Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea...

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Vydáno v:Petroleum science Ročník 21; číslo 4; s. 2758 - 2768
Hlavní autoři: Zhang, Rui, Cai, Bao-Ping, Yang, Chao, Zhou, Yu-Ming, Liu, Yong-Hong, Qi, Xin-Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Beijing Elsevier B.V 01.08.2024
KeAi Publishing Communications Ltd
Témata:
Abnormal sensor
Accuracy
Algorithms
Back propagation
Combinatorial algorithm
Combinatorial analysis
Control equipment
Control systems
False alarms
Fault diagnosis
Fault identification
Gas production
Mathematical models
Methods
Offshore engineering
Offshore platforms
Offshore structures
Oil and gas production
Production controls
Reasoning
Recognition
Sensors
Subsea production control system
Subsea production systems
Time series
Working conditions
South China Sea
Abnormal sensor
Subsea production control system
Fault identification
Combinatorial algorithm
ISSN:1995-8226, 1672-5107, 1995-8226
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Shrnutí:The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea sensors degrade rapidly due to harsh working environments and long service time. This leads to frequent false alarm incidents. A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed. A combinatorial algorithm is proposed to group sensors. The long short-term memory network (LSTM) is used to establish a single inference model. A counting-based judging method is proposed to identify abnormal sensors. Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method. The results show that the proposed method can identify the abnormal sensors effectively.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1995-8226
1672-5107
1995-8226
DOI:10.1016/j.petsci.2024.02.015