Geomodelling of a fluvial system with semi-supervised support vector regression

Détails

ID Serval
serval:BIB_9AB4DBB9E13A
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
Geomodelling of a fluvial system with semi-supervised support vector regression
Titre de la conférence
Proceedings of the 8th International Geostatistics Congress, Santiago, Chile
Auteur(s)
Demyanov V., Pozdnoukhov A ., Kanevski M., Christie M.
Editeur
Gecamin Ltd.
Statut éditorial
Publié
Date de publication
2008
Volume
2
Pages
627-636
Langue
anglais
Notes
Demyanov2008
Résumé
Fluvial deposits are a challenge for modelling flow in sub-surface
reservoirs. Connectivity and continuity of permeable bodies have
a major impact on fluid flow in porous media. Contemporary object-based
and multipoint statistics methods face a problem of robust representation
of connected structures. An alternative approach to model petrophysical
properties is based on machine learning algorithm ? Support Vector
Regression (SVR). Semi-supervised SVR is able to establish spatial
connectivity taking into account the prior knowledge on natural similarities.
SVR as a learning algorithm is robust to noise and captures dependencies
from all available data. Semi-supervised SVR applied to a synthetic
fluvial reservoir demonstrated robust results, which are well matched
to the flow performance
Création de la notice
25/11/2013 18:18
Dernière modification de la notice
20/08/2019 16:01
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