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This paper aims to provide alternative approaches for automatic classification of subsurface hydrocarbon-bearing regions from 2D seismic images driven by multi-layer perceptron neural networks (MLPs) (a kind of artificial neural network) and convolutional neural networks (CNNs). The first approach is based on a standard MLP whose features are controlled by Haralick's textural descriptors; the second one is developed with a multiple-layer CNN. Both techniques are studied to identify geologic "leads", instead of delineating other structures of the porous medium, such as salt bodies or seismic faults. The outcomes obtained from each approach are evaluated for a dataset of seismic images corresponding to the offshore SEAL Basin in Brazil's northeastern. Performance indicators (accuracy, recall, precision, F-measure and loss) are computed to verify training and validation of the network learning capabilities. It is shown that for both MLP and CNN configurations, good agreement is achieved in blind testing qualitatively and quantitatively. • Automatic classification of hydrocarbon accumulations in 2D seismic images. • Straightforward detection of the location of geologic leads. • Image processing through a multilayer convolutional neural network. • Feature extraction from several Haralick's textural descriptors. • Classification with good agreement in blind testing. [ABSTRACT FROM AUTHOR]