1

PETROLEUM SOCIETYCANADIAN INSTITUTE OF MINING, METALLURGY & PETROLEUM

PAPER 2002-026

CO2 Flooding PerformancePrediction for Alberta Oil Pools

J.C. ShawAdams Pearson Associates Inc.

S. BachuAlberta Energy and Utilities Board

This paper is to be presented at the Petroleum Society’s Canadian International Petroleum Conference 2002, Calgary, Alberta,Canada, June 11 – 13, 2002. Discussion of this paper is invited and may be presented at the meeting if filed in writing with thetechnical program chairman prior to the conclusion of the meeting. This paper and any discussion filed will be considered forpublication in Petroleum Society journals. Publication rights are reserved. This is a pre-print and subject to correction.

ABSTRACT

Attention in CO2 flooding for incremental oil recovery

and greenhouse gas (GHG) sequestration has prompted

the need for screening and ranking Alberta oil pools for

this EOR process. In a previous paper by the same

authors, over eight thousands of Alberta oil pools were

ranked for CO2 EOR suitability using a new parametric-

ranking software which utilized six essential reservoir

properties: oil density, residual oil saturation, minimum

miscibility pressure (MMP), reservoir temperature, net

pay thickness, and porosity. This continuation paper

describes the results of using an advanced method to

estimate production forecasts for numerous candidate

pools in Alberta.

A Microsoft Excel program with VBA based on the

modified Koval method (1963) by Claridge (1972) has

been developed to predict CO2 flooding performance

using the Alberta reserves database. The program

estimates live oil and CO2 viscosities at reservoir

conditions, oil MMP and reservoir heterogeneity based

on the rock type, to predict oil recovery at any specific

pore volume of CO2 throughput.

Over 8,000 Alberta pools were first screened for CO2-

flood suitability, and pertinent reservoir properties were

used for the remaining 4,729 pools to calculate oil

recovery. The predicted recoveries for all pools ranged

from 1.2-13.9%, 6.3-18.7% and 11.8-27.1% at

breakthrough and 0.25 and 0.5 hydrocarbon pore volume

(HCPV) injection respectively. These values compared

well to an average of 13% incremental oil recovery from

the field experience of CO2 floods. More importantly, the

results clearly identify the most suitable Alberta pools for

CO2 flooding.

2

INTRODUCTION

The recent high oil price and interest in reducing GHG

(Greenhouse Gas) emissions in response to global

warming may have created new business opportunities to

realize incremental value from depleted oil pools through

CO2 flooding. Not all oil pools in Alberta are suitable for

CO2 flooding. Thus, in a previous paper by the same

authors1, about 8,800 Alberta oil pools were ranked by

using a newly-developed VBA program capable of

retrieving pertinent information from the digitized

Alberta reserve database and perform parametric

technical rankings. Six parameters with different

assigned weightings were used in the technical ranking.

These include API gravity of oil, residual oil saturation,

ratio between reservoir pressure and predicted minimum

miscibility pressure (P/MMP), reservoir temperature, net

pay thickness, and porosity.

However, the screening software is not capable of

providing production forecasts of CO2 flooding, which is

the motivation of this study. Numerous active CO2

flooding projects in the United States and Canada have

provided valuable theoretical and practical information

on the technology. Desktop engineering prediction tools

such as US DOE “CO2 Prophet”2 have been developed

for quick technical and economic assessment. These

tools are based on sophisticated analytical equations

derived from theoretical calculations, numerical

simulation and field experience. However, we are not

aware of any tools that are capable of evaluating the

performance of large numbers of oil pools as reported in

this paper.

