A Probabilistic Assessment Methodology for the 

Evaluation of Geologic Carbon Dioxide Storage

By Sean T. Brennan, Robert C. Burruss, Matthew D. Merrill, Philip A. Freeman, and Leslie F. Ruppert

Open-File Report 2010–1127

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: 2010

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Suggested citation:

Brennan, S.T., Burruss, R.C., Merrill, M.D., Freeman, P.A., and Ruppert, L.F., 2010, A probabilistic assessment 

methodology for the evaluation of geologic carbon dioxide storage: U.S. Geological Survey Open-File Report 

2010–1127, 31 p., available only at http://pubs.usgs.gov/of/2010/1127. 

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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Contents

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

1.1. Purpose and Scope.......................................................................................................................................... 1

1.2. The Legislation................................................................................................................................................. 2

2. Assessment Framework ......................................................................................................................................... 4

2.1. Assessment Methodologies ............................................................................................................................. 4

2.2. Model for CO2 Storage Resources................................................................................................................... 5

2.3. Geologic Framework ........................................................................................................................................ 7

2.3.1. Storage Assessment Unit (SAU) – Storage Formation.............................................................................. 7

2.3.2. Storage Assessment Unit (SAU) – Seal Formation ................................................................................... 8

3. Storage Resource Calculations .............................................................................................................................. 8

3.1. Storage Formation Pore Volume...................................................................................................................... 9

3.2. Buoyant Trapping Calculation ........................................................................................................................ 10

3.3. Residual Trapping Calculation ....................................................................................................................... 11

3.4. Technically Accessible Storage Resource ..................................................................................................... 13

3.5. Storage in Oil and Gas Reservoirs................................................................................................................. 13

3.6. Final Assessment Products............................................................................................................................ 14

4. Example Storage Resource Calculation ............................................................................................................... 14

4.1. Data Sources ................................................................................................................................................. 14

4.2. Example Storage Assessment Unit................................................................................................................ 15

4.3. Form Inputs.................................................................................................................................................... 15

4.4. Results ........................................................................................................................................................... 17

5. Conclusions .......................................................................................................................................................... 18

Acknowledgments..................................................................................................................................................... 18

References Cited...................................................................................................................................................... 19

Glossary ................................................................................................................................................................... 24

Appendix A. Assessment Input Forms...................................................................................................................... 27

Storage Formation (SF) Assessment Method: Input Data Form ........................................................................... 27

Appendix B. GIS Data Manipulations: Creating the SAU.......................................................................................... 30

B.1. Required Data Rasters .................................................................................................................................. 30

B.2. Creation of Required Data Rasters................................................................................................................ 30

B.3. Generating SAU Extent and Modification of [DepthTop]................................................................................ 30

B.4. Isopach Generation and Modification ............................................................................................................ 31

Figures

1. A resource triangle depicting the relation between buoyant and residual storage resources that 

schematically illustrates the relation between uncertainties in knowledge of the resource and the relative 

volume of the resource. ................................................................................................................................. 6

2. A schematic cross section through a storage assessment unit illustrating the relation between buoyant and 

residual trapping styles in the storage formation............................................................................................ 7

3. Flow diagram of key steps for calculating buoyant trapping storage resources and residual trapping storage 

resources. ...................................................................................................................................................... 9
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4. Map showing the example of the Tensleep Sandstone storage assessment unit in the Wind River Basin, 

Wyoming. ..................................................................................................................................................... 16

Tables

1. Storage resource values of CO2 for five different classifications within the Tensleep Sandstone storage 

assessment unit........................................................................................................................................... 18

2. Storage resource values of CO2 for petroleum reservoirs in the Tensleep Sandstone storage assessment 

unit. .............................................................................................................................................................. 18
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Conversion Factors

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square inch (in2) 6.452 square centimeter (cm2)

square foot (ft2) 0.09290 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.004047 square kilometer (km2)

square meter (m2) 0.0002471 acre 

Volume

gallon (gal) 3.785 liter (L) 

barrel (bbl), (petroleum, 1 barrel=42 gal) 0.1590 cubic meter (m3) 

cubic foot (ft3) 0.02832 cubic meter (m3) 

1,000 cubic feet (MCF) 28.32 cubic meter (m3)

liter (L) 0.2642 gallon (gal)

cubic meter (m3) 6.290 barrel (petroleum, 1 barrel = 42 gal)

Mass

pound, avoirdupois (lb) 0.4536 kilogram (kg) 

ton, short (2,000 lb) 0.9072 megagram (Mg) 

ton, long (2,240 lb) 1.016 megagram (Mg) 

milligram (mg) 0.00003527 ounce, avoirdupois (oz)

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

megagram (Mg) = 1 metric ton (t) (1,000 kg) 1.102 ton, short (2,000 lb)

megagram (Mg) 0.9842 ton, long (2,240 lb)

million metric tons 1.102 million short tons

Pressure

atmosphere, standard (atm) 101.3 kilopascal (kPa)

bar 100 kilopascal (kPa) 

pound-force per square inch (lbf/in2 or psi) 6.895 kilopascal (kPa)

kilopascal (kPa) 0.009869 atmosphere, standard (atm)
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Multiply By To obtain

kilopascal (kPa) 0.01 bar

kilopascal (kPa)

kilopascal (kPa)

megapascal (MPa)

0.2961

0.1450

145

inch of mercury at 60°F (in Hg)

pound-force per square inch (lbf/in2) 

2 pound-force per square inch (lbf/in )

Pressure gradient

pound-force per square inch per foot 

(lb/in2/ft or psi/ft)

22.62 kilopascal per meter (kPa/m)

Density

pound per cubic foot (lb/ft3)

pound per cubic foot (lb/ft3)

kilogram per cubic meter (kg/m3) 

3 gram per cubic centimeter (g/cm )

16.02

0.01602

0.06242

62.4220

3 kilogram per cubic meter (kg/m )

3 gram per cubic centimeter (g/cm )

pound per cubic foot (lb/ft3) 

pound per cubic foot (lb/ft3) 

Electric power

megawatt electrical (MWe) 3,600,000,000 joule per hour (J/hr)

Hydraulic conductivity

meters per second (m/s) 3.281 foot per second (ft/s)

Hydraulic diffusivity

square meter per second (m2/s) 10.76 square foot per second (ft2/s)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F–32)/1.8

Concentrations of chemical constituents in water are given in parts per million (ppm). and milligrams per liter (mg/L). 
Abbreviations, Acronyms, and Symbols

ASF area of the storage formation

AU assessment unit (NOGA)

bbl petroleum barrel or barrels

BPV buoyant trapping pore volume; see glossary

BSE buoyant trapping storage efficiency; see glossary

BSR buoyant trapping storage resource; see glossary

BSV buoyant trapping storage volume; see glossary

BOE barrels of oil equivalent; see glossary

CO2 carbon dioxide

D Darcy

DOE U.S. Department of Energy

EISA Energy Independence and Security Act

EPA U.S. Environmental Protection Agency

FVF formation volume factor

FVFs formation volume factors

FVFGAS formation volume factor for gas

FVFOIL formation volume factor for oil and natural gas liquids

GIS geographic information system

GOR gas:oil ratio; see glossary

k permeability; see glossary

KR known recovery production volumes; see glossary

KRGAS known recovery production volumes of gas

KRNGL known recovery production volumes of natural gas liquids

KROIL known recovery production volumes of oil

KRRES known recovery production volumes converted to volumes at reservoir conditions

KRRSR known recovery replacement storage resource; see glossary

mD millidarcy

MMbbl million barrels

MMBOE million barrels of oil equivalent

mst million short tons

Mt million metric tons

NOGA National Oil and Gas Assessment; see glossary

ppm parts per million

psi pounds per square inch

RPV residual trapping pore volume; see glossary

RSE residual trapping storage efficiency; see glossary

RSR residual trapping storage resource; see glossary

RSV residual trapping storage volume; see glossary

R1PV residual trapping class 1 pore volume

R1SE residual trapping class 1 storage efficiency

R1SR residual trapping class 1 storage resource

R1SV residual trapping class 1 storage volume

R2PV residual trapping class 2 pore volume

R2SE residual trapping class 2 storage efficiency

R2SR residual trapping class 2 storage resource

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R2SV residual trapping class 2 storage volume

R3PV residual trapping class 3 pore volume

R3SE residual trapping class 3 storage efficiency

R3SR residual trapping class 3 storage resource

R3SV residual trapping class 3 storage volume

SAU storage assessment unit; see glossary

SAUs storage assessment units

SF storage formation; see glossary

SFPV storage formation pore volume; see glossary

TASR technically accessible storage resource; see glossary

TASV technically accessible storage volume; see glossary

TDS total dissolved solids; see glossary

TPI thickness of the porous interval; see glossary

USGS U.S. Geological Survey 

ρCO2 density of carbon dioxide

φ porosity; see glossary

φPI porosity of the porous interval
A Probabilistic Assessment Methodology for Evaluation 

of Geologic Carbon Dioxide Storage

By Sean T. Brennan, Robert C. Burruss, Matthew D. Merrill, Philip A. Freeman, and Leslie F. Ruppert 

1. Introduction 

1.1. Purpose and Scope 

In 2007, the Energy Independence and Security Act (Public Law 110–140) authorized the U.S. 

Geological Survey (USGS) to conduct a national assessment of potential geologic storage resources for 

carbon dioxide (CO2) in cooperation with the U.S. Environmental Protection Agency (EPA) and the 

U.S. Department of Energy (DOE). The first year of that activity was specified for development of a 

methodology to estimate storage potential that could be applied uniformly to geologic formations across 

the United States. After its release, the methodology was to receive public comment and external expert 

review. An initial methodology was developed and published in March 2009 (Burruss and others, 

2009), and public comments were received. The report was then sent to a panel of experts for external 

review. The external review report was received by the USGS in December 2009. 

This report is in response to those external comments and reviews and describes how the 

previous assessment methodology (Burruss and others, 2009) was revised. The resource that is assessed 

is the technically accessible storage resource, which is defined as the mass of CO2 that can be stored in 

the pore volume of a storage formation. The methodology that is presented in this report is intended to 

be used for assessments at scales ranging from regional to subbasinal in which storage assessment units 

are defined on the basis of common geologic and hydrologic characteristics. The methodology does not 

apply to site-specific evaluation of storage resources or capacity (see section 1.2). Calculations of 

subsurface pore volume for potential CO2 storage have been described in a number of publications 

(Bachu, 2003; Bradshaw, 2004; Bachu and others, 2007; U.S. Department of Energy, National Energy 

Technology Laboratory, 2008; van der Meer and Egberts, 2008). The methodology in this report is 

different in that it uses fully probabilistic methods to incorporate geologic uncertainty in calculations of 

storage potential. 

