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An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring

AP Gahlot, R Orozco, Z Yin, FJ Herrmann - arXiv preprint arXiv …, 2024 - arxiv.org
Physics paper physics.geo-ph Suggest
… m=1, simulation-based inference with CNFs proceeds by training an invertible neural network to approximate the posterior distribution, p(x y), by minimizing the forward …
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BibTeX

@article{2410.01218v1,
Author        = {Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Felix J. Herrmann},
Title         = {An uncertainty-aware Digital Shadow for underground multimodal CO2
  storage monitoring},
Eprint        = {2410.01218v1},
DOI           = {10.1093/gji/ggaf176},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.geo-ph},
Abstract      = {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.},
Year          = {2024},
Month         = {Oct},
Url           = {http://arxiv.org/abs/2410.01218v1},
File          = {2410.01218v1.pdf}
}

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