Michael J. Pyrcz
Cited by
Cited by
Geostatistical reservoir modeling
MJ Pyrcz, CV Deutsch
Oxford University Press, USA, 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
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
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?, D031S008R005, 2018
Improved geostatistical models of inclined heterolithic strata for McMurray Formation, Alberta, Canada
MM Hassanpour, MJ Pyrcz, CV Deutsch
AAPG bulletin 97 (7), 1209-1224, 2013
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
Integration of geologic information into geostatistical models
MJ Pyrcz
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
Fully coupled end-to-end drilling optimization model using machine learning
C Hegde, M Pyrcz, H Millwater, H Daigle, K Gray
Journal of Petroleum Science and Engineering 186, 106681, 2020
Rate of penetration (ROP) optimization in drilling with vibration control
C Hegde, H Millwater, M Pyrcz, H Daigle, K Gray
Journal of natural gas science and engineering 67, 71-81, 2019
Stochastic Pix2pix: a new machine learning method for geophysical and well conditioning of rule-based channel reservoir models
W Pan, C Torres-Verdín, MJ Pyrcz
Natural Resources Research 30 (2), 1319-1345, 2021
Declustering and debiasing
MJ Pyrcz, CV Deutsch
Newsletter 19, 1-14, 2003
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