A Characteristic Parameter-Based Method for Predicting Oil Production Dynamics During Chemical Flooding

Chemical flooding commonly exhibits a non-monotonic oil-production response characterized by an initial increase, peak formation, and subsequent decline. Quantitatively characterizing this response with limited field data remains challenging. This study develops a characteristic-parameter-based framework for reconstructing annual oil-production dynamics during polymer and alkaline–surfactant–polymer (ASP) flooding in Class I reservoirs. The production-response function is parameterized using peak time, maximum annual oil production, and a curve-shape parameter. Empirical regression models are then established to relate these characteristics to reservoir and injection variables. Data from nine polymer-flooding blocks and eight ASP-flooding blocks were used for regression analysis. Model performance was assessed through historical fitting and representative production reconstructions using annual weighted absolute percentage error (WAPE) and cumulative production error (CPE). For the polymer-flooding case, WAPE and CPE were 19.87% and 3.05%, respectively, while the corresponding errors for the ASP-flooding case were 22.85% and 7.37%. The results demonstrate that the framework captures the principal production-response trends and provides quantitative relationships between production characteristics and development conditions. This approach supports interpretable production-response analysis under the investigated reservoir and injection conditions.