field scale simulation of cyclic solvent injection (csi)2

Field-Scale Simulation of Cyclic SolventInjection (CSI)

Jeannine Chang, Devon Canada, and John Ivory, Alberta InnovatesTechnology Futures

Summary

Only 510% of the oil in Lloydminster heavy-oil reservoirs isrecovered during cold heavy-oil production with sand (CHOPS).CSI is currently the most active post-CHOPS process. In CSI, asolvent mixture (e.g., methane/propane) is injected and allowed tosoak into the reservoir before production begins (Fig. 1). CSI hasbeen focused on heavy-oil recovery from post-CHOPS reservoirsthat are too thin for an economic steam-based process. It has beenpiloted by Nexen and Husky and was a fundamental part of theCDN 40 million joint implementation of vapour extraction (JIVE)solvent pilot program that ran from 2006 through 2010.

This paper describes field-scale simulations of CSI performedwith a comprehensive numerical model that uses mass-transferrate equations to represent nonequilibrium solvent-solubilitybehaviour (i.e., there is a delay before the solvent reaches its equi-librium solubility in oil). The model contains mechanisms to con-sider foaming or to ignore it, depending on the field behaviour. Ithas been used to match laboratory experiments, design CSI oper-ating strategies, and to interpret CSI field pilot results.

The paper summarizes the impact on simulation predictions ofpost-CHOPS reservoir characterizations where the wormholeregion was represented by one of the following five configura-tions: (1) an effective high-permeability zone, (2) a dual-perme-ability zone, (3) a dilated zone around the well, (4) wormholes(20-cm-diameter spokes) extending from the well without branch-ing, and (5) wormholes extending from the well with branchingfrom the main wormholes. The different post-CHOPS configura-tions lead to dramatically different reservoir access for solventand to different predictions of CSI performance.

The impacts of grid size, upscaling, solvent dissolution andexsolution rate constants, and injection strategy were examined.The assumption of instant equilibrium solubility resulted in a 23%reduction in oil production compared with when a delay in solventdissolution and exsolution was allowed for. Increasing the grid-block size by a factor of nine reduced the predicted oil productionfive-fold. Assuming isothermal behaviour in the simulationsdecreased predicted oil production by 17%.

Introduction

Primary CHOPS is commonly used in Lloydminster and ColdLake to produce heavy-oil reservoirs (Sawatzky et al. 2002). As aresult of reservoir-pressure depletion and/or excessive water pro-duction, CHOPS becomes uneconomic after approximately515% of the initial oil has been recovered. Detailed studies ofthe cold-production process and enhanced-oil-production mecha-nisms, including the effect of fluid flow on sand production inheavy-oil reservoirs under solution gas drive, have been presentedpreviously (Bratli and Risnes 1981, Bratli et al. 1998; Chang2000; Dusseault and Santarelli 1989; Dusseault et al. 1998; Dus-seault and El-Sayed 1999; Geilikman and Dusseault 1997; Geilik-man 1999; Risnes et al. 1982; Sawatzky et al. 1996, 2002; Smith1988; Tremblay et al. 1998, 1999, 2009; Tremblay 2003).

In CSI, a solvent mixture is injected into the reservoir, allowedto soak, and then the well is put on production to a drawdown

pressure of approximately 50500 kPaa. The cycles are repeateduntil the process is no longer economic. Typically, the reservoir isrepressurized to initial reservoir conditions so that solvent solubil-ity will be high and the reservoir is re-energized. If the CSI well isnot an isolated well, then the injection pressure may not be builtup to the desired value, but CSI can still perform well at a lowerpressure with appropriate solvent-mixture selection. The solventcomposition is normally selected to suit the reservoir so that thesolvent mixture is slightly above its dewpoint at the end of injec-tion periods (Fig. 2). This creates high solvent solubility in theoil. Apart from that dissolved in the oil phase, the solvent shouldnot be liquid because excessive solvent inventory in the resevoirwould make the process uneconomic.

CSI has shown potential as a follow-up oil-recovery process toCHOPS and has undergone pilot testing. Wormholes, created dur-ing CHOPS, extend outward from production wells and provideaccess for solvent injection during CSI and larger contact area forsolvent dissolution in oil. There are dramatic opportunities forCSI field applications. Lloydminster reservoirs contain approxi-mately 3 billion m3 of heavy-oil resources. Most of these reser-voirs are under CHOPS. However, after CHOPS, where oilrecovery is only approximately 510%, the wells have been eitherpressure depleted or are watered out. Most of the original oil inplace still remains in the reservoir after CHOPS and is availablefor a follow-up process. In addition, CSI is applicable for thinpays where steam-based processes are uneconomic and it can usethe existing nonthermally completed CHOPS wells. CSI is consid-ered more environmentally friendly than steam-based processesbecause there is essentially no water usage, its energy requirementis lower, and if CO2 is part of the injected solvent, then some of itwill remain in the reservoir at the end of the solvent process. CSIhas been tested in the field by Nexen at Plover Lake and is cur-rently being piloted by Husky.

Looking at Darcys law, CHOPS increases the effective per-meability because of the wormhole formation, and in CSI, solventdissolves in the oil and reduces the live-oil viscosity. Both ofthese mechanisms enhance oil production. The solvent mixturealso creates other benefits (e.g., oil swelling and solution gasdrive).

, ............ (1)

drdpkq

=

(Primary) CHOPS

(EOR) CSI

where k is permeability (m2), l is viscosity (Pas), q is the flowrate per unit cross-sectional area (m2/s), and dp/dr is the pressuregradient (Pa/m).

Laboratory-scale numerical simulation of CSI has been inves-tigated at Alberta InnovatesTechnology Futures (AITF) for anumber of years (Ivory et al. 2010), and a focus is now to use theprevious learnings to improve field-scale simulation of CSI andother solvent processes [e.g., vapour-assisted petroleum extraction(VAPEX) and solvent drive], thereby facilitating the developmentof improved injection and production strategies. Improved repre-sentations of complex mechanisms (e.g., nonequilibrium dissolu-tion and exsolution of solvent, foamy oil behaviour, and relativepermeability hysteresis) are being developed. In particular, the

CopyrightVC 2013 Society of Petroleum Engineers

This paper (SPE 157804) was accepted for presentation at the SPE Heavy Oil ConferenceCanada, Calgary, 1214 June 2012, and revised for publication. Original manuscriptreceived for review 27 June 2012. Revised manuscript received for review 6 April 2013.Paper peer approved 29 April 2013.

July 2013 Journal of Canadian Petroleum Technology 251

physical delay in both gas dissolution and gas exsolution is repre-sented by nonequilibrium behaviour during both injection andproduction periods. In developing CSI numerical-simulation mod-els, the nonequilibrium representation of solvent (e.g., methaneand propane) solubility, solvent/oil-mixture (methane/propane/oil-mixture) viscosities, and the mixing parameters (diffusion, dis-persion) of the process were incorporated into the reservoir-fluidmodel. Previous work at AITF for a laboratory-scale experimentshowed the difference between the predicted propane mole frac-tion in oil and its equilibrium value during six CSI cycles (Fig. 3).

Predicted values for the 72% carbon dioxide/28% propane injec-tion experiment were dramatically different when nonequilibriumsolubility effects were considered relative to when they wereignored (Table 1). Allowance for nonequilibrium behaviour alsoaffects the predicted bottomhole pressure (BHP) during injectionperiods in field-scale simulations (Fig. 4). Frequency factor (FF)in this figure is the value of the frequency factor in the nonequili-brium reactions. For confidentiality reasons, the actual BHP val-ues are not shown in Fig. 4. In general, the quicker the solventdissolution, the slower is the rise in BHP during injection periods.

