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31

The Economics of Enhanced Oil Recovery:

Estimating Incremental Oil Supply and CO2Demand in the Powder River Basin

Klaas van t Veld* and Owen R. Phillips**

Expanding the use of CO2-enhanced oil recovery (EOR) promises to both

significantly increase recovery from existing U.S. oil reserves and possibly form

a bridge to large-scale CO2 capture and sequestration. An important input into

planning for such expansion are estimates of how both the supply of incrementaloil and the derived CO2 demand from EOR are likely to vary with the prices of

oil and CO2. We demonstrate how the analog method of predicting oil and

CO2 flows can be used to readily generate such estimates, and apply the method

to Wyomings Powder River Basin.

1. INTRODUCTION

As the policy issues of energy security and climate change have taken

center stage in recent years, the technique of CO2-enhanced oil recovery (CO2-

EOR) has received increasing attention from industry and government.1 One rea-son is that the technique promises significant increases in oil recovery from ex-

isting, mature oil fields. Typically, the primary phase of oil extraction from a new

1. See for example the statistics on CO 2-EOR growth in the biennial Oil & Gas Journal surveys

of EOR, the royalty relief and tax credits for CO 2-EOR in the Energy Policy Act of 2005, the

requirement to study pipeline construction for CO2-EOR in the Lieberman-Warner Climate Security

Act of 2008, and the further tax credits for CO2-EOR in the Emergency Economic Stabilization Act

of 2008 (the $700 billion federal bailout package for the financial industry). For a discussion of CO2-

EORs role in overall energy policy see Griffin (2009).

The Energy Journal, Vol. 31, No. 3, Copyright 2010 by the IAEE. All rights reserved.

* Corresponding author. Department of Economics & Finance, University of Wyoming, Dept

3985, 1000 E. University Ave., Laramie, Wyoming 82071-3985. E-mail: [email protected].

** Department of Economics & Finance and Enhanced Oil Recovery Institute, University of Wy-

oming.

We thank J. Michael Boyles for laying much of the groundwork for this study, and Brian F. Towler

and Vladimir Alvarado for helpful discussions. We also thank the editor, associate editor, and three

anonymous referees for many comments that improved our paper.


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32 / The Energy Journal

field, which utilizes the reservoirs natural pressure to bring oil to the surface,

extracts about 520% of the estimated original oil in place. The secondary phase,

which usually involves injection of water into the reservoir to augment or main-tain its pressure, extracts another 1020%. After the primary and secondary

phases of extraction, about two thirds of the original oil is left stranded. Ac-

cording to a recent Department of Energy study (DOE, 2008), the tertiary method

of CO2-EOR is technically able to recover about a third of this stranded oil, or

an additional 20% of the original oil in place. For all U.S. reservoirs combined,

this amounts to 87.1 billion barrels, of which the study estimates that (at an oil

price of $70 per barrel and a CO2 price of $45 per metric ton) 45 billion barrels

are economically recoverable.2

CO2-EOR recovers this additional oil by injecting slugs of CO2 at high

pressure into the reservoir, usually alternated with slugs of water. The injectedCO2 mixes with the reservoir oil, thereby reducing capillary forces that trap the

oil in pores of the rock and allowing oil that would otherwise remain stranded to

flow towards production wells.3 Most of the CO2 resurfaces with the recovered

oil and is separated, recompressed, and reinjected. In every pass through the

reservoir, however, a fraction of the CO2 remains sequestered underground. In

order to maintain a given CO2 injection rate, operators of CO2-EOR projects4

therefore need reliable sources of CO2 over extended periods of timeit is com-

mon for an EOR project to take 20 years or longer. As a result, EOR creates a

derived demand for relatively pure CO2 gas.

This steady demand for, and ultimately sequestration of, CO2 providesthe second reason for the recent interest in EOR. Realistically, the total amount

of CO2 that EOR projects might be able to sequester is limited: Dahowski et al.

(2005) estimate the sequestration capacity of depleted U.S. oil reservoirs (includ-

ing those depleted through EOR) at 10 GtCO2, which amounts to just over two

years worth of current U.S. CO2 emissions (EPA, 2008). Nevertheless, EOR may

2. To put these figures in perspective, technically recoverable reserves in the Arctic National

Wildlife Refuge (ANWR) are estimated by the U.S. Geological Survey at 10.4 billion barrels. See

the USGS National Assessment of Oil and Gas Resources Update (December, 2007) at http://

certmapper.cr.usgs.gov/data/noga00/natl/tabular/2007/ summary_07.pdf.3. To avoid fracturing the caprock overlying the reservoir, a small number of CO 2-EOR projects

are operated at pressures too low for the CO2 to mix with the oil. These so-called immiscible CO2floods generally recover significantly less of the stranded oil.

4. Hereafter, we drop the qualifier CO2 as understood. The term EOR is more generally used to

denote a variety of processes that enhance oil recovery beyond levels attained through primary and

secondary methods, including injection of steam, liquid chemicals, and gases other than CO 2. Ac-

cording to the Oil & Gas Journals most recent biennial survey of EOR (Moritis, 2008), CO2-EOR is

the fastest-growing EOR technique in the U.S., generating 250,000 barrels per day (bo/d) from 105

projects spread throughout the country. This amounts to about 5% of total U.S. oil production, and

is up from just 30,000 bo/d in 1986. So-called thermal EOR, which uses injections of mostly steam,

is slightly more prevalent in terms of production volume, generating 293,000 bo/d from currently 45

projects. Use of this method is almost entirely limited to heavy-oil fields in California, however, andthe aggregate production volume has steadily declined from a peak of 469,000 bo/d in 1986.


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The Economics of Enhanced Oil Recovery / 33

jump-start the building of pipelines and other infrastructure required for ulti-

mately much larger-scale sequestration in unmineable coal seams and saline aqui-

fers.If indeed EOR is to play a major role in expanding U.S. oil output as

well as providing a bridge to large-scale geological sequestration of CO2, an

important input into planning for the required infrastructure are estimates of how

both the supply of incremental oil and the derived CO2 demand from EOR are

likely to vary with the prices of oil and CO2. To our knowledge, very few such

estimates are currently available. Holtz et al.s (2001) study of EOR potential in

Texas, for example, uses only physical screening criteria to identify reservoirs

suitable for EOR, and then applies rule-of-thumb multipliers to estimate the in-

cremental oil that can be recovered and the CO2 that can be sequestered in these

reservoirs, without any reference to economics. Similar methods also are used toestimate EORs sequestration potential (without accompanying oil recovery es-

timates) in the DOEs Carbon Sequestration Atlas of the United States and Can-

ada (DOE, 2007). The above-cited study by Dahowski et al. (2005) does estimate

a cost curve for CO2 sequestration in 220 oil plays5 in the U.S., but does so for

just three oil prices, namely $15, $23, and $38/bo. It is also again based on rule-

of-thumb multipliers, adjusted only for API gravity6 and average depth of each

play. Finally, the above-cited DOE (2008) study uses physical screening criteria

to identify 1,111 large oil reservoirs amenable to EOR, and then uses reservoir-

simulation software to predict oil and CO2 flows for each reservoir. In principle,

this approach could be used to generate full oil supply and CO2 demand curves,but the study in fact examines only four price scenarios.7

In this paper, we introduce a procedure for estimating incremental oil

supply and CO2 demand curves that is based on the so-called analog method

of predicting oil and CO2 flows for a given reservoir. The method is more so-

phisticated than simple rule-of-thumb multipliers, while avoiding a key drawback

of using reservoir-simulation software. This drawback is that reservoir simula-

tions require reservoir-specific relative permeability curves as inputs, to predict

the rates at which different fluids (oil, water, CO2) will move through a given

reservoirs rock as their concentration levels in the reservoir change over time.

