  Evaluation of hydrocarbon reservoir requires classification of petrophysical
properties from available dataset. However, characterization of reservoir
attributes is difficult due to the nonlinear and heterogeneous nature of the
subsurface physical properties. In this context, present study proposes a
generalized one class classification framework based on Support Vector Data
Description (SVDD) to classify a reservoir characteristic water saturation into
two classes (Class high and Class low) from four logs namely gamma ray, neutron
porosity, bulk density, and P sonic using an imbalanced dataset. A comparison
is carried out among proposed framework and different supervised classification
algorithms in terms of g metric means and execution time. Experimental results
show that proposed framework has outperformed other classifiers in terms of
these performance evaluators. It is envisaged that the classification analysis
performed in this study will be useful in further reservoir modeling.
