International Journal of Greenhouse Gas Control 16S (2013) S35–S49
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International Journal of Greenhouse Gas Control
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ffects of mechanical dispersion on CO2 storage in Weyburn CO2-EOReld—Numerical history match and prediction
afiz Uddin ∗, Alireza Jafari, Ernie Perkinslberta Innovates – Technology Futures, 250 Karl Clark Road, Edmonton, Alberta, Canada T6N 1E4
r t i c l e i n f o
rticle history:eceived 14 August 2012eceived in revised form 22 January 2013ccepted 4 February 2013vailable online 15 March 2013
eywords:O2-EORO2 storageechanistic modeling
a b s t r a c t
Modeling the long-term fate of CO2 in the Weyburn CO2-EOR field is a key component of the IEAGHG Weyburn-Midale CO2 Monitoring and Storage Project located in Saskatchewan, Canada. This studypresents a mechanistic modeling approach to model the fate of CO2 in the CO2-EOR field. It evaluatesthe kinetics and dispersion mechanisms in CO2 partitioning and distributions in an area 2.15 km2 of theWeyburn CO2-EOR field. Numerical history matches were conducted for the three production phases:primary, secondary water flood and tertiary CO2 flood. The history matching results validate the reliabil-ity and accuracy of our proposed mechanistic approach for modeling CO2 partitioning and distributionsin the Weyburn CO2-EOR field. Long-term predictions for the period from 2010 to 2070 were conductedfor several operating scenarios. The predictive simulations showed an additional recovery of 19% of the
ispersioninetics reactionumerical simulation
original oil-in-place over the course of a 60-year miscible flood. Total CO2 stored in the simulated area atthe end of 70 years is 2.48 million tonnes with 12%, 18% and 70% stored in the oleic, aqueous and gaseousphases, respectively. The numerical simulations systematically demonstrated that the mechanical disper-sion plays a critical role in the performances of CO2-EOR and subsequent CO2 geological storage. Severalrecommendations were given, primarily targeting mechanical dispersion in the CO2-EOR for improvinglonger term oil recovery and CO2 geological storage.
. Introduction
.1. Weyburn CO2-EOR field
The Weyburn field, one of the largest medium-gravity crude oilelds in Canada, was discovered in 1955. The field covers approxi-ately 180 km2 in the southeastern corner of Saskatchewan and
roduces oil from the Midale Beds of the Mississippian Charlesormation (Elsayed et al., 1993). The IEA GHG Weyburn CO2 Mon-toring and Storage Project was designed to study methods for
onitoring CO2 movement in the subsurface and to determine theecurity of storing CO2 in depleting oil reservoirs for hundreds tohousands of years. Whittaker (2005) provided an overall summaryf the IEA GHG Weyburn CO2 Monitoring and Storage Project inontext with a geological characterization of the Weyburn field foreological storage of CO2.
Cenovus, the current operator of the field, is carrying out its CO2
ood in a number of well-defined stages. Fig. 1a shows the approvedOR area and four miscible flooding strategies in the Weyburneld as approximately 2000. The entire EOR area was divided into
75 patterns. Fig. 1b shows the major geological features in the Wey-burn unit. The Midale Beds are capped by the tight Midale Evaporite,and can be divided into two major lithologic units: an upper MarlyZone and a lower Vuggy Zone. The Marly predominantly comprisesdolostone, and the Vuggy is largely made up of limestone; bothunits contain anhydrite and small amounts of silica and silicateminerals. The Marly Zone has an average permeability of less than10 mD and an average porosity of 26%. The Vuggy Zone is a highlyfractured limestone with widely varying porosity and permeabil-ity, but typically lower porosity and higher permeability than theMarly. The dominant NE–SW fracture trend in the Vuggy produceshigher permeability in that direction, and was a significant fac-tor considered in development of the field. In CO2-EOR, reservoirheterogeneity plays a vital role in long-term CO2 partitioning anddistribution.
1.2. Field production
Development began shortly after discovery with the drillingof 675 wells on 32 ha (80 acre) spacing. Based on the fracture
trend, the lines of wells are oriented NE–SW and NW–SE. Aftereight years of primary production, water flooding was initiated in1964 using inverted nine-spot patterns. In the 1980s, a numberof infill wells were drilled, which reversed the trend of declining
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Nomenclature
AITF Alberta Innovates – Technology FutureAm mineral reactive surface areaCMG Computer Modelling GroupCO2 carbon dioxideDT total dispersionDd molecular diffusion coefficientDij mechanical dispersion coefficientEOS equation of stateGEM advanced compositional and GHG reservoir simula-
torGOR gas oil ratioh capillary headHCPV hydrocarbon pore volumekm mineral reaction rate constantKm chemical equilibrium constant for mineral reactionkr relative permeabilitykrg relative permeability to gaskrw relative permeability to waterkrow relative permeability to oil in the presence of waterkrog relative permeability to oil in the presence of gask0 initial mean formation permeabilitykE effective permeabilitymD milli Darcy (permeability unit)MPa mega PascalMw molecular weightMVWAG Marly Vuggy water alternating gasPcr critical pressurePcow capillary pressurePVT pressure volume temperature relationship (used in
reference to phase behavior studies)Qm mineral activity productr reaction rateMm3 million cubic metersSC (STD) surface condition or standard condition
(15.6 ◦C and 101.3 kPa)SG specific gravitySGI straight gas injectionSg gas saturationSgc critical gas saturationSo oil saturationSorw residual oil saturationSw water saturationSwc connate waterSwcrit critical water saturationSSWG simultaneous but separate injection of water and
roduction, and in the 1990s a number of horizontal wells wererilled, which again resulted in significant incremental production.y 1999, there were 824 active wells in the field, of which 514
ere vertical producers, 142 were horizontal producers and 168ere vertical water injectors (Hancock, 1999). Cumulative produc-
ion was 52 million m3, approximately 24% of the 220 million m3 ofriginal oil-in-place. By this time, production was declining rapidly,
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which led to plans for implementing a tertiary recovery strategy,miscible CO2 injection. This new phase of development for thefield was planned to begin in the fall of 2000. The planned tertiaryrecovery development was based on extensive research and reser-voir simulation studies. The work led to adoption of four differentrecovery strategies: “simultaneous but separate injection of waterand gas” (SSWG), “Vuggy water-alternating gas” (VWAG), “Marly,Vuggy water-alternating-gas” (MVWAG), and “straight gas” (SGI),each suited to the unique circumstances of different parts of thefield. Several of these strategies utilize horizontal wells, and somecombined water/CO2 injection (Hancock, 1999).
1.3. Field numerical simulation
Numerical simulations for the above miscible flooding strategieswere previously conducted at the Alberta Innovates – TechnologyFutures (AITF) under Phase I of the IEA GHG Weyburn CO2 Mon-itoring and Storage Project. Three single-pattern, SSWG (Cuthielland Law, 2002a, 2003), VWAG (Cuthiell and Law, 2002b; Lawet al., 2003) and MVWAG (Uddin et al., 2003) strategies utilizinga nine-layer simulation grid, a 75-pattern CO2-EOR field (Law andUddin, 2004) and a simplified two-layer grid were developed. Thethree single patterns are representatives of three of four plannedoperating strategies and are the building blocks used to carryout simulations of the entire 75-pattern CO2 flooding and storageproject.