Data required to determine these curves are rarely available.The analog method, explained in detail in the appendix to this paper, is

not new to reservoir engineers. Jarrell et al. (2002) discuss it, for example, in

their monograph on CO2 flooding published by the Society of Petroleum Engi-

5. An oil play is a grouping of geologically similar reservoirs in a given oil-producing region.

6. A standard measure of oil density introduced by the American Petroleum Institute.

7. A now dated study by the National Petroleum Council (NPC, 1984) used very similar proce-

dures. The main difference lies in the software used to predict CO2 and oil flows: whereas the NPC

study used the CO2PM package developed by Scientific Software-Intercomp, the DOE study uses

the more versatile CO2Prophet package later developed by the Texaco Exploration and Production

Technology Department. (Both packages are available from the DOEs National Energy TechnologyLaboratory website, http://www.netl.doe.gov.)


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neers. The method also underlies a spreadsheet model made available by Kinder

Morgan, Inc., which allows oil-field operators to estimate the likely profitability

of applying EOR to a given reservoir.8

The method is not widely known by energyeconomists, however. Nor has it, to our knowledge, ever been used to estimate

incremental oil supply curves and derived CO2 demand curves for all reservoirs

in an entire region or basin, as we do in this paper.

In essence, the analog method scales the historical production and in-

jection flows observed at some existing, mature EOR project (the analog) to

predict those of a new, proposed EOR project. The validity of doing so relies on

a central assumption of the method, namely that all the various dimensions across

which reservoirs may differlithology, area, thickness, porosity, permeability,

etc.are relevant to incremental oil and CO2 production only insofar as they

affect two key scaling factors: (i) per-pattern hydrocarbon pore volume and (ii)injectivity. The term pattern refers to a (typically square) sub-area of a reservoir

centered on a single injection well and bordered by production wells;9 hydrocar-

bon pore volume (HCPV) is the space originally occupied by oil in that sub-area

of the reservoir before any of the oil was produced; and injectivity is the rate at

which fluids can be injected into the reservoir, expressed in units of HCPV per

unit time.

Specifically, the analog method predicts that if the proposed EOR project

happens to have the same per-pattern HCPV and injectivity as the EOR analog

project, each of its patterns will generate roughly the same incremental oil and

CO2 flows over time as the analog project did historically. More generally, theproposed project will differ from the analog project in terms of either HCPV or

injectivity, in which case the predicted flows are scaled accordingly. If, for ex-

ample, the proposed project has twice the per-pattern HCPV but the same injec-

tivity, it is predicted to cumulatively produce twice as much from each pattern as

the analog project did at any given time after switching to EOR; if, on the other

hand, the proposed project has the same per-pattern HCPV but twice the injec-

8. The model is available at www.kindermorgan.com/business/co2/tech.cfm. It requires the op-

erator to enter engineering parameters for a proposed EOR project (e.g., the reservoir dimensions,the current rate and decline rate of oil production, the number of existing and planned injection and

production wells) as well as economic parameters (e.g., the price of oil and CO 2 anticipated by the

operator, royalty and tax rates, discount rate). The model then uses the analog method to project

incremental oil and CO2 flows for a single pattern (an injection well surrounded by production

wells) of the proposed project, multiplies these flows by the number of planned patterns, and combines

the result with the economic parameters to predict the projects NPV. Although the model uses a very

similar approach to ours, its implementation as a spreadsheet limits its application to a single project

at a time. The model is also cruder than ours in several respects. For example, it terminates the project

at an exogenously determined time, rather than optimally as in our model.

9. Common patterns are the five-spot, which has four production wells at the corners of the

square, and the nine-spot, which has four additional production wells at the square sides. These

patterns are typically repeated more or less regularly to cover the entire reservoir area, wherebyneighboring injection wells share the production wells on their common pattern borders.


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tivity, it is predicted to cumulatively produce as much as the analog project did,

but in half the time; etc.

Note that if the methods central assumption held exactly, all EOR pro-jects would literally trace out the same normalized production and injection paths.

A single analog project would therefore suffice to predict EOR flows at any and

all proposed projects, regardless of any differences between project reservoirs

besides per-pattern HCPV and injectivity. In practice, of course, the assumption

holds only approximately. For example, even if two project reservoirs have sim-

ilarly high injectivity levels, injectivity for project A may be uniformly high

throughout its reservoir, whereas that for project B may be concentrated in highly

permeable streaks or zones. If so, then in project A, injected CO2 is likely to push

oil uniformly towards production wells, whereas in project B, CO2 may flow

preferentially through the permeable zones, bypassing oil elsewhere in the res-ervoir. As a result, incremental oil recovery from project B is likely to be lower,

and would be overpredicted by a model using projectA as an analog.10 For reasons

such as these, the analog method is considered more reliable the more closely the

reservoir characteristics of a proposed project match those of the analog used.11

By way of illustration, we apply our procedure to the Wyoming portion

of the Powder River Basin (PRB). This basin, which covers the northeast corner

of the state, is a major oil-producing region in the US, with currently about 500

actively producing fields or (since many fields produce from several reservoirs)

over 700 actively producing field-reservoir combinations (FRCs). To date, 1.9

billion barrels of oil have been extracted from these fields, almost all throughprimary and secondary recovery. Enhanced oil recovery is just getting underway

in the PRB. On the western edge of the basin, in the Salt Creek Field, one operator

has been applying EOR since 2004. Several other oil-field operators have plans

to begin EOR projects in the near future.

Not all FRCs are suitable for EOR, however. For a given FRC, the size

and geological properties of the reservoir are factors that decide the profitability

of EOR, along of course with expected revenues from incremental oil production

and costs related to CO2 injection and recycling. As oil prices increase or CO2prices decline, more FRCs become profitable for EOR, giving rise to the incre-

mental oil supply and derived CO2 demand schedules that we map out for thebasin.

10. Similarly, even if two project reservoirs have the same original HCPV, they may experience

different degrees of compaction over time as fluids are removed during the various recovery phases.

This too might differentially impact EOR performance, although the effect would likely be small.

11. Consistent with this, Kinder Morgan provides two different versions of its spreadsheet model:

one uses the Denver Unit project in the San Andres formation of West Texas as its analog, while the

other uses an unspecified project in the Morrow formation of western Kansas and the Oklahoma

Panhandle. Unfortunately, the Morrow projects history is quite short, which reduces its usefulnessfor predicting the lifetime performance of candidate EOR projects.


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2. THE DATA AND MODEL

Extensive data were collected on all FRCs in the Wyoming portion ofthe Powder River Basin. These data describe the geology, oil composition, and

production and injection history of identified FRCs. Our data were collected from

numerous primary sources, including the Wyoming Geological Association, the

Wyoming Oil and Gas Conservation Commission, and the proprietary IHS data

bank. Journal descriptions of a number of FRCs were consulted to fill gaps.

Considerable work continues in updating and checking these data against different

source materials.

