Recognition of three dimensional geological features in seismic data sets is a daunting challenge, but new research from Prairie View A&M University promises enhanced fossil energy exploration and reduced drilling risks.
According to the university, their new Cloud-based software provides geophysicists an easy-to-use computing platform to analyze seismic data, well, reservoir, production data, and more.
“We developed the geological fault detection deep-learning model on top of the cloud, which reaches the close-to-human performance for geological fault interpretation,” said Dr. Lei Huang.
Huang, an associate computer science professor with Prairie View A&M’s Roy G. Perry College of Engineering, leads research at the Cloud Computing Research Lab. He also serves as the associate director in the Center of Excellence in Research and Education for Big Military Data Intelligence sponsored by the Department of Defense.
Huang and his team manage several projects sponsored by the National Science Foundation (NSF) and the Department of Defense in big data analytics, cloud computing, and high-performance computing.
Research for the Cloud platform was made possible with major funding from the NSF and the United States Army.
Since its inception, the Cloud Computing Platform research collaborated with a number of public and private organizations.
Baker Hughes, one of the world’s largest oil field services companies, provided well-production data for the cloud platform to test its performance, and Thermo Fisher Scientific, a biotechnology product development company, supplied its Open Inventor visualization software package for the cloud platform. TEC Application Analysis LLC helped with geophysics-domain consulting services, and the Texas A&M Commercialization Office gave advice on outreach and marketing.
In 2016 and 2017, the team demonstrated the cloud platform at conferences to petroleum researchers and managers from ExxonMobil, Chevron, Statoil, Halliburton, and Schlumberger.
Huang joined Prairie View A&M in 2011. He earned a PhD in computer science at University of Houston in 2006.