A ResNet-Based Method for Complex Channel Interpretation in Seismic Volumes

Abstract

Channels usually contribute to the accumulation of reservoirs and their identification is useful in the well placements for mining and oil field development. We consider the task of channel detection as a regression problem and propose a workflow to identify the channels in seismic volumes with complex structures using an end-to-end 3D convolutional neural network. To train the network, we automatically generate a training dataset containing more than 19000 3D synthetic seismic volumes and the corresponding channel labels, which are shown to be sufficient to train our network. Although only trained on the synthetic dataset, the network can automatically learn informative features for channel identification. Multiple synthetic and field applications show that the network can accurately and efficiently separation all the channel reflections in 3D seismic volumes with complex structures. With a NVIDIA P100 GPU, the predicting application takes only 0.6 hours for a 10GB seismic volume with size 1201×1601×1351.