Quantification of Uncertainties in Inline Inspection Data for Metal-loss Corrosion on Energy Pipelines and Implications for Reliability Analysis

One of the major threats to the oil and gas transmission pipeline integrity is metal-loss corrosion. Pipeline operators periodically inspect the size of the metal loss corrosion in a pipeline using in-line inspection (ILI) tools to avoid pipe failure which may lead to severe consequences. To predict pipe failure efficiently, reliability-based corrosion management program is gaining popularity as it effectively incorporates all the uncertainties involved in the pipe failure prediction. The focus of the research reported in this thesis is to investigate the unaddressed issues in the reliability-based corrosion assessment to assist in better predicting pipe failure. First, a methodology is proposed to facilitate the use of RSTRENG (Remaining Strength of Corroded Pipe) and CSA (Canadian standards association) burst pressure capacity models in reliability-based failure prediction of pipelines. Use of RSTRENG and CSA models require the detail geometric information of a corrosion defect, which may not be available in the ILI reports. To facilitate the use of CSA and RSTRENG models in the reliability analysis, probabilistic characteristics of parameters that relate the detailed defect geometry to its simplified characterizing parameters was derived by using the high-resolution geometric data for a large set of external metal-loss corrosion defects identified on an in-service pipeline in Alberta, Canada. Next, a complete framework is proposed to quantify the measurement error associated with the ILI measured corrosion defect length, effective length, and effective depth of oil and gas pipelines. A relatively large set of ILI-reported and field-measured defect data is collected from different in-service pipelines in Canada and used to develop the measurement error models. The proposed measurement error models associated with the ILI reported corrosion defect length, effective length, and effective depth is the weighted average of the measurement errors of the corresponding Type I and Type II defects and the weighted factor is the likelihood of ILI reported corrosion defect being a Type I defect (without cluster error) or a Type II defect (with clustering error). A log-logistic model is proposed to quantify the weighted factor. The application of the proposed measurement error models is demonstrated by evaluating probability of failure of a real corroded pipe joint through system reliability analysis

Seismic risk assessment of high-voltage transformers using Bayesian belief networks

Past earthquake records showed that a large magnitude earthquake can cause severe damage to high-voltage substations, which may lead to power disruption for a significant amount of time. A high-voltage transformer is one of the key components of a substation. This thesis proposes a probabilistic framework using Bayesian belief network (BBN) model to predict the vulnerability of a high-voltage transformer for a seismic event. BBN has many capabilities that make it well suited for the proposed risk assessment method. This thesis considers past studies, expert knowledge and reported causes of failures to develop an initial integrated risk assessment framework that acknowledges multiple failure modes. Therefore, the framework incorporates major causes of transformer vulnerability due to seismicity, such as liquefaction, rocking response of transformer, or interaction between interconnected equipment. To demonstrate the application of this framework, this thesis elaborates each step of the framework. Finally, the sensitivity analysis was carried out to evaluate the effects of input variables on transformer damage. The paper also illustrates two predictive models using response surface method (RSM) and Markov chain. The proposed framework is particularly handy to perform, and the results can be useful to support decisions on mitigation measures and seismic risk prediction.Applied Science, Faculty ofEngineering, School of (Okanagan)Graduat

Reliability Consistent Mitigation Criteria for Corrosion Defects on Natural Gas Transmission Pipelines

This article deals with reliability-based mitigation of metal-loss corrosion defects on steel natural gas transmission pipelines. The failure pressure ratios (FPRs) and probabilities of burst associated with corrosion defects with various sizes on representative gas pipelines are evaluated using the ASME B31G Modified model and first-order reliability method (FORM). The pipelines considered have different values of the maximum operating pressure, steel grade, wall thickness, outside diameter and utilization factor. The analysis results suggest that FPR and the reliability index corresponding to the probability of burst follow a linear relationship for pipelines with the same utilization factor. Based on the analysis results, linear regression equations that relate FPR to the reliability index are developed for pipelines with different utilization factors. The equation can be employed to determine the threshold FPR corresponding to a target reliability index (or allowable probability of burst) for a given utilization factor to identify critical corrosion defects for mitigation. This study will facilitate the risk- and reliability-based corrosion management of gas transmission pipelines.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author