CO2 FLOODING PREDICTIVE MODELS

The recovery efficiency prediction of CO2 flooding

can be used to provide useful estimates of financial

viability of the project. Ideally, the predictive models

should consider:

1. Whether miscibility can be achieved under

reservoir conditions;

2. Size of solvent slug and type of drive fluid;

3. Mobility of reservoir, solvent and drive fluids;

4. Vertical, areal and microscopic sweep

efficiencies;

5. Pressure distribution in the reservoir;

6. Flood pattern and well spacing; and

7. Reservoir heterogeneity.

However, due to the unavailability and uncertainties of

reservoir data and limitations of the analytical methods,

these predictions may be valid only under certain

conditions regardless the level of sophistication of the

models. Since the final objective of this study is to

provide CO2 flooding prediction for a large number of

Alberta oil pools, desktop methods are preferred for their

simplicity and applicability in this situation where there

are only limited reservoir data available from the Alberta

reserves database.

Modified Koval Method

Klins (1984)3 summarized the assumptions and

limitations of the modified Koval method as follows:

• Secondary recovery (Swi = Swc), where Swi is initial

water saturation, Swc is connate water saturation

• Five spot

• Trapped oil effect

• Gravity stabilisation

• Continuous or slug injection

This secondary recovery method is based on the

original work of Koval (1963)4 as modified by Claridge

(1972)5 for areal sweep. The following summarizes the

method by Claridge that is based on the concept of

apparent pore volume. It combines methods of predicting

areal coverage and linear displacement efficiency and is

used to calculate oil recovery for a series of assumed slug

sizes (hydrocarbon pore volume or HCPV) in a five spot

CO2 slug-waterflood pilot test.

Koval introduced the “Koval Factor” in his work and it

is defined as follows:

3

441

220780

µ

µ+==

/

s

o..HFHFEK

E = Effective viscosity or mobility ratio defined byKoval

441

220780

µ

µ+=

/

s

o..E

H = Heterogeneity factor

= 1 (homogeneous reservoirs)

= log10 H = ( )

− 2.01 DP

DP

V

V

(stratified reservoirs)

F = Gravity override factor

= 1, if assuming no gravity override

where:

µo = Oil viscosity, cp

µs = CO2 viscosity, cp

VDP = Dykstra-Parsons coefficient

Solvent breakthrough at:

KV pvdBT

1=

VpvdBT = invaded pore volumes injected at

breakthrough

( )MM

EABT ++=

14.0

1

EABT = areal sweep efficiency at breakthrough per

Caudle-Witte correlation6

pvdBTABTpiBT VEV ×=

VpiBT = actual pore volumes of solvent injected at

breakthrough

Finally, the oil produced from the miscible injection Np

is calculated by using Claridge’s simplified equation as

follows:

( )

−

−

=

−

− 26.0

28.1

61.0 0.1

6.1

0.1K

BTi

piBTi

Kp

piBTp

F

VF

N

VN

Fi = HCPV of solvent injection in an ideal five-

spot of unit thickness which will produce an

invaded area whose outer envelope

corresponds to EAFI times the area of the five-

spot

K = Koval factor

VpiBT = the value of Vpi at solvent breakthrough

Np = HCPV of oil produced, vol/vol

CO2 FLOODING PREDICTIVE MODELS

Estimation of oil and CO2 Viscosity

The following equations from Beggs and Robinson

(1975)7were used to estimate the dead and live oil

viscosity for all oil pools in Alberta:

Dead Oil Viscosity, µod (cp)

110 −=µ xod

where:

,10,601.0 zyyTx == −

)(033580.01646.2 APIz °−=

Live Oil Viscosity, µo (cp)

Bodo Aµµ =

where:

,)200(589.12 482.0−+= sRA090.0)15(276.1 −+= sRB

where:

Rs = Solution gas/oil ratio, scf/STB

T = Reservoir temperature, ˚ C

˚ API = Oil gravity

4

The viscosity and density of CO2 at original

temperature and pressure conditions was estimated based

on an interpolation method of two digitized CO2 viscosity

and density figures from the SPE Monograph on Miscible

Flooding by Stalkup (1983)8. It is assumed that the CO2

flooding will be conducted at the original reservoir

pressure to achieve miscibility. The viscosity ratio of

oil/CO2 plays an important role in the performance

prediction.

