Application of a Machine-Aided Technique for Instantaneous Gas Leak Detection: A Case Study of Real-Time Modeling for JK-52 Gas Processing Plant

Many fluid leak detection mechanisms rely on observation of volume changes and physical evidence of leak, which may take hours, days and sometimes weeks or months to be seen. This is a concern in gas plants where the proximity of the leakage may constitute environmental pollution as well as health hazards for personnel in the vicinity. Economic losses have also resulted from delays in mitigating a gas leak problem due to late detection.

This study applies a machine learning technique to develop an algorithm that can detect gas leak in real-time, where the only possible delay is the lag-time between the inlet gauges at the upstream valve and the outlet gauge at the downstream valve. In this case study of JK-52 gas processing plant, the difference pressure gauge readings were calibrated against the volume of the gas in the inlet section to quantify the leak volume. Because gaseous fluids do not present physical indication of volume, a pressure-based method was used for the detection, where drop in gauge pressure due to de-pressurisation indicate leakage in the absence of recorded gas supply or collection.

Python coding language, using Jupyter and Pycharm Integrated Development Environments (IDEs), was used for the programming. The machine learning algorithm analyses the incoming streaming pressure versus time datasets from the gauges during the residual and ramp-up flow phases to set the acceptable pressure difference cut-off. A minimun difference in gauge reading may be normal within an acceptable error margin. The change in the consistency of reading within this acceptable window defines the tolerance. The system is set-up to blare an alarm when there is leakage, usually based on a cut-off or tolerance, to be detected by the machine-aided process.