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International Journal of Enhanced Research Publications

Phase Behavior and Interfacial Properties of CO2-Saturated Malaysian Crude Oil

Author / Co-Author Names: Seyedeh Hosna Talebian 1, Isa Mohd Tan1, Mustafa Onur 2, Rahim Masoudi 3, Saeed Majidaie1

1 UniversitiTeknologi PETRONAS, Perak, Malaysia

2 Istanbul Technical University, Istanbul, Turkey

3 PETRONAS Nasional Berhad, Kulala Lumpur, Malaysia

Abstract: The fluid phase behavior properties of a shallow Malaysian reservoir, operating under low pressure (1800 psia) and high temperature (102 °C), are investigated to examine the efficiency of CO2 injection. Properties of CO2- oil mixtures, in terms of viscosity, swelling and interfacial tension are measured to provide insights into multi-phase flow and gas solubility in the oil reservoir.Experiments were performed to determine pressure-volume-temperature (PVT) properties of CO2-saturated oil mixtures. The changes in the mixtures’ density and viscosity with the increase in carbon dioxide concentration were also investigated to define the contribution of swelling and viscosity reduction mechanisms in an immiscible CO2 injection. Results of PVT tests were then used to tune an EOS model in order to predict the mixture behavior at different conditions. An axisymmetric drop shape analysis (ADSA) has been utilized to investigate the effect of pressure on the interfacial tension behavior of CO2-oil mixture at the reservoir temperature. The minimum miscibility pressure (MMP) of CO2/crude oil system was also determined by applying the vanishing interfacial tension (VIT) technique as well as slim-tube simulation model in a compositional simulator. The results showed that even at the immiscible condition, a CO2 injection process can benefit from gas solubility into the oil interface, which is followed by IFT reduction, oil viscosity reduction, and swelling mechanisms in order to mobilize the trapped oil.

Keywords: Malaysian Crude Oil, PVT analysis, Interfacial tension, Tuned EOS, Miscibility pressure.

Introduction

As a displacing agent used in EOR, CO2 is applied to obtain incremental oil. It is used in two ways; forming miscible interaction with oil (displacement efficiency), and/or reaching the un-swept parts of the reservoir (sweep efficiency). As a partner with oil, CO2 can increase oil displacement by reducing the IFT that it originally possesses with oil. With an IFT value approaching zero, CO2 forms a single homogeneous phase with the crude oil. CO2 dissolves in oil and at the same time, oil components transfer into the CO2-rich phase, and the resulting mixture can displace oil more efficiently in the CO2 swept zone (Schramm and Wassmuth, 1985). The success of a miscible CO2 flooding however, depends on reservoir temperature, pressure, and crude oil composition. The pressure at which miscibility with oil occurs is the minimum miscibility pressure (MMP). Such is the benefit of using CO2, even non-miscible CO2 injection is still economically attractive due to the multiple contacts and interfacial mass transfer between oil and CO2, which can lead to oil viscosity reduction and swelling (Srivastava, 2010; Farajzadeh et al., 2012). While swelling, by enhancing oil relative permeability, helps to reduce the effective residual oil saturation, viscosity reduction by CO2 helps to improve the mobility ratio and hence volumetric conformance (Zhang et al., 2010).

In this paper, phase behavior between CO2 and a Malaysian crude oil (MCO) system is reported. Density and viscosity of the CO2 spiked MCO are measured by conducting constant composition tests, and high pressure and high temperature viscosity and density measurements at different CO2 saturations. The bubble point pressure range of both recombined oil and dead oil samples are determined at different concentrations of CO2 saturation, to analyze the effect of light components on PVT properties estimations. Tuning of the equation of state (EOS) based on PVT properties was performed to predict mixture properties at different conditions and to export to the compositional simulator for further studies.

The VIT technique, which is based on the measurement of equilibrium IFT between crude oil and CO2 as the pressure increases to a point that the equilibrium interfacial tension between the two phases approaches zero, was employed to determine the miscible condition for the CO2-MCO system. The VIT technique can be performed in a shorter time compared to the slim tube experiments. Measuring IFTs at four to six different pressures is adequate to determine the MMP in this method. However, to ensure the validity of the data, a wider pressure range and repeated measurements of the IFT at a single pressure point for more than 3 times are required. Compositional 1-D slim-tube simulation is also performed to compare the experimentally estimated MMP with the simulation predicted result.

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Materials and Methodology

Dead oil samples for this study were collected from a Malaysian field which is currently operated under immiscible CO 2

injection. The properties of the stock tank oil (STO) are shown in Table 1. Complete set of phase behavior studies has been performed in Zain et al. (2001) work. However, no PVT data are available for the dead oil sample. The PVT data will be used later for chemical-EOR coreflood displacement studies, contact angle measurements, and interfacial tension tests.

In this work, PVT properties of dead oil is measured and reported. Moreover, the dead oil, pure CO 2 and C1 are physically recombined using a recombination cell to mimic the reservoir fluid live-oil. This will provide an understanding on the role of the light components on the measured bubble points of the mixtures. General properties of dead oil, and recombined-oil are given in Table 2.

