Effect of Oxygen on Minimum Miscibility Pressure in Carbon Dioxide


Effect of Oxygen on Minimum Miscibility Pressure in Carbon Dioxide Flooding ... Miscible CO2 flooding has become one of the most important enhanced oi...
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Effect of Oxygen on Minimum Miscibility Pressure in Carbon Dioxide Flooding Fulin Yang, Gui-Bing Zhao, Hertanto Adidharma,* Brian Towler, and Maciej Radosz Soft Materials Laboratory, Department of Chemical & Petroleum Engineering UniVersity of Wyoming, Laramie, Wyoming 82071-3295

Laboratory studies of the effect of oxygen content in CO2 on the minimum miscibility pressure (MMP) are conducted on the n-C5H12/n-C16H34 model oil and Cottonwood Creek crude oil with three injection gases of different oxygen contents. The results indicate that the MMPs for these oils increase unfavorably with increasing O2 concentration in the CO2 stream. The experimental results are also supported by our modeling work using a multiple-mixing-cell model, which is found to capture the effects of compositions and temperature, and is found to be a robust and predictive method for determining the MMP. Our experiments and calculations indicate that the effect of O2 contamination on the MMP is larger for heavier oil and the effect of N2 impurity on the MMP is larger than that of O2 impurity. 1. Introduction Miscible CO2 flooding has become one of the most important enhanced oil recovery (EOR) processes. The process development efforts have also been escalated, partly due to the increasing global awareness of the detrimental effects of anthropogenic CO2 on the environment. Flue gas from power plants is a significant potential source of CO2 for EOR processes. However, extracting CO2 from such a source will increase project costs. It is the remaining low percentage of non-CO2 gases (N2, SOx, H2S, and O2) that are more difficult and costly to remove. A possible approach that is more economical is thus to inject either impure CO2 or flue gas directly. One important consequence arising from the use of such an approach is the need to understand the effects of the impurities (non-CO2 gases) on the multicontact miscible displacement of the CO2 flooding process. A lot of effort has been made toward investigating the effects of contaminants in CO2, such as methane, N2, SOx, H2S, and C2-C4, on the minimum miscibility pressure (MMP). MMP is the pressure required for multicontact miscible displacement and is an essential criterion for screening and selecting reservoirs for the miscible displacement process.1-9 In general, the presence of H2S, C2H6, or intermediate hydrocarbons can lower the CO2 MMP, while the presence of CH4 or N2 in CO2 can substantially increase the CO2 MMP.10,11 Although O2 is one of the most common contaminants in CO2 from flue gas, the role of this gas in miscible CO2 flooding has never been explored. Understanding the effect of oxygen content in injection CO2 gas on the MMP is necessary for designing a cost-effective CO2 enhanced oil recovery process. Therefore, studies aimed at addressing the effect of O2 contaminant on the MMP are performed in this work. The MMPs of a model oil, i.e., n-C5H12/n-C16H34, and Cottonwood Creek crude oil with pure and O2-contaminated CO2 are measured. In addition, the experimental MMPs of model oils are compared with the calculated MMPs obtained from our recent multiple-mixing-cell model13 with a tie-line-length approach. 2. Experiment 2.1. Materials. The oil samples studied are from the Permian age Phosphoria formation in the Cottonwood Creek field in * To whom correspondence should be addressed. Tel.: (307) 7662909. Fax: (307) 766-6777. E-mail: [email protected]

Table 1. Physical Properties of Cottonwood Creek Crude Oil at 60 °C16 Properties dead oil gravity, ˚API density, g‚mL-1 viscosity, mPa‚s at reservoir temp asphaltene content, % (C7)

30.0 0.8879 12.3 2.3

Table 2. Slim-Tube Characteristics column material length, m internal diameter, cm glass beads packing (mesh) porosity, % permeability, darcy pore volume, cm3

