USGS Methodology for Assessing Continuous 

Petroleum Resources

By Ronald R. Charpentier and Troy A. Cook 

Open-File Report 2011–1167 

U.S. Department of the Interior 

U.S. Geological Survey 
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia 2011

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Charpentier, R.R., and Cook, T.A., 2011, USGS Methodology for Assessing Continuous Petroleum 

Resources: U.S. Geological Survey Open-File Report 2011–1167

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1 

USGS Methodology for 

Assessing Continuous 

Petroleum Resources

Ronald R. Charpentier & Troy Cook

U.S. Geological Survey

charpentier@usgs.gov tcook@usgs.gov
2 

Outline

• Principles of resource assessment

• Previous methods of assessing continuous 

resources

• Methodology issues addressed by the revised 

USGS methodology
3 

Outline (continued)

• Revised USGS methodology for assessing 

continuous resources

– Defining shale-gas assessment units (AU)

– Walk through the revised USGS methodology

• Applying analog data in data-poor AUs

• Data requirements for shale-gas AUs
4 

Principles of

Resource Assessment

• Resource assessments should be 

fundamentally based on geology.

• Probabilistic methods should be used.

• Assessment methodology is a means of 

quantifying geologic hypotheses and 

uncertainties.
5 

Previous Methods of 

Assessing Continuous 

Resources
6 

In-Place Methods

• Volumetric calculation

– Area * thickness * porosity * hydrocarbon 

saturation * temperature/pressure corrections

• Recovery factors generally conjectural

• Primary method used by the assessment 

community prior to 1995

• Still used extensively outside USGS

Commonly, the recovery factors for continuous accumulations have been poorly understood. 
7 

Productivity-Based Methods

• Based on productivities of cells

– Cells are roughly the size of the drainage area 

of a well

• Well productivities determined by declinecurve analysis

– Estimated ultimate recovery (EUR)

Productivity-based methods use drilled wells as “computers” to calculate recoverable resources.
8 

Creating an EUR from a Decline Curve

Natural Gas Production Fit and Forecast

1

10

100

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 96 102 108 114 120

Normalized Month of Production

MMCF

Production

Hyperbolic Fit

Extrapolation

A hyperbolic decline curve was fitted to the mean production decline of six Barnett Shale gas 

wells. This example demonstrates a calculation of estimated ultimate recovery (EUR) based on a 

90-month forecast of the expected decline.
9 

Productivity-Based Methods

• Estimates of numbers of cells based on:

– Area of assessment unit (AU)

– Percent of area already tested

– Area of a cell

• In USGS methodology, all of these are 

estimated as probability distributions
10

Advantages of

Productivity-Based Methods

• Well productivities (EURs) make 

assessments more realistic

• Much more production data available now 

compared to 30 years ago

– Many more wells with longer producing 

histories

– Many more plays
11

Disadvantages of

Productivity-Based Methods

• Assessments are tied to current engineering 

practice.

– No estimate of volumes recoverable by future 

technologies

• Large data requirements

Some organizations that need to study the effect of radical changes in production technology 

may wish to supplement these methods with in-place calculations. Although the methods have 

large data requirements, analog data can be used for areas that are data-poor. 
12

Methodology Issues 

Addressed by the 

Revised USGS Methodology
13

Applicable to Frontier

Assessment Units (AUs)

• No or little previous production

• Little information about well performance 

parameters

– Success ratios

– Drainage areas

– Estimated ultimate recoveries (EURs)

• Possibility of zero potential

• Solved by use of analogs
14

Accounts for PoorlyDeveloped Geologic and 

Engineering Models • Understanding of geologic and engineering 

controls on productivity is not as refined as 

for conventional petroleum.

• Still very hard to estimate ahead of drilling

• Great improvements in last decade

– Lots of models giving partial explanations of 

controls on productivity

• Revised method is robust.
15

Better Handling of Complex 

Historical Information

• Complex patterns

– For example, trends in EUR with time

• Not as “well behaved” as some classic 

conventional data sets

– Discovery process models less applicable

• Multiple trends going on at once

– Analysis and interpretation needed
16

This graph presents the EUR for 1,417 vertical wells in the Barnett Shale as of 2003. It shows 

the distribution of EURs for those wells with an EUR of at least 0.02 billion cubic feet (bcf). 

