Metal-loss corrosion is a major threat to the structural integrity and safe operation of underground oil and gas pipelines worldwide. The reliability-based corrosion management program has been increasingly used in the pipeline industry, which typically includes three tasks, namely periodic high-resolution inline inspections (ILIs) to detect and size corrosion defects on a given pipeline, engineering critical assessment of the corrosion defects reported by the inspection tool and mitigation of defects. This study addresses the core involved in the reliability-based corrosion management program. First, the stochastic process in conjunction with the hierarchical Bayesian methodology is used to characterize the growth of defect depth using imperfect ILI data. The biases, random scattering errors as well as the correlations between the random scattering errors associated with the ILI tools are accounted for in the Bayesian inference. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to carry out the Bayesian updating and numerically evaluate the posterior distributions of the parameters in the growth model. Second, a simulation-based methodology is presented to evaluate the time-dependent system reliability of pressurized energy pipelines containing multiple active metal-loss corrosion defects using the developed growth models. Lastly, a probabilistic investigation is carried out to determine the optimal inspection interval for the newly-built onshore underground natural gas pipelines with respect to external metal-loss corrosion by considering the generation of corrosion defects over time and time-dependent growth of individual defects. The proposed methodology will facilitate the reliability-based corrosion management for corroding pipelines.

Pipeline integrity management refers to an approach of understanding and operating pipelines in a safe and reliable manner. In this work, firstly, a probabilistic predictive model for internal corrosion of natural gas pipelines subject to aqueous CO2/H2S environment has been proposed. The model regards uniform and pitting corrosion as two main corrosion mechanisms and has been calibrated with the experimental data in a deterministic framework. Methodologies of simulating and accounting for temporal and spatial variabilities of operating parameters have been proposed and applied to the model for field applications. The model has been validated against field data from eight wet gas gathering pipelines in a probabilistic framework. Secondly, a reinforcement learning (RL)-based maintenance scheduler has been proposed for pipeline maintenance optimization problems by leveraging the proposed predictive corrosion model and the Q-learning and Sarsa algorithms. A case study has shown the superiority of the proposed maintenance scheduler over the periodic maintenance policy in reducing the maintenance costs. Finally, the previous two parts of work have been integrated into a pipeline system integrity management software featuring pipeline health monitoring, corrosion prognosis, system-level failure analysis, sensor placement optimization, and inspection/maintenance optimization. A case study has been provided to demonstrate the capabilities of the software.

This book describes technical and practical aspects of pipeline damage. It summarizes the phenomena, mechanisms and management of pipeline corrosion in-service. The topics discussed include pipelines fracture mechanics, damage mechanisms and evolution, and pipeline integrity assessment. The concept of acceptable risk is also elucidated and the future application of new knowledge management tools is considered.

Corrosion is one of the main deteriorating mechanisms that degrade the energy pipeline integrity, due to transferring corrosive fluid or gas and interacting with corrosive environment. Corrosion defects are usually detected by periodical inspections using in-line inspection (ILI) methods. In order to ensure pipeline safety, this study develops a cost-effective maintenance strategy that consists of three aspects: corrosion growth model development using ILI data, time-dependent performance evaluation, and optimal inspection interval determination. In particular, the proposed study is applied to a cathodic protected buried steel pipeline located in Mexico. First, time-dependent power-law formulation is adopted to probabilistically characterize growth of the maximum depth and length of the external corrosion defects. Dependency between defect depth and length are considered in the model development and generation of the corrosion defects over time is characterized by the homogenous Poisson process. The growth models unknown parameters are evaluated based on the ILI data through the Bayesian updating method with Markov Chain Monte Carlo (MCMC) simulation technique. The proposed corrosion growth models can be used when either matched or non-matched defects are available, and have ability to consider newly generated defects since last inspection. Results of this part of study show that both depth and length growth models can predict damage quantities reasonably well and a strong correlation between defect depth and length is found. Next, time-dependent system failure probabilities are evaluated using developed corrosion growth models considering prevailing uncertainties where three failure modes, namely small leak, large leak and rupture are considered. Performance of the pipeline is evaluated through failure probability per km (or called a sub-system) where each sub-system is considered as a series system of detected and newly generated defects within that sub-system. Sensitivity analysis is also performed to determine to which incorporated parameter(s) in the growth models reliability of the studied pipeline is most sensitive. The reliability analysis results suggest that newly generated defects should be considered in calculating failure probability, especially for prediction of long-term performance of the pipeline and also, impact of the statistical uncertainty in the model parameters is significant that should be considered in the reliability analysis. Finally, with the evaluated time-dependent failure probabilities, a life cycle-cost analysis is conducted to determine optimal inspection interval of studied pipeline. The expected total life-cycle costs consists construction cost and expected costs of inspections, repair, and failure. The repair is conducted when failure probability from any described failure mode exceeds pre-defined probability threshold after each inspection. Moreover, this study also investigates impact of repair threshold values and unit costs of inspection and failure on the expected total life-cycle cost and optimal inspection interval through a parametric study. The analysis suggests that a smaller inspection interval leads to higher inspection costs, but can lower failure cost and also repair cost is less significant compared to inspection and failure costs.

This book presents a unique collection of contributions from some of the foremost scholars in the field of risk and reliability analysis. Combining the most advanced analysis techniques with practical applications, it is one of the most comprehensive and up-to-date books available on risk-based engineering. All the fundamental concepts needed to conduct risk and reliability assessments are covered in detail, providing readers with a sound understanding of the field and making the book a powerful tool for students and researchers alike. This book was prepared in honor of Professor Armen Der Kiureghian, one of the fathers of modern risk and reliability analysis.

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.

Metal-loss corrosion is a major threat to the structural integrity and safe operation of underground oil and gas pipelines worldwide. The reliability-based corrosion management program has been increasingly used in the pipeline industry, which typically includes three tasks, namely periodic high-resolution inline inspections to detect and size corrosion defects on a given pipeline, engineering critical assessment of corrosion defects reported by the inspection tool and mitigation of defects. The work reported in this book addresses the engineering challenges involved in the reliability-based corrosion management program, including the probabilistic corrosion growth modeling based on imperfect inspection data, time-dependent reliability evaluation, and optimal inspection interval determination for corroding pipelines.