The Economics of Enhanced Oil Recovery: Estimating ... journal proof 022310.pdf · 31 The Economics of Enhanced Oil Recovery: Estimating Incremental Oil Supply and CO 2 Demand in

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The Economics of Enhanced Oil Recovery:Estimating Incremental Oil Supply and CO2

Demand in the Powder River Basin

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

Expanding the use of CO2-enhanced oil recovery (EOR) promises to bothsignificantly increase recovery from existing U.S. oil reserves and possibly forma bridge to large-scale CO2 capture and sequestration. An important input intoplanning 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 ofoil and CO2. We demonstrate how the “analog” method of predicting oil andCO2 flows can be used to readily generate such estimates, and apply the methodto Wyoming’s Powder River Basin.

1. INTRODUCTION

As the policy issues of energy security and climate change have takencenter 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 CO2-EOR growth in the biennial Oil & Gas Journal surveysof EOR, the royalty relief and tax credits for CO2-EOR in the Energy Policy Act of 2005, therequirement to study pipeline construction for CO2-EOR in the Lieberman-Warner Climate SecurityAct of 2008, and the further tax credits for CO2-EOR in the Emergency Economic Stabilization Actof 2008 (the $700 billion federal bailout package for the financial industry). For a discussion of CO2-EOR’s 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, Dept3985, 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. Towlerand Vladimir Alvarado for helpful discussions. We also thank the editor, associate editor, and threeanonymous referees for many comments that improved our paper.

32 / The Energy Journal

field, which utilizes the reservoir’s natural pressure to bring oil to the surface,extracts about 5–20% 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 10–20%. After the primary and secondaryphases 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 methodof CO2-EOR is technically able to recover about a third of this stranded oil, oran 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 oilprice of $70 per barrel and a CO2 price of $45 per metric ton) 45 billion barrelsare economically recoverable.2

CO2-EOR recovers this additional oil by injecting slugs of CO2 at highpressure into the reservoir, usually alternated with slugs of water. The injectedCO2 mixes with the reservoir oil, thereby reducing capillary forces that trap theoil in pores of the rock and allowing oil that would otherwise remain stranded toflow towards production wells.3 Most of the CO2 resurfaces with the recoveredoil and is separated, recompressed, and reinjected. In every pass through thereservoir, however, a fraction of the CO2 remains sequestered underground. Inorder to maintain a given CO2 injection rate, operators of CO2-EOR projects4

therefore need reliable sources of CO2 over extended periods of time—it is com-mon for an EOR project to take 20 years or longer. As a result, EOR creates aderived 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 amountof 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 twoyears’ 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 NationalWildlife Refuge (ANWR) are estimated by the U.S. Geological Survey at 10.4 billion barrels. Seethe 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 CO2-EOR projectsare operated at pressures too low for the CO2 to mix with the oil. These so-called “immiscible CO2

floods” 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 andsecondary methods, including injection of steam, liquid chemicals, and gases other than CO2. Ac-cording to the Oil & Gas Journal’s most recent biennial survey of EOR (Moritis, 2008), CO2-EOR isthe fastest-growing EOR technique in the U.S., generating 250,000 barrels per day (bo/d) from 105projects spread throughout the country. This amounts to about 5% of total U.S. oil production, andis 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 45projects. 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 aswell as providing a bridge to large-scale geological sequestration of CO2, animportant input into planning for the required infrastructure are estimates of howboth the supply of incremental oil and the derived CO2 demand from EOR arelikely to vary with the prices of oil and CO2. To our knowledge, very few suchestimates are currently available. Holtz et al.’s (2001) study of EOR potential inTexas, for example, uses only physical screening criteria to identify reservoirssuitable 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 thesereservoirs, without any reference to economics. Similar methods also are used toestimate EOR’s sequestration potential (without accompanying oil recovery es-timates) in the DOE’s Carbon Sequestration Atlas of the United States and Can-ada (DOE, 2007). The above-cited study by Dahowski et al. (2005) does estimatea cost curve for CO2 sequestration in 220 oil plays5 in the U.S., but does so forjust 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 eachplay. Finally, the above-cited DOE (2008) study uses physical screening criteriato 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 oilsupply and CO2 demand curves that is based on the so-called “analog” methodof 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 drawbackof using reservoir-simulation software. This drawback is that reservoir simula-tions require reservoir-specific “relative permeability curves” as inputs, to predictthe rates at which different fluids (oil, water, CO2) will move through a givenreservoir’s 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, isnot new to reservoir engineers. Jarrell et al. (2002) discuss it, for example, intheir 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 NPCstudy used the CO2PM package developed by Scientific Software-Intercomp, the DOE study usesthe more versatile CO2Prophet package later developed by the Texaco Exploration and ProductionTechnology Department. (Both packages are available from the DOE’s National Energy TechnologyLaboratory website, http://www.netl.doe.gov.)


