Integration of Deep Neural Networks into Seismic Workflows for Low-Carbon Energy

The synergistic application of rapidly evolving machine learning technology and modern seismic modeling and imaging algorithms can lead to cost-efficient workflows to provide values to low-carbon energy projects that require cost-effective subsurface characterization and monitoring. In this presentation we will show examples of convolutional neural networks applied to early waring in CCS projects and cost-effective reprocessing of old reflection seismic data.