Offshore staff
MIDDLESBROUGH, England, UK – Researchers from Teesside University are working with international partners to implement innovative machine learning techniques to detect leaks during underground carbon dioxide sequestration.
The international research partnership could help reduce the environmental and economic impact of leaks from gas transportation pipelines and underground carbon dioxide sequestration by using artificial intelligence and machine learning.
Academics in Teesside University’s School of Computing, Engineering & Digital Technologies are working with counterparts at Texas A&M University at Qatar (TAMUQ), Qatar University, Texas A&M University (USA), Birch Scientific (USA) and Rock-Oil Consulting from Canada to apply state of the art machine learning techniques to detect leaks during carbon dioxide underground sequestration in pipelines and well string.
The project, which is being led by Dr. Aziz Rahman, Associate Professor at Texas A&M University at Qatar supported by other distinguished international partners including Teesside University, led by Dr. Sina Rezaei Gomari, has been awarded $530,000 (approximately £430,000) by Qatar Foundation Priority Research.
Alongside the machine learning approach. The research team will also employ a novel digital twin for leak detection during single phase (crude oil or gas) and multi-phase (multiple materials) flow during the transportation and injection of carbon dioxide into the underground storage site. This involves creating a virtual representation of a pipeline which is updated in real-time via a network of sensors mounted and installed in the real gas pipelines.
Through use of computational fluid dynamics, in which artificial intelligence simulates the flow of liquids and gases, the team hopes to be able to accurately predict the likelihood and location of leaks in both single-phase and multi-phase flow.
It is hoped that these techniques will more accurately predict the location, size, number and orientation of both small chronic and larger leaks and ultimately take preventive action by artificial intelligence without requiring human interference.
08.23.2022
