My Publications
Peer Reviewed Papers
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
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. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. Özbayrak, F., Foster, J.T., and Pyrcz, M.J. Spatial bagging to integrate spatial correlation into ensemble machine learning, Computers and Geosciences, 186, [8 pages], Feb. 2024, https://doi.org/10.1016/j.cageo.2024.105558
65. 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 pages], 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 pages], 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 pages], 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 pages], Mar. 2024, https://doi.org/10.1177/01445987231210966
Other Publications and Contributions
Invited Keynote Talks
Presentation 1. Pyrcz, M.J., Clark, J, Drinkwater, N. and Sullivan, M., 2006, Event-Based Models as Laboratory for Testing Quantitative Rules: AAPG Annual Conference / SEPM Deepwater Research Meeting, Houston, TX, April 2006.
Presentation 2. Pyrcz, M.J., Sebastien Strebelle, Morgan Sullivan, Nicholas J. Drinkwater, Julian Clark, Andrea Fildani, and Tim McHargue, 2007, Event-based Geostatistical Modeling: Stanford Center for Reservoir Forecasting (SCRF), Energy Resources Engineering Department, Stanford, CA, April, 19th.
Presentation 3. Pyrcz, M.J., Morgan Sullivan, Nicholas J. Drinkwater, Julian Clark, Andrea Fildani and Tim McHargue, 2007, 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, April, 20th.
Presentation 4. Pyrcz, M.J., 2011, Applications of Rule-based Models and Geostatistics: Community Surface Dynamics Modeling Systems (CSDMS), Boulder, Colorado, October 28th to 30th, 2011.
Presentation 5. Covault, J.A., Romans, B.W., Fildani, A., Madof, A., Harris, A., Pyrcz, M.J., and Sun, T., 2012, Sediment budget framework for source to sink predictions: MYRES V The Sedimentary Record of Landscape Dynamics: Salt Lake City, UT, August 8-10.
Presentation 6. Pyrcz, M.J., 2014, Reservoir Geostatistics and New Process Mimicking Approaches: Rice University, Earth Science Lecture Series, Houston, TX, October 2nd.
Presentation 7. Pyrcz, M.J., and Sech, R., 2015, When Geostatistical Reservoir Models Become Unfit for Purpose (And Ways to Avoid This): Geological Society of London, Reservoir modeling Conference at University of Aberdeen, March 4-5, 2015.
Presentation 8. Pyrcz, M.J., 2015, What Geologists Need to Know About Geostatistical Reservoir Modeling, Texas A&M, AAPG Student Chapter Lecture Series, Nov. 5th, 2015.
Presentation 9. Pyrcz, M.J., 2015, Improved Geological and Geophysical Integration in Reservoir Modeling, The University of Texas at Austin, SEG Student Chapter Lecture Series, Nov. 12th, 2015.
Presentation 10. Pyrcz, M.J., Fall 2018, Data analytics/geostatistics workflows for modeling uncertainty for unconventionals, Gussow Conference: Canadian Society of Petroleum Geologists, Banff, Canada.
Presentation 11. Pyrcz, M.J., 2019-2020, Subsurface Data Analytics and Machine Learning, Distinguished Lecture, American Association for Petroleum Geologists. Multiple events.
Presentation 12. Pyrcz, M.J., Summer, 2020, Open source spatial data analytics in Python, Tutorial at TRANSFORM2020, Virtual Conference, Class with over 100 geoscience, data science, and engineering professionals and students.
Conference Presentations / Papers
Presentation 1. Pyrcz, M.J. and C.V. Deutsch, 2002, Debiasing for Improved Inference of the One-Point Statistic: 30th International Symposium on Computer Applications in the Mineral Industries (APCOM), Phoenix, USA, February 25-27.
Presentation 2. Deutsch, C.V., Pyrcz, M.J., and Tran, T.T., 2002, Geostatistical Assignment of Reservoir Properties on Unstructured Grids: The Society of Petroleum Engineers Annual Technical Conference and Exhibition held in San Antonio, USA, 29 September–2 October (SPE 77427).
