38 citations · 38 across the 3 of their papers we have counts for
7 papers
Computationally Efficient Multiscale Neural Networks Applied To Fluid Flow In Complex 3D Porous Media
Javier Santos, Ying Yin, Honggeun Jo +6
The permeability of complex porous materials can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particu…
Great SCO2T! Rapid tool for carbon sequestration science, engineering, and economics
Richard S. Middleton, Jeffrey M. Bielicki, Bailian Chen +12
CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pat…
Modeling nanoconfinement effects using active learning
Javier E. Santos, Mohammed Mehana, Hao Wu +5
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge…
PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data
B. Ahmmed, M. K. Mudunuru, S. Karra +3
Spectral induced polarization (SIP) is a non-intrusive geophysical method that is widely used to detect sulfide minerals, clay minerals, metallic objects, municipal wastes, hydroca…
Branching of Hydraulic Cracks in Gas or Oil Shale with Closed Natural Fractures: How to Master Permeability
Saeed Rahimi-Agham, Viet-Tuan Chau, Huynjin Lee +7
While the hydraulic fracturing technology, aka fracking (or fraccing, frac), has become highly developed and astonishingly successful, a consistent formulation of the associated fr…
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Max Schwarzer, Bryce Rogan, Yadong Ruan +8
We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these material…