In our new series “Managing Pipeline Threats – The Way Forward,” we explore the approach necessary to achieve a holistic maintenance mindset when it comes to the integrity management of oil and gas assets. In this first installment, our experts Roland Palmer Jones and Michael Beller primarily focus on diagnostics, the discipline of identifying and collecting the data needed to define a given status. Different approaches to making the most of the collected data and translating it into useful information, such as big data analytics, artificial intelligence and machine learning, will lead to a better use of data, a better understanding of defect histories and mechanisms, faster availability of information, and, ultimately, better management of pipelines in terms of safety, reliability and utilization.

Pipelines are a key element in global energy transportation and constitute the safest, most environmentally friendly way to transport large quantities of oil and gas. They are found in all segments of the oil and gas infrastructure in the up-, mid- and downstream sectors and will retain their important role in the future.

High-pressure pipelines are also important in other industries, including water, mining and chemicals. Within the energy industry, their use may be extended – for instance to the future transportation of hydrogen.

Apart from being extremely useful from a logistics perspective, pipelines are highly valuable assets, in a strategic as well as an economic sense, since they are needed to keep homes warm (or cool), industry running and transport moving. Therefore, they need to be protected. Their safe and economical operation must be ensured at all times.

This is the focus of an effective maintenance process, which includes the need to identify potential threats, assess their impact on mechanical integrity and derive useful information regarding actions to be taken.


In a three-part series starting in this issue, we will explore the approach necessary to achieve a holistic maintenance mindset.

In Part 1, we will address diagnostics, the discipline of identifying and collecting the data needed to define a given status.

In Part 2, we will see how this data can be used to provide information on the present and likely future mechanical integrity of a pipeline.

In Part 3, we will investigate how this condition information can assist in the development of a “predictive maintenance mindset,” address some of the new requirements raised by the Mega Rule for gas pipelines in the US and review some of those requirements from a global perspective.

In this series of articles, we will focus on the actual line pipe and not consider other important parts of the pipeline infrastructure, such as pumps, compressors, valves, etc. Limiting ourselves to the pipe, we need to understand the potential threats that may arise, the data needed to confirm whether those threats are active and the data needed to evaluate the significance of any damage caused by the threats.

API RP 1160 provides a list of the threats identified by the pipeline industry:

  • External corrosion
  • Internal corrosion
  • Selective seam corrosion (external or internal)
  • Stress Corrosion Cracking (SCC)
  • Manufacturing defects
  • Construction and fabrication defects
  • Equipment failure
  • Mechanical damage
  • Incorrect operations
  • Weather and outside force
  • The growth of an initially non-injurious anomaly into an injurious defect via pressure-cycle-induced fatigue

With the possible exceptions of incorrect operations and equipment failure, these threats or their effects can be identified by suitable external or internal inspection technologies and procedures. In many cases, factors that may contribute to the susceptibility to threats (for example depth of cover) or the significance of threats (for example material properties) can also be determined by internal inspection.

Managing pipeline threats requires an understanding of how these threats manifest themselves as well as assessing the effect they may have on the mechanical integrity and reliability of a given pipeline or pipeline system. Managing pipeline threats and minimizing the risk of failure is of course a major driver for regulations around the world.

Important questions arise: Why do certain threats occur? How can these threats be identified and assessed, possibly mitigated, or even prevented? All this results in the need for accurate and robust information about a given pipeline – information of critical importance for decision-making support.

Two major goals go hand in hand: to ensure the safety of a pipeline, protecting life and the environment, on the one hand, and to optimize the process of operating the pipeline, achieving the best possible balance of efficiency and effectiveness, on the other.

Achieving this, in short, requires us to know everything that could potentially affect the status of the pipeline – for every location along its linear extent and at any time.

While this may be a simple statement to make, it does have considerable consequences when you start to think about it.


Good decision-making requires robust, reliable and accurate information – useful information derived through data. Therefore, let us explore what data is required for assessment purposes and how it can be collected. Let us also repeat the challenge regarding fully understanding the state of a given pipeline or pipeline system:

Knowing everything for every location at any time.

