An improved Ant Colony Optimization (ACO) technique for estimation of flow functions (kr and Pc) from core-flood experiments

• Developing a new efficient version of Ant Colony Optimization (ACO) for the continuous inverse problem.
• Simultaneous estimation of kr and pc using Ant Colony Optimization (ACO) from corefoold experiments.
• Capability of the ACO technique to be linked to any simulation program for optimizing different engineering processes.

Oil and gas production from petroleum reservoirs are significantly affected by rock and fluid properties. Relative permeability (kr) and capillary pressure (Pc) are two key flow functions in the petroleum reservoir that are employed in numerical modeling to predict oil and gas production in future. The history matching (optimization) techniques are generally used to accurately estimate the flow functions from the results of core flood experiments.In this study, Ant Colony optimization (ACO) method is modified for the application in the continuous inverse problem to adjust some unknown variables by either minimization or maximization of objective function. Then this technique is implemented to estimate oil, water and gas flow functions from coreflood experiments. Some new ideas and innovations are proposed to improve the performance and convergence of ACO algorithm. This algorithm is an automated history matching technique that can estimate relative permeability and capillary pressure simultaneously. Moreover, this feature enables us to incorporate different mechanisms (i.e. viscous force, gravitational force and capillary force) of the core experiments in the estimation of flow functions. Having developed the algorithm, the validity of this method is tested using two sets of coreflood experiments including gas-oil and oil-water systems. The comparison between actual values of flow functions (kr and Pc) and those obtained from ACO method depicted good agreement and adequate accuracy. Furthermore, the investigation of objective function value versus iteration number demonstrated that the algorithm is converging to the optimal value.