Advancing Data Assimilation and Uncertainty Quantification for CO2 Sequestration through AI-Hybrid Methods

Conference Paper (2024)
Author(s)

Gabriel Serrão MR. SERRAO SEABRA (Petroleo Brasileiro S.A., TU Delft - Reservoir Engineering)

Nikolaj T. Mücke (Universiteit Utrecht, Centrum Wiskunde & Informatica (CWI))

V. Luiz Santos Silva (Imperial College London, Petroleo Brasileiro S.A.)

D. V. Voskov (TU Delft - Reservoir Engineering, Stanford University)

F.C. Vossepoel (TU Delft - Reservoir Engineering)

Research Group
Reservoir Engineering
DOI related publication
https://doi.org/10.3997/2214-4609.202437083
More Info
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Publication Year
2024
Language
English
Research Group
Reservoir Engineering
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Volume number
2
Pages (from-to)
886-908
ISBN (electronic)
9798331313319
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Abstract

In this comprehensive study, we discuss a novel approach to enhance data assimilation and uncertainty quantification in the field of Geological Carbon Sequestration (GCS). We specifically address the complexities of channelized reservoirs, which pose significant challenges due to non-Gaussian permeability distributions and the intricate non-linear physics of CO2 injection processes. Our innovative method integrates Fourier Neural Operators (FNOs) and Transformer UNet (T-UNet) with advanced data assimilation techniques - the Surrogate-based Hybrid Ensemble Smoother with Multiple Data Assimilation (SH-ESMDA) and the Surrogate-based Hybrid Randomized Maximum Likelihood (SH-RML). These techniques make use of the very efficient computation of gradients that neural networks provide and they not only improves the speed of data processing but also enhances the accuracy of predictions in synthetic data assimilation experiments for GCS applications. A key element of our approach is the use of proxy models alongside high-fidelity simulations, ensuring the consistency and reliability of physical posterior distributions. We utilized Alluvsim for detailed geological modeling and the Delft Advanced Research Terra Simulator (DARTS) for comprehensive fluid flow simulations, providing a comprehensive understanding of reservoir dynamics. A synthetic case study on a channelized reservoir model for CO2 sequestration demonstrates the effectiveness of these methods, with improvements in predicting CO2 plume migration and pressure dynamics within the reservoir. The results of our study show that the integration of FNOs and T-UNet with SH-ESMDA and SH-RML leads to enhanced prediction capabilities, particularly in the challenging context of channelized reservoirs. The SH-ESMDA method proves to be highly efficient in speeding up the data assimilation process without compromising accuracy, while SH-RML demonstrates a more effective history matching compared to standard Ensemble Smoother with Multiple Data Assimilation (ESMDA) techniques, indicating a robust strategy for assimilating complex data. This research not only contributes to the realm of GCS but also presents a novel solution for the integration of artificial intelligence with traditional methodologies that can be applied in various fields where data assimilation and uncertainty quantification are crucial. Our study paves the way for future advancements in this domain, highlighting the potential of AI-driven techniques in enhancing data assimilation and uncertainty quantification for GCS projects.

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