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Updates

**THIS STREAM BEGINS IN THE FALL SEMESTER AND IS NOT LISTED IN THE STREAM SORT FORM!**

 

Research Educator: Jesse Pisel

Principal Investigator: Michael Pyrcz, Associate Professor, Hildebrand Department of Petroleum and Geosystems Engineering

Credit Options: Spring & Fall

Can we use modern big data analytics and machine learning to improve characterization and modeling for improved utilization of subsurface resources?

Recent numerical developments and improved computational resources have led to a rapid expansion of big data analytics and machine learning implementations. These technologies are disrupting many industries. Oil and gas has a long history with big data from seismic surveys, production monitoring, remote sensing and well-based data. Additionally, there are various physics-based engineering and stochastic statistical workflows. There is an opportunity to adapt and tool big data analytics and machine learning to optimize subsurface development to maximize the value of national energy resources and to minimize environmental impacts.  

ConocoPhillips

Thank you to ConocoPhillips for sponsoring this stream! 

 

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Computer Science, Math