Summary
Accurate performance prediction of miscible enhanced-oil-recovery (EOR) projects or CO2 sequestration in depleted oil and gas reservoirs relies in part on the ability of an equation-of-state (EOS) model to adequately represent the properties of a wide range of mixtures of the resident fluid and the injected fluid(s). The mixtures that form when gas displaces oil in a porous medium will, in many cases, differ significantly from compositions created in swelling tests and other standard pressure/volume/temperature (PVT) experiments. Multicontact experiments (e.g., slimtube displacements) are often used to condition an EOS model before application in performance evaluation of miscible displacements. However, no clear understanding exists of the impact on the resultant accuracy of the selected characterization procedure when the fluid description is subsequently included in reservoir simulation.
In this paper, we present a detailed analysis of the quality of two different characterization procedures over a broad range of reservoir fluids (13 samples) for which experimental swelling-test and slimtube-displacement data are available. We explore the impact of including swelling-test and slimtube experiments in the data reduction and demonstrate that for some gas/oil systems, swelling tests do not contribute to a more accurate prediction of multicontact miscibility. Finally, we report on the impact that use of EOS models based on different characterization procedures can have on recovery predictions from dynamic 1D displacement calculations.
Introduction
During the past few decades, a significant effort has been invested in the studies and development of improved-oil-recovery processes. From a technical point of view, gas injection can be a very efficient method for improving the oil production, particularly in the case when miscibility develops during the displacement process. The lowest pressure at which a gas should be injected into the reservoir to obtain the multicontact miscible displacement—the minimum miscibility pressure (MMP)—has consequently attained a very important status in EOR studies.
Various methods for measuring and calculating the MMP have been proposed in the literature. Many of these are based on simplifications such as the ternary representation of the compositional space. This method fails to honor the existence of a combined mechanism controlling the development of miscibility in real reservoir fluids. Zick (1986) and Stalkup (1987) described the existence of the condensing/vaporizing mechanism. They showed that the development of miscibility (MMP) in multicomponent gas-displacement processes could, independent of the mechanism controlling the development of miscibility, be predicted accurately by 1D compositional simulations. A semianalytical method for predicting the MMP was later presented by Wang and Orr (1997), who played an important role in the development and application of the analytical theory of gas-injection processes. Jessen et al. (1998) subsequently developed an efficient algorithm for performing these calculations, reducing the MMP calculation time to a few seconds even for fluid descriptions of 10 components or more. Later, Jessen et al. (2001) used this approach to generate approximate solutions to the dispersion-free, 1D-displacement problem for multicomponent gas-injection processes.
Analytical and numerical methods for predicting the performance of a gas-injection process depend on an EOS to predict the phase behavior of the mixtures that form in the course of a displacement process. The role of the phase behavior in relation to numerical diffusion in compositional reservoir simulation has been pointed out previously by Stalkup (1990) and by Stalkup et al. (1990). Recently, Jessen et al. (2004) proposed a method to quantify the interplay of the phase behavior and numerical diffusion in a finite-difference simulation of a gas-injection process. By analyzing the phase behavior of the injection-gas/reservoir-fluid system, a measure of the impact, referred to as the dispersive distance, can be calculated. The dispersive distance is useful when designing and interpreting large-scale compositional reservoir simulations.
