A dataset for the article "Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling" by S. Ruchi and S. Dubinkina in Nonlin. Processes Geophys. 2018 Accurate estimation of subsurface geological parameters, e.g. permeability, is essential for the oil industry. This is done by combining observations of pressure with a mathematical model using data assimilation. We show that computationally affordable ensemble transform data assimilation methods are suitable for the parameter estimation. For a small number of uncertain parameters, ensemble transform particle filter performs comparably to ensemble transform Kalman filter in terms of the mean estimation. For a large number of uncertain parameters, ensemble transform particle filter performs comparably to ensemble transform Kalman filter only when either localization or the leading modes are used.

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Persistent URL dx.doi.org/10.4121/uuid:2d0018ea-fecc-4d19-8532-5a718c9f28ca
Citation
Dubinkina, S, & Ruchi, S. (2018). Data underlying the paper: Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling.. doi:10.4121/uuid:2d0018ea-fecc-4d19-8532-5a718c9f28ca