than about 20 AUs), another graphing tool has 

been added to the Microsoft Excel version of the database. This tool, Analog Histogram, can 

also be found under the Analog menu in the menu bar. The resulting graph shows the 

distribution of a selected utility variable for the analog set selected in the Geology worksheet. 

With this tool a distribution histogram is constructed but no maturity measure or identification of 

AU names is given (fig. 4). 

18 
Bin Frequency

0.0 960.9 431.8 432.7 233.6 94.5 115.4 86.3 47.2 08.1 29.0 09.9 2

10.8 111.7 012.6 213.5 114.4 015.3 016.2 017.1 118.0 018.9 0

ore 0

otal 246

M 

T 

 Figure 4. Example of chart generated by the Analog Histogram tool. MMBOE, million barrels of oil equivalent. 

19 
In constructing the histogram, data are copied to worksheet Chart Data. Column A 

contains the values of the utility variable in ascending sorted order. Column H contains the 

histogram class boundaries. The distribution of the utility variable data in column A is 

summarized in column K, where the number of values, minimum, median, maximum, and mean 

are given. These data are replaced when a new chart is constructed either by Analog Plot or by 

Analog Histogram. 

Example of Use with USGS Assessment Methodology 

The following example is based on the standard 2000 USGS methodology for assessing 

conventional oil and gas resources (Schmoker and Klett, 2000). The assessment input form (fig. 

5) requires distributions for numbers and sizes of oil and gas accumulations and the associated 

coproduct ratios

20 
Figure 5. Seventh approximation input data form, version 6. 

21 
22 
Selection of Analogs 

The example AU is in a deep-water environment with possible turbidite reservoirs; its 

area is 100,000 km2. No fields have yet been discovered in this area. A minimum assessed size 

of 5 MMBOE will be used. 

In order to choose a suitable set of analogs, a search was conducted using the Analog 

Search tool, within which the Depositional System variable (fig. 1) was searched for values that 

included “Slope, clinoforms, turbidites” (fig. 2). This yielded a set of 44 potential analogs (table 

3). Further examination could refine this analog set to a smaller number of analogs that are 

geologically more similar to the area being assessed, but with some possible loss of insight into 

the uncertainty. 

Table 3. Analog set for the numbers and sizes of fields used in text example 

(based on a search for the value "Slope, clinoforms, and turbidites" within the 

Depositional System variable) 

 

Assessment Unit Number and Name 

11120104 Central Offshore 

11120105 Iran Onshore/Nearshore 

11740101 Upper Jurassic-Cretaceous Sandstones 

20160201 Natih-Fiqa Structural/Stratigraphic 

37010102 Brunei-Sabah Turbidites 

38170102 Kutei Basin Turbidites 

39100201 Petrel 

39100301 Malita 

39130101 Late Jurassic/Early Cretaceous-Mesozoic 

39480101 Dingo-Mungaroo/Barrow 

39480201 Locker-Mungaroo/Barrow 

40250103 Central Graben 

40470101 Foreland Basin 

40470201 Deformed Belt 

40480101 Greater Hungarian Plain Basins 

40480201 Zala-Drava-Sava Basins 

40480601 Hungarian Paleogene Basin 

40600101 Neogene Flysch Gas 

60210101 Late Cretaceous-Tertiary Turbidites 

60220101 Amazon Delta and Submarine Fan 

60290102 Late Cretaceous-Tertiary Deep-Water Sandstones 

60340102 Late Cretaceous-Tertiary Slide Blocks and Turbidites 

60340103 Abrolhos Sub-Volcanic Structures 

60350101 Late Cretaceous-Tertiary Turbidites 

60350103 Salt Dome Province Tertiary Sandstones 

60360102 Salt-Structured Deep-Water Sandstones 

60370101 Pelotas Platform and Basin 

60450101 Sub-Andean Fold and Thrust Belt 

23 
24 

60450102 Foreland Basins 

60550103 Dorsal de Neuquen Structure 

60980202 Orinoco Delta and Offshore 

61030101 Carupano Basin Gas 

61070101 Inner Forearc Deformation Belt 

71920102 Akata Reservoirs 

72030201 Gabon Suprasalt 

72030302 Central Congo Turbidites 

72030401 Cuanza-Namibe 

73030101 Offshore 

80420102 Indus Fan 

80430102 Eocene-Miocene Cambay Deltaic 

80470201 Western Shelf and Slope 

80470301 Central Basin 

80470302 Eastern Fold Belt 

80480102 Irrawaddy-Andaman 

Numbers of Fields 

To capture the uncertainty in number of undiscovered fields, the density of fields per 

1,000 km2 was examined in the analog set. A minimum size of 5 MMBOE was used and the 

density of total fields was examined. Because of the relatively large number of analogs (more 

than 20), the Analog Histogram tool was used instead of the Analog Plot tool. Based on this 

set of 44 analogs, the field density ranges to about 10 fields per 1,000 km2, but most values are 

from 0 to 2 fields per 1,000 km2

 (fig. 6). 
Bin Frequency 

0.0 80.5 91.0 81.5 42.0 22.5 33.0 03.5 24.0 14.5 25.0 25.5 16.0 16.5 07.0 07.5 08.0 08.5 09.0 09.5 0

10.0 110.5 0

More 0

T otal 44

Figure 6. Plot of field density per 1,000 km2 for example given in text. MMBOE, million barrels of oil equivalent. 

25 
Values appropriate to the input form (fig. 5) should reflect these densities. A possible 

modal number of oil plus gas fields could be approximately 1 field per 1,000 km2 times the area 

of the AU, or 100 total fields. Dividing this number of fields between oil and gas fields is 

dependent on an understanding of source rocks and thermal history. Any previously discovered 

fields would need to be subtracted from these numbers. Similarly, the maximum number of oil 

plus gas fields could be as high as 10 fields per 1,000 km2 times the area of the AU, or 1,000 

total fields (to be divided between oil and gas fields). 

Sizes of Fields 

In a similar manner, uncertainty in sizes of undiscovered fields requires examination of 

the median and maximum field sizes in the analog set. A minimum size of 5 MMBOE was again 

used and the sizes of total fields were examined. Because of the relatively large number of 

analogs (more than 20), the Analog Histogram tool was used instead of the Analog Plot tool. 

The median field sizes range from 8 to more than 25 MMBOE (fig. 7), with most values about 

14 MMBOE. One of the values for maximum field size exceeds 22,000 MMBOE (fig. 8), but all 

the others are less than 3 MMBOE. 

26 
Bin Frequency

0 02 04 06 08 1

10 212 19

14 12

16 318 320 322 024 026 128 030 032 034 036 038 040 042 0

More 0

Total 44

Figure 7. Plot of median field size for example given in text. MMBOE, million barrels of oil equivalent. 

27 
28 

Bin Frequency

0 30

1190 82380 53570 04760 05950 07140 08330 09520 0

10710 0

11900 013090 014280 015470 016660 017850 019040 020230 021420 1

22610 023800 024990

o

0

M re 0T otal 44

Figure 8. Plot of maximum field size for example given in text. MMBOE, million barrels of oil equivalent. 
The values appropriate to the input form (fig. 5) should reflect these densities. The 

minimum size was defined in this example as 5 MMBOE. The median size could be about 14 

MMBOE. Because the data for maximum field size include an outlier value much larger than 

any of the others, the analog AU containing that value should be closely examined for geologic 

similarity to the others in the analog set. If this AU cannot be reasonably eliminated from the 

analog set, the high uncertainty suggests a maximum size in the 25,000 MMBOE range. Also, 

any previously discovered fields could reduce the maximum undiscovered field size. 

Coproduct Ratios 

The coproduct ratios relate to a different set of geologic characteristics than the numbers 

and sizes of fields, so a different set of analogs is required. In this example case, we chose 

analogs that have lacustrine source rock depositional environments and types I and II kerogen 

(table 4). Distributions of the three coproduct ratios are given in figures 9, 10, and 11. The 

smaller (22) size of the analog set allows use of the Analog Plot tool. Although this is a small 

analog set, a distribution of values for each of the coproduct ratios is shown by the plots. 

