Computing seismic attributes with deep-learning models

Seismic data contains valuable information about the Earth's subsurface, which is useful in oil and gas (O&G) exploration. Seismic attributes are derived from seismic data to highlight relevant data structures and properties, improving geological or geophysical data interpretation. However, when calculated on large datasets, quite common in the O&G industry, these attributes may be computationally expensive regarding computing power and memory capacity. Deep learning techniques can reduce these costs by avoiding direct attribute calculation. Some of these techniques may, however, be too complex, require large volumes of training data, and demand large computational capacity. This work shows that a conventional U-Net Convolutional Neural Network (CNN) model, with 31 million parameters, can be used to compute diverse seismic attributes directly from seismic data. The F3 dataset and attributes calculated on it were employed to train the models, each specialized in a specific attribute. The trained CNN models yield low prediction errors for most of the tested attributes. These results evince that simple CNN models are able to infer seismic attributes with high accuracy.