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Application of Genetic Algorithm (GA) in History Matching of the Vapour Extraction (VAPEX) Heavy Oil Recovery Process

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Abstract

This paper presents the application of genetic algorithm (GA) to the history-matching problem. As history matching of VAPEX (vapour extraction) experiments is a complex, highly nonlinear, and non-unique inverse problem, a modified GA was developed to assist the history-matching process. Compared to conventional GA, the computational time in this modified GA approach was reduced by 71 %, and an excellent match between the simulation data and experimental data was achieved, with the error being less than 1 %. This study is focussed on automatic history matching of the VAPEX heavy oil recovery process.

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Acknowledgments

The authors would like to thank the Petroleum Technology Research Centre (PTRC) and the Natural Sciences and Engineering Research Council (NSERC) for the financial support. The authors also wish to thank the anonymous reviewers for their detailed and valuable comments and suggestions.

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Authors and Affiliations

  1. Saskatchewan Research Council (SRC), Regina, SK, Canada

    Suxin Xu

  2. Faculty of Engineering and Applied Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Min Zhang, Fanhua Zeng & Christine Chan

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  1. Suxin Xu
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  2. Min Zhang
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  3. Fanhua Zeng
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  4. Christine Chan
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Corresponding author

Correspondence to Fanhua Zeng.

Additional information

The VAPEX process involves injection of vaporized hydrocarbon solvents into heavy oil and bitumen reservoirs; the solvent-diluted oil drains by gravity to a horizontal production well

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Xu, S., Zhang, M., Zeng, F. et al. Application of Genetic Algorithm (GA) in History Matching of the Vapour Extraction (VAPEX) Heavy Oil Recovery Process. Nat Resour Res 24, 221–237 (2015). https://doi.org/10.1007/s11053-014-9255-7

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