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In traditional channel sand body modeling methods, the method based on target modeling is difficult to condition, and multipoint geostatistics have difficulty reproducing the shape of continuous channel sand bodies, which makes the modeling results difficult to directly apply to oilfield production. Through depth learning, the depth generation model can generate a sufficiently accurate channel sand body model, reproduce the complex channel sand body shape, meet the well point conditions, and compensate for the shortcomings of the traditional modeling algorithm. In the process of modeling, 20 000 channel sand body models and corresponding condition sets are established based on the target simulation method and computer matching operation. Combined with the theory of variational autoencoders (VAE) and generative adversarial networks (GAN), a depth generation model is established, including a classifier, encoder, decoder and discriminator. The condition data and the real model are input into the depth generation model to obtain the corresponding channel sand body model. Through extensive training, a generator that can generate channel sand bodies that meet the well point conditions is established. Finally, the well point data are input into the generator to establish the corresponding channel sand body model. The results show that compared with the traditional modeling algorithm, the depth generation model modeling algorithm not only shows a continuous and clear channel sand body but can also generate multiple channel sand body models under given conditions. This modeling method overcomes the shortcomings of traditional channel sand modeling methods and provides a new solution for channel sand reservoir modeling. The established channel model can provide a reference for the oilfield development stage.