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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
Web
Task
carbon modeling

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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