Table 1: Stock tank oil properties.Temperature (˚C) 15

API Gravity 37.5˚Density at 14.69 psi (g/cc) 0.8358

Specific Gravity 0.836Asphaltenes (mass %) 0.027

Molecular weight (g/mol) 189.85Pour Point (˚C) 33

Table 2: Compositions of dead, recombined, and well-stream oil samples.Componen

tMW(g/mol) Composition (mole

%)Dead-Oil Live-Oil

CO2 44.01 0 15.047N2 28.01 0 0C1 16.04 0 13.773C2 30.07 0 0C3 44.09 0 0iC4 58.12 0 0nC4 58.12 0 0iC5 72.15 0 0nC5 72.15 0.004 0.003C6 86.17 1.864 1.327C7 100.2 7.713 5.490C8 114.2 5.997 4.269C9 128.3 3.675 2.619C10 142.3 4.679 3.330C11+ 213.35 76.068 54

TOTAL 143.97 100 100

A. Laboratory Fluid Analysis

Anton PaarDMA4500M digital densitometer was used to measure dead oil density (ρod ¿ at atmospheric pressure and temperature range of 40 to 85 °C, before mixing with CO2. A HTHP viscometer was used to determine the dead oil viscosity (µod ¿ at the target temperature of 102 °C as a function of pressure.

Both swelling and constant composition expansion (CCE) tests are performed using the MCO fluid samples, by employing HTHP Fluid-Eval PVT instrument from Vincci Technologies. A known amount of oil sample (50 cc) was charged into the visual PVT cell at reservoir temperature of 102 °C. CO2 of known amount was added to the fluid, step-wise from 20 to 80 mole % of the fluid sample. At each step of CO2 addition, equilibrium state of the mixture was assured by rocking and stirring the cell, until the PVT cell pressure was stabilized at the set value, which is a pressure higher than the initial reservoir pressure. The pressure was then reduced at constant temperature by removing mercury from the cell, and the routine CCE test was conducted in the cell as described by Ahmed (2007). The procedure is repeated at each step of CO2 addition, and the total hydrocarbon volume (V t) as a function of cell pressure obtained. The bubble-point pressure was observed and the corresponding volume recorded as a reference volume (V sat). The relative volume (V rel) curve, which is the ratio of V tto V sat vs. cell pressure, is the final product of the CCE test. The test also provides a direct weight/volume measurement of the mixture density in the PVT cell, at the saturation pressure (ρ sat).

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Density of the mixture at any pressure above the bubble-point can also be calculated as the ratio of ρ sat to V rel at the pressure of interest. The oil compressibility coefficient (co), above the bubble-point pressure also obtained from the relative volume data as described by Ahmed (2007), and reported in this study. The relationship of saturation pressure to volume of CO2 injected, and the volume of saturated fluid mixture in relation to the volume of the original reservoir oil, are the data obtained from the swelling test. These data are the required inputs in assessing the effect of mixing on the volume increase of the saturated fluid. After the completion of the CCE test at each step of CO2 incorporation, a sample of CO2-saturated mixture was transferred from the PVT-cell to a high pressure high temperature digital densitometer (Anton Paar DMA HPM), by maintaining the conditions of the PVT cell. A rolling ball viscometer (RBV 1000) was used to determine the viscosity of the CO2-saturated oil at reservoir temperature and a function of saturation pressures.

B. Pendant drop interfacial tension measurementsThe pendant drop IFT tensiometer (IFT 700) based on axisymmetric drop shape analysis (ADSA), provided by Vinci-

Technologies was implemented for measuring the equilibrium interfacial tensions of the CO2/MCO system. The high pressure apparatus consists of a view cell, fitted with a high-pressure capillary tube for generating pendant drop or standing (rising) bubble in a second immiscible fluid (Yang et al., 2005; Sarapardeh et al., 2013).

To perform the CO2/MCO IFT measurements at different pressure points and a fixed reservoir temperature, the following procedure was adopted: Before each test, the apparatus was cleaned using toluene solvent to dissolve any traces of the remaining liquid from the previous tests. The view cell and the needle were then purged with air, to remove any remaining contaminants. The view cell was filled with CO2 from the bulk tank which is connected to a gas booster to provide the desired pressure. Pendant drops of oil were generated from the needle which was fixed at the top of the view cell, after the cell temperature stabilized. The digital image of the drop size and the drop full contour were analyzed to obtain an interfacial profile of the drop surrounded by the bulk phase. The density of the fluids at the measuring temperature and pressure conditions were the required inputs for the calculation procedure. The exact value for IFT was then computed by fitting the Laplace equation of capillarity to an arbitrary array of coordinate points selected from the drop profile (Cheng, et al., 1990; Vinci Technologies, 2007). The resulted IFT is the average of calculated IFT data at the rate of 1 second per value.