To estimate the potential CO2 demand for enhanced oil recovery in the

PRB and the corresponding supply of incremental oil, we examined all FRCs that

met two criteria. First, given the large up-front capital costs of EOR projects, werequired the fields to be large. The cutoff chosen was an FRC that had cumu-

lative production of at least 5 million barrels of oil (MMbo) through the end of

2005. Smaller reservoirs were included if another reservoir in the same field met

the 5-MMbo cumulative production criterion. This is because reservoirs in the

same field can share capital facilities required for EOR. A total of 138 FRCs met

the first criterion. The second criterion is that a complete set of data had to be

available for the demand analysis. Key data were unavailable for 38 of the 138

FRCs, leaving 100 FRCs.

For each of these 100 FRCs, we first determined whether the reservoir

passed a key physical hurdle, namely the capability to be pressured to a level atwhich injected CO2 mixes with the oil. If this so-called minimum miscibility

pressure (MMP), which depends on the reservoirs temperature and the oils API

gravity, exceeds the maximum pressure that the reservoirs caprock can withstand,

then using CO2 for EOR becomes far less attractive.12 This was found to be the

case for 3 FRCs.

Table 1 lists the remaining 97 FRCs, together with their original oil in

place (OOIP)the estimated total amount of oil originally present in the reservoir

before any extraction took placeand their cumulative production up to mid-

2009. The accompanying Figure 1 shows the FRCs locations.

12. As pointed out in footnote 1, CO 2-based EOR projects can be operated at pressures belowMMP, but their performance drops significantly.


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Table 1. Field-reservoir combinations evaluated, with estimated original oilin place (OOIP, from various sources including McDaniel (1991),

the DOEs TORIS database, and estimates of HCPV) andcumulative oil production up to mid-2009 (Cum., from the IHSPI/Dwights PLUS database)

OOIP Cum.

# FieldReservoir Combination (MMbo)

1 AlphaMinnelusa C 13.2 5.7

2 Ash CreekShannon 17.3 4.6

3 Barber CreekFerguson 19.6 1.5

4 Big HandMinnelusa 9.0 6.1

5 Big MuddyDakota 11.7 7.3

6 Bone PileMinnelusa B 15.6 8.9

7 Buck Draw NorthDakota 36.0 24.4

8 Camp CreekMinnelusa B 10.5 4.9

9 Cellars RanchTensleep 34.1 6.2

10 ClaretonMuddy 149.0 3.2

11 Cole CreekDakota 30.0 1.1

12 Cole Creek SouthDakota 47.3 1.5

13 Cole Creek SouthLakota 34.3 5.4

14 CollumsMuddy 23.6 4.4

15 Coyote CreekDakota 54.4 13.3

16 Coyote Creek SouthDakota 11.4 5.4

17 Coyote Creek SouthTurner 9.2 1.2

18 Culp & Heldt DrawShannon 28.3 13.8

19 Dead Horse CreekFerguson 16.1 1.5

20 Dead Horse CreekParkman 26.4 2.0

21 Dillinger RanchMinnelusa A 24.1 8.0

22 Donkey CreekDakota 23.9 3.8

23 Donkey CreekMinnelusa 10.4 4.7

24 Dry GulchMinnelusa A 9.1 5.2

25 Duvall RanchMinnelusa A 24.9 14.6

26 EdselMinnelusa B 9.5 5.6

27 Fiddler CreekMuddy 25.9 2.9

28 Fiddler CreekNewcastle 55.9 1.2

(continued)


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the DOEs TORIS database, and estimates of HCPV) andcumulative oil production up to mid-2009 (Cum., from the IHSPI/Dwights PLUS database) (continued)

OOIP Cum.


29 Finn-ShurleyTurner 250.0 16.1

30 Finn-ShurleyWall Creek 15.5 1.4

31 Gas DrawMuddy 50.6 23.2

32 Glenrock SouthDakota 80.0 15.1

33 Glenrock SouthMuddy 62.1 19.4

34 GutheryMinnelusa B Upper 12.5 3.8

35 HalversonMinnelusa A 40.7 8.3

36 HammMinnelusa B Lower 20.3 8.1

37 Hartzog DrawShannon 353.3 114.9

38 HilightMuddy 110.0 74.3

39 House CreekSussex 67.0 38.2

40 Jepson-Holler DrawShannon 49.7 6.0

41 KayeTeapot 86.1 9.3

42 KittyMuddy 133.4 16.5

43 KummerfeldDakota 12.8 3.3

44 KummerfeldMinnelusa B 15.0 6.4

45 Lance CreekLeo 121.0 15.7

46 Lance Creek EastDakota 21.1 1.4

47 Little Mitchell Creek Minn. B 13.0 8.3

48 M-DMinnelusa B 12.0 5.8

49 MaysdorfMinnelusa A 11.5 5.4

50 Meadow CreekFrontier 7.2 2.3

51 Meadow CreekLakota 9.4 2.1

52 Meadow CreekShannon 34.0 5.4

53 Meadow CreekTensleep 40.5 13.8

54 Mellott RanchMinnelusa 19.1 5.0

55 Mikes DrawTeapot 23.0 14.6

56 Miller CreekDakota 17.0 4.4

(continued)


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OOIP Cum.


57 Moorcroft WestDakota 28.9 4.5

58 Moorcroft WestMinn. A 0.7 0.2

59 Moorcroft WestNewcastle 28.9 1.7

60 Mule CreekLakota 10.0 1.0

61 Mush CreekNewcastle 35.0 2.8

62 North ForkTensleep 55.6 21.5

63 OsageNewcastle 69.0 17.2

64 Pine TreeShannon 14.5 9.5

65 Poison DrawTeckla 13.4 7.6

66 Prong CreekMinnelusa 14.0 6.4

67 Raven CreekMinnelusa 73.8 43.0

68 RecluseMuddy 64.5 13.6

69 ReelMinnelusa 20.0 7.4

70 RenoMinnelusa 41.2 6.4

71 Robinson RanchMinnelusa 14.7 5.8

72 RozetMinnelusa 44.9 9.1

73 RozetMuddy 71.9 13.6

74 Rozet WestMinnelusa 22.7 9.8

75 Sand DunesFrontier 2.5 0.9

76 Sandbar EastMinnelusa B 32.1 9.0

77 ScottParkman 250.0 17.6

78 ScottTeapot 51.4 0.4

79 SemlekMinnelusa B 11.6 5.4

80 Semlek WestMinnelusa B 21.0 8.3

81 Skull CreekNewcastle 30.0 5.2

82 SlatteryMinnelusa 28.5 11.9

83 SlatteryMuddy 1.4 0.4

84 Springen RanchMuddy 22.8 8.8

(continued)


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OOIP Cum.