Oil and gas assessments conducted by the USGS evaluate the resource that is estimated to be 

technically recoverable using current and projected technologies. These assessments are fractions of the 

total in-place resource that may be recoverable with technological advances or unforeseen changes in 

economic factors. Similarly, this assessment methodology for CO2 storage resources focuses on the 

technically accessible resource, not a total in-place resource volume. The technically accessible storage 

resource is one that may be available using present-day geological and engineering knowledge and 

technology for CO2 injection into geologic formations. The methodology is not an economic 

assessment, nor does it incorporate engineering constraints in the estimation of the volume of the 

resource. However, the methodology does take into account the U.S. Environmental Protection Agency 

(2008) proposed limit of 10,000 ppm (parts per million; mg/L (milligrams per liter)) total dissolved 
2

solids (TDS) for injection of CO2 into storage formations. Potential storage formations with salinities 

less than 10,000 ppm (mg/L) TDS will not be assessed using this methodology. 

The methodology described in this report is for estimating the storage resource of an individual 

storage assessment unit, and it is designed as a guide for geologists to follow in the USGS national 

geologic CO2 storage assessment. During the course of the assessment we will develop additional 

methods to combine, or aggregate, resources of multiple storage assessment units within a basin and 

multiple regions and develop statistical algorithms that can address dependencies. The reporting of 

aggregated resource values ensures that policymakers and the public have access to information about 

CO2 storage resources for the Nation at a variety of scales. 

1.2. The Legislation

Section 711 of the Energy Independence and Security Act (Public Law 110–140) (hereafter 

referred to as “EISA”) specifies these requirements for the methodology: 

SEC. 711. CARBON DIOXIDE SEQUESTRATION CAPACITY ASSESSMENT.

(a) DEFINITIONS.—In this section— 

(1) ASSESSMENT.—The term ‘‘assessment’’ means the national assessment of onshore capacity for 

carbon dioxide completed under subsection (f). 

(2) CAPACITY.—The term ‘‘capacity’’ means the portion of a sequestration formation that can retain 

carbon dioxide in accordance with the requirements (including physical, geological, and economic 

requirements) established under the methodology developed under subsection (b). 

(3) ENGINEERED HAZARD.—The term ‘‘engineered hazard’’ includes the location and completion 

history of any well that could affect potential sequestration. 

(4) RISK.—The term ‘‘risk’’ includes any risk posed by geomechanical, geochemical, hydrogeological, 

structural, and engineered hazards.

(5) SECRETARY.—The term ‘‘Secretary’’ means the Secretary of the Interior, acting through the 

Director of the United States Geological Survey.

(6) SEQUESTRATION FORMATION.—The term ‘‘sequestration formation’’ means a deep saline 

formation, unmineable coal seam, or oil or gas reservoir that is capable of accommodating a volume 

of industrial carbon dioxide. 

(b) METHODOLOGY.—Not later than 1 year after the date of enactment of this Act, the Secretary 

shall develop a methodology for conducting an assessment under subsection (f), taking into

consideration— 

(1) the geographical extent of all potential sequestration formations in all States;

(2) the capacity of the potential sequestration formations; 

(3) the injectivity of the potential sequestration formations;

(4) an estimate of potential volumes of oil and gas recoverable by injection and sequestration of 

industrial carbon dioxide in potential sequestration formations; 

(5) the risk associated with the potential sequestration formations; and 

(6) the work done to develop the Carbon Sequestration Atlas of the United States and Canada that was 

completed by the Department.
3

A complete version of the EISA can be found at http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=110_cong_public_laws&docid=f:publ140.110.pdf. 

The EISA requires the USGS to assess the capacity of the sequestration formations; however, 

this term has become synonymous with “reserves” in the CO2 community (Burruss, 2009; Frailey and 

Finley, 2009; Gorecki and others, 2009). The USGS assesses resources, which are considered to be 

independent of economic constraints. Reserves are the fraction of the resource that has economic value 

(Juanes and others, 2010). The definition of “capacity” as specified in the EISA is equivalent to the 

term “resource"; therefore, this methodology describes a method to assess the storage resource, which is 

the mass of the free-phase CO2 that can be retained within a sequestration formation. Solubility trapping 

and mineral trapping of CO2 (Benson and Cook, 2005; Bradshaw and others, 2007) are not included in 

this methodology. 

The legislative definition of “risk” contains two distinct classes of risk factors: geologic and 

engineering. The first, as stated in section 711(a)(4) of the EISA, includes “geomechanical, 

geochemical, hydrogeological, [and] structural . . . hazards.” These are naturally occurring, geologically

based parameters, which are accounted for in the methodology through the input distributions for 

geologic parameters and storage efficiency that are described in section 3 of this report. In addition, we 

assume that sound engineering practices will be employed on a site-specific scale to minimize structural 

and environmental hazards. This assumption is necessary, because the quantification of risk is more 

appropriately a site-specific process that requires time and resources to yield meaningful results. These 

resources are not available for the investigation of the storage formations in this national assessment. 

Wiprut and Zoback (2000) and Lucier and Zoback (2008) provide examples of the potential interaction 

between CO2 injection and faults as an example of this research. The second risk is “engineered 

hazards,” which are primarily a result of the impact of drilling procedures, drilling history, and drilling 

density. Such hazards are potential engineering constraints that are not considered in this 

methodology’s evaluation of the probability of CO2 retention in the storage formation. However, 

illustrations of drilling density will be generated as part of the assessment process to provide 

information about the potential scale of engineered hazards. 

A sequestration formation, as defined in section 711(a)(6) of EISA, is “a deep saline formation, 

unmineable coal seam, or oil and gas reservoir that is capable of accommodating a volume of industrial 

carbon dioxide.” These three geologic environments may occur within the same geologic interval and 

may share common geologic characteristics that affect the size of the potential storage resource. This 

report only addresses the potential storage resource of two of these environments: deep saline 

formations and oil and gas reservoirs. Because there is no standard definition of “unmineable coal 

seams,” the potential storage resource of this environment will not be addressed in this report. The 

DOE, however, has published estimates for CO2 storage capacity in coal seams (U.S. Department of 

Energy, National Energy Technology Laboratory, 2008). When a commonly accepted definition of 

unmineable coal is determined, the USGS will incorporate the potential storage resource of this 

environment in future CO2 storage assessment reports. 

As described in detail in the following sections of this report, we will use the term “storage 

formation” (SF) for sequestration formation. The SF together with enclosing seal formations constitute 

a storage assessment unit (SAU) that is the fundamental geologic unit to be assessed for CO2 storage 

resources. 

Section 711(b)(3) of the EISA also specifies that the methodology consider “the injectivity of 

the potential sequestration formations.” Quantification of injectivity, as defined in the hydrologic and 

petroleum reservoir literature, requires measurement of pressure changes in a well resulting from

injection of a known volume of fluid for a known period of time (Craft and Hawkins, 1991). Although 
4

large volumes of fluid are injected into the subsurface during oil field and waste disposal operations, 

data from such operations, as reported by State and Federal agencies, are limited. This lack of adequate 

injection and associated pressure data from geologic formations across the country makes the 

development of performance-based, numerical “injectivity” criteria for an assessment methodology 

difficult. As a proxy, this assessment uses permeability categories in addition to lithologic information 

to more appropriately select the storage efficiencies used to calculate the storage resource values. 

Section 711(b)(4) of the EISA directs the USGS to provide “an estimate of potential volumes of 

oil and gas recoverable by injection and sequestration of industrial carbon dioxide in potential 

sequestration formations.” Methodologies for calculating enhanced oil recovery and enhanced gas 

recovery estimates are not included in this report. However, estimates of the volume of petroleum that 

can be recovered by CO2 sequestration to meet this requirement of the EISA will be discussed at a later 

time in a separate document. 

2. Assessment Framework 

2.1. Assessment Methodologies

Storage resources for geologic sequestration of CO2 have been estimated using many methodologies 

for a variety of scales, ranging from global to single basins. A review of many of these estimates 

(Bradshaw and others, 2005) concluded that assessments of similar areas varied by as much as a factor 

of 100 or more. In an attempt to provide consistent methods that are based on clearly defined input 

parameters, a working group of the “Carbon Sequestration Leadership Forum” created deterministic 

methods (Bradshaw and others, 2007; Bachu and others, 2007) that could be applied anywhere in the 

world. A group of researchers led by Scott Frailey at the Illinois State Geological Survey developed the 

methodologies for CO2 storage capacity that were used in the DOE Regional Carbon Sequestration 

Partnership program “National Carbon Atlas” (U.S. Department of Energy, National Energy 

Technology Laboratory, 2008, their appendix B.) Another important assessment method, formulated by 

van der Meer and Egberts (2008), incorporates the impact of the pressure front created by the injection 

of CO2. This pressure front propagates away from the injection site in advance of the plume of injected 

CO2, thereby affecting a much larger area of a storage formation. The methods described above are not

fully probabilistic. However, probabilistic methods have been used to calculate storage efficiency 

values (see definition and discussion below) but do not account for the full range of geologic 

uncertainty in the resource calculation (U.S. Department of Energy, National Energy Technology 

Laboratory, 2008; Gorecki and others, 2009). 

Over a number of years, the USGS has developed probabilistic methods for assessing energy 

resources (Charpentier and Klett, 2005; Schmoker and Klett, 2005) that can be applied to environments 

ranging from mature exploration provinces to relatively unexplored areas such as those found in the 

circum-Arctic (Bird and others, 2008; Gautier and others, 2009). Assessments of natural resources can 

be performed in many ways, and each method has advantages and disadvantages that are related to 

different input parameters, resource models, treatment of geologic uncertainty, and propagation of 

uncertainty to the results. Ahlbrandt and Klett (2005) reviewed and quantitatively compared seven

methods of oil and gas resource assessment with the USGS methods used in the 2000 World Energy 

Project. They concluded that the probabilistic methods used by the USGS are robust and reproducible. 

Probabilistic methods are needed for assessments because geologic data for large areas are rarely 

complete. Because rocks are heterogeneous, approximations are required to fill gaps in the data. 

Probabilistic methods provide a statistically sound method to make these approximations. However, as 

emphasized by Charpentier and Klett (2005) and demonstrated by Ahlbrandt and Klett (2005), a critical 
5

component of probabilistic resource assessment is careful integration of the geologic model of the 

resource with the statistical analyses of the probabilistic methods. 

2.2. Model for CO2 Storage Resources

This methodology is designed to assess the storage resources in mappable subsurface bodies of 

rock into which CO2 is injected and trapped; in this methodology, these mappable subsurface bodies of 

rock are referred to as storage assessment units (SAUs). The total volume of pore space within a SAU 

can be described as the total in-place resource. This report addresses the fraction of that total resource 

that may retain CO2. The mass retained in that fraction is the technically accessible storage resource 

(TASR). The technically accessible storage resource is defined as the mass of CO2 that can be stored in 

the pore volume of the SF taking into account present-day geologic knowledge and engineering practice 

and experience. The TASR is analogous to the term “technically recoverable resource” used in USGS oil 

and gas assessments.

