Session: Corrosion in Carbon Capture, Transportation, and Utilization and Storage (CCTUS) (Part I of III)
Robust Integrity Management Planning of Anthropogenic dense CO2 pipelines - Leveraging MFL Data Fusion (C2026-00363)
Wednesday, March 18, 2026
8:30 AM - 9:00 AM Central
Location: 342 DE
Earn .5 PDH
Interested in reading the entire paper? Click on the "Paper" button below to read on the AMPP Knowledge Hub!
*Please note, if your registration came with access to the conference proceedings don't forget to login to your AMPP Knowledge Hub account to access the paper for free. If you login and don't have access to the paper, you can purchase the individual paper or purchase the entire conference proceedings on your Knowledge Hub account.
Daniel Sandana, Angus Patterson, Kevin Siggers, Hazem Rahmah
Dense CO2 transportation by pipelines is a key element of the CCUS value chain. Currently, there are significant efforts to design safe/ practical CO2 compositional limits . Despite this, the historic experience from the natural gas transmission industry shows that, despite all care undertaken, operational upsets could be still expected. The arrangment of CCUS infrastructures into clusters with multiple feeders of various nature also increase the likelihood of anomalous scenarios. Finally, there is not yet a fully operational pipeline transporting man-made CO2. MFL Inspection programs are critical to confirm that internal corrosion is safely managed to prevent uncontrolled loss of containments. However, historical and conventional inspection and integrity assessment practices introduce conservatisms and uncertainties that can lead to unneccessary mitigation costs or at the other end of the spectrum potentially unsafe conclusions. These limits are futher compounded in the case of CO2 pipelines, for which the combination of complex corrosion morphologies, agressive rates , and high operational pressures can exert an onerous burden on the fitness-for-service and remaining life. This paper shows how the use of a machine-learning-based data fusion model to aligned MFL-A and MFL-C datasets can help to produce more robust integrity management planning of anthropogenic CO2 pipelines.
