investifgacion of cyclic solvent injection for heavy oil recovery

8/12/2019 Investifgacion of Cyclic Solvent Injection for Heavy Oil Recovery

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22 Journal of Canadian Petroleum Technology

IntroductionIn Cold Lake and Lloydminster heavy oil reservoirs, cold produc-tion is used to increase heavy oil production rates by producingsand along with the oil. Sand production results in the creation ofhigh-permeability wormholes that enhance oil production. Fol-lowing cold production, the pay zone has become a network ofwormholes, which extend radially outward from production wells.During a follow-up process, these wormholes can provide reservoiraccess for an injection fluid, such as solvent or steam.

After approximately 5% 10% of the initial oil in place has beenrecovered, the process becomes uneconomical because of reservoir

pressure depletion or water encroachment to the production well.Detailed studies of the cold production process have been presented

by many researchers [Bratli and Risnes (1), Bratli et al. (2) , Chang (3) ,Dusseault and Santarelli (4), Dusseault et al. (5), Dusseault and El-Sayed (6), Geilikman and Dusseault (7,8) , Risnes et al. (9), Sawatzky etal. (10,11) , Tremblay et al. (12,13) , Tremblay (14,15) ].

The experiment described in this paper was carried out to eval-uate the performance of a 28% C 3H8 72% CO 2 solvent mix-ture in a post-cold production CSI follow-up process. It indicatedthe potential viability of the cyclic solvent process using a 28%

C3H8 72% CO 2 solvent mixture as the oil recovery after primary production and six solvent cycles was 50%.

A numerical model was developed at AITF to simulate CSI pro-cesses. The model includes a representation of nonequilibrium be-haviour during solvent dissolution and exsolution. The model has

been validated based on the experiment described in this paper, aswell as by other experiments not discussed here.

ExperimentThe radial drainage physical simulation model was built to capture,at real-scale dimensions and time, radial flow of fluids into and outof a 6-cm diameter wormhole during primary cold production andfollow-up processes (Fig. 1).

Stepped cone laboratory models (3 m long) were constructedfrom sections of pipe that approximated the cross-sectional flowarea of the irregular cone (Fig. 2). The inside diameter of the

bottom cylinder of the stepped cone was 1 cm and that of the topcylinder was 9.7 cm.

The experimental model was packed with cleaned produced res-ervoir sand. Approximately 2.5 PV of water was pumped throughthe pack from the top end and its absolute permeability was de-termined. Approximately 1.5 PV of live Rush Lake oil, which had

been previously saturated with CH 4 at 3.0 MPa and 20 C, was pumped through the top of the pack to complete the saturation ofthe pack. Volumes of water and oil injected and produced duringsaturation were measured to calculate initial fluid saturations.

During the experiment, the model was configured in a verticalalignment with the narrow end down. The pressure was measuredat six locations along the model and the temperature measured at

five locations. In addition, the pressure difference between the

Investigation of Cyclic Solvent Injection

Process for Heavy Oil RecoveryJ. Ivory, J. Chang, R. Coates, and K. Forshner, Alberta Innovates Technology Futures

AbstractThis paper summarizes numerical and experimental simula-tion results of a cyclic solvent injection process study, whichwas part of a continuing investigation into the use of solventsas a follow-up process in Cold Lake and Lloydminster reser-

voirs that have been pressure-depleted by cold heavy oil pro-duction with sand (CHOPS). Typically only 5% 10% of theoriginal oil in place (OOIP) is recovered during cold produc-tion; therefore, an effective follow-up process is required.

The cyclic solvent injection (CSI) experiment consisted of primary production followed by six solvent (28% C 3H8 72%CO 2) injection cycles. Oil recovery after primary productionand six solvent cycles was 50%, which indicates the potentialviability of the CSI process.

Concurrently with the laboratory physical simulation, a nu-merical simulation model was developed to represent the phys-ical behaviour of the experimental results. A history match ofthe primary production portion of the experiment was obtainedusing an Alberta Innovates Technology Futures (AITF) foamyoil model. This resulted in the characterization (fluid satura-

tions and pressures) of the oil sandpack at the start of the sol-vent injection process. The history match of the subsequentsix solvent injection cycles was used to validate the numericalmodel of the CSI process developed at AITF.

This model includes nonequilibrium rate equations thatsimulated the delay in solvent reaching its equilibrium con-centration as it dissolves or exsolves in the oil in response tochanges in the pressure and/or gas-phase composition. Disso-lution of CH 4, C3H8 and CO 2 in oil and CO 2 in water were con-sidered, as was exsolution of CH 4, C3H8 and CO 2 from oil andCO 2 from water. Reduced gas-phase permeabilities resultingfrom gas exsolution were also included.

The history match simulations indicated that: The important mechanisms were represented in the simu -

lations.

Significant oil swelling by solvent dissolution occursduring solvent injection periods. This can reduce solvent injec-tivity and penetration into a heavy oil reservoir during solventinjection periods.

Low oil and gas-phase relative permeabilities are requiredduring production periods to match the experimental oil andgas production during solvent cycles.

A parametric simulation study showed that the quantity of gasinjected in an injection period was relatively insensitive to theoil-phase diffusion coefficients, but was sensitive to solvent sol-ubility in oil, dissolution rates, gas-phase diffusion coefficients,molar densities in the oil phase, gas-phase relative permeabilityand capillary pressure. It was shown that oil production is highlydependent on how quickly solvent can dissolve in the oil duringinjection and exsolve from the oil during production.


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September 2010, Volume 49, No. 9 23

top and bottom of the model was recorded. The production fluidsentered the top of the accumulators, each of which had a piston.Below the piston, the accumulators contained pressurized N 2,which was adjusted to control the experimental pressure. A Par-agon data logging and control system was used in combinationwith a Tesco pressure regulator to reduce the production pressureat a rate of 167 kPa/d during primary production and 125 kPa/dduring CSI production periods. Production periods were termi-nated when the cumulative oil production and cumulative gas pro-duction reached a level value.

