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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 25, issue 4
Nonlin. Processes Geophys., 25, 731–746, 2018
https://doi.org/10.5194/npg-25-731-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 25, 731–746, 2018
https://doi.org/10.5194/npg-25-731-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Nov 2018

Research article | 06 Nov 2018

Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling

Sangeetika Ruchi and Svetlana Dubinkina

Data sets

Data underlying the paper: Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling S. Dubinkina and S. Ruch https://doi.org/10.4121/uuid:2d0018ea-fecc-4d19-8532-5a718c9f28ca

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Short summary
Accurate estimation of subsurface geological parameters is essential for the oil industry. This is done by combining observations with an estimation from a model. Ensemble Kalman filter is a widely used method for inverse modeling, while ensemble transform particle filtering is a recently developed method that has been applied to estimate only a small number of parameters and in fluids. We show that for a high-dimensional inverse problem it is superior to an ensemble Kalman filter.
Accurate estimation of subsurface geological parameters is essential for the oil industry. This...
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