Statistical Analysis for Partial Inspection of Heat Exchanger Tube Bundles
Authors: Alathba M. , Jones R. , Laycock N. , Hoeve F. , Ostrowska A. , Terpstra S. , Kuniewski S. 
Published: March 2015

Abstract
The Pearl GTL plant in Qatar is the largest Gas-To-Liquids plant in the world, located north of Doha in Ras Laffan Industrial City. Like any other plant, it has to be maintained carefully, with the right tools and technology. During the forthcoming major turnaround, there is a need for the planned inspection of more than 60 shell and tube heat exchangers, with a total of more than 40,000 tubes. With the obvious desire to minimize the total shutdown cost and duration, it is desirable to only inspect the minimum fraction of the tubes that is necessary to determine their condition with a degree of confidence that is consistent with each individual item's criticality in terms of both safety and production. Statistical data analysis, and in particular a range of techniques based on Extreme Value theory, are an important tool that can assist in this endeavour. In general terms, there is increasing interest within the Oil and Gas industry in the use of statistical methods in asset integrity management, and one major area of application is the planning and evaluation of inspections. The potential benefits from applying these techniques are seen to include more effective management of the risks associated with in-service degradation and, although the techniques have been around for some time, these methods are gaining traction in the industry now that the latest inspection technologies make available more quantitative information. Indeed, statistical analysis is already a fundamental part of some aspects of integrity management; for example, it is a key requirement of the HOIS approach to NII [1].
For the inspection of the tubes in heat exchanger bundles, partial inspection of a bundle is attractive, but requires good understanding of the effectiveness of the inspection; for example, in terms of its ability to determine the likelihood of absence of damage above a critical depth in non-inspected parts of the bundle. Extreme Value Analysis (EVA) is a well known statistical methodology for analyzing extreme types of phenomena such as maximum sea levels or maximum corrosion pit depths [2–4]. However, its performance in inspection applications is strongly dependent on various key properties of the damage distribution in the subject equipment: the damage must be homogeneous, detectable, and sized with reasonable accuracy by the applied inspection technique.
The objective of any inspection is focused on two goals: first, to detect any damage already present that might cause an unwanted failure in the near-term; second, to provide data that will support a robust prediction of the equipment's remaining life. If the expected damage mechanisims are corrosion, thinning or cracking, the objectives of the inspection can be met using typical NDTs such as UT, IRIS, or EC. As mentioned earlier, partial inspection is attractive and is frequently employed. However, even if 30% or 40% of the tubes are inspected, that still leaves a lot of tubes that were not inspected and about which one should still be concerned. This is where EVA comes into play. [4] However, even though one knows in advance that this method will be used to analyse the inspection data, modern shut-downs are planned in extraordinary detail and it is necessary to decide in advance (with the most surety possible) how many tubes in each exchanger will be inspected. This is surprisingly difficult, since it depends on making a good estimate of the amount of damage (if any) that is expected to be present in the exchanger. For example, if there is zero or very little damage expected, then it seems reasonable to plan for only a small percentage inspection. On the other hand, if there is a reasonable chance of significant damage being present - though not enough that you are already planning for replacement of the tube bundle - then it is probably necessary to plan for a more complete inspection. And this decision-making process is further complicated by the need to consider the equipment criticality; i.e. other things being equal, for the more critical items of equipment there is a need for a greater degree of certainty in their condition, and this tends to imply a requirement for a greater percentage inspection.