A new model to evaluate two leak points in a gas pipeline

Natural gas is a clean fossil energy and an important sector in the energy consumption chart. Because of its reliability and low carbon-hydrogen ratio, the demand for natural gas increases steadily to replace coal and wood to better protect environment. To accommodate the ever rising in natural gas production and transportation, more gas pipelines are being constructed. Meanwhile, the existing gas pipelines are aging inevitably. One of the critical needs in natural gas flow assurance is detecting and locating pipeline leak in a timely manner. A reliable and timely detection of the leakage of gas pipeline can not only reduce the loss of hydrocarbon, but also limit the damage to facilities, possible loss of life, and the extent of environmental pollution. Two or more leakage points in a pipeline were observed in the field. Physical methods and mathematical models were employed to detect pipeline leakages. However, literature review indicates that no mathematical model has been developed to detect multiple leaks in the same pipeline. This study focused on the detection of two leak points in a pipeline. Multi-flowrate tests are proposed to evaluate the locations and sizes of leakages in two leak points. The new mathematical model is crucial when no physical inspection is available. The proposed model can be used to monitor possible leak in real-time because flowrate and pressure that are utilized to estimate multiple leaks are monitored in real-time and are available almost simultaneously. Therefore, the new method provides a practical, quick, and low computational cost approach to detect multiple leakages. The proposed method is important because existing mathematical models assumed single leak in a pipeline, which limits their applications because the detection will be misleading if there is more than one leakage in the same pipeline. The proposed model can differentiate single-leakage scenario from multi-leakage scenario based on multi-rate tests. The identification is critical because it guides the leakage detection to the right direction.