My Publications
Peer Reviewed Papers
2000 - 2010 PhD Publications
1. Faechner, T., Pyrcz, M.J., and Deutsch, C.V. Soil Remediation Decision Making in Presence of Uncertainty in Crop Yield Response. Geoderma, 97 (1-2), p. 21-38, Aug. 2000. https://doi.org/10.1016/S0016-7061(00)00024-0
2. Pyrcz, M.J., and Deutsch, C.V. Two Artifacts of Probability Field Simulation. Mathematical Geology, 33 (7), p. 775-799, Oct. 2001. https://doi.org/10.1023/A:1010993113807
3. Pyrcz, M.J., Catuneanu, O. and Deutsch, C.V. Stochastic Surface-based Modeling of Turbidite Lobes. American Association of Petroleum Geologists Bulletin, 89 (2), p. 177-191, Feb. 2005. https://doi.org/10.1306/09220403112
4. Pyrcz, M.J., and Deutsch, C.V. Semivariogram Models Based on Geometric Offsets. Mathematical Geology, 38 (7), 475-488, Oct. 2006. https://doi.org/10.1007/s11004-005-9025-5
5. Pyrcz, M.J., and Deutsch, C.V. Spectral Corrected Semivariogram Models. Mathematical Geology, 38 (7), p. 891-899, Jan. 2007. https://doi.org/10.1007/s11004-006-9053-9
6. Boisvert, J., Pyrcz, M.J., and Deutsch, C.V. Multiple-Point Statistics for Training Image Selection. Natural Resources Research, 16, p. 313-321, Jan. 2008. https://doi.org/10.1007/s11053-008-9058-9
7. Pyrcz, M.J., Boisvert, J. and Deutsch, C.V. A Library of Training Images for Fluvial and Deepwater Reservoirs and Associated Code. Computers and Geosciences, 34 (5), 542-560, May 2008. https://doi.org/10.1016/j.cageo.2007.05.015
8. Pyrcz, M.J., Boisvert, J.B. and Deutsch, C.V. ALLUVSIM: A Program for Event-based Stochastic Modeling of Fluvial Depositional Systems. Computers & Geosciences, 35 (8), 1671-1685, Aug. 2009. https://doi.org/10.1016/j.cageo.2008.09.012
9. Zhang, X., Pyrcz, M.J., and Deutsch, C.V. Stochastic Surface Modeling of Deepwater Depositional Systems for Improved Reservoir Models. Journal of Petroleum Science and Engineering, 68 (1-2), p. 118-134, Sept. 2009. https://doi.org/10.1016/j.petrol.2009.06.019
10. Boisvert, J.B., Pyrcz, M.J., and Deutsch, C.V. Multiple Point Metrics to Assess Categorical Variable Models. Natural Resources Research, 19, p. 165-175, May 2010. https://doi.org/10.1007/s11053-010-9120-2
2011 - 2017 Chevron Energy Technology Company Publications
11. McHargue,T., Pyrcz,M.J., Sullivan, M.D., Clark, J.D, Fildani, A., Romans, B.W., Covault, J.A., Levy, M., Posamentier, H.W. and, Drinkwater,N.J. Architecture of Turbidite Channel Systems on the Continental Slope: Patterns and Predictions. Marine and Petroleum Geology, 28 (3), 728-743, Mar. 2011. https://doi.org/10.1016/j.marpetgeo.2010.07.008
12. Hassanpour, M., Pyrcz, M.J., and Deutsch, C.V. Improved Geostatistical Models of Inclined Heterolithic Strata for McMurray Formation, Alberta, Canada. AAPG Bulletin, 97 ( 7), p. 1209-1224, Jul. 2013. https://doi.org/10.1306/01021312054
13. Pyrcz, M.J., and White, C.D. Uncertainty in Reservoir Modeling. Interpretation, v. 3 (2), SQ7-SQ19, May 2015. https://doi.org/10.1190/INT-2014-0126.1
14. Pyrcz, M.J., Sech, R.P., Covault, J.A., Willis, B.J., Sylvester, Z. and Sun, T. Stratigraphic Rule-based Reservoir Modeling. Bulletin of Canadian Petroleum Geology, 63 (4), pp. 287-303, Dec. 2015. https://doi.org/10.2113/gscpgbull.63.4.287
15. Pyrcz, M.J., Mariethoz and Caers: Multiple-Point Geostatistics. Mathematical Geosciences 48 (3), 349-351, Apr. 2017. https://doi.org/10.1007/s11004-016-9633-2 (book reveiw)
The University of Texas at Austin Publications (from 2018)
2018
16. Nwachukwu, A., Jeong, H., Pyrcz, M.J. and Lake, L.W. Fast Evaluation of Well Placements in Heterogeneous Reservoir Models Using Machine Learning. Journal of Petroleum Science and Engineering 163, 463-475, Apr. 2018. https://doi.org/10.1016/j.petrol.2018.01.019
17. Wang, Y.C., Pyrcz, M.J., Catuneanu, O. and Boisvert, J.B. Conditioning 3D Object-based Models to Dense Well Data. Computers & Geosciences v. 115, pp. 1-11, Jun. 2018. https://doi.org/10.1016/j.cageo.2018.02.006
18. Zhang, J., Covault, J., Pyrcz, M.J., Sharman, G.R., Carvajal, C., and Milliken, K. Quantifying Sediment Supply to Continental Margins: Application to the Paleogene Wilcox Group, Gulf of Mexico. American Association of Petroleum Geologists Bulletin 102 (9), pp. 1685-1702, Sept. 2018. https://doi.org/10.1306/01081817308
2019
19. Jo, H., and Pyrcz, M.J. Robust Rule-based Aggradational Lobe Reservoir Models. Natural Resources Research, v. 29, pp 1193-1213, Apr. 2019. https://doi.org/10.1007/s11053-019-09482-9
20. Jaing, H., Daigle, H., Tian, X., Pyrcz, M.J., Griffith, C., and Zhang, B. A Comparison of Clustering Algorithms applied to Fluid Characterization using NMR T1-T2 Maps of Shale. Computers & Geosciences, 126, p 52-61, May 2019. https://doi.org/10.1016/j.cageo.2019.01.021
21. Hedge, C., Millwater, H., Pyrcz, M.J., Daigle, H., and Gray, K.E. Rate of Penetration (ROP) Optimization in Drilling with Vibration Control. Journal of Natural Gas Science and Engineering, v. 67, p. 71-81, Jul. 2019. https://doi.org/10.1016/j.jngse.2019.04.017
22. Brown, C., Fadili, A., Holubnyak, Y., Kristensen, M., Leetaru, H., Pyrcz, M.J., Sullivan, C., Williams, M., and Reza, Z. Introduction to Special Section: Wastewater Disposal and CO2 Transport in the Subsurface. Interpretation, vol. 7(4), pp. SLi-SLii, Nov. 2019. https://doi.org/10.1190/INT-2019-0923-SPSEINTRO.1
23. Pyrcz, M.J. Data Analytics and Geostatistical Workflows for Modeling Uncertainty in Unconventional Reservoirs. Bulletin of Canadian Petroleum Geology, v. 67(4), pp 273-282, Dec. 2019. https://doi.org/10.35767/gscpgbull.67.4.273
2020
24. Hedge, C., Pyrcz, M.J., Millwater, H., Daigle, H., and Gray, K.E. Fully Coupled End-to-end Drilling Optimization Model Using Machine Learning. Journal of Petroleum Science and Engineering, v 186, p 106681 [14 pgs], Mar. 2020. https://doi.org/10.1016/j.petrol.2019.106681
25. Santos, J.E., Xu, D., Jo, H., Landry, C.J., Prodanović, M., and Pyrcz, M.J. PoreFlow-Net: A 3D Convolutional Neural Network to Predict Fluid Flow Through Porous Media. Advances in Water Resources v. 138, p. 103539 [12 pgs], Apr. 2020. https://doi.org/10.1016/j.advwatres.2020.103539
26. Jo, H., Santos, J.E., and Pyrcz, M.J. Conditioning Well Data to Rule-based Lobe Model by Machine Learning with a Generative Adversarial Network. Energy Exploration & Exploitation, 38 (6), p 2558-2578, Jul. 2020. https://doi.org/10.1177/0144598720937524
27. Santos, J.E., Mehana, M., Wu, H., Prodanovic, M., Kang, Q., Lubbers, N., Viswanathan, H., and Pyrcz, M.J. Modeling Nanoconfinement Effects Using Active Learning. Journal of Physical Chemistry C, v124 (40), p. 22200-22211, Sept. 2020. https://doi.org/10.1021/acs.jpcc.0c07427
28. Brusova, O., Corzo, M. and Pyrcz, M.J. Introduction to this Special Section: Machine Learning and AI. The Leading Edge, v39 (10), 689-764, Oct. 2020. https://doi.org/10.1190/tle39100700.1
29. Liu, W. and Pyrcz, M.J. A Spatial Correlation-Based Anomaly Detection Method for Subsurface Modeling. Mathematical Geosciences, 53, 809-822, Oct. 2020. https://doi.org/10.1007/s11004-020-09892-z
30. Khanna, P., Pyrcz, M.J., Droxler, A.W., Hopson, H.H., Harris, P.M., and Lehrmann, D.J. Implications for Controls on Upper Cambrian Microbial Build-ups Across Multiple-scales, Mason County, Central Texas, USA. Marine and Petroleum Geology, 121, p 104590 [15 pgs], Nov. 2020. https://doi.org/10.1016/j.marpetgeo.2020.104590
31. Pisel, J. and Pyrcz, M.J. Classifying Basin-Scale Stratigraphic Geometries from Subsurface Formation Tops With Machine Learning. The Depositional Record, 7 (1), p. 64-76, Nov. 2020. https://doi.org/10.1002/dep2.129
