Estimating Relative Permeability and Capillary Pressure Functions from Unsteady-state Core Flooding Data Using Dynamic Optimization Techniques

Accurate estimation of the relative permeability and capillary pressure functions is necessary for effectively managing oil and gas reservoirs. Various methods for estimating these properties exist, but mathematical modelling-based strategies have shown promise for accurate estimates. We propose a Pyomo-based mathematical modelling dynamic optimization approach for estimating relative permeability and capillary pressure functions from unsteady-state core flooding experimental data. The approach solves the inverse problem to estimate the unknown function parameters that characterize the evolution of relative permeability in the porous medium and the forward problem to verify the accuracy of the estimated parameters and estimate the capillary pressure function trend. Our results demonstrate the effectiveness of the proposed method in estimating relative permeability and capillary pressure functions from unsteady-state core flooding data and highlight the potential for this approach to enhance reservoir management strategies.