Ant colony optimization for history matching and uncertainty quantification of reservoir models

This paper introduces a new stochastic approach for assisted 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 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 for history matching has been illustrated on two reservoir simulation cases. The first case is Teal South model which is a real reservoir with a simple structure and a single producing well. History matching of this model is a low dimensional problem with eight parameters. The second case study is PUNQ-S3 reservoir which has a more complex geological structure than Teal South model. This problem entails solving a high dimensional optimization problem.

Research highlights
► ACO is a good candidate for generating multiple history-matched models.
► ACOR obtains better models than NA.
► ACOR converges faster than NA.
► Production data used in assisted history matching affects optimization algorithm.
► ACOR is successful in estimation of ultimate recovery in Teal South and PUNQ-S3 reservoirs.