In predicting the effectiveness of CSI and in developing oper-ating strategies, the assumed post-CHOPS reservoir situationresulting from sand production is of key importance. AITF hasdeveloped a sand-production model, which is supported in CMGSTARS software, and is an effective-permeability model in thatthe regions with a wormhole network are represented by a highpermeability determined by the sand-production model. For duediligence, other post-CHOPS representations can be considered.In this paper, the effectiveness of CSI was investigated for thefollowing post-CHOPS representations (Fig. 5): (1) an effectivehigh-permeability zone, (2) a dual-permeability zone, (3) a dilatedzone around the well, (4) wormholes (20-cm-diameter spokes)extending from the well without branching, and (5) wormholesextending from the well with branching from the mainwormholes.

Simulations were performed to examine the impact of Using nonequilibrium dissolution and exsolution reaction

kinetics to represent delays in solvent dissolution and exsolution Nonequilibrium solubility rate parameters

0

2,000

4,000

6,000

8,000

10,000

60 40 20 0 20 40

Pres

sure

(kPa

)

Temperature (C)

60/40 Methane/Propane

Bubblep

oint Line

Two-phas

e region

Dewpoint Line

CSI

Fig. 2Phase envelope for 60% methane/40% propane mixture.

Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 5 Cycle 60.50

0.40

0.30

0.20

0.10

0.00Prop

ane

Mol

e Fr

actio

n at

150

,1,1

0 100 200 300 400 500 600Time (days)

XC3H8Leqm

XC3H8L

Fig. 3Propane nonequilibrium solubility during a CSIexperiment.

Oil

Solvent dissolved in oil

CSI well on production

Pressure Profile

Oil

Solvent dissolved in oil

CSI well on injection

Pressure Profile

Bubbles

200 kPaa

3000 kPaa

Fig. 1Reservoir behaviour during CSI.

TABLE 1COMPARISON OF NONEQUILIBRIUM AND INSTANT EQUILIBRIUM SIMULATION

PREDICTIONS

Cumulative

Oil (cm3)

Propane

Recovery (%)

Carbon Dioxide

Recovery (%)

Net Solvent/Oil

Ratio (liquid cm3/cm3)

Nonequilibrium 1698 72.8 64.1 0.60

Instant equilibrium 809 92.3 97.4 0.34

Time (days)

Wel

l BH

P (kP

a)

Slow solvent dissolution

Instant solvent dissolution

Insoluble gas

FF=0.01

FF=0

Fig. 4Effect of dissolution rate on well BHP during injection.

252 July 2013 Journal of Canadian Petroleum Technology

Instant equilibrium vs. nonequilibrium dissolution andexsolution

Gridblock size.The solvent used in the simulations was a 60% methane/40%

propane mixture.

Development of CSI Models at AITF

Over the years, CSI models have been developed at AITF (Changand Ivory 2011) which consider important mechanisms (e.g., non-equilibrium solubility, foamy oil, gas expansion, oil swelling, dif-fusion, dispersion, and relative permeability hysteresis). AITFsCSI models have been validated with laboratory- and field-scalehistory matches. In the JIVE program, some CSI cycles were his-tory matched for a Colony well and a Waseca well in Huskys firstCSI Edam pilot. Following a history match of two cycles, differ-ent operating strategies were evaluated. JIVE was the CDN 40million solvent pilot program that ran from 2006 through 2010(Kristoff et al. 2008). The program was managed by PetroleumTechnology Research Centre. Operators were Canadian NaturalResources, Husky, and Nexen, and research providers were AITFand the Saskatchewan Research Council (SRC).

In representing nonequilibrium gas dissolution and exsolutionbehaviour, the delay in a gaseous component dissolving or exsolv-ing from the oil depends on the difference between its currentmole fraction in the oil phase (xi) and its equilibrium mole frac-tion in the oil phase (xieqm). The latter is determined from its molefraction in the gas phase, the temperature, and the pressure.

Equilibrium pressure/volume/temperature behaviour is repre-sented by the use of K-values for each component i, as follows

Ki yi=xi ; 2

where xi is the equilibrium mole fraction of i in the oil phase andyi is the equilibrium mole fraction of i in the gas phase.

In the CMG STARS simulator, the equilibrium K-value of aspecific gas is calculated using a modified version of the Antoineequation,

K kv1P

kv2 P kv3

exp kv4T kv5

; 3

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

Eq. 3 is based on the assumption that the K-value is independ-ent of composition and depends only on temperature and pressure.If kv2 and kv3 are set to zero as they were in the simulationsdescribed in this paper, then the K-value is inversely proportionalto pressure. As shown in Eq. 3, the K-value is exponentially de-pendent on temperature. Some allowance for the dependence ofthe K-value on composition can be obtained in STARS by provid-ing nonzero values for kv3 and kv4 to match experimental solubil-ity data at specified temperatures, pressures, and compositions.However, these experimental data are not available frequently inthe required detail and it is risky to use nonzero kv4 and kv5 valuesfor predictions outside the range of experimentally measured con-ditions because nonphysical K-values can then be obtained.

The methane and propane kv values that were used in the sim-ulations are provided in Table 2, and the resultant K-values formethane and propane at the reservoir temperature of 23.4C aredisplayed in Fig. 6a. The kv4 and kv5 values in Table 2 wereobtained from the CMG STARSTM simulator Users Guide. Thekv1 values were adjusted from the STARS values to meet meas-urements at AITF, including some oil/CH4/propane solubilitymeasurements. The higher the K-value is, the lower the solubility.Propane is approximately 30 times as soluble as methane.

Gas Dissolution. Reactions in STARS are used to represent thedelay in a solvent dissolving or coming out of solution. Theslower the reaction rate, the slower the solvent dissolution orexsolution. Slower dissolution rates are typically used for largefield gridblocks rather than for small laboratory-scale gridblocks.For example, dissolved propane is considered as one componentand propane gas as another component. A reaction is used to

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . .

Spoke

3

Spok

e 2

Permeability = 1.56 darcies

Wormhole diameter = 20 cm Spoke 1 Wormhole diameter = 20 cm Spoke 1

Spokes model

Spoke

3

Spok

e 2

Branch 2


Bran

ch 1

Spokes and Branches model

Porosity = 0.3Permeability = 1.56 darcies

Porosity = 0.7 Permeability = 1,000 darcy

Dilated-Zone model20

0 m

High permeabilityregion

Effective-Permeability model


Frac Perm = 10,000 darciesFrac Trans = 1,000In blocks with fractures,Frac Vol/Block Vol = 0.067

Dual-Permeability model

Dual permeability

VW(a) (b)

(c) (d) (e)

Fig. 5One-quarter symmetry representation of different reservoir configurations being tested.

TABLE 2METHANE AND PROPANE kv VALUES USED

IN THE SIMULATIONS

Methane Propane

kv1 (kPa) 476 664 726 374

kv2 (kPa1) 0 0

kv3 0 0

kv4 (C) 879.84 1872.46

kv5 (C) 265.99 247.99


represent the transformation from propane gas to dissolved gas.Similar reactions are used to represent propane coming out ofsolution.

The nonequilibrium behaviour of gas dissolution in oil is rep-resented by

CH4G CH4L ! 2 CH4L: 4

C3H8G C3H8L ! 2 C3H8L; 5

where CH4L is dissolved methane in the oil phase, CH4G is meth-ane in the gaseous phase, C3H8L is dissolved propane in the oilphase, and C3H8G is propane in the gaseous phase.

The dissolution rate for propane is

@NC3H8L@t

kC3H8 Nn1o xC3H8Geqm xC3H8Ln1

Nn2g yC3H8G; 6

where k is the rate constant for propane dissolution (m3/mol/d),Ng is the moles of gas phase/m

3 of the gridblock, NC3H8L is themoles of propane in the oil phase/m3 of the gridblock, No is themoles of oil phase/m3 of the gridblock and is the porosity oilsaturation oil molar density, n1 is the exponent for xC3H8eqm xC3H8, and n2 is the exponent for yC3H8.

