Pipeline Corrosion Prediction Method Based on Physics-informed Neural Networks

doi:10.11902/1005.4537.2024.363

Journal of Chinese Society for Corrosion and protection

2025, Vol. 45

Issue (5): 1320-1330 DOI: 10.11902/1005.4537.2024.363

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Pipeline Corrosion Prediction Method Based on Physics-informed Neural Networks

ZHOU Taotao¹^,²^,³, LIU Yingzheng¹^,²^,³(

), ZHENG Wenpei¹^,²^,³, JIANG Hengliang⁴, LIU Haipeng⁴, XIA Gang⁴

1 College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102249, China
2 Key Laboratory of Oil and Gas Safety and Emergency Technology, Ministry of Emergency Management, Beijing 102249, China
3 Key Laboratory of Oil and Gas Production Equipment Quality Inspection and Health Diagnosis, State Administration for Market Regulation, Beijing 102249, China
4 China National Oil and Gas Exploration and Development Corporation Limited, Beijing 102100, China

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ZHOU Taotao, LIU Yingzheng, ZHENG Wenpei, JIANG Hengliang, LIU Haipeng, XIA Gang. Pipeline Corrosion Prediction Method Based on Physics-informed Neural Networks. Journal of Chinese Society for Corrosion and protection, 2025, 45(5): 1320-1330.

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Abstract

With the increasing oil and gas exploration, the number of service pipelines is growing, making the accurate prediction of internal corrosion crucial for pipeline integrity management. To address the limitations of traditional machine learning methods in interpreting and generalizing corrosion rate predictions, the monotonic relationships between temperature, CO₂ partial pressure, and corrosion rate are incorporated into a Physics-Informed Neural Network (PINN). This approach is designed to adhere to mechanistic constraints, avoid overfitting and underfitting, and ensure physical consistency. The PINN model is shown to outperform Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), demonstrating superior accuracy and generalization.

Key words: internal corrosion rate prediction physics-informed neural networks monotonicity in physics

Received: 06 November 2024 32134.14.1005.4537.2024.363

ZTFLH:

TE985

Fund: National Natural Science Foundation of China(72301294);Cooperation Technology Projects of CNPC (China National Petroleum Corporation) and CUPB (China University of Petroleum, Beijing)(ZLZX2020-05);Program for Youth Talents by the China University of Petroleum-Beijing(2462023BJRC016)

Corresponding Authors: LIU Yingzheng, E-mail: liuyingzheng2024@126.com

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	XIA Gang

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https://www.jcscp.org/EN/10.11902/1005.4537.2024.363 OR https://www.jcscp.org/EN/Y2025/V45/I5/1320

Labels	Variable	Variable name	Unit	Range	Average
X₁	T	Temperature	℃	41.7-69.4	58.5
X₂	P	System pressure	Pa	1.52 × 10⁶-3.23 × 10⁶	2.28 × 10⁶
X₃	$p C O 2$	CO₂ partial pressure	Pa	2.9 × 10⁴-3.8 × 10⁴	3.3 × 10⁴
X₄	pH	pH	-	4.2-6.2	5.8
X₅	Vf	Fluid flow rate	m·s^-1	0.37-0.69	0.53
X₆	Cl^-	Cl^- concentration	mg·L^-1	3420-8280	6227
X₇	CO₂ eq	CO₂ concentration	mg·L^-1	15.8-27.4	22.7
X₈	HCO $3 -$	HCO $3 -$ concentration	mg·L^-1	107-208	150
X₉	WC	Water content	%	52.5-63.9	58.6
Y	V_corr	Internal corrosion rate	mm·a^-1	2.289-3.105	2.681

Table 1 Variables in prediction models of corrosion of pipelines

Fig.1 Framework of corrosion prediction model based on PINN

Fig.2 Comparisons of prediction results of various models

Table 2 Errors of prediction results of various models

Fig.3 Corrosion rate vs. temperature curves obtained for pipelines by SVM (a), XGBoost (b), ANN (c) and PINN (d) models under different CO₂ partial presses

Fig.4 Distributions of partial derivatives of prediction points by SVM (a), XGBoost (b), ANN (c) and PINN (d) models with respect to temperature and $p C O 2$

Fig.5 Corrosion rate vs. p $C O 2$ curves obtained for pipelines by SVM (a), XGBoost (b), ANN (c) and PINN (d) at different temperatures

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