Parallel Framework for Unsupervised Classification of Seismic Facies

Abstract

Seismic facies classification plays a critical role in characterizing & delineating the various features present in the reservoirs. It aims at determining the number of facies & their description for the available seismic data. During the past few decades, seismic attributes have been widely used for the task of seismic facies identification. It helps geologists to determine different lithological and stratigraphical changes in the reservoir. With the increase in the seismic data volume & attributes, it becomes difficult for the interpreters to examine each seismic line. One of the solutions given to this problem was to use some computer-assisted methods such as k-means, self-organizing map, generative topographic map and artificial neural network for analyzing the seismic data. Even though these computer-assisted methods performed well but due to the size of the 3-D seismic data the overall classification process becomes very protracted. In this paper, we introduce a parallel framework for unsupervised classification of the seismic facies. The method begins by calculating four different seismic attributes. Spark & Tensorflow based implementation of unsupervised facies classification algorithms are then used to identify the seismic facies based on the 4-D input attributes data. Further, the comparison of results (in terms of execution time & error) of Spark & Tensorflow based algorithms with already existing approach show that the proposed approach provides results much faster than previously existing MPI based approach.