38 citations · 43 across the 7 of their papers we have counts for
7 papers
Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting
Wendi Liu, Michael J. Pyrcz
Production forecast based on historical data provides essential value for developing hydrocarbon resources. Classic history matching workflow is often computationally intense and g…
A recommender system for automatic picking of subsurface formation tops
Jesse R. Pisel, Joshua A. Dierker, Sanya Srivastava +2
Geoscience domain experts traditionally correlate formation tops in the subsurface using geophysical well logs (known as well-log correlation) by-hand. Based on individual well log…
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…
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…