316 stainless steel 14.6 0.46 50-70 35.5 11 86.0

Wyoming. The physical properties of the Cottonwood Creek crude oil are presented in Table 1. The model oil is a mixture of n-C5H12/n-C16H34 with a mole fraction ratio of 0.43/0.57. The n-pentane and n-hexadecane were purchased from VWR International, Inc., and used without further treatment. The “pure” CO2 gas (99.5%) and O2contaminated gases (94.81% and 90.01% CO2) were purchased from Airgas, Inc., and used without further purification or adjustment. 2.2. Slim-Tube Apparatus and Procedure. The characteristics of the slim tube used are shown in Table 2. Prior to each run, the apparatus is carefully cleaned with toluene followed by distilled acetone and dried with N2 at a temperature of 100 °C. Then the slim tube is heated to the operating temperature. The oil sample is pumped into the slim tube at the desired operating pressure to saturate the tube. The gas solvent, maintained at the desired temperature and pressure, is injected into the slim tube to displace the oil using a positive displacement pump at a rate of 12.0 cm3/h. The following data are collected during the slim-tube experiment: the pore volume of injected gas, the volume of oil produced, and the volume of gas produced. Data are taken every 10 min. After 1.25 pore volumes (PV) of gas injection, the run is ended. In this work, we measure the MMPs of six systems: model oil, i.e., a mixture of 43 mol % n-C5H12 and 57 mol % n-C16H34, and Cottonwood Creek crude with three different injection gases (pure CO2, impure CO2 containing 5.19 mol % O2, and impure CO2 containing 9.99 mol % O2). Slim-tube experiments are conducted at five different pressures for each system. The

10.1021/ie061279g CCC: $37.00 © 2007 American Chemical Society Published on Web 01/12/2007

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Figure 1. Oil recovery and produced gas volume as a function of injected CO2 pore volume (PV) for model oil with pure CO2 at different pressures: (a) oil recovery vs PV; (b) produced gas volume vs PV. Table 3. Binary Interaction Parameters21 component N2 O2 CO2 CH4 C4H10 C5H12 C10H22 C16H34 a

N2

O2

CO2

CH4

C4H10 C5H12 C10H22 C16H34

0.0000 0.0000 0.0311 0.0711 0.1000 0.1550 0.1550

0.0000 0.0000 0.0000a 0.0000a

0.0000 0.1070 0.1333 0.1400 0.0145 0.0750a

0.0000 0.0133 0.0236 0.0500 0.0600

0.0000 0.0170 0.0000 0.0010 0.0000 0.0000 0.0010 0.0000 0.0000 0.0000

This work.

operating temperatures used are 50 °C for model oil and 60 °C for the Cottonwood Creek crude. For comparison with our modeling work, we also measure the MMP of the model oil with pure CO2 at 40 °C. 3. Modeling A multicontact miscibility process is controlled by a sequence of (nc - 1) key tie lines: the initial tie line, the injection tie line, and (nc - 3) crossover tie lines, where nc is the number of components.12,13 The initial tie line is an equilibrium tie line in the compositional space that extends to a point representing the

Figure 2. Rb and R1.2 vs P for model oil with three different gas injections at 50 °C: (a) pure CO2; (b) 5.19 mol % O2 in CO2; (c) 9.99 mol % O2 in CO2. Table 4. Parameters of the Multiple-Mixing-Cell Model total number of cells total batch number GOR cell volume, cm3 fractional flow function

1000 4000 0.3 1.0 1.0

oil composition, the injection tie line is an equilibrium tie line that extends to a point representing the injected gas composition, and the crossover tie line is an equilibrium tie line on a binodal

