Examining the facies of the reservoir can be of great help in identifying subsurface heterogeneity and identifying the characteristics of the different parts of the reservoir. The results can reduce the risk of drilling, reduce costs during exploration, harvesting and development, and increase production. In this study, we first analyzed the facies at the wells with unsupervised methods using petrophysical diagrams and compared their results to select the best ones, and then facies analysis will be done using support vector machine algorithm, which is a supervised method. To analyze the seismic facies, first attributes will be extracted and most effective of them will be selected. From seismic data, we try to obtain the facies using the information obtained from the well. In investigating the method, we have tried to perform cross-validation and obtain the mean of the algorithm error in several stages and consider it as the method error. The final results show the ability of the support vector classifier to analyze seismic facies.