Table 4. Analog set for the coproduct ratios used in text example (based on a search 

for the value "Lacustrine" within the Source Rock Depositional System variable and the 

value "Types I and II" within the Kerogen Type variable) 

 

Assessment Unit Number and Name 

11740201 Pre-Upper Jurassic 

31150101 Upper Paleozoic/Lower Mesozoic Nonmarine Coarse Clastics

31270101 Tertiary Lacustrine 

31270102 Pre-Tertiary Buried Hills 

31280101 Jurassic/Triassic Fluvial and Lacustrine Sandstone 

31420201 Jurassic Lacustrine 

31440101 Subtle Traps 

31440102 Anticlinal 

31540102 Kuche (Northern) Foldbelt 

37030101 South Malay Lacustrine 

37030102 North Malay Lacustrine 

38080101 Pematang/Sihapas Siliciclastics 

38240101 Sunda/Asri 

38280101 South Sumatra 

60290101 Western Pre-Aptian Reservoirs 

60350101 Late Cretaceous-Tertiary Turbidites 

60350102 Cretaceous Carbonates 

60350103 Salt Dome Province Tertiary Sandstones 

60600101 North Falklands Basin 

60600201 South Falklands Basin 

72030101 Gabon Subsalt 

72030301 Central Congo Delta and Carbonate Platform 

29 
Lacustrine, Types I and II

0 500 1000 1500 2000 2500 300

Gas-Oil Ratio (in Oil Fields)

Pre-Upper Jurassic

Upper Paleozoic/Lower Mesozoic Nonmarine Tertiary Lacustrine Pre-Tertiary Buried Hills

Jurassic/Triassic Fluvial and Lacustrine

Jurassic Lacustrine

Subtle Traps

Anticlinal

Kuche (Northern) Foldbelt

North Malay Lacustrine

Pematang/Sihapas Siliciclastics

Sunda/Asri

South Sumatra

Western Pre-Aptian Reservoirs

Late Cretaceous-Tertiary Turbidites Cretaceous Carbonates

Salt Dome Province Tertiary Sandstones

Gabon Subsalt

Central Congo Delta and Carbonate Platform

Number of values Minimum Median Maximum 

Mean 

Figure 9. Plot of gas-oil ratio in oil fields for example given in text. 

30 
Lacustrine, Types I and II

0 100 200 300 400 500 

NGL to Gas Ratio (in Oil Fields)

Pre-Upper Jurassic

Tertiary Lacustrine Pre-Tertiary Buried Hill

s

Jurassic Lacustrine

Anticlinal

Kuche (Northern) Foldbelt

South Malay Lacustrine

Pematang/SihapasSiliciclastics

Sunda/Asri

South Sumatra

Gabon Subsalt

Central Congo Deltaand Carbonate Platform

Number of values Minimum Median Maximum Mean 

Figure 10. Plot of natural gas liquids (NGL) to gas ratio in oil fields for example given in text. 

31 
32 

Lacustrine, Types I and II

0 10 20 30 40 50 60 70 

Liquids to Gas Ratio (in Gas Fields)

Pre-Upper Jurassic

Tertiary Lacustrine

Jurassic/Triassic Fluvial and Lacustrine

Jurassic Lacustrine Subtle Traps

South Malay Lacustrine

North Malay Lacustrine

Pematang/Sihapas Siliciclastics

Sunda/Asri

South Sumatra

Western Pre-Aptian Reservoirs

Cretaceous Carbonates

Central Congo Deltaand Carbonate Platform

Number of values Minimum Median Maximum Mean 

Figure 11. Plot of liquids to gas ratio in gas fields for example given in text. 
The input form (fig. 5) requires an estimate of uncertainty of the mean values for each 

ratio. The calculated mean values for each coproduct ratio in this analog set are given in the text 

box. These means might be modified after considering whether to include some of the outlier 

values, such as the very high value for NGL to gas ratio (fig. 10). 

References 

Attanasi, E.D., and Charpentier, R.R., 2002, Comparison of two probability distributions used to 

model sizes of undiscovered oil and gas accumulations—Does the tail wag the 

assessment?: Mathematical Geology, v. 34, no. 6, p. 767-777. 

Attanasi, E.D., and Charpentier, R.R., 2007, A program for partitioning shifted truncated 

lognormal distributions into size-class bins: U.S. Geological Survey Open-File Report 

2007-1260, 16 p. 

Drew, L.J., 1990, Oil and gas forecasting—Reflections of a petroleum geologist: New York, 

Oxford University Press, 252 p. 

Drew, L.J., Schuenemeyer, J.H., and Bawiec, W.J., 1982, Estimation of the future rates of oil and 

gas discoveries in the western Gulf of Mexico: U.S. Geological Survey Professional 

Paper 1252, 26 p. 

Gautier, D.L., Dolton, G.L., Takahashi, K.I., and Varnes, K.L., eds., 1995 National assessment 

of United States oil and gas resources—Results, methodology, and supporting data: U.S. 

Geological Survey Digital Data Series 30, one CD-ROM. 

Houghton, J.C., 1988, Use of the truncated shifted Pareto distribution in assessing size 

distribution of oil and gas fields: Mathematical Geology, v. 20, no. 8, p. 907-937. 

IHS Energy, Inc, 2004, IHS Energy exploration and production database: Houston, Texas, IHS 

Energy, Inc; database available from IHS Energy, Inc, P.O. Box 740619, Houston, Texas 

77274-0619 U.S.A.

Kaufman, G.M., 1993, Statistical issues in the assessment of undiscovered oil and gas resources: 

Energy Journal, v. 14, no. 1, p. 183-215. 

Klett, T.R., Schmoker, J.W., Charpentier, R.R., Ahlbrandt, T.S., and Ulmishek, G.F., 2000, 

Glossary, chap. GL of U.S. Geological Survey World Energy Assessment Team, U.S. 

Geological Survey World Petroleum Assessment 2000—Description and Results: U.S. 

Geological Survey Digital Data Series 60, 8 p. 

33 
NRG Associates, 1995, The significant oil and gas pools of Canada data base: Colorado Springs, 

Colorado, NRG Associates, Inc; database available from NRG Associates, Inc, P.O. Box 

1655, Colorado Springs, Colorado 80901 U.S.A. 

Root, D.H., and Attanasi, E.D., 1993, Small fields in the National Oil and Gas Assessment: 

American Association of Petroleum Geologists Bulletin, v. 77, no. 3, p. 485-490.

Rose, P.R., 1994, Chance of success and its use in petroleum exploration, in Steinmetz, R., ed., 

The business of petroleum exploration: American Association of Petroleum Geologists, 

Treatise of Petroleum Geology, Handbook of Petroleum Geology, v. 2, p. 71-85. 

Schmoker, J.W., and Klett, T.R., 2000, U.S. Geological Survey assessment model for 

undiscovered conventional oil, gas, and NGL resources—The seventh approximation, 

chap. AM of U.S. Geological Survey World Energy Assessment Team, ed., U.S. 

Geological Survey World Petroleum Assessment 2000—Description and results: U.S. 

Geological Survey Digital Data Series DDS-60, p. 1-18. 

Schuenemeyer, J.H., and Drew, L.J., 1983, A procedure to estimate the parent population of the 

size of oil and gas fields as revealed by a study of economic truncation: Journal of the 

International Association for Mathematical Geology, v. 15, no. 1, p. 145-161. 

U.S. Geological Survey World Energy Assessment Team, 2000, U.S. Geological Survey World 

Petroleum Assessment 2000—Description and Results: U.S. Geological Survey Digital 

Data Series 60, four CD-ROMs. 

34 
Appendix 1. 1995 Methodology for Small-Field Extrapolation 

In the 1995 U.S. National Oil and Gas Assessment (Gautier and others, 1995), a Pareto 

distribution was used to describe the size distribution of the set of undiscovered fields larger than 

1 million barrels of oil equivalent (MMBOE) in each play. After aggregating to the province 

level and adding the set of discovered fields, a separate Pareto distribution was used to describe 

the population of large fields (discovered plus undiscovered) plus small fields. This second 

Pareto distribution was estimated using binned field sizes (table 1) and the log-geometric 

distribution (the binned equivalent of the Pareto distribution). 

