SPE Online Education
Emerging Opportunities with Data Analytics and Machine Learning in Subsurface Modeling
Includes a Live Event on 07/13/2021 at 1:00 PM (EDT)
Every subsurface-oriented company that I visit is interested in growing new internal capabilities to add value with data analytics and machine learning. The geosciences have a long history of working with large, complicated datasets. In fact, we have been working with ‘big data’ for decades! We have an opportunity to build upon our strong foundation of geoscience and engineering interpretation and physics, along with spatial mapping, probability and geostatistics competencies, to augment our workflows with new emerging data analytics and machine learning methods.
Due to the unique challenges of the subsurface, for example data sparsity and uncertainty, many current data analytics and machine learning methods are not ready for subsurface application off-the-shelf. Given these gaps, a suite of new emerging, enabling technologies are required to fully realize the value of data analytics and machine learning to enhanced geoscience and engineering capabilities and impact. This supports optimum subsurface development and environmental stewardship decision making.
This webinar is categorized under the Health, Safety, Environment, and Sustainability technical discipline
All content contained within this webinar is copyrighted by Dr. Michael Pyrcz and its use and/or reproduction outside the portal requires express permission from Dr. Michael Pyrcz.
Dr. Michael Pyrcz
Associate Professor, The University of Texas at Austin
Dr. Michael Pyrcz is an associate professor in the Department of Petroleum and Geosystems Engineering, and the Jackson School of Geosciences, The University of Texas at Austin, where he researches and teaches on the topics of subsurface, spatial data analytics, geostatistics and machine learning. Michael is also the principal investigator of the freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin, an associate editor for Computers and Geosciences and a board member for Mathematical Geosciences, the International Association for Mathematical Geosciences, and the program chair for the Petroleum Data Driven Analytics Technical Section of the Society of Petroleum Engineers. Michael has written over 60 peer-reviewed publications, a Python package for spatial, subsurface data analytics, and coauthored a textbook on spatial data analytics, ‘Geostatistical Reservoir Modeling’. All of Michael’s university lectures are available on his YouTube channel, www.youtube.com/GeostatsGuyLectures to support his students and working professionals. To find out more about Michael’s work and shared educational resources visit his website at www.michaelpyrcz.com.
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