Presentation 3. Pyrcz, M.J., and Deutsch, C.V., 2003, Stochastic Surface Modeling in Mud Rich, Fine-grained Turbidite Lobes: American Association of Petroleum Geologists Annual Meeting, Salt Lake, USA. May 11-14.
Presentation 4. Pyrcz, M.J., and Deutsch, C.V., 2004, Stochastic Modeling of Inclined Heterolithic Stratification with the Bank Retreat Model: 2004 CSPG/CWLS/CHOA Joint Convention (ICE2004), Calgary, Canada.
Presentation 5. Pyrcz, M.J., and C. V. Deutsch, 2004, Conditioning Complex Curvilinear Lithofacies Models: Geostatistical Congress, Banff, Canada, Sept 26th - Oct 1st.
Presentation 6. Pyrcz, M.J., Leuangthong, O., Deutsch, C.V., 2005, Hierarchical Trend Modeling for Improved Reservoir Characterization: International Association of Mathematical Geology, Toronto, Canada.
Presentation 7. Pyrcz, M.J and Strebelle, S., 2006, Event-based Geostatistical Modeling of Deepwater Systems: Reservoir Characterization: Integrating Technology and Business Practices: Gulf Coast Section-SEPM Twenty-Sixth Annual Research Conference, Houston, USA.
Presentation 8. Pyrcz, M.J., Clark, J, Drinkwater, N., Sullivan, M., Fildani, A and McHargue, T., 2006, Event-based Models as a Quantitative Laboratory for Testing Quantitative Rules Associated with Deepwater Distributary Lobes: Reservoir Characterization: Integrating Technology and Business Practices: Gulf Coast Section-SEPM Twenty-Sixth Annual Research Conference, Houston, USA.
Presentation 9. Springhorn, S., Sullivan, M.D., Pyrcz, M.J., Alward, R., Skartvedt-Forte, M., Demucha, B., Spaeth, S. and Lawlor, N., 2007, Hierarchical Analysis of Channelized Deepwater Deposits: American Association of Petroleum Geologists Annual Meeting, Long Beach, USA.
Presentation 10. Khan, D., Strebelle, S., Hanggoro, D., Willis, B., and Pyrcz, M.J., 2008, Non-stationary Multiple Point simulation to model complex deltaic deposits for flow simulation, in Ortiz, J., and Emery, X. (eds.), Geostatistics Santiago 2008, Springer, Netherlands.
Presentation 11. Zhang, K., Pyrcz, M.J., and Deutsch, C.V., 2008, "Advanced Stochastic Surface-based Modeling”, in Ortiz, J. and Emery, X. (eds.), Geostatistics Santiago 2008, Springer, Netherlands.
Presentation 12. Sullivan, M.D., Pyrcz, M.J., Posamentier, H.W., McHargue, T.R., Fildani, A., Drinkwater, N.J., and Clark, J., 2008, 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.
Presentation 13. Pyrcz, M.J., Sullivan, M.D., McHargue, T.R., Fildani, A., Drinkwater, N.J., Clark, J., and Posamentier, H.W., 2008 , A showcase of event-based models., AAPG International Annual Convention, Cape Town, South Africa.
Presentation 14. Fildani, A., Pyrcz, M.J., Romans, B., McHargue, M., Sullivan, M., Clark, J., Drinkwater, D., Posamentier, H., and Hilley, G. 2008, Event-based modeling – a new frontier to explore deepwater systems, Geologic Society of America, Houston, Texas, USA.
Presentation 15. McHargue, T., Sullivan, M., Clark, J., Fildani, A., Pyrcz, M.J., Levy, M., Posamentier, H., Drinkwater, N., and Romans, B., 2008, Event-based forward modeling – visualizing and predicting turbidite channel architectures, Geologic Society of America, Houston, Texas, USA.
Presentation 16. Pyrcz, M.J., Sullivan, M.D., McHargue, T.R., Fildani, A., Drinkwater, N.J., Clark, J., and Posamentier, H.W., 2008, Numerical Modeling of Channel Stacking from Outcrop., SEPM research meeting, Kilkee Ireland.