The percentiles indicate what percent of the wells have an EUR of at least the indicated amount. 

Note that the range of EURs is greater than two orders of magnitude. (1 billion cubic feet = 

approximately 28 million cubic meters). 
17

This graph adds lines for those wells used in the preceding graph with single completions 

(1,240), dual completions (156), and triple completions (21). 
18

Peak Monthly Production for All Barnett Shale 

Gas Wells

All Barnett Shale Wells Vertical and Horizontal Wells

vertical

horizontal

These box-and-whisker plots show the distribution of peak monthly production for Barnett Shale 

vertical wells as of 2003. Peak monthly production is generally the production value for the first 

full month of production. Note the increase in information that results when the full distribution 

(on the left) is broken into two subgroups (on the right) showing much higher peak monthly 

production values for horizontal wells as compared to vertical wells. The box-and-whiskers 

plots show the parameters of the distributions: the green line is the mean, the top and bottom of 

the red box are the F25 and F75 fractiles, and the top and bottom of the lines are the F10 and F90 

fractiles. MMCF, million cubic feet. 
19

Peak Monthly Production for Barnett Shale 

Vertical And Horizontal Wells

The same data from the previous slide can be further subdivided to give even more information. 

Here the wells are divided into subgroups by order of drilling to show trends in peak monthly 

production with time. Vertical wells show a decrease with time, whereas horizontal wells have 

had relatively stable peak monthly production with time.
20

Even more subdivision provides additional information on data trends. 
21

Min Data

This graph shows trends in the average peak monthly production with time, separately for 

vertical and horizontal wells. MCF, thousand cubic feet. 
22

Allows Mixture of Sweet and 

Nonsweet Populations

• Study of some continuous deposits suggests 

that the productivities within a continuous 

deposit are a mixture of two populations

– Most clearly seen in some tight gas sands

– Not all deposits show this

• How do you define sweet?

– Are sweet spots just the high end of some 

distribution, or are they evidence of a mixture?
23

Barnett Shale Wells, circa. 2001

The spatial distribution of well productivity reflects spatial changes in those geologic and 

engineering characteristics that control the productivity. This map shows vertical wells drilled in 

the Barnett Shale as of 2001, defining a sweet spot. (20 miles = approximately 32 kilometers). 
24

Barnett Shale Wells, circa. 2009

This map shows a great expansion of the Barnett play that took place between 2001 and 2009. 

Most of the new wells were horizontal wells.
25

Fixes the Problem of

EUR Distribution “Collapse”

• A major methodological problem for 

assessing continuous resources is the effect 

on the EUR distribution of using thousands 

of cells/wells.

• Same result as if a single mean EUR value 

was used instead of an EUR distribution

– EUR distribution “collapses” around the mean.

When assessment models aggregate thousands (or tens of thousands) of EURs from an EUR 

distribution, the result is close to merely multiplying the number of cells by the mean EUR.
26

This graph shows a comparison of simulations performed with a shifted truncated lognormal 

distribution of EURs versus simulations with a constant mean EUR. When there are more than 

100 cells, the standard deviation of the resulting estimate of gas volume is practically the same 

whether a variable or a constant EUR is used. Most assessments are for thousands or tens of 

thousands of cells. This phenomenon is termed EUR distribution “collapse” around the mean. 
27

Fixes the Problem of

EUR Distribution “Collapse”

• Older methodology thus acts as if the mean 

of the EUR distribution was known exactly.

• In frontier AUs, the uncertainty about the 

mean EUR is a major part of the uncertainty 

in the estimate.

• Revised methodology directly estimates the 

uncertainty of the mean EUR.

