AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage

Gabriel Serrão Seabra*, Nikolaj T. Mücke, Vinicius Luiz Santos Silva, Denis Voskov, Femke C. Vossepoel

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

30 Downloads (Pure)

Abstract

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 the 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 CO2 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 a better uncertainty quantification when compared to the conventional ESMDA for the case study.

Original languageEnglish
Article number104190
Number of pages22
JournalInternational Journal of Greenhouse Gas Control
Volume136
DOIs
Publication statusPublished - 2024

Keywords

  • Data assimilation
  • Geological Carbon Storage (GCS)
  • Machine learning
  • Uncertainty quantification

Fingerprint

Dive into the research topics of 'AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage'. Together they form a unique fingerprint.

Cite this