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state-of-the-art-tech-could-help-detect-co2-leaks-during-underground-storage
state-of-the-art-tech-could-help-detect-co2-leaks-during-underground-storage

‘State of the art’ tech could help detect CO2 leaks during underground storage

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Innovative new machine learning techniques could help detect leaks during underground carbon dioxide (CO2) sequestration, protecting the environment while reducing unnecessary wastage.

A study being undertaken by researchers from Teesside University and international partners aims to explore the potential of machine learning and artificial intelligence (AI) to detect leaks during CO2 underground sequestration in pipelines and well string.  

Led by Dr Aziz Rahman, Associate Professor, Texas A&M University at Qatar $530,000 (£430,000) by Qatar Foundation Priority Research, the project is a response to the potential threat of CO2 leakage during storage.  

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