1.4. Objective
The objective of this study is to apply a mechanistic modelingapproach to accurately predict the long-term fate of CO2 during theCO2-EOR stage and the subsequent CO2 geological storage. The pro-posed model systematically evaluates the kinetics, diffusion andmechanical dispersion in CO2 partitioning and distributions in aselected SSWG area of 2.15 km2 in the Weyburn CO2-EOR field. TheCO2 dispersion was predicted using a Weyburn field representa-tive compositional model with brine chemistry and semi-reactivetransport options in a commercial simulator (CMG GEM).
In Sections 2–5 of this paper, we give the necessary descrip-tions of the study area, including the basic reservoir geologicalproperties and reservoir initial conditions, well development andinjection/production histories, Marly and Vuggy rock-fluid prop-erties, and compositional model. In particular, we emphasize thepossibility of handling mechanical dispersion phenomena in theCO2-EOR process. In Section 6, the numerical history matches ofthe primary and secondary water-flood and tertiary CO2-EOR pro-duction data are presented. Here we focus on incorporating thefield processes (such as roles of capillary, relative permeability,and dispersion) to achieve the matching. In Section 7, we presentnumerical predictions for several gas-oil-ratio (GOR) monitoringscenarios. The role of the mechanical dispersion on oil recoveryand CO2 inventory was explored. The prediction results can be agood guideline to design field operation.
The history-matching simulations were given a broader contextby examining the sensitivity of the results to reservoir hetero-geneity and mechanical dispersion coefficients. This is especiallyrelevant for future modeling of CO2-EOR processes as it beginsto explore a more mechanistic approach to CO2 partitioning anddistributions in oil reservoirs. Here, we describe the mechani-cal dispersion modeling approaches and numerical simulations
with several possible field-dispersion parameters. Some commentson time-dependent mixing processes are provided, emphasizingtheir possible utility in longer time field production which mightimprove oil recovery.
M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49 S37
F ana CS Resea
2
amoSituocmalaCc
2
osst
ig. 1. Weyburn oil reservoir (a) CO2-EOR field showing simulation area (source: EnC., Weyburn-Midale CO2 Storage and Monitoring Project, PTRC, UK CCSC Academic
. Study area – SSWG pattern
The study area is carefully chosen (based on geology, productionnd CO2-injection activities that were reported in early Weyburniscible flood simulation studies, 1992–1996, carried out by the
perator) to conduct the detailed mechanistic modeling for theSWG CO2-EOR flooding strategy. It employs horizontal Marly CO2njectors that are parallel to and within 50 m of the line-drive ver-ical water injectors. The main goal was to preferentially flood thepper Marly Zone, which contains significantly higher remainingil-in-place. The patterns selected for this operating strategy wereharacterized by a Vuggy shoal environment that demonstrated theaximum historical off-trend fluid rate greater than 400 m3/day
nd Marly pay greater than three meters thick. Considering thearge volume of CO2 injected over the 2000–2010 period, this isn attractive area to study the effect of mechanical dispersion onO2 distribution and subsequently storage. The dispersion modelan then be applied to the entire Weyburn CO2-EOR field.
.1. Reservoir properties
Three geological models representing three areas were built in
rder to evaluate the CO2 project. The areas were: Area 1, repre-enting mixed Vuggy rock, both shoal and intershoal; Area 2, repre-enting intershoal rock types; and Area 3, representing shoal rockypes. The Area 1, 2, and 3 geological models applied similar grid
orporation), and (b) major geological layers in Marly and Vuggy (source: Whittaker,rch Strategy Meeting, July 7th, 2010).
geometries. Each grid block has areal dimensions of 50 m × 50 m.There are three Marly layers and six Vuggy layers in each model.The thickness of each layer was defined by geological mapping.The Area 1 model covers about six patterns (approximately1100 m × 1100 m), using 23,000 grid blocks, while both Area 2 and3 models cover nine patterns giving a total of 3800 grid blocks permodel.
The geological model has been developed by the operator usingLandmark Graphics Corporation’s Stratamodel®. It contains a totalof 12 flow units, nine of which represent the Midale Beds, tworepresent non-reservoir units above the Midale (Evaporite andThree-Fingers), and one represents an underlying unit (Frobisher).The top two of the Midale flow units comprise the Marly, and thebottom seven, the Vuggy Zone of the reservoir. The nine Midaleflow units were subdivided into a total of 87 layers in the geologi-cal model. Using wireline logs, and extensive core-analysis data, thegeological model incorporated porosity, permeability, and watersaturation values for each block. There are significant variationsin porosity and permeability data from layer to layer. Eade (1994)plotted the available porosity and permeability data and showedvery different regression lines for Marly and Vuggy layers.
Cuthiell and Law (2003) extracted simulation grid for this SSWG
pattern together with a half pattern buffer from the above geolog-ical model. Their grid setup was used for our base-case simulation.This is a nine-layer simulation grid (a single simulation layer pergeological flow unit). The grid properties processed are porosity,
S38 M. Uddin et al. / International Journal of Green
Fts
pgfemnaOfa
tba
From 1994 to 2002, five wells in the pattern and eight wells
TR
ig. 2. Simulation area (a) plain view of Marly (M1) top layer showing porosity dis-ribution and well perforation at the end of year 2010, and (b) section view (NE–SW)howing simulation layers.
ermeability, saturation and hydrocarbon pore volume. When aeological unit is discontinuous, a thin layer (0.1 m thick) is addedor continuity. The grid properties of these thin layers were thenliminated using the simulator’s pinch-out option. The simulationodels behaved as if these thin layers did not exist. It should be
oted that some of the flow units are characterized by consider-ble variation of properties in each of the separate geological layers.ur current approach underestimates the progress of injected fluid
ronts within a flow unit (there is a tradeoff between simulationccuracy and computing time).
Fig. 2 shows the simulation grid blocks for the SSWG pattern
ogether with a half pattern buffer. In this grid setup, the num-er of blocks in the i- and j-directions is 32 and 42, respectively,nd there are nine layers in the k-direction. The total number of
able 1eservoir initial conditions – basic layer properties, and fluid in place.
Layers Areal permeability (mD) Porosity
Marly
M1 5.333 0.216
M3 7.373 0.254
Vuggy
V1 2.241 0.127
V1a 0.0 0.068
V1d 0.0 0.080
V2 6.166 0.106
V3 1.552 0.073
V4 6.958 0.090
V5 2.478 0.086
a Gas – dissolved gas in oil and water (an estimated solution gas-oil-ratio is 35 m3/m3,
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active grid blocks is 9110, and of pinch-out blocks is 2986. Thestudy area’s total bulk reservoir volume, pore volume and hydrocar-bon pore volume are 110.173, 14.610 and 7.916 Mm3, respectively.Fig. 2 shows the grid blocks’ porosity distribution for the Marlytop layer (M1) and one vertical section (NE–SW). Table 1 presentsthe average porosity and permeability distributions for the Marlyand Vuggy layers, and also shows the reservoir initial pressure,temperature, and fluid-in-place.
