Upscaling of Miscible Floods in Heterogeneous Reservoirs Considering Reservoir Mixing

Abstract

Summary Inaccurate modeling of reservoir mixing by using large gridblocks in compositional simulation can affect recoveries significantly in miscible gasfloods and lead to inaccurate predictions of recovery performance. Reservoir mixing or dispersion is caused by diffusion of particles across streamlines; mixing can be enhanced significantly if the surface area of contact between the reservoir and injected fluid is increased as fluids propagate through the reservoir. A common way to convert geological models into simulation models is to upscale permeabilities on the basis of reservoir heterogeneity. Upscaling affects the degree of mixing that is modeled, but the importance of reservoir mixing in upscaling is largely ignored. This paper shows how to estimate the level of mixing in a reservoir and how to incorporate mixing into the upscaling procedure. We derive the key scaling groups for first-contact miscible (FCM) flow and show how they have an impact on reservoir mixing. Heterogeneities are assumed to dominate the flow regime so that gravity effects are negligible. We examine only local mixing, not apparent mixing caused by variations in streamline path lengths (convective spreading). Local mixing is important because it affects the strength of the injected fluid and can cause an otherwise multicontact miscible (MCM) flood to become immiscible. More than 1,000 2D numerical simulations are carried out using experimental design to estimate dispersivity as a function of the derived scaling groups. We show that reservoir mixing is enhanced owing to fluid propagation through heterogeneous media. Because mixing is dependent on heterogeneities, upscaling is an iterative process in which the level of mixing in both the longitudinal and transverse directions must be matched from the fine to the coarse scale. The most important groups that affect mixing are the mobility ratio, dispersion number, correlation lengths, and the Dykstra-Parson coefficient. Large dispersion numbers yield greater dispersivities away from the injection well. We show through simulations of both FCM and MCM floods that gridblock size can be increased significantly when reservoir mixing is large. Heterogeneous reservoirs with large longitudinal correlation lengths can be upscaled to larger gridblocks than reservoirs with random permeability fields. This paper shows how to determine a priori the maximum gridblock size allowed in both the x- and z-directions to predict the oil recovery from miscible gasfloods accurately.