38 citations · 67 across the 13 of their papers we have counts for
6 papers · 1 filter
Machine learning-based porosity estimation from spectral decomposed seismic data
Honggeun Jo, Yongchae Cho, Michael J. Pyrcz +2
Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. Although impedance inversion is often used by combin…
Optimizing Oil and Gas Acquisitions Using Recommender Systems
Harsh Kumar, Geneva Allison, Jehil Mehta +2
Well acquisition in the oil and gas industry can often be a hit or miss process, with a poor purchase resulting in substantial loss. Recommender systems suggest items (wells) that…
StackGAN: Facial Image Generation Optimizations
Badr Belhiti, Justin Milushev, Avinash Gupta +4
Current state-of-the-art photorealistic generators are computationally expensive, involve unstable training processes, and have real and synthetic distributions that are dissimilar…
Automatic Feature Highlighting in Noisy RES Data With CycleGAN
Nicholas Khami, Omar Imtiaz, Akif Abidi +4
Radio echo sounding (RES) is a common technique used in subsurface glacial imaging, which provides insight into the underlying rock and ice. However, systematic noise is introduced…
Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning
Shankar Padmanabhan, Aidan Petratos, Allen Ting +4
The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs). In Albany County in Ne…
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…