Book Description

Pipelines are considered to be the most favored and reliable mode for transporting large quantities of gas/liquid. In recent years, because the pipelines are interconnected at a national and global level and because of increasing economic and regulatory constraints in dealing with aging and corroded pipeline systems, pipeline integrity management is an area of increasing relevance in the petroleum industry. Metal deterioration caused by corrosive soil is one of the major threats to the integrity of underground pipeline systems. External localized corrosion is one of the most common defects that can occur under normal operating conditions.In this Dissertation, we consider the external corrosion in pipeline structures in a probabilistic and dynamic manner. Multiple uncertainties from soil environment, pipeline structure, inspection process and maintenance are considered. The correlation between the spatial distribution of the external corrosion defects and the heterogeneous soil properties has been investigated. The correlation analysis is conducted by employing clustering techniques. As for the time domain, the long-time corrosion process is considered as a dynamic stable evolution process which contains three stages: nucleation, propagation and repassivation. In this study, the uncertainties introduced by the in-line inspections are investigated by using calibration methods and detection theories. Both random error and systematic error are assessed. Finally, a comprehensive pipeline maintenance strategy is proposed as an integrated solution to improve the efficiency of inspections and maintenances in industry practice.The contributions of this dissertation include: 1) a probabilistic model for the prediction of metal loss rate in underground pipeline structure is proposed. The model is able to account for the model uncertainty which comes from the imperfect prior knowledge and estimate the PDF of metal loss rate at a specified location. 2) A Bayesian approach for calibrating and estimating the actual external corrosion depth in buried pipeline structures based on an ultrasonic ILI inspection and the clustering technique was developed. We have taken the probability of defect existence into consideration. Hence a more realistic assessment of pipeline integrity can be achieved. 3) A clustering approach based on a hidden Markov random field is established for assessing the spatial distribution of external corrosion in a buried pipeline. The clustering approach presented in this study is easy to implement with the established ICM-EM algorithm. 4) A novel stochastic model framework for predicting the external corrosion growth in buried pipeline structures is presented and a time- and location- dependent maintenance strategy is established. We relate the soil properties with the corrosion growth propagation. The geometric Brownian bridge model was employed to present the corrosion rate evolution and hence is able to represent the inherent time-dependent dynamic property of corrosion growth.