The solvent, a mixture of 28 vol. % C 3H8 and 72 vol. % CO 2,was injected into the narrow end of the model from a pressurizedcylinder, through a pressure regulator set at the desired injection

pressure. The model was then left connected to the solvent cylinderthroughout the remainder of the injection period in order to main-

tain pressure as the solvent dissolved in the oil.Detailed information that explains the experiment is provided in

Tables 1 and 2.The experiment consisted of primary production and six solvent

injection cycles. During the solvent injection period, which lastedat least 62 days long in each cycle, the model was pressured to ap-

proximately 3.3 MPa and during the production period the model pressure was decreased to a minimum of approximately 0.6 MPa(Table 3). The solvent mixture was chosen to be totally in the va-

pour phase at the lower pressure and only in the two-phase regionat the upper pressure, as shown in Fig. 3.

A linear pressure drawdown strategy was applied during produc-tion periods (Fig. 4). The maximum pressure drop (between the

top and bottom of the sandpack) was approximately 200 kPa duringthe solvent cycle production periods as compared to 1,200 kPaduring primary production (Fig. 5). Solvent dissolution reducedthe oil viscosity and, as a result, foamy oil stability. It is expectedthat both of these effects were responsible for the lower pressuredrop during solvent cycles.

As a result of fluctuations in ambient temperature, the tempera-ture in the sandpack varied between 15 C and 25 C (Fig. 6) over

the course of the experiment. In later experiments, better control ofthe ambient temperature was achieved.

The oil recovery after primary production was 6.8% and forthe entire test it was 50.4% of the OOIP (Fig. 7). Because of in-creasing solvent penetration into the sandpack with each cycle, thegreatest oil production occurred during Cycles 4 and 5 where re-coveries of 9.6% and 11.9% were obtained. The CSI experiment

produced 50.4% oil recovery from the oil above a wormhole. It isanticipated that oil recovery in a field application of CSI would

be lower and would depend on the operating strategy used and the properties of the reservoir following CHOPS. Numerical simula-tions, based on history matches of field pilot data, are used in thedesign of CSI operating strategies and in estimating oil recovery for

particular field applications.The produced C 3H8 gas composition was initially high in the

Cycle 1 production period, but then declined with time (Fig. 8). Because of its higher solubility in oil, C 3H8 was preferentially dis-solved (relative to CO 2) in the oil at the bottom of the test bedduring solvent injection. When this oil was produced at the begin-ning of the production period, it contained more C 3H8 than CO 2,

but at later times in a production period, more of the produced fluidoriginated from higher in the test bed and the CO 2 concentrationincreased. With time, the C 3H8/(C 3H8+ CO 2) ratio approached that(0.28) in the injected solvent. Similar behaviour was also observedin subsequent cycles.

There was little CH 4 in the produced gas for the first 7 days(Fig. 9) of Cycle 1. This delay in CH 4 production was caused byCH 4 being displaced from the oil into the gas phase by the injectedC3H8 and CO 2 and its upward movement in the sandpack in the

Cycle 1 injection period.

Wormhole

Well

300 cm

E l e m e n

t

Fig. 1: Radial drainage experimental representation of field-scale CSI.

Fig. 2: Laboratory experiment setup and logging system.

TABLE 1SAND PACK CONDITIONS

Primary + 6 Solvent Cycles (28% C 3H8 and 72% CO 2)

Rush Lake dead oil density @ 20C (g/cm 3) 0.988

Dead oil viscosity (mPa.s) 39,320 @ 20C345 @ 75C

48.4 @ 120C

Permeability (darcy) 4.5Porosity (%) 38Initial GOR (std cm 3 /cm 3) 8Initial S o 0.88Initial S w 0.12

Initial pressure (MPaa) 3.3


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More CH 4 was produced in Cycle 1 of solvent production than

in primary production (Table 4). The injected solvent reducedthe CH 4 concentration in the gas phase resulting in CH 4 exsolvingfrom the oil phase in order to achieve an equilibrium state.

Following the experiment, samples were taken (at atmo-spheric pressure) at nine different locations along the test bedand a Dean Stark analysis was performed to determine their oil,water and solids contents. The fluid saturations were then calcu-lated assuming a sand density of 2.65 g/cm 3. The post-run sat-urations for the experiment are shown in Fig. 10. For greaterclarity, dashed lines connect the different sample points. The

post-run oil saturation was low at the bottom of the test bed be-

cause of oil production. Oil saturation was also low at the top be-

cause of its displacement by gas, which migrated to the top duringthe experiment.

Numerical SimulationsA numerical simulation model was developed for history matchingthe radial drainage experiment. The simulations were performedusing the CMG STARS simulator. A radial grid (300 1 1) wasused to represent the physical model for both primary produc-tion and CSI. Each of the 300 blocks was 1-cm long 0.78-cmthick 20C. The simulations were performed assuming iso-thermal behaviour.

70% CH 4, 30% C 3H8

28% C 3H8, 72% CO 2

Cyclic PressureRange

55% CH 4, 45% C 2H6

40% C 2H6, 60% CO 2

25% n-C 4H10 , 75% CO 2

10 000

8 000

6 000

4 000

2 000

0

60 40 20 0 20 40 60 80

Temperature, C

P r e s s u r e ,

k P a

Fig. 3: Solvent phase behaviour during CSI.

0 100 200 300 400 500 600 700

Time, days

4 000

3 500

3 000

2 500

2 000

1 500

1 000

500

0

P r e s s u r e ,

k P a

Cycle 1

Cycle 6

Test 8

P r i m a r y

Fig. 4: Measured pressure profile during injection periods.

TABLE 2PRODUCTION DATA FOR EXPERIMENT

Gas/Solvent (std. L)Produced Gas

Composition (std. L)Period(days)

OilProduced

(cm 3) Injected Produced CH 4 C 3H8 CO 2

ProducedGOR (std.cm 3 /cm 3)

OilRecovery

(%)

Primary 48 279 5.481 6.8Injection 62 121Cycle 1

Production 25 217 58.4 9.0 14.1 35.3 267 5.3Injection 80 208Cycle 2

Production 23 182 99.8 3.6 30.6 65.7 549 4.9Injection 64 224Cycle 3Production 26 234 108.2 0.4 42.7 63.8 462 5.9

Injection 63 298Cycle 4Production 17 389 147 0.03 73.7 72.4 378 9.6Injection 66 247Cycle 5Production 21 486 174 0.06 79.3 94.9 358 11.9Injection 78 159Cycle 6

Production 20 246 147 0.03 52.8 94.2 598 6.0

TABLE 3FINAL PRESSURE AT END OF EACH CYCLE

Final Drawdown Pressure (MPaa)