C. Prediction ToolsEmpirical correlation, phase behavior model, and numerical simulation have been used to predict and explain the

different properties of CO2-MCO mixtures, such as swelling factor, mixture densities and viscosities, and IFTs at different pressures.

1. Empirical correlationsBased on the properties of the samples of the dead oil used in this study, three sets of empirical correlations have been used to estimate the CO2-saturated oil properties, MMP, and the IFT between CO2 and oil. There are many correlations in the literature for calculating the properties of mixtures, but not all of them can be used for the purpose of our study, due to the following reasons; There are only a few correlations specifically designed to handle non-hydrocarbons such as CO2. The use of each correlation is limited to the range of reservoir and fluid conditions. Mehrotra-Svreck (1982) and Chung et al. (1988) correlations do not provide a match for the MCO properties. The correlation of Quail et al. (1988) for viscosity is also developed based on heavy oil database with viscosity range between 7.1 to 2600 cP, which is not recommended for this study. The correlations given by Emera-Sarma (2008) however have been examined and reported to be applicable over a wider range and conditions (Al-Jarba and Al-Anazi, 2009). Table 3 is a list of correlations which have been used for CO2-oil mixture physical properties estimations in this paper.

Table 3: Correlations used for predicting saturation pressures, swelling factor, density, and viscosity of CO2-oil mixtures.Authors Correlation/Model RemarksCO2 SolubilityEmera-Sarma(2008) sol (mole fracti on )=2.238−0.33 y+3.235 y0.6474−4.8 y0.25656

y=SG [0.006897×(1.8T +32)0.8

P sat ]exp ( 1MW )

Genetic algorithm-based correlation for CO2 solubility in dead oil,For temperatures greater than T c ,CO2(for all pressures), temperatures less than T c ,CO2(for pressures less than CO2 liquefaction pressure).

Oil Swelling FactorSimon-Graue(1965)

Graphical correlation, which is a function of CO2 solubility, oil MW, and oil density at 15.56 °C.

For dead oils at temperature range 43.33 to 121.1 °C, Pressures up to 15.86 MPa (2300 psi),Oil gravity from 12 to 33 °API.Presents lower accuracy at higher pressures.

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Emera-Sarma(2008)

SF=1+0.48411Y−0.9928Y 2+1.6019Y 3−1.2773Y 4+0.48267Y 5−0.06671Y 6

Y=1000 {[( SGMW )×sol(mole fraction)2]exp ( SG

MW)}

For light oils (MW < 300)

CO2-Oil DensityQuail et al.(1988)

ρ=(C1−C2 (T+273.16 )+C3 Ps)exp [−C 4 sol(mole fraction)][1+C5(CH 4 ,mole fraction)]

C1=1.1571, C2=0.6534E-3,C3=0.7989E-3,C4=35.8E-4,C5=50.86E-3

Based on heavy oil data from Senlac region,Used for pressure up to 17.0 MPa (2500 psi),Can be used for other oils by measuring some experimental data to rectify its coefficients.

Emera-Sarma(2008)

ρ=ρi−0.10276 F0.608+0.1407F0.6133

F=SG ρi(Psat−Pb)

1.25

1.8T+32

Initial oil density at the specific temperature

CO2-Oil ViscosityEmera-Sarma(2008) μ=Eμ i+A [ sol(mole fraction)μ i ]

E=xB

x=(C×μi( P sat

1.8T+32 )D

)SGsol (mole fraction )

Coefficients for dead oil:A B C D-9.5 -0.732 3.1429 0.23

A variety of correlations have been developed based on the regression of the experimental results for predicting the MMP of carbon dioxide and different types of oils. The MMP empirical correlations are quick and easy to use, especially when detailed fluid characterizations are not available. However, they are limited to a certain criteria and the conditions used to fit them. Hence, for choosing a correlation to predict the MMP of MCO and pure CO2, the properties of MCO system should satisfy the controlling parameters of each correlation. Table 4 provides the list of correlations used to estimate MMP of the MCO/CO2 system.

Table4: Correlations used for predicting MMP (CO2).Authors Correlation RemarksCronquistet al.(1978)

MMP=15.988T(0.7442+0.0011038MC5+¿+yC 1

)¿T= °FyC1

= mol% C1,N2

Yelling-Metcalf(1980)

MMP=1833.717+2.2518055T+0.0180067T 2-10394.93T−1T= °F

Johonson-Pollin(1981)

MMP=PC ,CO2+18.9(T-T C ,CO2

)+I(0.285 M STO−MCO2)2

I=-11.7+6.313×10−2M o-1.95×10−4 M o2+2.502×10−7M o

3+(0.1362+1.138

×10−5M o)API-7.222×10−5 API2

T= KM=MWoil

Glasoet al.(1985)

MMP=2947.9-3.404MC7+¿¿+17×10−9exp(786.8MC7+¿

−1.058¿)T-121.2xC2−C6

xC2−C6

<18%T= °F

The IFTs of the mixture of MCO and CO2 at various pressures can be calculated by using the expression of Parachor method described by Schechter and Guo (1998), as:

σ1

3.88=∑i=1

nc

Pi(x iρL

M L− y i

ρV

MV)eq .1

Where 𝜎 is the IFT, Pi is the Parachor for component i, x i and y i are the mole fractions of component i in the liquid and vapor phases, respectively. M L and M V are the molecular weights of the liquid and vapor phases, and ρL and ρV are the

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mass densities of the liquid and vapor phases, respectively. ncis the number of components present. The Parachor values for the components can be found in the literature (Danesh, 1998).

