72 citations · 99 across the 9 of their papers we have counts for
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cs.LG2023★ 10 cited
Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controls
Jungang Chen, Eduardo Gildin, John E. Killough
A physics-informed convolutional neural network is proposed to simulate two phase flow in porous media with time-varying well controls. While most of PICNNs in existing literatures…
physics.flu-dyn2023★ 4 cited
Physics-Informed Neural Network for the Transient Diffusivity Equation in Reservoir Engineering
Daniel Badawi, Eduardo Gildin
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-inform…
cs.LG2023★ 6 cited
Physics-informed Convolutional Recurrent Surrogate Model for Reservoir Simulation with Well Controls
Jungang Chen, Eduardo Gildin, John E. Killough
This paper presents a novel surrogate model for modeling subsurface fluid flow with well controls using a physics-informed convolutional recurrent neural network (PICRNN). The mode…