2021
32. Liu, W., Ikonnikova, S., Hamlin, S., Sivila, L. and Pyrcz, M.J. Demonstration and Mitigation of Spatial Sampling Bias for Machine-Learning Predictions. SPE Reservoir Evaluation & Engineering, 24 (01), 262-274, Feb. 2021. https://doi.org/10.2118/203838-PA
33. Santos, J.E., Yin, Y., Jo, H., Pan, W., Kang, Q., Viswanathan, H.W., Prodanović, M., Pyrcz, M.J., and Lubbers N., Computationally Efficient Multiscale Neural Networks Applied to Fluid Flow in Complex 3D Porous Media. Transport in Porous Media, 140, p. 241-272, May 2021. https://doi.org/10.1007/s11242-021-01617-y
34. Jo, H., Pyrcz, M.J. Automatic Semivariogram Modeling by Convolutional Neural Network. Mathematical Geosciences, 54, p. 177–205, Jul. 2021. https://doi.org/10.1007/s11004-021-09962-w
35. Salazar. J.J., and Pyrcz, M.J. Geostatistical Significance of Differences for Spatial Subsurface Phenomenon. Journal of Petroleum Science and Engineering, v203, 108694 [9 pgs], Aug. 2021. https://doi.org/10.1016/j.petrol.2021.108694
36. Maldonado-Cruz, E., and Pyrcz, M.J. Tuning Machine Learning Dropout for Subsurface Uncertainty Model Accuracy. Journal of Petroleum Science and Engineering, 205, 108975 [9 pgs], Oct. 2021. https://doi.org/10.1016/j.petrol.2021.108975
37. Pan, W., Torres-Verdin, C., and Pyrcz, M.J. Stochastic Pix2pix: A New Machine Learning Method for Geophysical and Well Conditioning of Rule-Based Channel Reservoir Models. Natural Resources Research 30, 1319–1345, Nov. 2021. https://doi.org/10.1007/s11053-020-09778-1
38. Zhu, P., Tavassoli, S., Ryu, J., Pyrcz, M.J., and Balhoff, M.T. Injection of Gel Systems for CO2 Leakage Remediation in a Fractured Reservoir. International Journal of Oil, Gas and Coal Technology, 29 (1), 52-74, Nov. 2021. https://doi.org/10.1504/IJOGCT.2022.119340
39. Jo, H., Pan, W., Santos, J.E., Jung, H., and Pyrcz, M.J. Machine Learning Assisted History Matching for a Deepwater Lobe System. Journal of Petroleum Science and Engineering, 109086 [18 pgs] Dec. 2021. https://doi.org/10.1016/j.petrol.2021.109086
2022
40. Tomski, J.R., Sen, M.K., Hess, T.K., and Pyrcz, M.J. Unconventional reservoir characterization by seismic inversion and machine learning of the Bakken Formation, AAPG Bulletin, [50 pgs], Jan. 2022, preprint. https://doi.org/10.1306/12162121035
41. Salazar, J.J., Garland, L., Ochoa, J., and Pyrcz, M.J. Fair Train-Test Split in Machine Learning: Mitigating Spatial Autocorrelation for Improved Prediction Accuracy. Journal of Petroleum Science and Engineering, 209, 109885 [13pgs], Feb. 2022. https://doi.org/10.1016/j.petrol.2021.109885
42. Santos, J.E., Pyrcz, M.J., and Prodanović, M. 3D Dataset of Binary Images: A Collection of Synthetically Created Digital Rock Images of Complex Media. Data in Brief, 40, 107797 [6 pgs], Feb. 2022. https://doi.org/10.1016/j.dib.2022.107797
43. Shakiba, M., Lake, L.W., Gale, J.F.W., and Pyrcz, M.J. Multiscale Spatial Analysis of Fracture Arrangement and Pattern Reconstruction using Ripley’s K-Function. Journal of Structural Geology, 155, 104531 [14 pgs], Feb. 2022. https://doi.org/10.1016/j.jsg.2022.104531
44. Farell, R., Pyrcz, M.J., and Bickel, E. Estimating Resources in Unconventional Assets: Spatial Bootstrapping with N-Effective. Journal of Petroleum Science and Engineering, 212, 110174 [8 pgs], May 2022. https://doi.org/10.1016/j.petrol.2022.110174
45. Maldonado-Cruz, E., and Pyrcz, M.J. Fast Evaluation of Pressure and Saturation Predictions with a Deep Learning Surrogate Flow Model. Journal of Petroleum Science and Engineering, 212, 110244 [14 pgs], May 2022. https://doi.org/10.1016/j.petrol.2022.110244
46. Jo, H., Cho, Y., Pyrcz, M.J., Tang, H., and Fu, P. Machine Learning-based Porosity Estimation from Multi-Frequency Post-Stack Seismic Data. Geophysics, 87 (5), p. 1-54, Jun. 2022. [Accepted] https://doi.org/10.1190/geo2021-0754.1
47. Santos, J.E., Gigliotti, A., Bihani, A., Landry, C., Hesse, M.A., Pyrcz, M.J., and Prodanovic, M. MPLBM-UT: Multiphase LBM Library for Permeable Media Analysis. SoftwareX, 18, 101097 [7 pgs], Jun. 2022. https://doi.org/10.1016/j.softx.2022.101097
48. Pan, W., Jo, H., Santos, J., Torres-Verdin, C., and Pyrcz, M. J. Hierarchical Machine Learning Workflow for Conditional and Multiscale Deepwater Reservoir Modeling, American Association of Petroleum Geologists Bulletin, Jul. 2022. https://doi.org/10.1306/05162221022
49. Pyrcz, M.J. Geoscience Data Analytics and Machine Learning, Special Issue Introduction. American Association of Petroleum Geologists Bulletin, 106(11), [3 pgs], Nov. 2022. https://doi.org/10.1306/bltnintro071922
50. Liu, L., Prodanović, and M, Pyrcz, M.J. Impact of geostatistical nonstationarity on convolutional neural network predictions, Computational Geosciences, 27, [9 pgs], Nov. 2022, https://doi.org/10.1007/s10596-022-10181-3
51. Hernandez-Mejia, J.L., Pisel, J., Jo, H., and Pyrcz, M.J. Dynamic time warping for well injection and production history connectivity characterization, Computational Geosciences 27, [9 pgs], Dec. 2022, https://doi.org/10.1007/s10596-022-10188-w
2023
52. Shakiba, M., Lake, L.W., Gale, J.F.W., Laubach, S.E., and Pyrcz, M.J. Multiscale Spatial Analysis of Fracture Nodes in Two Dimensions, Marine and Petroleum Geology, 149, 106093, [19 pgs], Jan. 2023, https://doi.org/10.1016/j.marpetgeo.2022.106093
53. Liu, W., and Pyrcz, M.J. Physics-informed Neural Network for Spatial-temporal Production Forecasting. Journal of Petroleum Science and Engineering, 223, 211486, [13 pgs], Jan. 2023, https://doi.org/10.1016/j.geoen.2023.211486
54. Liu, W., and Pyrcz, M.J. Spatial Ensemble Anomaly Detection Method for Exhaustive Map-based Datasets. Energy Exploration & Exploitation, 41(2), Mar. 2023 https://doi.org/10.1177/01445987221118697
55. Pan, W., Torres-Verdín, C., Duncan I. J., and Pyrcz, M. J., Reducing the uncertainty of multi-well petrophysical interpretation from well logs via machine-learning and statistical models. Geophysics, 88(2), [26 pgs], Mar. 2023, https://doi.org/10.1190/geo2022-0151.1
56. Maldonado-Cruz, E., and Pyrcz, M.J., Sonic Well-Log Imputation Thorugh Machine Learning-based Uncertainty Models, Petrophysics, 64(2), [17 pgs], Apr. 2023, https://doi.org/10.30632/PJV64N2-2023a7
57. Salazar, J., Ochoa, J., Garland, L., Lake, L., and Pyrcz, M.J., Spatial Data Analytics-Assisted Subsurface Modeling: A Duvernay Case Study, Petrophysics, 64(2), [15 pgs], Apr. 2023, https://doi.org/10.30632/PJV64N2-2023a9
58. Michalak M.P., Marzecb, P., Turobośc, F., Leonowiczd, P., Tepera, P., Gładkie, P., and Pyrcz, M.J. A New Methodology Using Borehole Data to Measure Angular Distances Between Geological Interfaces, Earth Science Infromatics, [19 pgs], Apr. 2023, https://doi.org/10.1007/s12145-023-01015-6
59. Jo, H., Laugier, F.L., Sullivan, M.D., and Pyrcz, M.J. Stratigraphic Controls on Connectivity and Flow Performance in Deepwater Lobe-Dominated Reservoirs, American Association of Petroleum Geologists Bulletin, 107(6), [19 pgs], June 2023, https://doi.org/10.1306/10102221083
60. Salazar, J., Maldonado-Cruz, E., Ochoa, J., L., and Pyrcz, M.J. Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability with Seismic Attributes, IEEE Transactions on Geoscience and Remote Sensing, 61, [18 pgs], Jun. 2023, https://doi.org/0.1109/TGRS.2023.3285820
61. Shakiba, M., Lake, L.W., Gale, J.F.W., and Pyrcz, M.J. Characterization of Spatial Relationships Between Fractures from Different Sets Using K-function Analysis, American Association of Petroleum Geologists Bulletin, 107(7), [20 pgs], Jul. 2023, https://doi.org/10.1306/11062222008
