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Deep learning-based modeling of CO2 corrosion rate prediction in oil and gas pipelines

Jian Cui

Jian Cui

Department of Industrial Engineering and Management, Peking UniversityBeijing, China
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Cui, Jian
, 
Kun Fang

Kun Fang

  • China Petroleum Engineering & Construction Corp. Beijing CompanyBeijing, China
  • Postdoctoral Workstation of China Construction BankBeijing, China
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Fang, Kun
, 
Xueyi Sun

Xueyi Sun

China Petroleum Engineering & Construction Corp. Beijing CompanyBeijing, China
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Sun, Xueyi
, 
Ya Gao

Ya Gao

Department of Industrial Engineering and Management, Peking UniversityBeijing, China
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Gao, Ya
 e 
Yilan Shen

Yilan Shen

Postdoctoral Workstation of China Construction BankBeijing, China
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Shen, Yilan
  
19 mar 2025
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Pubblicato online: 19 mar 2025
Ricevuto: 06 nov 2024
Accettato: 04 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0415
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© 2025 Jian Cui et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Main component analysis results
Main component analysis results

Figure 2.

Prediction of CO2 corrosion rate
Prediction of CO2 corrosion rate

Figure 3.

Relative error of prediction of different models
Relative error of prediction of different models

Analysis of corrosion factor correlation analysis

Evaluation factor G1 G2 G3 G4 G5 G6 G7 G8 G9
G1 1
G2 0.231 1
G3 -0.181 0.215 1
G4 0.123 0.231 0.345 1
G5 0.253 0.325 0.456 0.122 1
G6 0.189 0.123 0.485 0.213 0.213 1
G7 0.231 0.321 0.012 0.005 0.322 0.214 1
G8 0.321 0.231 0.030 0.023 0.125 0.216 0.038 1
G9 0.073 0.062 -0.031 0.082 0.123 0.133 0.052 0.715 1

Model predictability can be compared

Index DBN GAN Transformer Ours
MAE 0.925 0.121 0.212 0.035
MAPE(%) 3.480 4.486 5.872 1.893
RESE 0.0985 0.135 0.321 0.039
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro
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