Michael Pyrcz
researcher, ORCID id # 0000-0002-5983-219X
Facts
7 properties- Instance of
- human
- Occupation
- researcher
- Educated at
- University of Alberta (2000–2004), University of Alberta (1995–2000)[2][3]
- Imported from
- wholeness_audit_2026-05-02
- Social media followers
- 10,905[6]
🏷️ Also known as
en Michael Pyrcz
fa پیرکز، مایکل
🔖 Identifiers · 6
- ORCID iD
- 0000-0002-5983-219X
- OpenAlex ID
- A5061964004as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- DBLP author ID
- 22/10389
- NLAI ID
- 1640223
- Scopus author ID
- 6507450938
- X (Twitter) username
- GeostatsGuy (2017–present)
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 21
3D Dataset of binary images: A collection of synthetically created digital rock images of complex media,
A comparison of clustering algorithms applied to fluid characterization using NMR T1-T2 maps of shale,
Anisotropic resistivity estimation and uncertainty quantification from borehole triaxial electromagnetic induction measurements: Gradient-based inversion and physics-informed neural network,
A Spatial Correlation-Based Anomaly Detection Method for Subsurface Modeling,
Automatic Semivariogram Modeling by Convolutional Neural Network,
Conditioning 3D object-based models to dense well data,
Conditioning well data to rule-based lobe model by machine learning with a generative adversarial network,
Demonstration and Mitigation of Spatial Sampling Bias for Machine-Learning Predictions
+13 more
← Authored by 21
3D Dataset of binary images: A collection of synthetically created digital rock images of complex media,
A comparison of clustering algorithms applied to fluid characterization using NMR T1-T2 maps of shale,
Anisotropic resistivity estimation and uncertainty quantification from borehole triaxial electromagnetic induction measurements: Gradient-based inversion and physics-informed neural network,
A Spatial Correlation-Based Anomaly Detection Method for Subsurface Modeling,
Automatic Semivariogram Modeling by Convolutional Neural Network,
Conditioning 3D object-based models to dense well data,
Conditioning well data to rule-based lobe model by machine learning with a generative adversarial network,
Demonstration and Mitigation of Spatial Sampling Bias for Machine-Learning Predictions
+13 more
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Summary
Michael Pyrcz worked as a researcher[1].
Body
Education
Educated at University of Alberta[2], an autonomous university[7], in Canada[8], founded in 1906[9], headquartered in Edmonton[10].
FAQs
What did Michael Pyrcz do for work?
Michael Pyrcz worked as researcher[1].
Where did Michael Pyrcz go to school?
Michael Pyrcz was educated at University of Alberta[2] and University of Alberta[3].