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Speaker: Michael Pyrcz, Associate Professor, Cockrell School of Engineering and Jackson School of Geosciences

Host: Gail Christeson

Title: Emerging Opportunities with Data Analytics and Machine Learning for Subsurface Characterization and Modeling

Abstract: 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.

Yet, 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. The promise is enhanced understanding, utilization and environmental stewardship of the subsurface resources of our amazing planet.

About Michael: Michael 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 Michael investigates and teaches 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, associate editor for Computers and Geosciences and a board member for Mathematical Geosciences. Michael has written over 60 peer-reviewed publications, a Python geostatistics package, and a textbook on spatial data analytics. Find out more at www.michaelpyrcz.com.