Reservoir Monitoring and Continuous Model Updating Using Ensemble Kalman Filter

By Nævdal Geir, Liv Merethe Johnsen, S. Aanonsen, E. H. Vefring. 2005

354 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ocean Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/b9ed72d1fdec778bc24293466b4fa683e93a1d66

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. Louis J. Durlofsky
3. Dean S. Oliver
4. Yuqiong Liu
5. Ahmed H. Elsheikh
6. Dongxiao Zhang
7. H. H. Franssen
8. M. Clark

In Ocean Engineering

1. Louis J. Durlofsky
2. Jan Dirk Jansen
3. Dean S. Oliver
4. Yan Chen
5. Ahmed H. Elsheikh
6. Dongxiao Zhang
7. R. Hanea
8. Albert C. Reynolds

In Oceanography

1. Yan Chen
2. Dongxiao Zhang
3. E. Della Rossa
4. Dean S. Oliver
5. Arnold W. Heemink
6. Louis J. Durlofsky
7. Hamid Moradkhani
8. H. H. Franssen

Citations in the corpus

• Multimodal ensemble Kalman filtering using Gaussian mixture models (2011, methodology): “The method has been applied in ocean dynamical models [16], in meteorology [17] and, recently, in reservoir simulation models [23].”
• Efficient real-time reservoir management using adjoint-based optimal control and model updating (2006, methodology): “The ensemble Kalman filter has only recently been applied for history matching [5].”
• Efficient Ensemble-Based Closed-Loop Production Optimization (2009, methodology): “Details of the iterative EnKF can be found in Gu and Oliver (2007).”
• Characterization of reservoir simulation models using a polynomial chaos‐based ensemble Kalman filter (2009, methodology): “[3] Recently, the ensemble Kalman filter (EnKF) [Evensen, 2003] has been shown to be an efficient tool for history matching of reservoir models [Gao et al., 2006; Gu and Oliver, 2005; Liu and Oliver, 2005; Naevdal et al., 2003] enabling useful estimation of various reservoir parameters.”
• Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates (2014, methodology): “The multiphase flow of hydrocarbons in an oil reservoir is another example where model calibration is needed for accurate predictions [3,4].”
• 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.”
• Model-reduced gradient-based history matching (2011, background): “Reynolds (2002), and Oliver et al. (2008). In the adjoint approach the historymatching problem is treated as an optimal control problem where the control variables are the unknown model parameters, and where the objective function is minimized subject to the constraint that the state variables obey ”
• Data assimilation for transient flow in geologic formations via ensemble Kalman filter (2006, methodology): “Then, EnKF was further developed to tune the permeability field for simplified real field reservoir simulation models [20], which showed the capability of EnKF to deal with real large-scale problems.”
• Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, background): “The third approach is to modify the states and parameters simultaneously and quantify the uncertainty associated with them all within a sequential (or recursive) DA framework (e.g., Moradkhani et al., 2005b; Vrugt et al., 2005; Naevdal et al., 2003).”