The older assessment model acts as if the mean were known exactly. This underestimates the 

uncertainty in the result, especially for frontier AUs where the mean EUR is uncertain.
28

Revised USGS Methodology

for Assessing

Continuous Resources
29

 Features of

Revised USGS Methodology

• Wells instead of cells

• Risk more explicit

– AU risk, area risk, well risk

• Option to have mixture of two populations

– Sweet spots and nonsweet spots

• Direct estimation of uncertainty of mean 

EUR
30

 Robustness of

Revised USGS Methodology

• The revised methodology works for both datarich and data-poor areas.

– Analogs used for data-poor areas

• The revised methodology is not tied to a 

specific geologic model.

– As better understanding of geologic and 

engineering controls develops, this can be 

accommodated in the assessments.
31

Defining Shale-Gas 

Assessment Units

Before conducting a quantitative assessment, one must define assessment units and select which 

ones will be quantitatively assessed. A set of minimum requirements is needed to determine 

which proposed units may have significant resources to assess. These criteria are also used in the 

quantitative assessment itself. As an example, the minimum requirements for shale-gas 

assessment units are presented here.
32

Minimum Requirements

• Based on current USGS thinking of what is 

needed to have a successful shale-gas AU

• Could change in the future with increased 

geologic understanding

• This list is specific to thermally generated 

gas in its source rock.

• Biogenic gas would have different 

minimum requirements.
33

Minimum Requirements

• TOC > 2 weight percent

• Kerogen type I, II, or IIS

• Ro > 1.1 percent

• Net thickness > 15 meters

• Gas is thermogenic

TOC, total organic carbon; Ro, vitrinite reflectance. 
34

Desirable Characteristics

• High gamma-ray values in shale

• Hydrogen index of kerogen greater than 250 

mg/g

• Depth greater than 1,500 meters

• Not intensely structured

• Overpressured

Mg/g, milligrams per gram. 
35
36

AU Risk

• Based on 5 minimum requirements

• What is the probability that all 5 

requirements are met at some place within 

the AU?

– This shows the need for mapping the extents of 

the 5 requirements.
37

AU Risk – Alternate Questions

• What is the probability that at least one well 

within the AU will have production 

capacity of at least the minimum EUR (0.02 

bcf)?

• What is the probability that the petroleum 

system exists?

• Conversely, what is the probability that the 

AU will have no producible resources?
38

AU Risk

Assessment-Unit Probability:

What is the probability that an accumulation with producible resources exists?

This is the part of the official input form that concerns AU risk. 
39

Line 1 – Productive Area

• Again, based on 5 minimum requirements

• How large is the area within the AU where 

all 5 requirements are met?

– This shows again the need for mapping the 

extents of the 5 requirements, and 

understanding the uncertainties about their 

extents.
40

Line 1 – Productive Area

1. Total assessment-unit area (acres): (triangular)

calculated mean minimum mode maximum

In this line and others, the distribution represents the assessors’ uncertainty about a single value 

that exists in nature. (There exists one correct value of how large an area meets the five 

minimum requirements.) Distributions in nature are rarely triangular, but this is a distribution 

within the minds of the assessors. The minimum, mode, and maximum values of a triangular 

distribution are reasonable values to ask of an assessor to describe his/her uncertainty. 
41

Line 2 – Drainage Areas

• What is the average drainage area of a 

production well in this AU?

• Uncertainty about a fixed value

• Contingent on a development scheme

– Horizontal vs vertical wells

– Development scheme must be consistent with 

that implied by the EUR distribution
42

Line 2 – Drainage Areas

2. Uncertainty about average drainage area of wells (acres): (triangular)

calculated mean minimum mode maximum
43

Line 3 – Untested Area

• What percent of the area from line 1 is 

untested?

• Requires interpretation of previously drilled 

wells in the interval

– What constitutes a production test?

– Previous wells (perhaps vertical) may have tested 

less area each than the wells (perhaps horizontal) 

of the development scheme of line 2.