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main problems of this approach are the determination of initial guesses and the nonuniqueness of the solution. In this work, a new approach based on a multiple-mixingcell model22-24 is proposed to identify the key tie lines, and therefore to calculate the MMP. A multiple-mixing-cell model is a discrete model of a continuous gas injection process in the slim-tube experiment. A packed slim tube is discretized into a series of constant-volume cells, and the continuous gas injection process is discretized into a series of constant-volume batches. By assuming constant temperature and pressure in each cell, no physical dispersion among cells, no capillary force in each cell and among cells, and perfect mixing in each cell, this multiple-mixing-cell model is converted into a pure thermodynamic P/T flash calculation. A block-algebra simultaneous flash algorithm17 coupled with the Peng-Robinson (PR) cubic equation of state (EOS) is used in this work. The binary interaction parameters (kij) used and the parameters of the model are given in Table 3. The parameters of our multiple-mixingcell model are listed in Table 4. The total number of cells (Nc) is chosen to ensure that a steady-state compositional path of the process can be achieved. The cell volume, gas oil ratio (GOR), and fractional flow function do not affect the determination of the key tie lines;13 thus they do not affect the MMP calculation. The total batch number of gas injection can be calculated from

N b ) Nc

1.2 GOR

(1)

where the value of 1.2 is an extensively accepted criterion; i.e., the required amount of gas injected for oil recovery calculation from the slim-tube experiment is 1.2 PV. By examining the steady-state compositional path from a concentration-space analysis,12 for nc components, (nc + 1) constant-composition zones and (nc - 1) key tie lines can be easily determined. Upon increasing the batch number of the injected gas, these key tie lines appear in a reverse order; i.e., the initial tie line appears first and the injection tie line appears last. This approach provides a unique solution to find all of the key tie lines and is therefore particularly suitable for gas injection studies. Since all of the key tie lines can be robustly found through our multiple-mixing-cell model, our MMP calculation can be robustly based on the determination of tie-line length (TL), which is defined as

TL )

x∑

(xi - yi)2

(2)

i

Figure 3. Rb and R1.2 vs P for the Cottonwood Creek crude oil with the three different gas injections at 60 °C: (a) pure CO2; (b) 5.19 mol % O2 in CO2; (c) 9.99 mol % O2 in CO2.

surface at which the compositional path of the process starts to follow a different binodal surface. The MMP is defined as the lowest pressure at which any one of the key tie lines becomes a critical tie line.14 There are many criteria that can be used to determine the MMP, but in our recent work,13 we demonstrated that zero key-tie-line length is a robust criterion for this purpose. Therefore, the problem reduces to how to identify the key tie lines. Currently, the only method proposed to identify key tie lines is the tie-line-intersection approach,15 which states that any two adjacent key tie lines must intersect. By solving 2nc(nc - 1) equations, all (nc - 1) key tie lines can be found. The

where xi and yi are the equilibrium mole fractions of component i in the liquid and vapor phases, respectively. At the MMP, the tie-line length of one of the key tie lines reaches zero. It is important to mention that although the multiple-mixingcell model we use is somewhat similar to a one-dimensional compositional flood simulator, it offers an important advantage over the latter, i.e., the determination of key tie lines, and thus the MMP is not affected by the size of the mixing cell and the numerical dispersion induced by GOR and fractional flow.13 As also previously explained, the total number of cells is chosen to ensure that a steady-state compositional path of a process can be achieved. It does not matter what is the number of cells we choose: as long as the steady-state compositional path can be achieved, all of the key tie lines can be determined. In contrast, the results obtained from a one-dimensional compositional simulator are very sensitive to numerical dispersion

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Figure 4. Key tie-line lengths in the simulation of a model oil consisting of 50% CH4, 16.24% C4H10, and 33.76% C10H22 with a 50/50 mixture of N2 and CH4 as the injection gas at 344 K. Initial tie line becomes a critical tie line at 31.6 MPa. Table 5. Experimental Minimum Miscibility Pressure MMP1.2, MPa

|MMP1.2 - MMPb|, MPa

MMPb, MPa

gas

Cottonwood Creek oil, at 60 °C

model oil, at 50 °C

Cottonwood Creek oil, at 60 °C

model oil, at 50 °C

Cottonwood Creek oil

model oil

pure CO2 5.19 mol % O2 in CO2 9.99 mol % O2 in CO2

16.92 21.11 27.35

10.55 11.72 13.27

17.18 21.54 26.94

10.40 11.75 13.53

0.26 0.43 0.41

0.15 0.03 0.26

Table 6. Comparison of Measured and Calculated MMPs for Model Oil absolute relative deviation of calculated MMP ,%