2. PVT modelThe commercial simulator CMG’s EOS phase behavior program, WinProp-2012, was used to generate the phase behavior

model of fluid at each CO2 saturation step, and to tune the models against the CCE and swelling test experimental data. The plus fraction (C11+) was characterized by splitting into Single Carbon Number (SCN) fractions to decrease the uncertainty for the plus fraction molecular weight contribution. The two-stage exponential distribution method is selected as the distribution function, which is suitable for black-oil type fluid (WinProp User’s Guide, 2012). The distribution method also provides the lumping of SCNs within the splitting calculation, which can be determined in the distribution controlling parameters. Specific gravity (SG) and boiling point (Tb) values for the single and multiple carbon groups created would be determined from the correlations based on the SG and MW of the plus fraction. As stated in the literature describing the plus fraction with 4 groups of single or multiple carbon numbers should be adequate, in our model also C11+ was characterized by C11-14, C15-16, C17, and C18+

groups. The model was then regressed and tuned against the experimental data to eliminate deviations from the lab data, and to adjust the model parameters for further predictions. The critical properties (Pc, Tc) and the acentric factor (Ac) of all the pseudo components, as well as volume shift, omega A, omega B, and the interaction coefficient of the heavy end fraction are adjusted for regression. Saturation pressure (Ps), relative volume vs. pressure curves, and oil densities (ρo) are used as the matching parameters for regressed models.

3. Compositional simulation

MMP can be calculated rather than correlated, through the compositional simulation when the appropriate EOS-based PVT model is available. The WinProp-generated model was exported to CMG-GEM-2012 and the 1-D slim-tube compositional simulation was performed. The governing multi-component conservation laws are solved numerically for 1-D flow to obtain the oil recovery as a function of the displacement pressure to determine MMP which is considered the point that the recovery curve levels off.

Results and discussion

A. PVT analysisThe resulted relative volume curves of CCE tests are shown in Fig. 1. The decline in the relative volume curve by increasing the pressure can be attributed to the compressibility of CO2, and then to the mutual solubility of CO2 in MCO. The first change in the slope of the relative volume curve represents the saturation pressure (bubble-point), at each CO 2 incremental. As can be inferred from Fig. 1, determining the bubble-points for 60% and 80% of CO2 added cases was challenging as the slopes of the volume-pressure curves are changing relatively smoothly. The observed difference can be attributed to the presence of a large amount of CO2 in the mixture. This behavior can cause uncertainty in determining the bubble-point pressure. Therefore, using the prediction methods can be helpful for ascertaining the point where both the saturation and desaturation lines bisect.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Rel

ativ

e oi

l vol

ume

(frac

tion)

Pressure (psia)

80 mol% CO2

60 mol% CO2

40 mol% CO2

20 mol% CO2

T = 102 0C

Fig. 1. Relative volume (V /V sat ) of different steps of CO2 addition, at 102 °C.

The saturation pressure increase with CO2 addition is a key factor in determining the impact of gas injection on oil recovery. The relationship between saturation pressure and CO2 loading is given in Fig. 2, resulting from the swelling test. As illustrated in Fig. 2, the solubility of CO2 is a function of pressure. The more CO2 is dissolved, the higher is the saturation

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pressure. It is also useful to estimate the amount of CO2 that is solubilized in oil at different gas pressure injections in the reservoir.

20 30 40 50 60 70 80

1000

1500

2000

2500

3000

Sat

urat

ion

Pre

ssur

e (p

sia)

CO2 mole added (%)

Fig.2. The effect of CO2 dissolution on saturation pressure at T = 102 °C (swelling-test data).

The swelling test is repeated for the recombined oil, to observe the influence of the light components on the saturation pressure. As can be seen in Fig. 3, dead oil has a lower range of saturation pressure than live oil caused by the deficiency of light and intermediate fractions in the former.

0.2 0.3 0.4 0.5 0.6 0.7 0.8

1000

1500

2000

2500

3000

3500

4000

Sat

urat

ion

pres

sure

(psi

a)

CO2 added (mol%)

Live-oil Dead-oil

T=1020 C

Fig. 3. Comparison of saturation pressure range between live and dead oil samples, at a constant reservoir temperature.

