47 citations · 47 across the 4 of their papers we have counts for
7 papers
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…