neers. The method also underlies a spreadsheet model made available by KinderMorgan, Inc., which allows oil-field operators to estimate the likely profitabilityof 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 estimateincremental oil supply curves and derived CO2 demand curves for all reservoirsin 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) topredict those of a new, proposed EOR project. The validity of doing so relies ona central assumption of the method, namely that all the various dimensions acrosswhich reservoirs may differ—lithology, area, thickness, porosity, permeability,etc.—are relevant to incremental oil and CO2 production only insofar as theyaffect 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 reservoircentered 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-areaof the reservoir before any of the oil was produced; and injectivity is the rate atwhich fluids can be injected into the reservoir, expressed in units of HCPV perunit time.

Specifically, the analog method predicts that if the proposed EOR projecthappens to have the same per-pattern HCPV and injectivity as the EOR analogproject, each of its patterns will generate roughly the same incremental oil andCO2 flows over time as the analog project did historically. More generally, theproposed project will differ from the analog project in terms of either HCPV orinjectivity, 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 asthe analog project did at any given time after switching to EOR; if, on the otherhand, 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 andproduction wells) as well as economic parameters (e.g., the price of oil and CO2 anticipated by theoperator, royalty and tax rates, discount rate). The model then uses the analog method to projectincremental oil and CO2 flows for a single “pattern” (an injection well surrounded by productionwells) of the proposed project, multiplies these flows by the number of planned patterns, and combinesthe result with the economic parameters to predict the project’s NPV. Although the model uses a verysimilar approach to ours, its implementation as a spreadsheet limits its application to a single projectat a time. The model is also cruder than ours in several respects. For example, it terminates the projectat 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 thesquare, and the “nine-spot,” which has four additional production wells at the square sides. Thesepatterns 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.


tivity, it is predicted to cumulatively produce as much as the analog project did,but in half the time; etc.

Note that if the method’s 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 andall proposed projects, regardless of any differences between project reservoirsbesides per-pattern HCPV and injectivity. In practice, of course, the assumptionholds only approximately. For example, even if two project reservoirs have sim-ilarly high injectivity levels, injectivity for project A may be uniformly highthroughout its reservoir, whereas that for project B may be concentrated in highlypermeable streaks or zones. If so, then in project A, injected CO2 is likely to pushoil uniformly towards production wells, whereas in project B, CO2 may flowpreferentially 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 project A as an analog.10 For reasonssuch as these, the analog method is considered more reliable the more closely thereservoir characteristics of a proposed project match those of the analog used.11

By way of illustration, we apply our procedure to the Wyoming portionof the Powder River Basin (PRB). This basin, which covers the northeast cornerof the state, is a major oil-producing region in the US, with currently about 500actively producing fields or (since many fields produce from several reservoirs)over 700 actively producing field-reservoir combinations (FRCs). To date, 1.9billion barrels of oil have been extracted from these fields, almost all throughprimary and secondary recovery. Enhanced oil recovery is just getting underwayin the PRB. On the western edge of the basin, in the Salt Creek Field, one operatorhas been applying EOR since 2004. Several other oil-field operators have plansto begin EOR projects in the near future.

Not all FRCs are suitable for EOR, however. For a given FRC, the sizeand geological properties of the reservoir are factors that decide the profitabilityof EOR, along of course with expected revenues from incremental oil productionand costs related to CO2 injection and recycling. As oil prices increase or CO2

prices 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 experiencedifferent 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 theother uses an unspecified project in the Morrow formation of western Kansas and the OklahomaPanhandle. Unfortunately, the Morrow project’s history is quite short, which reduces its usefulnessfor predicting the lifetime performance of candidate EOR projects.


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, andproduction and injection history of identified FRCs. Our data were collected fromnumerous primary sources, including the Wyoming Geological Association, theWyoming Oil and Gas Conservation Commission, and the proprietary IHS databank. Journal descriptions of a number of FRCs were consulted to fill gaps.Considerable work continues in updating and checking these data against differentsource materials.

To estimate the potential CO2 demand for enhanced oil recovery in thePRB and the corresponding supply of incremental oil, we examined all FRCs thatmet 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 of2005. Smaller reservoirs were included if another reservoir in the same field metthe 5-MMbo cumulative production criterion. This is because reservoirs in thesame field can share capital facilities required for EOR. A total of 138 FRCs metthe first criterion. The second criterion is that a complete set of data had to beavailable for the demand analysis. Key data were unavailable for 38 of the 138FRCs, leaving 100 FRCs.

