A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
This work addresses reservoir characterization for petroleum geology, but it is incremental as it applies an existing method (SVDD) to a specific domain dataset.
The authors tackled the problem of classifying water saturation in hydrocarbon reservoirs using an imbalanced geological dataset, proposing a one-class classification framework based on Support Vector Data Description (SVDD) that outperformed other supervised classifiers in terms of g-metric means and execution time.
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.