Transient Model-Based Leak Detection and Localization Technique for Crude Oil Pipelines: A Case of N.P.D.C, Olomoro

Chuka, Chinwuko Emmanuel; Freedom, Ifowodo Henry; O, Umeozokwere Anthony

doi:10.21276/sjeat.2016.1.2.2

Cited by 7 publications

(3 citation statements)

References 7 publications

(13 reference statements)

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“…Its main component is methane, but it can contain non hydrocarbon gases and natural gas liquids as well. It comes from plants and animals that were initially in thick layers on ocean floors, the earth's surface, and other areas [12].…”

Section: Fluid Leaks Detection Mechanismmentioning

confidence: 99%

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

Usiabulu¹,

Joel²,

Nosike³

et al. 2023

JERR

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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.

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Section: Fluid Leaks Detection Mechanismmentioning

confidence: 99%

“…You can find a damaged gas pipe, dead houseplants, and if a smell is present, it'll be rotten eggs and sulfur. Near the gas line, you might see a white or dust cloud, and a whistling or hissing sound" [12].…”

Section: Environmental Pollutionmentioning

confidence: 99%

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

Usiabulu¹,

Joel²,

Nosike³

et al. 2023

JERR

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show abstract

“…In the Nigerian Bonny Light Crude Oil leak accident [ 20 ], P in = 120 kPa, the outlet pressure P out = 104 kPa, the pressure drop coefficient,

. The inlet mass flow rate is 82.17 kg/s, outlet mass flow rate is 60 kg/s.…”

Section: Evaluation Of Leakagementioning

confidence: 99%

Leakage Evaluation by Virtual Entropy Generation (VEG) Method

Zhang

Drapaca

Zhang

et al. 2017

Entropy

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Leakage through microscale or nanoscale cracks is usually hard to observe, difficult to control, and causes significant economic loss. In the present research, the leakage in a pipe was evaluated by the virtual entropy generation (VEG) method. In virtual entropy generation method, the "measured entropy generation" is forced to follow the "experimental second law of thermodynamics". Taking the leakage as the source virtual entropy generation, a new pipe leakage evaluation criterion was analytically derived, which indicates that the mass leakage rate should be smaller than the pressure drop rate inside a pipe. A numerical study based on computational fluid dynamics showed the existence of an unrealistic virtual entropy generation at a high mass leakage rate. Finally, the new criterion was used in the evaluation of leakage available in the literature. These results could be useful for leakage control or industry criteria design in the future.

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Single and multiphase flow leak detection in onshore/offshore pipelines and subsurface sequestration sites: An overview

Rahman,

Barooah,

Khan

et al. 2024

Journal of Loss Prevention in the Process Industries

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Transient Model-Based Leak Detection and Localization Technique for Crude Oil Pipelines: A Case of N.P.D.C, Olomoro

Cited by 7 publications

References 7 publications

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

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

Leakage Evaluation by Virtual Entropy Generation (VEG) Method

Single and multiphase flow leak detection in onshore/offshore pipelines and subsurface sequestration sites: An overview

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