Carbon dioxide is injected into the subsurface as a separate fluid phase that is less dense than 

formation water, migrates by buoyancy, and is retained in the subsurface by capillary forces. CO2 will 

rise buoyantly until it encounters a rock unit that has a capillary entrance pressure that is greater than 

the buoyancy or hydrodynamic forces. Additional CO2 will accumulate until the buoyancy force 

exceeds the capillary entrance pressure of the pore space vertically or laterally adjacent to the CO2

plume. If the overlying rock unit has sufficiently high capillary entrance pressure, it will act as a seal, 

allowing CO2 to accumulate in any structural or stratigraphic feature that has both vertical and lateral 

seals. In the geologic model for this assessment methodology, the pore space within large geologic 

structures that will retain CO2 in this manner is called the buoyant trapping storage resource (BSR). In 

basins that have produced oil and gas, many of these structures involve petroleum reservoirs. Buoyant 

trapping storage resource estimates are based on data from petroleum reservoirs that have more than 

500,000 barrels of oil equivalent (BOE) and, if available, information from other similar-size enclosures

(for example, dry traps). 

A mass of CO2 injected into a volume of rock will form a plume that migrates buoyantly and 

will continue to migrate after injection stops. The capillary entrance pressures of the pore spaces within 

the SF are heterogeneous. These heterogeneities within the SF may impede the flow of the migrating 

plume of CO2, causing some of the CO2 to be retained within the pore spaces. These heterogeneities 

range in size—as small as individual pores (Juanes and others, 2006) through meter-size features like 

clay drapes within crossbedded sandstones (Saadatpoor and others, 2010) to petroleum reservoir-size 

enclosures, which house the buoyant pore volume. The key concept is that the plume must migrate and 

leave behind some fraction of the CO2 trapped in these heterogeneities. The plume will continue to 

migrate until the mass of CO2 within the plume is so small that the buoyancy forces within the plume 

are less than the capillary injection pressures of the formation. In other words, these volumes of trapped 

CO2 from the migration of the CO2 plume are the residues of the migration process. The fraction of the 

pore space that physically traps CO2 in volumes less than petroleum reservoir-size enclosures is most 

typically referred to as residual CO2 trapping (Benson and Cook, 2005; Bradshaw and others, 2007; 

Gorecki and others, 2009), although Saadatpoor and others (2010) subdivide the pore-size and metersize retention mechanisms into residual and capillary trapping, respectively. In this methodology, all 

potential CO2 trapping within the SF that is smaller than the petroleum reservoir-size enclosures is 

defined as the residual trapping storage resource (RSR). 

The amount and quality of information available to estimate the mass of CO2 that could be 

stored vary. In geologic provinces that have oil and gas production, detailed information is available on 

some or most of the structural and stratigraphic features that are part of the buoyant trapping storage 
6

resource. However, such features are but a small fraction of the storage formation. The rest of the 

storage formation is a candidate for residual storage. The residual trapping storage resource is 

potentially much larger than the buoyant trapping storage resource, but within petroleum reservoir-size 

enclosures, the trapping processes and the relative fraction of pore space in which residual storage 

resources may occur are not as well understood as buoyant storage resources. The relative size of the 

two resources and the relative certainty with which we may be able to estimate the resources are 

illustrated in figure 1. The top part of the triangle represents the resource that is most understood but 

has the smaller resource; the bottom part of the triangle represents the resource that is least understood 

but has the larger resource. In provinces with no oil and gas production, most of the resource could be 

residual storage.

Figure 1. A resource triangle depicting the relation between buoyant and residual trapping storage resources 

that schematically illustrates the relation between uncertainties in knowledge of the resource and the relative 

volume of the resource. 

This general discussion of buoyant and residual trapping does not supplant the rich and diverse 

datasets and geologic models for petroleum entrapment that are based on 100 years of exploration and 

production. There is no comparable dataset for CO2 sequestration; only a few field trials have been 

undertaken thus far. As a consequence, the mechanics of assessing CO2 storage resources incorporates 

parameters that are poorly known due to limited data from CO2 injection projects. Adopting a 

probabilistic method does not serve as a cure for lack of knowledge, but merely quantifies the higher 

level of uncertainty that is necessarily inherent in CO2 storage resource assessments.
7

2.3. Geologic Framework 

2.3.1. Storage Assessment Unit—Storage Formation

The storage assessment unit (SAU) is a mappable volume of rock that consists of a porous flow 

storage unit and a bounding regional sealing formation. Within the SAU, the porous flow unit is defined 

as the storage formation (SF). A schematic cross section that extends downdip through a hypothetical 

SAU is shown in figure 2. The pore volume within the SF that contains the buoyant trapping storage 

resources and the residual trapping storage resources are shown in color. 

Figure 2. A schematic cross section through a storage assessment unit (SAU) illustrating the relation between 

buoyant and residual trapping styles in the storage formation. Recommended SAU depth limits of 3,000 ft (914 m)

and 13,000 ft (3,962 m) also are included.

The extent of the SF is defined by the physical properties of CO2. The upper vertical limit 

chosen for this methodology is 3,000 feet (914 m) (Burruss and others, 2009) because CO2 at this depth 

is typically subjected to temperatures and pressures that maintain the CO2 in a supercritical state. 

Supercritical CO2 has density values much higher than those of gaseous CO2 (National Institute of 

Standards, 2009). The lower vertical limit for the SAU of 13,000 feet (3,962 m) is based on the 

potential CO2 injection depth at pipeline pressures without additional compression at the surface. The 

justification for these limits is discussed in more detail in Burruss and others (2009). If rock properties 

suggest that a viable storage resource is present at depths below 13,000 ft, the assessment geologist may 
8

add an additional SAU for this deeper reservoir. The areal extent of the SAU on a map is defined by 

depth contours, from the land surface to the top of the SF, at 3,000 and 13,000 feet (914 and 3,962 m). 

Additional factors may limit the areal extent of the SF. For example, any formation water with 

salinities lower than 10,000 ppm (mg/L) TDS, regardless of depth, has the potential to be used as a 

potable water supply (U.S. Environmental Protection Agency, 2009). The U.S. Environmental 

Protection Agency (2008) has proposed the 10,000 ppm (mg/L) TDS limit for injection of CO2. 

Therefore, the potential storage resources for CO2 in formations with salinities less than 10,000 ppm 

(mg/L) TDS will not be assessed at this time, except to illustrate the methodology in section 4. 

Although salinities of formation water commonly increase with depth, formation water within an 

assessment unit may be less saline than the 10,000 ppm (mg/L) limit near the updip extent of the SF, 

eliminating a part of the SAU.

2.3.2. Storage Assessment Unit—Seal Formation

Seal formations (confining units, aquitards, cap rock) are regional geologic strata that inhibit the 

migration of fluids between adjacent geologic strata. The seal formation must be present for 

identification of the mappable extent of the SAU. Therefore, any area of the SF that is not beneath the 

seal formation is excluded from the area of the SAU. A seal may consist of single or multiple 

formations. 

Seals need to be evaluated for the leakage potential of CO2 and formation water from the SF. 

The two primary pathways of fluids through a seal that may account for seal failure are (1) leakage 

through fractures and faults and (2) diffuse porous flow where the capillary entrance pressure of the seal 

is exceeded by the pore pressure as determined by the column height of the fluids (Hermanrud and 

others, 2005). Diffuse porous flow is not considered to be a significant factor in the movement of fluids 

through seals (Couples, 2005). However, studies of seal fractures in oil and gas fields indicate that 

fracture flow can account for large volumes of fluid movement through the seal (Skerlec, 1999). The 

integrity of the fault seal may be affected by the juxtaposition of the strata within the fault plane, fluid 

pressures, mineralogy, and geometry of the fault system. 

3. Storage Resource Calculations

This methodology estimates the technically accessible storage resource (TASR), which is the 

mass of CO2 that can be stored in the pore volume of the SF. This resource is the product of CO2

density and storage volume, which is the fraction of the pore volume that will retain injected CO2. In 

order to estimate this fractional volume, the storage formation pore volume (SFPV) is calculated and 

then allocated between the pore volume available for buoyantly trapped CO2 and residually trapped 

CO2 (BPV and RPV,, respectively). The BPV and RPV are calculated separately because they are modified 

by different geologic uncertainties and storage efficiencies. Storage efficiency is the value that 

represents the fraction of the total available pore space that will retain free-phase CO2. The following 

sections (3.1, 3.2, and 3.3) describe SFPV, BPV, and RPV and how they are derived and used to generate 

probabilistic assessment storage resource estimates (fig. 3) using a Monte Carlo simulator. The 

assessment geologist is tasked with determining the input parameters using the information available. 

These input parameters are recorded on the input data form shown in appendix A. To facilitate the 

process, data, when available, will be provided to the geologist by the CO2 assessment team, a core 

group of USGS scientists working in support of the CO2 storage assessment activities.
9

Figure 3. Flow diagram of key steps for calculating buoyant trapping storage resources (left) and residual 

trapping storage resources (right). Injectivity categories are represented as class 1 (R1), class 2 (R2), and class 3 

(R3). See text for more information.

3.1. Calculating Storage Formation Pore Volume

The first step of the assessment process is to define the area of the SAU. Using available 

databases, the CO2 assessment team provides the assessment geologist with values for the area, net 

porous thickness, and porosity. The area of the SAU is determined via GIS techniques (appendix B) by 

the assessment team on the basis of the maps of the SF created using the parameters discussed in 

section 2.3.1. The values for net porous thickness and porosity can be further refined if relevant data are 

gathered by the assessment geologist during research for the framework geology of the SAU. Because
10

the area of the SF is typically clearly defined, the uncertainty for mean area of the SF is likely to be 

relatively small. However, thickness and porosity data that are either localized, such as data primarily 

from oil and gas fields, or sparse to nonexistent, such as data from a formation with few well 

penetrations, could lead to significant uncertainty about whether the mean values calculated from those 

data accurately represent the entire SF. These input values will be determined by the assessment 

geologist in consultation with the assessment team. The probabilistic product of mean SAU area, mean 

thickness of the net porous interval, and mean porosity is the storage formation porous volume as 

shown in equation 1. 

PV SF PI PI SF A T = ××φ (1)

where SFPV is the pore volume of the storage formation (fraction); 

ASF is the mean area of the storage formation (acres); 

TPI is the mean thickness of the porous interval (feet), where the porous interval is 

defined as the stratigraphic thickness of the storage formation with a porosity 

of 8 percent or higher; and

φPI is the mean porosity of the porous interval (fraction). 

3.2. Calculating Buoyant Trapping Pore Volume

The buoyant trapping pore volume (BPV) is a geologically determined, probabilistic distribution 

of the volume of the SF that can store CO2 by buoyant trapping. The assessment geologist is tasked to 

generate the BPV distribution by using all available information. However, defaults may be used for the 

input data that define the BPV distribution. 

