The study of natural events and those generated by human activity at sea has been a high priority for the Oil and Gas industry, due to the possibility of a dangerous event for the marine environment or the production area. In this context, the objective of this work is the evaluation of approaches based on deep learning for the classification of events in the sea using synthetic aperture radar images in the oil and gas area. Methods based on deep learning have shown an excellent performance through the use of convolutional layers, where the characteristics are extracted automatically through the definition of a kernel and stride. The following architectures are evaluated in this work: Inception V3, Xception, Inception ResNet V2, MobileNet, VGG16, and Deep Attention sampling. The assessment is made using a methodology for classifying events at sea using two radar image databases: the first contains 10 events commonly present in the Arctic Ocean, and the second describes an oil spill present near the Louisiana coast. In the experiments performed, the best results were obtained with the Deep Attention sampling architectures, which reached f1- score and Recall values of up to 0.82 a per cent nd 0.87 per cent respectively, for the class of interest in the oil spill dataset. For the dataset of natural events in the sea, high performance was evidenced for architectures based on the non-use of Inception modules, having higher values of F1-score and Recall for an Xception architecture. Also, an improvement of up to 10 per cent and 13 per cent in the metrics f1- score and recall in the use of attention was observed, concerning its base architecture (VGG16), and 4 per cent with other architectures based on Inception modules, this for the dataset of events at sea, demonstrating the advantages of using Attention Sampling carefully.