Estimation of Reservoir Heterogeneity

Since the CO2 flooding performance is also dependent

on the reservoir heterogeneity, it is necessary to provide

general estimations of the degree of heterogeneity. The

heterogeneity factor introduced by Koval requires input

of Dykstra-Parsons coefficient (VDP)9 which is not

available in the Alberta reserve database. It will be

extremely time-consuming to obtain the value by

systematically reviewing core analysis data from each oil

pool. Unfortunately, only very limited numbers of

Dykstra-Parsons coefficients are available in the

literature. In the SPE textbook series for Waterflooding

Willite (1986)1 0 , a range of these values were

summarized in a plot, which shows a range of 0.5-0.9 VDPwith an average of 0.7 for most rocks. Therefore, two

default values, 0.7 and 0.9, were used to represent VDP of

sandstone and carbonate rocks respectively. The resulting

heterogeneity factors (H), 8.5 and 26.3, were calculated

by using the above-mentioned heterogeneity equation.

The VDP values can be easily changed in this VBA

program to accommodate actual calculated results from

core analysis data.

A lookup table was designed to differentiate whether

the formation rock is either sandstone or carbonate for all

the producing formations in Alberta, which is included in

the VBA program.

PREDICTED RECOVERY OF CO2 FLOODING

The reservoirs suitable for EOR using CO2 flooding

have various degrees of suitability, depending on the

intrinsic reservoir and oil characteristics. The range of

reservoir and fluid properties suitable for CO2 miscible

injection is quite wide; however, reservoirs should have

an oil gravity API>30°, remaining oil saturation (So)

>25%, original reservoir pressure >10.3 MPa (1500 psi)

and ideally 1.4 MPa (200 psi) higher than MMP at the

time of CO2 injection. Immiscible CO2 flooding is much

less common; nevertheless it has being applied to heavy

and medium oils (10-25° API) and in-situ viscosities of

100 to 1000 cp. Thus, some oil reservoirs will be better

suited, hence more economic, than others, for CO2

flooding. These reservoirs should be the ones used first

for CO2 storage.

A total of 4,729 Alberta oil pools (out of a total of

8,630 pools initially) passed the following screening

criteria. This number was derived by eliminating 54

pools that have been on solvent flooding, 1,161 pools that

have reservoir temperature <31˚C, 24 pools that have

remaining oil saturation <40%, 336 pools that have no

reservoir pressure data and 2,326 pools that have P/MMP

<0.95. The screening was conducted because:

• The predicative model only predicts recovery for

pools that are miscible with CO2 and,

• CO2 critical temperature is 31˚C.

Calculated Fluid Properties

With the built-in program that calculates the CO2 and

oil properties, the output data are used for further

evaluation of recovery factor. For the 4,729 oil pools that

passed the screening, the API gravity ranges 17-72˚; live

oil viscosities 0.06-44.3 cp; CO2 viscosity of 0.02-0.1 cp

and CO2 density of 0.23-0.93 g/cc.

Recovery Factor

The recovery factors of CO2 miscible flood at solvent

breakthrough, 0.25 and 0.5 HCPV of solvent injection

were calculated by using the Excel VBA program. Of the

4,729-screened pools, the calculated recoveries ranges at

the solvent breakthrough, and 0.25 and 0.5 HCPV

injections are shown in Table 1. The results are compared

to an average of 13% incremental oil recovery from the

field experience of all CO2 floods.11 It is important to

note that the Koval heterogeneity factors used for the

calculations are 7.8 and 26.7 for sandstone and carbonate

respectively. Figure 1 shows the frequency distribution of

CO2 recovery factors at 0.25 and 0.5 PV injections.

