Over Pressure Risk Mitigation with SCADA in a Natural Gas Distribution System

Natural gas distribution providers have periodic and reactive maintenance plans that are not providing enough feedback and data to establish proper predictive analysis for failure, especially on distribution gas regulator station that are regulating gas from transmission pressure to a safe customer distribution pressure, these stations are the only overpressure safeguard and the highest risk at the same time. With the recent advances in computer and telecommunications technologies, some major gas utilities initiated more robust predictive risk analysis plans by introducing SCADA supervisory control and data acquisition to gas distribution, mainly monitoring gas regulator stations and their behavior based on their established overpressure prevention philosophies and long-term predictive analysis goals. This study outlines a full SCADA monitoring solution on a dual run gas distribution regulator station equipped with primary overpressure protection and redundancy options. RTU (Remote Terminal Units) containing flow and pressure monitoring transmitters are installed at specific pressure reduction points in the station piping. Unlike common monitoring practice, we are introducing mid-stream pressure monitoring between the monitor and regular valves, filter differential and flow measurement in our proposed SCADA set up. This system provides real time visibility and data to the gas control room to quickly recognize abnormal or emergency events and assess the current health of regulator equipment. The outlined risk mitigations in this study are created based on identified risks of overpressure under several regulation failure modes of the regulators in standby and working modes. Our SCADA solution introduces means to enhance deductive analysis and predictability for gas control departments and monitoring groups leading to a safer and more reliable gas distribution system, improving emergency response and creating more comprehensive maintenance plans driven by long term predictive risk plans.