Deep learning for seismic lithology prediction

Seismic prediction has been a huge challenge because of the great uncertainties contained in the seismic data. Deep learning (DL) has been successfully applied in many fields and brought revolutionary changes, such as computer vision and natural language processing. The traditional artificial neural networks have been studied to improve the accuracy and resolution of seismic prediction for years, but not DL. In this paper, we develop a new architecture for seismic reservoir characterization based on the DL technique. We apply the convolutional neural network (CNN), which is a DL framework, to predict lithology and have achieved better results compared with traditional methods. We also propose to use continuous wavelet transforms (CWTs) to get a time-frequency spectrum for neural networks. CWTs help to make full use of the frequency content of the post-stack seismic data. According to the difference in the convolution layers and the organization of the input data, we propose four DL architectures for seismic lithology prediction, namely the deep neural networks (DNNs), the CNNs, the CWT-DNNs and the CWT-CNNs. All of these four architectures are applied in the case study. The final results on blind wells, profile and horizontal slice show that CWT-CNN models have the best performance on post-stack seismic lithology prediction. CWT maps contain more information about thinner layers and convolution layers are better at feature extraction from CWT maps. The CWT-CNN model has higher accuracy and resolution, especially on medium and thin layer prediction.