62. Liu, L., Santos, J.E., Prodanovic, M., and Pyrcz, M.J. Mitigation of Nonstationarity with Vision Transformers, Computers and Geosciences, 178, [8 pgs], Sept. 2023, https://doi.org/10.1016/j.cageo.2023.105412
63. Rustamzade, E, Pan, W., Foster, J., and Pyrcz, M.J. Comparison of commingled and sequential production schemes by sensitivity analysis for Gulf of Mexico Paleogene Deepwater turbidite oil fields: A simulation study, Energy Exploration & Exploitation, 178, [23 pgs], Nov. 2023, https://doi.org/10.1177/01445987231195679
2024
64. Liu, L., Mehana, M., Chen, B., Prodanović, M., and Pyrcz, M.J., Pawar, R. Reduced-order models for the greenhouse gas leakage prediction from depleted hydrocarbon reservoirs using machine learning methods, International Journal of Greenhouse Gas Control, 132, [9 pgs], Feb. 2024, https://doi.org/10.1016/j.ijggc.2024.104072
65. Özbayrak, F., Foster, J.T., and Pyrcz, M.J. Spatial bagging to integrate spatial correlation into ensemble machine learning, Computers and Geosciences, 186, [8 pgs], Feb. 2024, https://doi.org/10.1016/j.cageo.2024.105558
66. Maldonado-Cruz, E., and Pyrcz, M.J. Multi-horizon well performance forecasting with temporal fusion transformers, Results in Engineering, 21, [19 pgs], Feb. 2024, https://doi.org/10.1016/j.rineng.2024.101776
67. Mabadeje, A.O., and Pyrcz, M.J. Rigid transformations for stabilized lower dimensional space to support subsurface uncertainty quantification and interpretation, Computational Geosciences, 2, [20 pgs], Mar. 2024, https://doi.org/10.1007/s10596-024-10278-x
68. Mabadeje, A.O., Salazar, J.J., Ochoa, J., Garland, L., and Pyrcz, M.J. A Machine Learning Workflow to Support the Identification of Subsurface Resource Analogs, Energy Exploration & Exploitation, 42(2), [22 pgs], Mar. 2024, https://doi.org/10.1177/01445987231210966
69. Shakiba, M., Lake, L.W., Gale, J.F.W, Laubach, S.E., and Pyrcz, M.J., Stochastic reconstruction of fracture network pattern using spatial point processes, Geoenergy Science and Engineering, 236, [19 pgs], May. 2024, https://doi.org/10.1016/j.cageo.2024.105558
70. Morales, M.M.,Torres-Verdin, C., and Pyrcz, M.J. Stochastic pix2vid: A new spatiotemporal deep learning method for image-to-video synthesis in geologic CO storage prediction, Computational Geosciences, [22 pgs], June. 2024, https://doi.org/10.1007/s10596-024-10298-7
71. Hernandez-Mejia, J.L, Imhoff, M., and Pyrcz, M.J. Anomaly detection for geological carbon sequestration monitoring, International Journal of Greenhouse Gas Control, [10 pgs], July. 2024, https://doi.org/10.1016/j.ijggc.2024.104188
72. Akmal, M.M., Sepehrnoori, K., Foster, J.T., and Pyrcz, M.J., Modelling of Joule-Thomson cooling effect using a modified shift-DeepONet method for predicting hydrate onset during CO2 sequestration, Geoenergy Science and Engineering, [17 pgs], Dec. 2024, https://doi.org/10.1016/j.geoen.2024.213320
2025
73. Morales, M.M., Mehana, M., Torres-Verdín, C., Pyrcz, M.J., and Chen, B., Optimal monitoring design for uncertainty quantification during geologic CO2 sequestration: A machine learning approach, Geoenergy Science and Engineering, [11 pgs], Jan. 2025, https://doi.org/10.1016/j.geoen.2024.213402
74. Liu, L., Salazar, J., Jo, H., Prodanovic, M., and Pyrcz, M.J., Minimum acceptance criteria for subsurface scenario-based uncertainty models from single image generative adversarial networks (SinGAN), Computational Geosceinces, [16 pgs], Jan. 2025, https://doi.org/10.1007/s10596-024-10330-w
75. Merzoug, A., and Pyrcz, M.J., Conditional Generative Adversarial Networks for Multivariate Gaussian Subsurface Modeling: How Good Are They?, Mathematical Geosciences, [25 pgs], Feb. 2025, https://doi.org/10.1007/s11004-025-10176-7
76. Morales, M.,Eghbali, A., Raheem, O., Pyrcz, M.J., and Torres-Verdin, C., Anisotropic resistivity estimation and uncertainty quantification from borehole triaxial electromagnetic induction measurements: Gradient-based inversion and physics-informed neural network, Computers & Geosciences, [20 pgs], Feb. 2025 https://doi.org/10.1016/j.cageo.2024.105786
77. Hernandez, J.L., and Pyrcz, M.J., Spatiotemporal Shapley Value-based Pressure Signal Decomposition for Enhanced Geological Carbon Sequestration Monitoring Under Uncertainty International Journal of Greenhouse Gas Control, [17 pgs], May 2025, https://doi.org/10.1016/j.ijggc.2025.104356
78. Liu, L., Chang, B., Prodanovic, M., and Pyrcz , M.J., AI-based Digital Rocks Aumentation and Assessment Metrics, Water Resources Research, [16 pgs], May 2025, https://doi.org/10.1029/2024WR037939
79. Özbayrak, F., Foster, J.T., and Pyrcz, M.J. Spatial bagging to for predictive machine learning uncertainty quantification, Computers and Geosciences, 186, [ pgs], May 2025, https://doi.org/10.1016/j.cageo.2025.105947
80. Morales, M.M., Kravchenko, K., Roales, A., Mendoza, A., Pyrcz, M.J., and Torres-Verdin, C., A Deep Learning–Based Dual Latent Space Method for the Estimation of Physical Flow Properties From Fibre‐Optic Measurements, Gephysical Prospecting , 73(6), [18 pages], July 2025, https://doi.org/10.1111/1365-2478.70063
81. Akmal, L., and Pyrcz, M.J. Physics-Based Discrepancy Modeling for Well Log Imputation, Mathematical Geosciences, [30 pgs], July 2025, https://doi.org/10.1007/s11004-025-10203-7
82. Liu, L., Maldonado-Cruz, E., Jo, H., Prodanovic, M., and Pyrcz, M.J., Data Conditioning for Subsurface Models with Single-Image Generative Adversarial Network (SinGAN), Mathematical Geosciences, [20 pgs], July 2025, https://doi.org/10.1007/s11004-025-10206-4
83. Merzoug, A., Liu, L., and Pyrcz, M.J., GenAI Minimum Acceptance Checks: Static and Dynamic Model-Checking of Conditioning Generative Artificial Intelligence Models for Subsurface Modeling, Mathematical Geosciences, [31 pages], July 2025, https://doi.org/10.1007/s11004-025-10206-4
84. Chacon-Buitrago, N. and Pyrcz, M.J., Machine Learning-Based Soft Data Checking for Subsurface Modeling, Geosceinces [13 pages], August 2025, https://doi.org/10.3390/geosciences15080288
85. Mabadeje, A. O., Morales, M., Torres-Verdin, C. and Pyrcz, M.J., Evaluating the Stability of Deep Learning Latent Feature Space for Subsurface Modeling, Mathematical Geosceinces [34 pages], August 2025, https://doi.org/10.1007/s11004-025-10223-3
86. Singind-Larsen, R., Eidsvik, J., Ellefmo, S., and Pyrcz, M.J., Introduction to the Special Issue: How do Big Data, AI, and ML Challenge Geostatistical and Bayesian Formalisms?, Mathematical Geosciences [6 pages], August 2025, https://doi.org/10.1007/s11004-025-10221-5
87. Merzoug, A., and Pyrcz, M.J., Generalized Conditioning of Generative Artificial Intelligence for History Matching Subsurface Models, Mathematical Geosciences [34 pages], October 2025, https://doi.org/10.1007/s11004-025-10240-2
88. Merzoug, A., Özbayrak, F., Foster, J.T., and Pyrcz, M.J., Beyond random forest: how spatial bagging and spatial random forest dominate for subsurface applications?, Computational Geosciences 29 (6), 52 [20 pages], December 2025, https://doi.org/10.1007/s10596-025-10388-0
2026
89. Chacon-Buitrago, N., Laugier F.L., Jo. H., and Pyrcz, M.J., GeoRulesLobePy: A Markov Chain-based approach for rule-based deepwater lobe training images in subsurface modeling, AAPG Bulletin, online first [43 pages], January 2026, https://doi.org/10.1306/01132624127
Other Publications and Contributions
1. Pyrcz, M.J., Leuangthong, O., Deutsch, C.V. Hierarchical Trend Modeling for Improved Reservoir Characterization: International Association of Mathematical Geology, Toronto, Canada, 2005.