The rate equation constants kC3H8, n1, and n2, can be deter-mined from experimental results (e.g., by history matching labo-ratory experiments or field tests). Similar equations are used formethane dissolution. The rate constants are dependent on temper-ature, but because of the small temperature changes and lack ofexperimental data available to determine this dependency, theywere assumed to be independent of temperature in the simulationsdiscussed in this paper. The dependency of phase behaviour ontemperature is partially captured in the dependency of the K-valueon temperature. Allowance for the temperature dependency of therate constants is available in STARS.

Gas Exsolution. Nonequilibrium gas exsolution with foam for-mation is represented as follows for methane and propane:

CH4L ! SBubCH4 ! CH4G: 7

C3H8L ! SBubC3H8 ! C3H8G; 8

where SBubCH4 is a small methane bubble in the oil phase andSBubC3H8 is a small propane bubble in the oil phase.

Unless specified otherwise, a nonequilibrium treatment wasused in this project for both gas dissolution and exsolution. As aresult of nonequilibrium behaviour, solvent components can stillbe dissolving at the beginning of a production period and comeout of solution (exsolve) during the initial part of an injectionperiod. Although the simulations are slower, it is more representa-tive of CSI to include all of the exsolution and reactions duringboth injection and production periods.

Solvent/Oil-Mixture Viscosity. The STARS default logarithmicmixing rule was used to determine the oil-phase viscosity.

lnllive oil X

Xi lnli; 9

where li is the the pseudoviscosity of component i.The dead-oil viscosity and the dissolved methane and propane

pseudoviscosities at 23.48C are provided in Fig. 6b. The dead-oilviscosity at this temperature was 18 775 mPas.

A rock compressibility of 2.9 106 kPa1 was used. Small gasbubbles were assigned a molar density of 41 gmol/std m3, asobtained from the ideal-gas law.

Solvent/Oil Mixing Process. Solvent and oil are mixed in a res-ervoir as a result of the combined effect of advection, diffusion,dispersion, and dissolution. Fluid flow by advection is the mainmechanism by which solvent is transported as gas and/or liquidinto the reservoir, especially through wormholes. Diffusion anddispersion allow it to spread within a phase and dissolution/exso-lution allows it to transfer between phases. In diffusion, the flowof a component toward regions of lower concentration in a fluidphase is represented by Ficks first law:

Jijk / Sj Dmij dCij=dk; 10

where Jijk is the flux of component i in phase j in the k direction(g mol/m2/d), / is the porosity, Sj is the saturation of phase j, Dmijis the molecular diffusion coefficient of component i in phase j(m2/d), and dCij/dk is the concentration gradient of component iin phase j in the k direction (g mol/m3/m).

In porous media, allowance is made for the increased flowlength caused by tortuous flow paths through the pore spaces. Asa result, the apparent diffusion coefficient (D) used for porousmedia is lower than the molecular diffusion coefficient and

D Dms; 11

where s 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 dis-rupt the streamlines. Mechanical dispersion in porous mediaarises from complex flow paths, which create mechanical mixingthat is independent of molecular diffusion. The mixing process iscaused by pore-to-pore difference, velocity gradients, and hetero-geneous flow paths.

Both longitudinal (in direction of flow) and lateral (orthogonalto flow) mixing are controlled by diffusion at low velocities andby advection at high velocities. Velocity variations parallel to themean flow direction are greater than those that are perpendicularto the main flow. Thus, longitudinal dispersion is greater thantransverse dispersion. At high velocities, Blackwell (1962)observed that longitudinal dispersion was approximately 24 timesthat of transverse dispersion.

Neuman (1990) examined over 130 longitudinal dispersivityvalues obtained from worldwide laboratory and field tracer studiesin porous and fractured media. The dispersivity values rangedfrom less than 1 mm to greater than 1 km for studies ranging fromless than 10 cm to greater than 100 km. For laminar-flow condi-tions in typical unconsolidated random packs, dispersion

. . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . .

. . . .

. . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

10

100

1000

10000

100000

0 0.2 0.4 0.6 0.8 1

Visc

osity

(mPa

s)

CH4 or C3H8 Mole Fraction in Oil

Oil-CH4

Oil-C3H8240

2

(a) (b)CH4C3H8

504540353025201510

50

K Va

lue

45.6

22.8

11.47.6 6.51.47 0.73 0.37 0.24 0.21

0 1000 2000 3000 4000Pressure (kPaa)

Fig. 6(a) K-values for C3H8 and CH4 at 23.4C and (b) live-oil viscosity.


correlations can be represented as (Perkins and Johnston 1963)the following:

Longitudinal Dispersion Correlation

KLDm

1F/

0:5r udpDm

;

whererudpDm

< 50

12

Transverse Dispersion Correlation

KTDm

1F/

0:0157r udpDm

;

whererudpDm

< 10413

where Dm is the molecular diffusion coefficient, dp is the particlediameter, u is the local fluid velocity, r is the inhomogeneity fac-tor, and F is the formation factor 1/(s/), which is also used forquantifying electrical conductivity through porous media.

In the simulations discussed in this paper, a gas phase diffu-sion coefficient of 0.0144 m2/d and an oil-phase diffusion coeffi-cient of 0.0000432 m2/d were used. The oil-phase diffusioncoefficient is typical for solvent diffusion in oil. Unless otherwisespecified, a mechanical dispersion coefficient of 0.005 m wasused for both the gas and oil phases. The diffusion and dispersioncoefficients were assumed to be independent of direction. Noallowance was made for the fact that diffusion and dispersioncoefficients are different for regions with wormholes because oftheir impact on tortuosity and flow patterns and velocities. Inaddition, time-dependent dispersive behaviour in the post-CHOPSreservoir was not incorporated into this study but was in laterstudies. Diffusion between gridblocks is not as significant for CSIas it would be for VAPEX because advection and dispersion aremore important. However, when looking within a gridblock, non-equilibrium behaviour is strongly dependent on diffusion andgridblock size. The impact of interblock and intrablock diffusionon simulation results will continue to be investigated.

The oil/water and oil/gas relative permeability values werebased on Stones Method 2 with gas-phase relative permeabilitiesbeing lower during production periods because of the impact oftrapped gas bubbles. The irreducible water saturation was 0.37,the critical gas saturation was 0.05, the water/oil residual-oil satu-ration was 0.29, and the gas/oil residual oil saturation was 0.15.The relative permeability values were based on previous historymatches at AITF. In reality, solvent injection can reduce the resid-ual-oil saturation because it progressively increases its mole frac-tion in the oil phase and reduces the oil-phase viscosity. In thesimulations, the residual oil saturation was not made dependenton the oil-phase mole fractions. However, low oil saturations canoccur near the well when the oil phase has a high solvent molefraction and the solvent vapourizes when the pressure is reducedduring production.

The complex interaction between nonequilibrium solvent solu-bility and the other fluid-transfer mechanisms (diffusion, disper-sion, and advection) in a reservoir model is represented in Fig. 7.In STARS, solvent dissolution and exsolution occur betweenphases in a single gridblock. Diffusion, dispersion, and advectionoccur within a single phase and between adjacent gridblocks. Dif-fusion is proportional to the concentration gradient, dispersion tothe concentration gradient and velocity, and advection to the pres-sure gradient. Solvent dissolution and exsolution rates depend onhow far the solvent mole fraction in the oil is from its equilibriumvalue. The latter is determined from its K-value at the particulartemperature and pressure.

Asphaltene deposition was not considered in the simulations,but will be represented in future work.

