Corrosion and Reliability Assessment of Inspected Pipelines

Onshore pipelines are the widest and most common means of transportation for hydrocarbons. Also, onshore pipelines are known to be the safest transportation means due to their low accident frequency and a small number of incidents affecting the population, in contrast to other means of transportation such as trains or trucks. Besides, the flexibility of building new paths makes onshore pipelines more cost-effective. Pipelines can be buried underground or lie above the ground in extremely corrosive soils or river crossings.

Pipelines have different safety barriers depending upon the nature of the fluids transported, i.e., flammable, toxic, and explosive substances, and the aggressiveness of the surroundings. For instance, cathodic protection and coatings are external protection barriers against corrosion. Regardless of these protection measures, aging, natural disasters, and third-party actions have produced significant LOC events with relevant economic, environmental, and human consequences. These consequences are reported in accident databases such as CONCAWE (Europe), PHMSA (United States), TSB (Canada), and ANLA(Colombia). This chapter presents the context of onshore pipelines, especially transmission pipelines for hydrocarbon transportation from processing plants to distribution centers/storage facilities.

Pipelines condition is space-dependent, defined by the soil properties surrounding the pipeline, the way pipes are installed (e.g., underground, aboveground), and the efficiency of protection measures (e.g., coatings, cathodic protection). Pipelines are subjected to different threats, which can be classified as manufacturing or mechanical failures, operational failures, third-party actions, natural forces, or corrosion attacks. This chapter focuses on the corrosion thinning at the inner or the outer pipelines walls. Corrosion defects may follow different degradation mechanisms depending on the properties of the soil surrounding the pipe, the fluid being transported, and the mechanical condition of the pipe. This chapter describes the pipelines main corrosion mechanisms and their common preventive measures. Corrosion mechanisms include uniform (general) corrosion, pitting, erosion-corrosion, stray current corrosion, Microbiologically-Influenced Corrosion (MIC), and stress corrosion cracking. This chapter also presents the main contributions from the soil properties, operation and fluid factors, and the material properties of the corrosion degradation. The main objective is to identify the principal factors favoring corrosion degradation of underground pipelines.

Corrosion represents a reduction of the effective wall thickness designed to support the operating conditions of the pipeline. This metal loss may trigger a loss of containment of the transporting fluid, leading to further socioeconomic or environmental consequences. In this regard, the pipe inspection and their evaluation results are central to defining a condition-based maintenance policy. The information obtained from In-Line Inspections is relevant because it supports decisions about future interventions upon acceptability criteria. However, the level of conservatism may lead to economic losses. ILI inspections commonly report metal loss concerning corrosion defects, cracks, gouges, and dents. These flaws reduce pipe resistance to withstand the intended operation and surrounding loads, which makes it more prone to failure to plastic deformation, leak, or burst depending on the wall thickness consumption and the material strength. In engineering design, the distinction between failure and safe condition is typically defined in terms of a limit state function using the systems resistance and applied load. This chapter presents the main approaches for evaluating a permanent deformation and burst for intact and corroded pipelines, considering their relevance in maintenance and risk assessment decisions. This chapter evaluates the main available approaches concerning their conservatism level regarding each models assumptions.

Pipeline corrosion is uncertain by nature. It depends on several uncontrolled parameters, such as the transporting fluid, the surrounding soil, pipeline geometry, and material strength. As mentioned in the previous chapter, pipeline operators can monitor corrosion at the inner/outer wall using ILI measurements every 2 to 6 years. However, these inspections are subject to uncertain measurements as well as how the corrosion will evolve between consecutive inspections. Uncertainties include the degradation model, the location of the defects, and the local uncertainties of the inspection tool. The degradation uncertainties are related to the lack of knowledge (epistemic uncertainty) of how each defect evolves. Also, local variations of features affect the degradation process, e.g., material properties, stress, temperature, or pressure. The corrosion degradation process changes with time, and finally, the data comes from imperfect inspection results. ILI measurements may hide existing defects that did not fulfill the detection requirement of the inspection tool. Also, the reported defects can have inaccurate locations and sizes, depending on the implemented PIG tool. This chapter focuses on the local inspection uncertainties and those associated with the temporal modeling of the degradation corrosion. The corrosion prediction is required to support further decisions to maintain adequate pipeline integrity. ILI measurements indicate the pipeline condition, which allows updating the corrosion predictions between inspections and identifying further critical pipeline segments. Different parameters such as soil aggressiveness, operating parameters, or the fluid would affect the obtained predictions. This chapter introduces the uncertainties associated with ILI inspections and the prediction of the degradation process.