2. Springhorn, S., Sullivan, M.D., Pyrcz, M.J., Alward, R., Skartvedt-Forte, M., Demucha, B., Spaeth, S. and Lawlor, N. Hierarchical Analysis of Channelized Deepwater Deposits: American Association of Petroleum Geologists Annual Meeting, Long Beach, USA, Apr. 1 – 4, 2007.
3. Keynote - Pyrcz, M.J., Clark, J, Drinkwater, N. and Sullivan, M. Event-Based Models as Laboratory for Testing Quantitative Rules: AAPG Annual Conference / SEPM Deepwater Research Meeting, Houston, TX, April 2006.
4. Keynote - Pyrcz, M.J., Morgan Sullivan, Nicholas J. Drinkwater, Julian Clark, Andrea Fildani and Tim McHargue. Event-based Geostatistical Modeling: Improved Geostatistical Models Through the Integration of Geologic Process: Sedimentary Research Group (SED), Department of Geological and Environmental Sciences, Stanford University, Stanford, CA, Apr. 20, 2007.
5. Springhorn, S., Sullivan, M.D., Pyrcz, M.J., Alward, R., Skartvedt-Forte, M., Demucha, B., Spaeth, S. and Lawlor, N. Hierarchical Analysis of Channelized Deepwater Deposits: American Association of Petroleum Geologists Annual Meeting, Long Beach, USA, Apr. 1 – 4, 2007.
6. Keynote - Pyrcz, M.J., Sebastien Strebelle, Morgan Sullivan, Nicholas J. Drinkwater, Julian Clark, Andrea Fildani, and Tim McHargue. Event-based Geostatistical Modeling: Stanford Center for Reservoir Forecasting (SCRF), Energy Resources Engineering Department, Stanford, CA, April 19, 2007.
7. Pyrcz, M.J., Sullivan, M.D., McHargue, T.R., Fildani, A., Drinkwater, N.J., Clark, J., and Posamentier, H.W. Numerical Modeling of Channel Stacking from Outcrop, SEPM Research Conference Outcrops Revitalized: Tools, Techniques and Application, Kilkee, Ireland, Jun. 22 – 28, 2008.
8. McHargue, T.R., Clark, J., Sullivan, M., Fildani, A., Drinkwater, N., Pyrcz, M.J., and Posamentier, H.W. Thicknesses of turbidite channel elements and their abandonment facies constrain stacking pattern and net sand, SEPM Research Conference Outcrops Revitalized: Tools, Techniques and Application, Kilkee, Ireland, Jun. 22 – 28, 2008.
9. Fildani, A., Pyrcz, M.J., Romans, B.W., McHargue, T., Sullivan, M., Clark, J., Drinkwater, N., and Posamentier, H.W. "Event-Based Modeling–a New Approach to Improve Our Understanding of Deep-Water Sedimentary Systems." In 2008 Joint Meeting of The Geological Society of America, Soil Science Society of America, American Society of Agronomy, Crop Science Society of America, Gulf Coast Association of Geological Societies with the Gulf Coast Section of SEPM. Sept. 29—Oct. 10, 2008.
10. McHargue, Tim, Morgan Sullivan, Julian Clark, Andrea Fildani, Michael J. Pyrcz, Marjorie Levy, Henry Posamentier, Nicholas Drinkwater, and Brian Romans. "Event-Based Forward Modeling–Visualizing and Predicting Turbidite Channel Architectures." In 2008 Joint Meeting of The Geological Society of America, Soil Science Society of America, American Society of Agronomy, Crop Science Society of America, Gulf Coast Association of Geological Societies with the Gulf Coast Section of SEPM. Sept. 29—Oct. 10, 2008.
11. Sullivan, M.D., Pyrcz, M.J., Posamentier, H.W., McHargue, T.R., Fildani, A., Drinkwater, N.J., and Clark, J. Recent advances in deepwater slope valley depositional models: Implications of channel-fill percent and stacking patterns on reservoir architecture and producibility, AAPG International Annual Convention, Cape Town, South Africa, Oct. 26 – 29, 2008.
12. Pyrcz, M.J., Sullivan, M.D., McHargue, T.R., Fildani, A., Drinkwater, N.J., Clark, J., and Posamentier, H.W. A showcase of event-based models., AAPG International Annual Convention, Cape Town, South Africa, Oct. 26 – 29, 2008.
13. Fildani, A., Pyrcz, M.J., Romans, B., McHargue, M., Sullivan, M., Clark, J., Drinkwater, D., Posamentier, H., and Hilley, G. Event-based modeling – a new frontier to explore deepwater systems, Geologic Society of America, Houston, Texas, USA, Oct. 5 – 9, 2008.
14. McHargue, T., Sullivan, M., Clark, J., Fildani, A., Pyrcz, M.J., Levy, M., Posamentier, H., Drinkwater, N., and Romans, B. Event-based forward modeling – visualizing and predicting turbidite channel architectures, Geologic Society of America, Houston, Texas, USA, Oct. 5 – 9, 2008.
15. McHargue, T., Clark, J., Sullivan, M., Fildani, A., Pyrcz, M.J., Romans, B., Levy, M., Posamentier, H., Covault, J. Assumed allocyclicity yields predictive model of turbidite channel architectures, SEPM Research Conference Stratigraphic Evolution of Deep-Water Architecture: Examples on Controls and Depositional Styles from the Magallanes Basin, Chile, Puerto Natales, Chile, Feb. 22 – 28, 2009 (best paper award).
16. Clark, J., Pyles, D., Bouroullec, R., Amerman, R., Hoffman, M., Moody, J., Moss-Russell, A., Setiawan, P., Silalahi, H., Heard, T., Guzofski, C., Fildani, A., Drinkwater, N., and Pyrcz, M.J. Structural Controls on Deepwater Architecture and Facies in the Eocene Ainsa Basin, Spanish Pyrenees. American Association of Petroleum Geologists Annual Meeting, New Orleans, USA, Apr. 11—14, 2010.
17. McHargue, T., Pyrcz, M., Sullivan, M., Clark, J., Levy, M., Fildani, A., Posamentier, H., Romans, B., and Covault, J.A., Predicting reservoir architecture of turbidite channel complexes; a general model adaptable to specific situation: The Geologic Society of America, v. 42, p. 51, Denver, CO, Oct. 31 – Nov. 3, 2010.
18. Romans, B.W., Fildani, A., Covault, J.A., Sullivan, M., Clark, J., Power, B., Pyrcz, M.J., Bracken, B., Willis, B., and Payenberg, T. Remembering the ‘source’ when applying source-to-sink concepts in clastic stratigraphy: American Association of Petroleum Geologists Annual Meeting, Houston, USA, Apr. 10-13, 2011.
19. McHargue, T.R., Pyrcz, M.J., Sullivan, M.D., Clark, J., Fildani, A., Drinkwater, N.J., Levy M., Posamentier, H.W., Romans, B. and Couvalt, J. Numerical Modeling of Channel Stacking from Outcrop., in Martinsen, O., Pulham, A., Haughton, P., and Sullivan, M. (eds.), SEPM special publication - Outcrops Revitalized: Tools, Techniques and Applications, Kilkee, Ireland, Jun. 22 – 28, 2011.
20. Pyrcz, M.J., Sullivan, M.D., McHargue, T.R., Fildani, A., Drinkwater, N.J., Clark, J., and Posamentier, H.W. Numerical Modeling of Channel Stacking from Outcrop., in Martinsen, O., Pulham, A., Haughton, P., and Sullivan, M. (eds.), SEPM special publication - Outcrops Revitalized: Tools, Techniques and Applications, Kilkee, Ireland, Jun. 22 – 28, 2011.
21. Keynote - Pyrcz, M.J. Applications of Rule-based Models and Geostatistics: Community Surface Dynamics Modeling Systems (CSDMS), Boulder, Colorado, Oct. 28 – 30, 2011.
22. Sullivan, M.D., Pyrcz, M.J., and Covault, J.A. Hierarchical Modeling of Deepwater Channelized Reservoirs: the Difference That Makes a Difference. The Geological Society of America Annual Meeting, Charlotte, USA, November 4-7, 2012.