CSI Simulations for Post-CHOPS ReservoirCharacterization on the Basis of AITF CHOPSModel

For the effective-permeability model, 2D radial primary-produc-tion simulations were first performed for an actual Lloydminsterwell using AITFs sand-transport and foamy-oil models. The res-ervoir was divided into seven layers on the basis of the well-loginformation. The match for the effective-permeability model wasmade using the radial grid. For the CSI simulations, the post-CHOPS reservoir configuration was transformed from radial toCartesian geometry. This step allowed for future simulation ofmultiwell and horizontal-well CSI processes. The CSI simulationsdescribed here were performed using the Cartesian grid.

Initial Pre-CHOPS Reservoir Conditions and CHOPSSimulation. Some initial reservoir conditions were

Depth 431 m Pressure 3300 kPaa Temperature 23.4C Dead-oil viscosity 18 775 mPas Gas/oil ratio (GOR) 7 std m3/m3The GOR of 7 std m3/m3 was determined with the K-value cor-

relation that was used for methane. A GOR of 7 to 8 std m3/m3 istypical for a Lloydminster reservoir with the initial reservoir con-ditions specified previously. Other pre-CHOPS reservoir condi-tions are provided in Table 3 and Figs. 8a and 8b.

. . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . .

IntrablockGridblock n Gridblock n Gridblock n + 1

Gas PhaseGas Phase

Oil Phase xi, n

yi, n

,

Oil PhasePn, ci, n Pn+1, ci, n+1

Gas Phase

Interblock

Fig. 7Solubility and fluid-transport mechanisms.

TABLE 3PRE-CHOPS RESERVOIR PROPERTIES

Layer

Thickness

(m) So Sw /Permeability

(md)

7 (top) 0.15 0.15 0.85 0.06 0.0

6 0.38 0.55 0.45 0.14 11.1

5 1.14 0.66 0.34 0.25 290.7

4 0.99 0.79 0.21 0.31 1,563

3 0.53 0.70 0.30 0.26 277.1

2 0.23 0.37 0.63 0.20 40.8

1 (bottom) 0.15 0.0 1.0 0.05 0.02


A good match of oil and water production (Figs. 9a and 9b)and reasonable sand production values were obtained duringCHOPS. BHP data were not available, but the predicted valueswere typical of what one would expect during CHOPS. The post-CHOPS permeability, porosity, saturations, and oil-phase molefractions obtained from the history match were used as the initialconditions for simulating the solvent cycles. Wormholes wereformed in Layer 4, which had the highest pre-CHOPS permeability(1.563 darcies) and porosity (0.31). The wormholes propogated to175 m from the well. The increased permeability occurring fromthe creation of wormholes was represented by high-effective-per-meability values in the Layer 4 gridblocks (Fig. 10a). The oil satu-ration following CHOPS is provided in Fig. 10b.

CSI Simulation and Injection/Production Strategy. The 3DCartesian geometry (Figs. 11a and 11b) for the CSI simulationsmaintained the porosity, permeability, fluid saturations, mole frac-tions, pressure, and temperature profiles of the 2D post-CHOPSradial geometry.

CSI generally consisted of three injection/production cycles inwhich the production period was 180 days in each cycle and theinjection period was 30 days in Cycle 1, 40 days in Cycle 2, and

50 days in Cycle 3. The composition of the injected gas was 60%methane and 40% propane, and the specified gas-injection ratewas 20 000 std m3/d in each cycle until the injection pressure con-straint of 3.5 MPaa was reached and the injection rate declined.Standard (surface) conditions were 15C and 101.3 kPaa.

During production, the minimum BHP was specified as either150 or 200 kPaa, the maximum gas-production rate as 20 000 stdm3/d, and the maximum liquid rate at downhole conditions as 50m3/d.

Results for Effective-Permeability Simulations. Effect ofIncreasing Reaction Rate Constants. Rate constant parameterscontrol the rate at which the solvent dissolves in CSI simulations.Selected rate parameter values are judged by how significantly themole fraction of solvent in the oil phase deviates from theequilibrium solvent mole fraction, which is calculated from thepressure, temperature, and solvent mole fractions in the gas phase,and from the predicted production rates and compositions.Increasing the dissolution and exsolution reaction rate constants(frequency factors) by a factor of 100 enhanced solvent injectivityand decreased the BHP during an injection period. Oil productionwas marginally decreased by 2% from 3524 to 3453 m3 in the first

md 500 m

3.58

m

1.000.900.800.700.600.500.400.300.200.100.00

2,0001,8001,6001,4001,2001,0008006004002000

< 0.1 40

< 0.1 40

280

1,560290

(a) (b)

Fig. 8Pre-CHOPS (a) permeability and (b) oil saturation.

20,000

15,000

10,000

5,000

0Cum

ulat

ive O

il sur

face

con

ditio

ns(S

C) (m

3 )

Cum

ulat

ive O

il SC

(m3 )

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Time (Date)1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Time (Date)

Oil R

ate

SC [m

3 /day

]

Wat

er R

ate

SC [m

3 /day

]

2

4

6

8

10

12

1,000

2,000

3,000

4,000

0

30

20

10

0

(a) (b)

Fig. 9CHOPS (a) oil production and (b) water production using AITF cold-production model.

Oil Saturation

3.58

m

250 m(a) (b)

Permeability

3.58

m

> 10,000 darcies

250 m

md1.000.900.800.700.600.500.400.300.200.100.002.00e2

1.00e+62.00e+63.00e+64.00e+65.00e+66.00e+67.00e+68.00e+69.00e+61.00e+7

Fig. 10Post-CHOPS (a) effective permeability and (b) oil saturation (AITF radial model).

md

> 3,000 darcies 03006009001,2001,5001,8002,1002,4002,7003,000

0.000.100.200.300.400.500.600.700.800.901.00

(a) (b)

Fig. 11Pre-CSI Cartesian model with one-quarter symmetry (a) effective permeability and (b) oil saturation.


cycle but the net propane/oil ratio was dramatically increasedfrom 0.04 to 0.15 liquid m3/m3. This indicates that moving thesolvent dissolution/exsolution closer to an instant equilibrium sit-uation reduces oil production and increases solvent loss in thereservoir.

Effect of Gridblock SizeUpscaling. One-quarter symmetrywas used in all of the CSI simulations, and they covered one-quar-ter of the 400 400-m area in the centre of which the vertical CSIwell was located. A 36 36 7 Cartesian grid was used for mostof the simulations (Fig. 12). Some simulations were performedusing a 16 16 7 grid with coarser gridblocks (Fig. 12) in orderto accelerate simulation speed.

When all other parameters remained the same, the use of thecoarse grid resulted in a much quicker reduction in BHP duringproduction (Fig. 13a) and much lower oil rates (Fig. 13b). Adjust-ments of dissolution and exsolution rate parameters and disper-sion coefficients can be made to compensate for changes inpredicted oil and gas production caused by increasing the size of

gridblocks. Simulation run Upscale 1 in Table 4 had the samekinetic parameters and dispersion coefficients as the baseline fine-grid run, and the predicted oil production was an order of magni-tude less than for the fine grid. Changing the FF rate parameterssignificantly impacted predictions (Upscale 2 through Upscale 6),as did changing the dispersion coefficients (Upscale 7 andUpscale 8). For example, Upscale 6 had the rate constant for theexsolution reactions increased from 0.0005 to 0.05 day1 andresulted in a reasonable approximation to the fine-grid case interms of gas injection, oil production, and net solvent/oil ratio(Table 4 and Fig. 14). Alternatively, increasing the dispersioncoefficients from 0.005 to 0.1 m (Upscale 8) also resulted inresults similar to those of the fine-grid case. Decreasing the dis-persion coefficients to 0 m (Upscale 7) resulted in satisfactory oilproduction, but the net solvent/oil ratio was significantly lowerthan for the fine-grid case (Table 4). Fine tuning of the dispersioncoefficients and/or nonequilibrium parameters can result in a closematch for oil, water, and gas production between coarse- and fine-grid predictions.

