Improved MMP Correlations for CO2 Floods Using Analytical Gas Flooding Theory

Abstract

Abstract Local displacement efficiency from CO2 gas injection is highly dependent on the minimum miscibility pressure (MMP). Correlations are often used to estimate the MMP where the injected fluid may or may not contain impurities such as methane. These correlations, however, are based on a limited set of experimental data and as such are not widely applicable. They also do not accurately account for the more complex condensing/vaporizing displacement process. This paper presents new MMP correlations for the displacement of multicomponent oil by CO2 and impure CO2. The approach is to use recently developed analytical theory for MMP calculations from equations-of-state (EOS) to generate MMP correlations for displacements by pure and impure CO2.1–8 The advantage of this approach is that MMPs for a wide range of temperatures and reservoir fluids can be calculated quickly and accurately without introducing uncertainties associated with slim-tube MMPs and other numerical methods. The improved MMP correlation is based solely on the reservoir temperature, molecular weight of C7+, and percentage of intermediates (C2 - C6) in the oil. The MMPs from the improved correlation are compared to currently used correlations and 41 experimentally measured MMPs. Correlations are also developed for impure CO2 floods, where the injection stream may contain up to 40% methane. The new correlations are significantly more accurate and applicable than currently used correlations. Introduction Whorton et al.9 received a patent in 1952 to improve oil recovery by the injection of CO2. CO2 injection has been ongoing ever since primarily because CO2 develops multicontact miscibility (MCM) with reservoir fluids at low pressures. There are also potential environmental benefits of CO2 injection in that subsurface sequestration of greenhouse gases has become an important U.S. priority.10 The minimum pressure for miscibility (MMP) is an important optimization parameter in CO2 floods. Recoveries from slim-tube experiments often give a slope change at the MMP. Above the MMP, slim-tube recoveries (or local displacement efficiencies) typically do not increase significantly with enrichment. Thus, the accurate determination of MMP is important in gas flood design. Pseudoternary diagrams have traditionally been used to explain the behavior of multicontact miscible (MCM) gas drive processes.11–16 Real oil displacements by CO2, however, have recently been shown to have features of both vaporizing and condensing drives (CV).2,17,18 The two-dimensional nature of pseudoternary diagrams often lead to incorrect interpretations especially for CV drives. Analytical theory has no such restrictions and can be applied for any number of components.1–8 The CV process greatly complicates the accurate estimation of MMP in that miscibility is developed not at the leading edge (condensing region) or trailing edge (vaporizing region) of the displacement, but is developed in between the condensing and vaporizing regions. Four primary methods have been used in recent years to determine MMPs for specific fluid displacements: slim-tube experiments,10 compositional simulation,12 mixing-cell models,19 and analytical methods.1–8 Each of these methods has advantages and disadvantages. Slim-tube experiments use real fluids, but are expensive and time consuming to perform and can give misleading results depending on the level of physical dispersion present.20 Fine-grid compositional simulations and mixing-cell models can suffer from numerical dispersion effects and are also time consuming to perform. Dispersion-free analytical methods are often very fast, but like simulation and mixing-cell models, they rely on an accurate fluid characterization by an equation-of-state (EOS). A variety of correlations for the estimation of MMP have been developed from regressions of slim-tube data. Although less accurate, correlations are quick and easy to use and generally require only a few input parameters. Hence, they are very useful for fast screening of reservoirs for potential CO2 flooding. They are also useful when detailed fluid characterizations are not available. One significant disadvantage of current MMP correlations is that the regressions use MMPs from slim-tube data, which are in themselves uncertain.