Steven J. Polasik

EFFECTIVE MODELING OF FATIGUE CRACK GROWTH IN PIPELINES

Pipeline operators must rely on fatigue crack growth models to evaluate the effects of operating pressure acting on flaws within the longitudinal seam to set re-assessment intervals. In most cases, many of the critical parameters in these models are unknown and must be assumed. As such, estimated remaining lives can be overly conservative, potentially leading to unrealistic and short reassessment intervals. This paper describes the fatigue crack growth methodology utilized by Det Norske Veritas (USA), Inc. (DNV), which is based on established fracture mechanics principles. DNV uses the fracture mechanics model in CorLAS™ to calculate stress intensity factors using the elastic portion of the J-integral for either an elliptically or rectangularly shaped surface crack profile. Various correction factors are used to account for key variables, such as strain hardening rate and bulging. The validity of the stress intensity factor calculations utilized and the effect of modifying some key parameters are discussed and demonstrated against available data from the published literature.Copyright

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

Determining Reassessment Intervals From Successive In-Line Inspections

Pipeline operators around the world use in-line inspections and corrosion control systems to manage the integrity of their systems. Determining when to inspect is a critical consideration, which depends in part on whether corrosion growth takes place between inspections. Remaining life estimates based on estimated corrosion growth rates typically form the basis for reassessment intervals.Remaining life assessments often use assumptions about corrosion rates that, while conservative, can lead to unrealistic results. Excess conservatism leads to short reassessment intervals and unnecessary mitigation. This paper discusses how data analyses can be used to identify and verify areas where corrosion is actually taking place. By identifying and addressing these areas, operators can minimize unnecessary mitigation in low growth areas, ensure high growth areas are mitigated in a timely manner, and extend overall reassessment intervals.This paper discusses an integrated approach to identifying corrosion activity using a combination of statistics, inspection signal comparisons, and engineering analyses. The approach relies on a full understanding of the mechanisms that cause corrosion and its growth. Pipeline operators can use this approach to calculate remaining life, prioritize repairs and mitigation, and extend reassessment intervals. This process is collectively known as Statistically Active Corrosion (SAC) 1,2,3.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

Quantifying the Impact of Assumptions on Predicted Burst Pressure Assessments

The ability for deterministic fracture mechanics assessments to correctly estimate the predicted burst pressure (PBP) is dependent upon the accuracy of the model as well as the assumptions made regarding mechanical properties and defect geometry. Within the assessment of defects identified through field investigation or through ILI programs, the use of extreme bounding values for all input variables can lead to unacceptably over-conservative predictions and impede the ability to achieve a target safety margin.This paper examines the effect multiple assumptions have on the bias between predicted and actual burst pressures for various kinds of defects that have caused in-service and hydrostatic pressure test failures in ERW and flash weld line pipe materials. The predicted failure pressures of defects documented within a recent U.S. Department of Transportation compendium of ERW and flash weld seam failures were analyzed using a variety of scenarios based on knowledge of key nominal parameters such as grade, outside diameter, wall thickness, and the maximum defect length and depth. The ratio of the predicted to actual failure pressure was statistically examined across the scenarios for the different defect types. Observations are made regarding the use of the PBP model in order to statistically quantify the accuracy of the model, which can be used as input for an operator to develop a process that achieves a target safety margin.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

Data Analysis in Parallel With GIS Systems

The processing and integration of data for direct assessment (DA) and in-line inspection (ILI) comparisons is critical to making sound integrity-based decisions. While geographic information systems (GIS) are now commonly used to model pipeline systems, most day-to-day data processing and integration occurs outside of the GIS, for example in Microsoft Excel™. As such, Det Norske Veritas (DNV) developed a data integration tool within Excel™ as part of a large scale stress corrosion cracking direct assessment (SCCDA) program for a major pipeline operator. Linear based data provided by the client (e.g., in-line inspections, girth welds, previous excavations, close interval survey, coating, grade and wall thickness, pressure history, road and water crossings, risk assessments, landowner information, etc.) is processed, analyzed and incorporated into the overlay. This tool provides the ability to integrate any linear based data in a graphical representation of the pipeline along continuous and parallel chainage. The overlay allows for identifying similar locations using criteria that are difficult to program into an algorithm and helps engineers to relate complex factors during the decision making process. The overlay also provides the ability to easily extract data relevant to sites selected for assessment along the pipeline. The data integration tool has already found many applications beyond SCCDA since it provides a robust process to integrate and analyze data in parallel with GIS systems. The overlay provides engineers with a method to make decisions without learning complex GIS programs and has the added ability to feed the results back into GIS systems. Such decision making processes and applications include direct assessment programs, cathodic protection enhancements, risk reduction programs, in-line inspection comparisons, and maintenance activities.Copyright