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.
Monitoring and forecasting CO2 storage in the Sleipner area with spatio-temporal CNNs
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Feng, Shihang, Zhang, Xitong, Wohlberg, Brendt, Symons, Neill, and Youzuo Lin. "Monitoring and forecasting CO2 storage in the Sleipner area with spatio-temporal CNNs." Paper presented at the SEG/AAPG/SEPM First International Meeting for Applied Geoscience & Energy, Denver, Colorado, USA and online, September 2021. doi: https://doi.org/10.1190/segam2021-3583695.1
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