Non-parametric Bayesian networks for parameter estimation in reservoir simulation: a graphical take on the ensemble Kalman filter. I
DOI10.1007/s10596-013-9365-zzbMath1393.86019OpenAlexW2047832693MaRDI QIDQ1663471
Maria Gheorghe, Anca Maria Hanea, Dan Ababei, Remus G. Hanea
Publication date: 21 August 2018
Published in: Computational Geosciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10596-013-9365-z
parameter estimationensemble Kalman filterhistory matchingreservoir engineeringprobabilistic model learningnon-parametric Bayesian networks
Monte Carlo methods (65C05) Geostatistics (86A32) Computational methods for problems pertaining to geophysics (86-08) Nonparametric inference (62G99)
Cites Work
- An iterative ensemble Kalman filter for reservoir engineering applications
- Mining and visualising ordinal data with non-parametric continuous BBNs
- An introduction to copulas. Properties and applications
- Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter
- Pair-copula constructions for non-Gaussian DAG models
- Uncertainty Analysis with High Dimensional Dependence Modelling
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Non-parametric Bayesian networks for parameter estimation in reservoir simulation: a graphical take on the ensemble Kalman filter. I
