SPE Online Education
Data Science Project from End to End: A Sucker-Rod Pump Example
The concepts behind the digital oilfield will be discussed and how they fit together into creating an ecosystem to deliver value. Concrete elements will be presented that illustrate the entire value chain and technology stack necessary to deliver on the promise of the digital oilfield. We illustrate this vision with a concrete example in which live data from an entire oilfield full of sucker-rod pumps is fed into a central facility, analyzed for predictive maintenance, and used to perform proactive maintenance on the pumps. Apart from various physical technologies, we will go into some detail on the analytics used to obtain the result. While the digital oilfield is a large complex made up of many moving parts, the central message of this webinar is that there are two essential factors that govern the success of the entire enterprise: Analytics and change management. Both of these will be discussed and the value of the final outcome will be quantified. Finally, we will present some lessons learned that can be applied to any digital oilfield.
This webinar is categorized under the Management and Information discipline.
All content contained within this webinar is copyrighted by Dr. Patrick Bangert and its use and/or reproduction outside the portal requires express permission from Dr. Patrick Bangert.
Dr. Patrick Bangert
Founder/CEO, Algorithmica Technologies
Dr. Bangert is the founder and CEO of Algorithmica Technologies, a machine learning company specializing in oil and gas applications. He was educated as a theoretical physicist and took his PhD in applied mathematics from University College London. After a few research positions at Los Alamos National Laboratory and NASA’s Jet Propulsion Laboratory, Patrick became Assistant Professor of applied mathematics at Jacobs University Bremen in Germany. In 2005, he founded Algorithmica in order to bring the machine learning methods from the ivory tower into real-life practice. He now analyzes empirical data from upstream and downstream plants the world over to provide production optimization and predictive maintenance using machine learning methods. Patrick lives in Cupertino, California.
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