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

Dataset

• Energy Exploration

data assimilation, inverse modeling, permeability

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

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.

• 2018-11-01 first online, published, posted

4TU.Centre for Research Data

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

• https://doi.org/10.5194/npg-2018-18

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

Centrum Wiskunde & Informatica, Amsterdam, The Netherlands