For each of these 100 FRCs, we first determined whether the reservoirpassed 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 miscibilitypressure (MMP), which depends on the reservoir’s temperature and the oil’s APIgravity, exceeds the maximum pressure that the reservoir’s caprock can withstand,then using CO2 for EOR becomes far less attractive.12 This was found to be thecase for 3 FRCs.

Table 1 lists the remaining 97 FRCs, together with their original oil inplace (OOIP)—the estimated total amount of oil originally present in the reservoirbefore any extraction took place—and their cumulative production up to mid-2009. The accompanying Figure 1 shows the FRCs’ locations.

12. As pointed out in footnote 1, CO2-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 DOE’s TORIS database, and estimates of HCPV) andcumulative oil production up to mid-2009 (Cum., from the IHSPI/Dwights PLUS database)

OOIP Cum.# Field–Reservoir Combination (MMbo)

1 Alpha–Minnelusa C 13.2 5.7

2 Ash Creek–Shannon 17.3 4.6

3 Barber Creek–Ferguson 19.6 1.5

4 Big Hand–Minnelusa 9.0 6.1

5 Big Muddy–Dakota 11.7 7.3

6 Bone Pile–Minnelusa B 15.6 8.9

7 Buck Draw North–Dakota 36.0 24.4

8 Camp Creek–Minnelusa B 10.5 4.9

9 Cellars Ranch–Tensleep 34.1 6.2

10 Clareton–Muddy 149.0 3.2

11 Cole Creek–Dakota 30.0 1.1

12 Cole Creek South–Dakota 47.3 1.5

13 Cole Creek South–Lakota 34.3 5.4

14 Collums–Muddy 23.6 4.4

15 Coyote Creek–Dakota 54.4 13.3

16 Coyote Creek South–Dakota 11.4 5.4

17 Coyote Creek South–Turner 9.2 1.2

18 Culp & Heldt Draw–Shannon 28.3 13.8

19 Dead Horse Creek–Ferguson 16.1 1.5

20 Dead Horse Creek–Parkman 26.4 2.0

21 Dillinger Ranch–Minnelusa A 24.1 8.0

22 Donkey Creek–Dakota 23.9 3.8

23 Donkey Creek–Minnelusa 10.4 4.7

24 Dry Gulch–Minnelusa A 9.1 5.2

25 Duvall Ranch–Minnelusa A 24.9 14.6

26 Edsel–Minnelusa B 9.5 5.6

27 Fiddler Creek–Muddy 25.9 2.9

28 Fiddler Creek–Newcastle 55.9 1.2

(continued)


Table 1. Field-reservoir combinations evaluated, with estimated original oilin place (OOIP, from various sources including McDaniel (1991),the DOE’s TORIS database, and estimates of HCPV) andcumulative oil production up to mid-2009 (Cum., from the IHSPI/Dwights PLUS database) (continued)


29 Finn-Shurley–Turner 250.0 16.1

30 Finn-Shurley–Wall Creek 15.5 1.4

31 Gas Draw–Muddy 50.6 23.2

32 Glenrock South–Dakota 80.0 15.1

33 Glenrock South–Muddy 62.1 19.4

34 Guthery–Minnelusa B Upper 12.5 3.8

35 Halverson–Minnelusa A 40.7 8.3

36 Hamm–Minnelusa B Lower 20.3 8.1

37 Hartzog Draw–Shannon 353.3 114.9

38 Hilight–Muddy 110.0 74.3

39 House Creek–Sussex 67.0 38.2

40 Jepson-Holler Draw–Shannon 49.7 6.0

41 Kaye–Teapot 86.1 9.3

42 Kitty–Muddy 133.4 16.5

43 Kummerfeld–Dakota 12.8 3.3

44 Kummerfeld–Minnelusa B 15.0 6.4

45 Lance Creek–Leo 121.0 15.7

46 Lance Creek East–Dakota 21.1 1.4

47 Little Mitchell Creek Minn. B 13.0 8.3

48 M-D–Minnelusa B 12.0 5.8

49 Maysdorf–Minnelusa A 11.5 5.4

50 Meadow Creek–Frontier 7.2 2.3

51 Meadow Creek–Lakota 9.4 2.1

52 Meadow Creek–Shannon 34.0 5.4

53 Meadow Creek–Tensleep 40.5 13.8

54 Mellott Ranch–Minnelusa 19.1 5.0

55 Mikes Draw–Teapot 23.0 14.6

56 Miller Creek–Dakota 17.0 4.4

(continued)




