A novel PINN-STAN model for predicting corrosion rate in subsea oil and gas pipelines
Year: 2026
Type: article
Abstract: This paper presents a novel Physics-Informed Neural Network with Spatio-Temporal Attention Network (PINN-STAN) for predicting corrosion rates in subsea oil and gas pipelines. Traditional data-driven models fail to capture multi-factor coupling effects and lack physical consistency under extreme conditions. PINN-STAN overcomes these limitations by embedding corrosion kinetics and mass conservation laws as trainable constraints. It employs a Bayesian spatio-temporal attention mechanism to dynamically adjust the importance of environmental factors and uses a 1D Convolutional Neural Network (CNN) for short-term events and Neural ODEs for long-term trends. Additionally, the model integrates a partial differential equation (PDE) to describe the physical evolution of corrosion processes, ensuring physical consistency by coupling the PDE solution with deep learning features. Experimental results on the CNOOC dataset demonstrate that PINN-STAN achieves a Root Mean Squared Error (RMSE) of 0.0165mm/a and a coefficient of determination (R 2 ) of 0.9950, showcasing its superior predictive accuracy and stability under extreme conditions. Compared to traditional linear regression and machine learning models, PINN-STAN better incorporates physical constraints and multi-scale prediction capabilities, making it well-suited for corrosion assessment in multi-physical field coupling environments. This framework provides a reliable solution for corrosion prediction in complex subsea environments.
Source: Ocean Engineering
Authors: Yawen Zhong, Xingxing Dong, Yifan Li, Sha He
Institutions: Southwest Petroleum University, Southwest Jiaotong University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W7123457519