An Uncertainty-Aware Digital Twin for Geological Carbon Storage
| Title | An Uncertainty-Aware Digital Twin for Geological Carbon Storage |
| Publication Type | Conference |
| Year of Publication | 2024 |
| Authors | Abhinav Prakash Gahlot, Rafael Orozco, Haoyun Li, Grant Bruer, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann |
| Conference Name | SIAM Conference on Uncertainty Quantification |
| Month | 03 |
| Keywords | Bayesian inference, CCS, deep learning, digital twin, GCS, Imaging, monitoring, SIAM, Uncertainty quantification |
| Abstract | 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. |
| Notes | (SIAM UQ, Trieste) |
| URL | https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/digital-twin |
| Citation Key | herrmann2024SIAMUQdt |