57 Moorcroft West–Dakota 28.9 4.5

58 Moorcroft West–Minn. A 0.7 0.2

59 Moorcroft West–Newcastle 28.9 1.7

60 Mule Creek–Lakota 10.0 1.0

61 Mush Creek–Newcastle 35.0 2.8

62 North Fork–Tensleep 55.6 21.5

63 Osage–Newcastle 69.0 17.2

64 Pine Tree–Shannon 14.5 9.5

65 Poison Draw–Teckla 13.4 7.6

66 Prong Creek–Minnelusa 14.0 6.4

67 Raven Creek–Minnelusa 73.8 43.0

68 Recluse–Muddy 64.5 13.6

69 Reel–Minnelusa 20.0 7.4

70 Reno–Minnelusa 41.2 6.4

71 Robinson Ranch–Minnelusa 14.7 5.8

72 Rozet–Minnelusa 44.9 9.1

73 Rozet–Muddy 71.9 13.6

74 Rozet West–Minnelusa 22.7 9.8

75 Sand Dunes–Frontier 2.5 0.9

76 Sandbar East–Minnelusa B 32.1 9.0

77 Scott–Parkman 250.0 17.6

78 Scott–Teapot 51.4 0.4

79 Semlek–Minnelusa B 11.6 5.4

80 Semlek West–Minnelusa B 21.0 8.3

81 Skull Creek–Newcastle 30.0 5.2

82 Slattery–Minnelusa 28.5 11.9

83 Slattery–Muddy 1.4 0.4

84 Springen Ranch–Muddy 22.8 8.8

(continued)




85 Stewart–Minnelusa B 40.9 11.1

86 Sussex–Frontier 5.4 0.3

87 Sussex–Shannon 4.6 2.1

88 Sussex–Sussex 11.7 3.5

89 Sussex–Tensleep 25.0 5.4

90 Sussex West–Shannon 35.0 13.4

91 Terrace–Minnelusa B 13.8 6.5

92 Timber Creek–Minnelusa 34.1 11.7

93 Timber Creek–Muddy 6.4 0.2

94 Ute–Muddy 43.9 9.7

95 Wallace–Minnelusa B 18.3 7.9

96 Well Draw–Teapot 95.0 33.5

97 Winter Draw–Minnelusa 9.2 6.5

An overview of the model

For each of the 97 FRCs, we estimated both the “baseline” net presentvalue of continuing with secondary oil recovery using water injection,basNPV

basT o op,bas R SP o p,bas�p 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 teorNPV � �K.� t(1�r)t�1

In these expressions, represents the price of oil (assumed constant over theoplifetime of the project), , projected baseline oil recovery in period t underop,basQt

the 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


Figure 1. Location of field-reservoir combinations listed in Table 1. Circleareas are proportional to projected incremental oil supply at ourreference oil price of $100/bo and CO2 price of $3/Mcf.

as severance and property taxes at combined rate . For the EOR project, weSPswe also subtract the projected cost of CO2 purchases, equal to the CO2 purchaseprice (also assumed constant over the lifetime of the project) times the pro-cpjected quantity purchased, , as well as the projected cost of recycling andcm rQ Ct

re-injecting CO2. This cost depends on the quantity of CO2 that is producedcpQt

together with the oil. Lastly, we subtract other operating costs , which dependoCin 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,bas�Qt

project, is the sum of water, oil, and CO2 produced, since before recyclingp,eor�Qt

the 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 ofthe project, denoted for the continued waterflood and for the CO2 flood,bas eorT Tis 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


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 anNPVFRC’s “response” to EOR—essentially a production function that projects timepaths of incremental oil recovery and CO2 production. As noted, we use the analogmethod laid out in the appendix to generate these estimates. Two analog schedulesare necessary to calculate . One schedule predicts incremental oil produc-eorNPVtion , which is then added to predicted baseline production to obtain overallop,incQt

EOR production. The other schedule predicts CO2 production , all of whichcpQt

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

by 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 withcost 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 EORproject underway; they are the variable in the expression for and areeorK NPVtypically 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 recyclingplant. Costs of drilling new wells are typically very high. This is particularly truein older fields, where many original wells may have been capped because theirsecondary-phase production declined to unprofitable levels. The spacing betweenthe 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 tubingthat 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 CO2 to individualwells. Lastly, CO2 recycling plant costs must be incurred to process the CO2 thateventually 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 takeconsiderable time, and therefore the investment of K could be spread over several months, or evenyears. Our analysis does not account for this possibility.

14. Our analysis does not consider possible alternative methods of EOR, such as injection ofmethane or nitrogen. In principle, nothing prevents operators from trying such methods even aftercompleting 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.