experimental, MPa gasa

temp, °C

MMP1.2

MMPb

predicted MMP, MPa

MMP1.2

MMPb

A B C

40 40 40

8.58

8.68

8.41 9.83 11.32

2.0

3.1

A B C

50 50 50

10.55 11.72 13.27

10.40 11.75 13.53

10.19 11.83 13.36

3.4 0.9 0.7

2.0 0.7 1.3

A B C

60 60 60

12.16 13.61 15.24

D E

50 50

12.65 15.38

a Gas A is pure CO , gas B is 5.19 mol % O in CO , gas C is 9.99 mol % O in CO , gas D is 5.19 mol % N in CO , and gas E is 9.99 mol % N 2 2 2 2 2 2 2 2 in CO2.

induced by the number of grid blocks used (the size of grid block); thus a series of simulations using various numbers of grid blocks needs to be performed and then the quantity of interest must be obtained from extrapolation to an infinite number of grid blocks. 4. Results and Discussion 4.1. Experiment. The oil recovery in % OOIP (Original Oil in Place) for each system is plotted as a function of injected gas pore volume (PV). For example, Figure 1 shows the oil recovery for model oil with pure CO2 at different pressures. The point at CO2 breakthrough, from which the breakthrough oil recovery (Rb) is obtained, is indicated in the plot of produced gas volume vs injected gas PV (Figure 1). Then, we plot Rb and the oil recovery after injecting 1.2 pore volume gas (R1.2) as a function of pressure (P). The MMP is defined as the

pressure at which a breakpoint occurred in this plot. The MMPs obtained from Rb vs P and R1.2 vs P are referred to as MMPb and MMP1.2, respectively. Figure 2 shows plots of Rb and R1.2 vs P for the model oil with three different gas injections at 50 °C. The solid lines are to assist us in determining the breakpoint. As summarized in Table 5, the difference between MMPb and MMP1.2 is small, and thus either MMP can be considered the MMP of the system. The MMP for the model oil with pure CO2 shows excellent agreement with that for the same system reported in the literature,18,19 indicating that the MMP determined in our lab is reliable. The results indicate that the effect of O2 impurity in CO2 gas is to increase the MMP. Figure 3 shows plots of Rb and R1.2 vs P for the Cottonwood Creek crude oil with the three different gas injections at 60 °C. The MMPs for these systems are also summarized in Table 5.

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agrees well with our experimental result. For this model oil, the MMP increases with temperature. From our predictions at 60 °C, the MMP of the model oil is increased by only 11.9% when 5.19 mol % O2 is used and 25.3% when 9.99 mol % O2 is used. For comparison, our experimental results show that the MMP of the Cottonwood Creek oil at the same temperature is increased by approximately 24.8% when 5.19 mol % O2 is used and by nearly 61.6% when 9.99 mol % O2 impurity is used. The larger increase in the MMP for the Cottonwood Creek oil indicates that the effect of O2 on the MMP is larger for heavier oil. The extent of the MMP increase is illustrated in Figure 5. As shown in Table 6, we also predict the MMP of the model oil when CO2 gas is contaminated with N2. The effect of N2 impurity on the MMP is found to be larger than that of O2 impurity, as depicted in Figure 6. Figure 5. Extent of MMP increase for Cottonwood Creek and model oils at 60 °C.

Figure 6. MMPs for model oils with O2- and N2-contaminated CO2 gases at 50 °C.

