Modeling of Smart Pigging for Pipeline Leak Detection
| Autor: | Caitlyn Thiberville, Paulo J. Waltrich, Wesley Williams, Seung Ihl Kam, Yanfang Wang |
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| Rok vydání: | 2020 |
| Předmět: |
Fuel Technology
Petroleum engineering Pigging Pipeline (computing) 010401 analytical chemistry 0202 electrical engineering electronic engineering information engineering Energy Engineering and Power Technology Environmental science 020206 networking & telecommunications 02 engineering and technology Leak detection 01 natural sciences 0104 chemical sciences |
| Zdroj: | SPE Production & Operations. 35:610-627 |
| ISSN: | 1930-1863 1930-1855 |
| DOI: | 10.2118/198648-pa |
| Popis: | Summary Although leak incidents continue, a pipeline remains the most reliable mode of transportation within the oil and gas industry. It becomes even more important today because the projection for new pipelines is expected to increase by 1 billion barrels of oil equivalent (BOE) through 2035. In addition, increasing the number and length of subsea tiebacks faces new challenges in terms of data acquisition, monitoring, analysis, and remedial actions. Passive leak-detection methods commonly used in the industry have been successful with some limitations, in that they often cannot detect small leaks and seeps. In addition to a thorough review of related topics, this study investigates how to create a framework for a smart pigging technique for pipeline leak detection as an active leak-detection method. Numerical modeling of smart pigging for leak detection requires two crucial components: detailed mathematical descriptions for fluid-solid and solid-solid interactions around pig and network modeling for the calculation of pressure and rate along the pipeline using iterative algorithms. The first step of this study is to build a numerical model that shows the motion of a pig along the pipeline with no leak (i.e., at a given injection rate, a pig first accelerates until it reaches its terminal velocity, beyond which the pig moves at a constant velocity). The second step is to construct a network model that consists of two pipeline segments (one upstream and the other downstream of the leak location) through which the pig travels and at the junction of which fluid leak occurs. By putting these multiple mechanisms together and using resulting pressure signatures, this study presents a new method to predict the location and size of a leak in the pipeline. |
| Databáze: | OpenAIRE |
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