University of Teesside research into underground carbon storage

TEESSIDE University is 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 gas transmission pipeline leaks and underground carbon dioxide sequestration using artificial intelligence and machine learning.

Academics from Teesside University’s School of Computing, Engineering and Digital Technologies work with their counterparts from Texas A&M University in Qatar (TAMUQ), Qatar University, Texas A&M (USA), Birch Scientific (USA) and Rock-Oil Consulting of Canada, to apply state-of-the-art machine learning techniques to detect leaks during underground carbon dioxide sequestration in pipelines and strings of wells.

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The project, which is led by Dr Aziz Rahman, Associate Professor at Texas A&M University in Qatar, supported by other distinguished international partners including Teesside University, led by Dr Sina Rezaei Gomari, has received 530 $000 (approximately £430,000) by Qatar Foundation Priority Search.

Along with machine learning approach. The research team will also use a new “digital twin” for leak detection during single-phase (crude oil or gas) and multi-phase (multiple materials) flow during transport and injection of carbon dioxide into the site. underground storage. It involves creating a virtual representation of a gas pipeline that is updated in real time via a network of sensors mounted and installed in real gas pipelines.

Through the use of computational fluid dynamics (CFD) through 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 single-phase and multi-phase flows.

It is hoped that these techniques will make it possible to more accurately predict the location, size, number and direction of small chronic and larger leaks and ultimately enable preventative action by artificial intelligence without require human intervention.

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Dr Sina Rezaei-Gomari said: “The University of Teesside is committed to research that uses new and disruptive technologies, processes and business models to forge a smarter and greener industrial economy.

“It is well documented how devastating pipeline leaks can be if not caught and dealt with quickly and efficiently.

“This research will examine how advanced computational techniques, including machine learning and digital pairing, can be applied to accurately predict where faults are occurring, without the need for remotely operated vehicles or aircraft to scan the pipeline, which can take time. and expensive.

“We will work with major oil and gas companies to ensure that this research can have real industrial applications.”

Dr. Aziz Rahman, who leads the TAMUQ project, added: “The objective of the funded project is to develop an industry-ready multi-phase flow leak detection model and visualization tool, integrating the machine learning and the digital twin technique.

“Developing this technology in a country like Qatar, which is primarily oil and gas focused, will present a unique opportunity to increase the efficiency of oil and gas transportation, resulting in lower capital overhead. and energy, and savings of millions of dollars every year.”

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Bonny J. Streater