It is apparent from Table 4 that for the coarse grid, decreasingboth the FF dissolution and FF exsolution from the 0.0005 valuein Upscale 1 to 0.00005 (Upscale 2) or increasing them to 0.05(Upscale 3) resulted in a dramatic increase in oil production.However, this is a complex problem with numerous interactingeffects. For example, increasing the FF for dissolution reactionsmeans more gas dissolves in the oil, which results in greater oil-viscosity reduction and potential for a greater solution gas driveduring production. However, a high FF dissolution value alsomeans that more of the solvent dissolves near the well and itseffect may be restricted to the near-wellbore region.

Increasing the FF for exsolution reactions increases the rate atwhich gas comes out of the solution. A low exsolution FF valuemeans the gas effectively stays in the solution and its effect isessentially oil-phase viscosity reduction, whereas a high

6.49 6.49 m

High-permeability High-permeabilityregion

Permeability:5,000 to 100,000 darcies

19.47 19.47 m

region

VWVW

1 m 19.47 m 1 m 6.49 m

36 36 7 16 16 70 100 200

0 100 200 0 100 200

0 100 200

Fig. 12Fine and coarse grids used in simulations.

4,000

3,000

2,000

1,000

00 200 400 600 0 200 400 600

Time (days) Time (days)

Wel

l BH

P (kP

a)

Cum

ulat

ive O

il SC

(m3 ) 3,000

2,000

1,000

0

(a) (b)Coarse grid

Coarse gridFine grid

Fine grid

Fig. 13Impact of grid size on (a) BHP and (b) oil production (one-quarter well basis).

TABLE 4EFFECT OF FFs AND DISPERSION COEFFICIENTS

Run Grid

FF

Dissolution

(m3/g mol/d)

FF

Exsolution

(day1)

Dispersion

Coefficient

(m)

CSI Cumulative Oil at End of

CycleFull Well Basis (m3)

Net Propane/Oil

(liquid m3/m3)

Cycle 1 Cycle 2 Cycle 3 Cycle 1 Cycle 2 Cycle 3

Baseline Fine 0.0005 0.0005 0.005 4,139 9,041 13,769 0.15 0.03 0.03

Upscale 1 Coarse 0.0005 0.0005 0.005 322 1,343 2,788 2.08 0.68 0.36

Upscale 2 Coarse 0.00005 0.00005 0.005 3,003 4,792 0.16 0.30

Upscale 3 Coarse 0.05 0.05 0.005 4,206 7,584 10,968 0.04 0.03 0.05

Upscale 4 Coarse 0.5 0.5 0.005 4,871 7,245 9,199 0.00 0.00 0.00

Upscale 5 Coarse 0.05 0.0005 0.005 4,059 7,982 11,651 0.05 0.04 0.04

Upscale 6 Coarse 0.0005 0.05 0.005 4,382 8,688 12,285 0.11 0.08 0.07

Upscale 7 Coarse 0.0005 0.0005 0 4,198 8,830 11,763 0.13 0.03 0.00

Upscale 8 Coarse 0.0005 0.0005 0.1 4,339 8,872 13,510 0.12 0.11 0.09


exsolution FF value means gas exsolves quickly and its effect isprimarily solution gas drive. It is intended to improve upscalingof gridblocks for CSI simulations.

Isothermal vs. Nonisothermal Simulations. Specific heat andthermal properties used in the nonisothermal simulations weretaken from the STARS manual. The temperature dependency ofthe rate constants was not introduced in these simulations becausethere are only small temperature changes in the process, whichare localized. Performing nonisothermal simulations resulted inincreased oil production in Cycle 1 by 17% (from 3524 to 4139m3, as shown in Table 5), but the net propane/oil ratio (propaneleft in reservoir oil produced) was unchanged at 0.15 at liquid m3/m3. Because of the heat of solution, the temperature rises duringsolvent dissolution in the oil and falls during solvent exsolution.During solvent dissolution, the rising temperature affects predic-tions because it causes a decrease in both the viscosity of the live-oil components (dead oil, dissolved methane, and dissolved pro-

pane) and the solubility of methane and propane. Allowing fornonisothermal behaviour typically slows down simulations; there-fore, this must be considered when deciding whether to use it.Unless stated otherwise, the simulations discussed in this paperwere conducted on the basis of nonisothermal conditions.

Equilibrium vs. Nonequilibrium Solubility. Ignoring none-quilibrium behaviour impacts predictions significantly. The use ofinstant equilibrium results in more-rapid gas exsolution when thepressure is decreased during production and low oil productionbecause of the severe reduction in the reservoir pressure and theelimination of bubble creation and foamy-oil drive. In addition,the initial high gas-flow rate negatively impacts oil production.When it was assumed that equilibrium solvent solubility wasobtained immediately during both gas dissolution and exsolution,lower injection pressures (Fig. 15a), greater gas injection (Fig.15b), lower oil production (Fig. 16a), and greater gas production(Fig. 16b) were obtained during CSI. Oil production was margin-ally greater in Cycle 1 for the instant equilibrium situation, butwas significantly lower in Cycles 2 and 3.

Because propane dissolved significantly more rapidly in oil forthe instant equilibrium solubility simulations, these simulationspredicted significantly lower reservoir pressures during an injectionperiod than did nonequilibrium solubility simulations (Fig. 17).This also resulted in reduced injection pressure similar to thatshown in Fig. 4. The 2D IJ profiles are shown for Layer 4 becausethis was the layer with a high-effective permeability, where Irefers to the gridblock number in the x-direction and J to the grid-block number in the y -irection. During a production period, the

4,000

3,000

2,000

1,000

0

Cum

ulat

ive O

il SC

(m3 )

0 200 400 600 800Time (days)

Fine gridUpscale 1Upscale 2Upscale 3Upscale 4Upscale 5Upscale 6Upscale 7Upscale 8

Fig. 14Upscaling simulation results of cumulative oil produc-tion (one-quarter well basis).

TABLE 5ISOTHERMAL VS. NONISOTHERMAL PREDICTIONS

Oil Production

(m3)

Net Propane/Oil

(liquid m3/m3)

Isothermal 3524 0.15

Nonisothermal 4139 0.15

4,000 600,000

500,000

400,000

300,000

200,000

100,000

0

3,000

2,000

1,000

0

Wel

l BH

P (kP

a)

0 200 400 600Time (days)

0 200 400 600Time (days)

Cum

ulat

ive G

as S

C (m

3 )

Nonequilibrium

Nonequilibrium

Equilibrium Equilibrium

(a) (b)

Fig. 15Impact of equilibrium vs. nonequilibrium on (a) BHP and (b) gas injection (one-quarter well basis).

4,000

3,000

2,000

1,000

0

Cum

ulat

ive O

il SC

(m3 )

0 200 400 600Time (days)

Cum

ulat

ive G

as S

C (m

3 ) 800,000

600,000

400,000

200,000

00 200 400 600Time (days)

Nonequilibrium

Nonequilibrium

Equilibrium

Equilibrium

(a) (b)

Fig. 16Impact of equilibrium vs. nonequilibrium on (a) oil and (b) gas production (one-quarter well basis).


pressure decreased more slowly in the instant equilibrium simula-tions as propane came rapidly out of solution and helped maintainthe reservoir pressure. In contrast, much of the propane was pro-duced in the oil phase in the nonequilibrium simulations.

Effect of Extra Exsolution Reaction. Foamy oil behaviourduring exsolution is represented in AITF CSI models by assumingthat each dissolved component first forms small gas bubbles dis-persed in the oil phase before entering the free gas. A refined paral-lel model allows for cases where rapid gas exsolution occurs. Anextra bypass reaction was added to the model to allow for gas tocome out of the solution and join the free gas phase without form-ing bubbles. This parallel bubble-destruction reaction can alsoremove small bubbles created during CHOPS before solvent injec-tion. Rate parameters for bubble-forming reactions were based onprevious modelling (including history matches) and examinationsof the predicted bubble oil phase mole fraction profiles.

