Improved USGS Methodology for Assessing 

Continuous Petroleum Resources Using Analogs

By Ronald R. Charpentier and Troy Cook

Open-File Report 2010–1309

U.S. Department of the Interior

U.S. Geological Survey 
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

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

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

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

http://www.usgs.gov or call 1-888-ASK-USGS

For an overview of USGS information products, including maps, imagery, and 

publications, visit http://www.usgs.gov/pubprod

To order this and other USGS information products, visit http://store.usgs.gov

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 materials contained within 

this report.

Suggested citation:

Charpentier, R.R., and Cook, Troy, 2010, Improved USGS methodology for assessing 

continuous petroleum resources using analogs: U.S. Geological Survey Open-File

Report 2010–1309, 27 p.
This talk was originally presented at the Third Symposium on Resource Assessment 

Methodologies in Canmore, Alberta, on September 17, 2010.

1
2
The current USGS methodology uses well production data, as opposed to volumetric 

calculations based on area, thickness, porosity, and hydrocarbon saturation.

3
4
There is need to be able to make assessments for areas that are data-poor. These 

changes will also lead to improved assessments for areas that are data-rich.

5
The “Lake Wobegon” effect is where all the wells are better than average.

6
The improved methodology is not tied to specific geologic and engineering models, but 

is robust enough to take into account improvements in geologic and engineering 

understanding.

7
Historical production data from data-rich areas show complex patterns that cannot be 

interpreted by simple models. Instead, significant effort is commonly required to 

interpret the data trends.

8
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 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.

9
This graph adds lines for those wells used in the preceding graph with single 

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

10
The spatial distribution of well productivities is not well understood, but in many cases 

the highly productive wells seem to be concentrated spatially in sweet spots.

11
When assessment models aggregate thousands (or tens of thousands) of EURs from an 

EUR distribution, the result is very close to merely multiplying the number of cells by 

the mean EUR. Thus the 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 very uncertain.

12
13
This change leads to less confusion about wells versus cells.

14
Risk in the new methodology is subdivided into three categories: well risk, area risk, 

and assessment unit (AU) risk.

15
16
This makes the improved methodology much more applicable to frontier areas.

17
Of the 100+ AUs for continuous oil and gas resources, 21 shale-gas AUs will be used for 

the example in the following graphs.

18
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 thus is a smooth curve. The graph thus presents 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 can vary for different shale-gas assessment 

units. The overall area defined by the variation in EUR distributions is termed “the 

cloud.”

19
Defining the cloud by using USGS estimates of EUR distributions of undrilled cells gives 

a good 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 the present sample captures much of the range of distributions based on 

current technology.

20
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.

21
In this graph, the EUR distributions for 11 sets of shale-gas wells are plotted against the 

cloud to put them into context. These red curves are smooth because the data have 

been fitted to distributions.

22
This is a repeat of the graph shown on a previous slide.

23
The data from the previous graph are now plotted against the cloud to put them in 

context.

24
Although these graphs presented a reasonable first approximation for the range of 

variation in EUR distribution, that approximation can be better refined by the ways 

listed here.

25
26
This website has links to references concerning the new USGS methodology, as well as 

references concerning previous methodologies.

27