23 citations · 43 across the 7 of their papers we have counts for
11 papers
Physics Informed Deep Learning for Flow and Transport in Porous Media
Cedric Fraces Gasmi, Hamdi Tchelepi
We present our progress on the application of physics informed deep learning to reservoir simulation problems. The model is a neural network that is jointly trained to respect gove…
Embedded Fracture Model for Coupled Flow and Geomechanics
I. Shovkun, T. Garipov, H. A. Tchelepi
Fluid injection and production cause changes in reservoir pressure, which result in deformations in the subsurface. This phenomenon is particularly important in reservoirs with abu…
Second Order Accurate Hierarchical Approximate Factorization of Sparse SPD Matrices
Bazyli Klockiewicz, Léopold Cambier, Ryan Humble +2
We describe a second-order accurate approach to sparsifying the off-diagonal blocks in the hierarchical approximate factorizations of sparse symmetric positive definite matrices. T…
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli +6
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. Whil…
Physics Informed Deep Learning for Transport in Porous Media. Buckley Leverett Problem
Cedric G. Fraces, Adrien Papaioannou, Hamdi Tchelepi
We present a new hybrid physics-based machine-learning approach to reservoir modeling. The methodology relies on a series of deep adversarial neural network architecture with physi…
Algebraically stabilized Lagrange multiplier method for frictional contact mechanics with hydraulically active fractures
Andrea Franceschini, Nicola Castelletto, Joshua A. White +1
Accurate numerical simulation of coupled fracture/fault deformation and fluid flow is crucial to the performance and safety assessment of many subsurface systems. In this work, we…