85 StewartMinnelusa B 40.9 11.1

86 SussexFrontier 5.4 0.3

87 SussexShannon 4.6 2.1

88 SussexSussex 11.7 3.5

89 SussexTensleep 25.0 5.4

90 Sussex WestShannon 35.0 13.4

91 TerraceMinnelusa B 13.8 6.5

92 Timber CreekMinnelusa 34.1 11.7

93 Timber CreekMuddy 6.4 0.2

94 UteMuddy 43.9 9.7

95 WallaceMinnelusa B 18.3 7.9

96 Well DrawTeapot 95.0 33.5

97 Winter DrawMinnelusa 9.2 6.5

An overview of the model

For each of the 97 FRCs, we estimated both the baseline net present

value of continuing with secondary oil recovery using water injection,basNPV

basT o op,bas R SP o p,basp Q (1 s ) (1 s ) C ( Q )t tbasNPV , t(1 r)t 1

and the net present value of switching to EOR,eorNPV

eorT o op,eor R SP c cm r cp o p,eor p Q (1 s )(1 s ) p Q C(Q ) C (Q )t t t t eorNPV K. t(1 r)t 1

In these expressions, represents the price of oil (assumed constant over theop

lifetime of the project), , projected baseline oil recovery in period t underop,basQtthe continued waterflood, and , projected oil recovery in that same periodop,eorQt

were the project to switch to a CO2 flood. To arrive at net operating profits ineach period, we subtract from pre-tax oil revenues any royalties at rate , as wellRs


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Figure 1. Location of field-reservoir combinations listed in Table 1. Circleareas are proportional to projected incremental oil supply at our

reference oil price of $100/bo and CO2 price of $3/Mcf.

as severance and property taxes at combined rate . For the EOR project, weSPs

we also subtract the projected cost of CO2 purchases, equal to the CO2 purchase

price (also assumed constant over the lifetime of the project) times the pro-cp

jected quantity purchased, , as well as the projected cost of recycling andcm r

Q Ctre-injecting CO2. This cost depends on the quantity of CO2 that is produced

cpQttogether with the oil. Lastly, we subtract other operating costs , which dependoC

in part on the total amount of liquids produced. For the baseline without EOR,

liquid production is just the sum of water and oil produced; for the EORp,basQtproject, is the sum of water, oil, and CO2 produced, since before recycling

p,eorQtthe CO2 is mixed in with the oil.

The remaining net operating profits are discounted to the present at the

internal rate of return r required by the FRC operator. The economic lifetime of

the project, denoted for the continued waterflood and for the CO2 flood,bas eor T T

is reached when operating profits turn negative. Switching to EOR in additioninvolves an up-front investment cost K. We assume that this entire cost is incurred


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immediately at time 0.13 Switching is optimal if and only if exceedseorNPV

.14basNPV

Predicting the response to EOR

Implementing the above estimation of requires estimates of anNPV

FRCs response to EORessentially a production function that projects time

paths of incremental oil recovery and CO2 production. As noted, we use the analog

method laid out in the appendix to generate these estimates. Two analog schedules

are necessary to calculate . One schedule predicts incremental oil produc-eorNPV

tion , which is then added to predicted baseline production to obtain overallop,incQtEOR production. The other schedule predicts CO2 production , all of which

cpQt

is assumed to be recycled and re-injected. Carbon purchases are calculatedcm

Qtby subtracting predicted production from CO2 injection.

Completing the analysis

Completing the economic analysis (for a given oil price and CO2 price)

requires combining the predicted production, recycling, and purchase paths with

cost data. There are a number of cost categories that enter in the calculation.NPV

Investment costs are the up-front expenditures needed to get an EOR

project underway; they are the variable in the expression for and areeorK NPV

typically large. Apart from the cost of constructing spur pipelines to connect aproject with trunk pipelines for CO2 and oil (a cost not included in our analysis),

the three main cost components are those of (i) drilling new wells, (ii) reconfi-

guring (working over) well equipment, and (iii) constructing a CO2 recycling

plant. Costs of drilling new wells are typically very high. This is particularly true

in older fields, where many original wells may have been capped because their

secondary-phase production declined to unprofitable levels. The spacing between

the remaining, active wells may then be too large for an EOR project. Well-

workover costs include the costs of replacing tubing in existing wells with tubing

that can resist the corrosion by carbonic acid created when CO2 mixes with water,

as well as the costs of laying new pipelines in the field to pump CO 2 to individualwells. Lastly, CO2 recycling plant costs must be incurred to process the CO 2 that

eventually comes back to the surface through producing wells.

Incremental operating costs for EOR projects also have three main com-

ponents. They consist of (i) standard operating costs for incremental liquid pro-

13. Realistically, converting an oil field currently under secondary recovery to EOR may take

considerable time, and therefore the investment of K could be spread over several months, or even

years. Our analysis does not account for this possibility.

14. Our analysis does not consider possible alternative methods of EOR, such as injection of

methane or nitrogen. In principle, nothing prevents operators from trying such methods even after

completing a CO2 flood. In practice, however, this is unlikely to occur, as the CO2 flood would leavelittle or no oil that these alternative (and generally more expensive) methods would be able to recover.


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Figure 2. Cumulative incremental oil supply at CO2 price /Mcf cp $3

using the Lost Soldier-Tensleep analog (LST) and the DenverUnit-San Andres analog (DSA)

duction and incremental wells, (ii) costs of purchasing CO2, and (iii) costs of

recycling CO2. Standard operating costs are those of labor, maintenance, and fuel

required for both waterflooding and EOR operations. Some of these are roughlyproportional to the amount of liquids produced, others to the number of wells

operated. CO2 purchasing costs cover the costs of CO2 production and compres-

sion at its source, and transportation from the source to the FRC. CO2 recycling

costs consist largely of the costs of energy (electricity or gas) required to fuel the

recompression pumps in the recycling plant, together with some labor and main-

tenance costs associated with that plant.

3. INCREMENTAL OIL SUPPLY AND CO2 DEMAND FOR THE PRB

The results of running the model to predict both the incremental oilsupply and the derived demand for CO2 from EOR in the PRB are shown in

Figures 2 and 3. More specifically, Figure 2 shows, for oil prices plotted on the

vertical axis and a CO2 price of $3 per thousand cubic feet (Mcf),15 the projected

15. Mcf is the standard quantity unit used by U.S. oil field operators for gases. Because the volumeof a gas depends on its temperature and pressure, the unit is defined at a reference pressure of 60


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Figure 4. Comparison of the dimensionless curves for incremental oilproduction for the Lost Soldier-Tensleep analog (LST) and theDenver Unit-San Andres analog (DSA)

both figures, the curve on the left is derived using the Lost Soldier-Tensleep (LST)

project in Wyoming as analog, whereas the curve on the right is derived using

the Denver Unit-San Andres (DSA) project in West Texas as analog.Figure 4 plots the dimensionless curves generated from the LST and

DSA projects historical EOR performance. It shows that, at the point in time

when cumulatively one HCPV worth of CO2 and water had been circulated

through the LST project after it switched to EOR, the project had produced cu-

mulatively 0.059 HCPV worth of incremental oil. In contrast, the DSA project

had at that same point produced 0.108 HCPV, and was therefore 1.8 times more

productive.

A number of differences between the LST and DSA projects may be

relevant to explaining their quite dissimilar EOR performance. One difference

concerns timing. According to Brokmeyer et al. (1996), CO2 flooding of the LSTproject commenced when 44.3% of OOIP had been recovered in the primary and

waterflooding stages, and when the waterflooding oil cut (the share of oil in

overall liquid production) had dropped to as little as 3%. In contrast, CO2 flooding

at the DSA project commenced when according to Hsu et al. (1997) only 35.5%

of OOIP had been recovered, and when according to Tanner et al. (1992) the oil

cut was still 14%. However, Jarrell et al. (2002) note that reservoir simulation

studies of west Texas reservoirs have shown that the rate of incremental oil

recovery to CO2 flooding is only slightly sensitive to the stage of waterflooding


16/26


when CO2 flooding starts, as long as the reservoir pressure is well above the

thermodynamic MMP. Both the LST and DSA projects satisfy the latter condi-

tion.More important, perhaps, is the difference in lithology of the project

reservoirs: whereas the Tensleep formation is a sandstone, the San Andres for-

mation is a carbonate. Several review studies of existing EOR projects (Holtz et

al., 1999; Christensen et al., 2001; Hustad, 2004) have noted that CO2 flooding

tends to yield somewhat higher incremental recovery rates in carbonate reservoirs

than in sandstone ones. Hustad (2004), for example, notes that of 115 worldwide

CO2 floods in a database maintained by the Norwegian consulting firm SINTEF

Petroleum Research, average incremental oil recoveries for sandstone and car-

bonate reservoirs are 12% and 17% of OOIP, respectively.

