Ants can do history matching

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 behavior 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 the 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 behavior - in our case the production history of the reservoir. The use of ACO in history-matching has been illustrated on PUNQ-S3 reservoir simulation model in North Sea with 45 unknown parameters. This model is fitted to multivariate production data coming from multiple wells. The ACO algorithm is also integrated within a Bayesian framework to quantify uncertainty of the predicted ultimate recovery. Results confirm that Ant Colony Optimization can be used to generate multiple history-matched reservoir models and accurately predict the final recovery from the reservoir after 16.5 years. We have compared the history matching results of ACO algorithm with the Neighborhood Algorithm (NA) and NA coupled with a geostatistical framework applied to the same problem. This comparison indicates that ACO obtains the history matched models from fewer simulations. We conclude that Ant Colony Optimization is a fast, reliable and promising tool for reservoir history matching and uncertainty quantification.