CO2 recovery mechanism in an immiscible procedure involves the reduction of oil viscosity, promoting swelling, dissolved gas drive, and the ability to vaporize and extract portions of crude oil. The ability of the injected CO2 to dissolve in oil is measured by the swelling factor (SF). SF for dead oil is defined as the volume of the CO2-saturated oil divided by the volume of the original oil at atmospheric pressure and reservoir temperature. SFs and mol% of CO2 saturation as a function of CO2

saturation pressure are plotted as in Fig. 4. The SF shows an increasing trend as pressure is increased. However, as the pressure approaches 1800 psia and at a CO2 loading in excess of 40%, there is a change in the slope of the mild SF curve compared to the increment at lower pressures and CO2 loading. Since the equilibrium procedure through the experiment remains the same, this behavior is associated with an excessive expansion of the oil at CO2 saturations higher than 40% as more and more CO2 is able to interact with oil at higher pressures. There is a simultaneous increase in CO 2 solubility which appears to support an increased affinity between CO2 and oil as pressure is increased.

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0 500 1000 1500 2000 2500 30000.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

SF Solubility

Pressure (psia)

SF

(frac

tion)

T = 102 oC

0

20

40

60

80

Sat

urat

ion

(%)

Fig. 4. Swelling factor and CO2-saturation mole fraction measured at saturation pressure at different steps of CO2-saturation process, at 102 °C.

Fig. 5 is the comparison of viscosity reduction behavior between the live and dead oil samples, where the first point in both trends represents oil viscosity before any addition of CO2. As shown in Fig. 5, a large reduction in the viscosity of dead oil occurs as it becomes saturated with CO2 at increasing pressures suggesting CO2 increased interaction with oil by providing less resistance to flow. The decrease in viscosity of the saturated-live oil is lower than that of the dead oil. The observation is in agreement with the literature that a larger percentage reduction occurs in the viscosity of the more viscous crudes during the process of CO2 saturation (Holm and Josendal, 1974).

1000 1500 2000 2500 3000 3500 4000

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

No CO2 added

Vis

cosi

ty (c

P)

Pressure (psia)

Live oil Dead oil

T= 102 OC

No CO2 added

Fig. 5. Comparison of viscosity reduction behavior of dead and live oil as saturating with CO2.

The effect of pressure on the dead oil viscosity and density, before and after CO2-saturation process is illustrated in Fig. 6.a and b, respectively. As shown in Fig. 6. a, both the density and viscosity of CO 2-deficient oil increase with increasing pressure at a constant temperature. For the case of oil saturated with CO 2, as shown in Fig. 6.b, as the gas in solution increases from 0% to 80%, the viscosity of the mixture tends to decrease. However, the increase in density declines at 1800 psia pressure and 40% saturation of CO2. The density of 80% CO2-saturated mixture at 3000 psia is decreased in comparison with the 40% mixture, but still remains higher than the CO2-free dead oil. This observation is in agreement with Holm and Josendal (1974) experimental data on mixture of stock-tank oil density with CO2. According to them, the mixture density increment is one of the mechanisms of oil displacement by CO2.Comparing swelling factor and density behavior in Figs. 4 and 6.b, although the density of CO2-saturated crude oil increased before 1800 psia, the swelling factor (SF) in Fig. 4 kept increasing. At this instant, the volume per unit oil mass expanded with pressure. The density change should be attributed to the competition between the expansion of oil per unit mass and the solubility of CO2 in oil.

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0 500 1000 1500 2000 2500 3000

1.4

1.5

1.6

1.7

1.8

T= 102 o C

Viscosity Density

Pressure (psia)

Vis

cosi

ty (c

P)

0.775

0.780

0.785

0.790

0.795

0.800

0.805

0.810

Den

sity

(g/c

c)

Fig. 6.a. Viscosity and density of CO2-free dead oil as a function of pressure at 102 °C.

0 500 1000 1500 2000 2500 30000.50

0.75

1.00

1.25

1.50

Pressure (psia)

Viscosiy Density

T = 102 0C

Vis

cosi

ty (c

p)

0.50

0.75

1.00

1.25

1.50

Den

sity

(g/c

c)

Fig. 6.b. Viscosity and density of CO2-saturated dead oil as a function of saturation pressure at 102 °C. heads unless they are unavoidable.

B. Tuned EOS-based PVT modelFig. 7 illustrates the experimental and simulated liquid volume for 40% CO2 saturated oil. As can be seen in Fig. 7, the simulated values and the experimentally observed values match each other.

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500 1000 1500 2000 2500 3000 35000.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

Pressure (psia)

Rel

ativ

e V

olum

e

Exp. ROV Final ROV Initial ROV

T = 102 0C

Fig. 7. Relative volume curve for 40% CO2 saturated oil, comparison of PVT model and experimental data before and after regression.

Fig. 8 presents the match between the results predicted by regressed Peng-Robinson cubic EOS (Pederson and Christensen, 2007) and the experimental data for density of oil saturated with 40% mole CO2.

