A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
This work addresses reservoir characterization for engineers by providing an incremental improvement in classification accuracy.
The paper tackled the problem of classifying water saturation from seismic attributes by proposing a novel Support Vector Data Description (SVDD) framework, achieving superior performance compared to existing classifiers as measured by G-metric means and execution time.
Water saturation is an important property in reservoir engineering domain. Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization. However, diverse and non-linear nature of subsurface attributes makes the classification task difficult. In this context, this paper proposes a generalized Support Vector Data Description (SVDD) based novel classification framework to classify water saturation into two classes (Class high and Class low) from three seismic attributes seismic impedance, amplitude envelop, and seismic sweetness. G-metric means and program execution time are used to quantify the performance of the proposed framework along with established supervised classifiers. The documented results imply that the proposed framework is superior to existing classifiers. The present study is envisioned to contribute in further reservoir modeling.