Bridging multipoint statistics and truncated Gaussian fields for improved estimation of channelized reservoirs with ensemble methods

DOI10.1007/s10596-014-9466-3zbMath1392.86054OpenAlexW2076561215MaRDI QIDQ723115

Bogdan Sebacher, Andreas S. Stordal, Remus G. Hanea

Publication date: 30 July 2018

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

Full work available at URL: https://doi.org/10.1007/s10596-014-9466-3

zbMATH Keywords

ensemble Kalman filtermultipoint geostatisticsfacies distributioniterative AGM

Mathematics Subject Classification ID

Geostatistics (86A32)

Related Items (8)

Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification ⋮ Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data ⋮ Channelized reservoir estimation using a low-dimensional parameterization based on high-order singular value decomposition ⋮ Bridging deep convolutional autoencoders and ensemble smoothers for improved estimation of channelized reservoirs ⋮ Investigation on principal component analysis parameterizations for history matching channelized facies models with ensemble-based data assimilation ⋮ Calibration of categorical simulations by evolutionary gradual deformation method ⋮ Complex geology estimation using the iterative adaptive Gaussian mixture filter ⋮ Regularized kernel PCA for the efficient parameterization of complex geological models

Uses Software

• EnKF
• GSLIB

Cites Work

• Bridging the ensemble Kalman filter and particle filters: The adaptive Gaussian mixture filter
• A probability conditioning method (PCM) for nonlinear flow data integration into multipoint statistical facies simulation
• An iterative version of the adaptive Gaussian mixture filter
• The probability perturbation method: a new look at Bayesian inverse modeling
• Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics
• Conditional simulation of complex geological structures using multiple-point statistics
• Combining knowledge from diverse sources: An alternative to traditional data independence hypotheses
• S-GeMS: The Stanford Geostatistical Modeling Software: A Tool for New Algorithms Development
• Evaluating Information Redundancy Through the Tau Model

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