A New Approach to Forecasting Miscible Water Alternating Gas Performance at the Field Scale

Abstract

Summary Full-field enhanced oil recovery (EOR) performance predictions are generally obtained from scale-up tools, because three-dimensional (3D) finite-difference simulations would be too CPU-inten- sive. Existing scale-up techniques require the user to define pattern elements and then to derive performance curves to apply to each injector-producer pair in the elements. Accurate assignment of these elements is difficult because the actual shape and size of the swept volumes are sensitive to reservoir faulting, well-rate changes, and regional flux. In reality, the actual sweep region is not an input parameter, but should be determined by the regional pressure field, which changes as well rates vary and new wells are drilled. Thus, a major source of error in using existing scale-up tools is trying to define representative pattern elements. In the current paper, we describe a scale-up technique in which the user does not have to define pattern elements or injector-producer pairs. In the new technique, the pressure field is computed at each timestep and then a front-tracking algorithm propagates water and miscible injectant throughout the reservoir. The miscible-gas process is modeled by means of an analogy between oil mobilization and adsorption/ desorption of tracers. The parameters for the model are obtained by fine-scale, two-dimensional (2D), compositional, finite-difference simulations in a vertical cross section. In the new approach, the injected solvent is divided into an effective and an ineffective portion. This approach reduces a 3D problem to a 2D, areal one in which the declining displacement efficiency of the solvent, which is caused by vertical effects, is captured by decreasing the injected concentration of effective solvent with time. In this paper, we show how the new scale-up tool has been used to model the miscible water-alternating-gas (MWAG) process in the Eastern Peripheral Wedge zone (EPWZ) of the Prudhoe Bay field. We show a comparison between field response and model predictions.