72 citations · 99 across the 9 of their papers we have counts for
9 papers
Physics-Informed Neural Networks for Multi-Phase Flow in Porous Media Considering Dual Shocks and Interphase Solubility
Jingjing Zhang, Ulisses Braga-Neto, Eduardo Gildin
Physics-Informed Neural Networks (PINNs) integrate physical principles into machine learning, finding wide applications in various science and engineering fields. However, solving…
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-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…
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…
Investigation of Proper Orthogonal Decomposition for Echo State Networks
Jean Panaioti Jordanou, Eric Aislan Antonelo, Eduardo Camponogara +1
Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped…
Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity
E. J. R. Coutinho, M. Dall'Aqua, L. McClenny +3
Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it h…
DREAMS: Drilling and Extraction Automated System
Mohamed Khaled, Srivignesh Srinivasan, Alkassoum Toure +14
Drilling and Extraction Automated System (DREAMS) is a fully automated prototype-drilling rig that can drill, extract water and assess subsurface density profiles from simulated lu…
Non-Intrusive Parametric Model Order Reduction With Error Correction Modeling for Changing Well Locations Using a Machine Learning Framework
Hardikkumar Zalavadia, Eduardo Gildin
The objective of this paper is to develop a global non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field, tha…
Deep Global Model Reduction Learning in Porous Media Flow Simulation
Siu Wun Cheung, Eric T. Chung, Yalchin Efendiev +3
In this paper, we combine deep learning concepts and some proper orthogonal decomposition (POD) model reduction methods for predicting flow in heterogeneous porous media. Nonlinear…