For the Pareto Distribution: 

f (x) = βLβ

x(β +1) = βLβ x(−β −1)

where: 

 L = location parameter (minimum field size) and 

β = shape factor (β > 0) 

The proportion of the population between size L (the minimum) and size X is: 

F(X) = f (x)dx = βLβ X(−β −1+1)

−β −1+1

⎡

⎣ ⎢ 

⎤

⎦ ⎥ 

L

X

∫ − βLβ L(−β −1+1)

−β −1+1

⎡

⎣ ⎢ 

⎤ 

⎦

⎥

F(X) = − Lβ

Xβ

⎡

⎣ ⎢ 

⎤

⎦ ⎥ − − Lβ

Lβ

⎡

⎣ ⎢ 

⎤ 

⎦

⎥

F(X) =1− Lβ

Xβ

Thus, appropriately, F(X) equals 0 where X = L and approaches 1 as X goes to infinity. 

35 
The proportion of fields larger than size X would be: 

1− F(X) =1− 1− Lβ

Xβ

⎡

⎣ ⎢ 

⎤

⎦ ⎥ = Lβ

Xβ

Relation Between Pareto and Log-Geometric Distributions 

Distribute a Pareto distribution among bins that have limits of X, PX, P2

X, P3X, and so 

on. Traditionally, the USGS uses a P of 2 with a starting value of X = 1 MMBOE, giving bin 

limits of 1, 2, 4, 8, and so on (table 1). 

The proportion of the population between X and PX is: 

F(X) = f (x)dx

X

PX∫ = − Lβ

( ) PX β

⎡

⎣ 

⎢ ⎢ 

⎤

⎦ 

⎥ ⎥ − − Lβ

( ) X β

⎡

⎣ 

⎢ ⎢ 

⎤ 

⎦

⎥

⎥

F(X) = Lβ

Xβ 1− 1

Pβ

⎡ 

⎣ ⎢ 

⎤

⎦

⎥

Similarly, the proportion of the population between PX and P2X is: 

F(X) = f (x)dx

PX

P 2

X

∫ = − Lβ

P2

( ) X β

⎡

⎣ 

⎢ 

⎢ 

⎤

⎦ 

⎥ 

⎥ − − Lβ

( ) PX β

⎡ 

⎣ 

⎢ ⎢ 

⎤

⎦

⎥

⎥

F(X) = Lβ

Xβ

1

Pβ − 1

P2β

⎡ 

⎣ ⎢ 

⎤

⎦

⎥

Also, the proportion of the population between P2

X and P3X is: 

F(X) = f (x)dx

P 2X

P 3

X

∫ = − Lβ

P3

( ) X β

⎡

⎣ 

⎢ 

⎢ 

⎤

⎦ 

⎥ 

⎥ − − Lβ

P2

( ) X β

⎡

⎣ 

⎢ 

⎢ 

⎤ 

⎦

⎥ 

⎥

36 
F(X) = Lβ

Xβ

1

P2β − 1

P3β

⎡ 

⎣ ⎢ 

⎤

⎦

⎥

For a binned distribution to be log-geometric, the ratios of frequencies (or proportions) 

between adjacent bins should be constant. 

Let r1 be the ratio between the proportion in bin X-PX to that in bin PX-P2

X: 

r

1 =

Lβ

Xβ 1− 1

Pβ

⎡

⎣ ⎢ 

⎤

⎦ ⎥ 

Lβ

Xβ

1

Pβ − 1

P2β

⎡ 

⎣ ⎢ 

⎤ 

⎦ ⎥ 

=

1− 1

Pβ

1

Pβ − 1

P2β

Multiplying numerator and denominator by Pβ : 

r

1 = Pβ −1

1− 1

Pβ

Let r2 be the ratio between the proportion in bin PX-P2

X to that in bin P2X-P3X: 

r2 =

Lβ

Xβ

1

Pβ − 1P2β

⎡

⎣ ⎢ 

⎤

⎦ ⎥ 

Lβ

Xβ

1

P2β − 1P3β

⎡ 

⎣ ⎢ 

⎤ 

⎦ ⎥ 

=

1

Pβ − 1P2β

1

P2β − 1P3β

Multiplying numerator and denominator by P2β : 

r2 = Pβ −1

1− 1

Pβ

Because r1 = r2, the binned distribution is log-geometric. 

37 
38 

Upon examining these r values for actual data sets in the Permian Basin of the United 

States, Drew and others (1982) and Drew (1990) concluded that field-size distributions were 

Pareto. They noted a pattern such as that shown in figure 12, drawn from data for the SupraDomanik Carbonates/Clastics Assessment Unit in the analog database. For field-size classes 

larger than about 200 MMBOE, the numbers of fields in each class are small and the ratios of 

adjacent classes are unstable. For classes smaller than 200 MMBOE, however, the ratio 

becomes more stable and approaches a constant of approximately 1.4. This implies a loggeometric distribution for the binned data and thus a Pareto distribution for the field sizes 

themselves. 
Supra-Domanik Carbonates/Clastics

0123456

1 10 100 1,000 

Field Size (MMBOE)

Ratio (r)

Figure 12. Ratio (r) between the numbers of discovered plus undiscovered fields in adjacent size classes for th

Carbonates/Clastics Assessment Unit (10150101) of the Volga-Ural Basin, Russia. MMBOE, million barrels of oil e

39 
Closer examination of these patterns using the analog database shows that the pattern is 

not always so clear (fig. 13). In some cases there is little convergence and in others the r’s tend 

to converge on a value greater than 2. This is problematic because an r value greater than 2 

implies that the resource approaches infinity as smaller and smaller size bins are considered. At 

an r value of 2, each bin has the same volume of resource. 

40 
Central Sirte Carbonates

0

0.5

1

1.5

2

2.5

3

1 10 100 1,000 

Field Size (MMBOE)

Ratio (r)

projfrom 

Figure 13. Ratio (r) between the numbers of discovered plus undiscovered fields in adjacent size classes for the CentAssessment Unit (20430102) of the Sirte Basin, Libya. MMBOE, million barrels of oil equivalent. 

41 
In the 1995 U.S. National Oil and Gas Assessment, r was calculated for each of the 58 

U.S. provinces with resource potential. Data on discovered oil and gas fields were binned by 

barrels of oil equivalent (BOE) (table 1). The Pareto distributions for undiscovered fields were 

divided into bins. This was the raw data set used for the calculation. The five size classes from 

class 7 (2 to 4 MMBOE) to class 11 (32 to 64 MMBOE) were used in the calculation. Class 6 (1 

to 2 MMBOE) was not used because of partial economic truncation at that size range. 

A least squares procedure was used to calculate a best-fit value for r. The number of 

fields in size classes 7 through 11 is: 

N = nc c= 7

c=11 ∑

where nc is the number of discovered fields plus the estimated mean value of number of 

undiscovered fields in class c. 

If the distribution were log-geometric with ratio r: 

Class 11 would have some number n11 of fields, 

Class 10 would have r n11 of fields, 

Class 9 would have r2

 n11 of fields, 

Class 8 would have r3 n11 of fields, and 

Class 7 would have r4 n11 of fields. 

Thus, the total number of fields in classes 7 to 11 would be: 

N = r

4

( )n + r3( )n + r

2

( )n + (rn)+ n

Given a trial value for r = rtrial, one can calculate expected values of numbers in each size 

class by: 

n11exp = N

rtrial

4 + rtrial3 + rtrial2 + r ( ) trial +1

n10 exp = rtrialN

rtrial

4 + rtrial3 + rtrial2 + r ( ) trial +1

n9 exp = rtrial

2 N

rtrial

4 + rtrial3 + rtrial2 + r ( ) trial +1

42 
43 

n8 exp = rtrial

3 N

rtrial

4 + rtrial3 + rtrial2 + r ( ) trial +1

n7 exp = rtrial

4 N

rtrial

4 + rtrial3 + rtrial2 + r ( ) trial +1

The expected value (given some trial r) of number of fields in each of the five size classes 

was compared to the number of discovered plus estimated undiscovered from the assessment and 

the sum of squared deviations calculated: 

SSQ = ( ) n11−n11exp

2

+ ( ) n10−n10 exp

2

+ (n9−n9 exp )

2

+ (n8 − n8 exp )

2

+ ( ) n7−n7 exp

2

A sum of squared deviations from the expected was calculated for each value from 1.4 to 

1.7 at 0.01 intervals (1.40, 1.41, 1.42 ... 1.69, 1.70). The trial value of r with the least sum of 

square deviations was chosen as r for the province. 

