  In carbon capture and sequestration, developing effective monitoring methods
is needed to detect and respond to CO2 leakage. CO2 leakage detection methods
rely on geophysical observations and monitoring sensor network. However,
traditional methods usually require the development of site-specific physical
models and expert interpretation, and the effectiveness of these methods can be
limited to different application locations, operational scenarios, and
conditions. In this paper, we developed a novel data-driven leakage detection
method based on densely connected convolutional neural networks. Our method
differs from conventional leakage monitoring methods by directly learning a
mapping relationship between seismic data and the CO2 leakage mass. To account
for the spatial and temporal characteristics of seismic data, our novel
networks architecture combines 1D and 2D convolutional neural networks. To
overcome the computational expense of solving optimization problems, we apply a
densely-connecting strategy in our network architecture that reduces the number
of network parameters. Based on the features generated by our convolutional
neural networks, we further incorporate a long short-term memory network to
utilize time-sequential information, which further improves the detection
accuracy. Finally, we employ our detection method to synthetic seismic datasets
generated based on flow simulations of a hypothetical CO2 storage scenario with
injection into a partially compartmentalized sandstone storage reservoir. To
evaluate method performance, we conducted multiple experiments including a
random leakage test, a sequential test, and a robustness test. Numerical
results show that our CO2 leakage detection method successfully detects the
leakage and accurately predicts the leakage mass, suggesting that it has the
potential for application in monitoring of real CO2 storage sites.
