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

Datacite citation style:
Dubinkina, Svetlana; Ruchi, S. (Sangeetika) (2018): Data underlying the paper: Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:2d0018ea-fecc-4d19-8532-5a718c9f28ca
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
usage stats
1152
views
575
downloads
categories
licence
cc-by.png logo CC BY 4.0
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.
history
  • 2018-11-01 first online, published, posted
publisher
4TU.Centre for Research Data
format
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
funding
  • research programme Shell-NWO/FOM Computational Sciences for Energy Research (CSER)
organizations
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

DATA

files (2)