AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage
<|reference_start|>AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage: This study investigates the integration of machine learning (ML) and data assimilation (DA) techniques, focusing on implementing surrogate models for Geological Carbon Storage (GCS) projects while maintaining high fidelity physical results in posterior states. Initially, we evaluate the surrogate modeling capability of two distinct machine learning models, Fourier Neural Operators (FNOs) and Transformer UNet (T-UNet), in the context of CO$_2$ injection simulations within channelized reservoirs. We introduce the Surrogate-based hybrid ESMDA (SH-ESMDA), an adaptation of the traditional Ensemble Smoother with Multiple Data Assimilation (ESMDA). This method uses FNOs and T-UNet as surrogate models and has the potential to make the standard ESMDA process at least 50% faster or more, depending on the number of assimilation steps. Additionally, we introduce Surrogate-based Hybrid RML (SH-RML), a variational data assimilation approach that relies on the randomized maximum likelihood (RML) where both the FNO and the T-UNet enable the computation of gradients for the optimization of the objective function, and a high-fidelity model is employed for the computation of the posterior states. Our comparative analyses show that SH-RML offers better uncertainty quantification compared to conventional ESMDA for the case study.<|reference_end|>
@article{seabra2024ai,
    title={AI enhanced data assimilation and uncertainty quantification applied to
  Geological Carbon Storage},
    author={G. S. Seabra (1, 2), N. T. M"ucke (3, 4), V. L. S. Silva (2, 5), D.
  Voskov (1, 6), F. Vossepoel (1) ((1) TU Delft, Netherlands, (2) Petrobras,
  Brazil, (3) Centrum Wiskunde & Informatica, Netherlands, (4) Utrecht
  University, Netherlands, (5) Imperial College London, United Kingdom, (6)
  Stanford University, USA)},
    journal={arXiv preprint arXiv:2402.06110},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06110},
    primaryClass={cs.LG}
}