Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation
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Publication:1698334
DOI10.1007/s11004-016-9659-5zbMath1387.86044OpenAlexW2528150856MaRDI QIDQ1698334
Publication date: 15 February 2018
Published in: Mathematical Geosciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11004-016-9659-5
data assimilationfaciesreservoir history matchingensemble smoother with multiple data assimilationnon-Gaussian priors
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Uses Software
Cites Work
- Unnamed Item
- Minimization for conditional simulation: relationship to optimal transport
- Large-scale history matching with quadratic interpolation models
- Combining sensitivities and prior information for covariance localization in the ensemble Kalman filter for petroleum reservoir applications
- A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation
- Data assimilation and uncertainty assessment for complex geological models using a new PCA-based parameterization
- Bridging multipoint statistics and truncated Gaussian fields for improved estimation of channelized reservoirs with ensemble methods
- Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
- Relation between level set and truncated pluri-Gaussian methodologies for facies representation
- A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models
- Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics
- Application of the EnKF and localization to automatic history matching of facies distribution and production data
- History matching with an ensemble Kalman filter and discrete cosine parameterization
- A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)
- Conditional simulation of complex geological structures using multiple-point statistics
- History matching of facies distribution with the EnKF and level set parameterization
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