Ant colony optimization algorithm for history matching

Yasin Hajizadeh, Mike Christie, Vasily Demyanov

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

    20 Citations (Scopus)

    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. Copyright 2009, Society of Petroleum Engineers.

    Original languageEnglish
    Title of host publicationSociety of Petroleum Engineers - 71st European Association of Geoscientists and Engineers Conference and Exhibition 2009
    Pages1753-1766
    Number of pages14
    Volume3
    DOIs
    Publication statusPublished - 2009
    Event71st European Association of Geoscientists and Engineers Conference and Exhibition 2009 - Amsterdam, Netherlands
    Duration: 8 Jun 200911 Jun 2009

    Conference

    Conference71st European Association of Geoscientists and Engineers Conference and Exhibition 2009
    CountryNetherlands
    CityAmsterdam
    Period8/06/0911/06/09

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