Skip to content
2009

Pipeline Revalidation using Quantum Sensors

Abstract

To ensure ongoing safety, compliance, and operational efficiency, WWU uses in-line inspection tools (commonly known as PIGs) to monitor the internal condition of these pipelines. These tools are critical for detecting corrosion, cracking, deformation, and other defects.

A significant challenge within the existing P18 pipeline network is the uncertainty surrounding weld integrity. For many legacy sections, the original inspection records are incomplete or unavailable; without reliable weld data, it is difficult to determine whether these joints remain structurally sound or if deterioration may be developing below detection thresholds.

The project endeavours to determine whether advanced sensing can effectively assess the condition of target pipeline welding and other features, defects, or degradation, and whether advanced sensors can be applied to existing or new PIGs to perform the weld inspection required.

The technology will provide a more comprehensive understanding of the condition of the existing network and its overall integrity and longevity, beyond what is achievable through current testing methods. This enhanced insight will enable Gas Distribution Networks (GDNs) to reclassify pipelines where appropriate, thereby avoiding significant capital expenditure associated with uncovering welds of unknown quality during capital projects or damage assessments. Furthermore, as reliance on natural gas declines, this capability will enable the repurposing of existing assets, rather than leaving them redundant or decommissioned.

The learnings of the project will endeavour to fully evidence understanding of weld integrity across the P18 pipeline sections, where approximately 90% of previously unknown welds are confidently classified as either compliant or requiring targeted remediation. Quantum-enabled screening is established as a routine repeatable inspection technique, delivering accurate weld condition data without unnecessary excavation or disruption. This is currently unproven in a gas distribution network. The learnings are expected to be of value to all GB networks.

file format pdf download NIA_WWU_03_07_NIA_Project_Eligibility_Assessment_2026-07-10.pdf
Loading

Article metrics loading...

/content/projects/NIA_WWU_03_07
2026-07-01
2026-07-15
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test