Data assimilation for transient flow in geologic formations via ensemble Kalman filter

By Yan Chen, Dongxiao Zhang. 2006

358 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/05675cc18449b4bc5a6206c16266600cbe147b4b

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In Environmental Engineering

In Ocean Engineering

In Water Science and Technology

Citations in the corpus

An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation (2007, methodology): “The method is now beginning to be applied for data assimilation in groundwater hydrology (Reichle et al. 2002; Chen and Zhang 2006) and in petroleum engineering (Nævdal et al.”
Efficient Ensemble-Based Closed-Loop Production Optimization (2009, methodology): “Ensemble Kalman filter (EnKF) The formulation of the EnKF has been well documented in Evensen (2003); Nævdal et al. (2005) and Chen and Zhang (2006).”
Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization (2009, background): “The EnKF may not work properly when the relationship between model parameters, state variables, and observations are strongly nonlinear and the resulting joint probability distribution is non-Gaussian.”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, background): “, 2002) and more recently also for groundwater modeling (Chen and Zhang, 2006).”
Physically based modeling in catchment hydrology at 50: Survey and outlook (2015, methodology): “…particle filter [Smith and Gelfand, 1992] are the most commonly used data assimilation techniques in hydrological modeling [Reichle et al., 2002; Chen and Zhang, 2006; Weerts and El Serafy, 2006; Camporese et al., 2009; Ng et al., 2009; Pauwels and De Lannoy, 2009; Hendricks Franssen et al.,…”
Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator (2009, methodology): “Typical numbers for ensemble-based methods range about 500 [e.g., Chen and Zhang, 2006; Hendricks Franssen and Kinzelbach, 2008b].”
A generalized grid connectivity–based parameterization for subsurface flow model calibration (2011, background): “, 1997] or those that simultaneously update an ensemble of prior sample realizations to generate an ensemble of conditional realizations [e.g., Evensen, 1994; Chen and Zhang, 2006; Wen and Chen, 2006; Nowak, 2009].”
A comparison of seven methods for the inverse modelling of groundwater flow. Application to the characterisation of well catchments (2009, background): “[97] and was started to be applied to groundwater problems in 2006 [32,44,68,90].”
Ensemble smoother with multiple data assimilation (2013, methodology): “Among the ensemble-based methods, the ensemble Kalman filter (EnKF) is the most popular for history-matching applications.”
Bayesian geostatistical design: Task‐driven optimal site investigation when the geostatistical model is uncertain (2010, methodology): “Each EnKF ensemble had a size of 4000 realizations, which is more than sufficient for Ensemble Kalman Filters in hydrogeological applications [ Chen and Zhang, 2006 ].”
Inverse methods in hydrogeology: Evolution and recent trends (2014, methodology): “…models, and non Gaussianity.506 The EnKF has been successfully applied for building geological models conditioned on piezometric head507 data (e.g., Chen and Zhang, 2006; Hendricks Franssen and Kinzelbach, 2008; Hendricks Franssen and Kinzel-508 bach, 2009; Kurtz et al., 2012; Panzeri et al.,…”