Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

DOI10.1007/s10596-016-9570-7zbMath1349.86050OpenAlexW2344983765MaRDI QIDQ341509

Siavash Hakim Elahi, Mohammadali Tarrahi, Behnam Jafarpour

Publication date: 16 November 2016

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-016-9570-7

zbMATH Keywords

data assimilationensemble Kalman filterhistory matchinglinearized forecastsubsurface flow

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Potentials, prospecting (86A20)

Related Items (4)

Iterative multilevel assimilation of inverted seismic data ⋮ Assessment of multilevel ensemble-based data assimilation for reservoir history matching ⋮ Assimilation of multiple linearly dependent data vectors ⋮ Sequential multilevel assimilation of inverted seismic data

Uses Software

• EnKF
• GSLIB

Cites Work

• Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications
• Cross-covariances and localization for EnKF in multiphase flow data assimilation
• A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation
• Improved initial sampling for the ensemble Kalman filter
• Conditional simulation of complex geological structures using multiple-point statistics
• Exploring the need for localization in ensemble data assimilation using a hierarchical ensemble filter
• Data Assimilation
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter