Conditioning reservoir models on rate data using ensemble smoothers
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Publication:1715362
DOI10.1007/s10596-018-9750-8zbMath1406.86031OpenAlexW2809251433WikidataQ57669369 ScholiaQ57669369MaRDI QIDQ1715362
Geir Evensen, Kjersti Solberg Eikrem
Publication date: 4 February 2019
Published in: Computational Geosciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10596-018-9750-8
Hydrology, hydrography, oceanography (86A05) Flows in porous media; filtration; seepage (76S05) Inverse problems in geophysics (86A22) Geostatistics (86A32)
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Uses Software
Cites Work
- Iterative regularization for ensemble data assimilation in reservoir models
- Levenberg-Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
- Bayesian estimation of reservoir properties -- effects of uncertainty quantification of 4D seismic data
- Analysis of iterative ensemble smoothers for solving inverse problems
- Recent progress on reservoir history matching: a review
- A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems
- Estimating and including observation-error correlations in data assimilation
- The ensemble Kalman filter for combined state and parameter estimation
- Correlated observation errors in data assimilation
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