Checks were conducted for data that did not fit the extrapolated field size. In some cases 

the number of discovered fields in one of classes 1 to 6 was larger than that estimated from the 

small-field extrapolation procedure. In this case, the extrapolated number of fields was used 

rather than the actual number of discovered fields. 

Changes to Small-Field Extrapolation Methodology 

For the 1995 U.S. National Oil and Gas Assessment, small oil and gas fields were 

estimated at the province level only. For the analog database a similar methodology was applied 

at the assessment unit level. Because of the smaller numbers of fields for each assessment unit 

there was an increased chance of poor distribution fit. Checks were thus made for cases where 

there were more discovered fields than the extrapolation estimated in the small field-size classes. 

In such cases, the additional discovered fields were eliminated and the database cell was colored 

gold in the Oil Bins, Gas Bins, and BOE Bins worksheets. 

The WPA 2000 used a shifted truncated lognormal distribution rather than a Pareto 

distribution for assessing large undiscovered fields. It also used a variable minimum assessed 

field size. Both of these changes made modification to the procedure necessary. In the 1995 

methodology, the minimum size assessed was 1 MMBOE. Because a Pareto distribution was 

used, the modal bin was always the 1 to 2 MMBOE class (class 6). The five bins used for 

calculation of r were the five starting with the 2 to 4 MMBOE class (class 7) up through the 32 to 

64 MMBOE class (class 11). In the present procedure, the modal bin may be somewhere above 

the minimum assessed size (fig. 14). The five bins above the modal bin are used for the 

calculation of r (fig. 15). 
Main Basin Platform of Timan-Pechora Basin

0

10203040506070

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 

Oil Accumulation Size Class

Number of Fields

WEP Disc

Figure 14. Numbers of discovered and undiscovered (from the 2000 USGS assessment) oil fields by size class in Platform Assessment Unit (10080102) of the Timan-Pechora Basin, Russia. Note the linear vertical scale. 

44 
45 

Main Basin Platform of Timan-Pechora Basin

1

10

100

1,000

10,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 

Oil Accumulation Size Class

Number of Fields

WEP 2000Discovered

5 classes above modal class

used to calculate r

Figure 15. Numbers of discovered and undiscovered (from the 2000 USGS assessment) oil fields by size class in the Main Assessment Unit (10080102) of the Timan-Pechora Basin, Russia, showing an extrapolation of small-field sizes using the datNote the logarithmic vertical scale.
The mean size of fields within a size class can be calculated for a lognormal or a Pareto 

distribution. The size distributions in the analog database, however, are combinations of a 

lognormal distribution and additional discrete (discovered) fields. As a first approximation, the 

1995 methodology used a mean size of 1.5 times the lower size-class boundary (half-way 

between the two bounds of the size class). 

In experiments with lognormal distributions, it can be shown that the mean size in each 

field size class varies in location from class to class (table 5). Below the mode of the field-size 

distribution, the mean size of a class is closer to the upper boundary of the size class. At the 

mode, the mean size of a class is approximately at the center of the size class. Above the mode, 

the mean size of a class is closer to the lower boundary and approaches the lower boundary even 

more closely as one goes to larger size classes. One can specify the mean size of a class by using 

a multiplier between 1 and 2 times the lower size-class boundary. Use of different values for that 

multiplier per size class would have greatly complicated the calculations for the database 

because the multiplier values depend not only on the size class but also on the distribution itself. 

Experiments showed that a multiplier value of 1.38 times the lower size class boundary gave the 

best approximation if only one value was to be used. The new methodology thus uses the value 

of 1.38, rather than the 1.5 that was used in 1995. 

46 
Table 5. Simulation of a lognormal distribution with mean of 50 million barrels of oil equivalent (MMBOE) and standard deviation of 10

USGS field-size classes. Right column shows relation of the mean in each class relative to the class boundaries. 

Class Minimum size Number Volume Mean size Mean s

1 0.03125 1 0.06 0.06 12 0.0625 27 2.93 0.11 1

3 0.125 151 31.04 0.21 14 0.25 1,173 466.55 0.40 15 0.5 5,665 4,422.19 0.78 16 1 21,401 32,905.38 1.54 17 2 59,212 179,567.86 3.03 1

8 4 121,337 724,043.50 5.97 1

9 8 186,682 2,192,422.75 11.74 1

10 16 215,607 4,976,658.55 23.08 111 32 185,660 8,425,796.98 45.38 112 64 118,833 10,603,253.55 89.23 113 128 56,751 9,961,970.76 175.54 1

14 256 20,621 7,111,576.20 344.87 1

15 512 5,545 3,768,982.60 679.71 1

16 1024 1,148 1,535,004.65 1,337.11 117 2048 166 443,442.94 2,671.34 1

18 4096 19 106,505.41 5,605.55 1

19 8192 0 0.00 0.00 020 16384 0 0.00 0.00 0

21 32768 0 0.00 0.00 0

Totals: 999,999 50,067,054 50.07

Sizes and volumes in millions of barrels equivalent 

47 
Sensitivity Tests 

A series of sensitivity tests was conducted to determine the effect of different 

assumptions on the calculation of r. The main assumptions are listed in table 6 and the results of 

the sensitivity analysis on several of these assumptions are given in table 7. Given the basic 

framework of accepting assumption 1, the tests determined that most of the assumptions were 

not particularly sensitive. The main exception was assumption 2, the constraint that r is 

constrained to lie between 1.4 and 1.7. 

Table 6. Assumptions for the 1995 small-field assessment methodology. 

1. The small fields have a log-geometric distribution. 

2. r can vary from 1.4 to 1.7. 

3. A least-squares procedure should be used to calculate r. 

4. Mean size in a bin is 1.5 times the lower bin limit. 

5. The least-squares procedure should not use bins with value less than 1.0. 

6. r should be fit to BOE totals, not separately for oil and gas fields. 

7. The least-squares procedure uses the five bins starting with the one larger than the modal bin. 

8. Oil and gas should be proportioned similar to the modal bin plus the next two larger bins. 

9. The least-squares fit should not use discovered fields of a type (oil/gas) not assessed. 

10. The oil or gas proportion should not use discovered fields of a type (oil/gas) not assessed. 

11. "Excess" discovered fields in a bin should be eliminated. 

12. Least squares should be based on numbers of accumulations rather than volumes of resources. 

13. r should be calculated at the province scale. 

48 
Table 7. Sensitivity studies of the calculation of r given different assumptions. 

assessment unit number and name base

case r

extended 

base case

multinomial r ratio 1 / 2 ratio 2 / 3 ratio 3 / 4 rat

4 / 

10080101 Northwest Izhma-Pechora Depression 1.70 3.00 3.00 2.56 3.54 4.86 16.10080102 Main Basin Platform 1.45 1.45 1.44 1.20 1.33 1.98 1.410080103 Foredeep Basins 1.70 2.22 2.20 2.17 1.99 2.44 3.010090101 Carboniferous-Lower Permian Clastics 1.60 1.60 1.60 1.46 1.64 1.65 1.710090102 Devonian Synrift 1.70 2.46 2.43 1.93 2.55 3.39 5.510150101 Supra-Domanik Carbonates/Clastics 1.40 1.40 1.39 1.45 1.04 1.68 1.810150102 Lower Volga 1.40 1.35 1.35 1.00 1.48 1.51 1.510150103 Sub-Domanik Devonian Clastics 1.45 1.45 1.43 1.24 1.64 0.98 3.310150201 Permian Reefs/Thrust Folds 1.70 1.75 1.71 1.43 1.44 1.56 13410160101 North and West Margins Subsalt Pinnacle Reefs 1.70 2.36 2.33 1.93 2.33 3.13 4.610160102 North and West Margins Subsalt Barrier Reefs 1.70 3.00 3.00 3.26 32.61 0.00 0.010160103 East and Southeast Margins Subsalt 1.70 1.79 1.76 1.80 1.59 1.43 4.610160104 South Margin Subsalt 1.40 1.39 1.38 1.24 1.16 1.75 1.710160106 Suprasalt 1.70 1.82 1.80 1.46 1.68 2.50 2.410500101 Kolguyev Terrace 1.70 2.21 2.20 1.77 2.48 2.53 3.110500102 South Barents and Ludlov Saddle 1.54 1.54 1.53 1.42 1.31 1.75 2.210500103 North Barents 1.70 1.79 1.78 1.57 1.74 1.99 2.311080101 Tertiary Foredeep 1.68 1.68 1.71 1.45 2.50 1.67 1.011080102 Foreland Slope 1.50 1.50 1.53 1.38 2.03 1.75 0.711080103 Subsalt Jurassic 1.70 2.44 2.37 2.70 1.20 7.56 4.411090101 Foldbelt-Foothills 1.70 1.94 1.88 1.64 1.65 1.69 42011090102 Terek-Sunzha Subsalt Jurassic 1.70 3.00 3.00 2.45 3.76 5.80 0.011090103 Foreland Slope and Foredeep 1.70 3.00 3.00 6.42 11.54 0.00 0.0

