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

TL;DR

This paper introduces a one-class SVDD-based framework for classifying reservoir water saturation from imbalanced petrophysical data, outperforming other classifiers in accuracy and efficiency.

Contribution

It presents a novel one-class classification approach using SVDD tailored for imbalanced geological datasets, improving reservoir property classification.

Findings

Outperforms other classifiers in g metric mean

Reduces classification execution time

Effective on imbalanced reservoir data

Abstract

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.