Many fluid leak detection mechanisms rely on observation of volume changes and physical evidence ... more 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,...
Natural gas is composed mostly of methane, the simplest hydrocarbon molecule, with only one carbo... more Natural gas is composed mostly of methane, the simplest hydrocarbon molecule, with only one carbon atom. But most gas at the wellhead contains other hydrocarbon molecules known as Natural Gas Liquids (NGL). Heavier gaseous hydrocarbons such as propane (C3H8), normal butane (n-C4H10), isobutane (i- C4H10) and pentanes, may also be processed in gas plants and exported as Liquified Natural Gas (LNG). During operational services in gas plant from inlet to outlet piping, gas leaks tend to occur undetected at some points in the facility. Apart from loss of gas resources, leaks and venting at natural gas processing plants release other pollutants besides methane (e.g., benzene, hexane, hydrogen sulfide) that can threaten air quality and public health. Hence, the need for early detection of gas leaks by using appropriate Machine Learning (ML) models. Insight from existing general flow equations was used to develop a new modelling approach for Machine Learning, in a test case: Gas Plant JK –...
Many fluid leak detection mechanisms rely on observation of volume changes and physical evidence ... more 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,...
Natural gas is composed mostly of methane, the simplest hydrocarbon molecule, with only one carbo... more Natural gas is composed mostly of methane, the simplest hydrocarbon molecule, with only one carbon atom. But most gas at the wellhead contains other hydrocarbon molecules known as Natural Gas Liquids (NGL). Heavier gaseous hydrocarbons such as propane (C3H8), normal butane (n-C4H10), isobutane (i- C4H10) and pentanes, may also be processed in gas plants and exported as Liquified Natural Gas (LNG). During operational services in gas plant from inlet to outlet piping, gas leaks tend to occur undetected at some points in the facility. Apart from loss of gas resources, leaks and venting at natural gas processing plants release other pollutants besides methane (e.g., benzene, hexane, hydrogen sulfide) that can threaten air quality and public health. Hence, the need for early detection of gas leaks by using appropriate Machine Learning (ML) models. Insight from existing general flow equations was used to develop a new modelling approach for Machine Learning, in a test case: Gas Plant JK –...
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