ProductionInjection

Pressure (MPaa)Drawdown

Rate (kPa/d)Top of

Test BedBottom ofTest Bed

Primary (CSI) 167 Cycle 1 3.32 125 0.60 0.70

Cycle 2 3.26 125 0.67 0.69Cycle 3 3.29 125 0.77 0.79

Cycle 4 3.31 125 1.27 1.31Cycle 5 3.47 125 1.34 1.38Cycle 6 3.43 125 1.01 1.04


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Primary Production ModelIn order to simulate the CSI follow-up process, it was first nec-essary to determine the conditions (fluid saturation and pressuredistributions) in the model at the end of primary production. Thiswas accomplished using a gas exsolution model [Uddin (16)]. In thismodel, a total of three oil-phase components (oil, dissolved CH 4 and dispersed CH 4 bubbles) and one gas-phase component (con-nected CH 4 bubbles) are used. The exsolution of dissolved CH 4 isrepresented by the following four steps:

CH 4LCH 4DB, ..........................................................................(1)

CH 4L+ CH 4DB2 CH 4DB ........................................................(2)

Equations (1) and (2) are forward pseudoreactions that represent bubble formation. The rate of Equation (1) is equal to N 1 (cCH 4 L cCH 4 Leqm), where cCH 4 L is the mole fraction of CH 4 L in the oil

phase, and cCH 4 Leqm is its equilibrium value. The rate of Equation(2) is equal to N 2 (cCH 4 L cCH 4 Leqm)2 cCH 4 DB, where cCH 4 DB is theconcentration of dispersed CH 4 bubbles in the oil phase. N 1 and N 2 are rate constants.

CH 4L+ CH 4DB2 CH 4G, ..........................................................(3)

CH 4DBCH 4G, ..........................................................................(4)

where CH 4L is dissolved methane in oil phase, CH 4DB is dispersedmethane bubble (considered to be in the oil phase) and CH 4G isgaseous methane.

Equations (3) and (4) are forward pseudoreactions that represent

bubble growth. The rate of Equation (3) is equal to G 1 cCH 4 DB. The

rate of Equation (4) is equal to G 2 (cCH 4 L cCH 4 Leqm)2 cCH 4 DB. G 1 and G 2 are rate constants.

The constants N 1, N 2, G 1 and G 2 were obtained by historymatching several laboratory rate and pressure depletion experiments.

Dissolved CH 4 and dispersed CH 4 bubbles are considered to be part of the oil phase and flow with the oil phase.

Development of CSI ModelIn developing the CSI numerical simulation model, the nonequilib-rium representation of solvent (C 3H8, CO 2 and CH 4) solubility, sol-vent/oil mixture viscosities (C 3H8/CO 2/CH 4/oil mixture) and themixing parameters of the process (diffusion and dispersion) were

incorporated into the reservoir fluid model. In the model, the delay

0 100 200 300 400 500 600 700

Time, days

1400

1200

1000

800

600

400

200

0

P r e s s u r e .

k P a

Fig. 5: Measured pressure drop profile during injection periods.

35

30

25

20

15

10

50 100 200 300 400 500 60

Time, days

T e m p e r a

t u r e ,

C

300 cm173 cm12 cm

Cycle 1 Cycle 3 Cycle 4 Cycle 5 Cycle 6Cycle 2

Primary

Fig. 6: Measured temperature during experiment.

0

400

800

1200

1600

2000

2400

C u m u l a

t i v e , c

m 3

Rate

Cumulative

Primary6.8% recovery

1st Cycle5.3% recovery

2nd Cycle4.9% recovery

3rd Cycle5.9% recovery

4th Cycle9.6% recovery



Total Recovery 50.4%

0 20 40 60 80 100 120 140 160 180

Time, days

400

360

320

280

240

200160

120

80

40

0

R a t e ,

c m

3 / d

Fig. 7: Measured oil production rate and cumulative oilproduction during experiment.

CO 2

C3 H8

CH 4

Cycle 160 day soak

Test 8

0 5 10 15 20 25 30Time, days

100

80

60

40

20

0

G a s

C o m p o s i

t i o n m o

l e %

Fig. 8: Measured composition of produced gas during Cycle 1injection.

Cycle 1

Cycle 2

Primary

Cycle 3

Test 8

0 5 10 15 20 25 30

Time, days

10 000

9 0008 000

7 000

6 000

5 000

4 000

3 000

2 000

1 000

0

C u m u

l a t i v e

C H

4 P r o

d . ,

s t d c

m 3

Fig. 9: Methane production during Cycle 1.


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in a gaseous component dissolving or exsolving from the oil de- pends on the difference between its current concentration in the oil phase ( xi) and its equilibrium concentration in the oil phase ( xieqm),as determined from its concentration in the gas phase, its tempera-ture and its pressure.

Equilibrium pressure, volume and temperature (PVT) relation-ship behaviour is represented by the use of equilibrium K values(gas/liquid equilibrium factor) for each component i as follows:

ii x

y K = , ............................................................................................................... (5)

where xi is equilibrium mole fraction of i in oil phase and yi is equi-librium mole fraction of i in the gas phase.

In the CMG STARS simulator, the equilibrium K value of a spe-cific gas is calculated using a modified version of the AntoineEquation,

)(exp)(

5

432

1

kvT

kvkv P kv

P

kv K

++= )( , ................................(6)

where P is the pressure (kPa), T is the temperature ( K) and kv1, kv2,kv3, kv4 and kv5 are the coefficients for specific gases.

In CSI simulations, effective prediction of oil-phase properties

and gas solubility in the oil is essential.

Gas Dissolution Nonequilibrium gas dissolution in oil is represented by:

CH 4G+ CH 4L2 CH 4L, .............................................................(7)

C3H8G+ C3H8L2 C 3H8L, ........................................................(8)

CO 2G+ CO 2L2 CO 2L, .............................................................(9)

where CH 4L is the dissolved CH 4 in oil phase, C 3H4G is the CH 4 ingaseous phase, C 3H8L is the dissolved C 3H8 in oil phase, C 3H8G isthe C 3H8 in gaseous phase, CO 2L is the dissolved CO 2 in oil phaseand CO 2G is the gaseous CO 2.