Similar to model oil, O2 impurity in CO2 increases the MMP for this system. 4.2 Modeling. To verify the accuracy of our algorithm, the MMP of a model oil consisting of 50% CH4, 16.24% C4H10, and 33.76% C10H22 with a 50/50 mixture of N2 (at 344 K) and CH4 as the injection gas is determined. The tie-line-length calculations are illustrated in Figure 4. For this quaternary system, there are three key tie lines and the initial tie line reaches zero at the MMP. As noted in the figure, the calculation is stopped at a pressure close to the MMP; the length of the initial tie line would be exactly zero at the MMP. This is due to the common difficulty encountered when one tries to do flash calculation in the critical region. We determine the MMP to be 31.5 MPa by extrapolation, which is in excellent agreement with the calculated MMP (31.3 MPa) reported in the literature.20 Since there is some disagreement in the literature about the value of kij between CO2 and C16H34, in this work we fit this parameter to reproduce the MMP of the model oil with pure CO2 at 50 °C. The MMPs of model oils with O2-contaminated CO2 are then predicted. As noted in Table 3, we also set kij’s involving O2 to zero. Table 6 summarizes our predictions, which are found to be reliable within the experimental error. The modeling results also demonstrate that O2 impurity in CO2 increases the MMP. We also predict the MMPs of these systems at different temperatures, i.e., 40 and 60 °C, as shown in Table 6. The calculated MMP of model oil with pure CO2 at 40 °C

5. Conclusions Laboratory studies of the effect of oxygen content in CO2 on the MMP are conducted on the n-C5H12/n-C16H34 model oil and Cottonwood Creek crude oil with three injection gases of different oxygen contents. The results indicate that the MMPs for these oils increase unfavorably as the O2 concentration in the CO2 stream increases. The experimental results are also supported by our modeling work using a multiple-mixing-cell model, which is found to capture the effects of compositions and temperature, and is found to be a robust and predictive method for determining the MMP. The extent of the MMP increase due to O2 contamination is shown to be dependent on the oil composition and temperature. At 60 °C, the MMP for Cottonwood Creek crude oil increases by 24.8% when the CO2 injection gas contains 5.19 mol % O2 and nearly 61.6% when the CO2 injection gas contains 9.99 mol % O2. For the model oil, with the same compositions of injected gases, the MMP increases by only 11.9% and 25.3%, respectively. Our calculations also indicate that the effect of N2 impurity on the MMP is larger than that of O2 impurity. Acknowledgment This work is funded by the University of Wyoming’s Enhanced Oil Recovery Institute. Literature Cited (1) Yuan, H.; Johns, R. T.; Egwuenu, A. M.; Dindoruk, B. Improved MMP Correlations for CO2 Floods Using Analytical Gasflooding Theory. SPE ReserVoir EVal. Eng. 2005, 8, 418-425. (2) Dong, M. Z; Huang, S.; Steve, B. D.; Mourits, F. M. A Comparison of CO2 Minimum Miscibility Pressure Determinations for Weyburn Crude Oil. J. Pet. Sci. Eng. 2001, 31, 13-22. (3) Zuo, Y. X.; Chu, J. Z.; Ke, Sh. L.; Guo, T. M. A Study on the Minimum Miscibility Pressure for Miscible Flooding Systems. J. Pet. Sci. Eng. 1993, 8, 315-328. (4) Bon, J.; Sarma, H. K.; Theophanous, A. M. An Investigation of Minimum Miscibility Pressure for CO2-Rich Injection Gases with Pentanesplus Fraction. SPE 2005, 97536, 1-12. (5) Emera, M. K.; Sarma, H. K. Use of Genetic Algorithm to Estimate CO2-Oil Minimum Miscibility PressuresA Key Parameter in Design of CO2 Miscible Flood. J. Pet. Sci. Eng. 2005, 46, 37-40. (6) Wang, Y.; Orr, F. M., Jr. Analytical Calculation of Minimum Miscibility Pressure. Fluid. Phase. Equilib. 1997, 139, 101-124. (7) Holm, L. W.; Josendal, V. A. Effect of Oil Composition in Miscibletype Displacement by Carbon Dioxide. SPE J. 1982, 87-95. (8) Kovarik, F. S. A Minimum Miscibility Pressure Study Using Impure CO2 and West Texas Oil System: Data Base, Correlations and Compositional Simulation. SPE 1985, 14689, 1-8. (9) Yellig, W. F.; Metcalfe, R. S. Determination and Prediction of CO2 Minimum Miscibility Pressures. J. Pet. Technol. 1980, 160-170.

