A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)

DOI10.1007/s10596-013-9357-zzbMath1393.86021OpenAlexW2103023994MaRDI QIDQ1663452

Bogdan Sebacher, Remus G. Hanea, Arnold W. Heemink

Publication date: 21 August 2018

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

Full work available at URL: https://doi.org/10.1007/s10596-013-9357-z

zbMATH Keywords

data assimilationsubsurface characterizationensemble Kalman filterfacies distributionprobability approachgeological uncertainties

Mathematics Subject Classification ID

Geostatistics (86A32)

Related Items (5)

Data assimilation in truncated plurigaussian models: impact of the truncation map ⋮ Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation ⋮ Recent developments combining ensemble smoother and deep generative networks for facies history matching ⋮ Complex geology estimation using the iterative adaptive Gaussian mixture filter ⋮ Integration of Markov mesh models and data assimilation techniques in complex reservoirs

Uses Software

• EnKF
• GSLIB

Cites Work

• A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation
• Application of the EnKF and localization to automatic history matching of facies distribution and production data
• Level set methods and dynamic implicit surfaces
• Conditional simulation of complex geological structures using multiple-point statistics
• History matching of facies distribution with the EnKF and level set parameterization
• Data Assimilation

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