On the potential for interim storage in dense phase CO2 pipelines

H. Aghajani, J. M. Race, B. Wetenhall, E. Sanchez Fernandez, Mathieu Lucquiaud, H. Chalmers

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This paper investigates the flexibility that exists within a dense phase carbon dioxide (CO2) pipeline system to accommodate upset conditions in the Carbon Capture and Storage (CCS) network by utilising the pipeline as a storage vessel whilst still maintaining flow into the pipeline. This process is defined in the pipeline industry as “line-packing” and the time available to undertake line-packing is termed the line-packing time. The longer the line-packing time, the more resilient the pipeline system is to flow variations or short term operational issues at the capture or storage site. The aims of the study were; to investigate the impact of typical CO2 pipeline design parameters (diameter, wall thickness and length) as well as CO2 mass flow rate and pipeline inlet and outlet pressure on the available line-packing time and; to derive relationships between the key variables to allow designers to optimise the line-packing time for a pipeline system.

The study was undertaken by developing a viable study set of dense phase CO2 pipelines using steady state hydraulic analysis and stress based design principles. The study set was designed to cover the range of design parameters, flow rates and pressures considered to be typical of dense phase pipelines in CCS systems. For each of the pipelines in the study set, the line-packing time was calculated using a transient hydraulic analysis approach. Although by interrogating the results, individual relationships could be identified between key input parameters and the line-packing time, the integration of all of the critical parameters could not be achieved through simple regression analysis techniques. Consequently, using the dataset of pipelines and line-packing times developed, an Artificial Neural Network (ANN) was designed to enable a comprehensive sensitivity analysis of the line-packing time to the input data to be conducted. It is also demonstrated how the ANN can be used as a design tool for the prediction of line-packing time.

As would be expected, the line-packing capacity of the pipeline can be increased by increasing the available internal volume of the pipeline, reducing the mass flow rate into the pipeline, increasing the allowable operating stress and managing the inlet pressure and outlet pressures. However, one of the key findings of the work is that, in the dense phase, line-packing times of only up to 8 hours can be achieved for pipeline dimensions typical of those considered for CCS schemes. Consequently it has been confirmed that the pipeline does not represent a long-term storage option for CCS systems.

However, if line-packing capability is considered at the design stage then the level of flexibility for the pipeline to act as short-term storage in the network increases. In particular, it is recommended that the effect of increasing the wall thickness on the line-packing time is considered at the design stage to determine the benefits of this option in enabling the pipeline to be used as a short-term storage option in the CCS system and prevent venting of CO2 during short-term outage events at the capture or storage site.
Original languageEnglish
Pages (from-to)276-287
Number of pages12
JournalInternational Journal of Greenhouse Gas Control
Early online date2 Nov 2017
Publication statusPublished - 2 Nov 2017


  • carbon capture and storage
  • dense phase pure CO2
  • line-packing time
  • pipeline transport
  • hydraulic analysis
  • optimisation
  • steady 27 state hydraulic analysis
  • stress based design principles
  • artificial neural network

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