A third difference concerns the degree of fracturing of the two reservoirs,which is much higher for the Tensleep. Injected CO2 is more likely to flow to

production wells through such fractures, bypassing much of the oil and hence

reducing incremental oil recovery. This may explain why the LST projects ulti-

mate recovery appears to below the sandstone average (its dimensionless curve

in Figure 4 asymptotes to roughly 9%).

Lastly, the injection history of the two projects has been quite different

as well. Whereas the LST project has consistently maintained a 1:1 ratio of water

to CO2 (commonly referred to as the water-alternating-gas or WAG ratio), the

DSA project started out injecting pure CO2, and thereafter gradually increased

the WAG ratio.Whatever the full explanation for the differential EOR performance of

the two analog projects may be, it is evident that the choice of analog significantly

affects estimates of how much incremental oil the examined candidate projects

in the PRB will produce, how much CO2 they will demand, and thereby how

profitable they are likely to be. At the individual project level, this is illustrated

in Figure 2 by the rightward jump in both oil supply curves when CO 2 flooding

of the large Finn-Shurley Turner field becomes profitable. If the LST analog is

used, this is estimated to require an oil price of at least $113, and the field is then

estimated to cumulatively produce 20.8 million incremental barrels of oil over its

economic lifetime. But if the DSA analog is used, the same field is estimated tobecome profitable when the oil price is only $93, and to cumulatively produce as

much as 56.5 million barrels. At the aggregate level, Figure 2 shows that at our

reference oil price of $100 and CO2 price of $3, estimated incremental oil supply

for the basin as a whole is more than three times as high if the DSA analog is

used than if the LST analog is used (although the ratio is smaller at both lower

and higher oil prices). Figure 3 shows that estimated CO2 demand is higher with

the DSA analog as well.

As noted in the previous section, the analog method is considered more

reliable the more closely the reservoir characteristics of a proposed project match

those of the analog used. Since all fields in our study produce from sandstone


17/26


rather than carbonate reservoirs16 and a handful in fact produce from reservoirs

that are part of the Tensleep formation, our estimates based on the LST analog

are perhaps more likely to be predictive. That said, many (about a third) of thefields in our study produce from the Minnelusa formation, a carbonate-rich sand-

stone that tends to be much less fractured than the Tensleep. For these fields, the

LST analog may well turn out to underpredict EOR performance.17

As for the maturity of the fields in our study (insofar as this matters for

EOR performance, despite Jarrell et als assertion to the contrary), the median

current recovery factor for the FRCs in our study is 30% of OOIP, which is closer

to that of the DSA project when it switched to EOR, but the median oil cut is

7%, which is closer to the initial oil cut of the LST project.

More generally, the considerable heterogeneity of reservoirs in the PRB

implies that the true aggregate curves are likely to differ from both of the curvesplotted. Nevertheless, subject to these caveats and inevitable data constraints,18

the results presented here provide at least a rough, order-of-magnitude estimate

of EOR market conditions, illustrating the potential usefulness of the analog

method.

To put the incremental oil supply estimates in perspective, cumulative

oil production to date from all 97 FRCs in our study combined is 978 MMbo, or

about 25% of their combined OOIP of 3.9 Bbo. Current combined oil production

is 5.9 MMbo/yr, but is declining: at our baseline oil price of $100, we estimate

cumulative future production under continued waterflooding to be just 63 MMbo.

Figure 2 shows that at our baseline CO2 price of $3/Mcf and using the LST analog,estimated cumulative future production from EOR is 74.5 MMbo higher, or 137.5

MMbo in total.

In Figure 1, FRCs that can profitably switch to EOR at these reference

prices are plotted as circles, with the circle areas proportional to each FRCs

projected incremental oil supply. Note that similar maps showing each FRCs

CO2 demand under various price scenarios could be used, for example, to plan

the trajectory of CO2 pipelines.

4. SENSITIVITY ANALYSIS

In practice, many of the geological and engineering parameters used in

the analog method (as listed in Table 3 of the appendix) are measured quite

imprecisely. For example, measures such as reservoir depth, thickness, and per-

meability are typically only available as averages for a given FRC, ignoring pos-

16. Some fields in the PRB do produce from carbonate reservoirs, but none of these met the

cumulative production hurdle for being part of our study.

17. Personal communication with J. Michael Boyles, a geologist previously with the Enhanced

Oil Recovery Institute at the University of Wyoming.

18. Better data will not be forthcoming until individual operators actively contemplate switching

to EOR, at which point they will want to invest in detailed geological studies and modeling exercisesfor their specific FRC.


18/26


Table 2. Results of sensitivity analysis using the Lost Soldier-Tensleep

analog. Geological/engineering parameters were variedindependently across FRCs to generate 95% confidence intervalsfor oil supply and CO2 demand. Cost and other parameters were

varied by 50% in the same direction for all FRCs simultaneously.

Change in

oil supply

Change in CO2demand

Geological/engineering parameters 95% conf. intvl. 95% conf. intvl.

Injectivity 7.4% 9.1% 12.9% 11.9%

Original oil in place 9.4% 11.0% 12.5% 11.1%

Reservoir area 2.7% 12.9% 8.3% 36.9%

Cost parameters f 50% F 50% f 50% F 50%

Royalties, severance & property taxes 23.5% 21.1% 53.1% 41.3%

Well drilling costs 20.9% 11.7% 38.1% 15.8%

Well conversion costs 15.5% 9.7% 36.2% 17.2%

Gas processing costs (fixed) 5.6% 3.7% 14.0% 9.2%

Gas processing costs (variable) 7.4% 5.4% 19.1% 12.2%

Other operating costs 7.6% 4.1% 20.6% 13.2%

Other parameters f 50% F 50% f 50% F 50%

Reference CO2 price 18.4% 21.0%

Reference oil price 74.9% 62.4%

Internal rate of return 39.5% 19.1% 92.0% 33.2%

Maximum pattern size 34.3% 35.5% 57.2% 81.9%

sibly significant heterogeneity in these measures across different sections of the

reservoir. The key scaling parameter of injectivity is usually estimated from his-

torical per-well water injection rates, even though these rates may be highly vari-able both across wells and over time. As for HCPV, the other key scaling param-

eter, this is sometimes based on published estimates of an FRCs original oil in

place; sometimes on a volumetric calculation combining estimates of the reser-

voirs area, thickness, porosity, and residual oil saturation; and in rare cases on

simple extrapolation from cumulative extraction to date. All three methods have

drawbacks, and the resulting estimate should not be taken as more than a rough

guess. Fortunately, for most of these parameters there is no reason to suspect a

systematic bias in one direction or another across all FRCs in a basin. As a result,

when it comes to estimating aggregate oil supply or CO2 demand for all FRCs

combined, it is reasonable to expect that errors will tend to cancel out.To investigate how sensitive our estimates are to variations in key geo-

logical and engineering parameters, we performed a Monte Carlo analysis, the

results of which are presented in the top panel of Table 2. Each of the parameters

listed in the first column was randomized by selecting, independently for each

FRC, 200 values from a symmetric beta distribution with mean equal to ourlicentral estimate of the parameter for the i-th FRC, support , and 95%(0,2l )i


19/26


confidence interval . The second and third columns show the re-(0.5l ,1.5l )i isulting 95% confidence intervals for incremental oil supply. The intervals are

expressed as percentage deviations from our estimate shown in Figure 2,o

QLSTafter averaging these deviations over the oil-price range shown (but dropping

values at which supply is zero). The fourth and fifth columns show the analogous

95% confidence intervals for CO2 demand. The first row of the Table therefore

shows, for example, that if the uncertainty about injectivity rates is represented

by the above-described beta distributions, then incremental oil supply lies be-

tween 7.4% below and 9.1% above the estimate shown in Figure 2 for 95%oQLSTof the simulation trials.