5

The calculated results are sorted in a descendant order

based on the breakthrough recovery efficiency. It is

interesting to find that, with the assumed heterogeneity

factors, all top-ranked pools are sandstones. The highest

ranked carbonate reservoir is at the 2,568th place. The

previous ranking study considered six reservoir

properties: oil gravity, oil saturation, reservoir pressure,

temperature, net pay thickness and porosity. As shown in

the previous ranking scores in Table 1, most of carbonate

pools were much higher ranked than the present

predictive results mainly due to the higher heterogeneity

factor that is used for these pools. This observation

points out the importance of better knowledge of

reservoir heterogeneity. Figure 2 shows the recovery

distribution with VDP of 0.75 for the carbonate rocks. The

top-ranked carbonate pool is at the 93rd place due to a

lower corresponding H of 9.8.

The characteristics of the top ranked pools are:

• Light oil;

• High initial reservoir pressure;

• Sandstone with lower heterogeneities and,

• Modest initial reservoir temperature.

It is important to note that the predictive model

assumes that CO2/waterflood flooding is implemented as

a secondary recovery process and there is no provision to

account for historic production. Since all Alberta oil

pools have gone through different stages of depletion, the

reader is cautioned not to draw blanket conclusions about

the CO2 flooding suitability without first checking the

status and other pertinent information of these pools.

This is especially true for pools that have been either

water or solvent flooded.

Due to the availability and uncertainties of reservoir

data and the limitations of the analytical methods, the

predictive results based on the developed desktop model

should only be used as a guide to rank-order large

numbers of oil pools. It is not intended to replace more

detailed laboratory and engineering studies that are

essential to any EOR design.

CONCLUSIONS

1. A total of 4,729 from 8,630 oil pools in Alberta

passed the following screening criteria:

• Not previously solvent flooded;

• Reservoir temperature > 31 ºC;

• Residual oil saturation > 40%;

• Available reservoir pressure data; and

• CO2 miscibility (P/MMP >0.95).

2. By using the newly developed Excel VBA program

based on the modified Koval (1963) method by

Claridge (1972), the CO2 flooding performance for

these 4,729 pools are reported at CO2 breakthrough,

0.25 and 0.5 pore volumes CO2 injection.

3 . Reservoir heterogeneity is accounted for by

assigning a default Dykstra-Parsons coefficient for

the rock type (sandstone vs. carbonate) of each

producing pool to calculate the heterogeneity factor.

4. CO2 and live oil viscosities at the initial reservoir

conditions were estimated for all of the screened

pools. In addition, CO2 density was also calculated

that will be useful in calculating storage capacity for

the future work.

5 . The user of this program can easily adjust the

miscibility cut-off, heterogeneity factor and pore

volume solvent injection to produce desirable results

for various conditions.

6 . The predicted recoveries for all pools range from

1.2-13.9% at solvent breakthrough, 6.3-18.7% and

11.8-27.1% at 0.25 and 0.5 pore volume injection

respectively. This is compared to an average of 13%

incremental oil recovery from the field experience of

all CO2 floods.

7. Due to the availability and uncertainties of reservoir

data and limitations of the analytical methods, the

predictive results based on the desktop model should

only be used as a guide to rank-order large numbers

of oil pools. It is not intended to replace more

detailed laboratory and engineering studies that are

essential to any EOR design.

8 . It is important to note that the predictive model

assumes that alternate CO2/water flooding is

implemented as a secondary recovery process and

there is no provision to account for historic field

production. Since all Alberta oil pools have gone

through different stages of depletion, the reader is

6

cautioned not to draw hasty conclusions of CO2

flooding suitability without first checking the status

and other pertinent information of these pools.

9. Since reservoir heterogeneity has a strong influence

on the volumetric sweep efficiency of the flood, it is

important to have this parameter better defined. The

present study uses two default values of the Dykstra-

Parsons coefficient, 0.7 for sandstone and 0.9 for

carbonate rocks. However, this coefficient can be

better estimated for individual pools based on core

data providing the resources are available.

REFERENCES

1. Shaw, J. C. and Bachu, S., “Screening and RankingAlberta Oil Pools for CO2 Flooding andSequestration”, CIM Paper 2001-157, presented atthe Petroleum Society’s Canadian InternationalPetroleum Conference, Calgary, Alberta, Canada,June 12 – 14, 2001.