One of the key objectives of this proposed mechanistic approachis to capture the changes in porosity and permeability caused byseveral hydrodynamic and geochemical processes in long-termCO2-EOR. In an earlier history matching study, Cuthiell and Law(2003) applied a local permeability modifier around some targetedwells to match the history of the field production data. Follow-ing up this earlier work, all localized permeability modifiers wereremoved and fully coupled fluid flow-dispersion processes used.In these simulations, grid porosity variations caused by the injec-tion/production pressures and mineral dissolution-precipitationprocesses were calculated in each time step. Subsequently, thechange in absolute permeability was continuously updated usingthe Carman–Kozeny function. The simulation showed that theinfluence of geochemical processes (i.e., mineral dissolution andprecipitation) on porosity/permeability was very small and was notnoticeable in the overall simulation results.
2.2. Well development
Numerical history matches were conducted for all three produc-tion phases: primary, secondary water flood, and tertiary CO2-EOR.The well-development and production/injection status in the studyarea were changed as the field development progressed. Fig. 2 high-lights some of the wells in the Marly layer at the end of year 2009.Table 2 shows the progressive well-development, injection andproduction status in this study area.
The primary production was mainly carried out during1956–1964. There were nine vertical producers in the pattern andanother 11 producers in the surrounding buffer zone. The produc-tivities for several wells were quite low and these wells were shutin early and later reopened as injection wells.
Between 1964 and 1994, eight wells in the pattern and ten wellsin the buffer zone, mainly vertical infill wells, were drilled. The newwells comprised five producers (two vertical and three horizontal)and three vertical water injectors in the pattern, and five producers(three vertical and three horizontal) and five water injectors in thebuffer. The field history shows a rapid drop of oil production forseveral of the infill producers and some were shut in and reopenedfor used as injection wells.
in the buffer were added. Among these new wells in the pattern,two are horizontal producers, two are vertical water injectors andone is a horizontal gas injector. Among the eight new wells in the
Water saturation Average conditions
Marly fluid in place (Mm3)0.419 Oil = 3.9760.419 Water = 2.268
Gas = 140.923a
Vuggy fluid in place (Mm3)0.630 Oil = 3.2400.631 Water = 4.1950.631 Gas = 116.118a
0.630 Other data0.627 Pore volume = 14.610 (Mm3)0.625 Pressure = 1460 kPa0.630 Temperature = 63 ◦C
STD).
M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49 S39
Table 2Well development and injection-production summary in the simulated area.
Year Well Cumulative injection (Mm3, SC) Cumulative production (Mm3, SC)
uffer, there are three horizontal producers and five horizontal gasnjectors. The well data for the central injector show that the totalolume of CO2 injected was 277.271 Mm3, STD for 3379 days ofperating time. A significant amount of CO2 was also injected inhe buffer zone.
From 2002 to 2009, 11 wells in the pattern and 24 wells in theuffer were added. Among these new wells in the pattern, thereere five horizontal producers, three vertical water injectors and
hree vertical gas injectors. Among the 24 new wells in the buffer,here were 18 producers mostly horizontal, three vertical waternjectors and three vertical gas injectors. Here, we noticed waterlternating gas (WAG) injection activities in several vertical wells.
Several common factors that contribute to overall productivitynclude reservoir conditions (reservoir pressure, fluid distributionnd gas breakthrough), locations of individual well perforations,nd well operating conditions. These factors were considered inndividual well history matching.
. Rock-fluid properties for Marly and Vuggy
.1. Capillary pressure
In this section, initial base rock-fluid properties for Marly anduggy layers are discussed. They include oil-water capillary pres-ure curves, relative permeability curves for water-oil and oil-gasystems. By tuning the initial rock-fluid properties, a final set ofurves was obtained via extensive numerical history matching forhe entire production phase (1956–2010).
From a special core analysis report provided by Cenovus (Hunt,979), we obtained air-brine capillary pressures for seven core sam-les from Marly layers and nine core samples from Vuggy layers.he core air-brine capillary pressure curves showed a significantariation. Based on these data, capillary curves for the Marly anduggy layers were chosen for the numerical simulations. Fig. 3ahows these capillary curves. Here we employ an adaptation of vanenuchten’s (1980) expression to describe water-oil capillary head,
, as,
= 1
˛{S(−1/m)w − 1}(1−m)
Fig. 3. Two sets of rock-fluid data used for the Marly and Vuggy layers, (a) ca
3.673 13.660 643.430b
where Sw = (Sw − Swcrit)/(1 − Sorw − Swcrit) is the normalized watersaturation, and m are curve shape parameters. Here, Swcrit andSorw are the critical water saturation and residual oil saturation,respectively. The chosen curve parameters for the Marly layers are:
= 15 cm−1, m = 0.77, Swcrit = 0.25; and for the Vuggy layers are: = 119 cm−1, m = 0.50, Swcrit = 0.55. The role of the capillary data
in simulation results was not fully explored. The air-brine capil-lary curves were not converted into oil-brine capillary curves. Weassumed that replacing non-wetting phase “air” with another non-wetting phase “oil” would not significantly impact on the overallsimulation results. This is an area for future studies, especially withrespect to the role of fractures on relative phase flows.
3.2. Relative permeability
The rock-fluid property is one of the most uncertain parametersin the reservoir simulation. We have examined several historic lab-oratory core data from different geological formations and chosenrelative permeability data for the Weyburn reservoir.
The water-oil relative permeability data for several Weyburn-Midale cores were plotted and fitted with standard forms ofrelative permeability functions. Fig. 3b shows these curves. Here,we employ the Parker et al. (1987) expressions (their Equations 31and 33) to describe the wetting phase water, krw, and non-wettingphase oil, krow, relative permeability curves for the Weyburn-Midale cores. These curves can be expressed as,
krw = krwro{S0.5w [1 − (1 − S
1/mw )
m]2}
krow = krocw{S0.5o [1 − (1 − So)
1/m]2m
}
where krwro and krocw are the permeability end points, Sw = (Sw −Swcrit)/(1 − Sorw − Swcrit) is the normalized water saturation, So =(1 − Sw) is the normalized oil saturation, and and m are curve
shape parameters. There are significant variations in the measuredlaboratory relative permeability data. Based on the field well logdata, one could envision using different curves within the Marlyand Vuggy layers for varying sedimentary types (i.e., silt, clay and
pillary pressure curves and (b) water-oil relative permeability curves.