The delay in CH 4, C 3H8 and/or CO 2 dissolving in the oil is rep-resented by a nonequilibrium equation whereby the rate of dissolu-tion of component i is proportional to ( xieqm xi)n.

For isothermal conditions, the dissolution rate for C 3H8 is:

( ) 2318383*83 ***83 n H C g n L H C Leqm H C o H C y N x x N k t N L H C =

, ............(10)

where k C 3 H 8 is the rate constant for propane dissolution, N g is themoles of gas phase/m 3 of gridblock, N C 3 H 8 L is the moles of C 3H8 in oil phase/m 3 of gridblock, N o is the moles of oil phase/m 3 ofgridblock and is equal to porosity oil saturation oil density, n1 isthe exponent for xC 3 H 8eqm xC 3 H 8 and n2 is the exponent for yC 3 H 8.

The rate equation constants k C 3 H 8 , n1 and n2 can be determinedfrom experimental results (e.g., by history matching laboratory ex-

periments or field tests). Similar equations are used for CH 4 andCO 2.

Gas Exsolution Nonequilibrium gas exsolution is represented as follows for CH 4,C3H8 and CO 2 by:

CH 4LCH 4G, ...........................................................................(11)

C3H8LC3H8G, ........................................................................(12)

CO 2LCO 2G .............................................................................(13)

During gas exsolution, foamy oil behaviour can be part of thenonequilibrium process. However, the allowance for foamy oil be-haviour was not part of the model when the simulations describedin this paper were performed. The allowance has since been in-cluded in the model and will be described in a future paper.

Solvent/Oil Mixing ProcessSolvent and oil are mixed in a reservoir as a result of the combinedeffect of the following mechanisms: convection, diffusion, disper-sion and dissolution.

In diffusion, the flow of a component toward regions of lowerconcentration in a fluid phase is represented by Ficks First Law:

k C DS J ij mij j ijk /)(= , ......................................................(14)

where ijk J is the flux of component i in Phase J in the k direction(gmoles/m 2/d), is the porosity, S j is the saturation of Phase J, Dm ij is the molecular diffusion coefficient of component i in Phase J(m2/d) and C ij / k is the concentration gradient of component i in

Phase J in the k direction (gmoles/m3/m).

SoSgSw

So

Sw

Sg

0 25 50 75 100 125 150 175 200 225 250 275 300

Model Height, cm

1.0

0.9

0.80.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

P o s

t - R u n

S a

t u r a

t i o n

Model height is the distance in cm fromnarrow (bottom) end of test bed

Fig. 10: Fluid saturations in test bed at end of experiment.

TABLE 4METHANE PRODUCTION IN THE EXP ERIMENT

CycleCH 4 Producedin Cycle (std L)

% Of Initial DissolvedCH 4 Produced in

Cycle

% Of Initial DissolvedCH 4 Produced(Cumulative)

Total Gas (CH 4+C 3H8 +CO 2) Produced in

Cycle (std L)

Primary 5.5 17.10 17.10 5.5

Cycle 1 9.0 27.61 44.71 58.4

Cycle 2 3.6 10.84 55.55 99.8

Cycle 3 0.4 1.25 56.79 108.2

Cycle 4 0.03 0.08 56.87 147Cycle 5 0.06 0.20 57.06 174

Cycle 6 0.03 0.08 57.15 147

Total 18.6 740


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In a porous media, allowance is made for the increased flowlength caused by the tortuous flow path through the pore spaces.Thus, the apparent diffusion coefficient ( D) used for porous mediais lower than the molecular diffusion coefficient and:

m D D =, ...................................................................................(15)

where is the tortuosity.Individual streamlines flow in a tortuous route through porous

media. A fluid particle can transfer (disperse) from one stream-line to another by diffusion or through turbulent eddies that disruptthe streamlines. Mechanical dispersion in porous media arises fromcomplex flow paths, which create mechanical mixing that is inde-

pendent of molecular diffusion. This is caused by velocity gradi-ents, heterogeneous flow paths and mechanical mixing.

Both longitudinal (in direction of flow) and lateral (orthogonalto flow) mixing are controlled by diffusion at low velocities and

by convection at high velocities. Velocity variations parallelto the mean flow direction are greater than those perpendicularto the main flow. Thus, longitudinal dispersion is greater thantransverse dispersion. At high velocities, Blackwell (17) observedthat longitudinal dispersion was approximately 24 times that oflateral dispersion.

Neuman (18) examined over 130 longitudinal dispersivity valuesobtained from worldwide laboratory and field tracer studies in po-rous and fractured media. The dispersivity values ranged from lessthan 1 mm to greater than 1 km for studies ranging from less than10 cm to greater than 100 km.

For laminar flow conditions in typical unconsolidated, random

packs, some dispersion correlations are as follows [Perkins andJohnston (19)]:

Longitudinal Dispersion Correlation

10.5 where 50 p p L

mmm

ud ud K D F D D

= + < ..................................(16)

Transverse Dispersion Correlation

41 0.0157 where 10 p pT

mmm

ud ud K D F D D


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History MatchAn excellent match was obtained for both primary oil and gas pro-duction (Figs. 11 and 12).

The post-run pressure and saturations in the test model obtainedfrom the primary history match were used as the initial conditionsfor simulating the solvent cycles. Capillary pressure was considered(Fig. 13). The relative permeabilities used in history matching ofthe experiments were provided previously [Ivory et al. (20) ] and al-lowed for the effect of gas exsolution in reducing gas-phase rela-tive permeability. K values at 0.6 and 3 MPa are shown in Table 6.

The diffusion coefficient for each component in the gas phasewas 0.04 m 2/d (4.63 10 3 cm 2/s) and 4.32 10 5 m 2/d (5 10 6 cm2/s) in the oil phase. Mechanical dispersion was not considered.

The overall match of oil production was reasonable (Fig. 14).However, oil production was higher in Cycles 2 and 3 in the simu-lation and in Cycles 4 and 5 in the experiment. This difference in

behaviour may be partly caused by ambient temperature changesduring the experiment, which was especially prevalent in Cycle 4(Fig. 6).

Predicted and experimental cumulative gas production was sim-ilar (Fig. 15). The simulation resulted in lower C 3H8 production(Fig. 16) and higher CH 4 and CO 2 production (Fig. 17) than theexperimental values. Improvements to the history match can bemade by tuning K values and dissolution and exsolution rate con-stants. In addition, allowance for changes in oil properties over the546 days it was exposed to solvent can be made.