23. Sun, T., Covault, J.A., Pyrcz, M.J., and Sullivan, M. Computer Simulations of Channel Meandering and the Formation of Point Bars: Linking Channel Dynamics to the Preserved Stratigraphy. American Geophysical Union Fall Meeting, San Francisco, USA, Dec. 3-7, 2012.
24. Pyrcz, M.J., McHargue, T., Clark, J. Sullivan, M.D., Sebastien, S. Event-Based Geostatistical Modeling Applications, American Association of Petroleum Geologists Annual Meeting, Long Beach, USA, Apr. 22 – 25, 2012.
25. Keynote - Covault, J.A., Romans, B.W., Fildani, A., Madof, A., Harris, A., Pyrcz, M.J., and Sun, T. Sediment budget framework for source to sink predictions: MYRES V The Sedimentary Record of Landscape Dynamics: Salt Lake City, UT, Aug. 8-10, 2012.
26. Sullivan, M.D., Pyrcz, M.J., and Covault, J.A. Hierarchical modeling of deepwater channelized reservoirs; the difference that makes a difference. Pacific Section AAPG, SPE, and SEPM Joint Technical Conference, Monterey, USA, April 19-25, 2013.
27. Covault, J.A., Carvajal, C., Fildani, A., Milliken, K., Pyrcz, M.J., Sun, T., and Zarra, L. Source-to-sink scaling relationships, sediment budgets, and landscape evolution for Paleogene Gulf of Mexico deepwater stratigraphic predictions. 63rdAnnual Convention of the Gulf Coast Association of the Geological Societies and Gulf Coast Section of SEPM, New Orleans, USA, October 6-8, 2013.
28. Pyrcz, M.J. Reservoir Geostatistics and New Process Mimicking Approaches: Rice University, Earth Science Lecture Series, Houston, TX, Oct. 2, 2014.
29. Willis, B.J., Sech, R., Sun, T., and Pyrcz, M.J. Predicting facies patterns within fluvial channel belts: American Geophysical Union Annual Conference, San Francisco, USA, Dec. 15 – 19, 2014.
30. Block, A., Perlmutter, M., Thorne, J., and Pyrcz, M.J. Toward an Objective Method to Distinguishing Delta Depositional Environments, American Geophysical Union Annual Conference, San Francisco, USA, Dec. 15 – 19, 2014.
31. Laugier, F., Covault, J., Pyrcz, M.J., Sech, R., Sun, T., and Sullivan M.D. Reservoir Modeling of Deepwater Depositional Lobes, American Association of Petroleum Geologists Annual Meeting, Denver, USA, May 31 – Jun. 3, 2015.
32. Keynote - Pyrcz, M.J., and Sech, R. When Geostatistical Reservoir Models Become Unfit for Purpose (And Ways to Avoid This): Geological Society of London, Reservoir modeling Conference at University of Aberdeen, Mar. 4 – 5, 2015.
33. Willis, B.J., Sech, R., Sun, T., Pyrcz, M.J., and Connell, S. Fluvial Channel Belt Reservoirs, 2015 AAPG Annual Convention and Exhibition, Denver, CO, May 31 – Jun. 3, 2015.
34. Pyrcz, M.J. What Geologists Need to Know About Geostatistical Reservoir Modeling, Texas A&M, AAPG Student Chapter Lecture Series, Nov. 5, 2015.
35. Pyrcz, M.J. Improved Geological and Geophysical Integration in Reservoir Modeling, The University of Texas at Austin, SEG Student Chapter Lecture Series, Nov. 12, 2015.
36. Kaplan, R., Pyrcz, M.J., and Strebelle, S. Deepwater reservoir connectivity reproduction from MPS and process-mimicking geostatistical methods, Geostatistics Congress Valencia 2016, Sep. 5 – 9, 2016.
37. Pyrcz, M.J., Janele, P., Weaver, D., and Strebelle, S. Geostatistical Methods for Unconventional Reservoir Uncertainty Assessments, Geostatistics Congress Valencia 2016, Sep. 5 – 9, 2016.
38. Strebelle, S., Vitel, S., and Pyrcz, M.J. Integrating New Data in Reservoir Forecasting Without Building New Models, Geostatistics Congress Valencia 2016, Sep. 5 – 9, 2016.
39. Zhang, J., Covault, J.A., Pyrcz, M.J., Carvajal, C., Milliken, K., and Sharman, G.R. Quantifying Sediment Supply to Continental Margins: Application to Paleogene Wilcox Group Deposition, Gulf of Mexico, 2017 AAPG Annual Convention and Exhibition, Houston, TX, Apr. 2 – 5, 2017.
40. Panel – Pyrcz, M.J., Lake, L., Ghosh, J. Big Data Analytics for Petroleum Engineering: Hype of Panacea, Center for Subsurface Energy and the Environment, The University of Texas at Austin, Dec. 8, 2017.
41. Invited Talk - Pyrcz, M.J. Everything Geologists Must Know About Geostatistics, Bureau of Economic Geology Invited Talk, The University of Texas at Austin, TX, Apr. 6, 2018.
42. Pyrcz, M.J. Geostatistics to Support Resource Modeling in Mexico, UT Austin – Mexico CONACYT Partnership Meeting, Mexico City, Mexico, Apr. 12, 2018.
43. Invited Talk - Pyrcz, M.J. Geostatistics for Unconventional Resource Modeling, Bureau of Economic Geology Invited Talk, Bureau of Economic Geology, The University of Texas at Austin, TX, Apr. 18, 2018.
44. Invited Talk - Pyrcz, M.J. Michael’s Unsolicited Advice for a Happy Career, Hildebrand Department of Petroleum and Geosystems Engineering Senior Banquest, Austin, TX, May 4, 2018.
45. Pyrcz, M.J., Covault, J. Model Resampling for Uncertainty Modeling, 2018 AAPG Annual Convention and Exhibition, Salt Lake City, UT, May 20 – 23, 2018.
46. Short Course - Pyrcz, M.J. 2 Day Geostatistics Short Course, Rocky Mountain Association of Geologists, Denver, CO, Jul 18-19, 2018.
47. Short Course - Pyrcz, M.J. Unconventional Oil and Gas Resources, ½ Day Course for Visiting China Petroleum University Scholars, Jul. 23, 2018.
48. Short Course - Pyrcz, M.J. Application of Big Data in the Energy Industry, ½ Day Course for Visiting China Petroleum University Scholars, Aug. 1, 2018.
49. Short Course - Pyrcz, M.J. Geostatistics, 2 Day Course for Anadarko, The Woodlands, TX, Aug. 20 – 21, 2018.
50. Short Course - Pyrcz, M.J. Reservoir Engineering, 3 Day Course for General Electric Baker Hughes, Florence, Italy, Sep. 5 – 7, 2018.
51. Pyrcz, M.J. Department Research Showcase Introduction and Host, Center for Subsurface Energy and the Environment, The University of Texas at Austin, TX, Aug. 23, 2018.
52. Keynote - Pyrcz, M.J. Data analytics/geostatistics workflows for modeling uncertainty for unconventionals, Gussow Conference: Canadian Society of Petroleum Geologists, Banff, Canada, Oct. 9 – 11, 2018.
53. Invited Talk - Pyrcz, M.J. Spatial Data Analytics to Support Induced Seismicity Modeling, TexNet-CISR Annual Meeting, Bureau of Economic Geology, The University of Texas at Austin, TX, Dec. 5, 2018.
54. Invited Talk - Pyrcz, M.J. Subsurface Data Analytics, Bureau of Economic Geology Invited Talk, Bureau of Economic Geology, The University of Texas at Austin, TX, Dec. 14, 2018.
55. Short Course - Pyrcz, M.J. Reservoir Engineering, 3 Day Course for General Electric Baker Hughes, Florence, Italy, Feb. 13 – 15, 2019.
56. Short Course - Pyrcz, M.J. Data Analytics Deep Dive, 1 Day Course for Equinor, Austin, TX, Feb. 19, 2019.
57. Short Course - Pyrcz, M.J. Spatial Data Analytics, 1 Day Course for Hess, Houston, TX, Feb. 21, 2019.
58. Short Course - Pyrcz, M.J. Spatial Data Analytics, 2 Day Course for Anadarko, The Woodlands, TX, Mar. 5 – 6, 2019.
59. Short Course - Pyrcz, M.J. Subsurface Data Analytics, 5 Day Course and Project Review for Hess, Houston, TX, Mar. 18 – 22, 2019.
60. Panel – Pyrcz, M.J. Digital Transformation, Pricewaterhouse Cooper, Austin, TX, Apri. 9, 2019.
61. Workshop Host - Pyrcz, M.J. Data Analytics, ½ Day Workshop with Chevron, Houston, TX, Apr. 23, 2019.
62. Short Course - Pyrcz, M.J., Sech, R., and Covault, J. Geological Heterogeneity, 1 Day Course for American Association of Petroleum Geologists, San Antonio, TX, May 18, 2019.
63. Liu, W., Ikonnikova, S., Pyrcz, M.J., Hamlin S. and Sivila, L. Spatial Sampling Bias in Decision Tree Machine Learning Method for Unconventional Resources, 2019 AAPG Annual Convention and Exhibition, San Antonio, TX, May 19 – 22, 2019.
