Graph network surrogate model for subsurface flow optimization
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Publication:6560712
DOI10.1016/j.jcp.2024.113132MaRDI QIDQ6560712
Louis J. Durlofsky, Haoyu Tang
Publication date: 23 June 2024
Published in: (Search for Journal in Brave)
reservoir simulationsubsurface flowgraph neural networkdeep learning surrogatewell placement and control optimization
Artificial intelligence (68Txx) Geophysics (86Axx) Flows in porous media; filtration; seepage (76Sxx)
Cites Work
- Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
- Use of low-fidelity models with machine-learning error correction for well placement optimization
- Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification
- Surrogate and inverse modeling for two-phase flow in porous media via theory-guided convolutional neural network
- Modeling the dynamics of PDE systems with physics-constrained deep auto-regressive networks
- Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology
- An Introduction to Reservoir Simulation Using MATLAB/GNU Octave
- Convolutional -- recurrent neural network proxy for robust optimization and closed-loop reservoir management
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