The viscosity of the oil phase had little variation along the modelat the start of an injection period, but varied at the end of InjectionPeriod 1 (Figs. 18 and 19) because of the dissolved solvent con-centration gradient. The viscosity decreased during a production

period as solvent came out of solution.At the end of the first solvent injection period, negligible gas

saturation was obtained at the top of the test bed (model height 140

cm 300 cm) in the simulation (Fig. 20). This resulted from oilswelling caused by solvent dissolution, which essentially eliminatedvoid areas and impeded solvent penetration. This implies that the

production period should extend long enough to create low-oil sat-uration near the well and increase subsequent solvent injectivity.At the end of the last production period, predicted gas saturationvalues were approximately 0.25 throughout the sandpack.

At the end of the first injection period, the composition of the oil phase varied throughout the test bed (Fig. 21). At the bottom of thetest bed, most of the oil phase on a molar basis was dissolved sol-vent (0.95 dissolved solvent mole fraction in oil phase). At the topof the test bed, the dissolved gas mole fraction in the oil phase was0.4. Thus, the oil phase at the bottom of the test bed had swollen

and restricted solvent flow to the top. Because of its high concen-

C u m u

l a t i v e

O i l

, c m

500

1000

1500

2000

0

Time, days

0 100 200 300 400 500 600

Fig. 14: Experimental and history matched oil production.

0 100 200 300 400 500 600

Time, days

800 000

600 000

400 000

200 000

0

C u m u

l a t i v e

G a s ,

s t d c m

3

Fig. 15: Experimental and history matched gas production.

C 3 H 8 Experiment

C 3 H 8 L

C 3 H 8 G

C 3 H 8Simulation

C u m u

l a t i v e

C 3

H 8 ,

s t d c m

3

100 000

200 000

300 000

0

0 100 200 300 400 500 600

Time, days

Fig. 16: Experimental and history matched C 3H8 production.

CO 2 Simulation

CO 2 Experiment

CO 2 L

CO 2 G

500 000

400 000

300 000

200 000

100 000

00 100 200 300 400 500 600

Time, days

C u m u

l a t i v e

C O

2 ,

s t d c m

3

Fig. 17: Experimental and history matched CO 2 production.

Start of Injection Period 1

End of Injection Period 1

End of Cycle 6

0 50 100 150 200 250 300

Model Height, cm

100 000

10 000

1 000

100

10

0

O i l V i s c o s

i t y ,

c P

Fig. 18: Oil viscosity in test bed during history match.


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tration in the gas phase, more CO 2 was dissolved in the oil thanCH 4 or C 3H8, even at the bottom of the model.

During Cycle 1, the C 3H8 and CO 2 mole fractions in the oil phase were much lower at the top of the model than at the bottom, but there was little variation in oil phase composition along the test bed at the end of Cycle 6 and most of the oil phase at that time wasdissolved solvent (Figs. 2124). In addition to solvent dissolu-tion, the dead oil mole fractions are low as a result of its high-mo-lecular weight in comparison to the solvent components.

The deviation with time of the C 3H8 concentration in the oil phase from its equilibrium value at gridblocks in the middle andtop of the model is shown in Figs. 25 and 26. At the midpoint,the C 3H8 concentration was less than the equilibrium value for thefirst cycle before increasing to the equilibrium value in the secondcycle. Concentration was greater than the equilibrium value for thelast four cycles (Fig. 25). This was caused by the complex interac-tion between the CH 4, C 3H8 and CO 2 having different equilibriumsolubility values and dissolving in and exsolving from the oil atdifferent rates. The C 3H8 concentration in the top gridblock gradu-ally moved toward its equilibrium value as C 3H8 penetrated to thetop of the model (Fig. 26).

Parametric InvestigationsSimulation runs were performed to determine the effect of a numberof parameters on the f irst CSI cycle. These parameters included:

Gridblock size. K values/dissolution rate constants. Oil-phase diffusion coefficients.

Gas-phase diffusion coefficients. Molar densities of oil-phase components. Oil-phase relative permeability. Gas-phase relative permeability. Maximum allowed injection rate. Capillary pressures ( P cow and P cgo). Allowance for CO 2 dissolution in water.The parametric study showed that the quantity of gas injected in

an injection period was relatively sensitive to nonequilibrium rateconstants, gas-phase diffusion coefficients, molar densities in the

E n

d o

f I n j e c

t i o n

P e r i o

d 1 ( 6 2 d a y s

)

E n

d o

f P r o

d u c

t i o n

P e r i o

d 1 ( 8 7 d a y s

)

E n

d o

f P r o

d u c

t i o n

P e r i o

d 6 ( 5 9 3 d a y s

)

mPa .smPa .s

S t a r t o

f I n

j e c

t i o n

P e r i o

d 1 ( 0 d a y s

)

30,00027,00024,00021,00018,00015,000

12,0009,0006,0003,0000

12,00010,8009,6008,4007,2006,000

4,8003,6002,4001,2000

Fig. 19: Oil-phase viscosity during history match simulation.

E n

d o

f I n j e c

t i o n

P e r i o

d 1 ( 6 2 d a y s

)

E n

d o

f P r o

d u c

t i o n

P e r i o

d 1 ( 8 7 d a y s

)

E n

d o

f P r o

d u c

t i o n

P e r i o

d 6 ( 5 9 3 d a y s

)

S t a r t o

f I n j e c

t i o n

P e r i o

d 1 ( 0

d a y s

)

0.35

0.31

0.20

0.25

0.21

0.17

0.14

0.11

0.07

0.04

0.00

Fig. 20: Gas saturation during history match simulation (contours).

Test 8x CH4

x CO2

x C3H8

x oil

0 50 100 150 200 250 300

Model Height, cm

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

C o m p o s i

t i o n o

f O i l P h a s e

@ E

n d o

f S o a

k 1

Fig. 21: Oil composition in test bed at end of Cycle 1 injection

period of history match.

x CH4x oil

x CO2

x C3H8

0 50 100 150 200 250 30 0

Model Height, cm

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

M o

l e F r a c

t i o n

i n O i l

Fig. 22: Oil composition in test bed at end of Cycle 6 of history

match.