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Table 3Basic oil properties based on the PVT model.
and layers). We have not done so due to lack of relative perme-bility core data. Our model utilizes only two sets of curves, one forhe Marly layers and one for the Vuggy layers (Fig. 3b). However,e have made some effort to estimate van Genuchten parame-
er, in particular shape parameter “m”. We then performed historyatching sensitivity studies by varying this parameter “m” within
hese limits. Some further discussions on water-oil and gas-oil rel-tive permeability curves are given in Section 6, Numerical Historyatching, of this paper.
. Mechanistic modeling setup
Our earlier simulation studies treated the Weyburn oil intoeven-component oil, one of the components being CO2. Table 3ummarizes the basic properties of the oil components. It waseveloped based on oil samples collected from the reservoir,ith PVT model parameters tuned to match oil density, viscos-
ty, GOR, and saturation pressure. Details of the model and itsevelopment are given in a separate report (Zhao et al., 2002). Theeng–Robinson equation of state (EOS) has been used for the PVTodel development, which makes its inclusion in our simulations
ery straightforward. The fluid model was augmented for our pur-oses by including solubility of CO2 in the water phase (Cuthiellnd Law, 2002a). In the PVT package we used Computer Modellingroup’s WinProp® 2002; CO2 solubility in water was described byenry’s law. We used the default parameters supplied by WinProp®
002, with point checks against published solubilities.The distributions of CO2 into oil, water and gas phases are
ictated by the PVT, brine and formation rock compositions. Weecognize that several mechanisms are needed to model in ordero fully capture the dynamics of CO2 partitioning and distribu-ion in the long-term CO2-EOR. One of the key mechanisms isispersion. To model this dispersion in the Weyburn CO2-EOR,
new compositional model was set up using oil with sevenomponents, brine with ten major species, and formation miner-logy with three dominate minerals as presented in the followingection.
.1. Reservoir water and rock compositions
Based on the overall understanding of the Weyburn field,e have chosen the following three aqueous equilibrium
eactions ((R-1)–(R-3)) and three rate-dependent mineral dissolu-ion/precipitation reactions: ((R-4)–(R-6)).
where dolomite, calcite and kaolinite are the minerals and all otherspecies are dissolved in the aqueous phase. Other minerals andaqueous species are also present in the reservoir but the first fivereactions were chosen as they play a critical role in CO2 distribu-tion. The clay mineral kaolinite in reaction (R-6) has a very smallrole in this CO2 EOR. The reservoir contains several types of clayminerals. In our generalized compositional model, we have chosenone clay member, kaolinite, as being representative.
The reactions ((R-1)–(R-3)) are homogeneous reactions thatinvolve only components in the aqueous phase. The reactions ((R-4)–(R-6)) are heterogeneous reactions that involve mineral speciesand aqueous species. Normally, a mineral reacts only with aqueousspecies and not with other minerals. Also, the reactions betweencomponents in the aqueous phase are fast relative to mineral dis-solution/precipitation reactions. Therefore intra-aqueous reactionsare represented as chemical-equilibrium reactions whereas min-eral dissolutions/precipitations are represented as rate-dependentreactions. The rate of the mineral dissolution and precipitation isdefined as,
r = Amkm
(1 − Qm
Km
)
where Am is the mineral reactive surface area, km is the mineralreaction rate constant, Km is the chemical equilibrium constant formineral reaction, and Qm is the mineral activity product. The activ-ity product, Qm is analogous to the activity product for aqueouschemical equilibrium reactions. More details can be found in CMG’sAdvanced Compositional and GHG Reservoir Simulator, GEM 2010.
Based on the PVT and EOS model, we obtained a sta-ble initial reservoir condition (i.e., condition at the beginningof the primary production at year 1956) by considering 10major species in water and three dominate formation miner-als. Knauss et al. (2005) described how to obtain steady-statecondition to expose the reservoir rock with very slowly mov-ing reservoir fluids. Here, we employed their approach withsome simplistic assumptions. Table 4 summarizes water and rockcompositions. We made an initial guess on water species andminerals based on CMG GEM templates for carbonate reser-voirs. Our present compositional model is restricted to tenspecies and three minerals. The steady state concentrationsfor the aqueous components are within the variation reportedby Hutcheon et al. (2004). The observed concentration rangesare as follows: Ca2+, 654.7–2536 mg/L; Mg2+, 315.3–460 mg/L;
Na+, 14,050–44,930 mg/L; and Cl−, 21,500–74,200 mg/L; alkalinityranged from 490.6 to 2423 mg/L; and the surface pH, from 5.30 to6.81. The variation of the minerals are: calcite, 0.7–89.9%; dolomite,5.0–72.7%; and kaolinite, 0.0–0.9%.
M. Uddin et al. / International Journal of Green
Table 4Initial formation water chemistry and mineral properties – background geochemicaltemplate for dispersion modeling.
In CO2-EOR, dispersion is the mixing and dissipation of misciblelug (CO2-diluted oil) caused by molecular diffusion (concentrationradients) and mechanical dispersion (local flow velocity gradientsnd reservoir heterogeneity).
CO2 in the oil phase diffuses from an area of greater concentra-ion toward an area of lower concentration. The mass flux of CO2 in aeservoir can be described using Fick’s law as: FCO2 = �Dd∇c, wherec is the concentration gradient, Dd is the molecular diffusion coef-cient, and � is the tortuosity (i.e., CO2 ion must follow longerathways as they travel around mineral grains). For CO2-EOR,here CO2 concentrations are changing during field development
such as injection and production histories), the changes in CO2oncentration can be defined as: �c/�t = ∇(FCO2 ).
In the mechanical dispersion process, CO2 in the oil, gas andater phases is continuously mixing caused by local flow-velocity
ariations in the reservoir. This results in a dilution of the CO2 athe advancing edge of flow. The most important variables influenc-ng this dispersion in the Weyburn CO2-EOR field are geologicalormations, magnitudes and spatial distributions of fractures (inarticular in the Vuggy Zone) and matrix porosity. Since thisechanical process depends upon the local velocity vector, a total
f nine dispersion components are needed to predict the CO2 plumen the field. In this study, the dispersion components are defined asollows (Bear, 1979): Dij = (˛L − ˛T )uiuj/�S|u| + ıij˛T |u|/�S, whereubscripts i and j indicate three principal flow directions. Here, ıij
s the Kronecker delta function (ı = 1 when i = j, ı = 0 when i /= j),L and ˛T are the longitudinal (parallel to the principal directionf flow) and transverse (perpendicular to the principal direction ofow) mechanical dispersion coefficients, � is the porosity, and S ishe saturation, ui and uj are the components of the velocity vectoru| in the i- and j-directions.
The molecular diffusion and mechanical dispersion are interde-endent processes in flowing reservoir fluids. In the simulations,he total dispersion coefficient is predicted for every time step byombing these two processes (DT = �Ddıij + Dij). The diffusion terms relatively small whereas the mechanical dispersion can vary by aew orders of magnitude depending on the flow velocity and flowength. In the Weyburn CO2-EOR field, both processes are expectedo play a vital role on CO2 distribution and subsequently CO2 storageuring long-term CO2-EOR process. The values of three dispersion
arameters (Dd, ˛L, ˛T ) are critical in history matching the fieldO2 plumes. The appropriate diffusion and dispersion parametersor the Weyburn CO2-EOR simulation are discussed in the followingections.
