Coupling Equation-of-State Compositional and Surfactant Models in a Fully Implicit Parallel Reservoir Simulator Using the Equivalent-Alkane-Carbon-Number Concept
- Choongyong Han (Chevron ETC) | Mojdeh Delshad (University of Texas at Austin) | Gary A. Pope (University of Texas at Austin) | Kamy Sepehrnoori (University of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- June 2009
- Document Type
- Journal Paper
- 302 - 310
- 2009. Society of Petroleum Engineers
- 2.5.2 Fracturing Materials (Fluids, Proppant), 5.5 Reservoir Simulation, 5.5.1 Simulator Development, 5.4.1 Waterflooding, 5.2.1 Phase Behavior and PVT Measurements, 4.3.4 Scale, 5.2.2 Fluid Modeling, Equations of State, 4.1.2 Separation and Treating, 5.7.2 Recovery Factors, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 5.6.5 Tracers, 5.3.2 Multiphase Flow, 5.1 Reservoir Characterisation, 4.6 Natural Gas
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Equation-of-state (EOS) compositional and surfactant models are coupled in a fully implicit parallel reservoir simulator using the equivalent alkane carbon number (EACN) of the oleic phase. The EACN of the oleic phase is computed using a mole-fraction-weighted carbon number for each component present in the oleic phase. Important microemulsion properties such as optimum salinity and optimum solubilization parameter as a function of the EACN of the oleic phase are implemented on the basis of known correlations. Type II(-) surfactant phase behavior is considered in this study. The simulator developed is validated using our implicit-pressure/explicit-concentration (IMPEC) chemical-flooding simulator. Case studies, including a large-scale simulation, emphasize that surfactant floods should be modeled carefully, taking the EACN of crude oil into consideration for more realistic and accurate oil-recovery predictions.
Surfactant flooding is one of the most effective methods to improve oil recovery: Dissolving oil in the aqueous phase results in lowering the interfacial tension between the oleic and aqueous phases. It is often accompanied by polymer flooding to increase sweep efficiency. The surfactant/polymer flooding has become a more competitive process under the current circumstances of high oil prices, and oil companies are considering the process more seriously to rejuvenate their mature fields (Chang et al. 2006; Anderson et al. 2006).
Phase behavior of a brine/oil/surfactant formulation is one of the key factors determining the enhanced oil recovery by surfactant flooding. Early experimental studies (Salager et al. 1979a; Salager et al. 1979b; Barakat et al. 1983; Baran et al. 1994) have shown that the surfactant-phase behavior is a strong function of the hydrocarbon composition of the crude oil. Surfactant properties such as optimum salinity and solubilization parameters were found as a function of the EACN of the oil. The EACN is a mole-fraction-weighted carbon number for each component present in the oleic phase. For example, the EACN is 9.0 if the oil consists of octane, nonane, and decane with mole fraction of 0.3:0.4:0.3, respectively.
When chemical flooding is applied to a reservoir with either free or dissolved gas, the oil composition may change considerably because of the mass transfer between the gaseous and oleic phases. This change results in spatial variation of the EACN of oil and, therefore, affects the surfactant-phase behavior and, ultimately, the oil recovery and process performance. However, the effect of oil composition or oil EACN on the surfactant-phase behavior often has been neglected when modeling such processes by assigning constant optimum salinity and solubilization parameters for the specific reservoir crude oil and surfactant.
Few chemical-flooding simulators other than the University of Texas Chemical Compositional Simulator, UTCHEM, have been developed to consider the effect of spatial variation of the EACN on the surfactant-phase behavior. The simulator is a 3D, multiphase, multicomponent chemical-flooding simulator and has been used extensively and validated with laboratory and field data (Delshad et al. 1996). However, it is IMPEC code and, in its current form, cannot run on parallel computers. Therefore, if a large number of gridblocks are necessary for either simulation of a large reservoir or refinement of a model for more accurate chemical-flooding simulation, run time can be very long because of the timestep restriction. Also, computational memory of a single processor can be insufficient for the large problem size. To overcome this computational limitation, a fully implicit, parallel, EOS compositional chemical flooding simulator, called the General Purpose Adaptive Simulator (GPAS), has been in development (Han et al. 2007).
Currently, GPAS can model only two phases under optimum Type II(-) surfactant phase behavior, which is explained in the next section. Several other modeling features relating to chemical flooding, such as cation exchange and chemical reactions, are not yet available in the simulator. However, with the fully implicit scheme and the capability of parallel computation, it has shown the ability to perform field-scale, high-resolution surfactant/polymer-flood simulations with more than one million gridblocks and taking large timesteps (Han et al. 2007).
