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Robust Approach in Hierarchical Clustering: Application to the Sectorisation of an Oil Field

  • Jean-Paul Valois
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Production data of oil fields are provided as decline curves (oil and water production vs time), that the user wants to gather in a limited number of clusters. Preprocessing of data is required to remove noise, and provides a complete data set, involving for each statistical unit (wells) extraction of attributes from smoothed or modelized curves. Hierarchical clustering is performed in two steps to avoid smaller or outlier cluster ; firstly the centroid clustering method is used to recognize and then discard clusters having a lower frequency, this is followed by application of the Ward-method. Finally, using the central part of these previous (Ward) clusters, discriminant analysis is performed, including all the discarded units. This sequence avoids the disturbing influence of outlying units, and also gives the probability for each unit to be classified in the clusters.

Keywords

Discriminant Analysis Centroid Method Outlying Unit Ward Method Decline Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2000

Authors and Affiliations

  • Jean-Paul Valois
    • 1
  1. 1.ELF AQUITAINE Exploration ProductionPAU CedexFrance

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