222 citations · 372 across the 17 of their papers we have counts for
18 papers
A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media
Ali Kashefi, Tapan Mukerji
Fourier neural operators (FNOs) are invariant with respect to the size of input images, and thus images with any size can be fed into FNO-based frameworks without any modification…
Generating Multiphase Fluid Configurations in Fractures using Diffusion Models
Jaehong Chung, Agnese Marcato, Eric J. Guiltinan +3
Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen…
Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks
Jaehong Chung, Rasool Ahmad, WaiChing Sun +2
This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images. We use the Mapper algorith…
Homogenizing elastic properties of large digital rock images by combining CNN with hierarchical homogenization method
Rasool Ahmad, Mingliang Liu, Michael Ortiz +2
Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic beh…
ChatGPT for Programming Numerical Methods
Ali Kashefi, Tapan Mukerji
ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numeric…
Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity
Ali Kashefi, Leonidas J. Guibas, Tapan Mukerji
Regular physics-informed neural networks (PINNs) predict the solution of partial differential equations using sparse labeled data but only over a single domain. On the other hand,…
Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction
Josue Fonseca, Anshuman Pradhan, Tapan Mukerji
Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian infere…
Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)
Divakar Vashisth, Tapan Mukerji
Petrophysical inversion is an important aspect of reservoir modeling. However due to the lack of a unique and straightforward relationship between seismic traces and rock propertie…
Computation of effective elastic moduli of rocks using hierarchical homogenization
Rasool Ahmad, Mingliang Liu, Michael Ortiz +2
This work focuses on computing the homogenized elastic properties of rocks from 3D micro-computed-tomography (micro-CT) scanned images. The accurate computation of homogenized prop…
Prediction of fluid flow in porous media by sparse observations and physics-informed PointNet
Ali Kashefi, Tapan Mukerji
We predict steady-state Stokes flow of fluids within porous media at pore scales using sparse point observations and a novel class of physics-informed neural networks, called "phys…
Consistency and prior falsification of training data in seismic deep learning: Application to offshore deltaic reservoir characterization
Anshuman Pradhan, Tapan Mukerji
Deep learning applications of seismic reservoir characterization often require generation of synthetic data to augment available sparse labeled data. An approach for generating syn…
Point-Cloud Deep Learning of Porous Media for Permeability Prediction
Ali Kashefi, Tapan Mukerji
We propose a novel deep learning framework for predicting permeability of porous media from their digital images. Unlike convolutional neural networks, instead of feeding the whole…
Improved POMDP Tree Search Planning with Prioritized Action Branching
John Mern, Anil Yildiz, Larry Bush +2
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. This paper proposes a method called PA-POMCPOW to sa…
Bayesian Optimized Monte Carlo Planning
John Mern, Anil Yildiz, Zachary Sunberg +2
Online solvers for partially observable Markov decision processes have difficulty scaling to problems with large action spaces. Monte Carlo tree search with progressive widening at…
Real-Time Well Log Prediction From Drilling Data Using Deep Learning
Rayan Kanfar, Obai Shaikh, Mehrdad Yousefzadeh +1
The objective is to study the feasibility of predicting subsurface rock properties in wells from real-time drilling data. Geophysical logs, namely, density, porosity and sonic logs…
Approximate Bayesian inference of seismic velocity and pore pressure uncertainty with basin modeling, rock physics and imaging constraints
Anshuman Pradhan, Huy Q. Le, Nader C. Dutta +2
We present a methodology for quantifying seismic velocity and pore pressure uncertainty that incorporates information regarding the geological history of a basin, rock physics, wel…
Seismic Bayesian evidential learning: Estimation and uncertainty quantification of sub-resolution reservoir properties
Anshuman Pradhan, Tapan Mukerji
We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimension…
Geological realism in hydrogeological and geophysical inverse modeling: a review
N. Linde, P. Renard, T. Mukerji +1
Scientific curiosity, exploration of georesources and environmental concerns are pushing the geoscientific research community toward subsurface investigations of ever-increasing co…