With increasing environmental concerns, regulatory and CSA Z662-23 standard requirements, leak detection in all pipelines has become a top priority for the energy industry in recent years. Real time leak detection on the upstream pipeline gathering networks carrying emulsions from oil wells to separation facilities is very challenging due to the presence of multiple fluid phases in the pipelines. Due
Currently, methane emissions account for approximately 25% of human-induced global warming, with the oil and gas sector ranking among the leading contributors. Early detection of methane leaks in pipelines significantly reduces the greenhouse gases emissions, aiding in the mitigation of adverse economic and environmental consequences associated with climate change. Computational Pipeline Monitoring (CPM) systems, tailored for leak detection, provide continuous
CO2 pipelines are a key component of carbon capture, utilization, and storage (CCUS) initiatives. These pipelines are being expanded to facilitate the transfer of captured CO2 from industrial sources to designated storage sites. Utilizing dynamic modeling for CO2 transportation pipelines offers a robust tool for ensuring the safety and efficiency of pipeline operations while minimizing the social and environmental impacts
The Pipeline and Hazardous Materials Safety Administration (PHMSA) has announced a new final rule [1] that took effect on October 5th , 2022. This new rule requires a leak detection method to be implemented on all hazardous liquid pipelines by October 1st, 2024. The rule includes several highlights specific to leak detection, such as the application to most Type A
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