64. Jo. H., Santos, J.E., Pyrcz, M.J. Conditioning stratigraphic, rule-based models with generative adversarial networks: a deepwater lobe example, 2019 AAPG Annual Convention and Exhibition, San Antonio, TX, May 19 – 22, 2019.
65. Panel – Pyrcz, M.J. et al. The Big Crew Change Panel, American Association for Petroleum Geologists 2019 Annual Meeting, San Antonia, TX, May 21, 2019.
66. Short Course - Pyrcz, M.J. Applied Machine Learning, 1 Day Course for American Association of Petroleum Geologists, San Antonio, TX, May 23, 2019.
67. Consortium - Pyrcz, M.J., and Foster, J. Introduction, Host and Conclusions for DIRECT Consortium Annual Meeting, The University of Texas at Austin, TX, Jun. 13, 2019.
68. Short Course - Pyrcz, M.J. Spatial Data Analytics, 1 Day Course for IHSMarkit, Houston, TX, Jun. 21, 2019.
69. Consortium - Pyrcz, M.J., and Foster, J. Introduction, Host and Conclusions for DIRECT Consortium Annual Meeting, The University of Texas at Austin, TX, Jun. 24, 2019.
70. Short Course - Pyrcz, M.J. Machine Learning, 2 Day Course for IHSMarkit, Houston, TX, Jul. 8 – 9, 2019.
71. Short Course - Pyrcz, M.J., and Foster, J. Data Science Bootcamp, Open Short Course, Houston, TX, Jul. 22 – 26, 2019.
72. Short Course - Pyrcz, M.J., Applied Machine Learning, ½ Day Course for Chevron, Houston, TX, Jul. 23, 2019.
73. Short Course - Pyrcz, M.J. Applied Machine Learning, 1 Day Course for Society of Petroleum Engineers, Rice University, TX, Sep. 26, 2019.
74. Short Course - Pyrcz, M.J., and Foster, J. Data Science Bootcamp, Short Course for BP Advanced Modeling Team, Austin, TX, Oct. 1 – 3, 2019.
75. Workshop Host - Pyrcz, M.J. Introductions and Host of Workshop, BP Research Campus Visit, The University of Texas at Austin, TX, Oct. 10, 2019.
76. Short Course - Pyrcz, M.J. Applied Machine Learning, ½ Day Course for Aramco, Houston, TX, Oct. 24, 2019.
77. Short Course - Pyrcz, M.J., and Foster, J. Data Science Bootcamp, Short Course for Noble Energy, Austin, TX, Nov. 14 – 15, 2019.
78. Pyrcz, M.J. Subsurface Machine Learning Opportunities, BP Leadership Team Visit to the Cockrell School of Engineering, The University of Texas at Austin, TX, Nov. 20, 2019.
79. Invited Talk - Pyrcz, M.J. Spatial Data Analytics to Support Induced Seismicity Modeling, TexNet-CISR Annual Meeting, Bureau of Economic Geology, The University of Texas at Austin, TX, Dec. 5, 2019.
80. Santos, J.E., Lubbers, N., Prodanovic, Mehana, M., Chen, Y., Prodanovic, M., Pyrcz, M.J., Andrew, M., Kang, Q., and Viswanathan, H.S. Active-learning For Upscaling Nano-confinement Effects, American Geophysical Union Fall Meeting 2019, San Francisco, CA, Dec. 9 – 13, 2019.
81. Shakiba, M., Pyrcz, M.J., Lake, L.W., and Gale, J. Using Stochastic Methods to Quantify Spatial Arrangement of Fractures, American Geophysical Union Fall Meeting 2019, San Francisco, CA, Dec. 9 – 13, 2019.
82. Short Course - Pyrcz, M.J. Applied Machine Learning, 1 Day Course for Society of Petroleum Engineers Computational Modeling Technical Section, Houston, TX, Feb. 19, 2020.
83. Short Course - Pyrcz, M.J., and Foster, J. Machine Learning for Executives, ½ Day Short Course for Noble Energy, Austin, TX, Feb. 27, 2020.
84. Short Course - Pyrcz, M.J., and Foster, J. Subsurface Machine Learning, 2 Day Short Course for Noble Energy, Austin, TX, Apr. 21 – 22, 2020.
85. Short Course - Pyrcz, M.J., and Foster, J. Subsurface Machine Learning, 2 Day Short Course for Noble Energy, Austin, TX, May. 20 – 21, 2020.
86. Keynote - Pyrcz, M.J. Open source spatial data analytics in Python, Tutorial at TRANSFORM2020, Virtual Conference, Class with over 100 geoscience, data science, and engineering professionals and students, Jun. 8, 2020.
87. Panel - Pyrcz, M.J. Energy Data Analytics, XStarter Mentoring: Roundtable on Data Science , Jun. 6, 2020.
88. Distinguished Lecture Series - Pyrcz, M.J. Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists, Latin America and Caribbean Region, American Association for Petroleum Geologists, Jul. 9, 2020.
89. Distinguished Lecture Series - Pyrcz, M.J., 2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists. Online, Jul. 13.
90. Pyrcz, M.J., 2020, Improved Feature Engineering, Global Lunch and Learn with Chevron, Online, Jul. 16.
91. Pyrcz, M.J., 2020, Teaching Data Analytics, Teachers’ Institute for High School Educators, Online, Jul. 22.
92. Webinar - Pyrcz, M.J., 2020, New Opportunities for Subsurface Engineers and Geoscientists in the Digital Revolution, Society of Petroleum Engineers, Gulf Coast Section, Online, Sep. 18.
93. Pyrcz, M.J., 2020, Spatial Data Analytics, ExxonMobil Geophysics Seminar Series, Online, Sep. 30.
94. Distinguished Lecture Series - Pyrcz, M.J., 2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, Fresno State University, Online, Oct. 8.
95. Distinguished Lecture Series - Pyrcz, M.J., 2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists, Virginia Tech, Oct. 23.
96. Jo, H. & Pyrcz, M. J. Conditioning Rule-based Models to Stratigraphy with Machine Learning: Demonstration in Deepwater Lobe System. Geological Society of America, GSA 2020 Connects Online, Virtual, Oct 26-30, 2020. http://doi.org/10.1130/abs/2020AM-359238
97. Short Course - Pyrcz, M.J., and Foster, J., 2020, Subsurface Machine Learning, 2 Day Short Course, Online, Oct. 26 - 30.
98. Distinguided Lecture Series - Pyrcz, M.J., 2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists, University of Georgia, Nov. 6.
99. Distinguished Lecture Series - Pyrcz, M.J., 2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists, University of North Dakota, Nov. 19.
100. Distinguished Lecture Series - Pyrcz, M.J., 2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists, University of Oklahoma.
101. Short Course - Pyrcz, M.J., and Foster, J., 2020, Subsurface Machine Learning, 2 Day Short Course for Oasis, Online, Dec. 1 - 3.
102. Pan, W., Torres-Verdin, C., Pyrcz, M. J., 2020, New Machine Learning Method for Integrated Subsurface Modeling, presented at American Geophysical Union 2020 Fall Meeting, Online, Dec. 1 - 17. https://doi.org/10.1002/essoar.10505300.1
103. Santos, J., Yin, Y., Lubbers, N., Prodanovic, M., Pyrcz, M.J., Viswanathan, H.S., Kang, Q. Multiscale Networks for Learning Fluid Flow Through Fractured Permeable Media. American Geophysical Union Fall Meeting. New Orleans, Dec. 8, 2020.
104. Santos, J.E., Ying, Y., Lubbers, N., Prodanovic, M., Pyrcz, M.J., Viswanathan, H.S., and Kang, Q. Multiscale networks for learning fluid flow through fractured permeable media, American Geophysical Union Fall Meeting 2020, Online, Dec. 1 – 17, 2020.
105. Webinar – Pyrcz, M.J. Bootstrap and Monte Carlo, daytum Data Science Webinar Series, Online, Jan. 7, 2021.
106. Webinar – Pyrcz, M.J. Advanced Clustering Methods, daytum Data Science Webinar Series, Online, Jan. 14, 2021.
107. Short Course - Pyrcz, M.J., and Foster, J. Subsurface Machine Learning, 2 Day Short Course for Chevron, Online, Jan. 25 & 28, 2021.
108. Webinar - Pyrcz, M.J., and Foster, J. Shapley Values – An Introduction to Energy Game Theory, daytum Data Science Webinar Series, Online, Feb. 11, 2021.
109. Webinar - Pyrcz, M.J. Subsurface Machine Learning, TexTalks with Executive Education, Cockrell School of Engineering, Online, Feb. 12, 2021.
110. Webinar - Pyrcz, M.J., and Foster, J. Becoming an Energy Data Scientist, Panel Discussion, Online, Feb. 18, 2021.
111. Webinar – Pyrcz, M.J. Introduction to Mineral and Mining Machine Learning, daytum Data Science Webinar Series, Online, Feb. 25, 2021.
