Unsupervised Seismic Data Classification Using Gaussian Mixture Models

Seismic facies analysis plays an important role in the studies of hydrocarbon reservoirs. Because in the beginning of exploration operations of hydrocarbon reservoirs, there is no or low number of wells in the area, the lateral changes and seismic facies analysis in a special horizon can be studied using pattern recognition algorithms and seismic attributes. Supervised and unsupervised methods have an important role in increasing the accuracy and the speed and decreasing the costs of data classification which a good analysis of seismic facies can be provided. The base of unsupervised methods, which is also the subject of this study, is the classification of all data in attribute space, and the result does not depend on prior information. In this method, the classification and interpretation of results are carried out by matching analysis between seismic facies, without using well data. There are several methods of unsupervised clustering. In this paper, the Gaussian Mixture Models (GMM) method has been employed which it uses some gaussian distributions and assigns membership probability to analysis samples in order to classify them. By using this method, seismic facies analysis is processed on a 3D seismic data set acquired in a hydrocarbon field in south of Iran. The analysis is carried out on two different horizons where the results show an acceptable facies classification by the GMM method, and the results are in a good agreement with reservoir quality analysis of electrofacies in some wells.