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5.1. Molecular diffusion
Values of molecular diffusion coefficients are well known andrange from 1 × 10−5 to 2 × 10−5 cm2/s at 25 ◦C. They do notvary much with concentration, but they are somewhat temper-ature dependent, being about 50% less at 5 ◦C (Robinson andStokes, 1965). The Stokes–Einstein equation relates the diffusioncoefficients with temperature and viscosity. Some diffusion corre-lations for solvent dispersion in oil reservoirs can be found in theliterature (Okazawa, 2007; Nghiem et al., 2001). In this study, dif-fusions in the gas and oil phases were calculated by the Sigmundmethod (Sigmund, 1976). Diffusion value in the aqueous phase wasassumed to be 2.0 × 10−5 cm2/s.
5.2. Mechanical dispersion
In an effort to choose reliable mechanical dispersion coefficientsin our CO2-EOR simulations, we systematically evaluated thefollowing related literature data. Perkins and Johnson (1963) pre-sented a comprehensive review of dispersion and diffusion in apermeable medium. They compiled dispersion data measured onunconsolidated sand packs and beads. Perkins and Johnson con-cluded that diffusion is the controlling mechanism at low rates,Peclet number (ratio between advection and dispersion processes),Pe < 0.02, and advection controls the displacement at high rates,Pe > 10. In groundwater, many laboratory and field tests have beenconducted to determine the mechanical dispersion coefficients(dispersivity values). Three excellent summaries of such tests aregiven by Lallemand-Barres and Peaudecerf (1978), Pickens andGrisak (1981) and Gelhar et al. (1985), in a report for the ElectricPower Research Institute (EPRI). In conclusion, the value of dis-persivity in a given formation varies with the distance over whichthe measurement is made (or scale effect). These results have gen-erated considerable discussion in the groundwater literature andalso have cast doubt about the validity of the advection–dispersionequation to model fluid transport through a permeable medium.Neuman (1990) presented comprehensive interpretations of howthe observed dispersivity values increase with scale. An importantfinding of these test results is that dispersivity values in the field aregenerally larger than laboratory ones by several orders of magni-tude. Based on these interpretations, different sets of dispersivityvalues for varying geological characteristics of Marly and Vuggylayers could be used.
In this study, a nine-layer single porosity simulation model wasused to simulate a 2.15 km2 study area with an areal grid spacing of50 m × 50 m. The grid blocks’ properties (such as porosity and per-meability) were defined using numerical history matching of thefield production records. The field scale dispersion (macrodisper-sion) at this scale was simulated using a longitudinal dispersivity,˛L, of ten meters and a transverse dispersivity, ˛T, of two meters.The dispersivity values can change during CO2 flooding due to othermixing processes in the field (Dagan, 1988; Lake, 1989; Neumanand Zhang, 1990). Three of the time-dependent mixing processesare flux-induced, dispersive, and capacitive. The time-dependentbehavior of the dispersion process is beyond the scope of this study;it remains to be evaluated in future long-term CO2-EOR studies ofthe Weyburn field.
6. Numerical history matching
This section presents the numerical history matches using our
new compositional model as discussed in the earlier sections.Numerical history matches were conducted for the entire threeproduction phases: primary (1956–1964), secondary water flood(1964–2000) and tertiary CO2 flood (2000–2010). One of the key
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42 M. Uddin et al. / International Journal of
bjectives of this numerical history matching is to emphasizeertiary CO2 flood period (2001–2010). All simulations were per-ormed both with and without dispersion options.
.1. History matching strategy
Our history match approach employs a two-stage parameter-election strategy in which model parameters were first separatednto two classes, one of which was made up of parameters tuneds part of the history matching process, and the other of param-ters fixed by reasonable estimates. Thereafter sensitivities to thessumed values of this second class of parameters were exploredeparately. The first set of “tuned parameters” was chosen as mostrocess-specific and hence most unknown with respect to CO2 EORrocess. The second class of “fixed parameters” had a variabilityssociated with geological uncertainty but are not specific to theOR process itself and hence it was felt could be assessed indepen-ently.
.1.1. History matching tuned parametersThe class one history matching parameters in the present mod-
ling approach are:
Global permeability modifiersRelative permeability tuning
In traditional reservoir history matching, one must satisfybserved total fluid production and the relative flow of the mobilehases. These are characterized by absolute permeability and rela-ive permeability adjustments, respectively.
.1.1.1. Absolute permeability. Global changes in permeability cane taken to be constant, by multiplying or adding to the origi-al matrix permeabilities. The multiplication option has the virtuef retaining more of the permeability heterogeneity present inhe matrix values (heterogeneity gets washed out if a large addi-ive permeability change is made). In some cases, permeabilityas changed anisotropically; changes were different in the on-
rend horizontal, off-trend horizontal and vertical directions. Thisoo is justified physically by the orientation of natural fracturesn the reservoir. The simulator uses a dimensionless transmissi-ility multiplier (transmissibility = permeability × area/length) toffect such changes. For the present, we refined old permeabilityodifiers used by Cuthiell and Law (2003). We removed all the
arlier local permeability modifiers around the wells with someustifications. We recognize that, for history matching individual
ells, it can be necessary to increase permeability in the imme-iate vicinity of the well to achieve target injection/production.he localized modification in permeability is justified physicallyn some field cases, such as induced fracturing or enhanced exist-ng fractures. However, it is important to note that the localizedermeability enhancement to match short-term production dataan override some other competing reservoir processes. One muste careful not to over-ride the fundamental longer term mecha-isms by focusing entirely on localized phenomena in this historyatch.
.1.1.2. Relative permeability. Relative permeability tuning is aommon modeling approach for numerical history matching of the
eld production data. This occurs even when basic relative per-eability curves are determined by laboratory tests of rock-core
amples. The relative permeability tuning is one of the major tasksf this work.
house Gas Control 16S (2013) S35–S49
6.1.2. History matching fixed parametersThe values of the history matching fixed parameters kept con-
stant during the history matching simulations are:
• Initial oil saturation distributions• Initial water composition and salinity• Molecular diffusion and mechanical dispersion
Our earlier numerical studies suggest that the above param-eters can affect the overall oil production, fluid partitioning anddistributions (Uddin et al., 2003; Law and Uddin, 2004).
6.2. History matching results
A successful history matching was achieved by tuning the globalpermeability modifiers to match total fluid production, and bytuning the relative permeability end points to match oil, water andgas productions. The history matching simulations were also givena broader context by examining the sensitivity of the results tothe dispersion parameters. The key accomplishment of this studyis that the CO2 dispersion processes in miscible flooding are nowfully coupled with fluid flow processes in the reservoir. The mech-anistic simulation captures the initial CO2 miscible flood responsefairly well. The rapidly increasing oil production after year 2000is evident in both the field data and the simulation. The com-parisons between the field data and the numerical simulationsfor oil, water, and gas productions are presented in the followingsections.