The minimum BPV input can be the volume of known recovery of petroleum, scaled to 

subsurface volumes. The CO2 assessment team will sum the known recovery production volumes (KR) 

of oil and gas reservoirs within the SAU from available databases and use formation volume factors 

(FVFs) to convert these surface volumes to volumes at reservoir conditions (KRRES). The KR for oil 

and natural gas liquids will be corrected to subsurface volumes by using an appropriate range of FVFs 

for the SAU from the Standing correlation (Beggs, 1987). The KR for gas will be corrected to 

subsurface volumes using appropriate compressibility factors for the range of temperatures and 

pressures (Garb and Smith, 1987) within the SAU. 

KR KR KR FVF KR FVF RES = +× +×   (( OIL NGL OIL GAS GAS ) ) ( )   (2)

where KRRES is the volume of known recovery corrected to reservoir conditions (volume); 

KROIL is the known recovery of oil (volume); 

KRNGL is the known recovery of natural gas liquids (volume); 

FVFOIL is the formation volume factor for oil and natural gas liquids (fraction); 

KRGAS is the known recovery of gas (volume); and

FVFGAS is the formation volume factor for gas (fraction). 

An additional estimation of buoyant trapping that can be used in this methodology is the 

reported mean values for USGS National Oil and Gas Assessment (NOGA) undiscovered petroleum 

resource volumes. NOGA mean resource volumes can be allocated to the SF; these allocated volumes 

are then corrected to reservoir conditions using equation (2). The corrected undiscovered NOGA 

volumes can be added to the KRRES value to estimate a more robust buoyant trapping volume. If there 

are any buoyant traps without petroleum production (“dry traps” within the SF), then it may be possible 
11

to incorporate their volumes into the KRRES and USGS NOGA undiscovered petroleum resource 

estimates to determine a median BPV. However, because there are no known available datasets on “dry 

trap” characteristics, estimating any volume comparable to USGS NOGA or KRRES values will be 

difficult. The maximum BPV input should likely include some estimate of the volume of the total pore 

space that is within large enclosures. However, the assessment geologist can estimate the BPV

distribution in any way using the data provided in conjunction with all other available geologic data. 

The BPV is multiplied by the buoyant trapping storage efficiency (BSE, fig. 3) to determine a 

buoyant trapping storage volume (BSV), as shown in equation 3. Typically, BSE is lower than 

hydrocarbon saturation values in oil or gas fields. This difference is due to the inability of a low 

viscosity fluid, such as supercritical CO2, to efficiently displace a high viscosity fluid, such as oil or 

water, without exceeding the fracture pressure of the storage or seal formation. Also, because there are 

no CO2 storage projects that have attempted to optimize the mass of CO2 that can be retained within the 

pore volume of an enclosure, buoyant trapping storage efficiencies are difficult to estimate. The 

buoyant trapping storage efficiencies (BSE) used in this study will be based on experimentally derived 

relative permeability curves (Bennion and Bachu, 2005, 2008; Burton and others, 2008). Values from 

such studies provide the best potential storage efficiencies that are currently available for buoyant 

storage, but these values could change with more research or field data.

B BB SV PV SE = × (3)

where BSV is the volume of CO2 stored by buoyant trapping within the storage formation 

(volume); 

BPV is the pore volume available for buoyant trapping (volume); and 

BSE is the storage efficiency of buoyant CO2 storage (fraction). 

To determine the buoyant trapping storage resource (BSR), which is the mass of CO2 that can be 

buoyantly trapped in the SF, the BSV is multiplied by the density of CO2 (as shown in equation 4 and 

illustrated in figure 3). A probabilistic distribution of the density of CO2 is calculated by the assessment 

team based on the upper and lower depth boundaries of the SF, temperature and pressure gradients 

appropriate for the area, and an equation of state for CO2. 

B B CO SR SV 2 = × ρ (4)

where BSR is the buoyant trapping storage resource (mass); 

BSV is the volume of CO2 stored by buoyant trapping with the storage formation 

(volume); and

ρCO2 is the density of CO2 (mass/volume). 

With this step, the calculation for BSR is complete (fig. 3).

3.3. Calculating Residual Trapping Pore Volume

Residual trapping is the second type of storage in the storage formation resource calculation. 

The pore volume available for residual trapping is the remaining SFPV that was not considered for the 

buoyant storage resource. During the iterative calculations using the Monte Carlo simulator, a single 

value is selected from the BPV distribution; this value is subtracted from the SFPV selected in the same 

iteration and yields a remaining pore volume that is equal to the residual trapping pore volume (RPV) 

(fig. 3). 
12

R SF B PV PV PV = − (5)

This residual pore volume is apportioned into three rock classes or “injectivity category 

allotments” (fig. 3) on the basis of permeability. The CO2 assessment team will provide permeability 

values from available databases; however, the assessment geologist should seek to augment these 

values with data available from literature, other databases, or other sources. These permeability values 

are used to estimate the percentages of the SF that comprise each class. Storage efficiencies associated 

with residual trapping are poorly understood because commercial-scale injection projects into these 

parts of the SF have not been undertaken. Because these values are not well constrained, we will use a 

standard set of minimum, mode, and maximum values based on modeled values from Gorecki and 

others (2009) that have been modified by the injectivity and mobility of CO2. 

Class 1 rocks are defined as those parts of the remaining SF that have very high permeabilities

(greater than 1 Darcy) and the highest injectivity values. These highly permeable rocks, however, 

typically have less pore-scale residual trapping (Juanes and others, 2006), due to the lack of small pore 

throats. Therefore, the majority of the trapping in these rocks will be the result of larger scale 

permeability heterogeneities (Saadatpoor and others, 2010) as described in section 2. In addition, CO2

plumes would move quickly in a highly permeable unit, which would increase the potential for leakage 

along sections of the SF that crop out at the surface updip from the SAU boundary. The corresponding 

storage efficiency values for these rocks are typically lower than class 2 rocks due to the lack of porescale residual trapping of a highly mobile CO2 plume. 

Class 2 rocks are defined as parts of the remaining SF that have (1) moderate permeabilities, 

typically on the order of 1 millidarcy to 1 Darcy; (2) minor to no injectivity issues as discussed in the 

guidelines of the U.S. Environmental Protection Agency (2008); and (3) a high potential for all types of 

residual trapping ranging from pore scale to larger scales. Class 2 rocks might have the highest storage 

efficiency values due to the full range of potential residual trapping types. 

Class 3 rocks are defined as parts of the remaining SF that have low permeabilities, on the order 

of less than 1 millidarcy, and have little to no injectivity. These rocks have low storage efficiency 

values, with minimum and mode values approaching or equal to zero, since little CO2 will enter these 

rocks without artificially fracturing the rock; artificial fracturing of the SF may not be permitted under 

U.S. Environmental Protection Agency requirements (2008). The maximum value is taken from the 

appropriate sandstone, limestone, or dolomite values from Gorecki and others (2009) to account for the 

possibility that some mass of CO2 could enter and be retained within this part of the SF. 

Using permeability data from the SF, the RPV is then allocated between these three classes (R1, 

R2, and R3). During each iteration, the classes are then multiplied by a value from their fixed storage 

efficiency probability distributions to determine their residual volume resources (RSV) (fig. 3). 

R RR SV PV SE = × (6)

where RSV is the residual trapping storage volume (volume); 

RPV is the residual trapping pore volume (volume); and

RSE is the residual trapping storage efficiency (fraction). 

The residual trapping storage resource (RSR) is determined by multiplying the RSV by the density 

of CO2 (fig. 3). A probabilistic distribution of the density of CO2 is calculated by the assessment team 

based on the upper and lower depth boundaries of the storage formation, temperature and pressure 

gradients appropriate for the area, and an equation of state for CO2. 
13

R R CO SR SV 2 = × ρ (7)

where RSR is the residual trapping storage resource (mass); and 

ρCO2 is the density of CO2 (mass/volume). 

With this step, the calculation for RSR is complete (fig. 3).

3.4. Technically Accessible Storage Resource 

In order to calculate the technically accessible storage resource (TASV) for the SF as a whole, 

the storage volumes calculated using the equations in sections 3.2 and 3.3 are summed iteratively 

(fig. 3). 

TA R1 R2 R3 B SV SV SV SV SV =+++ (8) 

where TASV is the technically accessible storage volume; 

R1SV is the residual trapping class 1 storage volume; 

R2SV is the residual trapping class 2 storage volume; 

R3SV is the residual trapping class 3 storage volume; and

BSV is the buoyant trapping storage volume. 

The technically accessible storage resource (TASR) is determined by multiplying the TASV by 

the density of CO2 (fig. 3). A probabilistic distribution of the density of CO2 is calculated by the 

assessment team based on the upper and lower depth boundaries of the storage formation, temperature 

and pressure gradients appropriate for the area, and an equation of state for CO2. 

TA TA CO SR SV 2 = × ρ (9)

where TASR is the technically accessible storage resource (mass); and

ρCO2 is the density of CO2 (mass/volume). 

With this step, the calculation for TASR is complete (fig. 3).

3.5. Storage in Oil and Gas Reservoirs

The EISA legislation defines a sequestration formation as a “deep saline formation ... or oil or 

gas reservoir.” Within SAUs that have petroleum production, the storage resource specific to petroleum 

reservoirs can be estimated by using the KRRES values determined in the BPV section. To convert this 

subsurface volume of KRRES to a storage resource, the total volume is reduced by a storage efficiency 

value. The storage efficiency distribution for the oil and gas reservoirs used for this resource estimation 

will be the same as the buoyant storage efficiency values. The known recovery replacement storage 

resource (KRRSR) is determined by multiplying the KRRES by the density of CO2. A probabilistic 

distribution of the density of CO2 is calculated by the assessment team based on the upper and lower 

depth boundaries of the storage formation, temperature and pressure gradients appropriate for the area, 

and an equation of state for CO2. 
14

KRR KR B CO SR RES SE 2 = ×× ( ) ρ (10) 

where KRRES is the known recovery corrected to a volume at subsurface conditions (volume), 

calculated in equation (2); 

BSE is the buoyant storage efficiency (fraction); and

ρCO2 is the density of CO2 (mass/volume). 

With this step, the calculation for KRRSR is complete.

3.6. Final Assessment Products

The final assessment products will include illustrations and storage resource values. The final 

assessment of each SAU will include illustrations of structure, depth below the surface, and isopach 

maps as well as well penetration cell maps for each SAU. The cell maps will include the total number 

of well penetrations through the SF per square mile grid blocks. The storage resource values will be 

reported as the F95 (denotes a 95 percent chance that the amount reported will be present within the 

SAU); other fractiles are defined similarly), F50, F5, and mean values of the mass of CO2 retained in 

the:

1. The total buoyant storage fraction; in other words, the buoyant trapping storage resource (BSR). 

2. The residual storage fractions; in other words, the residual trapping storage resource (RSR), with 

classes 1, 2, and 3 reported separately (R1SR, R2SR, R3SR). 

3. The total storage formation resource; in other words, the technically accessible storage 

resource (TASR).

4. The oil and gas reservoir storage; in other words, the known recovery replacement storage resource 

(KRRSR). 

