Oil pipeline network leakage intelligent self-adaptation monitoring system and method based on big data

Abstract

The invention provides an oil pipeline network leakage intelligent self-adaptation monitoring system and method based on big data, and belongs to the technical field of pipeline network internal detection methods. By means of the system and method, a large number of data collected on site can be effectively analyzed in reasonable time, the state of a pipeline network can be obtained by means of an intelligent self-adaptation method, and accordingly a topological structure of the pipeline network is obtained. By means of a flow balance method and with the combination of an information consistency theory, whether the pipeline network leaks is analyzed, and the method is visual, simple, high in sensitivity and low in false alarm rate. Besides, precise warning is well carried out on detection of small leakage amount and slow leakage. By means of a generalized regression neural network, leakage positioning of the pipeline network is carried out, and accuracy of a result is improved. Accordingly, leakage detection and positioning problems of the pipeline network are solved by means of strategies based on the big data and the intelligent self-adaptation method, and meanwhile the aims of high precision and high accuracy can be achieved.

Description

technical field [0001] The invention relates to the technical field of pipeline network internal detection methods, in particular to an intelligent self-adaptive monitoring system and method for oil pipeline network leakage based on big data. Background technique [0002] Pipeline transportation is an economical and convenient transportation method. Compared with other transportation methods, it has the advantages of high efficiency, safety, economy, and easy control and management. Therefore, it plays an important role in fluid transportation. According to the "Twelfth Five-Year Plan", by the end of 2015, the total length of my country's oil and gas pipelines will reach about 150,000 kilometers, of which: 25,000 kilometers of new oil pipelines and 44,000 kilometers of natural gas pipelines. However, due to the aging of pipeline equipment, changes in geographical conditions and man-made sabotage, pipeline leakage accidents often occur. When a leakage accident occurs in a pi...

AI Technical Summary

Problems solved by technology

Pipeline leak detection systems based on pressure signals have been widely used, but there are still some common problems in this type of system: one is that there are many false positives in the detection of small leaks and slow leaks, and the other is the ability of the system to resist disturbances in working conditions Not strong, the system has many false positives

[0004] At present, the research on the leakage detection and location method of a single pipeline has been relatively mature, but in actual engineering, there are many oil pipelines with one or more branches, that is, the pipeline network, and the information obtained from the oil pipeline network has a large amount of information. The characteristics of large data volume and the collection of signals such as pressure and flow are millisecond-level data, which fully reflects the characteristics of big data. In addition, the structure of the oil pipeline network is complex, which increases the difficulty of detecting leaks in the oil pipeline network. , but the current research on pipeline network transportation is still basically on a single pipeline transportation, and it is not possible to grasp and analyze the pipeline network well as a whole, and in the pipeline network transportation, the pressure wave is disturbed by the working conditions during the propagation process And the impact of system noise is greater, and the attenuation of the pressure signal will be more severe, which greatly reduces the sensitivity and positioning accuracy of leak detection

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