112. Webinar - Pyrcz, M.J., and Foster, J. Becoming an Energy Data Scientist, Panel Discussion, Online, Mar. 11, 2021.
113. Webinar – Pyrcz, M.J. Introduction to Mineral and Mining Machine Learning, daytum Data Science Webinar Series, Online, Mar. 25, 2021.
114. Panel - Pyrcz, M.J. Ask the Experts Panel, 2021 International Petroleum Technology Conference Annual Meeting, Online, Mar. 23 – Apr. 1, 2021.
115. Distinguished Lecture Series - Pyrcz, M.J. Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists. Stanford University, Apr. 6, 2021.
116. Webinar - Pyrcz, M.J. Academia vs. Industry, Chicano and Latino Engineers and Scientists Society (CALESS), University of California, Davis, Apr. 6, 2021.
117. Webinar – Pyrcz, M.J. Spatial Continuity in the Subsurface – What is a Variogram?, daytum Data Science Webinar Series, Online, Apr. 8, 2021.
118. Invited Talk - Pyrcz, M.J. Building Machine Learning on the Foundation of Geostatistics, Geostatistical Association of Southern Africa, Online, Apr. 15, 2021.
119. Short Course - Pyrcz, M.J. and Foster, J. Spatial Data Analytics and Machine Learning Refresher, ½ Day Workshop at the 2021 Energy Analytics Hackathon, The University of Texas at Austin, TX, Apr. 17, 2021.
120. Webinar – Pyrcz, M.J. Statistics in Machine Learning: Bayesian vs. Frequentist, daytum Data Science Webinar Series, Online, May 20, 2021.
121. Keynote - Pyrcz, M.J. Open source spatial data analytics in Python, Part II, Tutorial at TRANSFORM2021, Virtual Conference, Class with over 100 geoscience, data science, and engineering professionals and students, Jun. 19, 2021.
122. Invited Talk - Pyrcz, M.J. Data Analytics and Machine Learning for Subsurface Engineering and Geoscience, Machine Learning Laboratory Invited Talk, The University of Texas at Austin, Online, Jun. 7, 2021.
123. Webinar – Pyrcz, M.J. Bootstrap and Monte Carlo, daytum Data Science Webinar Series, Online, Jun. 10, 2021.
124. Webinar – Pyrcz, M.J. Advanced Clustering Methods, daytum Data Science Webinar Series, Online, Jun. 16, 2021.
125. Consortium - Pyrcz, M.J., and Foster, J. Introduction, Host and Conclusions for DIRECT Consortium Annual Meeting, The University of Texas at Austin, TX, Jun. 23, 2021.
126. Webinar – Pyrcz, M.J. Subsurface Neural Networks, daytum Data Science Webinar Series, Online, Jun. 30, 2021.
127. Keynote – Pyrcz, M.J. Texas Ground Water Commission Annual Meeting, Austin, TX, Jul. 1, 2021.
128. Keynote – Pyrcz, M.J. Emerging Opportunities with Data Analytics and Machine Learning in Subsurface Modeling, EAGE Machine Learning Workshop, Spring 2021 https://eage.eventsair.com/eage-machine-learning/keynote-speakers, Mar. 8-9, 2021.
129. Webinar – Pyrcz, M.J. Academia vs. Industry, daytum Data Science Webinar Series, Online, Jul. 8, 2021.
130. Pan, W., Torres-Verdin, C., Pyrcz, M. J.,Stochastic Pix2Pix Method for Conditional and Hierarchical Deepwater Reservoir Modeling, Geostats Congress 2021, Toronto, Canada. Jul. 12-16, 2021.
131. Jo, H., Santos, J.E., Pan, W., and Pyrcz, M. Machine Learning Assisted History Matching for a Deepwater Lobe System. Geostatistical Congress 2021, Toronto, Canada, Jul. 5, 2021.
132. Liu, W., Pyrcz, M. J. A Novel Geostatistical Heterogeneity Metric for Spatial Feature Engineering. Geostats Congress 2021, Toronto, Canada. Jul. 12-16, 2021.
133. Workshop - Pyrcz, M. J., 2021, An Introduction to machine learning. Geostats Congress 2021, Toronto, Canada. Jul. 12-16, 2021.
134. Short Course - Pyrcz, M.J., and Foster, J. Subsurface Game Theory, 2 Day Short Course for Chevron, Online, Jul. 19 – 21, 2021.
135. Webinar – Pyrcz, M.J. Feature Ranking and Selection, daytum Data Science Webinar Series, Online, Jul. 22, 2021.
136. Webinar – Pyrcz, M.J. Introduction to scikit-learn, daytum Data Science Webinar Series, Online, Jul. 29, 2021.
137. Keynote - Pyrcz, M.J. Open source spatial data analytics in Python Part II, Tutorial at TRANSFORM2021, Virtual Conference, Class with over 100 geoscience, data science, and engineering professionals and students, 2021.
138. Podcast - Pyrcz, M.J. Subsurface Data Science, Society for Petroleum Engineers, Online, Sept. 1, 2021.
139. Keynote - Pyrcz, M.J. Engineering Data Science Lessons from Subsurface Engineering, Institute of Industrial and Systems Engineers Lean6Data2021, Atlanta, GA, Sept. 20 – 21, 2021.
140. Keynote - Pyrcz, M.J. Subsurface Data Analytics and Machine Learning: A Geostatistical Perspective, TRANSAI2021, Online, Sept. 22, 2021.
141. Pan, W., Jo, H., Torres-Verdin, C., Duncan, I. J., Pyrcz, M. J. Machine learning assisted production history matching while retaining geological heterogeneity, GeoGulf 2021, Austin, TX, Oct. 28, 2021.
142. Jo, H. and Pyrcz, M. J. Feature Engineering in Well-Log Interpretation, GeoGulf 2021, Austin, TX, Oct. 28, 2021.
143. Maldonado-Cruz, E., and Pyrcz, M.J. Tuning Machine Learning Models for Geological Uncertainty Accuracy and Precision, GeoGulf 2021, Austin, TX, Oct. 28, 2021.
144. Hernandez Mejia, J.L., Pisel, J., and Pyrcz, M.J. Feature Engineering in Well-Log Interpretation, GeoGulf 2021, Austin, TX, Oct. 28, 2021.
145. Mabadeje, A.O., and Pyrcz, M.J. Machine Learning to Support Geological Analog Studies, GeoGulf 2021, Austin, TX, Oct. 28, 2021.
146. Pan, W., Torres-Verdin, C., Pyrcz, M.J., and Duncan, I.J. Feature Engineering in Well-Log Interpretation, GeoGulf 2021, Austin, TX, Oct. 28, 2021.
147. Short Course - Pyrcz, M.J. Subsurface Machine Learning, 1 Day Short Course for GeoGulf 2021, Austin, TX, Oct. 30, 2021.
148. Short Course - Pyrcz, M.J., and Foster, J. Subsurface Machine Learning, 1 Day Short Course for Society of Petroleum Engineers Continuing Education, Online, Nov. 11, 2021.
149. Prodanovic, M., Santos, J.E., Chang, B., Pyrcz, M.J., and Lubbers, N. Deep learning prediction of transport in porous and fractured media, American Geophysical Union Fall Meeting 2021, New Orleans, LA, Dec. 14, 2021.
150. Santos, J.E., Chang, B., Giliotti, A., Guiltinan, E.J., Mehana, M., Mohan, A., McClure, J., Kang, Q., Viswanathan, H.S., Lubbers, N. Prodanovic, M., and Pyrcz, M.J. Learning from a Big Dataset of Digital Rock Simulations, American Geophysical Union Fall Meeting 2021, New Orleans, LA, Dec. 14, 2021.
151. Podcast - Pyrcz, M.J. The Digital Revolution, Podcast with the Society of Petroleum Engineers, Gulf Coast Section, Online, Feb. 8, 2022.
152. Pan, W., Torres-Verdín, C., and Pyrcz, M. J. Reducing the uncertainty of multi-well petrophysical interpretation from well logs via machine-learning and statistical models. presented at the SPWLA 2022 Spring Topical Conference on Petrophysical Machine Learning, Mar. 23-24, 2022.
153. Short Course - Pyrcz, M.J. and Foster, J. Spatial Data Analytics and Machine Learning Refresher, ½ Day Workshop at the 2022 Energy Analytics Hackathon, The University of Texas at Austin, TX, Mar. 25, 2022.
154. Invited Talk - Pyrcz, M.J. Subsurface Machine Learning, Ivano-Frankovisk University, Ukraine, Online, Apr. 6, 2022.
155. Short Course - Pyrcz, M.J., Subsurface Modeling Technology for BP, Houston, TX, May 18, 2022.
156. Invited Talk - Pyrcz, M.J. Subsurface Machine Learning Built on a Foundation of Geostatistics, Canadian Society for Petroleum Geologists, Online, Jun. 16, 2022.
157. Host / PI - Pyrcz, M.J. and Foster, J., DIRECT Annual Consortium Meeting 2022, Aug. 19, 2022.
158. Talk – Lomask, J., Burrough, T., Gilmore, A., Pyrcz, M.J., Seismic fault proximity to production Jesse Lomask, Toby Burrough, IMAGE 2022 , Aug. 30, 2022.
