CCSNet.ai
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About
CCSNet.ai is a deep learning modeling suite for CO2 storage developed at Stanford University. It provides predictions for 2D and 3D saline reservoirs, considering various reservoir conditions, injection designs, rock properties, and permeability maps. The tool utilizes several AI models, including Nested FNO for high-resolution 3D predictions and U-FNO for 2D anisotropic permeability simulations. It significantly accelerates predictions compared to traditional numerical methods. The software is supported by the Stanford Center for Carbon Storage and ExxonMobil.
Platform
Task
Features
• supports anisotropic permeability maps
• permeability maps
• rock properties
• injection designs
• handles wide range of reservoir conditions
• co2 storage predictions for 2d and 3d saline reservoirs
• speeds up predictions 700,000 times compared to numerical simulators
• high-resolution, high-fidelity 3d spatial-temporal predictions
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