SFA-GTM: Seismic Facies Analysis Based on Generative Topographic Map and RBF

TL;DR

This paper introduces SFA-GTM, a non-linear seismic facies analysis method combining Generative Topographic Map and RBF interpolation to improve reservoir characterization from seismic data.

Contribution

It presents a novel non-linear approach using GTM and RBF to overcome limitations of linear methods in seismic facies identification.

Findings

Effective classification of seismic facies using GTM.

Improved handling of missing data with RBF interpolation.

Enhanced reservoir characterization accuracy.

Abstract

Seismic facies identification plays a significant role in reservoir characterization. It helps in identifying the various lithological and stratigraphical changes in reservoir properties. With the increase in the size of seismic data or number of attributes to be analyzed, the manual process for facies identification becomes complicated and time-consuming. Even though seismic attributes add multiple dimensions to the data, their role in reservoir characterization is very crucial. There exist different linear transformation methods that use seismic attributes for identification, characterization, and visualization of seismic facies. These linear transformation methods have been widely used for facies characterization. However, there are some limitations associated with these methods such as deciding the width parameters, number of clusters, convergence rate etc. Therefore, the present research work uses non-linear transformation approach that overcomes some of the major limitations of linear approaches. The proposed Seismic facies analysis approach based on Generative Topographic Map \& Radial Basis Function(SFA-GTM) works by calculating the set of four Gray Level Co-occurrence Matrix(GLCM) texture based attributes viz. energy, homogeneity, contrast, and dissimilarity. The Generative Topographic Map(GTM) is used for unsupervised classification of seismic facies based on the set of calculated texture attributes. Further, the present work uses Radial Basis Function(RBF) for interpolating the missing values in the data.