Feb. 12, 2024, 5:42 a.m. | G. S. SeabraTU Delft, Netherlands N. T. M\"uckeCentrum Wiskunde & Informatica, Netherlands V. L. S. SilvaPetrobras, Brazil D.

cs.LG updates on arXiv.org arxiv.org

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 …

capability carbon cs.lg data fidelity fourier integration machine machine learning machine learning models modeling posterior projects quantification storage study uncertainty

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