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Shihang Feng, Xitong Zhang, Brendt Wohlberg, Neill Symons and Youzuo Lin, "Monitoring and forecasting CO2 storage in the Sleipner area with spatio-temporal CNNs", in First International Meeting for Applied Geoscience \& Energy Expanded Abstracts, doi:10.1190/segam2021-3583695.1, Sep 2021


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.

BibTeX Entry

author = {Shihang Feng and Xitong Zhang and Brendt Wohlberg and Neill Symons and Youzuo Lin},
title = {Monitoring and forecasting {CO} $_{2}$ storage in the {S}leipner area with spatio-temporal {CNN}s},
year = {2021},
month = Sep,
urlhtml = {},
booktitle = {First International Meeting for Applied Geoscience \& Energy Expanded Abstracts},
doi = {10.1190/segam2021-3583695.1}