Figure 2. Cumulative incremental oil supply at CO2 price /Mcfcp �$3using 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 ofrecycling CO2. Standard operating costs are those of labor, maintenance, and fuelrequired for both waterflooding and EOR operations. Some of these are roughlyproportional to the amount of liquids produced, others to the number of wellsoperated. CO2 purchasing costs cover the costs of CO2 production and compres-sion at its source, and transportation from the source to the FRC. CO2 recyclingcosts consist largely of the costs of energy (electricity or gas) required to fuel therecompression 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 inFigures 2 and 3. More specifically, Figure 2 shows, for oil prices plotted on thevertical 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 3. Cumulative CO2 demand at oil price /bo using the Lostop �$100Soldier-Tensleep analog (LST) and the Denver Unit-San Andresanalog (DSA)

cumulative incremental oil recovery, denoted and measured in millions ofoQbarrels per year (MMbo), for all FRCs that at these prices could profitably switchto EOR. In other words, represents the difference between projected cumu-oQlative oil recovery from these FRCs were they to continue waterflooding andprojected cumulative oil recovery were they to switch to CO2 flooding. Similarly,Figure 3 shows, for CO2 prices on the vertical axis and an oil price of $100 perbarrel, the corresponding cumulative deliveries of CO2, denoted and measuredcQin billions of cubic feet (Bcf).

Note that, because the schedules show projected cumulative volumesrather than rates, they are not supply or demand curves in the standard sense(although we shall refer to them as such for brevity). The projected time frameover which the cumulative volumes are realized varies significantly across FRCsand depends also on the oil and CO2 price, but is usually on the order of severaldecades.

The main message of Figures 2 and 3 is that oil supply and CO2 demandprojections using the analog method are highly sensitive to the analog used. In

degrees Fahrenheit and a reference temperature of 14.7 psi (1 atmosphere). The actual pressure atwhich pipelines deliver CO2 to oil fields is typically much higher, between 1250 and 2250 psi (McCoyand Rubin, 2008). At these pressures and ambient temperatures, the CO2 is either a liquid or asupercritical fluid, where the latter has properties of both a liquid and a gas.

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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 usingthe Denver Unit-San Andres (DSA) project in West Texas as analog.

Figure 4 plots the dimensionless curves generated from the LST andDSA projects’ historical EOR performance. It shows that, at the point in timewhen cumulatively one HCPV worth of CO2 and water had been circulatedthrough 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 projecthad at that same point produced 0.108 HCPV, and was therefore 1.8 times moreproductive.

A number of differences between the LST and DSA projects may berelevant to explaining their quite dissimilar EOR performance. One differenceconcerns timing. According to Brokmeyer et al. (1996), CO2 flooding of the LSTproject commenced when 44.3% of OOIP had been recovered in the primary andwaterflooding stages, and when the waterflooding “oil cut” (the share of oil inoverall liquid production) had dropped to as little as 3%. In contrast, CO2 floodingat 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 oilcut was still 14%. However, Jarrell et al. (2002) note that reservoir simulationstudies of west Texas reservoirs have shown that “the rate of incremental oilrecovery to CO2 flooding is only slightly sensitive to the stage of waterflooding


when CO2 flooding starts, as long as the reservoir pressure is well above thethermodynamic MMP.” Both the LST and DSA projects satisfy the latter condi-tion.

More important, perhaps, is the difference in lithology of the projectreservoirs: whereas the Tensleep formation is a sandstone, the San Andres for-mation is a carbonate. Several review studies of existing EOR projects (Holtz etal., 1999; Christensen et al., 2001; Hustad, 2004) have noted that CO2 floodingtends to yield somewhat higher incremental recovery rates in carbonate reservoirsthan in sandstone ones. Hustad (2004), for example, notes that of 115 worldwideCO2 floods in a database maintained by the Norwegian consulting firm SINTEFPetroleum 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 toproduction wells through such fractures, bypassing much of the oil and hencereducing incremental oil recovery. This may explain why the LST project’s ulti-mate recovery appears to below the sandstone average (its dimensionless curvein Figure 4 asymptotes to roughly 9%).

Lastly, the injection history of the two projects has been quite differentas well. Whereas the LST project has consistently maintained a 1:1 ratio of waterto CO2 (commonly referred to as the “water-alternating-gas” or WAG ratio), theDSA project started out injecting pure CO2, and thereafter gradually increasedthe WAG ratio.

Whatever the full explanation for the differential EOR performance ofthe two analog projects may be, it is evident that the choice of analog significantlyaffects estimates of how much incremental oil the examined candidate projectsin the PRB will produce, how much CO2 they will demand, and thereby howprofitable they are likely to be. At the individual project level, this is illustratedin Figure 2 by the rightward jump in both oil supply curves when CO2 floodingof the large Finn-Shurley Turner field becomes profitable. If the LST analog isused, this is estimated to require an oil price of at least $113, and the field is thenestimated to cumulatively produce 20.8 million incremental barrels of oil over itseconomic 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 asmuch as 56.5 million barrels. At the aggregate level, Figure 2 shows that at ourreference oil price of $100 and CO2 price of $3, estimated incremental oil supplyfor the basin as a whole is more than three times as high if the DSA analog isused than if the LST analog is used (although the ratio is smaller at both lowerand higher oil prices). Figure 3 shows that estimated CO2 demand is higher withthe DSA analog as well.

