Uncertainty quantification of CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si193.svg" display="inline" id="d1e664"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>plume migration in highly channelized aquifers using probabilistic convolutional neural networks

Feng, Li; Mo, Shaoxing; Sun, Alexander Y.; Wu, Jichun; Shi, Xiaoqing

doi:10.1016/j.advwatres.2023.104607

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2024

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Cited by 3 publications

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Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

Feng,

Mo,

Sun

et al. 2024

Advances in Water Resources

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Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

Feng,

Mo,

Sun

et al. 2024

Advances in Water Resources

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CO2 storage efficiency in saline aquifers: Insight from the numerical modeling of immiscible displacement

Sypchenko,

Afanasyev

2024

Physics of Fluids

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We estimate the storage efficiency and the maximum migration distance of CO2 injected through a vertical well into a homogeneous open aquifer. The efficiency is regarded as a measure of the pore space usage for the subsurface CO2 disposal. Under the assumption of immiscible (no-soluble) displacement of the reservoir brine by the injected CO2, we derive the dimensionless equations governing the radial flow from the well. Using this theoretical development, we conclude that all parameters of the gas disposal can depend only on five similarity parameters, which can be considered the screening criteria for the CO2 storage. By employing reservoir simulations, we calculate the storage efficiency and the maximum migration distance as functions of those parameters and evaluate the most influencing criteria. We distinguish several limiting cases corresponding to either the gravity-driven flow of CO2 or its injection into an anisotropic reservoir characterized by a poor fluid communication in the vertical direction. We show that the storage parameters are governed by different scaling relationships in these limiting cases. We quantify the coefficients of the corresponding scaling laws. For the transient regimes of the CO2 flow away from the well, we present the maps allowing a back-of-the-envelope estimation of the storage efficiency for given similarity criteria. The maps and the scaling laws can be useful in the regional surveys for a preliminary screening and evaluation of the target reservoirs for carbon capture and storage.

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Gradient-Guided Convolutional Autoencoder for Predicting Co2 Storage in Saline Aquifers with Multiple Geological Scenarios and Well Placements

Hu,

Wang,

Yan

et al. 2024

Preprint

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Uncertainty quantification of CO2plume migration in highly channelized aquifers using probabilistic convolutional neural networks

Cited by 3 publications

References 63 publications

Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

CO2 storage efficiency in saline aquifers: Insight from the numerical modeling of immiscible displacement

Gradient-Guided Convolutional Autoencoder for Predicting Co2 Storage in Saline Aquifers with Multiple Geological Scenarios and Well Placements

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