Clifford J. Maier

Inelastic Fracture Mechanics Model for Assessment of Crack-Like Flaws

Fitness-for-service assessment of pressure vessels and piping often involves the evaluation of existing or potential crack-like flaws to guard against fracture or leaks that could be caused by the presence of such flaws. This paper presents an inelastic fracture mechanics model that has been developed to evaluate longitudinal surface cracks in pipelines, piping and pressure vessels subjected to internal pressure loading. The model uses the J-integral parameter to predict toughness-dependent failure and an effective flaw concept to predict flow-strength dependent failure. The concepts of the model are reviewed. Then, the model is used to evaluate the results of in-service failures and full-scale burst testing of steel pipe and pressure vessel samples. Application of the model to remaining life assessment based on inspection data and hydrostatic testing results is illustrated. Stress-corrosion cracking (SCC) and fatigue are considered as possible crack-growth mechanisms. Examples of typical remaining crack-growth life calculations are presented using both deterministic and probabilistic methods. The benefits of each method are discussed. Finally, planned future additions to the model are presented.Copyright

Application and Validation of Statistically Based Corrosion Growth Rates

When it comes to managing the integrity of corroded pipelines, operators are confronted with many difficult decisions — one of which is the level of conservatism that is used in pipeline integrity assessments. The financial implications associated with excavation, repair, rehabilitation, and inspection programs typically balance the level of conservatism that is adopted. More conservative approaches translate into more spending, so it is important that repair strategies developed based on the integrity assessment results are effective.As integrity assessment methodologies continue to evolve, so does the ability to account for local conditions. One development in recent years has been the ability to evaluate multiple MFL in-line inspections to determine areas of active corrosion growth, through the combined use of statistics, inspection signal comparisons, and engineering analysis. The authors have previously outlined one approach (commonly known as Statistically Active Corrosion (SAC)) that has been successfully used to identify areas of probable corrosion growth, predict local corrosion growth rates, and maximize the effectiveness of integrity assessments.[1]Validation of the SAC-predicted corrosion growth rates is important for establishing confidence in the process. This is achieved through inspection signal comparisons, integrating close interval survey (CIS) results, and (when possible) field verification. The means by which these methods are used for validating the SAC method are described in this paper.Copyright

IMPROVED COMPARISON OF ILI DATA AND FIELD EXCAVATIONS

The importance of comparing in-line inspectio n (ILI) calls to excavation data should not be underestimated. Ne ither should it be undertaken without a solid understanding of t he methodologies being employed. Such a comparison is not only a key part of assessing how well the tool performed , but also for an API 1163 evaluation and any subsequent use of the ILI data. The development of unity (1-1) plots and the associ ated regression analysis are commonly used to provide th e basis for predicting the likelihood of leaks or failures from unexcavated ILI calls. Combining such analysis with statistica lly active corrosion methods into perhaps a probability of exc eedance (POE) study helps develop an integrity maintenance plan for the years ahead. The theoretical underpinnings of standard reg ression analysis are based on the assumption that the indep endent variable (often thought of as x) is measured without error as a design variable. The dependent variable (often lab eled y) is modeled as having uncertainty or error. Pipeline c ompanies may run their regressions differently, but ILI to f ield excavation regressions often use the ILI depth as the x variable and field depth as the y variable. This is especially the case in which a probability of exceedance analysis is desired invol ving transforming ILI calls to predicted depths for a co mparison to a threshold of interest such as 80% wall thickness. However, in ILI to field depth regressions, both the measured d epths can have error. Thus, the underlying least squares reg ression assumptions are violated. Often one common result is a regression line that has a slope much less than the ideal 1-1 relationship.

A Review of Crack Detection In-Line Inspection Case Studies

This paper describes case studies involving crack detection in-line inspections and fitness for service assessments that were performed based on the inspection data. The assessments were used to evaluate the immediate integrity of the pipeline based on the reported features and the long-term integrity of the pipeline based on excavation data and probabilistic SCC and fatigue crack growth simulations. Two different case studies are analyzed, which illustrate how the data from an ultrasonic crack tool inspection was used to assess threats such as low frequency electrical resistance weld seam defects and stress corrosion cracking. Specific issues, such as probability of detection/identification and the length/depth accuracy of the tool, were evaluated to determine the suitability of the tool to accurately classify and size different types of defects. The long term assessment is based on the Monte Carlo method [1], where the material properties, pipeline details, crack growth parameters, and feature dimensions are randomly selected from certain specified probability distributions to determine the probability of failure versus time for the pipeline segment. The distributions of unreported crack-related features from the excavation program are used to distribute unreported features along the pipeline. Simulated crack growth by fatigue, SCC, or a combination of the two is performed until failure by either leak or rupture is predicted. The probability of failure calculation is performed through a number of crack growth simulations for each of the reported and unreported features and tallying their respective remaining lives. The results of the probabilistic analysis were used to determine the most effective and economical means of remediation by identifying areas or crack mechanisms that contribute most to the probability of failure.Copyright

