Articles | Volume 26, issue 3
https://doi.org/10.5194/npg-26-325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-26-325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Sep 2019
Research article |
| 17 Sep 2019
| 17 Sep 2019
Revising the stochastic iterative ensemble smoother
Patrick Nima Raanes
CORRESPONDING AUTHOR
NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway
Nansen Environmental and Remote Sensing Center, Thormøhlens Gate 47, 5006 Bergen, Norway
Andreas Størksen Stordal
NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway
Geir Evensen
NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway
Nansen Environmental and Remote Sensing Center, Thormøhlens Gate 47, 5006 Bergen, Norway
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Cited
24 citations as recorded by crossref.
- Impact of model and data resolutions in 4D seismic data assimilation applied to an offshore reservoir in Brazil D. Rosa et al. https://doi.org/10.1016/j.petrol.2022.110830
- Batch seismic inversion using the iterative ensemble Kalman smoother M. Gineste & J. Eidsvik https://doi.org/10.1007/s10596-021-10043-4
- Marginalized iterative ensemble smoothers for data assimilation A. Stordal et al. https://doi.org/10.1007/s10596-023-10242-1
- 4D seismic history matching D. Oliver et al. https://doi.org/10.1016/j.petrol.2021.109119
- Innovative Approach Based on Data Projection onto Ensemble Subspace for Improving Simultaneous Assimilation of Production and 4D Seismic Data C. Maschio et al. https://doi.org/10.2118/232787-PA
- On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems F. Parzer & O. Scherzer https://doi.org/10.1007/s00211-022-01314-y
- Localized ensemble Kalman inversion X. Tong & M. Morzfeld https://doi.org/10.1088/1361-6420/accb08
- A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet https://doi.org/10.5194/gmd-15-7641-2022
- Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method H. Yan et al. https://doi.org/10.1029/2021WR029610
- A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage H. Tang et al. https://doi.org/10.1016/j.ijggc.2021.103488
- Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano https://doi.org/10.5194/npg-28-93-2021
- Subspace Ensemble Randomized Maximum Likelihood with Local Analysis for Time-Lapse-Seismic-Data Assimilation G. Silva Neto et al. https://doi.org/10.2118/205029-PA
- An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation G. Evensen et al. https://doi.org/10.3934/fods.2021001
- Sequential multilevel assimilation of inverted seismic data M. Nezhadali et al. https://doi.org/10.1007/s10596-023-10191-9
- Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems Y. Yeung et al. https://doi.org/10.1016/j.jcp.2024.112788
- 4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method R. Soares et al. https://doi.org/10.1016/j.petrol.2020.107763
- Projection of 4D seismic onto the ensemble observation subspace for data assimilation A. Emerick & G. Neto https://doi.org/10.1016/j.geoen.2024.212835
- A review on optimization algorithms and surrogate models for reservoir automatic history matching Y. Zhao et al. https://doi.org/10.1016/j.geoen.2023.212554
- An ensemble-based decision workflow for reservoir management Y. Chang & G. Evensen https://doi.org/10.1016/j.petrol.2022.110858
- Iterative multilevel assimilation of inverted seismic data M. Nezhadali et al. https://doi.org/10.1007/s10596-021-10125-3
- Investigation on the production data frequency for assimilation with ensemble smoother A. Emerick & G. Neto https://doi.org/10.1016/j.geoen.2023.212356
- Handling Big Models and Big Data Sets in History-Matching Problems through an Adaptive Local Analysis Scheme R. Soares et al. https://doi.org/10.2118/204221-PA
- Performance assessment of an iterative ensemble smoother with local analysis to assimilate big 4D seismic datasets applied to a complex pre-salt-like benchmark case C. Maschio et al. https://doi.org/10.1093/jge/gxad099
- Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning X. Luo et al. https://doi.org/10.1016/j.petrol.2020.107961
24 citations as recorded by crossref.
- Impact of model and data resolutions in 4D seismic data assimilation applied to an offshore reservoir in Brazil D. Rosa et al. https://doi.org/10.1016/j.petrol.2022.110830
- Batch seismic inversion using the iterative ensemble Kalman smoother M. Gineste & J. Eidsvik https://doi.org/10.1007/s10596-021-10043-4
- Marginalized iterative ensemble smoothers for data assimilation A. Stordal et al. https://doi.org/10.1007/s10596-023-10242-1
- 4D seismic history matching D. Oliver et al. https://doi.org/10.1016/j.petrol.2021.109119
- Innovative Approach Based on Data Projection onto Ensemble Subspace for Improving Simultaneous Assimilation of Production and 4D Seismic Data C. Maschio et al. https://doi.org/10.2118/232787-PA
- On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems F. Parzer & O. Scherzer https://doi.org/10.1007/s00211-022-01314-y
- Localized ensemble Kalman inversion X. Tong & M. Morzfeld https://doi.org/10.1088/1361-6420/accb08
- A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet https://doi.org/10.5194/gmd-15-7641-2022
- Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method H. Yan et al. https://doi.org/10.1029/2021WR029610
- A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage H. Tang et al. https://doi.org/10.1016/j.ijggc.2021.103488
- Behavior of the iterative ensemble-based variational method in nonlinear problems S. Nakano https://doi.org/10.5194/npg-28-93-2021
- Subspace Ensemble Randomized Maximum Likelihood with Local Analysis for Time-Lapse-Seismic-Data Assimilation G. Silva Neto et al. https://doi.org/10.2118/205029-PA
- An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation G. Evensen et al. https://doi.org/10.3934/fods.2021001
- Sequential multilevel assimilation of inverted seismic data M. Nezhadali et al. https://doi.org/10.1007/s10596-023-10191-9
- Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems Y. Yeung et al. https://doi.org/10.1016/j.jcp.2024.112788
- 4D seismic history matching: Assessing the use of a dictionary learning based sparse representation method R. Soares et al. https://doi.org/10.1016/j.petrol.2020.107763
- Projection of 4D seismic onto the ensemble observation subspace for data assimilation A. Emerick & G. Neto https://doi.org/10.1016/j.geoen.2024.212835
- A review on optimization algorithms and surrogate models for reservoir automatic history matching Y. Zhao et al. https://doi.org/10.1016/j.geoen.2023.212554
- An ensemble-based decision workflow for reservoir management Y. Chang & G. Evensen https://doi.org/10.1016/j.petrol.2022.110858
- Iterative multilevel assimilation of inverted seismic data M. Nezhadali et al. https://doi.org/10.1007/s10596-021-10125-3
- Investigation on the production data frequency for assimilation with ensemble smoother A. Emerick & G. Neto https://doi.org/10.1016/j.geoen.2023.212356
- Handling Big Models and Big Data Sets in History-Matching Problems through an Adaptive Local Analysis Scheme R. Soares et al. https://doi.org/10.2118/204221-PA
- Performance assessment of an iterative ensemble smoother with local analysis to assimilate big 4D seismic datasets applied to a complex pre-salt-like benchmark case C. Maschio et al. https://doi.org/10.1093/jge/gxad099
- Accounting for model errors of rock physics models in 4D seismic history matching problems: A perspective of machine learning X. Luo et al. https://doi.org/10.1016/j.petrol.2020.107961
Saved (final revised paper)
Latest update: 01 Aug 2026
Short summary
A popular variational ensemble smoother for data assimilation and history matching is simplified. An exact relationship between ensemble linearizations (linear regression) and adjoints (analytic derivatives) is established.
A popular variational ensemble smoother for data assimilation and history matching is...
