Attention-based self-calibrated convolution neural network for efficient facies classification

Recent advances in deep learning and computer vision have resulted in giant leaps in automating some of the cumbersome oil and gas exploration and production operations. Deep convolutional neural networks have been widely used for seismic interpretation tasks including detection, classification, and segmentation of various subsurface geological phenomena. The downside of deep neural networks is that their data requirements increase heavily as their complexity increases. Although seismic data is abundantly available, such networks require annotated data which is an expensive and time-consuming process. In this work, we present a deep model for facies classification that leverages an attention-based self-calibrated convolution to achieve superior results while maintaining a relatively low model complexity. The model was trained and tested on a publicly available dataset for facies classification based on the Netherlands F3 block (Project F3 Demo 2020, 1987). The proposed model outperforms other models in the literature for facies classification while maintaining a lower complexity in terms of the number of parameters and the multiply-accumulate operations of the model.