Markus R. Dann

Influence of grade on the reliability of corroding pipelines

This paper focuses on a comparative analysis of the reliability associated with the evolution of corrosion between normal and high-strength pipe material. The use of high strength steel grades such as X100 and X120 for high pressure gas pipeline in the arctic is currently being considered. To achieve this objective, a time-dependent reliability analysis using variable Y/T ratios in a multiaxial finite strain analysis of thin-walled pipeline is performed. This analysis allows for the consideration of longitudinal grooves and the presence of companion axial tension and bending loads. Limit states models are developed based on suitable strain hardening models for the ultimate behavior of corroded medium and high strength pipeline material. In an application, the evolution of corrosion is modeled in pipelines of different grades that have been subjected to an internal corrosion inspection after a specified time which allows for a Bayesian updating of long-term corrosion estimates and, hence, the derivation of annual probabilities of failure as a function of time. The effect of grade and Y/T is clearly demonstrated.

Hierarchical Modeling of Pipeline Defect Growth Subject to ILI Uncertainty

Pipeline deterioration arises chiefly as the result of various types of internal and external corrosion processes, which are typically subject to several uncertainties. They include material uncertainties, uncertainties in external influences such as loading and environmental variations, uncertainties in operating conditions, various spatial and temporal uncertainties, inspection uncertainties, and modeling uncertainties. Typically, the metal loss time-path at one defect feature may be quite different from the metal loss time-path in a neighboring location even when subject to supposedly similar loading, material and environmental circumstances. On top of that, in-line inspections (ILI) of pipeline systems affected by deterioration are performed infrequently and suffer from considerable uncertainty due to sizing errors and detectability. The present paper provides a Hierarchical Bayes framework for corrosion defect growth. While a full Hierarchical Bayes analysis is practical only for selected critical defect features, we also develop a simplified method based on multi-level generalized least squares. The latter method is useful for scanning large defect inspection data sets. Two detailed examples of the approach are presented.Copyright

Reliability of Burst Limit States for Damaged and Corroded High Strength Pipelines

High strength steels (X100 and X120) that are being considered for high pressure gas pipelines differ from conventional steels by exhibiting lower work hardening capacity, lower strain to failure and softening of their HAZ. These differences impact burst limit state and tensile limit state, in addition to crack arrest. In this paper, the impact of the variations in mechanical properties on the reliability of pipe limit states involving ductile burst of damaged or corroded pipe is examined. The paper presents the results of burst limit state analysis using state-of-the-art plastic burst models of strain hardening pipe and considering all the uncertainties that impact the margin of safety of pipes subject to internal pressure. Intact pipes, corroded pipes and externally damaged pipes are considered. A case study comparing the differences between normal strength (X60) pipeline and high strength (X100) pipeline is also presented.Copyright

Automated matching of pipeline corrosion features from in-line inspection data

The integrity assessment of corroded pipelines is often based on in-line inspection (ILI) results. Before determining the corrosion growth for the integrity assessment, the detected corrosion features from two or more ILIs need to be matched with respect to their location in the pipeline. The objective of this paper is to introduce a framework for automated feature matching. The input for the framework is the locations of all detected corrosion features and girth welds from each ILI. Using a multi-step approach, the size of several ILIs with a possibly large number of features is reduced to a set of independent smaller problems to match efficiently the corrosion features. The results include the matched features for the subsequent corrosion growth analysis and the identification of outliers that cannot be matched. The applied probabilistic matching assigns to each feature pair a probability of being a match to reflect the inherent uncertainty in the matching process. The proposed framework replaces manual matching, which can be time intensive and prone to errors, particularly for internal corrosion with high feature densities. It reliably matches features in pipelines and supports the integrity and risk assessment of pipeline systems.

Assessing global change when data are sparse

Natural disasters and large-scale industrial accidents are rare events having very low probability of occurrence. They may result in high consequences for society, the environment and the economy. Media reports often suggest that the occurrence and severity of such hazards is perceived to be changing. Change may be a consequence of global environmental changes such as global warming, tectonic and geological changes, or changing human activity. The assessment of global hazard change using standard statistical methods can be challenging due to sparse or lacking data leading to increased parameter uncertainties. To compensate for the sparseness of data, a hierarchical model approach is introduced where similar hazards (or groups of hazards) are combined within one model. Statistical inference must then ‘borrow information’ from similar hazards resulting in informed risk analysis and decision making. As an example in this paper, the hierarchical approach is applied to the question: “Have tsunamis becoming more or less frequent in the last century?”