4. An Example of the Storage Resource Calculation

4.1. Data Sources

Geologic and assessment models are built on geologic framework studies from USGS oil and 

gas assessments. Such basin studies have resulted in structure and thickness models, stratigraphic 

columns, cross sections, and general knowledge of the geology that are used to identify the SAU. 

Additional comprehensive data searches from published and unpublished work by the USGS, State 

geological surveys, petroleum industry producer associations or agencies, commercial databases, and 

the general literature are conducted to obtain additional information for use by assessment geologists. 

Quantitative data at the oil and gas reservoir level, including composition, porosity, permeability, size, 

thickness, depths, lithology, drive mechanism, and production data, are available in a commercial 

database, “Significant Oil and Gas Fields of the United States” (Nehring Associates, Inc., 2008). The 

USGS CO2 storage resource assessment calculations include data from fields with at least 0.5 million 

barrels of oil equivalent (MMBOE). This minimum size is the same as that used by the USGS NOGA

methodology as well as Nehring Associates, Inc. Maintaining the same volume limits as NOGA ensures 

the applicability of their undiscovered petroleum volumes for use in this assessment’s calculations. 

Data at the individual well level, including location, producing formation, formation tops, 

bottom hole pressures, perforation zones, and production of oil, gas, and water, were obtained from the 
15

commercial database, “PI/Dwights PLUS on CD” (IHS Inc., 2008a). Volumes of produced and injected 

water, oil, gas, and natural gas liquid, along with core sample measurements, were obtained from a 

separate IHS database called “PIDM 2.5” (IHS Inc., 2008b).

Nehring Associates, Inc., and IHS Inc., commercial databases are all proprietary. No data from 

these datasets, whether tabular or spatial in nature, will be published or made available to the public in 

any way throughout the assessment process. Derivative aggregations of data and analytical results that 

are not related to specific wells, reservoirs, or fields will be included in assessment publications.

4.2. An Example—The Tensleep Sandstone Storage Assessment Unit

The Pennsylvanian-Permian Tensleep Sandstone of the Wind River Basin, Wyoming, was 

selected to demonstrate the USGS methodology for assessing CO2 storage resources. The Tensleep 

Sandstone provides a straightforward test case as a storage assessment unit because it lies within a 

mature petroleum basin with available production data, has a broadly uniform thickness, and is 

discussed at length in the literature. The assessment focused on the upper 100 feet (30 m), 

approximately, of the Tensleep Sandstone where petroleum is produced from a massive eolian 

sandstone unit, which has high porosity and permeability values. Furthermore, the recent assessment of 

the petroleum potential of the Wind River Basin (Kirschbaum and others, 2007) provided up-to-date 

data and interpretations that are highly useful for completing a CO2 storage capacity assessment. 

However, because the formation waters of the Tensleep Sandstone SAU have TDS concentrations 

below 10,000 ppm (mg/L), this particular SAU cannot be used for sequestration under current EPA 

guidelines. Therefore, storage resource values presented for this example are for illustrative purposes 

only. 

4.3. Form Inputs

This section describes what information needs to be entered on the input data form (appendix A) for 

the Tensleep Sandstone SAU example. The first section of the SF assessment model form 

(Identification Information) includes a description of the SAU, as well as a section for notes from the 

assessment geologist. The characteristics of the SAU are entered in the second section of the form

(Characteristics of the Storage Assessment Unit (SAU)). The SAU was created following the guidelines 

outlined in section 2.3.1 using GIS software with datasets from Kirschbaum and others (2007), Fox and 

others (1975), Nehring Associates, Inc. (2008), and IHS Inc. (2008a). A map of the SAU with structure 

contours is presented in figure 4. 
16

Figure 4. The Tensleep Sandstone storage assessment unit in the Wind River Basin, Wyoming, is confined to 

the parts of the formation where the top of the unit is at least 3,000 ft (914 m) below land surface and the bottom of 

the unit is no deeper than 13,000 ft (3,962 m). Figure modified from Kirschbaum and others (2007).

Storage formation pore volume (SFPV) is the product of three inputs. First, GIS software was 

used to calculate the area of the Tensleep Sandstone SAU. A mode value of 1,400,000 was entered on 

line 4 of the form, and values were entered for the minimum and maximum areas (1,300,000 and 

1,400,000, respectively) to account for the relatively limited uncertainty in the area calculation. The 

mean net porous interval thicknesses of 80, 100 and 120 feet (~24, 30, and 37 m) inputs were based on 

data from Fox and others (1975). Mean porosity distribution values were also based on data from Fox 

and others (1975); however, reservoir values from the Nehring database (Nehring Associates, Inc., 

2008) were also used.

Buoyant trapping pore volume is determined by the CO2 assessment team in conjunction with 

the assessment geologist using a variety of sources of data. Known recovery production volumes from 

reservoirs within the Tensleep Sandstone SAU from Nehring Associates, Inc. (2008), were converted to 

reservoir conditions using appropriate ranges of formation volume factors for both oil and gas (Beggs, 

1987; Garb and Smith, 1987) (see section 3.5). A value of 190 MMbbl was entered as the minimum 

buoyant trapping pore volume. USGS NOGA results were scaled proportionally to the SAU size and 

added to the minimum BPV to choose a mode BPV. Published mean undiscovered oil and gas assessment 

figures from Kirschbaum and others (2007) were modified using values determined from past 
17

production to isolate Tensleep Sandstone production only. Those volumes were scaled to account for 

the areal discrepancy between the boundaries of the NOGA assessment unit (Kirschbaum and others, 

2007) and the SAU (fig. 4). A volume, at reservoir conditions, of 15 MMbbl was calculated and added 

to the minimum of 190 MMbbl to produce the median of 205 MMbbl. 

For this example, a maximum BPV value was chosen using a volumetric calculation method 

similar to that used for SFPV, but for a much smaller area. The areas of potential enclosures, both dry 

traps and the water legs of existing fields, were determined from structure contour maps to be 

approximately 2.5 percent of the SAU area. A value of 4,100 MMbbl, which approximates 2.5 percent 

of the SFPV, was used as the maximum input for BPV. A log normal distribution is used for BPV in the 

probabilistic calculations.

Injectivity category allotment is the only input required for the residual trapping storage 

resource. Graphs of permeability information from Fox and others (1975) indicate that the majority of 

the porous interval in the Tensleep Sandstone has permeability values between 1 millidarcy (mD) and 1 

Darcy (D). Only a small percentage of the formation exhibits permeability values indicative of class 1 

or class 3 categories.

The storage efficiency values used in this example are included to facilitate understanding of the 

resource calculations but are not provided on the form and are not an input chosen by the assessment 

geologist. As stated in section 3.2, buoyant trapping storage efficiencies (BSE) used in this methodology 

are based on experimentally derived relative permeability curves (Bennion and Bachu, 2005, 2008; 

Burton and others, 2008). In this example, values of 10 percent, 30 percent, and 60 percent were used 

for minimum, mode, and maximum BSE. Storage efficiencies associated with residual trapping are 

poorly understood as mentioned in section 3.3. Inputs based on modeled values for sandstones from 

Gorecki and others (2009) modified by the injectivity and mobility of CO2 and the likelihood of 

residual trapping were used in this example. Values of 1 percent, 5 percent, and 7 percent for R1SE, 1 

percent, 7 percent, and 15 percent for R2SE, and 0 percent, 0 percent, and 7 percent for R3SE were used 

for minimum, mode, and maximum residual storage efficiency. 

The last three sections of the form cover the allocation of CO2 storage resource by area for 

Federal, State, tribal, and private lands, and other classifications. Areal allocations were designated 

using maps of these lands; however, volume percentage allocations are derived from the geologic 

information collected and analyzed throughout the example assessment project. Some areas or land 

types contain more or less of the resource than would be determined with a straight areal allocation. 

4.4. Results

Results from the probabilistic assessment indicate that the Tensleep Sandstone SAU has mean 

CO2 storage values of 13.4 million short tons (mst) (12.2 million metric tons (Mt)) of buoyant trapping 

storage resource, 36.9 mst (33.5 Mt) of residual trapping class 1 storage resource, 1,179 mst (1,070 Mt) 

of residual trapping class 2 storage resource, 19.9 mst (18.0 Mt) of residual trapping class 3 storage 

resource, and 1,244 mst (1,129 Mt) of technically accessible storage resource (table 1). In the Tensleep 

Sandstone, storage resources based on known recovery volumes are 7.6 mst (6.9 Mt) of CO2 (table 2). 
18

Table 1. Storage resource values of CO2 for five different classifications within the Tensleep Sandstone storage 

assessment unit.

[mst, million short tons; Mt, million metric tons] 

Tensleep 

Sandstone F95 F50 F5 Mean

Buoyant trapping 

storage resource

4.5 mst (4.1 Mt) 9.1 mst (8.3 Mt) 38.4 mst (34.8 Mt) 13.4 mst (12.2 Mt)

Residual trapping 

class 1 storage 

resource

16.7 mst (15.2 Mt) 38.0 mst (34.5 Mt) 58.6 mst (53.2 Mt) 36.9 mst (33.5 Mt)

Residual trapping 

class 2 storage 

resource

450 mst (408 Mt) 1,200 mst (1,089 Mt) 2,069 mst (1,877 Mt) 1,179 mst (1,070 Mt)

Residual trapping 

class 3 storage 

resource

1.6 mst (1.5 Mt) 19.0 mst (17.2 Mt) 47.1 mst (42.7 Mt) 19.9 mst (18.0 Mt)

Technically 

accessible 

storage resource

514 mst (466 Mt) 1,250 mst (1,134 Mt) 2,125 mst (1,928 Mt) 1,244 mst (1,129 Mt)

Table 2. Storage resource values of CO2 for petroleum reservoirs in the Tensleep Sandstone storage 

assessment unit.

[mst, million short ton

Tensleep 

Sandstone

Known recovery 

s; Mt, million metric

F95

3.9 mst (3.5 Mt)

 tons]

F50

7.4 mst (6.7 Mt)

F5

11.6 mst (10.5 Mt)

Mean

7.6 mst (6.9 Mt)

replacement 

storage resource 

5. Conclusions

This report describes a probabilistic methodology to assess the CO2 storage resource of geologic 

strata in sedimentary basins in the United States. The fully probabilistic method captures the uncertainty 

of (1) the geologic parameters which define the storage formation pore volume; (2) the fraction of that 

pore space in which CO2 will be retained; and (3) the mass of the CO2 retained in that pore volume. The 

resulting products created using this methodology include the storage resources, reported in mass of 

CO2, of (1) the entire storage formation; (2) the buoyant trapping and residual trapping within the 

storage formation; and (3) the storage within oil and gas reservoirs. 

Acknowledgments

The authors thank USGS authors from previous versions of this methodology; Mark F. Becker, 

William N. Herkelrath, Yousif K. Kharaka, Christopher E. Neuzil, Sharon M. Swanson, Troy A. Cook, 

Timothy R. Klett, Philip H. Nelson, and Christopher J. Schenk. Thanks to Ronald R. Charpentier 

(USGS) for expert guidance and advice on developing probabilistic models and calculations. 

