Application of particle swarms for history matching in the Brugge reservoir

Lina Mahgoub Yahya Mohamed, Mike Christie, Vasily Demyanov, Emmanuel Robert, Dick Kachuma

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

    Abstract

    Reservoir modelling is frequently used in the oil industry to measure the risk associated with alternative production scenarios. However, reservoir models themselves contain a high level of uncertainty because of the typically very limited, sparse and multi-scaled reservoir knowledge. The effect of this uncertainty can be assessed by producing a set of diverse models that match the production data reasonably well and using these models to quantify uncertainty in predicting the future performance of the reservoir. Evolutionary and swarm intelligence algorithms have become very popular for history matching due to their simplicity and parallel implementation capacity. This paper focuses on the application of Particle Swarm Optimisation (PSO) for history matching the Brugge field (a recent SPE benchmark case study). The parameterisation of the model is based on principal component analysis (PCA) for modelling spatially correlated random fields (e.g. porosity, net-to-gross and permeability) applied to the set of initial realisations which describe the range of prior beliefs. The PSO is then used to find the set of possible combinations of parameters, represented by the PCA eigenvalues, which match the historical data and honour the static data from the wells present in the initial realisations. We show that PSO is able to find multiple good and diverse history matched models for the Brugge reservoir without exhaustive sampling of the parameter space. Uncertainty of production forecasts are quantified by P10-P50-P90 uncertainty envelope obtained from the ensemble of PSO models. The history matching results are compared with the ones obtained with Ensemble Kalman Filter data assimilation method. These results show the ability of PSO to handle large history matching problems and obtain results comparable to the EnKF for this case study. Copyright 2010, Society of Petroleum Engineers.

    Original languageEnglish
    Title of host publicationSociety of Petroleum Engineers - SPE Annual Technical Conference and Exhibition 2010, ATCE 2010
    Pages4477-4492
    Number of pages16
    Volume6
    DOIs
    Publication statusPublished - 2010
    EventSPE Annual Technical Conference and Exhibition 2010 - Florence, Italy
    Duration: 20 Sep 201022 Sep 2010

    Conference

    ConferenceSPE Annual Technical Conference and Exhibition 2010
    Abbreviated titleATCE 2010
    CountryItaly
    CityFlorence
    Period20/09/1022/09/10

    Fingerprint

    history
    principal component analysis
    eigenvalue
    Kalman filter
    oil industry
    data assimilation
    modeling
    particle
    parameterization
    porosity
    permeability
    well
    sampling
    parameter

    Cite this

    Mohamed, L. M. Y., Christie, M., Demyanov, V., Robert, E., & Kachuma, D. (2010). Application of particle swarms for history matching in the Brugge reservoir. In Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition 2010, ATCE 2010 (Vol. 6, pp. 4477-4492) https://doi.org/doi:10.2118/135264-MS
    Mohamed, Lina Mahgoub Yahya ; Christie, Mike ; Demyanov, Vasily ; Robert, Emmanuel ; Kachuma, Dick. / Application of particle swarms for history matching in the Brugge reservoir. Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition 2010, ATCE 2010. Vol. 6 2010. pp. 4477-4492
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    title = "Application of particle swarms for history matching in the Brugge reservoir",
    abstract = "Reservoir modelling is frequently used in the oil industry to measure the risk associated with alternative production scenarios. However, reservoir models themselves contain a high level of uncertainty because of the typically very limited, sparse and multi-scaled reservoir knowledge. The effect of this uncertainty can be assessed by producing a set of diverse models that match the production data reasonably well and using these models to quantify uncertainty in predicting the future performance of the reservoir. Evolutionary and swarm intelligence algorithms have become very popular for history matching due to their simplicity and parallel implementation capacity. This paper focuses on the application of Particle Swarm Optimisation (PSO) for history matching the Brugge field (a recent SPE benchmark case study). The parameterisation of the model is based on principal component analysis (PCA) for modelling spatially correlated random fields (e.g. porosity, net-to-gross and permeability) applied to the set of initial realisations which describe the range of prior beliefs. The PSO is then used to find the set of possible combinations of parameters, represented by the PCA eigenvalues, which match the historical data and honour the static data from the wells present in the initial realisations. We show that PSO is able to find multiple good and diverse history matched models for the Brugge reservoir without exhaustive sampling of the parameter space. Uncertainty of production forecasts are quantified by P10-P50-P90 uncertainty envelope obtained from the ensemble of PSO models. The history matching results are compared with the ones obtained with Ensemble Kalman Filter data assimilation method. These results show the ability of PSO to handle large history matching problems and obtain results comparable to the EnKF for this case study. Copyright 2010, Society of Petroleum Engineers.",
    author = "Mohamed, {Lina Mahgoub Yahya} and Mike Christie and Vasily Demyanov and Emmanuel Robert and Dick Kachuma",
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    Mohamed, LMY, Christie, M, Demyanov, V, Robert, E & Kachuma, D 2010, Application of particle swarms for history matching in the Brugge reservoir. in Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition 2010, ATCE 2010. vol. 6, pp. 4477-4492, SPE Annual Technical Conference and Exhibition 2010, Florence, Italy, 20/09/10. https://doi.org/doi:10.2118/135264-MS

    Application of particle swarms for history matching in the Brugge reservoir. / Mohamed, Lina Mahgoub Yahya; Christie, Mike; Demyanov, Vasily; Robert, Emmanuel; Kachuma, Dick.

    Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition 2010, ATCE 2010. Vol. 6 2010. p. 4477-4492.

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

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    Mohamed LMY, Christie M, Demyanov V, Robert E, Kachuma D. Application of particle swarms for history matching in the Brugge reservoir. In Society of Petroleum Engineers - SPE Annual Technical Conference and Exhibition 2010, ATCE 2010. Vol. 6. 2010. p. 4477-4492 https://doi.org/doi:10.2118/135264-MS