Ant Colony Optimization for History Matching

Abstract

Abstract This paper introduces a new stochastic approach for automatic history matching based on a continuous ant colony optimization algorithm. Ant colony optimization (ACO) is a multi-agent optimization algorithm inspired by the behaviour of real ants. ACO is able to solve difficult optimization problems in both discrete and continuous variables. In the ACO algorithm, each artificial ant in the colony searches for good models in different regions of parameter space and shares information about the quality of the models with other agents. This gradually guides the colony towards models that match the desired behaviour – in our case the production history of the reservoir. The use of ACO history-matching has been illustrated on a reservoir simulation case for Gulf of Mexico which showed that Ant Colony optimization can be used to generate multiple history-matched reservoir models.