Associate Professor, Jackson School of Geosciences
Work: +1 512 471 3252
Recently, Michael made the move to the University of Texas at Austin to accept the role of Associate Professor in the Department of Petroleum and Geosystems Engineering, and the Department of Geological Sciences and Bureau of Economic Geology, Jackson School of Geosciences. At the University of Texas, Michael teaches and conducts research on subsurface data analytics, geostatistics and machine learning. In addition, Michael accepted the role of Principal Investigator in the College of Natural Sciences, The University of Texas at Austin, of the freshman research initiative in energy data analytics and teaches widely in the energy industry. Before joining The University of Texas at Austin, Michael conducted and led research on reservoir data analytics and modeling for 13 years with Chevrons technology company. He was an enterprise-wide subject matter expert, advising and mentoring on workflow development and best practice. Michael has written over 50 peer-reviewed publications, a Python package and a textbook on spatial data analytics with Oxford University Press. He is currently an associate editor with Computers and Geosciences, and on the editorial board member for Mathematical Geosciences. For more information see www.michaelpyrcz.com, and his course lectures at https://www.youtube.com/GeostatsGuyLectures, along with the demonstration numerical workflows at https://github.com/GeostatsGuy and contributions to outreach through social media at https://twitter.com/GeostatsGuy.
Areas of Expertise
geological modeling, geostatistics, spatial statistics, data analytics, machine learning
|2021||Fall||GEO 391||Subsurface Machine Learning|
My GitHub repositories with many well-documented Python, R and Excel subsurface data analytics, geostatistics and machine learning demonstration workflows and tutorials, other course content and my spatial data analytics Python package, GeostatsPy.
All of my lectures on subsurface data analytics, geostatistics and machine learning.