As part of their integrity management system, Oil and Gas operators carry out internal inspection of their pipelines by intelligent pigging. State of the art MFL and UT inspections are used to detect and accurately size the defects, which are present in the pipeline. The predominant type of defects reported is due to internal corrosion.
It is well established that corrosion is a naturally occurring phenomena. When the conditions are right for corrosion to develop, it starts by a single defect or very few defects which are shallow. Then as the pipeline is operated and corrosion further develops the defects increase in size and numbers.
This paper review several intelligent pigging reports data, and analyze the reported defects in terms of numbers and depth for several pipelines, in order to establish a correlation (mathematical model) between the number of internal corrosion defects and their depth. Defects counts will be made and equations will be developed for several pipelines. These equations will basically establish the number of defects as a function of their depth or vice versa.
More over when multiple intelligent pigging runs on same line are available, these derived equations will be compared with the objective to establish a novel model to determine corrosion growth rate in a non-conventional manner. In fact the models (# of defects and their depth equation) established for different inspections will be compared and a corrosion rate model establishing the increase in number of features and their number over time will be thereafter derived.
Number of Pages
American Petroleum Institute. API 570 Piping Inspection Code. 2018.
American Petroleum Institute. API Standard 1163 In-line Inspection Systems Qualification. 2013.
Pipeline Operators Forum. "Specifications and requirements for in-line inspection of pipelines." Vers. 2016. n.d. www.pipelineoperators.org.
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