Michael Pyrcz

Associate Professor

University of Texas at Austin

United States

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Michael Pyrcz is an Associate Professor at The University of Texas at Austin, USA. His research focuses on predictive machine learning, uncertainty quantification, and machine learning applications in geosciences, including petrophysical interpretation and seismic data analysis. Recent publications include work on anisotropic resistivity estimation, spatial bagging techniques, and modeling the Joule-Thomson cooling effect related to CO2 sequestration.

Articles (14)

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2025

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2022

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2022

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2022

Collaborators (4)

Masa Prodanovic

Professor

University of Texas at Austin

UNITED STATES

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J. Eric Bickel

University of Texas at Austin

UNITED STATES

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Carlos Torres‐Verdín

UNITED STATES

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Marc Hesse

Associate Professor

University of Texas at Austin

UNITED STATES

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Michael Pyrcz

Research Interests

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Articles (14)

Spatial bagging for predictive machine learning uncertainty quantification

Anisotropic resistivity estimation and uncertainty quantification from borehole triaxial electromagnetic induction measurements: Gradient-based inversion and physics-informed neural network

Modelling of Joule-Thomson cooling effect using a modified shift-DeepONet method for predicting hydrate onset during CO2 sequestration

Spatial bagging to integrate spatial correlation into ensemble machine learning

Physics-informed graph neural network for spatial-temporal production forecasting

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes

MPLBM-UT: Multiphase LBM library for permeable media analysis

3D Dataset of binary images: A collection of synthetically created digital rock images of complex media

Machine-learning-based porosity estimation from multifrequency poststack seismic data

Estimating resources in unconventional assets: Spatial bootstrapping with n-effective

Fast evaluation of pressure and saturation predictions with a deep learning surrogate flow model

Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy

Multiscale spatial analysis of fracture arrangement and pattern reconstruction using Ripley's K-function

Collaborators (4)

Masa Prodanovic

J. Eric Bickel

Carlos Torres‐Verdín

Marc Hesse

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