500 1000 1500 2000 2500 3000 35000.00

0.04

0.08

0.12

0.16

0.20

0.24

Pressure (psia)

Gas density Oil density Exp. oil density

T= 1020C

Gas

den

sity

(g/c

c)

0.850

0.855

0.860

0.865

0.870

Oil

dens

ity (g

/cc)

Fig. 8. PVT model for 40% CO2 saturated oil, comparison of measured and simulated oil densities.

The viscosity of oil saturated with 40% mole CO2 predicted by the modified Pederson corresponding states model (1987) (Pederson and Christensen, 2007), is shown in Fig. 9.

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500 1000 1500 2000 2500 3000 35000.0200

0.0205

0.0210

0.0215

0.0220

0.0225

0.0230

0.0235

0.0240

0.0245

0.0250

0.0255

0.0260

Gas Viscosity Oil Viscosity Exp. Oil Viscosity

T= 1020C

Pressure (psia)

Gas

Vis

cosi

ty (c

P)

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1.05

Oil

Vis

cosi

ty (c

P)

Fig. 9. PVT model for 40% CO2 saturated oil, comparison of measured and simulated oil viscosities.

Tables 5 to 8 provide the comparisons of phase behavior predicted by correlations and WinProp model with the measured data. There are many correlations in the literature for calculating the density of mixture, but not all of them can be used for the purpose of our study, due to the following reasons described. There are only a few correlations specifically designed to handle non-hydrocarbons such as CO2. Furthermore, the use of each correlation is limited to the range of reservoir and fluid conditions, which it is based on. For our studied case, Emera-Sarma and Quail et al. correlations are the only CO2-correlations that can be used for the studied oil properties.

Table 5: Measured and calculated saturation points in swelling experiments for the oil mixtures, at 102 °C.CO2 added (mol%) Saturation Pressure (psia)

Laboratory Emera-Sarma ∆Emera-Sarma Model ∆Model

20 1021 250 75 1023 0.2040 1657.3 551 66 1658 0.0460 2200 1102 49 2200.2 0.01080 2993.08 2161 27 2993.1 0.003

Table 6: Measured and calculated SFs for oil mixtures, at 102 °C.CO2 added (mol%) Swelling Factor

Laboratory Simon ∆Simon Emera-Sarma ∆Emera-Sarma Model ∆Model

20 1.17 1.05 10 1.23 2.5 1.1 5.840 1.26 1.14 9.5 1.391 10.3 1.22 3.160 1.58 1.34 15 1.690 1.2 1.5 5.080 2.40 NA - 2.15 9.2 2.1 8.3

Table 7: Measured and calculated mixture densities at saturation pressure, at 102 °C.CO2 added (mol%) Fluid Density (g/cc)

Laboratory Quail ∆Quail Emera-Sarma ∆Emera-Sarma Model ∆Model

0 0.777 0.905 16 0.844 8.70 0.776 0.020 -- 0.916 -- 0.849 -- 0.802 --40 0.84 0.919 8 0.852 0.63 0.852 0.360 -- 0.921 -- 0.854 -- 0.847 --80 0.807 0.925 14 0.857 6.14 0.814 0.37

Table 8: Measured and predicted mixture viscosities at saturation pressure, at 102 °C.CO2 added (mol%) Fluid Viscosity (cP)

Laboratory Emera-Sarma ∆Emera-Sarma Model ∆Model

40 0.753 1.24 65 0.75 0.380 0.644 1.69 160 0.645 0.15

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As can be inferred from the Tables 5-8, the model reproduces the measured data that is regressed for, very closely. However, it does not necessarily mean that the estimated parameters for other properties are valid to the same extent as the ones used for the fitting. This may contribute to the finding as to why the estimated SFs by model have larger differences (∆ model) than the saturation pressure, and densities, which are used for regression procedure.Data of the tuned model are then used to study the behavior of saturated mixture (density and viscosity) in comparison with the original stock tank oil, and pure CO2, at different saturation points, as reported in Table 9. CO2 density and viscosity values at different pressures are obtained from the CMG WinProp software.

Table 11: Comparison of density and viscosity between pure CO2, pure oil, and CO2/oil mixtures.Saturation Pressure (psia)

Density (g/cc) Viscosity (cP)

Sc-CO2 Original oil(Measured)

40% mixture(Tuned model)

Sc-CO2 Original oil (Measured)

40% mixture(Tuned model)

1021 0.1211 0.7902 0.8518 0.0209 1.509 0.881657.3 0.2238 0.79357 0.8513 0.0242 1.6289 0.752200 0.3245 0.799 0.8566 0.0289 1.689 0.782993.08 0.4632 0.8069 0.8645 0.0379 1.79 0.85

Figs. 10 and 11 show that the trends of densities and viscosities are as per discussed before (Table 11). It can be inferred from Fig. 10 that the predicted data by model for 40% saturated oil density is also supporting the oil density increment as it becomes saturated with CO2. As shown in Fig. 10, CO2 at its supercritical fluid state possesses a high density, which is comparable to the gas-free dead oil. That is why it can increase the density of saturated oil to more than the density of the CO2-free oil. However, there are no simple straight forward methods for calculating the density of CO2-saturated mixtures (Marra et al., 1988).

