Abstract
The task of seismic data interpretation is a time-consuming and uncertain process. Machine learning tools can help to build a shortcut between raw seismic data and reservoir characteristics of interest. Recently, techniques involving convolutional neural networks have started to gain momentum. Convolutional neural networks are particularly efficient at pattern recognition within images, and this is why they are suitable for seismic facies classification and interpretation tasks. We experimented with three different architectures based on convolutional layers and compared them with different synthetic and field datasets in terms of quality of the seismic interpretation results and computational efficiency. The architectures used in our study were three deep fully convolutional architectures: a 3D convolutional network with a fully connected head; a 2D fully convolutional network, and U-Net. We found the U-Net architecture to be both robust and the fastest when performing classification at the prediction stage. The 3D convolutional model with a fully connected head was the slowest, while a fully convolutional model was unstable in its predictions.