Uncertainty Quantification with Support Vector Regression Prediction Models

Détails

ID Serval
serval:BIB_8B928D0D37D2
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
Titre
Uncertainty Quantification with Support Vector Regression Prediction Models
Titre de la conférence
Proceedings of the Accuracy conference, Leicester, England
Auteur(s)
Demyanov V., Pozdnoukhov A., Kanevski M., Christie M.
Editeur
International Spatial Accuracy Research Association
Statut éditorial
Publié
Date de publication
2010
Pages
133-136
Langue
anglais
Résumé
Uncertainty quantification of petroleum reservoir models is one of
the present challenges, which is usually approached with a wide range
of geostatistical tools linked with statistical optimisation or/and
inference algorithms. The paper considers a data driven approach
in modelling uncertainty in spatial predictions. Proposed semi-supervised
Support Vector Regression (SVR) model has demonstrated its capability
to represent realistic features and describe stochastic variability
and non-uniqueness of spatial properties. It is able to capture and
preserve key spatial dependencies such as connectivity, which is
often difficult to achieve with two-point geostatistical models.
Semi-supervised SVR is designed to integrate various kinds of conditioning
data and learn dependences from them. A stochastic semi-supervised
SVR model is integrated into a Bayesian framework to quantify uncertainty
with multiple models fitted to dynamic observations. The developed
approach is illustrated with a reservoir case study. The resulting
probabilistic production forecasts are described by uncertainty envelopes.
Mots-clé
uncertainty, prediction, petroleum, machine learning, support vectors, data integration
Création de la notice
25/11/2013 18:18
Dernière modification de la notice
03/03/2018 19:12
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