Substantial improvements and modifications to earlier versions could not have occurred without the 

assistance of reviews and meetings with the members of an external review panel (Stefan Bachu 

(Alberta Innovates-Technology Futures, Edmonton, Alberta, Canada), Charles Gorecki (Energy & 
19

Environmental Research Center, University of North Dakota, Grand Forks, N. Dak.), Kenneth Hood 

(ExxonMobil Exploration Company, Houston Tex.), William Maze (ExxonMobil Upstream Research 

Company, Houston, Tex. – Chairman of the review committee), George Peridas (Natural Resource 

Defense Council, San Francisco, Calif.), Rusty Riese (BP Alternative Energy, Houston, Tex.), John 

Rupp (Indiana Geological Survey, Bloomington, Ind.), and Gary Teletzke (ExxonMobil Upstream 

Research Company, Houston, Tex))1. The content and presentation of this report benefited greatly from 

the technical reviews of Ronald R. Charpentier, Christopher J. Schenk, Laurie Burke, Phil Nelson, Troy 

Cook, and Lynn Tennyson. In the methodology development process, extensive collaboration was 

provided by the USGS Tertiary assessment team, especially Alex Karlsen, and the USGS Wind River 

Basin Province assessment team, in particular Mark Kirschbaum and Laura Roberts. Susan D. Hovorka 

and the Gulf Coast Carbon Center team at the Texas Bureau of Economic Geology provided data and 

helpful discussions on the potential for CO2 storage in the sediments of the Texas Gulf Coast. Eric 

Morrissey (USGS) was helpful in the redesign of several illustrations within the report. The authors 

thank Desiree Polyak (USGS) for her contributions to the project. The authors also wish to 

acknowledge the support of the USGS Enterprise Publishing Network. 

References Cited

Ahlbrandt, T.S., and Klett, T.R., 2005, Comparison of methods used to estimate conventional 

undiscovered petroleum resources—World examples: Natural Resources Research, v. 14, no. 3, 

p. 187–210, doi:10.1007/s11053–005–8076–0 [may require subscription to access]. 

Bachu, S., 2003, Screening and ranking of sedimentary basins for sequestration of CO2 in geological 

media in response to climate change: Environmental Geology, v. 44, p. 277–289, 

doi:10.1007/s00254–003–0762–9 [may require subscription to access]. 

Bachu, Stefan, Bonijoly, Didier, Bradshaw, John, Burruss, Robert, Holloway, Sam, Christensen, N.P., 

and Mathiassen, O.M., 2007, CO2 storage capacity estimation—Methodology and gaps: International 

Journal of Greenhouse Gas Control, v. 1, p. 430–443, doi:10.1016/S1750–5836(07)00086–2 [may 

require subscription to access]. 

Beggs, H. Dale, 1987, Oil system correlations, in Bradley, H.B., ed., Petroleum Engineering Handbook: 

Richardson, Tex., Society of Petroleum Engineers, p. 22–1 to 22–22. 

Bennion, Brant, and Bachu, Stefan, 2005, Relative permeability characteristics for supercritical CO2

displacing water in a variety of potential sequestration zones in the Western Canada Sedimentary 

Basin, in Society of Petroleum Engineers Annual Technical Conference and Exhibition, 9–12 October 

2005, Dallas, Tex., 2005, Proceedings: Richardson, Tex., Society of Petroleum Engineers, paper SPE 

95547, 15 p., doi:10.2118/95547–MS [may require subscription to access]. 

Bennion, D.B., and Bachu, Stefan, 2008, Drainage and imbibition relative permeability relationships for 

supercritical CO2/brine and H2S/brine systems in intergranular sandstone, carbonate, shale, and 

anhydrite rocks: Society of Petroleum Engineers Reservoir Evaluation and Engineering, v. 11, no. 3, 

p. 487–496, doi:10.2118/99326–PA [may require subscription to access]. 

_______________ 

1

Public Law 110–140, Section 711(d)(2), mandated the establishment of "a panel of individuals with expertise in the matters 

described. . .composed of representatives of Federal agencies, institutions of higher education, nongovernmental 

organizations, State organizations, industry, and international geoscience organizations to review the methodology and 

comments received" on the carbon dioxide sequestration capacity assessment. The reviews provided by members of that 

panel were for technical purposes only and do not constitute an endorsement of the methodology presented herein by either 

the reviewers or their institutions.
20

Benson, Sally, and Cook, Peter, 2005, Underground geological storage, chap. 5 of Intergovernmental 

Panel on Climate Change (IPCC), Working Group III, IPCC special report on carbon dioxide capture 

and storage (Metz, Bert, Davidson, Ogunlade, de Coninck, Heleen, Loos, Manuela, and Meyer, Leo, 

eds.): New York, Cambridge University Press, p. 195–276. (Also available at 

http://www.ipcc.ch/pdf/special-reports/srccs/srccs_chapter5.pdf.) 

Bird, K.J., Charpentier, R.R., Gautier, D.L., Houseknecht, D.W., Klett, T.R., Pitman, J.K., Moore, T.E., 

Schenk, C.J., Tennyson, M.E., and Wandrey, C.R., 2008, Circum-Arctic resource appraisal—

Estimates of undiscovered oil and gas north of the Arctic Circle: U.S. Geological Survey Fact Sheet 

2008–3049, 4 p. (Also available at http://pubs.usgs.gov/fs/2008/3049/.) 

Bradshaw, John, 2004, Geological sequestration of CO2; Why, where and what role for geoscientists 

[abs.], in Boult, P.J., Johns, D.R., and Lang, S.C., eds., Eastern Australasian Basins Symposium II, 

19–22d September 2004, Adelaide, South Australia [conference proceedings]: Sydney, Australia, 

Petroleum Exploration Society of Australia, p. 737. 

Bradshaw, John, Boreham, Chris, and La Pedalina, Frank, 2005, Storage retention time of CO2 in 

sedimentary basins; Examples from petroleum systems, in Rubin, E.S., Keith, D.W., and Gilboy, 

C.F., eds., Proceedings of the 7th International Conference on Greenhouse Gas Control Technologies, 

5–9 September 2004, Vancouver, Canada: Greenhouse Gas Control Technologies, v. 1, no. 4, p. 541–

550. (Also available at http://uregina.ca/ghgt7/PDF/papers/peer/427.pdf [may require subscription to 

access].) 

Bradshaw, John, Bachu, Stefan, Bonijoly, Didier, Burruss, Robert, Holloway, Sam, Christensen, N.P., 

and Mathiassen, O.M., 2007, CO2 storage capacity estimation—Issues and development of standards: 

International Journal of Greenhouse Gas Control, v. 1, p. 62–68, 

doi:10.1016/S1750–5836(07)00027–8 [may require subscription to access]. 

Burruss, R.C., 2009, CO2 storage resources, reserves, and reserve growth; Toward a methodology for 

integrated assessment of the storage capacity of oil and gas reservoirs and saline formations: Energy 

Procedia, v. 1, no. 1, p. 2679–2683, doi:10.1016/j.egypro.2009.02.036 [may require subscription to 

access].

Burruss, R.C., Brennan, S.T., Freeman, P.A., Merrill, M.D., Ruppert, L.F., Becker, M.F., Herkelrath, 

W.N., Kharaka, Y.K., Neuzil, C.E., Swanson, S.M., Cook, T.A., Klett, T.R., Nelson, P.H., and 

Schenk, C.J., 2009, Development of a probabilistic assessment methodology for evaluation of carbon 

dioxide storage: U.S. Geological Survey Open-File Report 2009–1035, 81 p., accessed May 12, 2010, 

at http://pubs.usgs.gov/of/2009/1035/. 

Burton, McMillan, Kumar, Navanit, and Bryant, S.L., 2008, Time-dependent injectivity during CO2

storage in aquifers, in Symposium on Improved Oil Recovery, 20–23 April 2008, Tulsa, Okla., 2008, 

Proceedings: Richardson, Tex., Society of Petroleum Engineers, paper SPE 113937, 15 p., 

doi:10.2118/113937–MS [may require subscription to access]. 

Charpentier, R.R., and Klett, T.R., 2005, A Monte Carlo simulation method for the assessment of 

undiscovered, conventional oil and gas, chap. 21 of USGS Southwestern Wyoming Province 

Assessment Team, National Assessment of Oil and Gas Project; Petroleum systems and geologic 

assessment of oil and gas in the Southwestern Wyoming Province, Wyoming, Colorado, and Utah: 

U.S. Geological Survey Digital Data Series 69–D, 5 p., on CD–ROM. (Also available at 

http://pubs.usgs.gov/dds/dds-069/dds-069-d/.) 
21

Couples, G.D., 2005, Seals; The role of geomechanics, in Boult, Peter, and Kaldi, John, eds., 

Evaluating fault and cap rock seals: American Association of Petroleum Geologists Hedberg Series, 

no. 2, p. 87–108, doi:10.1306/1060758H21906 [may require subscription to access]. 

Craft, B.C., and Hawkins, M.F, 1991, Applied petroleum reservoir engineering (2d ed., revised by R.E. 

Terry): Englewood Cliffs, N.J., Prentice Hall, 431 p. 

Dandekar, A.Y., 2006, Petroleum reservoir rock and fluid properties: New York, Taylor and Francis 

Group, 460 p. 

Fox, J.E., Lambert, P.W., Mast, R.F., Nuss, N.W., and Rein, R.D., 1975, Porosity variation in the 

Tensleep and its equivalent the Weber Sandstone, western Wyoming—A log and petrographic 

analysis, in Bolyard, D.W., ed., Deep drilling frontiers of the central Rocky Mountains: Rocky 

Mountain Association of Geologists Guidebook, p. 185–216. 

Frailey, S.M., and Finley, R.J., 2009, Classification of CO2 geologic storage—Resource and capacity: 

Energy Procedia, v. 1, no. 1, p. 2623–2630, doi:10.1016/j.egypro.2009.02.029 [may require 

subscription to access]. 

Garb, F.A., and Smith, G.L., 1987, Estimation of oil and gas resources, in Bradley, H.B., ed. Petroleum 

Engineering Handbook: Richardson, Tex., Society of Petroleum Engineers, p. 40–1 to 40–38. 

Gautier, D.L., Bird, K.J., Charpentier, R.R., Grantz, Arthur, Houseknecht, D.W., Klett, T.R., Moore, 

T.E., Pitman, J.K., Schenk, C.J., Schuenemeyer, J.H., Sorensen, Kai, Tennyson, M.E., Valin, Z.C., 

and Wandrey, C.J., 2009, Assessment of undiscovered oil and gas in the Arctic: Science, v. 324, no. 

5931, p. 1175–1179, doi:10.1126/science.1169467 [may require subscription to access].

