Uncertainty Quantification and Feedback Control Using a Model Selection Approach Applied to a Polymer Flooding Process

Abstract

Polymer flooding is economically successful in reservoirs where the water flood mobility ratio is high, and/or the reservoir heterogeneity is adverse, because of the improved sweep resulting from mobility-controlled oil displacement. The performance of a polymer flood can be further improved if the process is dynamically controlled using updated reservoir models and by implementing a closed-loop production optimization scheme. This paper presents a feedback control framework that: assesses uncertainty in reservoir modeling and production forecasts, updates the prior uncertainty in reservoir models by integrating continuously monitored production data, and formulates optimal injection/production rates for the updated reservoir models. The value of the feedback control framework is demonstrated with a synthetic example of polymer flooding where the economic performance was maximized.