Seismic fault interpretation based on deep convolutional neural networks

Seismic fault interpretation has always been a key task in the process of oil and gas exploration and development. Conventional fault interpretation is mainly based on human-computer interaction, which is of low efficiency and causes the results with many uncertainties. In addition, conventional methods for fault interpretation usua-lly set multiple parameters, whose controls accuracy of the predicted faults. This paper proposes a method using seismic data based on convolutional deep neural networks. Taking the advantages of ResNet for effectively training deep convolutional neural network and U-Net architecture for characterizing multi-scale and multi-layer characteristic information, this method combines deep residual neural network and U-Net architecture to construct a network architecture (SeisFault-Net) for fault interpretation based on seismic data. The U-Net architecture consists of an encoding sub-network and a decoding sub-network. They enable the SeisFault-Net to train models in an end-to-end manner. The residual neural network can suppress the gradient dispersion of deep network, and effectively improve the training efficiency of the SeisFault-Net. After trained, the SeisFault-Net can perform fault interpretation based on seismic data without setting any parameters. This avoids the empirical error and uncertainties caused by parameters artificially set in conventional methods. Applications to raw data have proved that the SeisFault-Net me-thod can effectively and accurately detect fault loca-tions, and the faults have good vertical continuity and clear outlines. The detailed information of faults interpretated by the SeisFault-Net method is more abundant and accurate than the coherent algorithm. And the calculating efficiency of the SeisFault-Net method is very high in seismic fault interpretation. © 2021, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.