As noted in the previous section, the analog method is considered morereliable the more closely the reservoir characteristics of a proposed project matchthose of the analog used. Since all fields in our study produce from sandstone


rather than carbonate reservoirs16 and a handful in fact produce from reservoirsthat are part of the Tensleep formation, our estimates based on the LST analogare 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, theLST analog may well turn out to underpredict EOR performance.17

As for the maturity of the fields in our study (insofar as this matters forEOR performance, despite Jarrell et al’s assertion to the contrary), the mediancurrent recovery factor for the FRCs in our study is 30% of OOIP, which is closerto that of the DSA project when it switched to EOR, but the median oil cut is7%, which is closer to the initial oil cut of the LST project.

More generally, the considerable heterogeneity of reservoirs in the PRBimplies 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 estimateof EOR market conditions, illustrating the potential usefulness of the analogmethod.

To put the incremental oil supply estimates in perspective, cumulativeoil production to date from all 97 FRCs in our study combined is 978 MMbo, orabout 25% of their combined OOIP of 3.9 Bbo. Current combined oil productionis 5.9 MMbo/yr, but is declining: at our baseline oil price of $100, we estimatecumulative 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 137.5MMbo in total.

In Figure 1, FRCs that can profitably switch to EOR at these referenceprices are plotted as circles, with the circle areas proportional to each FRC’sprojected incremental oil supply. Note that similar maps showing each FRC’sCO2 demand under various price scenarios could be used, for example, to planthe trajectory of CO2 pipelines.

4. SENSITIVITY ANALYSIS

In practice, many of the geological and engineering parameters used inthe analog method (as listed in Table 3 of the appendix) are measured quiteimprecisely. 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 thecumulative production hurdle for being part of our study.

17. Personal communication with J. Michael Boyles, a geologist previously with the EnhancedOil Recovery Institute at the University of Wyoming.

18. Better data will not be forthcoming until individual operators actively contemplate switchingto EOR, at which point they will want to invest in detailed geological studies and modeling exercisesfor their specific FRC.


Table 2. Results of sensitivity analysis using the Lost Soldier-Tensleepanalog. Geological/engineering parameters were variedindependently across FRCs to generate 95% confidence intervalsfor oil supply and CO2 demand. Cost and other parameters werevaried by 50% in the same direction for all FRCs simultaneously.

Change inoil supply

Change in CO2

demand

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 thereservoir. 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 FRC’s original oil inplace; sometimes on a volumetric calculation combining estimates of the reser-voir’s area, thickness, porosity, and residual oil saturation; and in rare cases onsimple extrapolation from cumulative extraction to date. All three methods havedrawbacks, and the resulting estimate should not be taken as more than a roughguess. Fortunately, for most of these parameters there is no reason to suspect asystematic 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 FRCscombined, 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, theresults of which are presented in the top panel of Table 2. Each of the parameterslisted in the first column was randomized by selecting, independently for eachFRC, 200 values from a symmetric beta distribution with mean equal to ourli

central estimate of the parameter for the i-th FRC, support , and 95%(0,2l )i


confidence interval . The second and third columns show the re-(0.5l ,1.5l )i i

sulting 95% confidence intervals for incremental oil supply. The intervals areexpressed as percentage deviations from our estimate shown in Figure 2,oQLST

after averaging these deviations over the oil-price range shown (but droppingvalues at which supply is zero). The fourth and fifth columns show the analogous95% confidence intervals for CO2 demand. The first row of the Table thereforeshows, for example, that if the uncertainty about injectivity rates is representedby 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%oQLST

of 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 centralestimates 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 inthe first column, the average effects on incremental oil supply and CO2 demandof simply reducing or increasing the relevant per-unit costs by 50%. The first rowof the panel shows, for example, that if the royalty rate as well as severance andproperty tax rates were to be simultaneously reduced (increased) by 50%, theresulting incremental oil supply would on average increase by 23.5% (drop by21.1%) relative to the estimate shown in Figure 2. Clearly, the relativeoQLST

magnitudes of the induced changes are reflective of the different cost categories’share of overall EOR costs. Next to taxes, the up-front investment costs associatedwith 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 keyparameters 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 varyingtaxes or drilling costs. Dropping our reference oil price from $100/bo to $50/boshifts in the CO2 demand curve by 74.9% on average, whereas increasing thereference price to $150/bo increases demand by 62.4% on average.

