A multi-resolution workflow to generate high-resolution models constrained to dynamic data

DOI10.1007/s10596-011-9223-9zbMath1254.86029OpenAlexW2057568335MaRDI QIDQ695713

Jef Caers, Louis J. Durlofsky, Céline Scheidt, Yu-Guang Chen

Publication date: 17 December 2012

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

Full work available at URL: https://doi.org/10.1007/s10596-011-9223-9

zbMATH Keywords

upscalinguncertainty quantificationhistory matchingerror modelingdistance-based techniqueskernel KL expansion

Mathematics Subject Classification ID

Geostatistics (86A32)

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Uses Software

• GSLIB

Cites Work

• Unnamed Item
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• A flow-based pattern recognition algorithm for rapid quantification of geologic uncertainty
• Kernel principal component analysis for efficient, differentiable parametrization of multipoint geostatistics
• A distance-based prior model parameterization for constraining solutions of spatial inverse problems
• Representing spatial uncertainty using distances and kernels
• Modeling Uncertainty of Complex Earth Systems in Metric Space
• Risk management for petroleum reservoir production: A simulation-based study of prediction