System reliability analyses and optimal maintenance planning of corroding pipelines

The failure of corroding pipeline joints may induce severe consequences. However, maintenance is expensive due to the cost of excavating and repairing a single joint and typically a significant number of joints that need repair. It is central to develop an optimal cost-effective maintenance strategy that balances cost and safety. A key component of the strategy is the reliability based condition evaluation of pipeline joints. The focus of the research reported in this thesis is therefore developing efficient reliability assessment methods for pipeline individual joints, and developing an optimal maintenance framework for the entire pipeline system. First, efficient system reliability methods relying on the first-order reliability method (FORM) and important sampling (IS) are developed for the assessment of the time-dependent probabilities of small leak and burst failure of pipeline joints containing multiple corrosion defects. In addition, a novel method is developed within the FORM to obtain the design points efficiently. An improved equivalent component approach for evaluating multi-normal integrals is also developed to improve the efficiency of the FORM for system reliability analysis. In addition, a multi-objective optimization-based maintenance framework for corroding pipeline systems is formulated optimizing three objectives, i.e. the conditioned probabilities of burst and small leak, respectively, and repair cost. An improved genetic algorithm with a pre-training population is utilized to investigate the optimal Pareto front. The benefits of this framework enable decision makers to access a series of non-dominated optimal repairing solutions with respect to multiple conflicting objectives

Numerical study for reliability assessment of a corroding pipe to flange weld joint over its lifetime

Numerical model of a corroded weld joint connecting pipe to flange using a finite element method was developed, and coupled with usual stress models. The structure material has been characterized experimentally. Monte Carlo simulations, FORM and SORM methods have been applied for estimating reliability and failure probability under corrosion and residual stress effects. A numerical case with various corrosion rates was conducted to determine the reliability of the welded connection pipe to flange. The obtained results show that the heat affected zone is very sensitive to corrosion effects due to the welding process

Development of Probabilistic Corrosion Growth Models with Applications in Integrity Management of Pipelines

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

Comparative study on the efficiency of simulation and meta-model-based Monte Carlo techniques for accurate reliability analysis of corroded pipelines

Estimation of the failure probability for corroded oil and gas pipelines using the appropriate reliability analysis method is a task with high importance. The accurate prediction of failure probability can contribute to the better integrity management of corroded pipelines. In this paper, the reliability analysis of corroded pipelines is investigated using different simulation and meta-model methods. This includes five simulation approaches, i.e., Monte Carlo Simulation (MCS), Directional Simulation (DS), Line Sampling (LS), Subset Simulation (SS), and Importance Sampling (IS), and two meta-models based on MCS as Kriging-MCS and Artificial Neural Network based on MCS (ANN-MCS). To implement the proposed approaches, three limit state functions (LSFs) using probabilistic burst pressure models are established. These LSFs are designed for describing the collapse failure mode for pipelines constructed of low, mid, and high strength steels and are subjected to corrosion degradation. Illustrative examples that comprise three candidate pipelines made of X52, X65, and X100 steel grade are employed. The performance and efficiency of the proposed techniques for the estimation of the failure probability are compared from different aspects, which can be a useful implementation to indicate the complexity of handling the uncertainties provided by corroded pipelines

Dynamic corrosion risk-based integrity assessment of marine and offshore systems

Corrosion poses a serious integrity threat to marine and offshore systems. This critical issue leads to high rate of offshore systems degradation, failure, and associated risks. The microbiologically influenced corrosion (microbial corrosion), which is a type of corrosion mechanism, presents inherent complexity due to interactions among influential factors and the bacteria. The stochastic nature of the vital operating parameters and the unstable microbial metabolism affect the prediction of microbial corrosion induced failure and the systems’ integrity management strategy. The unstable and dynamic characteristics of the corrosion induced risk factors need to be captured for a robust integrity management strategy for corroding marine and offshore systems. This thesis proposes dynamic methodology for risk-based integrity assessment of microbially influenced corroding marine and offshore systems. Firstly, a novel probabilistic network based structure is presented to capture the non-linear interactions among the monitoring operating parameters and the bacteria (e.g., sulfate-reducing bacteria) for the microbial corrosion rate predictions. A Markovian stochastic formulation is developed for the corroding offshore system failure probability prediction using the degradation rate as the transition intensity. The analysis results show that the non-linear interactions among the microbial corrosion influential parameters increase the corrosion rate and decrease the corroding system's failure time. Secondly, a dynamic model is introduced to evaluate the offshore system's operational safety under microbial corrosion induced multiple defect interactions. An effective Bayesian network - Markovian mixture structure is integrated with the Monte Carlo algorithm to forecast the effects of defects interactions and the corrosion response parameters’ variability on offshore system survivability under multispecies biofilm architecture. The results reveal the impact of defects interaction on the system's survivability profile under different operational scenarios and suggest the critical intervention time based on the corrosivity index to prevent total failure of the offshore system. Finally, a probabilistic investigation is carried out to determine the parametric interdependencies' effects on the corroding system reliability using a Copula-based Monte Carlo algorithm. The model simultaneously captures the failure modes and the non-linear correlation effects on the offshore system reliability under multispecies biofilm structure. The research outputs suggest a realistic reliability-based integrity management strategy that is consistent with industry best practices. Furthermore, a dynamic risk-based assessment framework is developed considering the evolving characteristics of the influential microbial corrosion factors. A novel dynamic Bayesian network structure is developed to capture the corrosion's evolving stochastic process and the importance of input parameters based on their temporal interrelationship. The associated loss scenarios due to microbial corrosion induced failures are modeled using a loss aggregation technique. A subsea pipeline is used to demonstrate the model performance. The proposed integrated model provides a risk-based prognostic tool to aid engineers and integrity managers for making effective safety and risk strategies. This work explores the microbial corrosion induced failure mechanisms and develops dynamic risk-based tools under different operational scenarios for systems’ integrity management in the marine and offshore oil and gas industries

