Cockrell School of Engineering
The University of Texas at Austin

Recognizing the oil and gas industry was entering a period of data disruption, Dr. Eric van Oort had the vision to launch a state-of-the-art real-time operations center (RTOC) at UT PGE in 2012. Now, the industry is looking at the enormous amount of data flowing in by the second from its rigs to optimize drilling performance.

Van Oort, who built industry’s first large RTOC for Shell in its New Orleans office, knew creating a lab dedicated to big data and automation would pave the way for successfully training the next generation of drilling engineers and industry leaders.

Companies have made a calculated move to incorporate big data across several industries including healthcare and agriculture, positioning big data as an essential tool. According to The Wall Street Journal, a recent survey by NewVantage Partners found 70 percent of firms now say that big data is of critical importance to their firms, a significant increase from 21 percent in 2012. With the low-price oil environment, the gold standard for drilling has become productivity.

“The future involves automation, so we are shaping it,” said van Oort. “We will be monitoring wells, finding ways to minimize inefficiencies and creating a safer work environment.”

drilling for data web

Real-time operations center

In the spring of 2016, van Oort and his team partnered with an energy company to create a research program housed within his Rig Automation Performance Improvement in Drilling (RAPID) consortium. The program’s goal is to provide undergraduate and graduate students experience and skills in analyzing data. In addition, it aims to give the industry partner meaningful recommendations on how to improve the drilling performance of its onshore unconventional wells.

Theresa Baumgartner (PhD PE ‘17) is one of two graduate students who has been working on the program since it launched, addressing industry’s challenges.

“Operators collect huge amounts of data during their drilling operations, but they don’t have the resources and capability to process it,” said Baumgartner. “This is where we come in.”

Baumgartner is serving as a mentor to the six undergraduates who are working on the project. She is training them on understanding huge data files and encourages them to drive the assignment to gain valuable leadership skills.

“The department doesn’t offer a dedicated undergraduate course in automation, so we are trying to close the loop on their learning,” said Baumgartner. “Students will need these skills in industry and I think it will help set them apart from students at other universities.”

In addition to learning from the graduate students, the undergraduate students also gain expertise from the project’s manager and UT PGE research scientist Pradeep Ashok. A PhD graduate from UT Austin’s Department of Mechanical Engineering, Ashok worked for the railway industry to automate shift yards upon his graduation in 2007. He collected data and wrote control algorithms to help automate the process. While Ashok brings experience in automation to his current project, he says the problems in oil and gas are much more complex, but that is what intrigued him.

Will DuBois (BS PE ’17), a petroleum engineering undergraduate student, worked on the project in the spring. He saw Ashok as a mentor, valuing his knowledge and familiarity with the field. DuBois was excited to get involved with the project as he didn’t realize this type of work is being conducted in the department.

“When I think about big data, I think Silicon Valley, so it’s exciting Dr. van Oort and his team are working on it right here at UT,” said DuBois.

A unique aspect of the program, which is a priority for Ashok, is bringing together majors from petroleum and mechanical engineering as well as computer science. Each area of expertise is critical to ensuring the work is successful. Ashok’s focus is teaching the computer engineers what they are looking for in the data, so they can excel in analyzing it and developing recommendations.

A significant challenge the students face with the data is manually combing through hundreds of pages of information that comes in through various formats. Their goal is to develop a software program that will standardize the data. This will save the researchers substantial time enabling them to provide more suggestions for how industry can decrease down rig time.

DuBois enjoyed the problems he tackled during his research internship as he believes big data is here to stay and will help industry succeed.

“This situation reminds me of the movie ‘Moneyball’ – instead of going on their gut feelings, the Oakland Athletics’ management took a deep look at player data and figured out how to do more with less,” said DuBois. “I think industry can do this as well.”