Presentation 17. McHargue, T.R., Clark, J., Sullivan, M., Fildani, A., Drinkwater, N., Pyrcz, M.J., and Posamentier, H.W., 2008, Thicknesses of turbidite channel elements and their abandonment facies constrain stacking pattern and net sand, SEPM research meeting, Kilkee Ireland.
Presentation 18. McHargue, T., Clark, J., Sullivan, M., Fildani, A., Pyrcz, M.J., Romans, B., Levy, M., Posamentier, H., Covault, J., 2009, Assumed allocyclicity yields predictive model of turbidite channel architectures, SEPM Research Conference – Magallanes Basin, Chile. February 22-28 (best paper award).
Presentation 19. Clark, J., D.R. Pyles, R. Bouroullec, R. Amerman, M. Hoffman, J. Moody, A. Moss, P. Setiawan, H. Silalahi, T. Heard, C. Guzofski, A. Fildani, N. Drinkwater, M. Pyrcz, 2010, Structural controls on deepwater architecture and facies in the Eocene Ainsa Basin, Spanish Pyrenees: American Association of Petroleum Geologists Annual Meeting, New Orleans, USA.
Presentation 20. McHargue, T., Pyrcz, M., Sullivan, M., Clark, J., Levy, M., Fildani, A., Posamentier, H., Romans, B., and Covault, J.A., 2010, Predicting reservoir architecture of turbidite channel complexes; a general model adaptable to specific situation: The Geologic Society of America, v. 42, p. 51, https://gsa.confex.com/gsa/2010CD/finalprogram/abstract_173634.htm.
Presentation 21. Romans, B.W., Fildani, A., Covault, J.A., Sullivan, M., Clark, J., Power, B., Pyrcz, M.J., Bracken, B., Willis, B., and Payenberg, T., 2011, Remembering the ‘source’ when applying source-to-sink concepts in clastic stratigraphy: American Association of Petroleum Geologists Annual Meeting, Houston, USA, April 10-13.
Presentation 22. Payenberg, T., Willis, B., Bracken, B., Posamentier, H.W., Pyrcz, M.J., Pusca, V., and Sullivan, M.D., 2011, Remembering the ‘source’ when applying source-to-sink concepts in clastic stratigraphy: American Association of Petroleum Geologists Annual Meeting, Houston, USA, April 10-13.
Presentation 23. 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., 2011, 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.
Presentation 24. Pyrcz, M.J., Sullivan, M.D., McHargue, T.R., Fildani, A., Drinkwater, N.J., Clark, J., and Posamentier, H.W., 2011, 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.
Presentation 25. Sullivan, M.D., Pyrcz, M.J., and Covault, J.A., 2012, Hierarchical modeling of deepwater channelized reservoirs; the difference that makes a difference: The Geological Society of America Annual Meeting, Charlotte, USA, November 4-7.
Presentation 26. Sun, T., Covault, J.A., Pyrcz, M.J., and Sullivan, M., 2012, 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, December 3-7 (invited abs.).
Presentation 27. Pyrcz, M.J., McHargue, T., Clark, J. Sullivan, M.D., Sebastien, S., 2012, Event-Based Geostatistical Modeling Applications, American Association of Petroleum Geologists Annual Meeting, Long Beach, USA.
Presentation 28. Covault, J.A., Sun, T., Carvajal, C., Milliken, K., Fildani, A., Pyrcz, M.J., and Zarra, L., 2013, Source-to-sink scaling relationships, sediment budgets, and landscape evolution for Paleogene Gulf of Mexico deep-water stratigraphic predictions: Gulf Association of the Geological Societies Annual Convention, New Orleans, USA, October 6-8.
Presentation 29. Sullivan, M.D., Pyrcz, M.J., and Covault, J.A., 2013, Hierarchical modeling of deepwater channelized reservoirs; the difference that makes a difference: American Association of Petroleum Geologists Pacific Section Annual Convention, Monterey, USA, April 19-25.
Presentation 30. Sprunt, E., Howes, S. and Pyrcz, M.J., 2014, Attraction and Retention of Employees, Results of 2013 Society of Petroleum Engineers Talent Council Survey (SPE paper number 16811).