49 
11090201 South Mangyshlak (Entire) 1.70 1.74 1.74 1.25 3.82 0.74 3.2

11090301 Offshore Prikumsk Zone 1.70 3.00 3.00 2.81 3.27 4.04 6.911090302 Onshore Stavropol-Prikumsk 1.70 1.91 1.90 1.70 1.40 5.68 1.011090303 Central Caspian Offshore 1.70 3.00 3.00 6.07 22.60 0.00 0.011120101 Apsheron-Pribalkhan Zone 1.45 1.45 1.46 1.14 1.88 1.85 0.811120102 Lower Kura Depression and Adjacent Shelf 1.46 1.46 1.45 1.38 1.33 1.31 2.611120103 Gograndag-Okarem Zone and Adjacent Shelf 1.64 1.64 1.65 1.34 1.91 1.84 1.411120104 Central Offshore 1.70 1.75 1.74 1.41 1.71 2.09 2.511120105 Iran Onshore/Nearshore 1.70 1.99 1.97 1.52 1.99 2.60 3.411500101 Mesozoic Sandstone Reservoirs 1.70 1.87 1.87 1.51 2.20 1.89 2.211500201 Jurassic-Tertiary Reservoirs 1.69 1.69 1.70 1.55 1.68 3.22 0.711500301 Upper Paleozoic Carbonates 1.70 2.08 2.06 1.79 2.05 2.43 2.911540101 Northern and Western Areas 1.70 1.88 1.87 1.60 1.78 2.75 1.711540102 Karabil-Badkhyz (Southern Area) 1.70 1.92 1.90 1.80 1.84 1.76 3.311540103 Murgab Depression Suprasalt 1.55 1.55 1.54 0.94 1.33 3.81 1.411540104 Murgab Depression Subsalt 1.40 1.24 1.25 1.08 2.02 0.82 1.211740101 Upper Jurassic-Cretaceous Sandstones 1.46 1.46 1.47 1.51 1.39 1.59 1.311740201 Pre-Upper Jurassic 1.70 1.74 1.74 1.64 1.78 1.85 1.711740301 Northern West Siberian Onshore Gas 1.40 1.37 1.37 1.13 1.32 1.82 1.211740302 South Kara Sea Offshore 1.40 1.40 1.39 1.15 1.47 1.32 1.912070101 Yenisey Foldbelt Riphean-Craton Margin Riphean 1.61 1.61 1.62 1.31 1.94 1.87 1.212100101 Baikal-Patom Foldbelt Riphean-Craton Margin Vendian 1.55 1.55 1.55 1.29 1.43 2.48 1.213220101 Onshore and Offshore Northeastern Shelf 1.64 1.64 1.65 1.16 2.30 1.79 1.420040101 Ma'Rib-Al Jawf/Shabwah/Masila 1.70 1.80 1.78 1.47 1.46 3.38 1.820140101 Ghaba-Makarem Combined Structural 1.54 1.54 1.52 1.29 1.10 2.45 2.520160101 Fahud-Huqf Combined Structural 1.70 2.15 2.19 2.50 2.95 1.31 1.420160201 Natih-Fiqa Structural/Stratigraphic 1.40 1.28 1.31 1.02 2.24 2.13 0.320190101 Cretaceous Reservoirs in Northwest Desert Anticlines 1.40 1.28 1.28 1.33 1.08 1.70 1.0

50 
20190102 Cretaceous Reservoirs in South Gulf Suprasalt Structural 1.50 1.50 1.47 1.26 1.00 2.19 3.3

20190103 Mesozoic/Tertiary Foredeep Fold and Thrust 1.69 1.69 1.72 1.33 2.05 4.44 0.420190201 Jurassic Reservoirs in Northwest Desert Anticlines 1.52 1.52 1.51 0.97 1.70 1.65 2.720190202 Jurassic Reservoirs in South Gulf Suprasalt/Qatar Arch Structural 1.60 1.60 1.61 1.54 1.78 1.69 1.220190301 Khuff Carbonates in Salt Structures 1.70 1.81 1.79 1.59 1.45 2.70 2.520190302 Paleozoic Reservoirs 1.40 1.36 1.36 1.27 1.30 1.42 1.520210101 Central Arch Horst-Block Anticlinal Oil and Gas 1.70 1.93 1.92 1.45 2.11 2.18 3.420210102 North Gulf Salt Basin Structural Gas 1.51 1.51 1.52 1.37 1.69 1.61 1.320210201 Horst-Block Anticlinal Oil 1.66 1.66 1.63 1.31 1.08 2.92 6.220210202 Salt-Involved Structural Oil 1.56 1.56 1.61 1.63 1.74 7.08 0.120230101 Horst/Graben-Related Oil and Gas 1.70 1.90 1.87 1.62 1.81 1.90 4.420230201 Platform Horst/Graben-Related Oil 1.70 1.89 1.86 1.42 2.07 1.85 4.620230202 Basinal Oil and Gas 1.70 2.86 2.79 2.27 2.43 8.40 0.020300101 Cretaceous Reservoirs 1.47 1.47 1.46 1.22 1.55 1.38 2.120300102 Tertiary Reservoirs 1.66 1.66 1.66 1.89 2.59 0.71 2.120300201 Northern Qatar Arch Extension 1.46 1.46 1.44 1.16 1.56 1.20 3.020430101 Southeast Sirte Clastics 1.55 1.55 1.57 1.63 3.16 0.63 1.620430102 Central Sirte Carbonates 1.67 1.67 1.66 1.70 1.35 1.83 2.420430103 Offshore Sirte Hypothetical 1.70 2.34 2.32 1.87 2.41 3.12 4.220430104 Southeast Sirte Hypothetical 1.70 2.22 2.19 1.62 2.31 3.25 4.620480101 Bou Dabbous-Tertiary Structural/Stratigraphic 1.64 1.64 1.67 1.51 2.56 1.32 1.120480201 Jurassic-Cretaceous Structural/Stratigraphic 1.70 1.77 1.74 1.18 2.52 1.13 8.920540101 Tanezzuft-Oued Mya Structural/Stratigraphic 1.70 1.75 1.77 3.43 1.74 0.83 1.420540201 Tanezzuft-Melrhir Structural/Stratigraphic 1.70 1.70 1.69 1.30 1.86 1.89 2.120540301 Tanezzuft-Ghadames Structural/Stratigraphic 1.40 1.26 1.26 1.08 1.23 1.68 1.0

51 
20560101 Tanezzuft-Illizi Structural/Stratigraphic 1.70 2.00 2.02 1.56 2.79 2.34 1.3