Error independence across FRCs is of course not a reasonable assump-

tion for the cost parameters discussed in Section 2; deviations from our central

estimates for these parameters will clearly affect all FRCs in the same way. Thesecond panel of Table 2 therefore shows, for each of the cost categories listed in

the first column, the average effects on incremental oil supply and CO2 demand

of simply reducing or increasing the relevant per-unit costs by 50%. The first row

of the panel shows, for example, that if the royalty rate as well as severance and

property tax rates were to be simultaneously reduced (increased) by 50%, the

resulting incremental oil supply would on average increase by 23.5% (drop by

21.1%) relative to the estimate shown in Figure 2. Clearly, the relativeoQLSTmagnitudes of the induced changes are reflective of the different cost categories

share of overall EOR costs. Next to taxes, the up-front investment costs associated

with drilling new wells are the most important cost category, followed by the up-front costs associated with converting and equipping wells for EOR use.

The third panel of Table 2 shows the effects of varying four other key

parameters by 50% in either direction. Doing so for our reference CO2 price of

$3/Mcf has an effect on oil supply comparable in magnitude to that of varying

taxes or drilling costs. Dropping our reference oil price from $100/bo to $50/bo

shifts in the CO2 demand curve by 74.9% on average, whereas increasing the

reference price to $150/bo increases demand by 62.4% on average.

A further parameter of particular interest is the discount rate applied by

oil-field operators in calculating a proposed projects NPV, i.e., their internal rate

of return (IRR). According to industry contacts, a common IRR applied to profitsnet of royalties, severance taxes, and property taxes but gross of income taxes is

20%; this is the rate used in the simulations underlying Figures 2 and 3. Note

that the rate is considerably above conventional riskless rates of return, reflecting

the high level of project failures in the oil industry. The next-to-last row of Table

2 reports the average effects of reducing the IRR for all FRCs to 10%, or raising

it to 30%.

A final parameter considered in our sensitivity analysis is the maximum

pattern size applied to EOR floods. Because many oil fields in the PRB are ap-

proaching the economic limit of their secondary recovery phase, it is not uncom-

mon for operators to either temporarily or permanently shut down wells of un-derperforming patterns. Were these operators to switch to EOR, however, they


20/26


may well bring temporarily abandoned wells back into operation, or even drill

new wells to achieve a desired well configuration. Our baseline simulations as-

sume that operators will do so up to the point where the average EOR patternsize is at most 80 acres.19 Reducing this maximum pattern size has two opposing

effects on EOR profitability. On the one hand, it reduces profitability, by increas-

ing the number of new wells that must be drilled and thereby the up-front capital

cost of switching to EOR. On the other hand, reducing the pattern size also

increases the rate at which CO2 cycles through a reservoir, thereby speeding up

incremental oil recovery and increasing profitability.20 We find, however, that the

former effect dominates over the price ranges for oil and CO2 considered: reduc-

ing the maximum pattern size to 40 acres shifts in the oil supply curve by 34.3%

on average, while increasing the pattern size to 120 acres shifts out supply by

35.5% on average.

5. CONCLUDING DISCUSSION

In this paper, we have shown how, given sufficiently detailed data on

the geology and extraction history of FRCs, along with the documented experi-

ences of mature or completed EOR projects, it is possible to forecast which FRCs

in an oil-producing region can profitably implement EOR at given prices for oil

and CO2. Aggregating these forecasts generates region-wide incremental oil pro-

duction and CO2 demand schedules.

The availability of such forecasts has clear benefits to EOR development.Knowing the location of other candidate EOR projects facilitates cost sharing and

coordination of CO2 and oil shipments by projects in proximity to each other.

Knowing the derived demand for pure CO2 allows emitters to consider locating

closer to points that will demand CO2 as a commodity. It may also help pipeline

authorities to more efficiently design delivery systems connecting emitters to EOR

projects.

The analog method used to generate the forecasts essentially extrap-

olates experience at an existing, mature EOR projectthe analogto new, can-

didate projects. Not surprisingly, we find that the forecasts are quite sensitive to

the specific choice of analog. Ideally, this choice should be driven by the geolog-ical properties of each candidate project, which should match those of the analog

as closely as possible. Currently, however, only a small number of analogs for

both incremental oil production and CO2 circulation are available. This problem

should diminish over time, as EOR becomes more widely applied.

19. For the common five-spot pattern (see footnote 9) this corresponds to an average well spacing

of 40 acres.

20. In terms of Figure 4, halving the pattern size halves the time by which, for example, 1 HCPV

worth of CO2 and water can be injected into the reservoir, thereby halving the time by with 0.059HCPV worth of incremental oil is recovered.


21/26


An important set of questions we leave for future work is how potential

subsidies for CO2 sequestration might affect market conditions for EOR, and how

the analog method might be adapted to forecast these effects. As noted for ex-ample by Jessen et al. (2005), EOR operators may turn to co-optimization of oil

recovery and CO2 sequestration, by adjusting the CO2 content of their injection

stream. They may also choose to continue CO2 injection even after incremental

oil has been extracted. Although such changes would not invalidate the analog

method used in this paper, they would imply a need for adjustments. Specifically,

given that existing EOR projects have been operated if anything with the aim ofminimizing CO2 sequestration, dimensionless curves based on such projects ex-

perience are likely to become less accurate as predictors of future projects per-formance. Instead, new dimensionless curves will have to be developed, possibly

based on reservoir simulations.

REFERENCES

Brokmeyer, R. J., D. C. Borling, and W. T. Pierson (1996). Lost Soldier Tensleep CO 2 Tertiary

Project, Performance Case History: Bairoil, Wyoming. Paper # SPE 35191.

Christensen, J. R., E. H. Stenby, and A. Skauge (2001). Review of WAG Field Experience. SPE

Reservoir Evaluation & Engineering, 4(2): 97106.

Dahowski, R. T., J. J. Dooley, C. L. Davidson, S. Bachu, and N. Gupta (2005). Building the Cost

Curves for CO2 Storage: North America. Report 2005/3, IEA Greenhouse Gas R&D Programme.

DOE (2006). Basin Oriented Strategies for CO2 Enhanced Oil Recovery: Rocky Mountain Region.

Washington, DC: Department of Energy, Office of Fossil Energy, Office of Oil and Natural Gas,

Prepared by Advanced Resources International.DOE (2007). Carbon Sequestration Atlas of the United States and Canada. Washington, DC: De-

partment of Energy, Office of Fossil Energy, National Energy Technology Laboratory.

