Big data-based digital risk management and control analysis method and system, and medium

By collecting pressure signals at different locations in oil and gas pipelines and calculating fluctuation factors, the pressure signals are corrected, and leakage risk analysis is conducted. This solves the accuracy problem of oil and gas pipeline leakage detection and enables accurate location of leaks.

CN122170361APending Publication Date: 2026-06-09DAQING OILFIELD CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
DAQING OILFIELD CO LTD
Filing Date
2024-12-06
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In existing technologies, the accuracy of oil and gas pipeline leak detection is poor, especially in sections of road with uneven ground, where the pressure values ​​collected by pressure sensors cannot accurately reflect the leak situation, resulting in large detection errors.

Method used

By acquiring the initial pressure signals of oil and gas pipelines at different preset locations and the height of their adjacent locations, the fluctuation factor is calculated to correct the pressure signals, and a leakage risk analysis is performed to screen out the leaking sub-pipelines and locate the leaking locations.

Benefits of technology

It improves the accuracy of oil and gas pipeline leak detection, reduces the impact of ground undulations on detection, and enables accurate location of leak points.

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Abstract

This invention relates to the field of pressure data processing technology, specifically to a digital risk management and analysis method, system, and medium based on big data. The method includes: acquiring initial pressure signals of the oil and gas pipeline to be detected at different preset locations, and determining the fluctuation factor corresponding to each initial pressure signal; correcting each initial pressure signal; identifying sub-pipelines between each adjacent preset location as target sub-pipelines, and analyzing the pipeline leakage pressure variation pattern of each target sub-pipeline based on the target pressure signals at preset locations adjacent to each target sub-pipeline; and selecting leaking sub-pipelines from all target sub-pipelines based on the leakage risk signals corresponding to the target sub-pipelines, and determining the leakage location of each leaking sub-pipeline. This invention achieves leakage detection of oil and gas pipelines by processing initial pressure signals and improves the accuracy of leakage detection.
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