nanditadoloi/PIML
My pytorch based implementation of the paper 'Limitations of Physics Informed Machine Learning for Nonlinear Two-Phase Transport in Porous Media'
This project helps reservoir engineers improve their predictions of fluid flow in porous media, like oil and gas reservoirs. It takes observed data about reservoir conditions and integrates it with known physics equations to produce more accurate forecasts of how fluids will move over time. Geoscientists, petroleum engineers, or anyone modeling subsurface fluid dynamics would use this tool.
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Use this if you are a reservoir engineer or geoscientist looking to make more accurate forecasts of fluid transport in porous media by incorporating fundamental physics into your data-driven models.
Not ideal if you do not have access to physics-based analytical equations for your specific problem or if your primary interest is in general machine learning without domain-specific physical constraints.
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Last pushed
Mar 09, 2021
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