Gorecki, C.D., Sorensen, J.A., Bremer, J.M., Knudsen, D.J., Smith, S.A., Steadman, E.N., and Harju, 

J.A., 2009, Development of storage coefficients for determining the effective CO2 storage resource in 

deep saline formations, in Society of Petroleum Engineers International Conference on CO2 Capture, 

Storage, and Utilization, 2–4 November 2009, San Diego, Calif., 2009, Proceedings: Richardson, 

Tex., Society of Petroleum Engineers, no. 126444, doi:10.2118/126444–MS [may require 

subscription to access]. 

Hermanrud, Christian, Nordgård Bolås, H.M., and Tiege, G.M.G., 2005, Seal failure related to basinscale processes, in Boult, Peter, and Kaldi, John, eds., Evaluating fault and cap rock seals: American 

Association of Petroleum Geologists Hedberg Series, no. 2, p. 13–22, doi:10.1306/1060753H23159

[may require subscription to access]. 

IHS Inc., 2008a, PI/Dwights PLUS on CD, v. 18, issue 7–8: Englewood, Colo., IHS Inc. 

IHS Inc., 2008b, PIDM 2.5: Englewood, Colo., IHS Inc. 

Juanes, Ruben, Spiteri, E.J., Orr, F.M., Jr., and Blunt, M.J., 2006, Impact of relative permeability 

hysteresis on geological CO2 storage: Water Resources Research, v. 42, W12418, 

doi:10.1029/2005WR004806 [may require subscription to access]. 

Juanes, Ruben, MacMinn, C.W., and Szulczewski, M.L., 2010, The footprint of the CO2 plume during 

carbon dioxide storage in saline aquifers; Storage efficiency for capillary trapping at the basin scale: 

Transport in Porous Media, v. 82, no. 1, p. 19–30, doi:10.1007/s11242–009–9420–3 [may require 

subscription to access]. 

Kirschbaum, M.A., Lillis, P.G., and Roberts, L.N.R., 2007, Geologic assessment of undiscovered oil 

and gas resources in the Phosphoria Total Petroleum System of the Wind River Basin Province, 

Wyoming, chap. 3 of USGS Wind River Basin Province Assessment Team, National Assessment of 

Oil and Gas Project; Petroleum systems and geologic assessment of oil and gas in the Wind River 
22

Basin Province, Wyoming: U.S. Geological Survey Digital Data Series DDS–69–J, 27 p., on CDROM. (Also available at http://pubs.usgs.gov/dds/dds-069/dds-069-j/.) 

Klett, T.R., Schmoker, J.W., Charpentier, R.R., Ahlbrandt, T.S., and Ulmishek, G.Fr., 2005, Glossary, 

chap. 25 of USGS Southwestern Wyoming Province Assessment Team, National Assessment of Oil 

and Gas Project; Petroleum systems and geologic assessment of oil and gas in the Southwestern 

Wyoming Province, Wyoming, Colorado, and Utah: U.S. Geological Survey Digital Data Series 

69–D, 3 p., on CD–ROM. (Also available at http://pubs.usgs.gov/dds/dds-069/dds-069-d/.) 

Lucier, Amie, and Zoback, Mark, 2008, Assessing the economic feasibility of regional deep saline 

aquifer CO2 injection and storage—A geomechanics-based workflow applied to the Rose Run 

sandstone in eastern Ohio, USA: International Journal of Greenhouse Gas Control, v. 2, no. 2, 

p. 230–247, doi:10:1016/j.ijggc.2007.12.002 [may require subscription to access]. 

National Institute of Standards, 2009, Thermophysical properties of fluid systems models: NIST 

standard reference database number 69, accessed January 13, 2009, at 

http://webbook.nist.gov/chemistry/. 

Nehring Associates, Inc., 2008, Significant oil and gas fields of the United States database [includes 

data current as of December 31, 2006]: Colorado Springs, Colo., Nehring Associates, Inc. 

Public Law 110–140, 2007, Energy Independence and Security Act of 2007, U.S. Government Printing 

Office, accessed May 12, 2010, at http://frwebgate.access.gpo.gov/cgi-bin/ 

getdoc.cgi?dbname=110_cong_public_laws&docid=f:publ140.110.pdf. 

Saadatpoor, Ehsan, Bryant, S.L., and Sepehrnoori, Kamy, 2010, New trapping mechanism in carbon 

sequestration: Transport in Porous Media, v. 82, no. 1, p. 3-17, doi:10.1007/s11242–009–9446–6

[may require subscription to access].

Schmoker, J.W., and Klett, T.R., 2005, U.S. Geological Survey assessment concepts for conventional 

petroleum accumulations, chap. 19 of USGS Southwestern Wyoming Province Assessment Team, 

National Assessment of Oil and Gas Project; Petroleum systems and geologic assessment of oil and 

gas in the Southwestern Wyoming Province, Wyoming, Colorado, and Utah: U.S. Geological Survey 

Digital Data Series 69–D, 6 p., on CD–ROM. (Also available at http://pubs.usgs.gov/dds/dds069/dds-069-d/.) 

Skerlec, G.M., 1999, Evaluating top and fault seal, in Beaumont, E.A., and Foster, N.H., eds., 

Exploring for oil and gas traps, v. 3 of Treatise of petroleum geology; Handbook of petroleum 

geology: Tulsa, Okla., American Association of Petroleum Geologists, p. 10.3–10.94. (Also available 

at http://search.datapages.com/data/specpubs/beaumont/ch10/ch10.htm?q=%2Bauthor%3Askerlec [may 

require subscription to access].) 

U.S. Department of Energy, National Energy Technology Laboratory, 2008, Carbon sequestration atlas 

of the United States and Canada (2d ed.; Atlas II): 142 p., accessed May 12, 2010, at 

http://www.netl.doe.gov/technologies/carbon_seq/refshelf/atlasII/2008%20ATLAS_Introduction.pdf. 

U.S. Environmental Protection Agency, 2008, Federal requirements under the underground injection 

control (UIC) program for carbon dioxide (CO2) geologic sequestration (GS) wells: Washington, 

D.C., U.S. Environmental Protection Agency, proposed rule, accessed January 14, 2009, at 

http://www.epa.gov/fedrgstr/EPA-WATER/2008/July/Day-25/w16626.htm. 

U.S. Environmental Protection Agency, 2009, Safe Drinking Water Act (SDWA): Washington, D.C., 

U.S. Environmental Protection Agency Web site, accessed January 14, 2009, at 

http://www.epa.gov/ogwdw/sdwa/index.html.
23

van der Meer, Bert, and Egberts, Paul, 2008, Calculating subsurface CO2 storage capacities: The Leading

Edge, v. 27, no. 4, p. 502–505, doi:10.1190/1.2907182 [may require subscription to access].

Wiprut, David, and Zoback, M.D., 2000, Fault reactivation and fluid flow along a previously dormant 

normal fault in the northern North Sea: Geology, v. 28, no. 7, p. 585–598, doi:10.1130/0091–7613

[may require subscription to access]. 
24

Glossary

barrels of oil equivalent (BOE) A unit of petroleum volume in which the gas part is expressed in terms 

of its energy equivalent in barrels of oil. For this assessment, the energy equivalent (not the volume 

equivalent) of 6,000 cubic feet of natural gas equals 1 barrel of oil equivalent (Klett and others, 2005). 

brine Water having a salinity higher than that of average seawater, that is, more than 35,000 parts per 

million (milligrams per liter) total dissolved solids. 

buoyancy Upward force on one phase (for example, a fluid) produced by the surrounding fluid (for 

example, a liquid or a gas) in which it is fully or partially immersed, caused by differences in density.

buoyant trapping CO2 in communication across pore space creating a column that is held in place by a 

top and lateral seal, either a seal formation or a sealing fault.

buoyant trapping pore volume (BPV) A geologically determined, probabilistic-distribution of the 

volume fraction of the storage formation that can store CO2 by buoyant trapping. This distribution 

minimum is typically defined by existing plus forecast undiscovered oil and gas production volumes. 

The maximum is probabilistically calculated from distributions of geologic parameters describing the 

known trapping structures within the storage formation. 

buoyant trapping storage efficiency (BSE) A distribution of efficiency values that describes the fraction 

of buoyant trapping that can occur within a volume of porous media. 

buoyant trapping storage resource (BSR) Mass of CO2 retained in the storage formation by buoyant 

trapping. 

buoyant trapping storage volume (BSV) Volume of CO2 retained in the storage formation by buoyant 

trapping. 

capillary entrance pressure The pressure necessary to displace a wetting fluid from a porous medium 

by a nonwetting phase (for example, displacing water with gas or mercury). The surface tension 

between the phases is a function of the radius of curvature of the interface between the phases, causing 

capillary entrance pressures to increase as the diameter of the pores and pore throats decreases. Very 

fine grained rocks, like mudstones, have very high capillary entrance pressures allowing them to be 

barriers to flow (seals) for nonwetting fluids, such as oil, gas, and supercritical CO2. 

capillary force Capillary forces in a petroleum reservoir are the result of the combined effect of surface 

and interfacial tensions, pore size, geometry, and wetting characteristics of a given system (Dandekar, 

2006, p. 145). 

carbon dioxide plume The subsurface extent, in three dimensions, of an injected carbon dioxide 

stream.

column height The thickness defined by the highest and lowest levels within the strata where the CO2

phase is continuously connected. This column is held in place by top and lateral seals, and its thickness 

is controlled by the geometry of the closure and (or) the seal adequacy.

enhanced oil recovery Any process of tertiary oil recovery that follows recovery using natural 

reservoir pressure (primary recovery) and water flooding (secondary recovery). Common methods 

include steam-flooding for heavy oil, polymer and surfactant flooding, and CO2 miscible gas flooding. 

gas:oil ratio (GOR) Ratio of gas to oil (in cubic feet per barrel) in a hydrocarbon accumulation. This 

ratio is calculated by using volumes of gas and oil at surface conditions. 
25

gas reservoir A hydrocarbon accumulation composed primarily of gas, defined by the USGS as an 

accumulation having a gas:oil ratio of 20,000 cubic feet per barrel or greater. 

geologic storage of CO2 The long-term retention of carbon dioxide in subsurface geologic formations. 

injectivity The rate and pressure at which fluids can be pumped into the rock without fracturing the 

formation. Although injectivity is typically reported as a rate, this methodology addresses this 

requirement by using permeability values to divide the residual storage component of the storage 

formation into three classes.

known recovery production volumes (KR) The cumulative petroleum production and proved reserves 

for a given reservoir.

known recovery replacement storage resource (KRRSR) The storage resource calculated from known 

recovery production volumes. 

minimum size The lower limit for inclusion of oil and gas field information in assessment calculations. 

Volumetric data from accumulations with less than 0.5 million barrels of oil equivalent total production 

are not included in any of the calculations in this methodology. 