Presentation 31. Carvajal, C., Sullivan, M.D., Pyrcz, M.J., and Covault, J.A., 2014, Hierarchical modeling of deepwater channelized reservoirs; the difference that makes a difference: American Association of Petroleum Geologists Annual Meeting, Houston, USA, April 6-9.
Presentation 32. Sun, T., Covault, J., Harris, A., Pyrcz, M.J., and Perlmutter, M., 2014, Upstream and downstream controls on sediment routing and deposition in a complete “source-to-sink” system from a simple computer model: American Association of Petroleum Geologists Annual Meeting, Houston, USA, April 6-9.
Presentation 33. Covault, J.A., Carvajal, C., Milliken, K., Pyrcz, M.J., Sun, T., and Zarra, L., 2014, Sediment routing-mass balance workflows for Paleogene Gulf of Mexico deep-water stratigraphic predictions: American Association of Petroleum Geologists Geosciences Technology Workshop, Trinidad and Tobago, March 9-11 (invited pres.).
Presentation 34. Sech, R., Willis, B., Pyrcz, M.J. and Bracken, B., 2014, Representing Fluvial Channel Belt Heterogeneity in Reservoir Models, American Association of Petroleum Geologists Annual Meeting, Houston, USA, April 6-9.
Presentation 35. Payenberg, T., Willis, B., Pusca, V., Sixsmith, P., Bracken, B., Posamentier, H., Pyrcz, M,J, Sech, R., Connell, S., and Sullivan, M.D., 2014, Channel Belt Rugosity in Reservoir Characterization, American Association of Petroleum Geologists Annual Meeting, Houston, USA, April 6-9.
Presentation 36. Covault, J.A., Carvajal, C., Milliken, K., Pyrcz, M.J., Sun, T., and Zarra, L., 2014, Source-to-sink sediment budgets for Paleogene Gulf of Mexico deep-water stratigraphic predictions: Gulf Coast Section Society for Sedimentary Research Perkins Research Conference, Houston, USA, January 26-28.
Presentation 37. Pyrcz, M.J., Sech, R., Covault, J., and Sun, T., 2014, Process-mimicking modeling consideration: Gussow Geoscience Conference, Banff, Canada, 22-24 September 2014 (invited abs.).
Presentation 38. Willis, B.J., Sech, R., Sun, T., and Pyrcz, M.J., 2014, Predicting facies patterns within fluvial channel belts: American Geophysical Union Annual Conference, San Francisco, USA, Dec. 15th – 19th.
Presentation 39. Block, A., Perlmutter, M., Thorne, J., and Pyrcz, M.J., 2014, Toward an Objective Method to Distinguishing Delta Depositional Environments, American Geophysical Union Annual Conference, San Francisco, USA, Dec. 15th – 19th.
Presentation 40. Laugier, F., Covault, J., Pyrcz, M.J., Sech, R., Sun, T., and Sullivan M.D., 2015, Reservoir Modeling of Deepwater Depositional Lobes, American Association of Petroleum Geologists Annual Meeting, Denver, USA.
Poster Presentations
Poster 1.Pyrcz, M.J, and Strebelle, S., 2006, “Event-based Geostatistical Modeling of Deepwater Systems”, Reservoir Characterization: Integrating Technology and Business Practices: Gulf Coast Section-SEPM Twenty-Sixth Annual Research Conference, Houston, USA, December 2006.
Poster 2.Pyrcz, M.J., Clark, J, Drinkwater, N., Sullivan, M., Fildani, A and McHargue, T., 2006, "Event-based Models as a Quantitative Laboratory for Testing Quantitative Rules Associated with Deepwater Distributary Lobes", Reservoir Characterization: Integrating Technology and Business Practices: Gulf Coast Section-SEPM Twenty-Sixth Annual Research Conference, Houston, USA, December, 2006.
Poster 3. Kaplan, R., Pyrcz, M., Covault, J., Sech, R., and Sullivan M.D., 2015, Flow Significant Architectures for Deepwater Reservoirs, American Association of Petroleum Geologists Annual Meeting, Denver, USA.