Not all penetrations of a stratigraphic interval are tests of that interval. Interpretation of those 

wells is needed, but the number of tests may be uncertain. Date of drilling may provide 

information, in that wells drilled before the recognition of production potential in that interval 

are unlikely to be tests (especially in an underpressured interval). 
44

Line 3 – Untested Area

3. Percentage of total assessment-unit area that is untested (%): (triangular)

calculated mean minimum mode maximum
45

Line 4 – Sweet vs Non-sweet

• Within the untested area (as defined by lines 

1 and 3), what percent of the area falls 

within sweet spots?

• Can be 100 percent if assessors choose to 

model the accumulation with one 

population (one success ratio and one EUR 

distribution) rather than two
46

Line 4 – Sweet vs Non-sweet

4. Percentage of untested assessment-unit area in sweet spots (%): (triangular)

calculated mean minimum mode maximum
47

Line 5a – Success Ratio

• Within the untested sweet spots (as defined 

by lines 1, 3, and 4), what percent of wells 

will have productivity greater than the 

minimum?

• Line 6a is the equivalent for the non-sweet 

spots.

Some of the non-successes would have no or insufficient flow to result in completing the well as 

a producer. Other non-successes would be completed as producers, but have an EUR less than 

the minimum.
48

Line 5a – Success Ratio

5a. Future success ratio (%): (triangular)

calculated mean minimum mode maximum
49

Line 5b – EUR Distribution

• Within the untested sweet spots (as defined 

by lines 1, 3, and 4), what is the average

EUR?

• Uncertainty about a fixed value

• Development scheme must be consistent 

with that implied by the drainage area

• Line 6b is the equivalent for the non-sweet 

spots.

Experimentation with analog data suggested that the uncertainty about the average EUR could be 

more skewed that can be reasonably represented by a triangular distribution. Thus a shifted 

truncated lognormal distribution is used here. 
50

Line 5b – EUR Distribution

5b. Uncertainty about average EUR (mmbo for oil; bcfg for gas): (shifted truncated lognormal)

calculated mean minimum median maximum

mmbo, million barrels of oil; bcfg, billion cubic feet of gas. 
51

Coproduct Ratios

• Each continuous AU has a primary product 

of either oil or gas.

• Uncertainty about the average ratios

• Oil AU

– Gas to oil ratio

– NGL to gas ratio

• Gas AU

– Liquids to gas ratio

NGL, natural gas liquids. 
52

Coproduct Ratios

Oil assessment unit: minimum mode maximum

 Gas/oil ratio (cfg/bo)

 NGL/gas ratio (bngl/mmcfg)

Gas assessment unit:

 Li quids/gas ratio (bliq/mmcfg)

(triangular)

UNCERTAINTY ABOUT AVERAGE COPRODUCT RATIOS FOR UNTESTED WELLS

cfg/bo, cubic feet of gas per barrel of oil; bngl/mmcfg, barrels of natural gas liquids per million 

cubic feet of gas; bliq/mmcfg, barrels of liquids per million cubic feet of gas. 
53

Ancillary Data

• Not used in the volumetric calculations

• Useful for cost analysis or supply modeling
54

Ancillary Data

Oil assessment unit: minimum median maximum

 A PI gravity of oil (degrees)

 S ulfur content of oil (%)

 Depth (m) of water (if applicable)

 Drilling depth (m) minimum F75 median F25 maximum

(no specified distribution type)

SELECTED ANCILLARY DATA FOR UNTESTED WELLS

There is a 75 percent chance of the depth being greater than the F75 value. Similarly, there is a 

25 percent chance of the depth being greater than the F25 value. 
55

Allocations

• Commonly need to report by geographic 

entity (country, state, province; onshore vs 

offshore)

• Point estimates of percent of resource 

volume

– Problems with distributions constrained to add 

to 100 percent

• Default is to use areal percent determined 

by GIS
56

Allocations

Surface Allocations (uncertainty of a fixed value)

1. is % of the AREA of the AU

mean VOLUME % in entity

2. is % of the AREA of the AU

mean VOLUME % in entity

ALLOCATIONS OF POTENTIAL ADDITIONS TO RESERVES TO STATES
57

Applying Analog Data

in Data-Poor AUs
58

EUR Analog Sets

• For this presentation, preliminary analog 

sets were built based on the USGS estimates 

of future EUR distribution for 100+ AUs.

