Here is my team of graduate students that I have the privilege to work with here at the University of Texas at Austin. Here's a list of my students and some details on their projects.
Our research focused on subsurface, spatial data analytics, geostatistics, and machine learning.
PhD Student, cosupervised with Prof. Carlos Torres-Verdin
Novel pix-2-pix method for deep learning-based subsurface models with improved conditioning to geological concepts, local observations (well data) and large scale, exhaustive imaging (seismic data).
New, geologically realistic rule-based subsurface modeling methods and workflows for training subsurface deep learning
PhD student, cosupervised with Prof. Eric Bickel
New geostatistical and spatial data analytics methods to support subsurface development decision making
PhD student
New geostatistics, spatial data analytics, machine learning methods and workflows for unconventional reservoirs
New methods for spatial anomaly detection, heterogeneity measures and spatial debiasing for spatial predictive machine learning.
PhD student, cosupervised with Prof. Mary Wheeler
Integration of physics into machine learning subsurface flow through porous media surrogate modeling.
PhD student, cosupervised with Prof. Larry Lake
New geostatistics, spatial data analytics, machine learning methods and workflows for unconventional reservoirs
New spatial data analytics and spatial statistics methods and workflows for geostatistical significance and trend modeling.
PhD student
New workflows and methods for generalizable machine learning-based large-scale surrogate models for forecasting flow through porous media
PhD student
New geostatistics, spatial data analytics, machine learning methods and workflows for unconventional reservoirs
PhD student
New data analytics and machine learning workflows to address various sources of spatial/subsurface bias that impact decision making.
New workflows to use spatial data analytics to detect and automatically correct for spatial data sampling bias/clustered sampled.
PhD student, cosupervised with Prof. Carlos Torres-Verdin
Deep learning for the integration of 4D seismic for subsurface resource modeling.
PhD student, cosupervised with Prof. John Foster
Deep learning for the integration of 4D seismic for subsurface resource modeling.
PhD student, cosupervised with Prof. Masa Prodanovic
Impact and mitigation of nonstationarity for spatial deep learning prediction models.
Data analytics and machine learning-assisted subsurface resource modeling.
PhD defended Fall 2018, cosupervised with Prof. Larry Lake
New subsurface machine learning methods and workflows for subsurface modeling and physics surrogate temporal forecasting with artificial neural networks and long-short term memory (LSTM), recurrent neural networks.
PhD defended Spring 2021, cosupervised with Prof. Eric van Oort
Automated surface measurements of non-Newtonian fluid properties for real-time, optimum drilling control.
PhD defended Summer 2021
New, geologically realistic subsurface modeling methods and workflows with deep convolutional generative adversarial networks (DCGANS)
New, geologically realistic rule-based subsurface modeling methods and workflows for training subsurface deep learning.
PhD defended Fall 202, cosupervised by Prof. Larry Lake
New geostatistical and spatial statistics to characterize and model lineaments/fractures for spatial/subsurface settings. Methods for accounting for limited spatial samples and correcting for edge effects.
PhD defended Fall 2021, cosupervised by Prof. Masa Prodanovic
Multiscale flow through porous media modeling with deep learning.
Convolutional neural network-based machine learning intragranular flow through porous media surrogate models.
MSc completed Summer 2021, cosupervised with Prof. John Foster
Geostatistical modeling, multivariate, spatiotemporal modeling for
Texas induced seismicity statistical analysis, statistical modeling in collaboration with the Bureau of Economic Geology of the Jackson School of Geosciences.
Copyright © 2021 Professor Michael J. Pyrcz, The University of Texas at Austin
All Rights Reserved.
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