As stated above, most of the identified threats can be identified using a variety of inspection techniques. Breaking this down slightly, we can refer to non-destructive examination performed from the inside of the pipe, such as in-line inspection (ILI), or external inspection, either manual or automated.

Using the language of ILI, the manifestation of the threats listed above can be grouped into defect categories, such as geometric anomalies, metal loss, cracks and leaks, and changes in material properties.

In addition, we can identify milestones in the life of a line pipe at which these defects can potentially occur: during steel production in the steel mill, during the manufacturing of the line pipe in the pipe mill, during construction and commissioning of the pipeline, and, finally, during the operational life of the pipeline.

Achieving the above requires the deployment of the latest technology, including advanced sensors and data collection systems, internally and externally with the required resolutions, accuracies and threshold levels. It also requires making the best use of collected data and turning it into useful information. This information must be visualized in a way that is easy to understand and use. Finally, it also requires availability of advanced methods for using the information to define current and future condition for all types of pipelines, including those in harsh and challenging environments.


There is indeed already a wide range of “tools” available today to screen, monitor and inspect pipelines. Just consider all the internal and external inspection techniques, including in-line inspection, the various direct assessment methodologies, and the information provided by SCADA systems or leak detection systems and the associated real-time transient models used.

Let us start with in-line inspection tools (ILI). These tools were originally developed to look for defects in the pipe wall. Different tools and non-destructive testing technologies are used to identify different defects such as dents, metal loss and cracks. Data collected provides information on the geometry, i.e. size and shape, of these features, which in turn is used for integrity assessment purposes. Important parameters are the measurement thresholds that can be achieved (i.e. what is the smallest defect I can pick up?), measurement accuracy (how precisely can I measure the depth, length and width of a defect?) and repeatability (how reliably can I identify a change?).

But defects can interact. Areas of corrosion can be treated as isolated from each other or as interacting, depending on their size and vicinity to each other. However, a specific defect category, such as a metal loss, for instance, can also interact with other defect categories like, say, cracks. But they can not only interact, there can also be a causality between them, i.e. a dent contributing to the initiation of a crack.

This has resulted in the need for ever higher resolution and measurement accuracies in tools as well as the use of multiple data sets, either from combined inspection runs or correlation of consecutive runs.

But the potential threats not only relate to the steel wall of the pipe. The coating, applied for protection, or the cathodic protection system installed to ideally prevent corrosion from taking place, can also deteriorate or malfunction. You need to know about it when it happens – and for the entire line.

Defect assessment, however, does not only need input regarding the presence and geometry of defects, it also requires knowledge of the actual loading conditions on the pipeline at the location of the defect as well as the true material properties.

The stool shown in Figure 2 can be used to visually symbolize the input required for an assessment of the mechanical integrity of the pipe wall. In order to be “stable,” the stool requires three legs to stand on.

Let us look at them:

Figure 2 – The integrity “stool” using a data collection perspective

Figure 2 – The integrity “stool” using a data collection perspective


In order to assess the effect a defect has, we need to find it first. Then we need to size it accurately and, obviously, locate it. This is all included in the leg named “sizing.” It is a strong leg, because today we have a multitude of different and highly accurate inspection technologies that help us to achieve this task.

The next input we need is related to the loading experienced by the pipe. Pipelines are cylindrical pressure vessels. Usually, the internal pressure acting in the line is seen as the major load, but actually a variety of external loads can act on the pipe wall. Examples are bending loads, possibly due to pipeline movement, or axial loads or external pressures caused, for instance, by something very heavy placed on the ground over the pipe.

All the above are forces or moments acting on the pipe wall and inducing mechanical stresses. If a defect is present, it means the load-bearing cross-sectional area of the pipe wall is reduced, causing additional notch stresses. But it does not stop there. Residual stresses are also often present in the weld- or heat-affected zone or locked in stresses from offshore installation or onshore segment tie-in.