159. Invited Talk - Pyrcz, M.J., Data Analytics and Machine Learning for Geology, Trinity University, Sept. 20, 2023.
160. Short Course - Pyrcz, M.J., Machine Learning for Executives, Houston, TX, Nov. 6, 2022.
161. Short Course - Pyrcz, M.J., Machine Learning for Executives for PetroSkills Conclave, Houston, TX, Dec. 6, 2022.
162. Hackathon Host - Pyrcz, M.J. and Foster, J., Energy AI Hackathon 2023, UT Austin, TX, Jan. 20 – 22, 2023.
163. Short Course - Pyrcz, M.J., J. Subsurface Machine Learning for Chord Energy, Houston, TX, Feb. 28 – Mar. 1, 2023.
164. Short Course - Pyrcz, M.J., J. Spatial Data Analytics for Chord Energy, Houston, TX, Mar. 9, 2023.
165. Panel - Prochnow, S., Andrew, S., Pyrcz, M.J., W-12: Recent Challenges, Advances, and Future Applications of Machine Learning in the Geosciences, IMAGE2023, Sept. 1, 2023.
166. Keynote - Pyrcz, M.J., Data Analytics and Machine Learning for Geostatistics, International Association for Mathematical Geosciences IAMG 2023, Aug. 8, 2023.
167. Short Course - Pyrcz, M.J., Subsurface Machnine Learning for Woodside, Houston, TX, Aug. 1-2, 2023.
168. Short Course - Pyrcz, M.J., Spatial Data Analytics for Woodside, Houston, TX, Aug. 3, 2023.
169. Host / PI - Pyrcz, M.J. and Foster, J., DIRECT Annual Consortium Meeting 2023, Aug. 18, 2023.
170. Invited Talk - Pyrcz, M.J., Data Analytics and Machine Learning for Geostatistical Reservoir Modeling, IMAGE2023, Sept. 1, 2023.
171. Short Course - Pyrcz, M.J., Machine Learning for Executives for Woodside, Houston, TX, Sept. 11, 2023.
172. Panel - Application of Artificial Intelligence in Petroleum Engineering Industry and Academy Part II, ATCE Annual Meeting 2023, San Antonio, TX, USA, Oct. 17, 2024.
173. Keynote – Pyrcz, M.J., Data Analytics and Machine Learning from Geostatistics, Subsurface Engineers and Geoscientists, Apr. 11, 2024.
174. Webinar - Applying Machine Learning as a Competent Engineer or Geoscientist, CSEE, UT Austin, July 11, 2024.
175. Host / PI - Pyrcz, M.J. and Foster, J., DIRECT Annual Consortium Meeting 2024, Aug. 30, 2024.
176. Keynote - Pyrcz, M.J., Data Analytics and Machine Learning for Energy Engineering and Geoscience Modeling, Geostatistics Congress 2024, Ponta Delgada, Azores, Portugal, Sept. 2024.
177. Invited Talk - Pyrcz, M.J., Forecast Uncertainty: a Geostatistics and Machine Learning PerspectiveModeling, U.S. Energy Information Adminstration, Sept. 2024.
178. Invited Talk – Pyrcz, M.J., Applying Machine Learning as a Competent Engineer or Geoscientist, Santos Global Open Forum, Sept. 2024.
179. Panel – Pyrcz, M.J., Airhardt, M., Rai, V., and James, R., AI Energy Policy, Year of AI Symposium, LBJ School, Oct. 2024. https://lbj.utexas.edu/ut-austin-hosts-signature-year-ai-symposium-policy-leadership-lbj-school
180. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, University of Houston, International Association for Mathematical Geosciences, Oct. 2024.
181. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, Petroleum and Natural Gas Engineering, Penn State, International Association for Mathematical Geosciences, Oct. 2024.
182. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, AAPG Student Session, Rice University, International Association for Mathematical Geosciences, Oct. 2024.
183. Distinguished Lecture – Pyrcz, M.J., Machine / Deep Learning for Building Subsurface Models, MineralX, Stanford University, International Association for Mathematical Geosciences, Oct. 2024.
184. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, Montana Tech, International Association for Mathematical Geosciences, Oct. 2024.
185. Distinguished Lecture – Pyrcz, M.J., Inferential Machine Learning, Montana Tech, International Association for Mathematical Geosciences, Oct. 2024.
186. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, Virginia Tech, International Association for Mathematical Geosciences, Nov. 2024.
187. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, Oklahoma University, International Association for Mathematical Geosciences, Nov. 2024.
188. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, Missouri Tech, International Association for Mathematical Geosciences, Dec. 2024.
189. Hackathon Host – Pyrcz, M.J., Energy AI Hackathon, Host and Workshop Instructor, The University of Texas at Austin, Jan. 2025.
190. Workshop Invited Talk – Pyrcz, M.J., Data Science in Energy, Data to Decisions in the E&P – Bridging the Gap with Machine Learning, King Fahd University of Petroleum and Minerals, International Association for Mathematical Geosciences , Feb. 2025.
191. Panel – Pyrcz, M.J., Faculty Panel for Admitted Students, Hildebrand Department of Petroleum and Geosystems Engineering, Mar. 2025.
192. Panel – Pyrcz, M.J., Be UT PGE, Hildebrand Department of Petroleum and Geosystems Engineering, Mar. 2025.
193. Webinar - Pyrcz, M.J., Integrating Uncertainities With Data Analytics and Machine Learning for Subsurface Modeling, SPE IRMTS / DSEATS Technical Sections, Society for Petroleum Engineers, April 2025.
194. Short Course – Pyrcz, M.J., Geostatistics 1/2-day Course, Geophysical Society of Houston, Houston, TX, May 2025.
195. Short Course – Pyrcz, M.J., Machine Learning 1/2-day Course, Geophysical Society of Houston, Houston, TX, May 2025.
196. Annual Consortium Meeting – Pyrcz, M.J., DIRECT Consortium Host, Co-author and Speaker, 18 talks, 3 given and 13 co-authored, The University of Texas at Austin, May 2025.
197. Short Course – Pyrcz, M.J., Geostatistics 2-day Course, Hess Energy, Houston, TX, May 2025.
198. Short Course – Pyrcz, M.J., Machine Learning 1-day Course, SPE/AAPG/SEG URTeC, Houston, TX, June 2025.
199. Hackathon Host and Instructor – Pyrcz, M.J., Energy AI High School Hackathon, The University of Texas at Austin, June 2025.
200. Hackathon Host and Instructor – Pyrcz, M.J., Energy AI High School Hackathon, The University of Texas at Austin, July 2025.
201. Distinguished Lecture – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, Inha University, International Association for Mathematical Geosciences , July 2025.
202. Distinguished Lecture – Pyrcz, M.J., Machine / Deep Learning for Building Subsurface Models, Korean National Lab, KIGAM, International Association for Mathematical Geosciences , July 2025.
203. Distinguished Lecture – Pyrcz, M.J., Machine / Deep Learning for Building Subsurface Models, Korean National Ol Company, KNOC, International Association for Mathematical Geosciences , July 2025.
204. Conference Talk – Christie, M., Pyrcz, M.J., Borrough, T., Lomask, J., and Chaparro, M., AL / ML / Geostatistics Best Practices for Geoscience Automation at Scale, IMAGE 2025, Aug. 2025.
205. Conference Talk – Chaparro, M., Pyrcz, M.J., Borrough, T., and Christie, M., Novel Feature Selection with Information Theory for Improved Subsurface Machine Learning Models, IMAGE 2025, Aug. 2025.
206. Conference Poster – Chacon-Buitrago N., and Pyrcz, M.J., Satellite-Based Delta Characterization via Latent Space, IMAGE 2025, Aug. 2025
207. Conference Poster - Q. Zhou*, X. Ma, M. Prodanović and M.J. Pyrcz, Prediction of 3D fluid velocity fields in fractured media using multiscale network for hierarchical regression (ms-net), IMAGE 2025, Aug. 2025.
208. Conference Talk – Merzoug, A., and Pyrcz, M.J., Continuous vs. categorical encodings: Channel orientation-aware self-attention conditional generative adversarial networks, IMAGE 2025, Aug. 2025.
209. Conference Talk – Elhossary, D., Pyrcz, M.J., and Mohanty, K., Predicting CO2 Storage and Enhanced Gas Recovery (CSEGR) Performance under Uncertainty using Ensemble-Based Reservoir Heterogeneity Modeling, IMAGE 2025, Aug. 2025.
210. Invited Talk – Pyrcz, M.J., New Subsurface Applications in Latent Space, University of Alberta, Edmonton, Canada, Sept. 2025.
211. Invited Talk – Pyrcz, M.J., Integrating Geostatistics into Data Analytics and Machine Learning, University of Alberta, Edmonton, Canada, Sept. 2025.
212. Consortium Review Host – many of my graduate students presented to my consortium member companies, Online, Dec. 2025.
213. Short Course - Pyrcz, M.J., Geostatistics 1-day Course, Coord Energy, Houston, TX, Dec. 2025.
214. Short Course - Pyrcz, M.J., Machine Learning 2-day Course, Coord Energy, Houston, TX, Dec. 2025.
