Improved Fluid Characterization for Miscible Gas Floods
- Azubuike M. Egwuenu (U. of Texas at Austin) | Russell T. Johns (U. of Texas at Austin) | Yinghui Li (U. of Texas at Austin)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2008
- Document Type
- Journal Paper
- 655 - 665
- 2008. Society of Petroleum Engineers
- 5.2.1 Phase Behavior and PVT Measurements, 5.5 Reservoir Simulation, 4.1.5 Processing Equipment, 5.2 Reservoir Fluid Dynamics, 5.4.1 Waterflooding, 4.6 Natural Gas, 5.4.2 Gas Injection Methods, 5.6.4 Drillstem/Well Testing, 4.3.4 Scale, 5.2.2 Fluid Modeling, Equations of State, 5.2 Fluid Characterization, 5.3.2 Multiphase Flow, 4.1.4 Gas Processing, 5.3.1 Flow in Porous Media, 5.4.9 Miscible Methods, 4.1.2 Separation and Treating
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Equations of state (EOSs) are typically tuned to black-oil pressure/volume/temperature (PVT) data such as constant volume-depletion, constant-composition-expansion, differential-liberation, and separator tests. Other PVT data more appropriate for gas injection could include multicontact and swelling tests and slimtube tests. The standard method of tuning, however, does not typically incorporate important displacement parameters, such as the minimum miscibility pressure (MMP), minimum miscibility enrichment (MME), or the likely compositions that result in a reservoir from condensing-vaporizing (CV) displacements.
This paper demonstrates an improved reservoir-fluid-characterization procedure for miscible gas floods that can represent the interaction of flow and phase behavior more accurately. We demonstrate the approach for two displacements, an 11-component CO2 flood and a 12-component enriched-gas flood. The method-of-characteristic (MOC) theory is used to determine the MME (or MMP) of both lumped and unlumped models. The results show that by tuning to the calculated MME/MMP, fewer pseudocomponents are required to characterize the fluid than with conventional tuning methods. For the cases studied, fluids lumped to as few as four or five pseudocomponents can provide a good match to the composition profiles and oil recoveries of the unlumped models.
Gas injection into oil reservoirs results in complex interactions of flow with phase behavior that often are not modeled accurately by black-oil simulation. This is especially true for miscible or nearly miscible floods in which significant mass transfer occurs between the hydrocarbon phases. Such floods are modeled best by compositional simulation.
A significant disadvantage of compositional simulation, however, is that it is more computationally intensive than black-oil simulation. The primary reason for the increased computational time is the result of solving repeated flash calculations with cubic EOSs. The use of fewer pseudocomponents could reduce the flash computation time, but fewer components results in poor fluid characterizations and reduced accuracy.
Reservoir oils typically are subjected to standard black-oil PVT experiments that give volumetric behavior for recovery predictions from conventional methods, such as waterflooding. These experiments include constant-volume-depletion, differential-liberation, constant-composition-expansion, and separator tests. Standard PVT experiments, however, do not provide sufficient phase-behavior data in the range of compositions that result from mixing of gas with resident oil.
For gas floods, multicontact experiments, along with swelling tests and slimtube experiments, are sometimes performed (Pedersen et al. 1989). Most gasfloods, such as those with CO2 and enriched-gas injection, however, have features of both condensing and vaporizing drives (Zick 1986; Stalkup 1987; Johns et al. 1993). Miscibility in these CV drives is developed in the transition zone between the condensing and vaporizing regions at an equilibrium tie line, known as the crossover tie line (Johns et al. 1993, 2002; Johns and Orr 1996). Multicontact tests attempt to mimic the composition paths that result from either vaporizing or condensing drives, but not both. Thus, these tests do not provide sufficient PVT data in the compositional range of interest, especially in the transition zone near the critical region in which miscibility is developed in CV drives.
Slimtube tests can and should be used to tune an EOS by matching the experimental recoveries with 1D compositional simulations (Shanin and Kremesec 1992). Slimtube tests, however, are expensive and time-consuming to obtain, and their recoveries can be affected by dispersion and relative permeabilities (Johns et al. 1994; Solano et al. 2001). Slimtube tests are not always available, and even if they are, it would be helpful to have a method that is not dependent on the level of dispersion or relative permeability parameters, and one that is very fast so that regression of the MMP/MME is possible. Recent research has shown how to calculate the dispersion-free MMP/MME from an EOS by MOC (Jessen et al. 1998; Wang and Orr 2002; Yuan 2003; Yuan and Johns 2005).
EOS are used to predict the compositions and volumetric behavior that result when oil and gas mix in the reservoir. These EOS fluid characterizations must be tuned to match the PVT behavior of the original reservoir fluid. The process of tuning an EOS involves: (1) selection of the pseudocomponents, (2) determination of EOS properties for the pseudocomponents, and (3) adjustment of pseudocomponent EOS properties by regression to the PVT data.
The fluid characterizations that result from the lumping and tuning process are dependent on the method used and the experimental PVT data available (Pedersen et al. 1989). Often the tuning process involves iteration and subjectivity concerning which parameters to regress and the number of pseudocomponents to use. The usual approach is first to lump the original fluid analysis to as few as 12 to 15 components and pseudocomponents. This EOS model is tuned to match the available PVT data, and it can be lumped into fewer pseudocomponents as needed.
There are several methods for lumping components into pseudocomponents and determining their EOS properties (Danesh 1998; Pedersen and Christensen 2006). The simplest methods assign pseudocomponents on the basis of component mole fractions (Cotterman and Prausnitz 1985), mass fractions (Pedersen et al. 1985), ranges in molecular weights (Whitson 1983), and K -values (Li et al. 1985; Newley and Merrill 1991) pore-complex methods include the statistical approach of Mehra et al. (1982). The method used in this research is that of Newley and Merrill (1991), which is based on K-values at some selected feed composition. We use this method because analytical theory has demonstrated that components within the reservoir are chromatographically separated by their K-values (Orr 2007).
Several regression procedures have been suggested for tuning EOS characterizations (Hong 1982; Fong et al. 1992; Khan et al. 1992; Liu 1999; Zurrita and McCain 2002). The selection of parameters to tune to match a set of PVT data is more of an art than an exact science. Adjusting too many parameters could result in poor PVT predictions away from the range of the measured PVT data. Jhaveri and Youngren (1984) recommend classifying PVT experimental data into volumetric and compositional data. Preselected EOS parameters are adjusted to match the compositional data first, and, then, volumetric data are matched by adjusting the volumetric-shift parameters. Pedersen and Christensen (2006) showed that fluid characterizations can predict fluid properties better when most binary-interaction parameters (BIPs) between hydrocarbon components are zero. Typically, the parameters associated with the heaviest pseudocomponents are adjusted by up to 10% to match the compositional data because these components have properties with the largest measurement uncertainties (Danesh 1998; Christensen 1999; Pedersen and Christensen 2006).
This paper presents a method to improve fluid characterizations that can account for the complex composition paths that result from a CV process. Such a method can be used to reduce the number of required pseudocomponents for use in compositional simulation. The proposed method is based on matching all available PVT data and the analytical calculation of MMP/MME from the lumped EOS models to the original unlumped fluid characterizations. The lumping and tuning procedure is demonstrated for 11-component and 12-component oil displacements by gas using the Peng-Robinson EOS (Peng and Robinson 1976).
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