Super resolution (SR) is a topic of notable importance in domains of assorted knowledge, such as the medical, monitoring, and security areas. The use of deep neural networks to solve this task is something extremely recent in the seismic field, with few references, which began to be published less than 2 years ago. However, the literature presents a wide range of methods, using neural networks for the super resolution of natural images. With this in mind, the objective of this work is to explore such approaches applied to synthetic seismic data from reservoirs. For this, models of chronological importance in the literature were used and compared with a classic interpolation method and with models of the literature of super resolution of seismic images. These models are: SRCNN, RDN, the Deep Image Prior approach and SAN. The results show that the PSNR obtained by architectures developed for the seismic domain is equivalent to 38.23 and the best result of the proposed architectures is 38.62, showing the progress that such models bring to the seismic domain.