A further parameter of particular interest is the discount rate applied byoil-field operators in calculating a proposed project’s NPV, i.e., their internal rateof return (IRR). According to industry contacts, a common IRR applied to profitsnet of royalties, severance taxes, and property taxes but gross of income taxes is20%; this is the rate used in the simulations underlying Figures 2 and 3. Notethat the rate is considerably above conventional riskless rates of return, reflectingthe high level of project failures in the oil industry. The next-to-last row of Table2 reports the average effects of reducing the IRR for all FRCs to 10%, or raisingit to 30%.

A final parameter considered in our sensitivity analysis is the maximumpattern 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


may well bring temporarily abandoned wells back into operation, or even drillnew 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 opposingeffects 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 capitalcost of switching to EOR. On the other hand, reducing the pattern size alsoincreases the rate at which CO2 cycles through a reservoir, thereby speeding upincremental oil recovery and increasing profitability.20 We find, however, that theformer 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 by35.5% on average.

5. CONCLUDING DISCUSSION

In this paper, we have shown how, given sufficiently detailed data onthe geology and extraction history of FRCs, along with the documented experi-ences of mature or completed EOR projects, it is possible to forecast which FRCsin an oil-producing region can profitably implement EOR at given prices for oiland 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 andcoordination of CO2 and oil shipments by projects in proximity to each other.Knowing the derived demand for pure CO2 allows emitters to consider locatingcloser to points that will demand CO2 as a commodity. It may also help pipelineauthorities to more efficiently design delivery systems connecting emitters to EORprojects.

The “analog” method used to generate the forecasts essentially extrap-olates experience at an existing, mature EOR project—the analog—to new, can-didate projects. Not surprisingly, we find that the forecasts are quite sensitive tothe 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 analogas closely as possible. Currently, however, only a small number of analogs forboth incremental oil production and CO2 circulation are available. This problemshould 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 spacingof 40 acres.

20. In terms of Figure 4, halving the pattern size halves the time by which, for example, 1 HCPVworth of CO2 and water can be injected into the reservoir, thereby halving the time by with 0.059HCPV worth of incremental oil is recovered.


An important set of questions we leave for future work is how potentialsubsidies for CO2 sequestration might affect market conditions for EOR, and howthe 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 oilrecovery and CO2 sequestration, by adjusting the CO2 content of their injectionstream. They may also choose to continue CO2 injection even after incrementaloil has been extracted. Although such changes would not invalidate the analogmethod 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, possiblybased on reservoir simulations.

REFERENCES

Brokmeyer, R. J., D. C. Borling, and W. T. Pierson (1996). “Lost Soldier Tensleep CO2 TertiaryProject, Performance Case History: Bairoil, Wyoming.” Paper # SPE 35191.

Christensen, J. R., E. H. Stenby, and A. Skauge (2001). “Review of WAG Field Experience.” SPEReservoir Evaluation & Engineering, 4(2): 97–106.

Dahowski, R. T., J. J. Dooley, C. L. Davidson, S. Bachu, and N. Gupta (2005). “Building the CostCurves 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 Pressure—A Key Parameter in Design of CO2 Miscible Flood.” Journalof Petroleum Science and Engineering, 46(1–2): 37–52.

EPA (2008). Inventory of U.S. greenhouse gas emissions and sinks: 1990–2006. Washington, DC:Environmental Protection Agency, EPA 430-R-08-005.

Fox, Charles E. (1995). “Cost Estimation Parameters.” University of Texas of the Permian Basin’sCenter 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 Economist’s 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 Emissionsthrough Underground CO2 Sequestration in Texas Oil and Gas Reservoirs.” Digital PublicationSeries, 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): 187–199.

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): 4–11.

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Hustad, Carl-W. (2004). “Large-Scale CO2 Sequestration on the Norwegian Continental Shelf: ATechnical, Economic, Legal and Institutional Assessment.” Project No., 151393/210, NorwegianResearch 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 inoil recovery.” Energy Conversion and Management, 46(2): 293–311.

Lucken, J. E. (1969). “Raven Creek.” WGA Earth Science Bulletin, 2(4): 24–27.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 GreenhouseGas Control, 2(2): 219–229.

McPherson, Brian J. O. L., Weon Shik Han, and Barret S. Cole (2008). “Two Equations of StateAssembled for Basic Analysis of Multiphase CO2 Flow and in Deep Sedimentary Basin Condi-tions.” Computers & Geosciences, 34(5): 427–444.

Moritis, Guntis (2008). “More US EOR projects start but EOR production continues decline.” Oil &Gas Journal, 106(15): 41–46.

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-sizedfield 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, canbe estimated either as or as 43,560(ft2/acre)/5.615(ft3/rb). Sim-OOIP•B Ah�S •oi oi

ilarly, injectivity can be estimated either from past water injection rates or fromreservoir thickness h and permeability k.

