Full waveform seismic monitoring of CO2 injection and storage with broadband and fiberoptic sensors

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A critical geoscientific priority in Canada is to enable Carbon Capture, Utilization, and Storage (CCUS), in which greenhouse gases are permanently sequestered in geological structures, reducing their impact on climate change.  Applied seismology stands to play a critical monitoring role in this. My group and I will seek to make breakthroughs in our ability to use seismic methods to monitor CO2 storage, making full use of data, reliably and at low cost. The research will be supported by collaboration with the Consortium for Research in Elastic Wave Exploration Seismology, a group with the capacity to carry out custom land seismic experiments, and with the Containment and Monitoring Institute through their Field Research Station (CaMI.FRS). My HQP and I will leverage this collaborative environment to create new, full waveform inversion-based seismic methods for ensuring safe and stable storage. In the FWI variants needed for reservoir model-building, basic mathematical and numerical issues exist, that make mapping of multiple petrophysical properties, and their changes with time, more complicated to produce. We will address this through algorithm-building and numerical analysis, and validate the results with custom field data acquired at the CaMI.FRS.  My group and I will also target the computational expense of FWI-based monitoring. In recent years, hardware/software advances have made computational tools for FWI more easily available, and, critically, they have also made available two new technologies which will be central to the problem of low-cost, reliable monitoring. The first is Distributed Acoustic Sensing (DAS), in which fiberoptic cables are used as flexible, easily-deployable acquisition systems. The reduction in cost associated with DAS data makes it a key to new monitoring technology, but the unusual properties of the data mean that special methods will be needed to leverage its potential. My group and I will merge DAS with high-frequency FWI as one of our core proposed research objectives. The second technology is the suite of computational platforms associated with deep learning, and the possibilities they introduce for extension, and computationally efficient optimization, of FWI. My group has a record of developing deep learning tools for seismic inversion, but the data-rich problem of CO2 monitoring has not yet been broached. In the proposed plan, my group and I will develop custom-designed neural networks, constrained with physics-guiding involving wave theory and petrophysics, to output indicators of containment and/or storage failures. In this research plan, the science needed to create tools for monitoring of geological storage of CO2 will be carried out playing a role in helping Canada achieve its climate objectives. Importantly, by means of mentoring, outreach, and targeted recruitment, this research will be accomplished at the same time as we train a diverse cohort of HQP, in an inclusive environment.