6.2.1. Well performanceBy 2010, a total of 86 wells were drilled in the study area.
These wells can be categorized into four groups of producers: pri-mary vertical, infill vertical and infill horizontal, and two groups ofinjectors: vertical and horizontal. The field development (such aswell drilling, well perforation, production/injection histories) andnumerical simulations were presented in our earlier report (Uddinet al., 2011). The report was prepared for the Petroleum TechnologyResearch Centre (PTRC) on behalf of the IEA GHG Weyburn-MidaleCO2 Monitoring and Storage Project.
The production history match simulations in this studyinclude two primary vertical wells, one infill vertical welland one infill horizontal well. All four producers are locatedin the central SSWG pattern and have a relatively longerproduction history then other wells in this study area. Theeffect of mechanical dispersion was highlighted against thenumerical simulations with no mechanical dispersion option.The numerical history matches for the two primary verticalproducers (P1 and P2) are shown in Fig. 4. The locations of these twoproducers in the reservoir are also shown in Fig. 4. Here, the cen-tral producer, P1, only operated as a producer during the primaryproduction phase, then switched to water injector for the waterflood and CO2-EOR stages. The numerical results show an excellentagreement with the field data. The effect of mechanical dispersionon oil production is not noticeable over the primary product phase(1956–64). This can be explained as there are no injection activi-ties during the primary production phase, and there is no significantcompositional change in the reservoir fluids. The corner producer,P2, operated during the entire secondary water-flooding and thetertiary CO2-flooding phases. Again, the P2 history match is verygood, except during the final four to five years of the water-flood(1996–2000) and the tertiary CO2-flood (2000–2010) phases, whenthe simulation under-predicts oil production. Here, history match
improvement is clearly noticeable with allowing the mechanicaldispersion.
The numerical history matches for two infill producers (P3 andP4) are shown in Fig. 5. The locations of these two producers
M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49 S43
Fig. 4. Oil production history matches for two primary producers and the effect ofmechanical dispersion, P1 – vertical producer located at the centre of the SSWGpattern and P2 – vertical producer located at the corner of the SSWG pattern. P1h
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Fig. 5. Oil-production history matches for two infill producers and the effect of
acceptable pressure contours in the reservoir. Here, we recog-nize the field complexities and some uncertainties associated with
istory match plot at the top shows the locations of P1 and P2 in the reservoir.
n the reservoir are also shown in Fig. 5. The infill vertical pro-ucer, P3, and infill horizontal producer, P4, both operated overhe secondary and the tertiary phases. The history match plotsor P3 and P4 show a very good agreement between the disper-ion simulations and field production data. The plots show that theeviation between two separate simulations (i.e., simulations withnd without mechanical dispersion options) increases as CO2 floodontinues. The field data show that most of the production duringhe CO2 flood was from horizontal wells, and the simulation showshat horizontal wells had faster, earlier production. Generally, indi-idual well matches are not as good as the overall field matchescribed in the next section, but most are reasonably good. Theseatches are very dependent on the detailed displacement path of
he injected water and CO2. It is also quite likely that the simulationas not captured some of the details of the reservoir heterogeneity,hich will strongly affect the water and CO2 floods sweep. In theresent single porosity model, we have imposed uniform perme-bility enhancement to represent fracturing, whereas, in reality,racture distribution is certainly not uniform in the reservoir. Ineneral, CO2 displacement is an unstable process, affected signifi-antly by both viscous instability and by reservoir heterogeneity; its not unexpected that breakthrough (which was observed during
ong-term prediction) in the simulation would be later than in theeld.
mechanical dispersion, P3 – infill vertical producer and P4 – infill horizontal pro-ducer. P3 history match plot at the top shows the locations of P3 and P4 in thereservoir.
6.2.2. Field temporal performanceAn initial goal of this mechanistic simulation was to match the
overall mass balance properties of this study area including the ini-tial oil-in-place and the total injected and produced fluids from thearea (as summarized in Table 2). The simulated oil-, water- andgas-production histories in comparison with the actual field pro-duction data are shown in Figs. 6 and 7. The numerical simulationswith dispersion show a very good agreement with the field oil andgas productions. The role of dispersion on water production simu-lation is not noticeable. The results clearly demonstrate that the oiland gas production during the tertiary CO2-flooding phase can besignificantly changed with dispersion.
We also evaluated the simulated field pressures and gas-oil-ratio (GOR). These results are shown in Fig. 7b. The newsimulations show a higher average field pressure during the sec-ondary water-flooding period. It should be noted that there weresignificant variations in the very limited field pressure data. Again,the simulated spatial and temporal pressures vary with severalfield variables such as PVT data, initial oil saturation distribu-tion and dispersion. Considering uncertainties associated with thefield variables, we concluded that the numerical model simulated
several important model parameters such as initial oil saturationdistribution, PVT data in particular initial oil compositions, brine
S44 M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49
Fig. 6. Field-production history matches and the effect of mechanical dispersion:(
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Fig. 7. Field production history matches and the effect of mechanical dispersion:
a) oil rate and cumulative oil, and (b) water rate and cumulative water.
hemistry, and dispersion. The history matching results were sub-equently tuned with grid-block size, PVT data, initial oil moleraction, initial oil saturation distribution and dispersion. Again,onsidering the uncertainties associated with field variables, weoncluded that our proposed mechanistic model obtained success-ul history matches of the production data and effectively capturedhe spatial fluid distributions.
.2.3. Field spatial performanceThe effect of mechanical dispersion on the spatial distributions
f CO2 component in oil, water and gas phases has been systemat-cally studied. Fig. 8 illustrates the effect of mechanical dispersionn the spatial distributions of CO2 global mole fractions. The resultshow how dispersion can alter extension of CO2 distribution pat-ern and could be partly due to numerical dispersion. It shoulde noted here that, unlike an analytical solution, the numericalolution is not exact. Numerical dispersion arises because of theruncation of the Taylor series used to generate finite differencepproximation. This artificial dispersion can cause smearing of theO2 plume in a continuously varying flow field. In this paper, CO2ispersion is assumed to be physical dispersion caused by the dif-usion and mechanical dispersion processes. A sensitivity study
as conducted on two physical dispersion components (molecu-
ar diffusion and mechanical dispersion) against several numericalolution methods in the present CMG GEM simulator. The contribu-ion of numerical truncation error or artificial numerical dispersion
(a) gas rate and cumulative gas, and (b) gas-oil-ratio (GOR) and an average reservoirpressure.
was found to be much smaller than total physical dispersion. Thenumerical dispersion in the solution was assumed to be negligi-ble. It should be noted that the molecular diffusion component isa much smaller quantity compared with the mechanical disper-sion component. One could suspect that the diffusion componentwithout mechanical dispersion may be overridden by the numeri-cal truncation error or so called numerical dispersion if numericalsolution criteria are loosely defined.