Conference Session Chair
Session Chair 1. Pyrcz, M., Gladczenko, T., and Caers, J., 2007, “3D Modeling: Geostatistics, Upscaling and Flow Simulation”, American Association of Petroleum Geologists Annual Convention 2007 – Long Beach, USA.
Session Chair 2. Pyrcz, M., and Tomasso, M., 2008, “Advanced Quantitative Stratigraphy and Model Construction: Outcrop, Experiment and Process to Subsurface”, American Association of Petroleum Geologists Annual Convention 2008 – San Antonia, USA.
Session Chair 3. Pyrcz, M., and Pirmez, C., 2008, “Experimental and Numerical Models of Deep-Water Processes”, American Association of Petroleum Geologists International Annual Convention 2008, Cape Town, South Africa.
Session Chair 4. Hajek, L., and Pyrcz, M.J., 2013, Outcrop, Subsurface, and Simulation: Perspectives on Quantitative Modeling of Sedimentary Systems, American Association of Petroleum Geologists International Annual Convention, Pitsburg, USA (oral and poster).
Session Chair 5. Dixon, B., Pyrcz, M.J., Jobe, Z.R., 2014, Sedimentology, Architecture and Process Controls of Deepwater Siliciclastic Systems: American Association of Petroleum Geologists International Annual Convention, Houston, USA.
Session Chair 6. Pyrcz, M.J., Stright, L. and Hajek, L., 2014, Outcrops, Stratigraphy, and Geomodeling: Canadian Society for Petroleum Geologists Gussow Conference, Banff, Canada.
Session Chair 7. Pyrcz, M.J., Hampson, G., Kim, W., 2015, Quantitative Characterization and Modeling of Sedimentary Systems: American Association of Petroleum Geologists International Annual Convention, Denver, USA (oral and poster).
Session Chair 8. Stright, L., Pyrcz, M.J., 2016, Reservoir Modeling: American Association of Petroleum Geologists International Annual Convention, Calgary, Canada (oral and poster).
Session Chair 9. Casey, M., Pyrcz, M.J., Srivastava, M., Fall 2018, Gussow Conference: Canadian Society of Petroleum Geologists, Banff, Canada (chair, organizing committee, and presenter).
Authorship and Taught Courses
Sullivan, M., Mooney, T., McHargue, T., Clark, J., Drinkwater, N., Fildani, A., Stelting, C., and Pyrcz, M.J., 2006, Hierarchical Analysis of Deep Water Reservoir Prediction and Characterization, Chevron Energy Technology Company, Houston, TX.
Meddaugh, W. and Pyrcz, M.J., 2006, 2008, 2011, 2013 Geostatistics Two Day Theory, Chevron Energy Technology Company, Houston, TX (various versions of same course)
Deutsch, C.V., Leuangthong, O., Nguyen, H, Norrena, K, Ortiz, J, Oz, B, Pyrcz, M.J., and Zanon, S. (2001) Principles of Monte Carlo Simulation, A One-Day Short Course, University of Alberta.
Pyrcz, M.J., Fall, 2017, PGE 383 Stochastic methods for reservoir modeling, The University of Texas at Austin, TX, USA, Graduate class of 5 students.
Pyrcz, M.J., Spring, 2018, PGE 337 Introduction to Data Analytics and Geostatistics, The University of Texas at Austin, TX, USA, Undergraduate class of 35 students.
Pyrcz, M.J., Summer, 2018, Geostatistics for Geoscientists, Rocky Mountain Association of Geologists, Denver, CO, USA, a 2-day short course to 30 professionals.
Pyrcz, M.J., Summer, 2018, Geostatistics for Geoscientists, Anadarko Corporation, Midland, TX, USA, a 2-day short course to 30 professionals.
Pyrcz, M.J., Santos, J., Fall, 2018, Reservoir Characterization and Development, Baker Hughes a General Electric company, Oil and Gas University, Florence, Italy, 3-day short course to 30 professionals.
Pyrcz, M.J., Fall, 2018, PGE 337 Introduction to Geostatistics, University of Texas at Austin, TX, USA, Undergraduate class of 55 students.
Pyrcz, M.J., Jo, H., Spring, 2019, Reservoir Characterization and Development, Baker Hughes a General Electric company, Oil and Gas University, Florence, Italy, 3-day short course to 30 professionals.
