An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation

By Yaqing Gu, Dean S. Oliver. 2007

302 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ocean Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7c4b19381fef1b101d2913d0149cd1d8c104c4ee

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Yan Chen
2. Dean S. Oliver
3. Albert C. Reynolds
4. Laurent Bertino
5. Wolfgang Nowak
6. Pavel Sakov
7. Dongxiao Zhang
8. Geir Evensen

In Ocean Engineering

1. Dean S. Oliver
2. Albert C. Reynolds
3. Yan Chen
4. Wolfgang Nowak
5. Sy T. Do
6. Dongxiao Zhang
7. Sergey Oladyshkin
8. Holger Class

In Water Science and Technology

1. Wolfgang Nowak
2. Yan Chen
3. Dean S. Oliver
4. Albert C. Reynolds
5. Sergey Oladyshkin
6. Dongxiao Zhang
7. H. H. Franssen
8. Wolfgang Kinzelbach

Citations in the corpus

• A consistent interpretation of the stochastic version of the Ensemble Kalman Filter (2020, background): “…to samples from the posterior, such as in iterative Ensemble Kalman Filters or Smoothers (e.g., the discussion in van Leeuwen and Evensen, 1996, Gu and Oliver, 2007, Emerick and Reynolds, 2013, Asch et al., 2016), or explore transportation particle filters (e.g., Villani 2008, Reich, 2011, Liu…”
• Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (2013, methodology): “, in extended Kalman filtering, in iterative ensemble Kalman filters, or in the quasi-linear geostatistical approach [14, 28, 34, 55].”
• Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother (2011, methodology): “Although the dimension of the model variables, Nm, is generally large (104–106), the size of the ensemble, Ne, is typically fairly small (102) so the effort required to compute the SVD is affordable.”
• Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification (2013, methodology): “Equations 2 and 3 form the basis for several iterative forms of the ensemble Kalman filter [9, 10] and iterative ensemble smoothers [5].”
• Theoretical connections between optimization algorithms based on an approximate gradient (2013, methodology): “EnRML is effectively an iterative ensemble Kalman filter where the Gauss–Newton iteration is done using an approximate sensitivity matrix.”
• Efficient Ensemble-Based Closed-Loop Production Optimization (2009, methodology): “Details of the iterative EnKF can be found in Gu and Oliver (2007).”
• Real‐time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem (2008, methodology): “A more radical option [Gu and Oliver, 2007] is to resolve the groundwater flow equation in its forward mode from t = 0 onward until new measurement data (that were not used for assimilation before) become available.”
• Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization (2009, unknown): “All rights reserved.”
• An Iterative EnKF for Strongly Nonlinear Systems (2012, methodology): “Gu and Oliver (2007) introduced a local iterative scheme called EnRML.”