Ensemble smoother with multiple data assimilation
By Alexandre Anozé Emerick, Albert C. Reynolds. Computational Geosciences, 2013
871 citations recorded by the source corpus.
Subfields: Geophysics, Mechanical Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/4866679ea6c6253ff1bbd57141d8f49d926f7a48
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Geophysics
In Mechanical Engineering
- Yan Chen
- Dean S. Oliver
- Hamidreza Hamdi
- Mario Costa Sousa
- Tom D'haene
- Ivo Couckuyt
- P. V. Leeuwen
- Albert C. Reynolds
In Ocean Engineering
- Yan Chen
- Dean S. Oliver
- Hamidreza Hamdi
- Mario Costa Sousa
- Tom D'haene
- Ivo Couckuyt
- P. V. Leeuwen
- Albert C. Reynolds
Citations in the corpus
- Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, methodology): “Different variants of ES such as iterative ES [4], and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) [5] have also been introduced to enhance the performance of data assimilation method in field problems.”
- A consistent interpretation of the stochastic version of the Ensemble Kalman Filter (2020, background): “Iterative variants of those, such as stochastic iterative ensemble smoothers (e.g., Gu and Oliver, 2007; Emerick and Reynolds, 2013), and the “Ensemble of Data Assimilations” scheme advocated by operational centres (e.g., Žagar et al., 2005; Isaksen et al., 2011), are also expected to benefit from…”
- Localization and regularization for iterative ensemble smoothers (2017, methodology): “Emerick and Reynolds [7] suggested setting the critical range for localization to be the sum of the length scale of the prior covariance of model parameters and the length scale of the well drainage area (from streamline simulation) and used Gaspari-Cohn function to compute elements of ρmd and ρdd, ”
- Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification (2013, methodology): “Figure 4 compares the marginal distribution of the samples of the model parameters (permeability at each grid block) from LM-EnRML (at the 10th iteration) and from MCMC. Results from MCMC are reproduced from Emerick and Reynolds [6].”