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A Bayesian mixture‐modeling approach for flow‐conditioned multiple‐point statistical facies simulation from uncertain training images
Author(s) -
Khodabakhshi Morteza,
Jafarpour Behnam
Publication year - 2013
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2011wr010787
Subject(s) - facies , training (meteorology) , sampling (signal processing) , image (mathematics) , computer science , bayesian probability , flow (mathematics) , artificial intelligence , point (geometry) , machine learning , statistics , pattern recognition (psychology) , algorithm , data mining , geology , mathematics , computer vision , geography , geomorphology , meteorology , geometry , filter (signal processing) , structural basin
Key Points Preserving prior higher‐order statistics of facies using flow data integration Incorporating the uncertainty in the training image during facies simulation Efficiently sampling from multiple training images based on flow response