National Oil and Gas Assessment (NOGA) U.S. Geological Survey National Oil and Gas Assessment, 

described at http://energy.cr.usgs.gov/oilgas/noga/.

net porous thickness (TPI) Defined in this methodology as the stratigraphic thickness of the storage 

formation with a porosity of 8 percent or higher. 

oil reservoir A hydrocarbon accumulation composed primarily of oil, defined by the USGS as an 

accumulation having a gas:oil ratio of less than 20,000 cubic feet per barrel. 

original oil in place The total in-place volume of oil within a hydrocarbon reservoir or field, or within a 

larger region such as a basin or country, prior to the onset of production. The volume of the oil is

reported at surface conditions, not reservoir conditions. 

permeability (k) A measure of the ability of a rock to transmit fluids, controlled by pore size and pore 

throat geometry. Typically reported in Darcy units. 

porosity (φ) The part of a rock that is occupied by voids or pores. Pores can be connected by passages 

called pore throats, which allow for fluid flow, or pores can be isolated and inaccessible to fluid flow. 

Porosity is typically reported as a volume, fraction, or percentage.

pressure gradient The change in pore pressure per unit depth, typically in units of pound-force per 

square inch per foot or kilopascals per meter. 

residual trapping Discrete droplets, blobs, or ganglia of CO2 as a nonwetting phase, essentially 

immiscible with the wetting fluid, trapped within individual pores where the capillary forces overcome 

the buoyant forces. 

residual trapping class 1 (R1) Residually trapped storage formation rock having a permeability of 

greater than 1 Darcy. 

residual trapping class 2 (R2) Residually trapped storage formation rock having a permeability of 

greater than 1 millidarcy and less than than 1 Darcy. 

residual trapping class 3 (R3) Residually trapped storage formation rock having a permeability of less 

than 1 millidarcy. 

residual trapping pore volume (RPV) A calculated value equal to the storage formation pore volume 

(SFPV) minus the buoyant trapping pore volume (BPV). The value represents the pore volume within the 
26

storage formation that can be used to store CO2 by residual trapping; it is calculated during iterations of 

the Monte Carlo simulator after a value from the buoyant trapping pore volume distribution is chosen. 

residual trapping storage efficiency (RSE) A distribution of efficiency values that describes the fraction 

of residual trapping that can occur within a volume of porous media. 

residual trapping storage resource (RSR) The mass of CO2 retained in the storage formation by residual 

trapping. 

residual trapping storage volume (RSV) The volume of CO2 retained in the storage formation by 

residual trapping. 

salinity A measurement of the water properties determined by the total dissolved solids, generally 

reported in parts per million or milligrams per liter.

seal formation The confining rock unit within the storage assessment unit. The seal formation is a rock 

unit that sufficiently overlies the storage formation and has a capillary entrance pressure low enough to 

effectively inhibit the upward buoyant flow of CO2. 

seal A geologic feature that inhibits the mixing or migration of fluids and gases between adjacent 

geologic units. Typically a rock unit or a fault; it can be a top seal, inhibiting upward flow of buoyant 

fluids, or a lateral seal, inhibiting the lateral flow of buoyant fluids. 

storage assessment unit (SAU) A mappable volume of rock that includes the storage formation, a 

reservoir flow unit for CO2 storage, and a regional seal formation. 

storage formation (SF) The reservoir component of the storage assessment unit. The sedimentary rock 

layers that are saturated with formation water with total dissolved solids greater than 10,000 parts per 

million (milligrams per liter). In the CO2 assessment methodology, the storage formation resource 

calculation is the main resource calculation and consists of two parts, a buoyant trapping resource and a 

residual trapping resource. 

storage formation pore volume (SFPV) The available pore space in the storage formation calculated 

from area, thickness of the net porous interval, and porosity. This value is used in the calculation of 

residual trapping pore volume (RPV).

supercritical CO2 Carbon dioxide is in a supercritical fluid state when both the temperature and 

pressure exceed the critical temperature of 88°F (31°C) and pressure of 74 bars (7,400 kilopascal) at 

which liquid and vapor CO2 can no longer coexist. 

technically accessible storage resource (TASR) The CO2 storage resource that may be available for CO2 

injection and storage estimated by using present-day geologic and hydrologic knowledge of the 

subsurface and engineering practices. Analogous to the term “technically recoverable resource” used in 

USGS oil and gas assessments.

technically accessible storage volume (TASV) The CO2 storage volume that may be available for CO2 

injection and storage estimated by using present-day geologic and hydrologic knowledge of the 

subsurface and engineering practices. 

total dissolved solids (TDS) Synonymous with salinity; see salinity.

trapping The physical and geochemical processes by which injected CO2 is retained in the subsurface.
27

Appendix A. Assessment Input Forms

Storage Formation (SF) Assessment Method: Input Data Form

The input sections for the storage formation form are described in section 4.3. 
28

STORAGE FORMATION (SF) ASSESSMENT MODEL: INPUT DATA FORM

IDENTIFICATION INFORMATION

Assessment Geologist: USGS Date:

Region: North America Number: 5

Province: Wind River Basin Number: 5035

Storage Assessment Unit (SAU): Tensleep Sandstone Number: S50350101

SAU Relationship to NOGA AU: NOGA AU Tensleep-Park City Conventional Oil and Gas

Based on Data as of: Nehring Associates, Inc., 2008, data current through 2006;

Fox and others, 1975; Kirschbaum and others, 2007

Notes from Assessor:

CHARACTERISTICS OF THE STORAGE ASSESSMENT UNIT (SAU)

1) Storage formation depth (ft): minimum: 3,000 mode: 9,500 maximum: 13,000

2) Area of storage formation at depths between 3,000 ft and 13,000 ft, or another interval (acres): 1,400,000

3) Total Storage Formation 

 Thickness (3,000-13,000 ft): minimum: 200 mode: 275 maximum: 600

STORAGE FORMATION PORE VOLUME (SFPV) PROBABILISTIC CALCULATION INPUTS

4) Mean SAU Area (line 2): minimum: 1,300,000 mode: 1,400,000 maximum: 1,500,000

5) Mean Thickness,

 Net Porous Interval (ft): minimum: 80 mode: 100 maximum: 120

6) Mean Porosity (fraction): minimum: 0.1 mode: 0.15 maximum: 0.2

BUOYANT TRAPPING PROBABILISTIC CALCULATION INPUTS

7) Buoyant Trapping Pore 

 Volume (MMbbl): minimum: 190 mode: 205 maximum: 4,100

RESIDUAL TRAPPING PROBABILISTIC CALCULATION INPUTS

8) Injectivity Category Allotment (Sum = 1.0)

a) Fraction with Class 1 Injectivity (k > 1 D) 0.05

b) Fraction with Class 2 Injectivity (1 mD < k < 1 D) 0.9

c) Fraction with Class 3 Injectivity (< 1 mD) 0.05

Does the Class 1 fraction have a direct updip path to outcrop that may be a leakage risk? no
29

ALLOCATIONS OF THE SF TO STATES

Surface Allocations (uncertainty of a fixed value)

1) Wyoming represents 100 area % of the SAU

Volume % in entity mode: 100

ALLOCATIONS OF THE SF TO GENERAL LAND OWNERSHIPS

Surface Allocations (uncertainty of a fixed value)

1) Federal Lands represents 35.19 area % of the SAU

Volume % in entity mode: 45

2) Private Lands represents 29.36 area % of the SAU

Volume % in entity mode: 25

3) Tribal Lands represents 29.91 area % of the SAU

Volume % in entity mode: 25

4) Other Lands represents 0.45 area % of the SAU

Volume % in entity mode: 0.5

5) WY State Lands represents 5.08 area % of the SAU

Volume % in entity mode: 4.5

ALLOCATIONS OF THE SF TO CRITICAL LAND AREAS

Surface Allocations (uncertainty of a fixed value, does not total 100%)

1) Urban Lands represents 0.02 area % of the SAU

Volume % in entity minimum: 0.01 mode: 0.02 maximum: 0.04

2) Offshore Storage represents 0 area % of the SAU

Volume % in entity minimum: 0 mode: 0 maximum: 0
30

Appendix B. GIS Data Manipulations: Creating the SAU

Creating SAU boundaries can be accomplished by using a number of different spatial 

calculation techniques and will be dependent on the data available. These instructions are intended for 

those with experience using GIS software and specifically raster calculations.

B.1. Required Data Rasters

[DepthTop] = Raster of depth from surface to top of formation in feet with negative values. 

[DepthBase] = Raster of depth from surface to base of formation in feet with negative values. 

[Isopach] = Raster of the thickness of the reservoir formation in feet with positive values. 

[WaterTDSLimit] = Raster of the depth from surface to the potable/saline water contact at 10,000 ppm 

(mg/L) TDS in feet with negative values (see section 2.3.1 for discussion). 

A [DepthTop] map and either an [Isopach] map or a [DepthBase] map is required initially. With a 

[DepthTop] and either the [Isopach] or [DepthBase], the final raster can be calculated. If elevations are 

relative to sea level, rather than the land surface, use a digital elevation model to add the appropriate 

elevation to the file. Directions below are specific to ESRI ArcGIS software. Other spatial analysis 

programs are suitable.

B.2. Creation of Required Data Rasters

To generate an [Isopach] map from [DepthTop] and [DepthBase], use ESRI ArcGIS Spatial Analyst 

raster calculator.

Raster Calculator: [Isopach] = [DepthTop]-[DepthBase]

To generate a [DepthBase] map.

Raster Calculator: [DepthBase] = [DepthTop]-[Isopach] 

B.3. Generating Storage Assessment Unit Extent and Modification of [DepthTop]

Step 1: Select area of [DepthTop] that is at depths of between 3,000 and 13,000 ft. 

Raster Calculator: [ExtentDepthTop3-13k] = [DepthTop] >= -13000 AND [DepthTop] <= -3000 

Step 2: Remove area of [DepthTop] that is not at depths of between 3,000 and 13,000 ft. 

Reclassify [ExtentDepthTop3-13k] so the removed area is “NoData.” 

Spatial Analyst Options: Set Analysis Mask to reclassified [ExtentDepthTop3-13k]. 

Raster Calculator: [DepthTop3-13k] = [DepthTop] 

Step 3: Select area of [DepthTop3-13k] that is within potable water. 

Raster Calculator: [ExtentDepthTop3-13kW] = [DepthTop3-13k] > [WaterTDSLimit]

Step 4: Remove area of [DepthTop3-13k] that is within potable water.

Reclassify [ExtentDepthTop3-13kW] so the removed area (potable water) is “NoData.” 

Spatial Analyst Options: Set Analysis Mask to reclassified [ExtentDepthTop3-13kW]. 

Raster Calculator: [DepthTop3-13kW] = [DepthTop] 

[DepthTop3-13kW] is the final product of this part of the calculation and is SAU area. 
31

B.4. Isopach Generation and Modification

Step 5: Spatial Analyst Options: Set Analysis Mask to [DepthTop3-13kW]. 

Raster Calculator: [Isopach3-13kW] = [DepthTop3-13kW] – max([DepthBase], -13000) 

[Isopach3-13kW] is the final product of this part of the calculation and represents the thickness of the 

storage formation from the top of the formation to whatever is shallower: the depth to base or 13,000 

feet.