20580101 Tanezzuft-Timimoun Structural/Stratigraphic 1.40 1.21 1.18 1.21 7.17 0.13 2.020580201 Tanezzuft-Ahnet Structural/Stratigraphic 1.70 1.97 1.95 1.01 3.48 1.50 43.20580301 Tanezzuft-Sbaa Structural/Stratigraphic 1.70 1.84 1.80 1.80 5.32 0.25 16820580401 Tanezzuft-Mouydir Structural/Stratigraphic 1.40 1.00 3.00 2.75 3.45 4.59 0.020580501 Tanezzuft-Benoud Structural/Stratigraphic 1.70 1.95 1.94 1.90 1.74 2.13 2.620580601 Tanezzuft-Bechar/Abadla Structural/Stratigraphic 1.70 1.93 1.91 1.46 1.94 2.50 3.120710101 Gulf of Suez Block-Fault Fairway 1.40 1.36 1.35 1.01 1.26 2.09 1.220710102 Gulf of Suez Qaa Plain 1.40 1.00 2.17 1.71 2.24 2.93 3.920710103 Southern Gulf of Suez 1.65 1.65 1.63 1.32 1.32 2.48 2.720710201 Red Sea Coastal Block Faults 1.57 1.57 1.57 1.29 1.55 1.98 1.620710202 Red Sea Salt Basin 1.70 3.00 3.00 2.92 2.93 3.33 4.431150101 Upper Paleozoic/Lower Mesozoic Nonmarine Coarse Clastic 1.40 1.39 1.39 1.27 1.53 1.26 1.631150201 Jurassic/Tertiary Fluvial and Lacustrine Sandstone 1.70 1.98 2.08 1.33 6.04 3.87 0.231270101 Tertiary Lacustrine 1.70 1.78 1.78 1.67 1.47 3.41 1.131270102 Pre-Tertiary Buried Hills 1.70 1.79 1.81 1.56 2.61 1.65 1.131280101 Jurassic/Triassic Fluvial and Lacustrine Sandstone 1.70 1.82 1.80 0.72 3.50 1.54 0.031420101 Southeastern Fold Belt 1.48 1.48 1.46 0.86 1.60 1.74 2.931420102 Northwestern Depression/Foldbelt 1.70 1.72 1.69 1.62 2.05 0.83 10.31420201 Jurassic Lacustrine 1.62 1.62 1.62 1.15 2.79 1.09 2.031420401 Leshan-Longnusi Paleohigh 1.70 1.76 1.73 1.78 1.81 1.06 5.631420402 Lower Paleozoic of Southeastern Fold Belt 1.70 2.37 2.35 1.89 2.44 3.19 4.231440101 Subtle Traps 1.70 1.79 1.76 1.33 2.52 0.84 0.031440102 Anticlinal 1.40 1.16 1.17 4.01 0.49 1.09 1.031440201 Structural Traps 1.70 3.00 3.00 3.55 4.84 11.79 0.031540101 Tarim Basin Excluding Marginal Foldbelts 1.70 2.21 2.18 1.53 2.40 3.61 4.031540102 Kuche (Northern) Foldbelt 1.70 3.00 3.00 2.89 3.03 4.41 17.31540103 Southwest Foldbelt 1.70 2.48 2.45 2.01 2.48 3.41 5.2

52 
37010101 Brunei-Sabah Deltaics 1.40 1.38 1.37 1.13 1.70 1.12 1.7

37010102 Brunei-Sabah Turbidites 1.66 1.66 1.65 1.14 1.89 2.20 1.737020101 Central Luconia 1.52 1.52 1.52 1.46 1.28 1.99 1.537020102 Balingian 1.70 2.04 2.04 1.97 3.66 0.74 4.137020201 East Natuna 1.70 2.60 2.57 2.33 2.45 3.24 4.537030101 South Malay Lacustrine 1.62 1.62 1.61 1.09 1.68 2.38 1.937030102 North Malay Lacustrine 1.70 3.00 3.00 2.21 3.69 6.27 29.37030201 South Malay Coaly 1.40 1.38 1.39 1.29 4.89 0.34 1.538080101 Pematang/Sihapas Siliciclastics 1.52 1.52 1.51 1.51 1.03 2.23 2.038170101 Kutei Basin Deltaics 1.44 1.44 1.43 1.33 1.16 1.83 1.738170102 Kutei Basin Turbidites 1.64 1.64 1.62 1.28 1.58 1.96 2.438170103 Kutei Basin Fold and Thrust Belt 1.70 2.31 2.30 1.53 3.12 3.05 3.938220101 North Sumatra 1.70 1.93 1.92 1.82 1.76 2.23 2.338220102 Mergui 1.70 1.89 1.88 1.63 1.86 2.16 2.638240101 Sunda/Asri 1.70 1.74 1.72 1.42 1.66 1.91 3.138240201 Ardjuna 1.62 1.62 1.63 1.11 2.18 2.19 1.138280101 South Sumatra 1.40 1.33 1.32 1.11 1.09 1.76 1.639100101 Barnett 1.70 3.00 3.00 2.26 4.43 9.33 0.039100201 Petrel 1.70 2.27 2.25 1.33 3.08 3.71 6.339100202 Vulcan Graben 1.70 3.00 3.00 2.50 3.71 6.35 0.039100301 Malita 1.70 1.79 1.81 1.17 2.38 3.66 0.839130101 Late Jurassic/Early Cretaceous-Mesozoic 1.51 1.51 1.54 1.71 1.78 1.97 0.639300101 Gippsland 1.70 1.85 1.87 1.51 2.90 1.50 1.539480101 Dingo-Mungaroo/Barrow 1.70 1.84 1.83 1.57 1.95 1.94 2.339480201 Locker-Mungaroo/Barrow 1.67 1.67 1.73 2.25 2.12 2.20 0.440170101 Halten Terrace-Trondelag Platform 1.70 2.31 2.24 2.11 1.61 3.50 28.40170102 Mid-Norway Continental Margin 1.50 1.50 1.49 1.28 1.44 1.66 1.940250101 Viking Graben 1.43 1.43 1.41 1.59 1.07 1.46 2.140250102 Moray Firth 1.62 1.62 1.60 1.33 1.82 1.22 3.840250103 Central Graben 1.67 1.67 1.65 1.26 1.76 1.79 2.740360101 Southern Permian Basin-U.K. Onshore 1.70 2.60 2.58 7.91 0.00 0.00 0.0

53 
40360102 Southern Permian Basin-Europe Onshore 1.40 1.23 1.21 1.06 0.98 1.28 2.2

40360103 Southern Permian Basin-Offshore 1.70 1.73 1.71 1.45 1.58 2.17 2.340470101 Foreland Basin 1.70 1.73 1.74 2.07 1.56 1.62 1.640470201 Deformed Belt 1.70 1.85 1.79 2.16 1.00 2.34 7.040470301 Paleozoic Reservoirs 1.70 3.00 3.00 2.69 4.01 7.03 0.040480101 Greater Hungarian Plain Basins 1.70 1.92 1.91 1.83 1.54 3.17 1.640480201 Zala-Drava-Sava Basins 1.70 1.91 1.93 1.65 3.35 1.07 2.140480301 Danube Basin 1.55 1.55 1.62 2.25 2.59 4.09 0.140480401 Transcarpathian Basin 1.70 3.00 3.00 1228.79 0.00 0.00 0.040480601 Hungarian Paleogene Basin 1.70 2.77 2.76 1.60 5.10 4.94 13.40570101 Transylvanian Neogene Suprasalt Gas 1.40 1.31 1.28 1.16 1.11 1.04 3.840600101 Neogene Flysch Gas 1.60 1.60 1.60 1.09 1.82 1.94 2.040600201 Thermal Triassic 1.40 1.19 1.16 0.80 0.89 1.37 2.840610101 Moesian Platform 1.49 1.49 1.48 1.50 1.28 1.50 2.040610201 Romania Flysch Zone 1.70 2.11 2.07 2.10 1.39 3.26 5.240610202 Romania Ploiesti Zone 1.40 1.26 1.25 0.92 1.35 1.40 1.440680101 Subsalt 1.67 1.67 1.66 1.32 1.62 2.01 2.552000101 Northeast Greenland Shelf Rift Systems 1.60 1.60 1.59 1.34 1.55 1.79 2.052150101 Jeanne d'Arc 1.70 1.83 1.83 1.91 3.30 0.63 3.352430101 Keg River Gas 1.70 1.72 1.71 1.28 1.68 2.71 1.752430102 Keg River Oil and Gas 1.70 1.91 1.89 2.31 1.35 2.27 2.052430201 Leduc Gas 1.70 1.86 1.83 1.79 1.75 1.57 3.852430202 Leduc Oil and Gas 1.51 1.51 1.51 1.37 1.58 1.48 1.752430301 Exshaw-Rundle Gas 1.62 1.62 1.61 1.00 1.79 2.18 2.452430302 Exshaw-Rundle Oil and Gas 1.70 1.91 1.90 1.76 1.98 1.69 2.952430401 Combined Triassic/Jurassic Gas 1.70 1.87 1.88 1.26 3.01 1.77 1.852430402 Combined Triassic/Jurassic Oil and Gas 1.70 2.03 2.03 2.06 2.31 1.49 2.552430501 Manville Gas 1.70 2.34 2.33 2.14 2.36 2.84 2.252430601 Second White Specks-Cardium Gas 1.70 2.76 2.67 3.79 1.51 2.17 0.052430602 Second White Specks-Cardium Oil and Gas 1.70 1.95 1.97 1.96 2.22 2.04 1.2