• Analog sets can similarly be built for other 

well-performance parameters, such as well 

drainage areas

Of the 100+ AUs for continuous oil and gas resources, 21 shale-gas AUs will be used in the 

following graphs.
59

This graph shows 21 shale-gas EUR distributions, based on USGS assessments of undiscovered 

shale-gas resources. Each distribution is a truncated shifted lognormal, and is therefore a smooth 

curve. The graph thus represents the “distribution of the distributions.” Each distribution is a 

USGS estimate of the EUR distribution for undrilled productive cells of a particular assessment 

unit. This graph is termed a “spaghetti plot” which shows how EUR distributions vary for 

different shale-gas assessment units. The overall area defined by the variation in EUR 

distributions is termed “the cloud.” 
60

First Generation Analog Set

• Good approximation of full EUR 

distribution

– Estimates of future EUR distribution were 

always close to EUR distribution of previously 

drilled wells

– Effect of earlier (learning curve) wells 

minimized

– Effect of geological differences in undrilled 

areas included

Defining the cloud by using USGS estimates of EUR distributions of undrilled cells gives a good 

rough approximation of the range of distributions. Assessments have been conducted over the 

last decade in a wide variety of reservoirs, using a variety of completion practices, and thus the 

present analog set probably captures much of the range of distributions based on current 

technology. 
61

This graph adds the EUR distributions for three recent USGS sets of shale-gas wells, plotted 

against the cloud shown previously, to put the three distributions in context. The three 

additional curves are not smooth because they are based on actual well data and not on fitted 

distributions. 
62

Distributions Estimated for Undrilled

Distributions Fit to Drilled

In this graph, the EUR distributions for eleven sets of shale-gas wells are plotted against the 

cloud to provide context. These red curves are smooth because distributions have been fitted to 

the data.
63

This graph presents the estimated ultimate recovery (EUR) for 1,417 vertical wells in the Barnett 

Shale as of 2003. It shows the distribution of EURs for those wells with an EUR of at least 0.02 

bcf. The percentiles indicate what percent of the wells have an EUR of at least the indicated 

amount. Note that the range of EURs is greater than two orders of magnitude. This graph also 

has lines for single completions (1,240), dual completions (156), and triple completions (21). (1 

billion cubic feet = approximately 28 million cubic meters). 
64

The data from the previous graph is now plotted against the cloud to provide context.
65

Choosing Analogs

• Use multiple analogs, not just the single 

“best” analog.

– Multiple analogs give some measure of 

variability.

• Analogs for drainage areas and EURs must 

be based on similar development schemes

– For example, horizontal versus vertical wells
66

Modifying Analogs

• Analogs may be modified in such cases as:

– Increased EURs where the productive section is 

so thick as to accommodate additional 

completions
67

Data Requirements

for Shale-Gas AUs

Again, shale gas will be used as an example to show what data should be collected and analyzed 

as part of an assessment.
68

Geologic Data

• Where possible, data should be mapped and 

contoured.

• Uncertainty of data values is important.

• This is not an exhaustive list. Any other 

data that may be relevant to productivity 

should be included.

• Geologic data requirements for other 

continuous resources are similar.
69

Shale Characteristics

• Extent of shale

• Thickness of shale

– Gross thickness

– Thickness of highly organic shale

• Structure maps
70

Shale Characteristics

• Lithofacies

• Mineralogy

• Natural fractures

• Regional stress field
71

Shale Maturation

• Total organic carbon content

• Organic composition

• Maturity indicators

• Thermal history models
72

Exploration and Production

• Well locations

• Production tests

• Decline-curve analyses based on production 

histories 
73

Additional Information at:

energy.cr.usgs.gov/

oilgas/noga/methodology.html

charpentier@usgs.gov

tcook@usgs.gov

This website has links to references concerning the revised USGS methodology, as well as 

references concerning previous methodologies.