This website uses cookies to improve your experience. If you continue without changing your settings, you consent to our use of cookies in accordance with our cookie policy. You can disable cookies at any time.

×

Monitoring and forecasting CO2 storage in the Sleipner area with spatio-temporal CNNs

Authors:

Abstract

We have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated seismic images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models in monitoring and forecasting the CO2 storage using real 4D post-stack seismic imaging data acquired at the Sleipner CO2 sequestration field.