Song wins prestigious award for autonomous shale oil drilling research
Dr. Xingyong Song, assistant professor in the Department of Engineering Technology and Industrial Distribution with a joint appointment in the Department of Mechanical Engineering, was recently presented the prestigious Doctoral New Investigator Award from the American Chemical Society (ACS) for his research on autonomous shale oil drilling.
Each year the ACS holds a highly selective competition through peer review to award funding to top young researchers with a track record of producing innovative fundamental research.
Established in 1954, the goal of the competition has been to support "advanced scientific education, and the careers of scientists, to aid in significantly increasing the world's energy options."
The award provides funding of $110,000 for two years and will support Song on some fundamental studies in his proposed research, "Dynamics Modeling and Process Control of Drilling System for Unconventional Oil and Gas Production."
Shale oil is a "tight oil" trapped in hard rocks that, in the past, has been very difficult to extract. Since the United States has large reserves of shale oil, it has become a priority to engineers to figure out how to ease the process of extraction.
In addition to the possible transition from the U.S. being an oil and gas importer to a producer, shale oil and gas can potentially be cleaner than its crude counterpart.
However, extraction is highly difficult due to the depth of shale oil and the requirement of new types of drills that must operate in a 3-D space.
"There are three major challenges to shale oil extraction," says Song. "Cost, environmental impact and safety."
Song's research focuses on drilling automation - transitioning from a human drilling operator to a drill that can operate itself, similar to a self-driving car or spacecraft.
"Machine learning can optimize the drilling path using analysis and data in ways that a human operator cannot," says Song. "You can potentially remove human error and effectively drill in a three-dimensional path."
Song is focused on building the algorithm that autonomous drills would use to make intelligent decisions on their own, resulting in cost-effective, environmentally friendly and safe shale oil drilling. Autonomous drilling could also save companies crucial time in locating and extracting the oil, allowing them to utilize their drillers in other areas that require human control.