Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.558 IF 1.558
  • IF 5-year value: 1.475 IF 5-year
    1.475
  • CiteScore value: 2.8 CiteScore
    2.8
  • SNIP value: 0.921 SNIP 0.921
  • IPP value: 1.56 IPP 1.56
  • SJR value: 0.571 SJR 0.571
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 55 Scimago H
    index 55
  • h5-index value: 22 h5-index 22
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

Viewed

Total article views: 2,001 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,382 535 84 2,001 75 73
  • HTML: 1,382
  • PDF: 535
  • XML: 84
  • Total: 2,001
  • BibTeX: 75
  • EndNote: 73
Views and downloads (calculated since 23 Mar 2018)
Cumulative views and downloads (calculated since 23 Mar 2018)

Viewed (geographical distribution)

Total article views: 1,602 (including HTML, PDF, and XML) Thereof 1,596 with geography defined and 6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

No saved metrics found.

Saved (preprint)

No saved metrics found.

Discussed (final revised paper)

No discussed metrics found.

Discussed (preprint)

No discussed metrics found.
Latest update: 14 Aug 2020
Publications Copernicus
Download
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...
Citation