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Found 5 papers, 1 papers with code
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
To tackle this, we use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization at critical reservoir locations.
A Robust Deep Learning Workflow to Predict Multiphase Flow Behavior during Geological CO2 Sequestration Injection and Post-Injection Periods
For the post-injection period, it is key to use cumulative CO2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation.
Improving Deep Learning Performance for Predicting Large-Scale Porous-Media Flow through Feature Coarsening
Physics-based simulation for fluid flow in porous media is a computational technology to predict the temporal-spatial evolution of state variables (e. g. pressure) in porous media, and usually requires high computational expense due to its nonlinearity and the scale of the study domain.
A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Media
We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators.
A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media
Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.
