High-Resolution Prediction of Enhanced Condensate Recovery Processes

Abstract

Summary This paper investigates the accuracy of first- and high-order numerical methods in simulating enhanced condensate processes in 1D, 2D, and 3D. We compare the predictions of a standard single point upwind (SPU) scheme with a third-order accurate finite difference (FD) simulator based on a third-order essentially nonoscil-latory (ENO) flux reconstruction with matching temporal accuracy. We include physical dispersion in the mathematical model of these multiphase, multicomponent systems. The comparisons demonstrate that SPU schemes may fail to predict the formation of the mobile liquid bank at the leading edge of the displacement unless an impractical number of gridblocks is used in the simulations. In contrast, the high-order FD simulator is demonstrated to accurately predict the liquid bank at much lower grid resolution, providing for a more efficient simulation approach. In 2D displacement calculations with gravity included, the CPU requirement of the SPU scheme was found to be more than 50 times larger than for the ENO scheme for a given level of accuracy. In 2D vertical cross-sections, the predicted component recovery is demonstrated to vary upward of 8% depending on the selected numerical scheme for a given grid resolution and disper-sivity. In these settings, the SPU solutions converge to the ENO results upon significant grid refinement. In 3D displacement calculations, the magnitude ofthe predicted condensate bank is also found to be very different depending on the selected numerical scheme. Relative to the 2D displacement calculations, condensate banking and gravity segregation is observed to have less impact on the process performance prediction because of the permeability configuration in the 3D model used here, but it could have a high impact in different settings. We include an explicit representation of longitudinal and transverse dispersion in the porous medium to demonstrate the grid resolution required to resolve physical dispersion at a given simulation length scale, and to show that condensate banks can also form in more realistic dispersive systems. Grid-refinement studies in 1D and 2D demonstrate, again, that the ENO scheme outperforms the SPU scheme for a given Peclet (Pe) number. Converged solutions are obtained with the ENO scheme using a relatively small number of grid cells. In addition, we show the behavior of the two schemes for varying Peclet numbers on a fixed simulation grid. For this grid, the ENO scheme is shown to be sensitive to the Peclet number, signifying that physical dispersion is not overwhelmed by numerical diffusion. For the SPU scheme, however, the solutions are almost independent of the Peclet number, which indicates that numerical diffusion dominates.