CCSNet.ai
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About
CCSNet.ai is a comprehensive deep learning modeling suite developed at Stanford University for predicting CO2 storage in saline reservoirs. It allows users to model various conditions using 2D and 3D frameworks, optimizing reservoir simulation via machine learning. The framework, Nested FNO, excels in providing high-resolution predictions of CO2 gas saturation and pressure buildup, significantly outperforming traditional simulation methods. Users can choose from different models to visualize and predict outcomes in real-time. Developed in collaboration with prominent academic institutions and industry partners, CCSNet offers a user-friendly experience for those involved in carbon capture and storage.
Platform
Task
Features
• real-time predictions
• high-resolution outputs
• user manual and demo cases available
• interactive models
• multiple reservoir models (2d and 3d)
• technical blog for insights
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