Use of the reaction bypassing bubble formation resulted ingreater gas production in Cycle 1 because gas could come out ofsolution more quickly (Fig. 18a). Oil production over two cycleswas increased by 9% from 9041 to 9871 m3 (Fig. 18b) by incorpo-rating the extra gas exsolution reaction. Production of gaseous pro-pane (C3H8G) and dissolved propane (C3H8L) was also increased(Figs. 19a and 19b) when the extra reaction was used. Althoughthe new reaction (dissolved gas to free gas) competed with thebubble-forming reaction, it actually increased the amount of bothpropane and methane bubbles (Figs. 19c and 19d) produced at thewell as a result of its effect in increasing the pressure drawdownrate during production because of enhanced gas production. Itshould be noted that the bypass reaction (as shown in the followingreactions) was only included in the simulation discussed in thissection and not in any of the other simulations in this paper.

Bubble Destruction

Dissolved Gas

Small Bubbles

Free Gas

Dissolved Gas

Small Bubbles

Free Gas

Bubble Formation

Bubble Destruction

Bypa

ss

Alternative Post-CHOPS Characterizations

Reservoir Models Considered. Different CHOPS models havebeen developed in the last 20 years. AITF has 2D and 3D effec-tive-permeability-CHOPS models which incorporate both sand-transport and foamy-oil behaviour mechanisms. On the basis ofsand production, the models substantially increase the permeabil-ity in regions where wormholes are formed and these are repre-sented as high-effective-permeability regions. AITF can alsoconvert its effective-permeability model into a dual-permeabilitymodel where the fractures represent the wormhole structure. SRChas a CHOPS model that results in wormholes growing from thevertical well similar to spokes in a wheel. Other conceptual mod-els, including a dilated region around the vertical wells and spokeswith/without branches, were also considered in this study.

In addition to the AITF effective-permeability model dis-cussed in the preceding section, CSI performance was predictedusing the other post-CHOPS configurations in Fig. 5 after firstperforming primary-production simulations. The intent was toexamine how different post-CHOPS configurations would gener-ally affect CSI predicted behaviour. For the dual-permeability,dilated-zone, spokes, and spokes-and-branches models, the simu-lations started at the initial pre-CHOPS pressure, temperature, andsaturations with the pressure being drawn down before the initia-tion of CSI. Nonequilibrium solubility conditions were used formost of the effective-permeability-model simulations and for thespokes and spokes-and-branches simulations.

Dual-Permeability Model. A dual-permeability model (Fig.5b) was set up with fractures in Layer 4. The matrix representsthe intact reservoir, and the fractures represent the wormhole net-work. The fluid flow between the matrix and fracture representsthe fluid flow between the intact reservoir and the wormhole net-work. The fracture porosity (which is equal to fracture volume/block volume) selected was 0.0067 and the fracture permeabilitywas 10,000 darcies. The horizontal fractures were spaced 10 cmapart in the vertical direction. The initial properties (pressure,temperature, permeability, porosity, and saturations) in the reser-voir matrix were the same as the pre-CHOPS values that wereused to obtain the initial (post-CHOPS) reservoir for the effec-tive-permeability model. Primary production was modelled with-out sand production and instant equilibrium solubility behaviourwas assumed because simulation of nonequilibrium behaviourwas slow and unstable. Different tuning parameters can be used in

Equilibrium (30 days) Equilibrium (210 days)

Nonequilibrium (30 days) Nonequilibrium (210 days)

kPaa3,5003,1702,8402,5102,1801,8501,5201,190

860530200

Fig. 172D (IJ) pressure profile in high-permeability Layer 4 atend of first injection and production periods.

4e+5

3e+5

2e+5

1e+5

0e+0

Cum

ulat

ive G

as S

C (m

3 )

Cum

ulat

ive O

il SC

(m3 )

3,000

2,500

2,000

1,500

1,000

500

00 100 200 300 400 0 100 200 300 400Time (days) Time (days)

Extra reaction Extra reaction

No extra reaction

No extra reaction

(a) (b)

Fig. 18Impact of extra exsolution (bypass of bubble formation) reaction on (a) gas and (b) oil production (one-quarter wellbasis).


STARS for the fracture and matrix, but in this study the same tun-ing parameters were used.

Dilated-Zone Model. In the dilated zone model (Fig. 5c), itwas assumed that there was dilation in a region (153.28m 153.28 m 23 525 m2 on a full-well basis) around the wellcausing a high permeability (1,000 darcies) and that there was nowormhole formation. As a result of slow run times, an instantequilibrium model was used to represent CSI. The high-perme-ability region was in place at the beginning of primaryproduction.

Spokes Model. In the spokes model, it was assumed that worm-holes emanated from the well (Fig. 5d). This model was not theSRC model. The spokes were only in Layer 4 because it wasassumed that this was the only layer with high enough porosity andpermeability for wormholes to propogate during CHOPS. Thespokes were represented by 20-cm-diameter source/sink horizontalwells, whose length increased with time during primary produc-tion, as shown in Table 6. There was no dilated region around thespokes. They were assigned a minimum BHP during primary pro-duction and CSI production periods of 150 kPaa and a maximumBHP during CSI injection periods of 3500 kPaa. It was assumed

that if large-diameter wormholes did exist, then the pressure dropin them would be low and they could be represented by source/sinkhorizontal wells. Representation of the spokes as shut-in horizontalwells where backflow is permitted is an alternative approach thatmay be evaluated in the future. Also, the use of CMG s wellboremodel Flexwell to represent large wormholes may be an option.

Spokes-and-Branches Model. The spokes-and-branches modelincluded 20-cm-diameter spokes extending in Layer 4 from the CSIwell with offshoot 20-cm-diameter branches emanating from thespokes (Table 6 and Fig. 5e). The branches were orthogonal to thespokes from which they emanated. There was no dilated regionaround the spokes or branches. They were assigned a minimumBHP during primary production and CSI production periods of150 kPaa and a maximum BHP during CSI injection periods of3500 kPaa.

Results for Alternative Post-CHOPS Characterizations.Primary Production. Gas injection and oil, water, and gas pro-duction during primary production and CSI are summarized inTable 7 for the different models. As outlined previously, the

8e+4

6e+4

4e+4

2e+4

0e+0

6e+4

5e+4

4e+4

3e+4

1e+4

2e+4

0e+0Cum

ulat

ive G

as (C

3H8G

) SC

(m3 )

C 3H

8 Bu

bble

s SC

(m3 )

Cum

ulat

ive G

as (C

3H8L

) SC

(m3 )

Cum

ulat

ive G

as (S

BubC

H 4) S

C (m

3 )

0 100 200 300 400Time (days)

0 100 200 300 400Time (days) 0 100 200 300 400Time (days)

0 100 200 300 400Time (days)

0

200

400

600

250

200

150

100

50

0

Extra reactionExtra reaction

Extra reactionExtra reaction

No extra reaction

No extrareaction

No extrareaction

No extrareaction

(a) (b)

(c) (d)

Fig. 19Impact of extra exsolution (bypass of bubble formation) reaction on production of (a) C3H8G, (b) C3H8L, (c) SBubC3H8, and(d) SBubCH4 (one-quarter well basis).

