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Data underlying the paper: Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling

dataset
posted on 01.11.2018 by Svetlana Dubinkina, S. (Sangeetika) Ruchi
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.

Funding

research programme Shell-NWO/FOM Computational Sciences for Energy Research (CSER)

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Contributors

Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Publisher

4TU.Centre for Research Data

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media types: application/octet-stream, application/x-matlab-data, application/x-sharedlib, application/zip, text/plain, text/x-c, text/x-c++, text/x-matlab

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