Multivariate data cleaning and reclassification of particle size distribution data for the Joslyn lease

CIM Journal, Vol. 6, No. 2, 2015, pages 75-90

P. Babak
Penn West Exploration, Calgary, Alberta, Canada
(formerly with Total E&P Canada, Calgary, Alberta, Canada)

P. Henriquel and E. Insalaco
Total E&P, Paris, France

O. Babak
Cenovus Energy, Calgary, Alberta, Canada
(formerly with Total E&P Canada, Calgary, Alberta, Canada

http://dx.doi.org/10.15834/cimj.2015.8
Abstract Detection of outliers is an important primary step in geomodelling. Geological datasets often contain errors due to problems such as measurement device limitations and recording glitches. In this paper, a robust Mahalanobis distance–based approach is successfully applied to particle size distributions of the Joslyn lease, an oil sands development site near Fort McMurray, Alberta, Canada. Automatic outlier detection and geological review were combined. Outliers from the Mahalanobis distance approach were compared with their respective core photographs to determine if laboratory measurements and facies classification were correct. A checking procedure for laboratory technicians is proposed.
Keywords: Data cleaning, Mining, Multivariate sampling, Particle size distribution (PSD)
Résumé La détection des valeurs aberrantes constitue une étape primaire importante de la modélisation géologique. Les ensembles de données géologiques contiennent souvent des erreurs en raison de problèmes tels que les limitations des instruments de mesure et des pépins d’enregistrement. Dans le présent article, une méthode robuste basée sur la distance de Mahalanobis a été mise en oeuvre avec succès aux granulométries du bail minier Joslyn, un site de sables bitumineux en développement à proximité de Fort McMurray, Alberta, Canada. La détection automatique des valeurs aberrantes a été combinée à une étude géologique. Les valeurs aberrantes de la méthode de la distance de Manalanobis ont été comparées avec leurs photographies respectives de carottes de forage afin de déterminer si les mesures en laboratoire et la classification des faciès étaient correctes. Une procédure de vérification pour les techniciens de laboratoire est proposée
Mots-Clé Échantillonnage à plusieurs variables, exploitation minière, granulométrie, nettoyage des données
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