Sedimentary facies modeling is an important part of reservoir modeling and there are many methods to build sedimentary facies models. Traditional modeling methods need to describe the spatial structure information of variables by various parameters such as variogram and data patterns，and then reproduce the spatial structure in the realizations. With different strategies，the reservoir modeling based on Generative Adversarial Nets（GANs）learns a large number of images（models）to generate the model possessing highly similar characteristics with the learning samples. enerative Adversarial Nets based on the single image（SinGAN）only need one image for training to generate highly similar images，improving the traditional GANs. With the sedimentary microfacies diagram of two layers in N gas field as an example，the corresponding sedimentary facies model is built. Compared with the classical multiple-point geostatistics method Simpat，the SinGAN method obtains more similar spatial structure of sedimentary microfacies with that depicted by training images and has a good application prospect.