Oil and gas are important in the world economy, used as raw materials in various products. For the extraction of these products, it is necessary to carry out the characterization of the hydrocarbon reservoirs. This characterization extracts a volume with seismic data from the region of interest. These data are interpreted to identify various features, such as the classification of seismic facies, horizons, faults, and gas. A large amount of seismic volume data makes manual interpretation increasingly challenging. Many researchers in the field of seismic interpretation have invested in methods using neural networks. Convolutional Neural Networks (CNN) are widely used in computer vision problems and get great results in many situations with 2D data. The present work aimed to apply convolutional neural networks in the supervised mapping of seismic horizons. We evaluated our proposal using the F3 block with seismic facies annotations. The data representation in the input layer are patches of sections. In the horizon forecast, we evaluate the architectures of ResUnet and DC-Unet. We use the Generalized Dice and the Focal Tversky loss functions for the loss function. The method delivered promising results with the ResUnet and Focal Tversky loss function on data based on 128x128 patches, reaching approximately 56 percent on the Dice metric. The full implementation and the trained networks are available at https://github.com/mayaragomys/seismic_horizons.