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        Ensemble History Matching

        Conditioning Reservoir Models on Dynamic Data

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        Author(s)
        Evensen, Geir
        Oliver, Dean S.
        Hanea, Remus G.
        Language
        English
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        Abstract
        This open-access book aims to formulate the history-matching problem consistently and present state-of-the-art ensemble solution methods. The content aims to help practitioners in the field understand the properties of ensemble methods better when used to history-match reservoir models. The book provides educational information for graduate students and researchers in petroleum, geothermal, and hydrological engineering and sciences. It introduces and explains various algorithms used in data assimilation and parameter estimation, focusing on ensemble methods, particularly the most popular ones in the petroleum community. It discusses challenges associated with these techniques, such as dealing with high-dimensional models, finite number of realizations, parameterization, and handling uncertainties in the observations and model parameters.
        URI
        https://library.oapen.org/handle/20.500.12657/109296
        Keywords
        Open Access; Ensemble history matching; Robust optimization; Closed loop reservoir managment; Reservoir characterization; Randomized Maximum-Likelihood Sampling
        DOI
        10.1007/978-3-031-99155-4
        ISBN
        9783031991554, 9783031991554, 9783031991547
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2026
        Imprint
        Springer
        Series
        Earth and Environmental Science; Earth and Environmental Science (R0),
        Classification
        Earth sciences
        Probability and statistics
        Pages
        199
        Public remark
        Funded by: NORCE Norwegian Research Center
        Rights
        http://creativecommons.org/licenses/by/4.0/)/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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