2. Paul, G. W. (Principal Investigator), “Developmentand Verification of Simplified Prediction Models forEnhanced Oil Recovery Application: CO2 (MiscibleFlood) Predictive Model,” U.S. DOE Report DE-AC19-80BC10327, Intercomp-Denver (1983).

3 . Klins, M. A., “Carbon Dioxide Flooding; BasicMechanisms and Project Design”, InternationalHuman Resources Development Corporation,Boston, MA, 1984.

4 . Koval, E. J., “A Method for Predicting thePerformance of Unstable Miscible Displacement inHeterogeneous Media”, Soc. Pet. Eng. J. (June 1963)145-154.

5. Claridge, E. L., “Prediction of Recovery in UnstableMiscible Flooding”, Soc. Pet. Eng. J. (April 1972)143-154.

6. Caudle, B. H. and Witte M. D., “Production PotentialChanges During Sweep-Out in a Five Spot Pattern”,Trans. AIME, 1959, Vol. 216, p. 447.

7. Beggs, H. D. and Robinson, J. R., “Estimating theViscosities of Crude Oil Systems”, JPT, (Sept. 1975)1140-1141.

8. Stalkup, F. I., “Miscible Displacement”, MonographSeries, SPE, Dallas, New York, 1983.

9 . Dykstra, H., and Parsons, R. L., “SecondaryRecovery of Oil in the United States”, API,Washington (1950).

10. Willhite, G. P., “Waterflooding”, SPE TextbookSeries Vol. 3, 1986, p.172.

11. Holt, T., Jensen, J.I., Lindeberg, E.: “UndergroundStorage of CO2 in Aquifers and Oil Reservoirs,”Energy Conv. Mgmt., 1995, 36, 335-338.

TABLE 1SUMMARY OF RECOVERY FACTORS OF 4,729 ALBERTA

POOLS WITH ASSUMED HETEROGENEITY FACTORS

BreakthroughRecovery (%)

Recovery @0.25 HCPV

Injection (%)

Recovery @0.5 HCPV

Injection (%)

Minimum 1.2 6.3 7.0

Maximum 13.9 18.7 27.2

Average 5.1 11.1 14.9

7

TABLE 2- SUMMARY OF PREDICTED RESULTS OF THE TOP 20 RANKED POOLS

Field Name Pool NamePreviousScores1

APIGravity

InitialSolutionGOR

Temp(ºC)

So(%)

InitialPressure(kPa)

MMP(kPa)

P/MMPLive OilViscosity(cp)

CO2

Viscosity(cp)

Oil/CO2

Viscosity

DensityCO2

(g/cc)

Bre

ak-t

hro

ug

hR

eco

very

of

CO

2

(%)

Oil

Rec

ove

ry @

0.25

PV

CO

2

Inje

ctio

n (

%)

Oil

Rec

ove

ry @

0.50

PV

CO

2

Inje

ctio

n (

%)

