Abstract
Summary In applications of polymer flood for enhanced oil recovery (EOR), polymer injectivity is of great concern because project economics is sensitive to injection rates. In-situ non-Newtonian polymer rheology is the most crucial factor that affects polymer injectivity. There are several ongoing polymer-injection field tests in which the field injectivities differ significantly from the simulation forecasts. We have developed an analytical model to more accurately calculate and predict polymer injectivity during the field projects to help with optimum injection strategies. Significant viscosity variations during polymer flood occur in the vicinities of wellbores where velocities are high. As the size of a wellblock increases, velocity smears, and thus polymer injectivity is erroneously calculated. In the University of Texas Chemical Flooding Simulator (UTCHEM), the solution was to use an effective radius to capture the “grid effect,” which is empirical and impractical for large-scale field simulations with several hundred wells. Another approach is to use local grid refinement near wells, but this adds to the computational cost and limits the size of the problem. An attractive alternative to previous approaches is to extend the Peaceman well model (Peaceman 1983) to non-Newtonian polymer solutions. The polymer rheological model and its implementation in UTCHEM were validated by simulating single-phase polymer injectivity in coreflood experiments. On the basis of the Peaceman well model and UTCHEM polymer rheological models covering both shear-thinning and shear-thickening polymers, an analytical polymer-injectivity model was developed. The analytical model was validated by comparing results of different gridblock sizes and radial numerical simulation. We also tested a field case by comparing results of a fine-grid simulation and its upscaled coarse-grid model. A pilot-scale polymer flood was simulated to demonstrate the capability of the proposed analytical model. The model successfully captured polymer injectivity in all these cases with no need to introduce empirical parameters.
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