Step 1: Estimate per-pattern HCPV. Mack and Duvall (1984) provide adirect estimate of RCM’s OOIP, so we estimate HCPV for the reservoir as a wholeas � 81,169,000 rb. (The alternative formulaOOIP•B Ah�S • 43,560/5.615oi oi

would 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 baselinemaximum pattern size) the CO2 flood will have pat-n�ceil(0.8•2,975/80)�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 EORproject is equal to that for the waterflood, so bl/ptn-mnth.i, eor wiq �q �36,8330

Assuming 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 Universityof Wyoming’s 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.

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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 TORIST Temperature 200 �F TORISMMP 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) TORIS

pnc Existing producer wells 15 WOGCCinc Existing injector wells 13 WOGCCtan Temporarily abandoned wells 12 WOGCCopQ0 Oil production (end of waterflood) 6,833 stb/mo IHSg (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 T using DOE (2006) correlation combined0.87022� �MWC �4247.98641API5

with Emera and Sarma (2005) correlation .1.164 1.2785�MMP�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 opQ̂ 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 monthsof 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, eor0.5q �18,416 q �0.5q /B �CO2

Mcf/ptn-mnth.41,788


Step 3: Estimate time paths of incremental oil and CO2 production usingthe dimensionless curves. Dividing by H gives a total injectivity of 0.01361i, eorqHCPV/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 forincremental oil production (see Figure 4). Reading off the corresponding pointon the vertical axis yields cumulative incremental oil production by that samenumber of months in HCPV/ptn, or, after multiplying by , in stb/ptn. Dif-H/Boc

ferencing the cumulative values yields the corresponding time path of incrementaloil production rates, , in stb/ptn-mnth. Lastly, multiplying by the number ofop,incqt

patterns n yields the predicted time path of incremental oil production for thefield as a whole, , in stb/mnth. Applying the same steps to the dimensionlessop, incQt

curve for CO2 production (but multiplying cumulative production in HCPV/ptnon 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, waterproduction, non-CO2 (hydrocarbon) gas production, and EOR purchases of CO2.Baseline oil production from a continued waterflood is assumed to con-op, basQt

tinue 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 t

manner that keeps overall liquids (oil plus water plus possibly CO2) productionat 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 cpQ �nq �Q B �Q Bt t oc t CO2

production is assumed to maintain a constant ratio to oil production, soand . 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 purchasesR�0are calculated as the difference between CO2 production (all of which iscmQt

recycled) and injection, so .cm cp ciQ �Q �nqt t

Step 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�30the CO2 flood will require new wells to be drilled, atp i ta2n�n �n �n �20c c

(given RCM’s 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 forexisting producers, and $325,000 plus $19/ft for newly drilled wells or existingnon-producers.23 Total costs sum to about $45.0 million.

Step 6: Estimate the up-front capital cost of gas recycling. The requiredcapacity 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., inCasper, Wyoming. All costs are for the year 2006, the most recent year for which oil-industry costindices are available from the Energy Information Administration (EIA, 2007).

23. Based on personal communication with Charles E. Fox, Vice President of Operations and


bined production of CO2 and (in this case zero) hydrocarbon gas, which for RCMis 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 pressuresp �225of psi. Subtracting from this the pressure gain offmax(p �200,MMP)�5,348

psi obtained from the CO2 column in the injection0.28(psi/ft)�8,380�2,346well yields the plant’s required discharge pressure, psi. Based on adp �3,001rule-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 /php, which at a cost per hp of $1,52024 yields a capital cost for the recycling plantof about $9.7 million.

Step 7: Estimate operating costs of gas recycling. Labor and maintenanceoperating costs for gas recycling are $80 per hp-year. Electricity costs, at 7,000kWh/hp-year and Wyoming’s 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 barrelof total liquids produced plus $31,000 per well-year for each of RCM’s current28 wells. Based on a rule of thumb given in Fox (1995), the corresponding costsfor the CO2 flood are assumed to be 10% higher, whereby the per-well costs applyto the CO2 flood’s 60 wells.

Step 10: Determine the terminal times for the continued waterflood andCO2 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 reference20.5CO2 price of $3/Mcf, net revenues from the CO2 flood’s 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 eorNPV NPVing to a CO2 flood, net of initial capital costs, is substantially higher, at $89.6million. Switching is therefore optimal.

Step 12: Calculate cumulative incremental oil production and CO2 de-mand. Cumulative oil production under the continued waterflood, up to , isbasT1.016 million barrels, while that under the CO2 flood, up to , is 6.870 millioneorTbarrels. Incremental production is therefore 5.854 million barrels, or 7.9% ofOOIP. 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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