Hongliang Xiao

Supercritical CO₂ pipeline transportation is a critical component of the carbon capture, utilization and storage (CCUS) industry chain, where long distance operation introduces inherent risks of accidental leakage. During the leakage process of supercritical CO₂ pipelines, throttling pressure reduction and the Joule–Thomson effect generate distinct negative pressure wave characteristics. The magnitude of the leakage directly impacts localization effectiveness, particularly under small leakage conditions where negative pressure wave signals are less pronounced, so the leakage is difficult to effectively detect. To solve this problem, the mutual correlation function model for pipeline leakage was developed by using the mutual correlation analysis method, and it was verified by the dense-phase CO₂ leakage data from Trondheim University of Technology. Based on the TGNET software, the actual pipeline model of the Yanchang oilfield is established, and the captured leakage signal is imported into MATLAB for differential pressure conversion, using the verified cross-correlation function model of the differential pressure signal to calculate the time difference between the arrival of the negative pressure wave at the two ends of the pipeline. Finally, the actual leakage location was determined. The simulation results indicate that the leakage detection method based on mutual correlation analysis of negative pressure wave signals exhibits varying localization performance under different leakage rates. By enhancing negative pressure wave characteristics and utilizing mutual correlation analysis, this method effectively addresses the challenges of indistinct negative pressure wave features and difficult localization during small leakage conditions. When leakage exceeds 5%, the relative error is controlled within ±5.40%, meeting the preliminary localization requirements for rapid identification and regional determination in engineering applications. Through the application of actual engineering cases, it is shown that this method has high accuracy in pipeline leakage detection. These findings provide theoretical and methodological support for supercritical CO₂ pipeline leakage detection in the CCUS projects currently under construction.