All this is represented by the leg called “loading.” This leg is thinner than the sizing leg. Why is this? The stool represents the data collection perspective. Collected data on the true local stress distribution is not readily available and often not known. Notch stresses can be calculated, but residual stresses are not readily available. Also, from an inspection point of view, there are currently only limited options to identify local stresses acting in the pipe wall. That is why in many cases integrity assessment, at least in a first iteration, is performed using the stresses induced by the internal pressure, predominantly hoop stress.

In-line inspection technologies are already available to measure some forms of deformation (which are of course strains), and these can be used to deduce stresses. A further step would be to develop ILI technologies or external techniques to measure stresses directly. Such technologies are currently being developed and may be commercially available in the not-too-distant future.

Finally, we need to know the material properties. Material properties are sometimes not known for a given pipeline, for instance if records are not complete or have been lost. This may result in the need to estimate values or introduce appropriate safety factors during an integrity assessment, which often leads to very conservative results. In some cases, the actual strength properties may be significantly higher than the specified or recorded values, which can result in unrealized potential.

While information on minimum yield strength (SMYS) and ultimate tensile strength is usually readily available, this is not the case for fracture toughness. The latter is a material property value needed to assess cracks. Instead, fracture toughness is usually estimated based on a simple qualitative impact test (Charpy value), but there are discussions on the expert level regarding which correlations between Charpy and fracture toughness are best to use.


Data on material properties is represented by the third leg of the stool, called “material.” This again is a rather weak leg because we are using the inspection perspective.

Internal and external inspection technologies are already being introduced, and will be further developed in the future, to address material properties such as yield strength, ultimate tensile strength and, especially, fracture toughness. Currently, there are only limited choices for in-situ (internal or external) measurement of material properties.

Collecting data in order to derive useful information also requires that the data can be collected for all relevant locations along the length of a pipeline. Pipelines are linear in nature and often run through different terrain. In addition to new pipelines still to be built, the majority of global pipelines are in operation; not all of them are ideally suited for the inspection or monitoring of equipment, including the use of in-line inspection tools.

Therefore, it is often a matter of how to get the inspection device – the sensor that takes readings – to the location of interest. This does not only apply to the high pressure transmission pipelines for which traditional ILI tools have typically been designed for, it also affects the challenging or difficult-to-inspect pipelines that require specialized tools and procedures.

The actual propulsion of an inspection device is thus also paramount. Today, we have a range of free-swimming tools, cable-operated tools and tools that incorporate their own drive. Further developments of inspection devices incorporating their own drive all the way to fully autonomous devices for onshore and offshore use are underway, and fascinating new tools will be introduced in the near future.


The development of technologies to collect data on loading conditions and materials (the two historically neglected legs of the defect assessment stool) combined with ways to deploy inspection technology into ever more challenging situations are all examples of collecting more data about a pipeline – or the pipe wall, to be precise. But there is more usable information based on data collected during the steel and pipe manufacturing process, not to mention pipeline construction.

Further data is available through the SCADA system of a pipeline, and we can derive data relating to the environment of the pipeline, for instance from optical cables to detect the movements and vibrations associated with external mechanical interference. In the future, we will need to use even more of the data available, such as weather records and forecasts, and data on ground level changes, earthquakes and flooding.

This takes us to the next point: Data! Beyond just collecting more and more data, the challenging part is to make sense of that data and translate it into useful information. Not only does this relate to analyzing the collected data, it also concerns the correlation of all that data from different sources to identify trends and recognize patterns. A major part of future technologies in our industry will therefore be the use of big data analytics, artificial intelligence and machine learning to make the most of the available data.

The industry has already started this process, but the effort must and will intensify. All this must be done for pipelines being inspected, for pipelines that have been inspected in the past, for those that may be inspected in the future, and for those that will never be inspected. This path will lead to a better use of data, a better understanding of defect histories and mechanisms, faster availability of information, and, ultimately, better management of pipelines in terms of safety, reliability and utilization.