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

• URL: http://arxiv.org/abs/2410.01218v1
• Date: Wed, 2 Oct 2024 03:58:45 GMT
• Title: An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring
• Authors: Abhinav Prakash Gahlot, Rafael Orozco, Ziyi Yin, Felix J. Herrmann,
• Abstract summary: Geological Carbon Storage GCS is arguably the only scalable net-negative CO2 emission technology available. A machine learning based data-assimilation framework is introduced and validated on realistic numerical simulations. This work represents the first proof of concept of an uncertainty-aware in-principle scalable Digital Shadow.
• Score: 1.1249583407496222
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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