Perpustakaan Digital ITB

This study applies the Design of Experiments (DoE) methodology to evaluate the performance of micellar polymer flooding (MPF) in three reservoir models using the Chemical Flooding Predictive Model (CFPM) in EORgui. A two-level full factorial design with 64 simulation runs was implemented to quantify the effects of six key parameters: pattern area, porosity, permeability, net pay thickness, Dykstra Parsons coefficient, and oil viscosity. The results show that volumetric parameters, particularly pattern area, porosity, and net pay thickness, consistently have the strongest influence on cumulative oil production across the three reservoirs. Interaction terms, such as pattern area × porosity and pattern area × thickness, were also found to be significant, showing the importance of volumetric and geometric synergy in chemical flood performance. Although regression analysis highlights these dominant drivers, residual diagnostics indicate limitations in predictive adequacy, especially at higher recovery estimates. However, the study confirms the usefulness of DoE in ranking the relative importance of reservoir and production parameters, providing a screening-level framework to guide micellar polymer flooding design.