CCSNet
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About
CCSNet is a deep learning modeling suite for CO2 storage, developed by Gege Wen at Stanford University. It provides CO2 storage predictions for 2D and 3D saline reservoirs, accounting for a wide range of reservoir conditions, injection designs, rock properties, and permeability maps. The suite includes interactive models like Reservoir-scale 2D isotropic (using R-U-Net), 2D anisotropic (using U-FNO), and Basin-scale 3D predictions (using Nested FNO). Nested FNO offers high-resolution, high-fidelity 3D spatial-temporal predictions of CO2 gas saturation plume and pressure buildup, speeding up predictions nearly 700,000 times compared to state-of-the-art numerical simulators. Users can select a model from the sidebar to predict and visualize in real-time. User manuals and demo cases are available for download.
Platform
Task
Features
• achieves predictions nearly 700,000 times faster than numerical simulators
• radial 2d isotropic predictions using r-u-net
• radial 2d anisotropic predictions using u-fno
• basin-scale high-resolution 3d predictions using nested fno
• interactive models for real-time prediction and visualization
• handles wide range of reservoir conditions, injection designs, rock properties, and permeability maps
• provides co2 storage predictions for 2d and 3d saline reservoirs
• deep learning modeling suite for co2 storage
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