9/12


oil phase, solvent solubility in oil, gas-phase relative permeabilityand capillary pressures.

Effect of Grid SizeThe radial grid used in most of the simulations was 300 1 1. Inorder to determine the effect of using larger gridblocks, a simulationwas run for a radial grid of 5 1 1 where the five blocks in the I direction were 1 cm, 9 cm, 90 cm, 100 cm and 100 cm, respectively.The inner block was kept at 1 cm so that the same well index could

be used for both. In both simulations an initial (preCSI) pressure of500 kPa was used and the initial oil, gas and water saturations were0.84, 0.04 and 0.12, respectively. The initial dead oil and CH 4 molefractions in the live oil were 0.96 and 0.04. Less gas (29%) was in-

jected for the case with larger gridblocks. This was because of arapid increase in small bottom-block gas saturation when solventwas injected, which results in a shift in the gas-phase relative perme-ability. This effect is more pervasive in the small-block case.

Effect of K Values/Dissolution Rate ConstantReducing the component K value increases solvent solubility and,consequently, more gas can be dissolved. Reducing the K valuesof CH 4, C 3H8 and CO 2 by 48% increased the solvent injected by afactor of 10.

Dissolution rate constants are obtained by matching exper-imental or field data. In the field, the bottomhole pressure ismatched for specif ied injection rates. The more soluble the injected

gas and the quicker it dissolves, the lower the rise in bottomhole pressure during an injection period. In contrast, the injection of alow solubility or slowly dissolving gas will cause a relatively rapidrise in injection pressure.

The rate constants will depend on gridblock size as greater non-equilibrium behaviour occurs in larger gridblocks (i.e., it takeslonger to reach equilibrium in a large block). Diffusion, dispersion,PVT and nonequilibrium behaviour are all interrelated and thismakes the modelling of field behaviour more complex.

Effect of Diffusion CoefficientThe cumulative gas injected in the f irst injection period was essen-tially the same whether the oil-phase diffusion coefficient was 0.0m2/d, 0.0000432 m 2/d or 0.000864 m 2/d.

The effect of increasing the gas-phase diffusion coefficient forinjection periods was complicated by the CO 2 mole fraction in thegas at the top of the model becoming greater than its mole fraction

at the bottom. Because C 3H8 is more soluble, it is preferentiallydissolved near the injection well (bottom of the test bed) and moreof the CO 2 moves to the top. Thus, diffusion causes C 3H8 to moveupward, CO 2 to move downward and the overall effect on solventinjection is difficult to predict especially because the concentrationgradients of the different components change significantly during asolvent cycle. Of four diffusion coefficients considered (0.0, 0.01,0.04 and 0.1 m 2/d), 0.01 m 2/d resulted in the greatest quantity ofsolvent injected during the first injection period (Fig. 27).

C3H8eqm

C3H8

x

x

0 50 100 150 200 250 300

Time, days

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 C 3

H 8

M o

l e F r a c

t i o n

i n O i l a

t 1 5 0

, 1

, 1

Fig. 25: C 3H8 mole fraction at midpoint of model duringhistory match.

C3H8eqm

C3H8

x

x

0 50 100 150 200 250 300

Time, days

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 C 3

H 8

M o

l e F r a c

t i o n

i n O i l a

t 3 0 0

, 1

, 1

Fig. 26: C 3H8 mole fraction at top of model during history match.

E n

d o

f I n j e c

t i o n

P e r i o

d 1 ( 6 2 d a y s

)

S t a r t o

f I n j e c

t i o n

P e r i o

d 1 ( 0 d a y s

)

E n

d o

f P r o

d u c

t i o n

P e r i o

d 1 ( 8 7 d a y s

)

E n

d o

f P r o

d u c

t i o n

P e r i o

d 6 ( 5 9 3 d a y s

)

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

Fig. 23: Propane mole fraction in oil phase during historymatch simulation.

E n d o f I n j e c t i o n P e r i o d 1 ( 6 2 d a y

s )

E n d o f P r o d u

c t i o n P e r i o d 1 ( 8 7 d a y

s )

E n d o f P r o d u

c t i o n P e r i o d 6 ( 5 9 3 d a y

s )

S t a r t o f I n j e c t i o n P e r i o d 1 ( 0 d a y

s )

0.70

0.63

0.56

0.49

0.42

0.35

0.28

0.21

0.14

0.07

0.00

Fig. 24: CO 2 mole fraction in oil phase during history matchsimulation (contours).


10/12


Effect of Oil-Phase Molar DensitiesConcentration and temperature dependent molar densities for deadoil and solvent components should be determined from experi-mental measurements. The values selected had a significant impacton volume of solvent injected (Fig. 28). A higher-molar densityfor dissolved solvent reduced oil-phase volume and allowed greatergas space for additional injected solvent. The molar densities usedin all the simulations, with the exception of some of those usedto obtain Fig. 28, were 18,750 moles/m 3 (CH 4), 11,539 moles/m 3 (C3H8) and 18,795 moles/m 3 (CO 2). These values were based onthe densities for pure CH 4, C 3H8 and CO 2 liquids at atmospheric

pressure as obtained from the GPSA Engineering Data Book (21) .

Effect of Capillary PressureCapillary pressure had a significant impact on solvent injected,oil produced and gas produced in solvent cycles. Increasing thecapillary pressure by a factor of 10 increased the gas injection by7%, gas production by 11% (Fig. 29) and oil production by 9%(Fig. 30). Increasing the capillary pressures by a factor of 100 in-creased the gas injection by 26%, gas production by 29% and oil

production by 59%.

Effect of Maximum Allowed Injection RateReducing the maximum allowed injection rate, which is specifiedin the model, from 20 std L/d to 10 std L/d resulted in only 4%lower gas injection. This was because the injection rate was limited

because of the confined volume and the oil swelling that occurredduring injection periods.

Effect of CO 2 Dissolution in WaterTo evaluate the effect of CO 2 nonequilibrium dissolution and exso-lution from the water phase, components CO 2W (CO 2 in the water

phase) and CO 2G (CO 2 in the gas phase) and the following twomass transfer reactions were used:

CO 2G+ CO 2W2 CO 2W, .........................................................(19)

CO 2WCO 2G ...........................................................................(20)

Published solubility data [Chang et al. (22) ] for CO 2 in water wereused to determine K values at 20 C over the pressure range of in-terest (0.5 to 3.5 MPa).

