403 citations · 698 across the 4 of their papers we have counts for
4 papers
Deep-learning-based coupled flow-geomechanics surrogate model for CO sequestration
Meng Tang, Xin Ju, Louis J. Durlofsky
A deep-learning-based surrogate model capable of predicting flow and geomechanical responses in CO2 storage operations is presented and applied. The 3D recurrent R-U-Net model comb…
Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
Meng Tang, Yimin Liu, Louis J. Durlofsky
Data assimilation in subsurface flow systems is challenging due to the large number of flow simulations often required, and by the need to preserve geological realism in the calibr…
Multiphase flow prediction with deep neural networks
Gege Wen, Meng Tang, Sally M. Benson
This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able…
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
Meng Tang, Yimin Liu, Louis J. Durlofsky
A deep-learning-based surrogate model is developed and applied for predicting dynamic subsurface flow in channelized geological models. The surrogate model is based on deep convolu…