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Innovative research to detect leaks in underground carbon dioxide storage

11 August 2022

 

Researchers from Teesside University are working with international partners to implement innovative machine learning techniques to detect leaks during underground carbon dioxide sequestration .

Dr Sina Gomari
Dr Sina Gomari

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 using Computational Fluid Dynamics (CFD) whereby artificial intelligence simulates the flow of liquids and gases the team hope to be able to accurately predict the likelihood and location of leaks in both single-phase and multi-phase flow.

Teesside University is committed to research which utilises novel and disruptive technologies, processes and business models to forge a smarter, greener industrial economy.

Dr Sina Rezaei-Gomari

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.

Dr Sina Rezaei-Gomari said: “Teesside University is committed to research which utilises novel and disruptive technologies, processes and business models to forge a smarter, greener industrial economy.

“It is well-documented just how devastating leaks from pipelines can be if they aren’t spotted and acted upon in a timely and efficient manner.

“This research will look at how state of the art computational techniques including machine learning and digital twinning 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 be both time-consuming and costly.

“We will be working with alongside leading oil and gas companies to ensure that this research can have real industrial applications.”

Dr Aziz Rahman who is leading the Project from TAMUQ added: “The objective of the funded project is to develop a multiphase flow leak detection model and visualisation tool that is ready to be used by the industry, integrating the machine learning and digital twin technique.

“The development of this technology in a country like Qatar which is predominantly oil and gas driven will present a unique opportunity for increased efficiency in oil and gas transportation, resulting in lower capital and energy overheads, and savings of millions of dollars every year.”


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