Allowing for CO 2 solubility in water had a minor impact byincreasing solvent injection by 2.7%, oil production by 0.7% anddecreasing gas production by 0.7%. At high-water saturations,CO 2 solubility may have a more significant effect and it wouldthen be important to use the additional component (CO 2W) andrate equations.

ConclusionsThe cyclic solvent process shows potential (50% recovery in sixcycles) as a post-cold production process.

The primary production foamy oil model used resulted in an ex-

cellent history match.The CSI model captures the main mechanisms involved in CSI.

More data on PVT and nonequilibrium behaviour is required fortuning the CSI simulation model.

At a laboratory scale, the quantity of gas injected is relativelyinsensitive to the oil-phase diffusion coefficients and the oil-phaserelative permeability, but is sensitive to dissolution rates, gas dis-

persion coefficients, molar densities in the oil phase, K values, rela-tive permeability to gas and capillary pressures.

Increasing the block size reduces the predicted solventinjection.

0.01 m 2 /d

0.04 m 2 /d0.1 m 2 /d

0.0 m

DISP_GAS = 0.0 m 2 /dDISP_GAS = 0.01 m 2 /dDISP_GAS = 0.04 m 2 /dDISP_GAS = 0.1 m 2 /d

2 /d

0 20 40 60 80

Time, days

140 000

120 000

100 000

80 000

60 000

40 000

20 000

0

C u m u

l a t i v e

G a s ,

s t d c m

3

Fig. 27: Effect of gas phase diffusion coefficients on solventinjection in Cycle 1.

800 000

600 000

400 000

200 000

00 20 40 60 80

Time, days

C u m u

l a t i v e

G a s ,

s t d c m

3

CH 4 C 3 H8 CO 2

Molar Densities: 18750, 11539, 18795 moles/m 3

Molar Densities: 21563, 13270, 21614Molar Densities: 20000, 20000, 20000Molar Densities: 25000, 25000, 25000Molar Densities: 30000, 30000, 30000Molar Densities: 40000, 40000, 40000

Fig. 28: Effect of oil-phase component molar densities onsolvent injection in Cycle 1.

00 20 40 60 80 100

Time, days

200 000

150 000

100 000

50 000Pc

HM = history matchcapillary pressure

100 * Pc HM

10* Pc

HM

Pc HM

Experiment

No capillary pressure

C u m u

l a t i v e

G a s ,

s t d c m

Fig. 29: Effect of capillary pressures on solvent injection in Cycle 1.

0 20 40 60 80 100

Time, days

500

400

300

200

100

0

C u m u

l a t i v e

O i l

, c m

100 * Pc HM

10 * Pc HM Pc HM

Experiment

No capillarypressure

Fig. 30: Effect of capillary pressures on oil production in Cycle 1.


11/12


Oil swelling may have a significant impact on cyclic solvent pro-cesses when initial oil saturations are high because it impedes sol-vent injection.

As a result of its higher solubility in oil and its initial productionin the oil phase, the C 3H8 concentration of the produced gas wasinitially higher than that of CO 2. As production began to originatefrom higher in the model, the CO 2 concentration became signifi-cantly greater than the C 3H8 concentration.

There was a delay in CH 4 production during each production pe-riod of a solvent cycle as a result of CH 4 being displaced upward inthe test bed by injected solvent.

AcknowledgementsThe assistance of Dennis Coombe of CMG, Ron Sawatzky andMafiz Uddin is appreciated.

Nomenclature bc = connected bubble component in the gas phase bd = dispersed bubble component in the oil phase CH 4L = dissolved methane in oil phase CH 4G = gaseous methane C3H8L = dissolved propane in oil phase C3H8G = gaseous propane CO 2L = dissolved carbon dioxide in oil phase CO 2G = gaseous carbon dioxide CO

2W = dissolved carbon dioxide in water phase

CDOR = (calendar day oil rate) total oil production/totalnumber of days, m 3/d

gd = dissolved gas component in the oil phase GOR = volume of dissolved gas per unit volume of oil, std

m3/m3 k h = absolute permeability in horizontal direction, darcy k

rgro= gas relative permeability at S wc+ S org

k rocw

= oil relative permeability at irreducible watersaturation

k row = oil-phase relative permeability in the presence ofwater

k rw = water-phase relative permeabilityk

rwc= water relative permeability at residual oil saturation

k v = absolute permeability in vertical direction, darcy

K i = yi / xi OOIP = original oil in place, m 3 P = pressure, kPa PV = pore volume, cm 3 S

gr= critical gas saturation

S org

= residual oil saturation for oil-gas system S

orw= residual oil saturation for oil-water system

S W = water saturation S

wr = irreducible water saturation

visi = viscosity of component I in oil phase, mPa.s visoil = oil-phase viscosity, mPa.s xi = mole fraction of component i in oil phase yi = mole fraction of component i in gas phase = porosity

References 1. Bratli, R.K. and Risnes, R. 1981. Stability and Failure of Sand Arches.

SPE J . 21 (2): 236238. SPE-8427-PA. doi: 10.2118/8427-PA.2. Bratli, R.K., Dusseault, M.B., Santarelli, F.J., and Tronvoll, J. 1998.

Sand Management Protocol Increases Production Rates, ReducesCompletion Costs. Presented at the 12th Biennial Business and Tech-nology Conference, Trinidad and Tobago, 10-13 March.

3. Chang, J. 2000. System Dynamics Approaches for Sand ProductionSimulation and Prediction (A Semi-Analytical Implementation). MSthesis, University of Waterloo, Waterloo, Ontario.

4. Dusseault, M.B. and Santarelli, F.J. 1989. A Conceptual Model forMassive Solid Production in Poorly-Consolidated Sandstone. Proc.,International Symposium on Rock at Great Depth, Pau, France, 2831August, 789797.

5. Dusseault, M.B., Geilikman, M.B., and Spanos, T.J.T. 1998. Mecha-nisms of Massive Sand Production in Heavy Oils. Proc. , 7th UnitarConference on Heavy Crude and Tar Sands, Beijing, 2730 October,119.

6. Dusseault, M.B. and El-Sayed, A. 1999. CHOP-Cold Heavy Oil Pro-duction. Paper 086 presented at the 10th European Symposium onImproved Oil Recovery, Brighton, UK, 1820 August.

