Michael J. Pyrcz
Michael J. Pyrcz
Associate Professor, The University of Texas at Austin
Verified email at - Homepage
Cited by
Cited by
Geostatistical reservoir modeling
MJ Pyrcz, CV Deutsch
Oxford university press, 2014
Architecture of turbidite channel systems on the continental slope: patterns and predictions
T McHargue, MJ Pyrcz, MD Sullivan, JD Clark, A Fildani, BW Romans, ...
Marine and petroleum geology 28 (3), 728-743, 2011
Stochastic surface-based modeling of turbidite lobes
MJ Pyrcz, O Catuneanu, CV Deutsch
AAPG bulletin 89 (2), 177-191, 2005
PoreFlow-Net: A 3D convolutional neural network to predict fluid flow through porous media
JE Santos, D Xu, H Jo, CJ Landry, M Prodanović, MJ Pyrcz
Advances in Water Resources 138, 103539, 2020
ALLUVSIM: A program for event-based stochastic modeling of fluvial depositional systems
MJ Pyrcz, JB Boisvert, CV Deutsch
Computers & Geosciences 35 (8), 1671-1685, 2009
Fast evaluation of well placements in heterogeneous reservoir models using machine learning
A Nwachukwu, H Jeong, M Pyrcz, LW Lake
Journal of Petroleum Science and Engineering 163, 463-475, 2018
The whole story on the hole effect
MJ Pyrcz, CV Deutsch
Geostatistical Association of Australasia, Newsletter 18, 3-5, 2003
Multiple-point statistics for training image selection
JB Boisvert, MJ Pyrcz, CV Deutsch
Natural Resources Research 16, 313-321, 2007
A library of training images for fluvial and deepwater reservoirs and associated code
MJ Pyrcz, JB Boisvert, CV Deutsch
Computers & Geosciences 34 (5), 542-560, 2008
Stochastic surface modeling of deepwater depositional systems for improved reservoir models
X Zhang, MJ Pyrcz, CV Deutsch
Journal of Petroleum Science and Engineering 68 (1-2), 118-134, 2009
Improved geostatistical models of inclined heterolithic strata for McMurray Formation, Alberta, CanadaGeostatistical Models of Inclined Heterolithic Strata, Alberta
MM Hassanpour, MJ Pyrcz, CV Deutsch
AAPG bulletin 97 (7), 1209-1224, 2013
Integration of geologic information into geostatistical models
MJ Pyrcz
Stratigraphic rule-based reservoir modeling
MJ Pyrcz, RP Sech, JA Covault, BJ Willis, Z Sylvester, T Sun
Bulletin of Canadian Petroleum Geology 63 (4), 287-303, 2015
Machine learning-based optimization of well locations and WAG parameters under geologic uncertainty
A Nwachukwu, H Jeong, A Sun, M Pyrcz, LW Lake
SPE improved oil recovery conference, 2018
Computationally efficient multiscale neural networks applied to fluid flow in complex 3D porous media
JE Santos, Y Yin, H Jo, W Pan, Q Kang, HS Viswanathan, M Prodanović, ...
Transport in porous media 140 (1), 241-272, 2021
Declustering and debiasing
MJ Pyrcz, CV Deutsch
Newsletter 19, 1-14, 2003
Multiple point metrics to assess categorical variable models
JB Boisvert, MJ Pyrcz, CV Deutsch
Natural resources research 19, 165-175, 2010
Quantifying sediment supply to continental margins: Application to the Paleogene Wilcox Group, Gulf of Mexico
J Zhang, J Covault, M Pyrcz, G Sharman, C Carvajal, K Milliken
AAPG Bulletin 102 (9), 1685-1702, 2018
Prediction of yield response to soil remediation
T Faechner, MJ Pyrcz, CV Deutsch
Geoderma 97 (1-2), 21-38, 2000
Modeling nanoconfinement effects using active learning
JE Santos, M Mehana, H Wu, M Prodanovic, Q Kang, N Lubbers, ...
The Journal of Physical Chemistry C 124 (40), 22200-22211, 2020
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