A Physics-informed Latent Variables of Corrosion Growth in Oil and Gas Pipelines

Corrosion defect in oil and gas pipelines is the major risk factor that threatens the structural integrity of buried pipelines. Pipeline corrosion management which typically requires high-resolution inline inspections (ILI) to characterize the corrosion growth process is an important task to assess the corrosion defects. However, the corrosion process is inherently stochastic, temporal-spatial dependent, and driven by various hidden physics-related variables. In the literature, the growth of corrosion defects in pipelines is usually modeled by various stochastic processes imposing many unjustified assumptions about the mean growth path and probability distribution of the corrosion rate. In this paper, we proposed a physics-informed latent variable corrosion growth model that integrates the known physics from complex processes into modeling the relations between observable and latent variables and the actual stochastic process that generate the corrosion time series data. The proposed method consists of 3 main steps. Firstly, the latent and observed variable relations and the underlying stochastic processes governing the corrosion process were modelled by physics-informed regression models. The latent groupings that give rise to the observed time series were identified by using the agglomerative hierarchical clustering method. Finally, the prediction algorithm based on the learned physic-informed regression model was proposed to forecast the process progression. We validated the model by a case study on a simulated corrosion process in oil and gas pipelines, in which both latent variables and ILI data were sampled from pre-defined distributions. The results indicated that the model could predict the growth of corrosion defects and capture the variance of the stochastic processes demonstrated by the low mean absolute percentage errors (MAPE) of 3.0082%, 3.9532%, and 3.6831%, which corresponds to the three corrosion growth processes causes by three types of soil. The proposed model can be used to facilitate the development of reliability models and corrosion management.