STRACHAN SECOND WHITE SPECKS A 43.97 47.38 399 82 0.97 30782 10680 2.88 0.18 0.06 2.97 0.74 0.13 0.18 0.26

GARRINGTONVIKING CC MANV B &

LOWER MANNVILLE VVV 48.22 46.08 385 68 0.93 32119 10680 3.01 0.22 0.07 3.00 0.82 0.13 0.18 0.26

CAROLINE BASAL MANNVILLE MU #3 47.60 42.97 483 92 0.82 30916 10680 2.89 0.21 0.06 3.69 0.70 0.12 0.17 0.25

PECO CARDIUM G 45.20 47.16 210 77 0.93 31372 10680 2.94 0.24 0.07 3.73 0.78 0.12 0.17 0.25

RICINUS CARDIUM 000 49.46 46.26 323 78 0.95 26853 10680 2.51 0.22 0.06 3.74 0.72 0.12 0.17 0.25

KAKWA C CARDIUM B 49.38 47.61 268 55 0.92 20632 9650 2.14 0.25 0.06 3.82 0.77 0.12 0.17 0.25

CAROLINE CARDIUM K 53.20 44.93 312 74 0.90 27372 10680 2.56 0.25 0.06 3.96 0.75 0.12 0.17 0.25

KAKWA A CARDIUM APRIMARY AREA

46.81 46.71 254 52 1.00 21702 9650 2.25 0.27 0.07 3.98 0.80 0.12 0.17 0.25

RICINUS CARDIUM E & KKK 55.85 45.08 323 78 0.95 26389 10680 2.47 0.23 0.06 4.07 0.71 0.12 0.17 0.25

BRAZEAU RIVER CARDIUM I 54.07 46.93 240 76 0.86 25567 10680 2.39 0.24 0.06 4.13 0.71 0.12 0.17 0.25

WESTPEM OSTRACOD F 40.90 42.76 260 96 0.84 38634 11720 3.30 0.27 0.07 4.19 0.75 0.12 0.17 0.25

RICINUS CARDIUM III 55.49 43.84 363 75 0.99 25817 10680 2.42 0.25 0.06 4.20 0.72 0.12 0.17 0.25

BRAZEAU RIVER LOWER MANNVILLE I 44.22 45.52 280 90 0.93 27664 10680 2.59 0.23 0.05 4.23 0.67 0.12 0.17 0.25

BRAZEAU RIVER CARDIUM K 51.04 46.26 245 76 0.95 25949 10680 2.43 0.25 0.06 4.23 0.71 0.12 0.17 0.25

BRAZEAU RIVER LOWER MANNVILLE P 37.60 43.4 248 96 0.97 36874 11720 3.15 0.27 0.06 4.24 0.74 0.12 0.17 0.25

PECO CARDIUM D 51.83 47.38 200 74 0.91 25145 10680 2.35 0.25 0.06 4.28 0.71 0.12 0.17 0.25

KAKWA MAIN CARDIUM A 47.96 47.61 192 53 0.95 20561 9650 2.13 0.28 0.07 4.32 0.78 0.12 0.17 0.24

CAROLINE CARDIUM F 52.18 45.08 246 77 0.88 28125 10680 2.63 0.26 0.06 4.32 0.74 0.12 0.17 0.24

CAROLINE CARDIUM G 56.62 45.08 312 69 0.98 22181 10680 2.08 0.25 0.06 4.42 0.69 0.12 0.17 0.24

SYLVAN LAKE SECOND WHITESPECKS D

48.33 46.71 177 47 0.99 22607 8270 2.73 0.33 0.07 4.43 0.84 0.12 0.17 0.24

8

FIGURE 1-FREQUENCY DISTRIBUTION OF CO2 RECOVERY FACTOR AT 0.25 AND 0.5 HCPV INJECTION ASSUMING VDP OF 0.7 (SANDSTIONE) AND 0.90 (CARBONATE)

FREQUENCY DISTRIBUTIONS

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

3% 6% 9% 12% 15% 18% 21% 24% 27% 30%

Recovery Percentage

Cou

nt

Oil Recovery @ 0.25 PV CO2 Injection Oil Recovery @ 0.50 PV CO2 Injection

FIGURE 2-FREQUENCY DISTRIBUTION OF CO2 RECOVERY FACTOR AT 0.25 AND 0.5 HCPV INJECTION ASSUMING VDP OF 0.7 (SANDSTIONE) AND 0.75 (CARBONATE)

FREQUENCY DISTRIBUTIONS

0500

1,0001,5002,0002,5003,0003,5004,0004,500

3% 6% 9% 12% 15% 18% 21% 24% 27% 30%

Recovery Percentage

Cou

nt

Oil Recovery @ 0.25 PV CO2 Injection Oil Recovery @ 0.50 PV CO2 Injection