Michael J. Pyrcz

Michael J. Pyrcz

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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, with an assignment in the Bureau of Economic Geology, Jackson School of Geosciences. At The University of Texas at Austin, Michael teaches and supervises research on subsurface data analytics, geostatistics and machine learning. In addition, Michael accepted the role of Principal Investigator for the College of Natural Sciences, The University of Texas at Austin, freshman research initiative in energy data analytics. Before joining The University of Texas at Austin, Michael conducted and lead research on reservoir data analytics and modeling for 13 years with Chevron’s Energy Technology Company. He became an enterprise-wide subject matter expert, advising and mentoring on workflow development and best practice. Michael has written over 45 peer-reviewed publications, an open source Python data analytics package and a textbook with Oxford University Press. He is currently an associate editor with Computers and Geosciences, editorial board member for Mathematical Geosciences and the Program Chair for the Petroleum Data Driven Analytics Technical Section (PD²A) for the Society of Petroleum Engineers International. For more information go to www.michaelpyrcz.com, see his course lectures at http://y2u.be/j4dMnAPZu70, along with the demonstration numerical workflows at https://github.com/GeostatsGuy and contributions to outreach through social media at https://twitter.com/GeostatsGuy.


Video Presentation


  • 55547 Every energy company that I visit is interested in growing internal capabilities to add value with data analytics and machine learning. Energy has a long history of working with large, complicated geoscience and engineering datasets and there is a growing toolbox of old and new emerging data-driven methods available that may offer improved efficiency and potentially new insights from vast and complicated subsurface datasets. This talk is an opportunity to link subsurface data analytics and machine learning to fundamental concepts from probability, statistics, geoscience and engineering and to provide an enthusiastic, but at times critical perspective on what we may expect in the data-driven science revolution. Data Analytics and Machine Learning for Energy Geoscience and Engineering https://www.aapg.org/career/training/in-person/distinguished-lecturer/abstract/articleid/55547/data-analytics-and-machine-learning-for-energy-geoscience-and-engineering
    Data Analytics and Machine Learning for Energy Geoscience and Engineering