Polymer Flooding Design and Optimization Under Uncertainty

Abstract

Abstract We use a streamline-based simulator that accurately captures non-Newtonian rheology and controls numerical dispersion to investigate polymer flooding design. First, we develop and test a parallel design algorithm to optimize polymer floods with respect to net present value in terms of slug size, polymer concentration and initiation; in which simulations are ran simultaneously and the results are combined through scaling of optimal slug size. In terms of optimal strategies, the optimization results illustrate that polymer-flooding design – with respect to concentration, slug size, and initiation – is more intuitive than earlier expected. It is always beneficial to start polymer flooding as soon as possible preferably before any waterflooding. The optimal slug size is close to being continuous. The optimal concentration is generally high and represents a balance between mobility gains and injectivity losses. Second, we quantify the impact of uncertainty on both the design and profitability of polymer flooding. This serves as a guide to associated data acquisition efforts, where pre-polymer flooding initiation, efforts can be focused on reducing uncertainties of high impact factors thereby increasing the probability of success. The same methods can be applied to other augmented waterfloods, such as low salinity flooding.