1000 1500 2000 2500 3000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Den

sity

(g/c

c)

Pressure (psia)

40% mixture dead oil pure CO2

T = 102 0C

Fig. 10. Comparison of the density of pure CO2, CO2-free crude, and 40% CO2-saturated oil as a function of pressure.

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1000 1500 2000 2500 30000.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Vis

cosi

ty (c

P)

Pressure (psia)

dead oil 40% mixture pure CO2

T = 102 OC

Fig. 11. Comparison of the viscosity of pure CO2, CO2-free crude, and CO2-oil mixture as a function of pressure.

Fig. 12 presents the measured densitiesresulting from the digital densitometer in comparison with the average densities, as well as calculated densities using the WinProp model, mixing rule (∑ ρi z i), Emera-Sarma, and Quail et al. correlations. The average density is defined as the total fluid mass (mass of crude + mass of carbon dioxide charged at each step) divided by the total cell volume monitored at each pressure (Rahman et al., 2010).It can be inferred from Fig. 12 that while average method and the model are predicting the trend similar to the measured data points, correlations and the mixing rule fail to show the non-linear behavior of saturated mixture densities.

1000 1500 2000 2500 30000.50

0.55

0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

Quail et al. Winprop model Emera-Sarma AntonPaar Avg. method Mixing rule

Den

sity

(g/c

c)

Pressure (psia)

Fig. 12. Density of mixture at saturation pressure, comparison the trend of different methods, at 102 °C.

C. Minimum miscibility pressure estimation1. VIT Technique

Although the interfacial tension between oil and CO2 in immiscible injection is not zero, it can still decrease to lower values due to the solubility of gas in oil (Kang et al., 2013). It is even stated that the interfacial tension between oil and water can be dropped down in the presence of CO2 (Jha, 1986). This behavior is attributed to the molecular diffusion of CO 2 into the both phases in the water-wet rock system (Grogan and Pinczewski, 1987).

The IFTs between MCO and CO2 were measured at two different temperatures (80 °C, 102 °C) and pressure ranges from 14.7 to 3200 psia. The IFTs were measured after enough equilibrium time of around 1 h was given to the system. The reported IFTs are the average of measurements for at least three different oil drops to ensure adequate repeatability. The main challenge during the IFT measurements for pendant drops of oil in CO2 bulk phase is the stability of the oil drop in the CO2

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bulk phase, and the drop contour visibility in contrast with the bulk area, at some pressure points. The stability of a pendant drop is represented by the resting time of the drop at the tip of the needle before it detaches from the tip due to the force imbalance between the downward gravity and the upward buoyncy force. The visibility of drop and sharp drop contours are also required since the IFT calculations are highly dependent on the drop geometry analysis. During the IFT measurements in our experiments, it was difficult to maintain a stable oil drop at pressures of around 1030 psia and temperature of 102 °C. Even when the drop was stable, the drop resolution was too low to be able to calculate IFT using the digital image data. The observed behavior is attributed to the turbulences the CO2 bulk is experiencing in the transition zone from the CO2 gas to the critical fluid state. This limitation is tackled by measuring the IFTs at pressures below and higher than the critical pressure (1030 psia). The pendant oil drops that are formed in the CO 2 environment at different pressures from P=1650psia (near supercritical gas state) to P= 3000psia (already supercritical fluid state) at the reservoir temperature are shown in Figs. 13.a to 13. c, respectively.

a. P1=1660 psia b. P2=2200 psia c. P3=3000 psiaFig. 13. Pendant oil drop in CO2 phase at T=102 °C, at different pressures.

It can be seen in Fig. 13 that increasing the cell pressure from 1600 to 3000 psia improves the resolution of the pendant drop in the CO2 environment significantly. When the pressure is high above the transition zone (3000 psia), where CO 2 is predominantly a super-critical fluid, the drop resolution as well as drop stability in CO 2 phase is further enhanced compared to when the drop is at lower pressures. However, the stable oil pendant drop in CO 2 bulk at 3000 psia is also a sign that the miscibility between oil and CO2 has yet to be achieved. At very low IFT values, the stability of the pendant drop in the bulk phase is again influenced, and the use of a thinner needle is recommended. Finally, at conditions close to the miscibility region, no pendant drops can be formed because no interface exists between the two phases (Danesh, 1998; Yang et al., 2005). Fig. 14 shows the measured dynamic IFTs of the system at two different pressures and a constant temperature of 102 °C. The fluctuations of the IFT for a drop of oil measured over a 60 sec duration increased as the pressure was increased from 15 psia to 3000 psia. This observation can be attributed to the increased mutual solubility of oil and CO2 at higher pressure representative of reaching near miscibility condition.

