CCSNet
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About
CCSNet is a deep learning modeling suite for CO2 storage, offering predictions for 2D and 3D saline reservoirs across a wide range of reservoir conditions, injection designs, rock properties, and permeability maps. Developed by Gege Wen at Stanford University and supported by the Stanford Center for Carbon Storage, it provides interactive models for real-time prediction and visualization. The suite includes Nested FNO for high-resolution, high-fidelity 3D spatial-temporal predictions of CO2 gas saturation plume and pressure buildup, speeding up simulations by nearly 700,000 times. It also features Radial 2D models for anisotropic permeability prediction using U-FNO and isotropic permeability prediction using R-U-Net convolutional neural networks, enabling efficient and accurate analysis of CO2 storage outcomes.
Platform
Task
Features
• interactive real-time prediction and visualization
• r-u-net for radial 2d isotropic permeability
• u-fno for radial 2d anisotropic permeability
• 700,000x faster than numerical simulators
• nested fno for 3d co2 plume and pressure predictions
• handles diverse reservoir conditions and properties
• 2d and 3d saline reservoir co2 storage predictions
• deep learning modeling for co2 storage
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