Chapter 5 discussed sources of uncertainty identified during metal loss detection and degradation. These uncertainties affect the extent and location of the corrosion defects reported by the ILI measurements, posing additional challenges for pipeline monitoring. In this chapter, some relevant questions are raised in terms of corrosion spatial variability, such as the following: How the soil influenced the corrosion spatial distribution? Is it possible to describe the spatial variability of defects’ depth? How can an onshore pipeline be divided depending on its actual condition? Is there a spatial correlation among corrosion defects, or are they randomly located? For this purpose, a statistical analysis of the corrosion spatial variability is proposed considering three different scales. The first scale implements the records from the entire pipeline, with a basic soil classification to study the effect of the surrounding soil and identify the main features of deep defects. The objective is to characterize the soil in the surroundings and to provide tools for predicting pipe corrosion based on the reported measurements. The second scale divides the pipeline to identify segments with critical conditions in which further analysis should be implemented. Finally, the third scale evaluates information about the corrosion spatial autocorrelation. The spatial dependencies of the corrosion measurements are characterized based on statistical indicators such as Moran’s I. The objective is to give some insights into how defects are affected by their neighbors.

The statistical “blind-approach” in the last chapter focused on how spatial variability could be described at three main scales (i.e., full, segment, and defect-based). This approach considered the data reported from at least one ILI measurement and basic soil classification. However, new defects will appear between consecutive inspections, mainly because of the aggressiveness of the surrounding soil, the fluid being transported, and the inspection capabilities. Dealing with new defects implies two main processes determining the number of defects for a given time and spatial distribution. Based on the above, this chapter proposes an approach to identify and characterize new defects between two consecutive inspections. The characterization considers how they are distributed, interact with old defects, and how new defects can be simulated. For this purpose, a point pattern analysis is proposed based on the reported locations of each corrosion defect. This chapter considers a matching process between the two inspections to identify the defects not reported in the first inspection. This process is not straightforward because defects may be shifted from their real location (i.e., location errors). Hence, point pattern matching seeks a feasible transformation that maps the location of the defects from the first to the second inspections. Besides, defect distribution is evaluated considering the Complete Spatial Randomness (CSR) assumption, and the new defects are spatially predicted based on a non-homogeneous Poisson process.

This chapter describes the methodology to evaluate the reliability of corroded pipelines. The overall methodology distinguishes three main sections: the generation of new corrosion defects, the corrosion degradation process, and the reliability approach. The generation of new defects starts by identifying rates in time and after simulating their initiation time and random locations. The corrosion degradation section uses existing and generated defects to identify potential clusters or “corrosion colonies”. Each of these clusters follows a degradation process. Regarding the reliability assessment, a failure probability is evaluated following spatial and temporal analyses.

The proposed approach shown in Part I will be implemented in a real case study based on the results from two consecutive In-Line Inspections 2 years apart. The inspections were determined using an MFL tool at an underground pipeline that includes an aerial section due to a river crossing. This chapter presents the main parameters of the case study, including the type of soil and the detected corrosion defects. Besides, this chapter assesses the main extent of the metal loss based on three scales described in Chapter 6: A full-scale using the entire records; a medium-scale using a partition of the records in the axial or circumferential direction; and finally, a small-scale assessment that focuses on particular defect locations, orientation, and separation.

This chapter presents the results from the statistical “blind-approach” proposed in Chapter 6. As mentioned above, three main scales were considered: a full, segmented, and defect-based scale. These scales do not intend to make multiscale modeling, i.e., to assess the corrosion degradation in a coupled way; on the contrary, the intention is to analyze different scales of the condition of the case study. The full-scale analysis implements the seven soil categories mentioned before to detect which conditions tend to be more aggressive at the outer wall. The analysis was also implemented with the data at the inner wall for comparison purposes. In the inner wall, the influence of the soil parameters would be diminished, and no influence is expected. The segmented scale uses the changepoint approach to divide the pipeline based on the depth, length, and width measurements. The segmentation allows decision-makers to focus on critical segments instead of the entire pipeline. Finally, the defects scale uses Moran’s scatterplot and correlogram and the relationship between the areas and volumes of the neighbors of each defect to inspect their spatial autocorrelation. This autocorrelation allows identifying further clusters where spatial independence assumptions are no longer valid, and further spatial predictions can be proposed.

This chapter presents the results of new corrosion points based on the framework proposed in Chap. 7. This framework uses two consecutive inspections in a matching process to classify the corrosion records as new or old. The spatial distribution of these new corrosion points is evaluated under a null hypothesis of Complete Spatially Randomness (CSR) against the alternatives of clustered or dispersed distributions. Finally, the location of these new corrosion points is simulated using a non-homogeneous Poisson process. The possible interaction with old corrosion points is evaluated following a repulsion or attraction permutation test. This chapter aims to present the main results of this framework based on the measurements from both pipe walls and inspections.

This chapter presents the results for the reliability framework proposed in Chap. 8. The framework consists of three main stages: In the first place, new corrosion defects are generated and located on the pipeline. This approach contemplates a Homogeneous Poisson Process (HPP) to determine the number of new defects and the Non-Homogeneous Poisson Point Process (NHPPP) described in the previous chapter to simulate their location on the pipe. The second stage clusters the defects and implements a stochastic degradation process. For this purpose, three clustering criteria and two synthetic learning approaches are compared to identify the “corrosion colonies” and their equivalent dimensions. Regarding the degradation process, a stochastic approach based on Lévy process is implemented to simulate the corrosion evolution and the Mean Time to Failure (MTTF). Finally, the third stage evaluates a combined failure probability under a spatial and temporal assessment, using dynamic segmentation and a failure region.