Deep Machine Learning Application for Supervised Facies Classification

Abstract

Summary Applications of Machine Learning (ML) algorithms to solve problems in seismic reservoir characterization (SRC) are drawing widespread attention in the last few years. One of the challenging problems to solve is doing facies classification based on seismic amplitude/ Amplitude Versus Offset (AVO) attributes only without employing commonly used results from seismic inversion. The objective of this study is to leverage the new developments in Deep Learning (DL) techniques to perform a supervised facies classification based on available post/pre-stack seismic attributes. This simple ML workflow will help geoscientists perform a reconnaissance of the available seismic data and formulate a program for detailed analysis using more advanced tools. Results of the facies classification were then validated by comparison of the facies volume with Lithofacies logs from both training and blind wells, and additionally compared with pre-stack seismic inversion results.