Recently, a research paper written and presented by grad students, Cristina Ruse and Jamal Ahmadov from the University of Louisiana at Lafayette has won national accolades, including second place in the Society of Petrophysicists and Well Log Analysts’ International Student Paper Competition held in June.
Both grad students, Ruse and Ahmadov, are pursuing graduate degrees in petroleum engineering and working as research assistants in the Tuscaloosa Marine Shale Laboratory, a multidisciplinary consortium of geologists, petroleum engineers, geophysicists and economic development experts from UL Lafayette and four other institutions. In addition to UL Lafayette, the Tuscaloosa Marine Shale Laboratory includes researchers from New Mexico’s Los Alamos National Lab, the Missouri University of Science and Technology, the University of Oklahoma, and the University of Southern Mississippi.
Researchers involved in the Tuscaloosa Marine Shale Laboratory are studying how to best recover the substantial bounty of oil and gas that is offered by its namesake. The entire Tuscaloosa Marine Shale touches 28 total parishes in south and central Louisiana and several southwesten Mississippi counties. Portions of the formation are 15,000 feet (2.9 miles) beneath the surface with the entire shale containing an estimated 7 billion barrels of light, sweet crude oil. Though, due to the sheer size, depth, and frequently unstable geology, this play– the name the energy industry gives an area where oil and gas exist – is among the most expensive places in the country to drill.
Enter: grad students, Ruse and Ahmadov, who in their paper suggest using machine learning to predict the formation’s geomechanical properties. Ruse said knowing these properties is essential to hydraulic fracturing, one of the methods by which the oil and gas can be extracted. Machine-learning algorithms use statistics to find patterns in large amounts of data. They provide an alternative to other analytical tools, Ruse explained. “Many of the tedious calculations associated with these analytical methods can be eliminated by using the machine-learning model,” that she and Ahmadov propose in their paper.
In Early 2018 the lab’s creation was funded by a $9.7 million grant from the U.S. Department of Energy and several energy companies. The consortium is headed by Dr. Mehdi Mokhtari, an associate professor in UL Lafayette’s Department of Petroleum Engineering. The grant funding the lab’s creation is part of an initiative by the Energy Department’s Office of Fossil Energy to examine unconventional oil and gas plays.
The nonprofit Society of Petrophysicists and Well Log Analysts was founded in 1959. Its annual symposium enables students to present research findings and offers scholarship opportunities.
ULL grad student, Ruse received SPWLA scholarships in 2018, 2019 and again this year with Ahmadov being a two-time recipient, in both 2019 and 2020. Also among this year’s scholarship recipients from UL Lafayette were Asiman Saidzade, a petroleum engineering graduate student who’s also a research assistant in the Tuscaloosa lab, and Shelby J. Stewart, an MBA student and the lab’s research coordinator.
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