Interpretation of External Cracking on Underground Pipelines

When external cracks are discovered on underground pipelines, it is sometimes difficult to identify the cracking mechanism. This is especially true when the cracking coincides with local stress discontinuities such as dents, where significant stress fluctuations can occur. Additionally, cracking may initiate by one mechanism and another mechanism may subsequently dominate. For purposes of developing integrity programs for the affected pipeline segment, proper interpretation of the mechanism for crack formation is important. This paper describes the characteristics of, and methods to distinguish between, the common types of time-dependent cracking found on underground pipelines. It also discusses the relationships between the mechanisms for the environmentally assisted cracking phenomena.© 2006 ASME

Probabilistic Assessment of Minor Mechanical Damage

In-line inspection (ILI) of the Trans Alaska Pipeline System (TAPS) using high resolution metal loss tools indicated 77 locations with suspected minor mechanical damage features (MDF). The tools used are able to detect the presence of a suspected feature, and measure indented dimensions, but are insufficient to detect the presence of cracks or gouges needed to reliably assess feature severity based solely on the ILI data. Excavations of 42 sites deemed most severe provided important field data characterizing residual deformation dimensions, the occurrence of gouges or cracks, and allowing a reliable field assessment of defect severity. Upon completion of the excavations, 35 possible MDF locations remained unexcavated. An engineering evaluation was undertaken to assess whether or not these remaining minor MDF pose a threat that is significant enough to warrant excavation. Multiple assessment methods were utilized including deterministic, probabilistic, and risk assessment methods. The probabilistic assessment of 35 unexcavated MDFs was performed using PCFStat; or P ressure C ycle F atigue Stat istical Assessment, which uses Monte Carlo simulation to estimate remaining fatigue life. PCFStat performs 1,000’s of simulations for each case where the input parameters are randomly selected from expected distributions. Of particular importance is the fatigue environment of the location. The results of the probabilistic assessment were used to estimate the potential for failure of remaining MDFs. The results suggest that 25 of 35 unexpected damage features had a POF of less than 10−4 over the remaining expected pipeline life cycle and thus are unlikely to fail. Alyeska considered a combination of probabilistic, deterministic and risk assessment results to decide on the actual locations to be examined. The results of probabilistic analysis also were found to support the outcome of the operator’s risk-based evaluation process.Copyright

Probabilistic Assessment of Axial Crack-Like Flaws and Remaining Life in Onshore Pipelines

The predicted failure pressure and estimated remaining life of axial crack-like flaws are two key parameters pipeline operators use to develop excavation programs and set re-assessment intervals following an assessment (in-line inspection or hydrostatic pressure test, for example). Deterministic approaches routinely use conservative input values, such as specified minimum or worst-case conditions, leading to potentially overly conservative conclusions. Probabilistic approaches, on the other hand, can account for inherent variability and provide probabilities of failure; however, there are no current approaches to define an acceptance threshold in the onshore pipeline industry. This paper discusses the probabilistic approach Det Norske Veritas (USA), Inc., (DNV) uses to assess axial crack-like flaws. DNV’s approach incorporates an inelastic fracture mechanics model in combination with Monte Carlo simulations and Paris Law fatigue crack growth to estimate the cumulative probability of failure over time. Topics include the application of this methodology for two primary cases: (1) the defect population can be described with a certain degree of confidence (in-line inspection) and (2) the defect population cannot (hydrostatic pressure test). The potential for using the methodology for determining a case-by-case acceptance threshold will also be explored.Copyright

Pressure Cycle Fatigue: A Statistical Assessment Approach

Operational pressure cycle fatigue (PCF) is one of the integrity threats managed by pipeline operators. Usually, hazardous liquid pipeline operators are most interested in the effects of pressure cycles since these pipelines inherently experience more significant pressure cycles than natural gas pipelines. The parameters considered in the assessment of operational pressure cycles include pipe geometry (diameter and wall thickness), mechanical properties of the pipe, distribution of hypothetical defect sizes that may exist in the pipeline, and pressure cycles. In performing these assessments, the most conservative value for each parameter is commonly used for predicting a time to failure. As such, the results are inherently overly conservative. A statistical assessment method, PCFStat, has been developed to more appropriately model the input parameters used in the assessment of operational pressure cycle fatigue; especially for cases where the deterministic approach identifies relatively short remaining fatigue lives. A distribution of each of the input parameters is developed, and then a Monte Carlo simulation of these parameters is performed. The results produced by this analysis are then used to evaluate the probability of a failure (leak or rupture) for a defined time interval.Copyright