7. Geilikman, M.B. and Dusseault, M.B. 1997. Dynamics of Wormholesand Enhancement of Fluid Production. Paper presented at the 48thAnnual Technical Meeting of the Petroleum Society in Calgary, Cal-gary, 811 June.

8. Geilikman, M.B. and Dusseault, M.B. 1999. Sand Production Caused by Foamy Oil Flow. Transport in Porous Media 35 (2): 259272. doi:10.1023/A:1006532804609.

9. Risnes, R., Bratli, R.K., and Horsrud, P. 1982. Sand Arching- A CaseStudy. Proc. , European Petroleum Conference, London, 2528 Oc-tober, EUR 310, 313318.

10. Sawatzky, R.P., Lillico, D.A., Vilcsak, G., and Tremblay, B. 1996.Initiation of Sand Production in the Cold Production Process. Paper

presented at the Petroleum Society of CIM Annual Technical Meeting,Calgary, 1012 June.

11. Sawatzky, R.P., Lillico, D.A., Tremblay, B.R., and Coates, R.M. 2002.Tracking Cold Production Footprints. Paper CIPC 2002-086 pre-sented at the Canadian International Petroleum Conference, Calgary,1113 June. doi: 10.2118/2002-08.

12. Tremblay, B., Sedgwick, G., and Forshner, K. 1998. Modelling ofSand Production from Wells on Primary Recovery. J Can Pet Technol37 (3): 4150. JCPT Paper No. 98-03-03. doi: 10.2118/98-03-03.

13. Tremblay, B., Sedgwick, G., and Vu, D. 1999. A Review of ColdProduction in Heavy Oil Reservoirs. Paper presented at the 10th Eu-ropean Symposium on Improved Oil Recovery, Brighton, UK, 1820August.

14. Tremblay, B. 2003. Modelling of Sand Transport Through Wormholes.Paper CIPC 2003-101 presented at the Canadian International Petro-leum Conference, Calgary, 1012 June.

15. Tremblay, B. 2009. Cold Flow: A Multi-Well Cold Production(CHOPS) Model. J Can Pet Technol 48 (2): 2228. JCPT Paper No.09-02-22. doi: 10.2118/09-02-22.

16. Uddin, M. 2005. Numerical Studies of Gas Exsolution in a LiveHeavy-Oil Reservoir. Paper SPE 97739 presented at the SPE/PS-CIM/

CHOA International Thermal Operations and Heavy Oil Symposium,Calgary, 13 November. doi: 10.2118/97739-MS.

17. Blackwell, R.J. 1962. Laboratory Studies of Microscopic DispersionPhenomena. SPE J . 2 (1), 18; Trans. , AIME, 225 . SPE-1483-G. doi:10.2118/1483-G.

18. Neuman, S.P. 1990. Universal Scaling of Hydraulic Conductivitiesand Dispersivities in Geologic Media. Water Resources Research 26(8): 17491758. doi: 10.1029/WR026i008p01749.

19. Perkins, T.K. and Johnston, O.C. 1963. A Review of Diffusion andDispersion in Porous Media. SPE J . 3 (1): 7084; Trans. , AIME, 228 .SPE-480-PA. doi: 10.2118/480-PA.

20. Ivory, J., Chang, J., Coates, R., and Forshner, K. 2009. Investigation ofCyclic Solvent Injection Process for Heavy Oil Recovery. Paper CIPC2009-161 presented at the Canadian International Petroleum Confer-ence, Calgary, 1618 June. doi: 10.2118/2009-161.

21. GPSA Engineering Data Book, Vol. 2, eleventh edition, 23-222-3.1998. Tulsa, Oklahoma: Gas Processors Suppliers Association.

22. Chang, Y., Coats, B.K., and Nolen, J.S. 1998. A Compositional Modelfor CO 2 Floods Including CO 2 Solubility in Water. SPE Res Eval &

Eng 1 (2): 155160. SPE-35164-PA. doi: 10.2118/35164-PA.

This paper (2009-161) was accepted for presentation at the 10th CanadianInternational Petroleum Conference (the 60th Annual Technical Meeting ofthe Petroleum Society), Calgary, 16-18 June, 2009, and revised for publi-cation. Original manuscript received for review 27 March 2009. Revised

paper received for review 29 June 2010. Paper peer approved 7 July 2010as SPE Paper 140662.


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S b 2010 V l 49 N 9 33

Authors Biographies

John Ivory is a senior research engineer atAITF (formerly Alberta Research Council)in the areas of enhanced oil recovery (pri-marily solvent, steam, steam-solvent and in-situ combustion processes) and gasseparation/purification using membranes,adsorption or absorption technologies since1981. He is currently leader of AITFs reser-voir simulation group. He has extensive ex-

pertise in both designing experiments and performing numerical simulations related to enhanced heavy oiland bitumen recovery processes. Ivory holds a Ph.D. degree inchemical engineering from the University of Alberta.

Jeannine Chang is a research scientist atAITF in Edmonton, Alberta. She is cur-rently focusing on reservoir simulation ofEOR technologies, including the cyclic in-

jection processes (solvent, steam and steam-solvent), VAPEX, SAGD and primary

production. She has expertise in petroleumgeomechanics and hydrogeology and has

been an environmental consultant focusingon environmental assessments and petro-

leum contaminant remediation. Chang holds an M.Sc. degree inearth sciences from the University of Waterloo.

Roy Coates is a senior research engineerwith the heavy oil and oil sands research de-

partment of AITF. He has been extensivelyinvolved in researching heavy oil and bi-tumen recovery processes for over 30 years,including in-situ combustion, steam assistedgravity drainage and cold production. Re-cently he has been a member of the team in-vestigating solvent-based and in-situcombustion heavy oil recovery processes

and has been the program manager for the AERI/ARC CarbonateResearch Program. He holds a degree in chemical engineering fromthe University of Alberta and is a member of APPEGA.

Ken Forshner is a senior research technolo-gist at AITF in the areas of enhanced oil re-covery (primarily solvent, steam and in-situcombustion processes) and development ofx-ray scanning. He is currently a lead tech-nologist in AITFs reservoir engineeringgroup. He has extensive expertise in bothdesigning and fabricating experiments re-lated to enhanced heavy oil and bitumen re-covery processes.

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