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Many multivariate statistical techniques have the ability to handle large data sets or a great number of parameters. Therefore, these multivariate statistical approaches are widely used in clastic sedimentology for facies analysis. Furthermore, most of the techniques which try to separate more or less homogeneous subsets can be subjective. This subjectivity raises several questions about the significance and confidence of clustering. The goal of this study is to optimize clustering and to evaluate the proper number of clusters needed in order to describe sedimentary and lithological facies through common characteristics. Also, with the interpretation of the clusters, the parametrized geometry adds further but quasi-subjective information to a 3D geologicalmodel. Two assumptions must be met: (1) well-definable geometries must correspond to the architectural elements (2) it is assumed that exactly one sedimentary or lithological facies belongs to each structural element and the flow properties are determined by these structural elements. This approach was applied to the clastic depositional data from a Miocene hydrocarbon reservoir (Algyő field, Hungary) to demonstrate the fidelity of the clustering method yielding an optimum of five cluster facies. The revealed clusters represent lithological characteristics within a (delta fed) submarine fan system. The paper deals with two stressed clusters in particular, showing sinusoid channels which were recognizable and measureable using parametrisation.
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