Ind. Eng. Chem. Res., Vol. 46, No. 4, 2007 1401 (10) Alston, R. B.; Kokolis, G. P.; James, C. F. CO2 Minimum Miscibility Pressure: A Correlation for Impure CO2 Streams and Live oil Systems. SPE J. 1985, 268-274. (11) Zhang, P. Y.; Huang, S.; Sayegh, S.; Zhou, X. L. Effect of CO2 Impurities on Gas Injection EOR process. SPE 2004, 89477, 1-10. (12) Orr, F. M., Jr.; Dindoruk, B.; Johns, R. T. Theory of Multicomponent Gas/Oil Displacements. Ind. Eng. Chem. Res. 1995, 34 (8), 26612669. (13) Zhao, G. B.; Adidharma, H.; Towler, B.; Radosz, M. Using a Multiple-Mixing-Cell Model to Study Minimum Miscibility Pressure Controlled by Thermodynamic Equilibrium Tie lines. Ind. Eng. Chem. Res. 2006, 45 (23), 7913-7923. (14) Orr, F. M., Jr.; Johns, R. T.; Dindoruk, B. Development of Miscibility in Four Component CO2 Floods. SPE ReserVoir Eng. 1993, 8 (2), 135-142. (15) Johns, R. T.; Dindoruk, B.; Orr, F. M. Analytical theory of combined condensing/vaporizing gas drives. SPE AdV. Tech. Ser. 1993, 2, 7-16. (16) Xie, X.; Weiss, W. W.; Tong, Z.; Morrow, N. R. Improved Oil Recovery from Carbonate Reservoirs by Chemical Stimulation. SPE J. 2005, 276-285. (17) Chen, C.; Duran, M. A.; Radosz, M. Phase equilibria in polymer solutions. Block-algebra, simultaneous flash algorithm coupled with SAFT equation of state, applied to single-stage supercritical antisolvent fractionation of polyethylene. Ind. Eng. Chem. Res. 1993, 32 (12), 31233127.

(18) Christiansen, R. L.; Haines, H. K. Rapid Measurement of Minimum Miscibility Pressure with the Rising-Bubble Apparatus. SPE ReserVoir Eng. 1987, 523-527. (19) Elsharkawy, A. M.; Poettmann, F. H.; Christiansen, R. L. Measuring Minimum Miscibility Pressure: Slim-Tube or Rising-Bubble Method. SPE/ DOE 1992, 24114, 1-14. (20) Jessen, K.; Stenby, E. H.; Orr, F. M., Jr. Interplay of Phase Behavior and Numerical Dispersion in Finite-Difference Compositional Simulation. SPE J. 2004, 193-201. (21) Danesh, A. PVT and Phase BehaViour of Petroleum ReserVoir Fluids; Elsevier Science B.V: New York, 1998. (22) Jaubert, J.-N.; Wolff, L.; Neau, E.; Avaullee, L., A Very Simple Multiple Mixing Cell Calculation To Compute the Minimum Miscibility Pressure Whatever the Displacement Mechanism. Ind. Eng. Chem. Res. 1998, 37 (12), 4854-4859. (23) Jaubert, J.-N.; Arras, L.; Neau, E.; Avaullee, L. Properly Defining the Classical Vaporizing and Condensing Mechanisms When a Gas Is Injected into a Crude Oil. Ind. Eng. Chem. Res. 1998, 37 (12), 48604869. (24) Jaubert, J.-N.; Avaullee, L.; Pierre, C. Is It Still Necessary to Measure the Minimum Miscibility Pressure? Ind. Eng. Chem. Res. 2002, 41 (2), 303-310.

ReceiVed for reView October 4, 2006 ReVised manuscript receiVed November 21, 2006 Accepted December 4, 2006 IE061279G