| Geostatistical reservoir modeling MJ Pyrcz, CV Deutsch Oxford University Press, USA, 2014 | 2270 | 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 | 437 | 2011 |
| 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 | 210 | 2020 |
| Stochastic surface-based modeling of turbidite lobes MJ Pyrcz, O Catuneanu, CV Deutsch AAPG bulletin 89 (2), 177-191, 2005 | 189 | 2005 |
| 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 | 143 | 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 | 142 | 2018 |
| Multiple-point statistics for training image selection JB Boisvert, MJ Pyrcz, CV Deutsch Natural Resources Research 16, 313-321, 2007 | 101 | 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 | 98 | 2008 |
| The whole story on the hole effect MJ Pyrcz, CV Deutsch Geostatistical Association of Australasia, Newsletter 18, 3-5, 2003 | 94 | 2003 |
| 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 | 92 | 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 | 70 | 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 | 68 | 2018 |
| Fair train-test split in machine learning: Mitigating spatial autocorrelation for improved prediction accuracy JJ Salazar, L Garland, J Ochoa, MJ Pyrcz Journal of Petroleum Science and Engineering 209, 109885, 2022 | 64 | 2022 |
| 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 | 63 | 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 | 62 | 2009 |
| 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 | 60 | 2019 |
| Integration of geologic information into geostatistical models MJ Pyrcz | 58 | 2004 |
| 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 | 53 | 2020 |
| 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 | 51 | 2021 |
| 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 | 51 | 2018 |
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