A novel multiclassSVM based framework to classify lithology from well logs: a real-world application

TL;DR

This paper presents a multiclass SVM framework for lithology classification from well logs, demonstrating improved accuracy over other classifiers and exploring kernel parameter selection, with potential for seismic data integration.

Contribution

Introduces a multiclass SVM framework for lithology classification using well logs, with a focus on one-against-all strategy and kernel optimization, advancing geophysical classification methods.

Findings

Multiclass SVM outperforms other classifiers in accuracy.

Kernel function choice significantly affects classification performance.

Framework can incorporate seismic attributes for broader applications.

Abstract

Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies. In this paper, an attempt has been made to classify lithology using a multiclass SVM based framework using well logs as predictor variables. Here, the lithology is classified into four classes such as sand, shaly sand, sandy shale and shale based on the relative values of sand and shale fractions as suggested by an expert geologist. The available dataset consisting well logs (gamma ray, neutron porosity, density, and P-sonic) and class information from four closely spaced wells from an onshore hydrocarbon field is divided into training and testing sets. We have used one-against-all strategy to combine the results of multiple binary classifiers. The reported results established the superiority of multiclass SVM compared to other classifiers in terms of classification accuracy. The selection of kernel function and associated parameters has also been investigated here. It can be envisaged from the results achieved in this study that the proposed framework based on multiclass SVM can further be used to solve classification problems. In future research endeavor, seismic attributes can be introduced in the framework to classify the lithology throughout a study area from seismic inputs.