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Evaluation of hydrocarbon reservoir requires classification of petrophysicalproperties from available dataset. However, characterization of reservoirattributes is difficult due to the nonlinear and heterogeneous nature of thesubsurface physical properties. In this context, present study proposes ageneralized one class classification framework based on Support Vector DataDescription (SVDD) to classify a reservoir characteristic water saturation intotwo classes (Class high and Class low) from four logs namely gamma ray, neutronporosity, bulk density, and P sonic using an imbalanced dataset. A comparisonis carried out among proposed framework and different supervised classificationalgorithms in terms of g metric means and execution time. Experimental resultsshow that proposed framework has outperformed other classifiers in terms ofthese performance evaluators. It is envisaged that the classification analysisperformed in this study will be useful in further reservoir modeling.

A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset