Data Science and Machine Learning SIG: Hybrid Machine Learning and Physics-based Modeling for Drilling - Aug 16th
Complete Title: Hybrid Machine Learning and Physics-based Modeling for Drilling (GITC22 Webinar Series) Sponsored By: Enthought
Online only event - You must pre-register to receive access information.
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Speaker: Dr. John Hedengren, Brigham Young University
The oil and gas industry faces cyclical market conditions that motivate the use of simulation and optimization to reduce costs and decrease exploration and production variability. Physics-based and empirical models have advantages and opportunities for predictive monitoring and control. Current progress, challenges, and opportunities to control critical drilling conditions such as downhole pressure in Managed Pressure Drilling (MPD) are explored. In automated rig systems, there is additional potential to unlock the predictive capabilities of physics-based models to "see" into the near future to optimize and coordinate control actions. A convergence of several key technologies creates an opportunity to use sophisticated mathematical models in drilling. A significant challenge is the size of the physics-based models that have too many adjustable parameters or are too slow in simulation to extract actionable information. This presentation shows how fit-for-purpose models can be used directly in automation and optimization solutions. These fit-for-purpose models have unlocked new ways of thinking. Hybrid modeling uses the strengths of both physics-based and data-informed modeling approaches. A hybrid approach uses a priori knowledge in the form of a nonlinear physics-based model with empirical model elements. The challenges and opportunities for combining physics-based and data-driven elements are discussed. The methods are demonstrated with drilling automation.
Speaker Biography: Dr. John Hedengren, Brigham Young University
Dr. Hedengren received a PhD degree in Chemical Engineering from the University of Texas at Austin and is a Professor at Brigham Young University. He previously worked with ExxonMobil on Advanced Process Control. His primary research focuses on accelerating machine learning and automation technology across industries. Other research interests include fiber optic monitoring, Intelli-fields, reservoir optimization, drilling automation, nuclear hybrid energy systems, and unmanned aerial systems. His professional service includes IEEE CSS Chair of Control Education, Communications Chair for the American Automatic Control Council. He served as a Society of Petroleum Engineers (SPE) Distinguished Lecturer for 2018-2019, visiting 22 local sections to deliver a presentation on "Drilling Automation and Downhole Monitoring with Physics-based Models".
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