TABLE 6LENGTH OF SPOKES AND BRANCHES VS. TIME

Time

(days)

Spoke 1

Length (m)

Spoke 2

Length (m)

Spoke 3

Length (m)

Branch 1

Length (m)

Branch 2

Length (m)

0 0.2 0.2 3.2 0 0

12 8.4 8.4 11.9 8.4 8.4

26 21.4 21.4 30.2 21.4 21.4

40 34.4 34.4 48.6 34.4 34.4

54 47.3 47.3 66.9 34.4 34.4

68 60.3 60.3 85.3 34.4 34.4

83 73.3 73.3 103.7 34.4 34.4

98 86.3 86.3 122.0 34.4 34.4

114 99.3 99.3 140.4 34.4 34.4

122 105.7 105.7 149.5 34.4 34.4

3,657 105.7 105.7 149.5 34.4 34.4


effective-permeability instant equilibrium and nonequilibriumCSI models started with the post-CHOPS reservoir characteriza-tion determined using AITFs CHOPS model. For the other mod-els, an attempt was made to match the specified primary-production oil rates.

For the dual-permeability model, the oil rate was specified tobe the actual field value for the first part of the primary-productionsimulations. The match of sand-production values was obtainedas part of the AITF CHOPS model from which the dual-perme-ability characterization was derived. The oil rate was averagedduring the latter half of the dual-permeability primary-productionsimulations in order that a constant rate would speed up the simu-lations. A match of the specified production was achieved.

The dilated-zone model resulted in a significant underpredic-tion of oil, water, and gas rates during primary production. Forboth the spokes and the spokes-and-branches models, the speci-fied oil production during primary production could not beachieved during primary production (Table 7 and Fig. 20), eventhough the oil rate was a well-control parameter. No sand-trans-port behaviour was represented in the model.

CSI Process. The greater contact area caused by wormholeformation resulted in high gas injectivity, particularly for theeffective-permeability models (Fig. 21). For these models, the oilrate was much higher during CSI than during primary production.

For the effective-permeability nonequilibrium simulations, thesolvent penetrated to near the edge of the reservoir although atlow concentrations (Figs. 22 and 23, note the log scale used inthese figures), whereas for the instant equilibrium version of thismodel, the propane moved vertically from Layer 4 but there wasmuch less lateral penetration beyond the high-permeability regionthan for the effective-permeability nonequilibrium model. Thiswas probably because the instant equilibrium solubility condi-tions, which were assumed in these latter simulations, allowedrapid dissolution of injected propane and therefore it remainedcloser to the well.

As compared with both the effective-permeability nonequili-brium and instant equilibrium models, the dual-permeability modelresulted in the same gas injection in Cycle 1, more gas injection inCycle 2, and less gas injection in Cycle 3. For the latter model, oilwas produced at a similar rate during CSI as it had been during pri-mary production (Fig. 20). Although the dual-permeability and effective-permeability models both matchedthe specified primary oil production, there was substantially less oilproduction for three CSI cycles for the dual-permeability model(1968 m3) as compared with the effective-permeability models (13769 m3 for nonequilibrium and 9988 m3 for instant equilibrium).

The high oil saturation (Fig. 24) in the fracture networkreduced gas injectivity in Cycle 3 of the dual-permeability simula-tion (Fig. 21). The oil saturation in the matrix adjoining the frac-tures was reduced to approximately 70% during primaryproduction and to approximately 60% during CSI. Although theoil saturations were significantly different in the matrix and frac-tures, their propane mole fractions in the oil phase were similar(Fig. 23). As for the effective-permeability instant equilibriummodel, the propane did move vertically from Layer 4 in the dual-permeability model, but there was much less penetration beyondthe high-permeability region.

Gas injection during CSI for the dilated-zone model was one-seventh of that obtained with the effective-permeability model,where instant equilibrium was also assumed (Fig. 21). As a result,

TABLE 7PRIMARY PRODUCTION (FULL-WELL BASIS)

FOR DIFFERENT MODELS

Model Oil (m3) Water (m3) Gas (std m3)

Effective permeability 15 677 3593 568 901

Dual permeability 14 606 228 929 036

Dilated zone 5460 33 35 708

Spokes 10 518 2946 99 874

Spokes and branches 6005 1512 79 580

0

1000

2000

3000

4000

5000

6000

7000

8000

0 500 1000 1500 2000 2500 3000 3500 4000Time (days)

Cum

ulat

ive O

il (m3

)

CHOPS Production CSICHOPS

Effective Pe

rmeability

Nonequilibriu

m

Spokes Nonequi

librium

Dilated zone Nonequilibrium

Spokes and Branches

Dual Perm Equ

ilibrium

EPE

qm.

Fig. 20Cumulative oil produced for different models (one-quarter well basis).

Cum

ulat

ive G

as In

jected

SC

(m3 )

6.E+05

5.E+05

4.E+05

3.E+05

2.E+05

1.E+05

0.E+050 500 1000 1500 2000 2500 3000 3500 4000

Time (days)

CSICHOPS

EPEqm.

SPokesDual Perm

Spokes and Branches

Dilated zone

EP nonequilibrium (NE)

Fig. 21Cumulative gas injected for different models (one-quarter well basis).

End Inj. 3

End Prod. 3

< 0.00001

< 1 105

< 1 105< 1 105

< 1 105

5

1.00e+04.64e12.15e11.00e14.64e22.15e21.00e24.64e32.15e31.00e34.64e42.15e41.00e44.64e52.15e51.00e5

(a) (b) (c)

Fig. 22Propane mole fraction in oil phase for (a) effective-permeability (nonequilibrium), (b) effective-permeability (equilibrium),and (c) dual-permeability (equilibrium) models.


oil production for the former model was approximately one-thirdof that obtained for the latter model (Fig. 20).

For the Spokes model, significantly lower rates were obtainedduring CSI as compared with the effective-permeability model.The oil rate was a little lower in the first CSI cycle than at the endof primary production, but it was significantly higher in Cycle 2(Fig. 20). As compared with the effective-permeability instantequilibrium model, propane dissolution in oil in the spokes modelwas limited to the region near the spokes (Figs. 23 and 25a), andthe extent of this region was actually increased during productionperiods.

Predicted oil production for two CSI cycles with the spokesmodel was only 3235 m3 as compared with 9041 m3 for twocycles with the effective-permeability nonequilibrium model. Oil-and gas-saturation changes were mainly limited to areas where thespokes were located (Figs. 26a and 26b). Oil flowed from theouter regions of the reservoir to the spokes in the high-permeabil-ity Layer 4. This flow occurred even during injection periods as a

result of the high pressure at the outer regions of the reservoir.Although not shown here, this oil flow from the outer regions to-ward a CSI well also occurred in effective-permeability simula-tions during injection periods until the outer reservoir pressure hadbeen somewhat depleted. In Layer 4, most of the pressure changesduring CSI injection and production periods occurred where thespokes were located (Fig. 27a). There was limited gas-phase flowfrom the outer regions toward the spokes (Fig. 26b) because meth-ane remained in solution when the pressure was still high. Therewas low gas saturation at the end of primary production and littlegas formation during CSI for the spokes model. Similar behaviourwas observed for the spokes-and-branches model.

For Cycle 1 with the spokes-and-branches model, the desiredgas-injection rate of 20 000 std m3/d (full-well basis) was main-tained for the entire 30-day injection period. Despite this, pre-dicted oil production for Cycle 1 was only 168 m3 as comparedwith 4139 and 630 m3, respectively, for the effective-permeabilityand spokes models. The branches were too close to the verticalwell and as a result had a negative rather than a positive impacton oil production.

The production-period pressure profiles for the spokes andspokes-and-branches models were similar except for the regionnear the branches, which had lower pressure values even duringthe injection period. There was little pressure buildup during theinjection period of the spokes-and-branches-model simulation.The low pressure values obtained during injection resulted in theminimal oil production during CSI. The pressure from Layer 4was transmitted vertically; thefore, a pressure profile for any layerwas similar to that for Layer 4 (Fig. 27b).

