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September 17, 2025

Generative Adversarial Network for Modeling of CO2 Plume Evolution in Geological Carbon Storage Systems

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Deep learning approach shows accurate predictions of CO2 saturation and pressure in geological systems, indicating efficiency advantages.

GANs achieved R2 values of 0.989 and 0.996 for CO2 saturation and pressure buildup predictions, respectively.
Normalized Absolute Error remained around 1% across all predictions, highlighting model accuracy.
Using physics-based simulations, Latin-Hypercube sampling helped create a diverse set of reservoir parameters.
GANs showed computational efficiency with prediction times of just 0.01 seconds per case, contrasting with 1000 seconds for traditional simulations.

Tariq et al. (2025) studied this question.

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September 17, 2025

View Full Paper

Ask AI

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Authors

Deep learning approach shows accurate predictions of CO2 saturation and pressure in geological systems, indicating efficiency advantages.

GANs achieved R2 values of 0.989 and 0.996 for CO2 saturation and pressure buildup predictions, respectively.
Normalized Absolute Error remained around 1% across all predictions, highlighting model accuracy.
Using physics-based simulations, Latin-Hypercube sampling helped create a diverse set of reservoir parameters.
GANs showed computational efficiency with prediction times of just 0.01 seconds per case, contrasting with 1000 seconds for traditional simulations.

Tariq et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark