Ant colony optimization for history matching

Yasin Hajizadeh, Michael A. Christie, Vasily Demyanov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationEUROPEC/EAGE Conference and Exhibition 2009
PublisherSociety of Petroleum Engineers
ISBN (Print)9781613994276
Publication statusPublished - 2009
Event2009 SPE EUROPEC/EAGE Annual Conference and Exhibition - Amsterdam, Netherlands
Duration: 8 Jun 200911 Jun 2009


Conference2009 SPE EUROPEC/EAGE Annual Conference and Exhibition

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Geochemistry and Petrology


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