DOE (2008). Storing CO2 with Enhanced Oil Recovery. Washington, DC: Department of Energy,

National Energy Technology Laboratory, Prepared by V. Kuuskraa, R. Ferguson, Advanced Re-

sources International, DOE/NETL-402/1312/02-07-08.

EIA (2006). Oil and Gas Lease Equipment and Operating Costs 1988 Through 2006. Energy In-

formation Administration. Document available online at http://www.eia.doe.gov/pub/oil_gas/ nat-

ural_gas/data_publications/cost_indices_equipment_production/current/coststudy.html.

Emera, Mohammed K. and Hemanta K. Sarma (2005). Use of Genetic Algorithm to Estimate CO2-

Oil Minimum Miscibility PressureA Key Parameter in Design of CO 2 Miscible Flood. Journal

of Petroleum Science and Engineering, 46(12): 3752.

EPA (2008). Inventory of U.S. greenhouse gas emissions and sinks: 19902006. Washington, DC:Environmental Protection Agency, EPA 430-R-08-005.

Fox, Charles E. (1995). Cost Estimation Parameters. University of Texas of the Permian Basins

Center for Energy & Economic Diversification (CEED) CO2 Flooding Shortcourse No. 2, Septem-

ber 12, 1995, Section F.

Griffin, James M. (2009). A Smart Energy Policy: An Economists Rx for Balancing Cheap, Clean,

and Secure Energy. New Haven, CT: Yale University Press.

Holtz, Mark H., Peter K. Nance, and Robert J. Finley (1999) Reduction of Greenhouse Gas Emissions

through Underground CO2 Sequestration in Texas Oil and Gas Reservoirs. Digital Publication

Series, 99-01, Gulf Coast Carbon Center (GCCC).

Holtz, Mark H., Peter K. Nance, and Robert J. Finley (2001). Reduction of Greenhouse Gas Emis-

sions through CO2 EOR in Texas. Environmental Geosciences, 8(3): 187199.

Hsu, C-F., J. I. Morell, and A. H. Falls (1997). Field-scale CO2-Flood simulations and their impacton the performance of the Wasson Denver Unit. SPE Reservoir Engineering, 12(1): 411.


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Hustad, Carl-W. (2004). Large-Scale CO2 Sequestration on the Norwegian Continental Shelf: A

Technical, Economic, Legal and Institutional Assessment. Project No., 151393/210, Norwegian

Research Council.

Jarrell, P. M., C. E. Fox, M. H. Stein, and S. L. Webb (2002). Practical Aspects of CO2 Flooding.SPE Monograph Series. Society of Petroleum Engineers.

Jessen, Kristian, Anthony R. Kovscek, and Franklin M. Orr, Jr. (2005). Increasing CO2 storage in

oil recovery. Energy Conversion and Management, 46(2): 293311.

Lucken, J. E. (1969). Raven Creek. WGA Earth Science Bulletin, 2(4): 2427.

Mack, J. C. and M. L. Duvall (1984). Performance and Economics of Minnelusa Polymer Floods.

Paper # SPE 12929.

McCoy, Sean T. and Edward S. Rubin (2008). An Engineering-Economic Model of Pipeline Trans-

port of CO2 with Application to Carbon Capture and Storage. International Journal of Greenhouse

Gas Control, 2(2): 219229.

McPherson, Brian J. O. L., Weon Shik Han, and Barret S. Cole (2008). Two Equations of State

Assembled for Basic Analysis of Multiphase CO2 Flow and in Deep Sedimentary Basin Condi-

tions. Computers & Geosciences, 34(5): 427444.Moritis, Guntis (2008). More US EOR projects start but EOR production continues decline. Oil &

Gas Journal, 106(15): 4146.

NPC (1984). Enhanced Oil Recovery. Washington, DC: National Petroleum Council.

Tanner, C. S., P. T. Baxley, J. G. Crump, III, and W. C. Miller (1992). Production Performance of

the Wasson Denver Unit CO2 Flood. Paper # SPE 24156.

APPENDIX

In this appendix, we illustrate the analog method using a medium-sized

field in our study area, Raven Creek-Minnelusa (RCM), as a worked example. 21

Table 3 lists the data required for applying the analog method, although strictlyspeaking the list contains some redundancies. Reservoir HCPV, for example, can

be estimated either as or as 43,560(ft2/acre)/5.615(ft3/rb). Sim-OOIPB AhS oi oiilarly, injectivity can be estimated either from past water injection rates or from

reservoir thickness h and permeability k.

Step 1: Estimate per-pattern HCPV. Mack and Duvall (1984) provide a

direct estimate of RCMs OOIP, so we estimate HCPV for the reservoir as a whole

as 81,169,000 rb. (The alternative formulaOOIPB AhS 43,560/5.615oi oiwould yield a somewhat higher estimate of 85,052,000 rb.) Assuming that 80%

of the total reservoir area will be covered with 80-acre patterns (our baseline

maximum pattern size) the CO2 flood will have pat-n ceil(0.82,975/80) 30terns. Letting H denote per-pattern HCPV, we therefore have H 2,706,000 rb.

Step 2: Estimate water and CO2 injection rates. We assume that, aver-

aged over the water and CO2 injection cycles, per-well injectivity for the EOR

project is equal to that for the waterflood, so bl/ptn-mnth.i, eor wiq q 36,8330Assuming equal-sized alternating slugs of water and CO2 will be injected (i.e., a

21. Readers interested in further detail are referred to a presentation available from the University

of Wyomings Enhanced Oil Recovery Institute (EORI), at http://eori.gg.uwyo. edu/downloads/

CO2_Conf_2009/Presentation%20PDF/ProfitableCO2ProWyo092909.pdf. The results shown in thatpresentation are based on less recent production/injection data and cost figures, however.


23/26


Table 3. Data requirements for the analog method, with example valuesfor the Raven Creek-Minnelusa field

Variable Value Source

OOIP Original oil in place 73,790,000 stba Mack and Duvall (1984)

Boi Oil formation vol. factor (initial) 1.1 rbb/stb TORISc

Boc Oil formation vol. factor (current) 1.089 rb/stb TORIS

API Oil gravity 33 API TORIS

T Temperature 200 F TORIS

MMP Minimum miscibility pressure 3,082 psi Derivedd

BCO2 CO2 formation vol. factor 0.7001 rb/Mcf Derivede

fp Fracture pressure 5,548 psi WOGCCf

d Depth 8,380 ft Lucken (1969)

h Thickness 37 ft Lucken (1969)

A Area 2,975 acres Lucken (1969) Porosity 0.12 (fraction) Lucken (1969)

k Permeability 50 md Lucken (1969)

Soi Oil saturation (initial) 0.83 (fraction) TORISpnc Existing producer wells 15 WOGCCinc Existing injector wells 13 WOGCCtan Temporarily abandoned wells 12 WOGCCopQ0 Oil production (end of waterflood) 6,833 stb/mo IHS

g (estimated) h

d Oil production decline rate (e.o.w.) 0.45 %/mo IHS (estimated) i

wpQ0 Water production (e.o.w.) 396,536 bl/mo IHS (estimated)j

wiq0 Water injection per well 36,833 bl/wl-mo IHS (estimated)k

R Gas-oil ratio 0 Mcf/bl IHS (estimated) l

a Stock-tank barrels (42 gallons U.S. at 60 F and 14.7 psi).b Reservoir barrels (42 gallons U.S. at reservoir temperature and pressure).c Total Oil Recovery Information System database, U.S. Department of Energy, National Petroleum

Technology Office.http://www.netl.doe.gov/technologies/oil-gas/Software/database.html.d Derived from API and Tusing DOE (2006) correlation combined0.87022MWC 4247.98641API5

with Emera and Sarma (2005) correlation .1.164 1.2785MMP 0.00726538 T (MWC )5e Derived from T and MMP using sw_SPECIFIC_DENSITY.m Matlab subroutine provided by

McPherson et al. (2008) at http://www.iamg.org/documents/oldftp/VOL34/v34-05-01.zipf Wyoming Oil and Gas Conservation Commission. http://wogcc.state.wy.us.g IHS PI/Dwights PLUS database. http://energy.ihs.com.h Predicted end value from linear regression of log monthly oil production on aop opQ logQ dt0 t

time trend for the most recent 36 months of production data.t 35, 34,...0i Estimated coefficient on t in above regression.j Estimated using same procedure as that for oil production, but using log monthly water production.k Median water injection rate per actively injecting well since 1985.l Median ratio of monthly hydrocarbon gas production to oil production for the most recent 36 months

of production data.