0 10 20 30 40 50 60 70

2

4

6

8

10

12

14

16

18

20

15 psia 3000 psia

T = 102 0C

CO

2 / M

CO

dyn

amic

IFT

(mN

/m)

Time (seconds)

Fig. 14.Effect of pressure on dynamic IFTs of the MCO/CO2 system at T=102 °C.

Fig. 15 reports the measured equilibrium IFTs between MCO/CO2 system in comparison with the calculated IFTs using the version of Parachor model described by Schechter and Guo (1998), at the reservoir temperature and as a function of equilibrium pressure. As can be seen in Fig. 15, there are two distinct ranges of experimentally determined IFT values below and above the CO 2 critical

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region. This is also found for the IFT values calculated using the Schechter and Guo model. It can be inferred from the comparison of experimental and correlation values that as the pressure increases, the accuracy of the IFT model increases and the calculated data are much closer to the measured values.

0 500 1000 1500 2000 2500 3000 3500 40000

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

Schechter-Guo Model Experimental

T = 102 oCIF

T M

CO

/CO

2 (m

N/m

)

Prressure (psia)

Fig. 15. Comparison of measured and correlated IFTs of MCO/CO2 system at reservoir temperature as a function of pressure.

The CO2-MCO multi-contact miscibility (MCM) pressure or MMP, and the first contact miscibility (FCM) pressure can be estimated from the data in Fig. 15 by applying the VIT technique. According to the VIT technique, the extrapolation of the measured IFTs in the first and second ranges to zero can lead to the system MMP (3750 psia) and FCM pressure (4000 psia), respectively.

2. MMP Correlations and Slim-tube Simulation ResultsFig. 16 illustrates the results of slim-tube numerical simulation for MCO/CO2 system at the reservoir conditions. The dashed line in Fig. 16 represents the predicted MMP value (3680 psia) by simulation method.

1000 1500 2000 2500 3000 3500 4000 4500 5000 5500

55

60

65

70

75

80

85

90

95

Oil

Rec

ovry

(%)

Pressure (psia)

Slim-tube simulationT = 102 oC

Fig. 16. Oil recovery vs. pressure resulted from slim-tube simulation model.Table 12 illustrates the MMP predictions by different correlations in comparison with the values generated from VIT and simulation methods.

Table 12: Predicted MMP values by different methods.Model Predicted MMP (psia)

Slim-tube simulation 3687VIT 3750

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Cronquist et al. 2688.61Yelling - Metcalf 3108Johonson-Pollin 4796

Glaso et al. 2305

Conclusion/Results

The physical properties including the pressure-volume behavior, saturation pressure, solubility, swelling effect, mixture density and viscosity for both dead and live oils have been studied under the reservoir conditions.

According to the results, the differences in phase behavior of dead and live oil should not be disregarded. The saturation pressure contrast between live and dead oil for the same CO2 loading is 1000 psia. This amount corresponds to the contribution of the light ends of live oils. The contribution of light ends should not be neglected in displacement tests of a chemical EOR when dead oil samples are used.

The available correlations and as well as the WinProp model have been used to match with the phase behavior data. While the WinProp model results for density are in good agreement with the experimental data, the empirical correlations fail to predict the non-linear density trend as observed in our PVT experiment. This finding emphasizes the importance of selecting a proper prediction tool for phase behavior studies.

VIT and slim-tube simulation model based on the regressed PVT model provided similar values for MMP value of the dead oil sample with CO2 at reservoir temperature.

Nomenclature

A-B-C-D Emera-Sarma correlation constants for mixture viscosityC1to C5 Quail et al. correlation constants for mixture densityCCE Constant composition expansionCO2 Carbon dioxideE Emera-Sarma correlation constant for mixture viscosityEOR Enhanced oil recoveryEOS Equation of stateF Emera-Sarmacorrelation constant for mixture densityHTHP High pressure, high temperatureIFT Interfacial tension, mN/mLBC Lohrenz-Bray-Clark method

µ CO2-oil viscosity, cPµi Initial dead oil viscosity at the specific temperature, mPa.s = cP

MCO Malaysian crude oilMMP Minimum miscibility pressureMW Oil molecular weight

n Number of components in the mixturePb Bubble point of the original oil, 0.1034 MPa (15 psia) used for Emera-Sarma correlationPSat Saturation Pressure, MPa for correlations definitions, psia for PVT report

PR3 Cubic Peng Robinson equation of statePVT Pressure-volume-temperature

ρ CO2-oil density, g/ccρi Initial oil density at specific temperature, g/cc

SG Specific gravitySF Oil swelling factor, fractionsol CO2 solubility, mole fractionT Temperature, °Cx Emera-Sarma correlation constant for mixture viscosityY Emera-Sarma correlation constant for SFy Emera-Sarma correlation constant for solubility

z i Mole fraction of each component∆ |(Exp. value-calculated value)|*100/Exp. value

Acknowledgment

Page | 15


The authors would like to thank Mr. Rezwan Ahmad and Mr. Shaharul for their effort and technical support during the phase behavior measurements. The authors are indebted to PETRONAS EPTC-UTP project for the research grant.

References

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