Pyrcz, M.J., Spring, 2019, Data analytics, geostatistics and machine learning workshop, Equinor, Austin, TX, USA, a 1-day course with about 20 geoscience, data science and engineering professionals.
Pyrcz, M.J., Spring, 2019, Data analytics, geostatistics and machine learning workshop, Anadarko, The Woodlands, TX, USA, 2-day course with about 20 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Spring, 2019, Data analytics, geostatistics and machine learning workshop, Hess, Houston, TX, USA, a 5-day course with about 20 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Spring, 2019, PGE 383 Stochastic methods for reservoir modeling, The University of Texas at Austin, TX, USA, Graduate class.
Pyrcz, M.J., Spring, 2019, Data analytics workshop, Andarko, Houston, TX, USA, 1-half day course with about 20 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Spring, 2019, Data analytics workshop, Chevron ETC, Houston, TX, USA, 1-half day course with about 20 geoscience, data science and engineering professionals.
Pyrcz, M.J., and Didi, O., Spring, 2019, Geostatistics and machine learning for geoscientists, American Association of Petroleum Geologists International Annual Convention, TX, USA, a1 day short course professional development.
Pyrcz, M.J., Covault, J., and Sech, R., Spring, 2019, Reservoir characterization to modeling, American Association of Petroleum Geologists International Annual Convention, TX, USA, a 1 day short course professional development.
Pyrcz, M.J., Foster, J.T., Summer, 2019, Data science boot camp, Houston, TX, USA, a 5-day class with about 10 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Summer, 2019, Introduction to subsurface data analytics and geostatistics, IHSMarkit, Houston, TX, USA, 2-day class with about 20 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Foster, J.T., Summer, 2019, Data science boot camp, Houston, TX, USA, a 5-day class with about 15 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Summer, 2019, Introduction to subsurface data analytics and machine learning, IHSMarkit, Houston, TX, USA, 2-day class with about 20 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Fall, 2020, Subsurface machine learning, Society for Petroleum Engineers, Rice University, TX, USA, 1-day class with about 150 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Fall, 2019, Introduction to subsurface machine learning, BP Advanced Modeling Team, The University of Texas at Austin, TX, USA, a 3-day class with about 10 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Fall, 2019, Introduction to subsurface machine learning, Aramco Americas, Houston, TX, USA, 1-half day class with about 10 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Fall, 2019, PGE 383 Subsurface machine learning, The University of Texas at Austin, TX, USA, Undergraduate/Graduate class with about 50 students.
Pyrcz, M.J., Fall, 2019, Introduction to subsurface machine learning, Noble Energy, Houston, TX, USA, 2-day class with about 20 geoscience and engineering professionals.
Pyrcz, M.J., Winter, 2020, Subsurface machine learning, Society for Petroleum Engineers, Houston, TX, USA, a 1-day class with about 50 geoscience, data science, and engineering professionals.
Pyrcz, M.J., Summer, 2020, Introduction to subsurface machine learning, Noble Energy, Houston, TX, USA, 4-half day class with about 20 geoscience and engineering professionals.
Pyrcz, M.J., Fall, 2020, Energy Data Analytics, The University of Texas at Austin, TX, USA, Undergraduate/Graduate class with about 50 students.
Pyrcz, M.J., Fall, 2020, The University of Texas at Austin, TX, USA, Undergraduate/Graduate class with about 50 students.
Pyrcz, M.J., Summer, 2020, Introduction to energy machine learning, Chevron MCBU, Houston, TX, USA, 4-half day class with about 20 geoscience and engineering professionals.
Pyrcz, M.J., Summer, 2020, Subsurface machine learning, Chevron MCBU, Houston, TX, USA, 2-half day class with about 20 geoscience and engineering professionals.
Pyrcz, M.J., Summer, 2020, Energy machine learning for executives, Noble Energy, Houston, TX, USA, Class with >120 HR and finance professionals.
Pyrcz, M.J., Fall, 2020, PGE 383 Subsurface machine learning, The University of Texas at Austin, TX, USA, Undergraduate/Graduate class with about 50 students.
