Permeability is one of the most important hydrocarbon reservoir’s characters. This parameter plays a fundamental role in assessing the production rate of a reservoir, field performance, and reservoir management and development. In fact, this property indicates the ability to pass fluid from the porous medium without changing the structure of the building or replacing it with parts of it. Permeability is mainly obtained through laboratory measurements on the core or well data. However, because of the high cost and low frequency of this type of data, using of well logging data has a particular importance in permeability prediction. In the following, permeability was calculated using a regression model called support vector machine based on radial basic function using data from four wells in the aforementioned field. To enhance the model's generalizability, the data of three wells was used to train the regression model whereas well number four was hidden and also was used to evaluate the efficiency and accuracy of the model. To do that, the correlation coefficient was calculated between the actual and estimated permeability values for each electro-facies in well number four. High values of correlation coefficient confirmed the ability and accuracy of the proposed method for permeability prediction.