Several 4D seismic images for the Marly Zone throughout Wey-burn’s 19 patterns, including this study area, after one year of CO2flooding were reported in earlier studies (Uddin et al., 2003, seetheir Figure 40). The seismic data collected show a spatial distribu-tion clearly attributable to movement of CO2 through the reservoir.Both the reported seismic and the present simulation images showprogress of the CO2 front with more fingering for some injec-tors than others. There are clear similarities with the simulatorimages of global CO2 (mole fraction) shown in layers 2 (M3) and6 (V2). However, a thorough quantitative comparison of seismicimages and reservoir simulation results is not entirely straightfor-ward, since the seismic data do not directly measure the quantitiesused in reservoir simulations such as saturations, compositions andpressures. Rather, a typical seismic image reflects the sonic prop-erties of the reservoir, which in turn depend in some complex way
on the typical simulator parameters. This is one of the reasons wehave developed this new composition model (brine chemistry andsemi-reactive transport options).
M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49 S45
f CO2
6
pr
•
•
Fig. 8. The effect of mechanical dispersion on the spatial distributions o
.3. History matching refinement
In addition to the current history matched model, severalarameters associated with dispersion and fluid flow should beefined along with the field development.
Dispersion parameters: Within the scope of this study, the simu-lated CO2 plume was not compared with any field observations.It should be noted that some limited 4D seismic data have beenreported and can be utilized to refine the dispersion parame-ters. By modifying the longitudinal and transverse mechanicaldispersion coefficients, the shape as well as concentration of pre-dicted CO2 plume can be matched with the observed CO2 plume.In addition, a further refinement of the simulation grids shouldbe considered to capture the steep CO2 concentration front.Rock-fluid parameters: A good history match of the field produc-tion through the primary, water flooding, and ten years of CO2flooding was obtained. To achieve this match, the permeabili-ties in the geological model had to be increased by significantamounts. These increases are justified by extensive fracturingin the reservoir. An alternative, which has not been pursuedhere as it is not within the scope of this work, would be toutilize a dual-porosity model; however, in practical terms, thepermeability-enhanced, single-porosity model is adequate tocapture the reservoir behavior with respect to oil, gas and water.Two sets of rock-fluid data were used, one for the Marly layers
and one for the Vuggy layers. The capillary-pressure and relativepermeability curves are significantly different. In future studies,different curves for the shoal and intershoal sub-zones of theVuggy could be used.
global mole fraction at the end of years (a) 2004, (b) 2006, and (c) 2008.
7. Numerical prediction
Numerical predictions were made for several gas oil ratio(GOR) production scenarios (GOR of 2000, 4000, 6000 and 8000at standard conditions). The effect of mechanical dispersion on thesimulated oil recovery, CO2 distributions, and brine compositionswere thoroughly evaluated. In evaluating the forward-modeledscenarios, we conducted a series of numerical simulations by vary-ing maximum GOR constraints from 1000 to 60,000. In thesescenarios, the producer productivity was checked every 30 daysand the well was shut-in at the maximum target GOR and reopenedautomatically if the simulated GOR was less than the target value.The simulation results showed a very good prediction performance(i.e., higher oil recovery) when maximum GOR between 4000 and8000. We observed some gas breakthrough in several producers atGOR of 8000 (Uddin et al., 2011).
The predictive simulations were carried out for a period of 60years beginning with the end of the history match and ending in2070. Gas and water injection rates were chosen in the simulationto ensure that reservoir pressure remained above the MMP, whichis about 18 MPa. Field development was assumed to be consistentwith current development, though it is clearly impossible in such asimulation to anticipate the fine-tuning of reservoir managementin the future. Under this condition, total CO2 injected in the studyarea of 2.15 km2 up to year 2010 (end of history match) and 2070(end of 60 years prediction) were 2.02 × 106 and 9.41 × 106 tonnes,respectively. Total CO2 produced in gas and oil phases at year 2010
and 2070 are 1.4 × 105 and 6.94 × 106 tonnes, respectively. Table 5summarizes the injected CO2, the stored CO2, and the producedCO2 over the length of 70 years of miscible flooding. The simulatedresults principally depend on the geological characteristics of the
S46 M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49
Table 5Predicted CO2 inventory and recovery status in the Weyburn CO2-EOR.
Year (year) CO2 injection (tonnes) CO2 production (tonnes) CO2 storage (tonnes) Oil recovery (%)
eservoir and the CO2-EOR process. Table 5 shows that 51% of theotal injected CO2 (2.02 × 106 tonnes) was produced by the end of010. This substantially increased to 74% of the total injected CO26.94 × 106 tonnes) over the following 60 years. The total storedO2 reaches 49% of the injected CO2 by end of 2010 and is 26% ofhe injected CO2 by 2070. The produced CO2 in oil, water and gashases is assumed to be re-injected and the fraction of the injectedO2 retained in the reservoir is assumed to be stored. The relatively
ow amount of storage in the simulation can be attributed to lack ofontrol of excessive gas production. The prediction results indicatehat the simulated area is not very effectively swept by CO2. This is
artly due to reservoir heterogeneity, which causes channeling ofhe gas, and partly due to lack of management of high GOR wells.
In Table 5, oil recovery status is given over the lengthf a 70-year miscible flood. This predictive simulation shows
Fig. 9. Numerical predictions, (a) oil production, (b) water and CO2 inje
307,419 435,594 1,738,000 72.528
additional recovery of 19% of the original oil-in-place over thecourse of a 60-year miscible flood. More active management ofthe flood, particularly controlling high GOR producers to improveareal sweep of the pattern, could substantially improve thisperformance.
By 2070, a total of 2.48 × 106 tonnes of CO2 has been stored in thepattern, despite significant gas production from some of the pro-ducers. Of this total, 3.1 × 105 tonnes (13%) is stored in the oil phase,1.74 × 106 tonnes (70%) in the gas phase, and 4.4 × 105 tonnes (18%)in the water phase. Less than 0.5% of the stored CO2 is presentin the mineral phase. Based on the predictive simulations, both
oil recovery and CO2 storage could be enhanced by better indi-vidual well control which results in better reservoir sweep duringthe simultaneous gas and water injection. Earlier simulation stud-ies showed that CO2 storage can be increased by shortening or
ction and production, (c) oil recovery status, and (d) CO2 storage.
M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49 S47
super
eppuaaau
fiFa6cofltreettct2hoiC
Fig. 10. The overall field CO2 storage inventory: (a)
liminating water injection. This would be a departure from thelanned SSWG strategy and would impact the economics of therocess. It is inevitable that considerable tuning to manage individ-al patterns to achieve a target of CO2 recycle or to promote betterreal sweep will occur. This is not straightforward using a simulatornd is beyond the scope of this study. In these simulations, limiteddjustments to well rates to reduce excessive recycling have beenndertaken.
