Abstract: The subsurface resource industry has a long history of working with large, complicated geoscience and engineering datasets. Our community has been working with ‘big data’ for decades, driving the development and establishment of the geostatistical toolbox. As a result, we are uniquely prepared for the data science revolution, as we are further down the road of data-driven workflow development and have insights and experiences to share with the other scientific and engineering domains. The foundation of geostatistics, domain expertise, spatial continuity, scale and uncertainty remain essential, even with advanced deep learning architectures; therefore, many new data analytics and machine learning technologies are not ready off-the-shelf for subsurface applications. I elucidate opportunities to integrate geostatistics into data analytics and machine learning for success in the subsurface.
Bio: Michael Pyrcz is a professor in the Cockrell School of Engineering, and the Jackson School of Geosciences, at The University of Texas at Austin, where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also the principal investigator of the Energy Analytics 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. Michael has written over 70 peer-reviewed publications, a Python package for spatial data analytics, co-authored a textbook on spatial data analytics, ‘Geostatistical Reservoir Modeling’ and author of two recently released e-books, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy and Applied Machine Learning in Python: a Hands-on Guide with Code.
All of Michael’s university lectures are available on his YouTube channel with links to 100’s of Python interactive dashboards and well-documented workflows on his GitHub account, to support any interested students and working professionals with evergreen content. To find out more about Michael’s work and shared educational resources visit his website, www.michaelpyrcz.com.