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Collaborative Prediction and Warning of Associated Process Parameters in Natural Gas Regional Production
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Yong ZHAO1, Ai-jun YIN2, *, Hao CHENG3, Qian LI1, Lin-cheng JI1, Qian-ying WU1
Science Technology and Engineering | 2025, 25(2) : 560 - 566
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Science Technology and Engineering | 2025, 25(2): 560-566
Papers·Petroleum and Natural Gas Industry
Collaborative Prediction and Warning of Associated Process Parameters in Natural Gas Regional Production
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Yong ZHAO1, Ai-jun YIN2, *, Hao CHENG3, Qian LI1, Lin-cheng JI1, Qian-ying WU1
Affiliations
  • 1 Chongqing Gas Mine of Southwest Oil and Gas Field Branch of Petrochina, Chongqing 400021, China
  • 2 College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
  • 3 School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
Published: 2025-01-18 doi: 10.12404/j.issn.1671-1815.2308734
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In order to detect the abnormal working conditions such as overpressure and leakage, that may occur in pipelines and installations in the process of natural gas regional production, the current industrial control and alarm systems cannot accurately reflect the real state of the equipment, and the single-parameter early warning has a higher rate of error judgement, which is insufficient in practicality. A collaborative prediction and warning method for process parameters related to upstream and downstream stations in a natural gas production area was tested. Aiming at the characteristics of natural gas region with many stations, complex production process and diverse monitoring data, firstly, the parameters of each station were downgraded to extract the key process parameters of each station. Then, the key parameters are evaluated and grouped by correlation, and a multivariate nonlinear lasso regression prediction model was established with the highly correlated parameters in the same group as the independent variables. At the same time, a long and short-term memory prediction model was established for the key parameters, and a comparison analysis of the prediction results was performed to determine the dynamic prediction and early warning of natural gas production. Comparative analysis of the prediction results of the two models was used to determine the dynamic thresholds for coordinated early warning of regional production. The results show that the method can not only effectively reduce the misjudgment of single-value anomalies, but also locate the anomalous stations and points, which is of high practical value.

natural gas regional production  /  alarm  /  process parameters  /  correlation degree  /  collaborative early warning
Yong ZHAO, Ai-jun YIN, Hao CHENG, Qian LI, Lin-cheng JI, Qian-ying WU. Collaborative Prediction and Warning of Associated Process Parameters in Natural Gas Regional Production[J]. Science Technology and Engineering, 2025 , 25 (2) : 560 -566 . DOI: 10.12404/j.issn.1671-1815.2308734
Year 2025 volume 25 Issue 2
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Article Info
doi: 10.12404/j.issn.1671-1815.2308734
  • Receive Date:2023-11-08
  • Online Date:2025-12-05
  • Published:2025-01-18
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  • Received:2023-11-08
  • Revised:2024-10-18
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Affiliations
    1 Chongqing Gas Mine of Southwest Oil and Gas Field Branch of Petrochina, Chongqing 400021, China
    2 College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
    3 School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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