An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring
<|reference_start|>An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring: Geological Carbon Storage GCS is arguably the only scalable net-negative CO2 emission technology available While promising subsurface complexities and heterogeneity of reservoir properties demand a systematic approach to quantify uncertainty when optimizing production and mitigating storage risks which include assurances of Containment and Conformance of injected supercritical CO2 As a first step towards the design and implementation of a Digital Twin for monitoring underground storage operations a machine learning based data-assimilation framework is introduced and validated on carefully designed realistic numerical simulations As our implementation is based on Bayesian inference but does not yet support control and decision-making we coin our approach an uncertainty-aware Digital Shadow To characterize the posterior distribution for the state of CO2 plumes conditioned on multi-modal time-lapse data the envisioned Shadow combines techniques from Simulation-Based Inference SBI and Ensemble Bayesian Filtering to establish probabilistic baselines and assimilate multi-modal data for GCS problems that are challenged by large degrees of freedom nonlinear multi-physics non-Gaussianity and computationally expensive to evaluate fluid flow and seismic simulations To enable SBI for dynamic systems a recursive scheme is proposed where the Digital Shadows neural networks are trained on simulated ensembles for their state and observed data well and/or seismic Once training is completed the systems state is inferred when time-lapse field data becomes available In this computational study we observe that a lack of knowledge on the permeability field can be factored into the Digital Shadows uncertainty quantification To our knowledge this work represents the first proof of concept of an uncertainty-aware in-principle scalable Digital Shadow.<|reference_end|>
@article{gahlot2024an,
    title={An uncertainty-aware Digital Shadow for underground multimodal CO2
  storage monitoring},
    author={Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Felix J.
  Herrmann},
    journal={arXiv preprint arXiv:2410.01218},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01218},
    primaryClass={physics.geo-ph cs.AI cs.LG physics.comp-ph}
}