54 
52440101 Yeoman Oil 1.70 3.00 3.00 3.61 5.68 15.25 0.0

52440201 Brightholme Oil 1.70 3.00 3.00 1.47 75.14 0.00 0.052440301 Bakken Sandstone 1.70 3.00 3.00 1.84 23.50 0.00 0.052440401 Lodgepole Oil 1.40 1.31 1.32 1.22 1.29 2.26 0.653050101 El Abra-Like Reef and Backreef Limestone 1.70 1.70 1.70 2.59 1.01 2.09 1.553050102 Tamabra-Like Debris-Flow-Breccia Limestone Overlying Evaporites 1.40 1.38 1.38 1.13 1.35 1.86 1.253050103 Tamabra-Like Debris-Flow-Breccia Limestone and Overlying Strata 1.70 1.88 1.85 1.73 1.39 3.09 2.453050104 Tamabra-Like Debris-Flow-Breccia Limestone of the Golden Lane 1.40 1.20 1.18 0.55 1.40 1.43 1.753050105 

Tamaulipas-Like Basinal Limestone and Tertiary Strata Without Underlying Evaporites 

1.40 1.01 1.04 1.94 1.31 0.51 1.0

53050106 Tamaulipas-Like Basinal Limestone and Tertiary Strata Overlying Evaporites 1.70 2.10 2.09 1.28 2.63 3.81 2.753050107 Tamabra-Like Debris-Flow-Breccia Limestone North of Campeche 1.70 1.82 1.81 1.51 1.78 2.14 2.660210101 Late Cretaceous-Tertiary Turbidites 1.44 1.44 1.43 1.21 1.38 1.60 1.860210102 Cretaceous Carbonates 1.70 1.78 1.77 1.45 1.75 2.11 2.560210103 Late Cretaceous-Tertiary Nearshore Sandstones 1.70 1.83 1.81 1.49 1.79 2.17 2.660220101 Amazon Delta and Submarine Fan 1.70 2.10 2.07 1.76 1.82 3.21 3.360290101 Western Pre-Aptian Reservoirs 1.40 1.24 1.26 1.68 1.26 1.74 0.560290102 Late Cretaceous-Tertiary Deep-Water Sandstones 1.40 1.37 1.36 1.01 1.47 1.30 2.360340101 Espirito Santo Shelf 1.70 1.88 1.88 0.57 7.20 1.03 0.060340102 Late Cretaceous-Tertiary Slide Blocks and Turbidites 1.45 1.45 1.44 1.20 1.39 1.62 1.960340103 Abrolhos Sub-Volcanic Structures 1.70 1.75 1.74 1.52 1.70 1.95 2.260350101 Late Cretaceous-Tertiary Turbidites 1.40 1.30 1.28 1.08 1.05 1.44 2.260350102 Cretaceous Carbonates 1.56 1.56 1.58 1.08 1.97 3.52 0.560350103 Salt Dome Province Tertiary Sandstones 1.59 1.59 1.58 1.28 1.53 1.86 2.260360101 Santos Shelf 1.63 1.63 1.62 1.46 1.53 1.80 2.1

55 
60360102 Salt-Structured Deep-Water Sandstones 1.70 1.76 1.75 1.38 1.73 2.16 2.7

60370101 Pelotas Platform and Basin 1.70 1.95 1.93 1.55 1.94 2.44 3.060410101 Hollin-Napo 1.40 1.24 1.23 1.05 1.27 1.18 1.560410201 Ene 1.70 2.75 2.71 1.98 3.04 5.26 13.60450101 Sub-Andean Fold and Thrust Belt 1.40 1.35 1.34 1.67 1.02 1.48 1.460450102 Foreland Basins 1.66 1.66 1.65 1.25 1.74 1.93 2.360450103 Foreland Central Chaco High 1.68 1.68 1.67 1.37 1.64 1.96 2.360550101 Neuquen Extensional Structures 1.63 1.63 1.63 1.23 1.49 2.78 1.560550102 Neuquen Foothills Structure 1.52 1.52 1.50 1.13 1.40 1.85 2.660550103 Dorsal de Neuquen Structure 1.70 1.84 1.86 3.31 0.87 5.13 0.560580101 San Jorge Extensional Structures 1.40 1.22 1.23 1.20 1.65 0.89 1.260580102 San Bernardo Fold Belt Structures 1.70 1.76 1.78 2.32 2.80 0.73 1.660590101 Magallanes Extensional Structures 1.59 1.59 1.60 1.36 1.93 1.58 1.460590102 Andean Fold Belt Structures 1.70 2.28 2.26 1.85 2.33 2.95 3.760600101 North Falklands Basin 1.70 1.83 1.82 1.63 1.80 2.02 2.360600201 South Falklands Basin 1.70 1.83 1.82 1.63 1.80 2.02 2.360630101 Malvinas Extensional Structures 1.70 1.98 1.96 1.63 1.96 2.39 2.960810101 Cretaceous-Paleogene Basin 1.44 1.44 1.43 0.91 1.69 1.59 2.060830101 Neogene Pull-Apart Basin 1.45 1.45 1.46 1.13 1.47 3.12 0.660830201 Cretaceous-Paleogene Santa Elena Block 1.62 1.62 1.65 1.84 1.83 2.20 0.660900101 Northern 1.40 1.35 1.38 1.43 1.82 2.25 0.360900102 Southern 1.40 1.17 1.18 1.01 1.70 0.91 1.160900103 Eastern 1.70 2.22 2.21 1.28 3.37 3.16 4.160900104 La Luna and Older 1.70 2.82 2.79 2.38 2.83 3.88 6.760960101 Central 1.40 1.19 1.18 1.01 0.80 1.88 1.460960102 Peripheral 1.40 1.28 1.28 1.09 1.40 1.35 1.260980101 East Venezuela Fold and Thrust Belt 1.40 1.18 1.18 1.10 1.23 1.31 1.060980102 Guarico Sub-Basin 1.40 1.37 1.36 1.19 1.17 1.70 1.660980103 Maturin Sub-Basin 1.40 1.21 1.22 1.20 1.23 1.48 0.960980201 Trinidad Basins 1.43 1.43 1.42 1.82 1.02 1.78 1.260980202 Orinoco Delta and Offshore 1.62 1.62 1.61 1.42 1.57 1.78 2.0

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57 

60990101 Main Maracaibo Basin 1.40 1.38 1.37 1.00 1.45 1.50 1.960990102 Southwest Maracaibo Basin Fold Belt 1.41 1.41 1.40 1.28 1.12 1.69 2.161030101 Carupano Basin Gas 1.40 1.39 1.40 1.38 1.61 1.18 1.461070101 Inner Forearc Deformation Belt 1.70 1.72 1.71 1.38 1.68 2.05 2.561170101 North Cuba Fold and Thrust Belt 1.64 1.64 1.63 1.61 1.47 1.87 1.770130101 Coastal Plain and Offshore 1.52 1.52 1.51 1.09 1.38 2.26 2.071830101 Coastal Plain and Offshore 1.70 1.71 1.68 1.37 1.49 2.04 3.771920101 Agbada Reservoirs 1.40 1.39 1.38 1.19 1.34 1.37 2.171920102 Akata Reservoirs 1.51 1.51 1.50 1.23 1.46 1.72 2.072030101 Gabon Subsalt 1.68 1.68 1.64 2.17 1.12 1.09 15.72030201 Gabon Suprasalt 1.61 1.61 1.59 1.53 1.37 1.45 3.772030301 Central Congo Delta and Carbonate Platform 1.40 1.32 1.32 1.08 1.51 1.31 1.372030302 Central Congo Turbidites 1.70 2.09 2.06 1.64 1.86 3.71 2.872030401 Cuanza-Namibe 1.40 1.37 1.37 1.34 1.71 1.09 1.373030101 Offshore 1.44 1.44 1.45 1.53 1.10 3.68 0.580260101 Kohat-Potwar Intrathrust Basin 1.70 1.83 1.77 1.22 0.87 52.85 0.080340101 Sylhet-Kopili/Barail-Tipam Composite 1.53 1.53 1.56 1.25 3.37 1.11 0.880420101 Greater Indus Foreland and Foldbelt 1.57 1.57 1.57 1.18 1.90 1.69 1.680420102 Indus Fan 1.70 2.08 2.06 1.79 2.05 2.43 2.980430101 Eocene-Miocene Bombay Shelf 1.70 1.72 1.71 1.22 1.95 2.34 1.780430102 Eocene-Miocene Cambay Deltaic 1.60 1.60 1.60 1.37 2.00 1.35 1.880470201 Western Shelf and Slope 1.70 1.74 1.73 1.57 1.71 1.87 2.080470301 Central Basin 1.46 1.46 1.46 1.26 1.40 1.60 1.880470302 Eastern Fold Belt 1.40 1.38 1.38 1.13 1.17 2.25 1.180480101 Central Burma Basin 1.66 1.66 1.64 1.17 1.62 2.02 3.380480102 Irrawaddy-Andaman 1.40 1.35 1.34 1.21 1.30 1.21 2.0
The base case r given in table 7 is that calculated by algorithms as close as possible to 

