CCSNet.ai
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About
CCSNet.ai is a deep learning modeling suite for CO2 storage, providing 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, this app is supported by the Stanford Center for Carbon Storage. It includes models like Nested FNO for high-resolution, high-fidelity 3D spatial-temporal predictions of CO2 gas saturation plumes and pressure buildup, speeding up predictions nearly 700,000 times compared to traditional simulators. It also features U-FNO for radial 2D anisotropic permeability and R-U-Net for radial 2D isotropic permeability. Users can interactively select models to predict and visualize results in real-time, and download user manuals and demo cases.
Platform
Task
Features
• user manual and demo cases available
• anisotropic and isotropic permeability modeling
• accelerated simulation (up to 700,000x faster)
• high-resolution 3d spatial-temporal predictions
• handles diverse reservoir parameters
• real-time prediction and visualization
• 2d and 3d saline reservoir prediction
• deep learning co2 storage modeling
Pricing Plans
Free
Free Plan• CO2 storage predictions
• 2D and 3D saline reservoir modeling
• Real-time visualization
• Access to Nested FNO, U-FNO, R-U-Net models
• User manual and demo cases download
Job Opportunities
There are currently no job postings for this AI tool.
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