The subject of this paper is the implementation of EACN and its effect on surfactant phase behavior in GPAS. Currently, this implementation considers only Type II(-) surfactant phase behavior. However, this work is the first and crucial step toward a more accurate model of chemical phase behavior in oil reservoirs with dissolved or free gas present and the processes that involve the coinjection of gas and surfactant such as foam or surfactant alternating gas (SAG).
|File Size||1 MB||Number of Pages||9|
Anderson, G.A., Delshad, M., King, C.B., Mohammadi, H., and Pope, G.A. 2006.Optimization of Chemical Floodingin a Mixed-Wet Dolomite Reservoir. Paper SPE 100082 presented at theSPE/DOE Symposium on Improved Oil Recovery, Tulsa, 22-26 April. doi:10.2118/100082-MS.
Aoudia, M., Wade, W.H., and Weerasooriya, V. 1995. Optimum MicroemulsionsFormulated With Propoxylated Tridecyl Alcohol Sodium Sulfates. Journalof Dispersion Science and Technology 16 (2): 115-135.doi:10.1080/01932699508943664.
Barakat, Y., Fortney, L.N., Schechter, R.S., Wade, W.H., Yiv, S.H., andGraciaa, A. 1983. Criteria for StructuringSurfactants to Maximize Solubilization of Oil and Water: II. Alkyl BenzeneSodium Sulfonates. J. of Colloid and Interface Science92 (2): 561-574. doi:10.1016/0021-9797(83)90177-7.
Baran, J.R. Jr., Pope, G.A., Wade, W.H., Weerasooriya, V., and Yapa, A.1994. MicroemulsionFormation with Mixed Chlorinated Hydrocarbon Liquids. J. of Colloid andInterface Science 168 (1): 67-72.doi:10.1006/jcis.1994.1394.
Barker, J.W. 1990. Co-Deployment of Surfactant/Polymerand Miscible Gas Enhanced Oil Recovery Processes: A Simulation Study. PaperSPE 20236 presented at the SPE/DOE Enhanced Oil Recovery Symposium, Tulsa,22-25 April. doi: 10.2118/20236-MS.
Chang, H.L., Zhang, Z.Q., Wang, Q.M., Xu, Z.S., Guo, Z.D., Sun, H.Q., Cao,X.L., and Qiao, Q. 2006. Advancesin Polymer Flooding and Alkaline/Surfactant/Polymer Processes as Developed andApplied in the People's Republic of China. J. Pet Tech58 (2): 84-89. SPE-89175-PA. doi: 10.2118/89175-MS.
Delshad, M., Pope, G.A., and Sepehrnoori, K. 1996. A Compositional Simulatorfor Modeling Surfactant Enhanced Aquifer Remediation, 1 Formulation. J.Contamin. Hydrol. 23 (4): 303-327.doi:10.1016/0169-7722(95)00106-9.
Han, C., Delshad, M., Sepehrnoori, K., and Pope, G.A. 2007. A Fully Implicit, Parallel,Compositional Chemical Flooding Simulator. SPE J. 12(3): 322-338. SPE-97217-PA. doi: 10.2118/97217-PA.
Hand, D.B. 1939. Dineric Distribution: I. The Distribution of a ConsoluteLiquid Between Two Immiscible Liquids. J. of Physics and Chem.34: 1961-2000.
Høier, L. and Whitson, C.H. 2001. Compositional Grading--Theory andPractice. SPE Res Eval & Eng 4 (6): 525-535.SPE-74714-PA. doi: 10.2118/74714-PA.
Lake, L. 1989. Enhanced Oil Recovery. Englewood Cliffs, New Jersey:Prentice Hall.
Salager, J.L., Bourrel, M., Schechter, R.S., and Wade, W.H. 1979a. Mixing Rules for OptimumPhase-Behavior Formulations of Surfactant/Oil/Water Systems. SPE J.19 (5): 271-278. SPE-7584-PA. doi: 10.2118/7584-PA.
Salager, J.L., Morgan, J.C., Schechter, R.S., Wade, W.H., and Vasquez, E.1979b. Optimum Formulation ofSurfactant/Water/Oil Systems for Minimum Interfacial Tension or PhaseBehavior. SPE J. 19 (2): 107-115. SPE-7054-PA. doi:10.2118/7054-PA.