Application of Bayesian Networks to Integrity Management of Energy Pipelines

Metal-loss corrosion and third-party damage (TPD) are the leading threats to the integrity of buried oil and natural gas pipelines. This thesis employs Bayesian networks (BNs) and non-parametric Bayesian networks (NPBNs) to deal with four issues with regard to the reliability-based management program of corrosion and TPD. The first study integrates the quantification of measurement errors of the ILI tools, corrosion growth modeling and reliability analysis in a single dynamic Bayesian network (DBN) model, and employs the parameter learning technique to learn the parameters of the DBN model from the ILI-reported and filed-measured corrosion depths. The second study develops the BN model to estimate the probability of a given pipeline being hit by third-party excavations by taking into account common preventative and protective measures. The parameter learning technique is employed to learn the parameters of the BN model from datasets that consist of individual cases of third-party activities. The ILIs are infeasible for a portion of buried pipelines due to various reasons, which are known as unpiggable pipelines. To assist with the corrosion assessment for the unpiggable pipelines, the third study develops a non-parametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties as the predictors. The last study develops an optimal sample size determination method for collecting samples to reduce the epistemic uncertainties in the probabilistic distributions of basic random variables in the reliability analysis of corroded pipelines

DEVELOPING HYBRID PHM MODELS FOR PIPELINE PITTING CORROSION, CONSIDERING DIFFERENT TYPES OF UNCERTAINTY AND CHANGES IN OPERATIONAL CONDITIONS

Pipelines are the most efficient and reliable way to transfer oil and gas in large quantities. Pipeline infrastructures represent a high capital investment and, if they fail, a source of environmental hazards and a potential threat to life. Among different pipeline failure mechanisms, pitting corrosion is of most concern because of the high growth rate of pits. In this dissertation two hybrid prognostics and health management (PHM) models are developed to evaluate degradation level of piggable pipelines, due to internal pitting corrosion. These models are able to incorporate multiple sensors data and physics of failure (POF) knowledge of internal pitting corrosion process. This dissertation covers both cases when in some pipeline's segments the pit density is low and in some segments it is high. In addition, it takes into account four types of uncertainty, including epistemic uncertainty, variability in the temporal aspects, spatial heterogeneity, and inspection errors. For a pipeline segment with a low pit density, a hybrid defect-based algorithm is developed to estimate probability distribution of maximum depth of each individual pit on that segment. This algorithm considers change in operational condition in internal pitting corrosion degradation modeling for the first time. In this way a two-phase similarity-based data fusion algorithm is developed to fuse POF knowledge, in-line inspection (ILI) and online inspection (OLI) data. In the first phase, a hierarchical Bayesian method based on a non-homogeneous gamma process is used to fuse POF knowledge and in-line inspection (ILI) data on multiple pits, and augmented particle filtering is used to fuse POF knowledge and online inspection (OLI) data of an active reference pit. The results are used to define a similarity index between each ILI pit and the OLI pit. In the second phase, this similarity index is used to generate dummy observations of depth for each ILI pit, based on the inspection data of the OLI pit. Those dummy observations are used in augmented particle filtering to estimate the remaining useful life (RUL) of that segment after the change in operational conditions when there is no new ILI data. For a pipeline segment with a high pit density, a hybrid population-based algorithm is developed to estimate the probability density function of maximum depth of the pit population on that segment. This algorithm eliminates the need of matching procedure that is computationally expensive and prone to error when the pit density is high. In this algorithm three types of measurement uncertainty including sizing error, probability of detection (POD), and probability of false call (POFC) are taken into account. In addition, initiation of new pits between the last ILI and a prediction time is modeled by using a homogeneous Poisson process. The non-linearity of the pitting corrosion process and the POF knowledge of this process is modeled by using a non-homogeneous gamma process. The estimation of these two algorithms are used in a series system to estimate the reliability of a long pipeline with multiple segments, when in some segments the pit density is low and in some segments it is high. The output of this research can be used to find the optimal maintenance action and time for each segment and the optimal next ILI time for the whole pipeline that eventually decreases the cost of unpredicted failures and unnecessary maintenance activities

Risk-based evaluation of pitting corrosion in process facilities

Pitting is one of the most challenging forms of corrosion to study and model due to complex pit behavior. Pitting can occur in different engineering alloys and can lead to catastrophic consequences. Pits are usually latent or difficult-to-detect and resulting degradation often causes in-service failure of process equipment. Therefore, the ability to predict pit behavior is key to design and maintenance of assets. In particular, pitting corrosion is a significant challenge in marine environments and offshore operations due to remoteness of operations and hidden damage under insulations. Thus, the ability to assess risk and estimate remaining life of assets affected by pitting corrosion is necessary for timely maintenance and safe operation of assets. This thesis proposes a methodology to assess and dynamically update the risk of pressurized components affected by pitting corrosion. To take into consideration the time-dependent growth of pits, the application of non-homogenous Markov process is proposed to model the maximum pit depth. The integration of the developed maximum pit model into a pressureresistance model is proposed to predict the failure probability of affected components. An economic consequence analysis model is developed to estimate both business and accidental losses due to failure of the affected component. Then, risk is estimated by integrating models developed for probability of failure and associated consequences. The application of Bayesian analysis is proposed to update estimated risk as new inspection data gets available and also as economic condition of the process evolves. This work also proposes a risk management strategy including corrosion prevention, control and monitoring measures to make effective decision related to pitting corrosion. The application of the proposed methods is demonstrated using different case studies