An Uncertainty-Aware Digital Twin for Geological Carbon Storage

TitleAn Uncertainty-Aware Digital Twin for Geological Carbon Storage
Publication TypeConference
Year of Publication2024
AuthorsAbhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Grant Bruer, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann
Conference NameSIAM Conference on Uncertainty Quantification
KeywordsBayesian inference, CCS, deep learning, digital twin, GCS, Imaging, monitoring, SIAM, Uncertainty quantification

Arguably, Geological Carbon Storage constitutes the only truly scalable net-negative carbon emission technology. To mitigate its risks and optimize its operations, an uncertainty-aware Digital Twin is being developed. To leverage existing fluid-flow and seismic simulation and imaging capabilities, the envisioned twin combines techniques from sequential and simulation-based Bayesian inference to train its deep generative neural networks to draw samples from the posterior of the Digital Twin's state. Because these samples are conditioned on observed time-lapse field data, these twins are capable of capturing the dynamics of CO2 plumes and their uncertainty.


(SIAM UQ, Trieste)

Citation Keyherrmann2024SIAMUQdt