38 citations · 67 across the 13 of their papers we have counts for
13 papers
PointSSIM: A novel low dimensional resolution invariant image-to-image comparison metric
Oscar Ovanger, Ragnar Hauge, Jacob Skauvold +2
This paper presents PointSSIM, a novel low-dimensional image-to-image comparison metric that is resolution invariant. Drawing inspiration from the structural similarity index measu…
Data Conditioning for Subsurface Models with Single-Image Generative Adversarial Network (SinGAN)
Lei Liu, Eduardo Maldonado-Cruz, Honggeun Jo +2
The characterization of subsurface models relies on the accuracy of subsurface models which request integrating a large number of information across different sources through model…
Rigid Transformations for Stabilized Lower Dimensional Space to Support Subsurface Uncertainty Quantification and Interpretation
Ademide O. Mabadeje, Michael J. Pyrcz
Subsurface datasets inherently possess big data characteristics such as vast volume, diverse features, and high sampling speeds, further compounded by the curse of dimensionality f…
Mitigation of Spatial Nonstationarity with Vision Transformers
Lei Liu, Javier E. Santos, Maša Prodanović +1
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix por…
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…
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…
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…