Pipeline Corrosion Growth Modeling for In-Line Inspection Data Using a Population-Based Approach

To manage the integrity of corroded pipelines reliable estimates of the current and future corrosion growth process are required. They are often obtained from in-line inspection data by matching defects from two or more inspections and determining corrosion growth rates from the observed growth paths. In practice only a (small) subset of the observed defects are often reliably matched and used in the subsequent corrosion growth analysis. The information from the remaining unmatched defects on the corrosion growth process are typically ignored. Hence, all decisions that depend on the corrosion growth process such as maintenance and repair requirements and re-inspection intervals, are based on the information obtained from the (small) set of matched defects rather than all observed corrosion anomalies.A new probabilistic approach for estimating corrosion growth from in-line inspection data is introduced. It does not depend on defect matching and the associated defect matching uncertainties. The reported defects of an inspection are considered from a population perspective and the corrosion growth is determined from two or more defect populations. The distribution of the reported defect sizes is transformed into the distribution of the actual defect sizes by adjusting it for detectability, false calls, and sizing uncertainties. The obtained distribution is then used to determine the parameters of the assumed gamma-distributed corrosion growth process in order to forecast future metal loss in the pipeline. As defect matching is not required all reported corrosion defects are used in the probabilistic analysis rather than the truncated set of matched defects. A numerical example is provided where two in-line inspections are analyzed.Copyright

Pragmatic Approach to Estimate Corrosion Rates for Pipelines Subject to Complex Corrosion

Corrosion is a common degradation process for most oil and gas pipelines in operation that can lead to leak and rupture failures. To avoid failures due to corrosion, integrity management plans for pipelines require fitness-for-service (FFS) assessments and remaining life analysis of the corrosion features that are detected by in-line inspections (ILIs). The objective of the present paper is to support the deterministic integrity and remaining life assessment of pipelines by introducing a pragmatic approach for the determination of corrosion rates from two inspections. The proposed approach is primarily tailored towards upstream and subsea pipelines that are subject to very high density internal corrosion rather than transmission pipelines with low to moderate densities of external features.ILI data may be subject to significant measurement errors and feature matching for two ILIs can become highly unreliable if high-density corrosion is present. To address these uncertainties, the backbone of the proposed approach is to focus on corrosion clusters rather than individual corrosion pits and a filtering process is utilized to identify true corrosion growth. The introduced approach is supported by theoretical knowledge and practical experience. The approach can be easily executed in spreadsheet software tools without the application of advanced statistical and probabilistic methods for the deterministic remaining life assessment in practice.Copyright

Probabilistic FAD and Ductile Tearing Assessment

The assessment of cracks in pipelines is one of the key elements for the integrity verification and the remaining lifetime evaluation of pipelines. This crack assessment is commonly performed through Failure Assessment Diagram (FAD) methods in which two failure criteria are combined, i.e. brittle fracture and plastic collapse. FAD assessments are, however, highly deterministic and do not take into account the various uncertainties that affect cracking. A probabilistic FAD tool is developed that accounts for these uncertainties and determines the probability of failure as a function of the crack dimensions, steel grade and pipeline characteristics. The failure probabilities are associated with a reliability index β, which is commonly used to describe structural safety. The results allow an objective comparison of different cracks across a wide range of operating characteristics, and can be used to construct a reliability-based inspection plan. Multiple parameter studies are executed in which the influence of specific variables on the reliability level is quantified.

Spatial Hazard Rate Modeling Using Hierarchical Bayes Methods

Abstract Risk mitigation and accident prevention within a spatially distributed system require a good handle on the spatial distribution of accident rates and fatality rates. This is helpful in optimising and prioritising decisions with respect to the design, maintenance and risk management of new and existing infrastructure. Hierarchical Bayes methods of analysis are used (i) to correlate accident fatalities and damages to site-specific indicators (in the case of a road network they include traffic density, speed limit and other indicators); and (ii) to perform a spatial analysis of hazard rates. The (unknown) random field of accident fatality rates is estimated and/or other accident- related hazard rates based on detailed accident reports of events spread randomly throughout the system are considered. To demonstrate the method of analysis, accident data from the national road and traffic data bank for the southern region of Norway are matched to an existing geographical database containing spatial information regarding possible causal factors.

The extraordinary in decision-making: myth, apology or opportunity?

Abstract Robustness, resilience and anticipation are essential aspects of a system’s first line of defence against exceptional hazards and high-impact unexpected events. This paper questions both the principles and the need of digging too deep into the often intangible world of high-consequence low-probability events that have the potential to impact systems and society as a whole. The engineering community has recently taken to referring to certain events as black swans, perfect storms or events caused by a priori unknowable unknowns, as if such appellation would constitute an apology for their sudden occurrence. We show that many of these metaphoric notions are either conceptually inappropriate, mostly from an epistemic point of view; or else, that they have little operational bearing on our knowledge base and on best practice rational decision-making. The case studies illustrate their conceptual and metaphorical role in a world of interconnected networks of everything.