Training image-based scenario modeling of fractured reservoirs for flow uncertainty quantification

DOI10.1007/s10596-013-9372-0zbMath1393.86004OpenAlexW2146434317MaRDI QIDQ1663483

Andre Jung, Jef Caers, Darryl H. Fenwick

Publication date: 21 August 2018

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

Full work available at URL: https://doi.org/10.1007/s10596-013-9372-0

zbMATH Keywords

geostatisticsuncertainty quantificationdiscrete fracture networksfracture modelingpattern modelingdual medium

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Geostatistics (86A32) Computational methods for problems pertaining to geophysics (86-08)

Related Items

Uncertainty quantification in reservoir prediction. II: Handling uncertainty in the geological scenario

Uses Software

• R
• Silhouettes
• cluster (R)
• SGeMS
• RFOC

Cites Work

• Dynamic data integration for structural modeling: Model screening approach using a distance-based model parameterization
• Representing spatial uncertainty using distances and kernels
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Conditional simulation of complex geological structures using multiple-point statistics
• Modeling Uncertainty in the Earth Sciences
• Stochastic simulation of patterns using distance-based pattern modeling