A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes

TL;DR

This paper introduces a new SVDD-based classification framework that effectively distinguishes high and low water saturation from seismic attributes, outperforming existing classifiers and aiding reservoir modeling.

Contribution

A novel SVDD-based framework for classifying water saturation from seismic attributes, demonstrating superior performance over traditional classifiers.

Findings

Framework outperforms existing classifiers in accuracy.

Uses seismic impedance, amplitude envelope, and seismic sweetness.

Contributes to improved reservoir modeling.

Abstract

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.