Crude oil transportation pipelines depend on Computational Pipeline Monitoring (CPM) systems for leak detection. Accurate prediction of the volume of vapor phase in the pipeline is very challenging when crude oil goes through phase change (column separation) in the pipeline. It is also challenging to accurately predict the vapor phase volume when the pipeline is started from extended shut-in period
A novel model is used to predict the inception moment and location of a phenomenon known as the column separation or slack line. The existing models for prediction of the cavitation phenomenon in internal flow systems in general are primarily formulated for the capture of the inception moment and initial location of the event only. Hence questions such as where
Leaks may occur at any time and location in a liquid transmission pipeline. One of the significant complexities in the leak detection process arises when the leak happens in a pipeline in the presence of phase change. A set of experiments are conducted on a 374-meter (1227-ft) pipe to examine the performance of CPM-based Leak-Detection algorithms. Different intensities of slack
There is a discontinuity in the tabulated thermodynamic properties as the fluid is going across the critical point. In this paper, utilizing regularization techniques, correlations are established to continuously determine fluid properties during the transition through the critical point and into the supercritical region. An augmented model for the dense phase is proposed by integrating our derived correlations into the
