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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
 and 
Yilan Shen

Yilan Shen

Postdoctoral Workstation of China Construction BankBeijing, China
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Shen, Yilan
  
Mar 19, 2025
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Published Online: Mar 19, 2025
Received: Nov 06, 2024
Accepted: Feb 04, 2025
DOI: https://doi.org/10.2478/amns-2025-0415
Keywords
© 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
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Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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