Fig. 9 illustrates the long-term prediction trends for the overalleld production and CO2 storage with the maximum GOR of 6000.ig. 9a and b shows the effect of dispersion on the oil productionsnd the overall field recovery. The oil recovery at the end of the0-year prediction is little over 72% when allowing the mechani-al dispersion. The CO2 flood yields an additional recovery of 19%f the original oil-in-place over the course of a 60-year miscibleood. Fig. 9c and d shows the cumulative water and CO2 produc-ion/injection status and CO2 storage inventory. The simulationesults demonstrated that the mechanical dispersion significantlynhanced the oil recovery and CO2 distribution in the CO2-EOR. Theffect of the mechanical dispersion on the overall field CO2 inven-ory is shown in Fig. 10. The plots show the effect of dispersion onhe CO2 distributions in oil and aqueous phases is relatively higherompared with CO2 in supercritical and mineral phases. The spa-ial distributions of CO2 global mole fraction at the end of years010, 2020, 2030 and 2040 are shown in Fig. 11. Our simulations
ave demonstrated that the effect of mechanical dispersion is vitaln CO2 partitioning, distribution and storage over the long termn the Weyburn CO2-EOR field. The dispersion process spreads theO2 more widely in the reservoir. As a result, this leads to increase
-critical, (b) dissolved, (c) aqueous, and (d) mineral.
of CO2 storage in both EOR and saline storage, and maximizes oilrecovery in EOR.
The dispersion sensitivity simulations showed a higher oilrecovery at a higher mechanical dispersion. Mechanical dispersioncan be enhanced through setting well spacing, operating condi-tions such as, controlling reservoir pressure to maintain miscibility,constraining producers’ bottom hole pressure (BHP) to minimizeeffects of loss of miscibility near the wells, and controlling CO2 recy-cle to promote better areal sweep. These can be optimized througha detailed simulation studies.
7.1. Prediction refinement
• The numerical prediction of the current pattern, simultaneousbut separate injection of water and gas pattern (SSWG), shouldfurther be evaluated against water flooding (WF), and wateralternating gas injection (WAG) scenarios. WF and WAG can betargeted only for the vertical wells.
• The prediction scenarios should be evaluated against morecontrolled operating conditions. The most viable operating con-ditions can be identified as gas injectors can be shut in at targetedhydro-carbon pore volume (HCPV) (for example, 70%, 80%, 90%)and the water-injection rates altered.
• In future forward modeling, production and injection rates canbe monthly updated based on the simulated pressure and fluid-
distribution contours. Predicting scenarios should also determinethe well shut in and reopen strategies, and potential new welldevelopments should be considered and discussed with the fieldoperators.
S48 M. Uddin et al. / International Journal of Greenhouse Gas Control 16S (2013) S35–S49
on at t
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8
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Fig. 11. The spatial distributions of CO2 global mole fracti
Here, our recommendations are primarily targeted towardmproving mechanical dispersion and CO2 mixing strategies fornhancing longer term oil recovery. More systematic numericalnd experimental studies need to be carried out on how to enhanceiffusion and mechanical dispersion and how to control the timevolution fundamental mixing processes (such as flux, disper-ive and capacitive induced mechanisms) in long-term CO2-EORrocess.
Several key variables such as the Peclet (ratio between advectionnd dispersion processes) and the Capillary (ratio between viscousnd capillary forces) Numbers should be considered in field model-ng to retain the fluid-flow driver and, at the same time, to enhancehe fundamental CO2 partitioning, mixing and distribution mecha-isms (i.e., solubility, diffusion and dispersion). One can manipulatehese variables in a given reservoir by selecting well-operatingressures, and production and injection rates. In addition, the hand-
ing of the Peclet and the Capillary Numbers can have importanteld consequences, in mobilizing the trapped oil saturation whenhe oil-in-place in the reservoir has declined significantly. In theseases, enhanced or hybrid production schemes such as the injectionf CO2 with other solvents may need to be considered.
. Conclusions
This paper forms the basis for mechanistic modeling of CO2 par-itioning and distributions of a longer term CO2-EOR productionn the Weyburn field. The study has systematically demonstratedhat field dispersion plays a critical role in spatial distributions ofO2 in CO2-EOR and subsequent CO2 geological storage. The CO2ispersion was modeled using a Weyburn field representative com-ositional model with brine chemistry and semi-reactive transportptions in a commercial simulator (CMG GEM). The general formf the compositional setup contains oil with seven componentsone of the components is CO2), brine with ten major species, for-
ation mineralogy with three dominant minerals, three aqueousquilibrium reactions, and three rate-dependent mineral dissolu-
ion/precipitation reactions. The history matching results validatehe reliability and accuracy our proposed mechanistic approachor accurately modeling CO2 partitioning and distributions in the
eyburn CO2-EOR field.
he end of years (a) 2010, (b) 2020, (c) 2030, and (d) 2040.
This study presents long-term predictions over the period fromyear 2010 to 2070 for several gas-oil-ratio (GOR) productionmonitoring scenarios. The overall field oil production and spatialdistributions of CO2 can significantly be enhanced by the mechani-cal dispersion process. The numerical predictions showed higher oilrecovery at GOR of 6000 (m3/m3, STP). Based on this scenario, 72%of the oil will be recovered at the end of 2070. Total CO2 injectedin the study area up to year 2010 (end of the history match) and2070 (end of 60 years prediction) were 1156 and 5036 Mm3, STD,respectively. 51% of the total injected CO2 (2.02 × 106 tonnes) bythe end of 2010 was recovered (i.e., produced in oil, water and gasphases) which substantially increased to 74% of the total injectedCO2 (6.94 × 106 tonnes) over the length of the 60-year predictionperiod. The remaining fractions of the injected CO2 were retainedin the reservoir and assumed to be stored. Total CO2 stored in thesimulated area at the end of 60 years is 2.48 million tonnes with12%, 18% and 70% stored in the oleic, aqueous and gaseous phases,respectively. Predicted amounts of mineral dissolution in the sim-ulated area are 8200 tonnes of calcite, 1200 tonnes of dolomite andtwo tonnes of kaolinite. The total CO2 change due to mineral dis-solution is approximately 4200 tonnes at 2070, and is less than 1%of the total CO2 stored in oleic, aqueous and gaseous phases.
The proposed mechanistic modeling approach should be con-tinued for other single-pattern simulation (Marly and Vuggy wateralternating gas, MVWAG, pattern; Vuggy water alternating gas,VWAG, pattern), and finally for the 75-pattern simulation. For oilrecovery, our recommendations are primarily targeted to improv-ing mechanical dispersion and CO2 mixing strategies for enhancinglonger term oil recovery. More systematic numerical and experi-mental studies need to be carried out on how to enhance diffusionand mechanical dispersion and how to control the time evolutionfundamental mixing processes (such as flux, dispersive and capac-itive induced mechanisms) in long-term CO2-EOR process.
Acknowledgements
The authors acknowledge the funding support from thePetroleum Technology Research Centre (PTRC), and appreciate theassistance of Cenovus staff for their support in collecting field dataand some suggestions and comments on numerical simulation. We
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hank J. Ivory and S. Talman of AITF, Ben Rostron from U of A,nd Steve Whittaker from PTRC, for their inspiring discussions anduggestions for the final stage of this modeling work.
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