The net propane/oil ratio was considerably higher for thespokes and spokes-and-branches models as compared with theeffective-permeability predictions (Table 8). The branches in thespokes-and-branches model changed where the propane dissolvedas compared with when the spokes model was used (Fig. 25b).The propane did not penetrate as far along the middle spoke as itdid when there were no branches (spokes model). In the formercase, there was more propane dissolved nearer the vertical well. Ifbranches were farther from the vertical well and extended into oilsand that would not be contacted by the main spokes, thenincreased oil production could be expected although interferencebetween the spokes and branches could still occur.

Conclusions

Changing the FFs for gas exsolution and/or dissolution and/orchanging the dispersion-coefficient values is an effective

Spokes Model Dual-Permeability Model Effective-Permeability Model

End

Injec

tion 2

End

Prod

uctio

n 2

FractureMatrixEquilibriumNonequilibrium

FractureMatrixEquilibriumNonequilibrium

< 0.00001

< 0.00001

1.00e5

2.15e5

4.64e5

1.00e4

2.15e44.64e4

1.00e3

2.15e34.64e3

1.00e2

2.15e24.64e2

1.00e1

2.15e1

4.64e1

1.00e+0

Fig. 23Propane mole fraction in oil phase in CSI Cycle 2 for different CHOPS models.

FracturesMatrix

0 days

3,707 days, end of Injection Period 3

3,227 days, end of Primary Production

3,887 days, end of Production Period 3

1.000.950.900.850.800.750.700.650.600.550.500.450.400.350.300.250.200.150.100.050.00

Fig. 24Dual-permeability model. Oil saturation in Layer 4matrix and fractures.


upscaling strategy. As compared with fine gridblocks, the use ofcoarse gridblocks in effective-permeability-model simulationsresulted in a much quicker reduction in BHP during productionand much lower oil rates as a result of rapid reservoir depressuri-zation. One needs to adjust parameter(s) to compensate for thisbehaviour if using coarse gridblocks. Adjustments of dissolutionand exsolution rate parameters and of dispersion coefficients cancompensate for changes in predicted production caused byincreasing the size of gridblocks. For example, increasing thedispersion coefficients by a factor of 20 from 0.5 to 10 cmresulted in a good match of oil production (Upscale 1). Alterna-

tively, increasing the reaction rate constants for the exsolutionreactions by a factor of 100 produced a reasonable approxima-tion to the fine grid case in terms of oil production (Upscale 2).

Ignoring nonequilibrium behaviour impacts predictions signifi-cantly, especially for large gridblocks. The use of instant equi-librium results in rapid gas exsolution during production andlow oil production as a result of the severe reduction of the res-ervoir pressure and the elimination of foamy oil behaviour.

Increasing the reaction rate constants (FFs) from 0.0005 to 0.05increased propane injectivity and decreased the BHP during aninjection period. The impact on oil production was small(decreased by only 2%).

Gas exsolution rates were increased when an extra reaction wasused to bypass gas-bubble formation during gas exsolution. Itresulted in a 9% increase in oil production. The new reactionincreased the amount of free bubbles produced at the well becauseit increased the pressure drawdown rate during production.

As compared with isothermal simulations, nonisothermal simu-lations decreased the injection BHP and the amount of gasinjected and resulted in increased oil production by 17%.Allowing for nonisothermal behaviour typically slows downsimulations; therefore, this must be considered when decidingwhether to use it.

Although the dual-permeability and effective-permeabilitymodels both matched specified primary oil production, therewas an order of magnitude less oil production during CSI for

End Inj. 1

End Prod. 1< 0.00001

< 0.00001

< 0.00001< 0.000011.00e+04.64e12.15e11.00e14.64e22.15e21.00e24.64e32.15e31.00e34.64e42.15e41.00e44.64e52.15e51.00e5

(a) Spokes (b) Spokes and Branches

Fig. 25Propane mole fraction in oil phase for (a) spokes and(b) spokes-and-branches models.

End of Injection Period 1 End of Production Period 1




1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

1.00

0.95

0.90

0.85

0.80

0.75

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

(a) (b)

Fig. 26Spokes model (a) oil saturation and oil velocity vectors and (b) gas saturation and gas velocity vectors in high-permeabil-ity Layer 4.

3,227 days. End ofPrimary Production

3,257 days. End ofInjection Period 1

3,437 days. End ofProduction Period 1

kPaa37503375300026252250187515001125

750375

0

kPaa3,6003,2602,9202,5802,2401,9001,5601,220

880540200

(a) Spokes Spokes and Branches (b) Spokes Spokes and Branches

Fig. 27(a) Pressure in Layer 4 and (b) reservoir pressure for spokes and spokes-and-branches models.


the dual-permeability model using instant equilibrium solubilityconditions as compared with nonequilibrium or instant equilib-rium effective-permeability-model predictions.

For both the spokes and spokes-and-branches models, the pre-dicted primary oil production was significantly less than theactual field values, even though the oil rate was used as a wellcontrol. Significantly lower oil rates were also obtained duringCSI as compared with the effective-permeability model.

Predicted oil production for two CSI cycles with the spokesmodel was approximately one-third of that predicted by theeffective-permeability model.

Predicted oil production for one CSI cycle with the spokes-and-branches model was approximately one-seventh of that pre-dicted by the effective-permeability model.

The reservoir model chosen for CHOPS has a profound effect onCSI predictions. Therefore, a number of different post-CHOPSreservoir realizations can be used for CSI simulations in a post-CHOPS reservoir in order to evaluate the uncertainty involved.

A single cycle should not be used to estimate solvent recoverybecause it will be low from an unrecoverable (by pressurereduction alone) solvent inventory being built up in the part ofthe reservoir into which solvent penetrates. In later cycles, agreater percentage of the injected solvent is recovered becausethe total solvent retention at the end of each cycle increasesonly by a relatively small amount.

Oil recovery from CSI is mostly limited to high-permeabilityregions created during CHOPS.

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Jeannine Chang is a reservoir engineer at Devon CanadaCorporation. Before joining Devon, she was a senior researchscientist at AITF. In the past Chang also worked as an en-vironmental consultant focusing on environmental hydrogeol-ogy and petroleum contaminant remediation. Her work hasbeen focused primarily on primary and thermal heavy-oil

TABLE 8COMPARISON OF EFFECTIVE-PERMEABILITY-, DUAL-PERMEABILITY-, DILATED-

ZONE-, SPOKES-, AND SPOKES-AND-BRANCHES-MODEL PREDICTIONS FOR FIRST CYCLE

Eqm or NE

Oil

Production (m3)

Net Propane/Oil

(liquid m3/m3)

Effective permeability NE 4,139 0.15

Effective permeability Eqm 4,849 0.07

Dual permeability Eqm 656 0.82

Dilated zone Eqm 1,281 0.00

Spokes NE 630 1.14

Spokes and branches NE 168 4.91

Eqm instant equilibrium solubility.NENonequilibrium solubility.Net Propane/Oil Ratio (propane injected propane produced)/oil produced.


development, reservoir simulation, and laboratory experi-ments of enhanced-oil-recovery (EOR) technologies, includingcyclic injection processes (solvent, steam, steam/solvent, andsteam/air), steam-assisted gravity drainage, CHOPS, VAPEX,and steam additives.

John Ivory is the Heavy Oil and Oil Sands Subsurface PortfolioManager at AITF in the areas of EOR (primarily solvent, steam,

steam/solvent, and in-situ combustion processes) and leadsAITFs Reservoir Simulation Group. He has extensive expertise indesigning experiments, performing numerical simulations, andbeing involved in field pilots related to enhanced heavy-oil- andbitumen-recovery processes in clastic and carbonate reservoirs.Ivory also has investigated gas separation/purification usingmembranes, adsorption, and absorption technologies, and hasbeen involved in investigatingwaxdeposition in flowlines.


field scale simulation of cyclic solvent injection (csi)2

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