Tagging and tracking oil-gas mixtures in multiphase pipelines

Pipeline transportation of multiphase products, such as gas and oil mixtures, exhibits complex and varying flow regimes: as a result, analytical approaches or conventional methods cannot accurately describe the composition or the propagation characteristics of the fluid mix inside the transportation system itself. We address such an issue by presenting a methodology, driven by the data and applied to a real case history, where basic pressure transients are used to tag and track, along a pipeline, different batches of a multiphase medium. Several statistical indicators, computed from the pressure data and on different window lengths, are employed to train a machine learning model, which learns to distinguish the characteristic behavior of two different oil-gas slugs: in practice, each different combination of fluid phases (in terms of gas/oil ratio in a given batch of product) and each different sequence of slugs (in terms of gas/oil ratio variability between successive batches) behaves like a coded tag linked to the flowing fluid. The key innovation consists in the possibility of tracking such multiphase slugs along the flowline and at each monitoring station: this allows one to determine in real-time the fluid composition entering/exiting the line, its position, and its movement along the pipe. As such, we obtain also a virtual metering system, able to provide estimates of the flow rate and phases ratio. Moreover, by having several recording stations accurately synchronized, one can also leverage real-time transmission and multichannel processing of the data, enabling the opportunity for online monitoring applications. The results on the test cases and the accuracy scores obtained for the metrics considered validate the tagging and tracking approach.