A Dimensionless Leakage Estimation System and Method for Oil Products Pipeline

Abstract

The present invention proposes a dimensionless product oil pipeline leakage estimation system and method, which relates to the technical field of product oil pipeline leakage detection methods. The present invention effectively analyzes a large amount of data collected on site within a reasonable time, using pressure The sensor array obtains the status of the entire pipeline section and obtains the pressure change trend of the pipeline. At the same time, it uses the flow balance method and combines the information consistency theory to analyze whether there are abnormal conditions in the pipeline. The method is intuitive and simple, with high sensitivity and low false alarm rate; And it can accurately detect small leaks and slow leaks; the BP neural network is used to calculate the pipeline pressure decay rate, which improves the accuracy of the final results. At the same time, the dimensionless modeling method is used to greatly It reduces the amount of calculation and improves the calculation accuracy.

Description

technical field [0001] The invention relates to the technical field of a method for detecting the leakage of a product oil pipeline, in particular to a system and a method for estimating the leakage of a product oil pipeline based on a dimensionless method. Background technique [0002] At present, the pipeline leakage detection system based on the negative pressure wave detection method has been widely used, but there are still some common problems in such systems: the product oil pipeline leakage monitoring system cannot evaluate the leakage level of the leakage accident, that is, the leakage of the pipeline Quantitative estimation of quantities has been technically impossible. [0003] At present, the accurate estimation of the leakage of oil product pipelines is related to many factors, the most critical of which is the pressure wave attenuation rate of the current pipeline medium. Through the accurate pressure wave attenuation rate, a corresponding model can be establis...

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Problems solved by technology

[0002] At present, the pipeline leakage detection system based on the negative pressure wave detection method has been widely used, but there are still some common problems in this type of system: the product oil pipeline leakage monitoring system cannot evaluate the leakage level of the leakage accident, that is, the leakage of the pipeline Quantitative estimation of the quantity has not been technically possible

[0003] At present, the accurate estimation of the leakage of refined oil pipelines is related to many factors, the most critical of which is the pressure wave attenuation rate of the current pipeline medium. Through the accurate pressure wave attenuation rate, a corresponding model can be established to estimate the leakage; long-term transportation During the hydraulic transient process of the product oil pipeline, the direct water hammer pressure will propagate along the pipeline. Due to the existence of residual flow, wherever the direct water hammer pressure wave goes, the flow velocity will continue to change, and the direct water hammer pressure wave front There is obvious attenuation of the peak value, which is the so-called pressure wave attenuation; there are different degrees of loss in the transmission of pressure wave in the pipeline, and the pressure wave is attenuated every time it is transmitted, and the pressure wave is attenuated according to different media and distances. However, due to the long distance between long-distance pipelines, many environmental factors, and the boundary conditions of the medium of each pipeline are different, the relevant theory on the degree and form of pressure wave attenuation in product oil pipelines has not yet been formed. Data collection research is carried out with targeted pipe sections. Therefore, finding a suitable way to calculate the pressure wave attenuation rate and establishing a suitable model to estimate the leakage is the general direction of future research.

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