The international research partnership seeks to implement AI and machine learning to detect the leaks in pipelines and well string.
Copyright: energylivenews.com – “New AI tech could help detect leaks in underground carbon storage”
Innovative machine learning techniques could help detect leaks during underground carbon dioxide (CO2) sequestration, helping reduce their environmental and economic impacts.
Academics from various universities have formed an international research partnership to implement artificial intelligence (AI) and machine learning to detect the leaks in pipelines and well string.
They will also employ a novel “digital twin” for leak detection during single phase – crude oil or gas – and multi-phase flow during the transportation and injection of carbon dioxide into the underground storage site.
This involved 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.
The team will use Computational Fluid Dynamics (CFD), whereby AI simulates the flow of liquids and gases and hope to be able to accurately predict the likelihood and location of leaks in both the single phase and multi-phase flows.
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These techniques are expected to more accurately predict the location, size, number and orientation of both small chronic and larger leaks and ultimately take action by AI without requiring human interference.[…]
Read more: www.energylivenews.com
The international research partnership seeks to implement AI and machine learning to detect the leaks in pipelines and well string.
Copyright: energylivenews.com – “New AI tech could help detect leaks in underground carbon storage”
Academics from various universities have formed an international research partnership to implement artificial intelligence (AI) and machine learning to detect the leaks in pipelines and well string.
They will also employ a novel “digital twin” for leak detection during single phase – crude oil or gas – and multi-phase flow during the transportation and injection of carbon dioxide into the underground storage site.
This involved 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.
The team will use Computational Fluid Dynamics (CFD), whereby AI simulates the flow of liquids and gases and hope to be able to accurately predict the likelihood and location of leaks in both the single phase and multi-phase flows.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
These techniques are expected to more accurately predict the location, size, number and orientation of both small chronic and larger leaks and ultimately take action by AI without requiring human interference.[…]
Read more: www.energylivenews.com
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