Digital twin-driven offshore oil underwater production system fault prediction method and system

Abstract

The invention belongs to the field of petroleum engineering, and particularly relates to a digital twin-driven offshore oil underwater production system fault prediction method and system. The digitaltwin-driven offshore oil underwater production system fault prediction method comprises five big steps: determining degradation modes of modules, modeling a performance degradation process dynamic Bayesian network, calculating dynamic reliability, calculating residual life and managing and updating life information based on digital twinning. A digital twin-driven fault prediction system for an offshore oil underwater production system comprises an overwater control module data acquisition and processing subsystem, a hydraulic power unit data acquisition and processing subsystem, an electric power unit data acquisition and processing subsystem, an underwater control module data acquisition and processing subsystem, an underwater Christmas tree data acquisition and processing subsystem anda digital twin life monitoring control system.

Description

technical field [0001] The invention belongs to the field of petroleum engineering, and in particular relates to a fault prediction method and system for a digital twin-driven offshore oil underwater production system. Background technique [0002] As an important medium to complete offshore oil exploitation, the underwater production system has become an important part of deep-sea marine engineering technology. Due to the integrity of the system and the complexity of its structure, the failure of any component may lead to the failure of the entire system. If the fault is not eliminated in time, it may lead to a vicious accident, causing huge casualties and property losses. The main equipment of the subsea production system includes subsea wellhead, subsea Christmas tree, manifold, cross-boundary pipe, pipeline terminal and umbilical cable, control system, distribution system, etc. [0003] The traditional failure prediction method is based on historical life data, and dete...

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Problems solved by technology

[0003] The traditional failure prediction method is based on historical life data, and determines the time of equipment failure through statistical analysis of life data. However, for some large-scale equipment, the life data often cannot reflect the failure status and life information of each component, resulting in subsequent maintenance. Incomplete information and other issues

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