1:1 WAG ratio), the average rate of water injection will then be wi, eorq

bl/ptn-mnth, while that of CO2 will bei, eor ci i, eor

0.5 q 18,416 q 0.5 q /BCO2Mcf/ptn-mnth.41,788


24/26


Step 3: Estimate time paths of incremental oil and CO2 production using

the dimensionless curves. Dividing by Hgives a total injectivity of 0.01361i, eorq

HCPV/ptn-mnth. Cumulating this for any given number of months after startingthe CO2 flood yields a point on the horizontal axis of the dimensionless curve for

incremental oil production (see Figure 4). Reading off the corresponding point

on the vertical axis yields cumulative incremental oil production by that same

number of months in HCPV/ptn, or, after multiplying by , in stb/ptn. Dif-H/Bocferencing the cumulative values yields the corresponding time path of incremental

oil production rates, , in stb/ptn-mnth. Lastly, multiplying by the number ofop,incqtpatterns n yields the predicted time path of incremental oil production for the

field as a whole, , in stb/mnth. Applying the same steps to the dimensionlessop, incQtcurve for CO2 production (but multiplying cumulative production in HCPV/ptn

on the vertical axis by ) yields the predicted time path of CO2 production,H/BCO2, in Mcf/mnth.cpQt

Step 4: Estimate time paths of baseline and EOR oil production, water

production, non-CO2 (hydrocarbon) gas production, and EOR purchases of CO2.

Baseline oil production from a continued waterflood is assumed to con-op, basQttinue declining at rate , so . EOR oil production is thenop, bas op t dd Q Q et 0

. Overall water production is assumed to change in aop, eor op, bas op, incQ Q Qt t tmanner that keeps overall liquids (oil plus water plus possibly CO2) production

at reservoir conditions constant over time, and equal to overall liquids injection.

Baseline water production is therefore . EORwp, bas wp op t dQ Q Q (1 e )Bt 0 0 oc

water production is . Hydrocarbon gaswp, eor i, eor op, eor cp

Q nq Q B Q Bt t oc t CO2production is assumed to maintain a constant ratio to oil production, so

and . For RCM, no gas production is re-gp, bas op, bas gp, eor op, eorQ RQ Q RQt t t t ported, however, which we interpret to imply that . Lastly, CO2 purchasesR0

are calculated as the difference between CO2 production (all of which iscmQt

recycled) and injection, so .cm cp ciQ Q nqt tStep 5: Estimate up-front costs of well drilling, conversion, and equip-

ment. With on average one injector and one producer for each of patterns,n 30

the CO2 flood will require new wells to be drilled, atp i ta2 n n n n 20c c

(given RCMs depth in the PRB) $1.31 million per well.22 Converting and equip-

ping any kind of well (newly drilled, existing injector, existing producer, or tem-porarily abandoned) for use as a CO2-flood injector cost $152,000 plus $19/ft;

converting and equipping wells for use as CO2-flood producers cost $142,000 for

existing producers, and $325,000 plus $19/ft for newly drilled wells or existing

non-producers.23 Total costs sum to about $45.0 million.

Step 6: Estimate the up-front capital cost of gas recycling. The required

capacity of the gas recycling plant is determined by the peak of projected com-

22. Based on personal communication with Bob King, engineer at Wold Oil Properties, Inc., in

Casper, Wyoming. All costs are for the year 2006, the most recent year for which oil-industry cost

indices are available from the Energy Information Administration (EIA, 2007).23. Based on personal communication with Charles E. Fox, Vice President of Operations and


25/26


bined production of CO2 and (in this case zero) hydrocarbon gas, which for RCM

is 36.9 thousand Mcf/day. We assume the gas plant operates at inlet pressure

psi, and that the CO2 flood is operated at a reservoir injection pressures

p 225of psi. Subtracting from this the pressure gain of fmax(p 200,MMP) 5,348

psi obtained from the CO2 column in the injection0.28(psi/ft) 8,380 2,346

well yields the plants required discharge pressure, psi. Based on adp 3,001

rule-of-thumb formula provided by Fox (1995), the gas throughput capacity and

ratio combined yield an estimated power requirement for the plant of 6,361d sp /p

hp, which at a cost per hp of $1,52024 yields a capital cost for the recycling plant

of about $9.7 million.

Step 7: Estimate operating costs of gas recycling. Labor and maintenance

operating costs for gas recycling are $80 per hp-year. Electricity costs, at 7,000

kWh/hp-year and Wyomings electricity price for industrial users of 4.03 cents/kWh, amount to an additional $282 per hp-year. The overall gas recycling op-

erating cost is about $2.30 million/year.

Step 8: Estimate other operating costs. Based on data in EIA (2007),

operating costs were the waterflood to continue are estimated at $0.233 per barrel

of total liquids produced plus $31,000 per well-year for each of RCMs current

28 wells. Based on a rule of thumb given in Fox (1995), the corresponding costs

for the CO2 flood are assumed to be 10% higher, whereby the per-well costs apply

to the CO2 floods 60 wells.

Step 10: Determine the terminal times for the continued waterflood and

CO2 flood. At our reference oil price of $100, oil revenues net of 16% royalty,6% severance tax, and 6% property tax drop below operating costs after basT

years of continued waterflooding. At the same oil price and our reference20.5

CO2 price of $3/Mcf, net revenues from the CO2 floods total oil production (i.e.,

baseline and incremental oil production combined) drop below operating costs,

including the cost of CO2 purchases, after years.eorT 13.5

Step 11: Compare the NPV of the continued waterflood and CO2 flood.

At the reference prices and our baseline internal rate of return of , ther 0.20

of continuing the waterflood is $15.1 million, while the of switch-bas eor NPV NPV

ing to a CO2 flood, net of initial capital costs, is substantially higher, at $89.6

million. Switching is therefore optimal.Step 12: Calculate cumulative incremental oil production and CO2 de-

mand. Cumulative oil production under the continued waterflood, up to , isbasT

1.016 million barrels, while that under the CO2 flood, up to , is 6.870 millioneorT

barrels. Incremental production is therefore 5.854 million barrels, or 7.9% of

OOIP. Cumulative CO2 demand is 50.4 Bcf.

Technology at Kinder Morgan CO2 Company, LP in Houston, Texas, updating figures given in Fox

(1995).

24. Based on personal communication with Mark Nicholas, President of Nicholas ConsultingGroup in Midland, Texas.


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