those used in 1995. The differences between the 1995 methodology and that used in the base 

case for the analog database were that the calculations were done at an assessment unit scale 

rather than at a province scale (table 6, assumption 13) and the distinction that the modal class 

was not always class 6 (because of use of the lognormal distribution). 

Assumption 2 restricts r to a range from 1.4 to 1.7. Tests were run allowing the broader 

range of 1.0 to 3.0; table 7 refers to this as the extended base case. Even more extreme values of 

r would have resulted if these limits were further extended. The extended base case was the case 

used for all the other sensitivity tests. That is, the other sensitivity tests used r’s limited to the 

range from 1.0 to 3.0. 

Assumption 3 refers to the use of the original 1995 least squares method to calculate r. 

To examine the sensitivity of assumption 3, a maximum likelihood measure was used to choose 

r. The maximum likelihood calculation was based on the fit to a multinomial distribution 

(“multinomial r” in table 7); the results (table 7) show no significant change in r. 

An additional examination of assumption 3 was done with calculations of r based on 

individual ratios of counts in pairs of classes above the mean. The column in table 7 entitled 

“ratio 1 / 2” gives the ratio of the count of the class one step above the modal class to that of the 

count of that class two steps above the modal class. The columns “ratio 2 / 3,” “ratio 3 / 4,” and 

“ratio 4 / 5” are defined similarly. The “mean ratio” is the mean of these four values. As 

expected, “ratio 1 / 2” is closest to the extended base case value. Because of small numbers of 

fields in some of the higher classes, “ratio 4 / 5” can have extreme values, which affects the 

“mean ratio” also. 

The columns “1.4 mean” and “1.38 mean” in table 7 examine the sensitivity of 

assumption 4 of table 6. They show minimal changes from the extended base case. 

The “no <1 constraint” column in table 7 relates to assumption 5 of table 6. In the 

original 1995 methodology, any bins with a count less than 1 were not used in the calculations. 

By dropping this constraint, several assessment units with low field counts showed changes in r. 

Unresolved Issues 

The main unresolved issue for the methodology used in constructing our analog database 

is the form of field-size population distributions. An extensive literature argues that the 

underlying distribution of field sizes is either lognormal (Kaufman, 1993; Rose, 1994) or Pareto 

(Schuenemeyer and Drew, 1983; Houghton, 1988). Attanasi and Charpentier (2002) studied the 

effects of distribution type on the 1995 U.S. National Oil and Gas Assessment distributions. One 

alternative to the Pareto procedure used in our database may be to fit the small-field end of the 

distribution using robust regression estimation procedures. 

58 
Another important unresolved issue is the constraining of r to values of 1.4 to 1.7. In the 

original 58 assessed provinces of the 1995 National Assessment, 16 values were calculated as 1.4 

and 29 values as 1.7 (table 8). Only 13 provinces had values of r between 1.4 and 1.7. Thus, the 

constraint significantly affected the calculation of r. 

Table 8. Calculated r values by province from the 1995 USGS National Assessment (Gautier and others, 

1995). 

 

Province * Calculated r 

1 Northern Alaska 1.40 

2 Central Alaska 1.58 

3 Southern Alaska 1.40 

4 Western Oregon-Washington 1.70 

5 Eastern Oregon-Washington 1.70 

7 Northern Coastal 1.70 

8 Sonoma-Livermore Basin 1.70 

9 Sacramento Basin 1.55 

10 San Joaquin Basin 1.40 

11 Central Coastal 1.70 

12 Santa Maria Basin 1.40 

13 Ventura Basin 1.40 

14 Los Angeles Basin 1.40 

17 Idaho-Snake River Downwarp 1.70 

18 Western Great Basin 1.68 

19 Eastern Great Basin 1.70 

20 Uinta-Piceance Basin 1.70 

21 Paradox Basin 1.70 

22 San Juan Basin 1.52 

23 Albuquerque-Santa Fe Rift 1.70 

24 Northern Arizona 1.70 

25 Southern Arizona-South West New Mexico 1.40 

27 Montana Thrust Belt 1.41 

28 Central Montana 1.70 

29 Southwest Montana 1.70 

31 Williston Basin 1.70 

33 Powder River Basin 1.69 

34 Big Horn Basin 1.40 

35 Wind River Basin 1.40 

36 Wyoming Thrust Belt 1.40 

37 Southwest Wyoming 1.40 

38 Park Basins 1.70 

39 Denver Basin 1.70 

40 Las Animas Arch 1.40 

41 Raton Basin-Sierra Grande Uplift 1.70 

43 Palo Duro Basin 1.70 

44 Permian Basin 1.42 

45 Bend Arch-Fort Worth Basin 1.70 

59 
46 Marathon Thrust Belt 1.70 

47 Western Gulf 1.40 

49 Louisiana-Mississippi Salt Basins 1.58 

50 Florida Peninsula 1.40 

51 Superior 1.70 

53 Cambridge Arch-Central Kansas Uplift 1.70 

55 Nemaha Uplift 1.70 

56 Forest City Basin 1.70 

58 Anadarko Basin 1.51 

59 Sedgwick Basin 1.57 

60 Cherokee Basin 1.46 

61 Southern Oklahoma 1.49 

62 Arkoma Basin 1.40 

63 Michigan Basin 1.70 

64 Illinois Basin 1.65 

65 Black Warrior Basin 1.70 

66 Cincinnati Arch 1.70 

67 Appalachian Basin 1.70 

68 Blue Ridge Thrust Belt 1.70 

69 Piedmont 1.40 

* Only those provinces with small-field assessments are included in this table. 

The present USGS assessment methodology uses a truncated shifted lognormal 

distribution to approximate the numbers and sizes of fields larger than some minimum field size. 

If a small-field extrapolation is applied to this distribution, additional fields are added to the size 

class or classes just above the minimum field size. This leads to two different numbers of fields 

larger than the minimum: the number of fields larger than size X (as originally assessed) versus 

the number of fields larger than size X (with small field extrapolation). The median and mean 

field sizes above the minimum field size are similarly affected. Additional methodological work 

is needed to connect the values in the analog database (which include the small-field correction) 

with the values needed for the assessment input form, which not only do not include the smallfield correction but also are for the undiscovered portion of the field population. 

Appendix 2. Database Files 

For those users with Microsoft Excel, the following workbook includes the complete 

analog database as well as several additional tools to aid use of the database. 

WorldAnalogs.xls 

LINK TO DOWNLOAD FILE HERE 

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61 

For those users without Microsoft Excel, the database is distributed among eight files. 

Each file is tab-delimited to make it easy to import into the spreadsheet program of your choice. 

The additional tools are not included. The eight files are: 

WA_geology.tab the classification variables 

WA_oil.tab the utility variables for oil fields 

WA_gas.tab the utility variables for gas fields 

WA_BOE.tab the utility variables for all fields (in BOE) 

WA_oilbins.tab the counts of discovered and undiscovered oil fields by size class 

WA_gasbins.tab the counts of discovered and undiscovered gas fields by size class 

WA_BOEbins.tab the counts of discovered and undiscovered all fields (in BOE) by size class 

WA_ancillary.tab the utility variables for ancillary data 

LINKS TO DOWNLOAD FILES HERE