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.
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References
BAYNE, C.K., BEAUCHAMP, J.J., BEGOVITCH, C.L., and KANE, V.E. (1980): Monte-Carlo comparisons of selected clustering procedures. Pattern Recognition, 12, 51–62.
CAUSSINUS, H. (1992): Projections revelatrices. In: J.J. Droesbecke, B. Fichet and Ph. Tassi (Eds.): Modéles pour l’analyse des données. Economica, Paris, 241–266.
CLEVELAND, W.S. (1993): Vizualising data. Hobart Press, Summit, New Jersey.
GNANADESIKAN R. (1977): Methods for Statistical Data Analysis of multivariate observations. Willey-Interscience, New-York.
JAIN, K.A. and DUBES, R.C. (1988): Algorithms for clustering data. Prentice Hall, Englewood Cliffs, New Jersey.
HARRIS, C.W. and KAISER H.F. (1964): Oblique factor analytic solutions by orthogonal transformations. Psychometrica, 29, 4, 347–362.
HARMAN, H.H. (1976): Modern Factor Analysis. Univ. of Chicago Press, Chicago.
KAUFMAN, L. and ROUSSEEUW P.J. (1990): Finding groups in data, An introduction to cluster analysis. Willey-Interscience, New-York.
MILLIGAN, G.W. and COOPER, M.C. (1985): An examination of procedures for determining the number of clusters in a data set. Psychometrica, 50, 159–179.
VALOIS, J.-P. (2000): L’approche graphique en analyse des données, Journal de la Soc. Fr. de Stat. to be published.
VONEIFF, G.W. (1996):A new Approach to Large Sale Infill evaluations applied to the Ozona(Canyon) Gas Sands. In: Permian Basin Oil and gas Conference, Midland, Texas, 495–510, rf. SPE: 35203
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Valois, JP. (2000). Robust Approach in Hierarchical Clustering: Application to the Sectorisation of an Oil Field. In: Kiers, H.A.L., Rasson, JP., Groenen, P.J.F., Schader, M. (eds) Data Analysis, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59789-3_15
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DOI: https://doi.org/10.1007/978-3-642-59789-3_15
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