Improvements in the Engineering Assessment of Metal Loss in ILI
By: Ian Smith, Senior Consulting Engineer, Quest Integrity Group
As seen in the February 2013 issue of BIC Magazine. Download the PDF version.
and assessment capabilities in the pipeline industry are constantly improving
thanks to competitive technology developments. More advanced in-line inspection
(ILI) tools yield better data on pipeline condition, which in turn drives the
need for advanced assessment capabilities to leverage the improved data quality
(FFS) assessments have become increasingly accepted across the pipeline industry
over the past few years. FFS standard API 579/ASME FFS-1 (API 579-2007)
provides guidelines for assessing types of damage affecting pipelines across
loss may be internal or external, in the form of isolated pitting, general
corrosion, axial or circumferentially oriented, or some combination of those
geometries. The high number of variables makes assessing metal loss flaws
complex and highlights the need for a more comprehensive metal loss assessment
method than one that relies on boxed flaws.
identify and size metal loss flaws, ILI data is reviewed through a traditional data
analysis process. To do this, individual metal loss flaws are bounded by a box,
and length, width and depth predictions are provided. Metal loss flaws may be
combined into clusters based on interaction rules. After the data analysis
process is complete, the results are reported in a spreadsheet format.
is common to apply remaining strength pressure calculations (e.g., B31G or 0.85dl)
to those features identified in the spreadsheet. The length and depth that have
been established through data analysis and reported in the ILI report
spreadsheet are the only inputs into the metal loss remaining strength assessments.
differences between actual and predicted flaw dimensions will be reflected in
the results of the assessment. Since these results are used in pressure
de-rating and flaw repair decisions, any inaccuracies can impact the safety of
the pipeline. Errors in length can be due to the often subjective nature of
flaw boxing and interaction during data analysis. Any errors in the maximum
predicted depth will have a direct impact upon the accuracy of the calculated
reduced pressure due to the flaw.
in data processing make it possible for continuous improvement in automated
processing of ILI data. Automated processing is necessary to traverse the very
large ILI data sets acquired today. The combination of automated processing and
human expert intervention form the basis of the improved data analysis process.
more advanced method of assessment of ILI thickness data is to perform a
continuous effective area calculation as described in API 579-2007 directly to
the data set as validated through the data analysis process. In this method,
the data analyst validates the wall thickness data and all of the validated
data is used in the effective area pressure assessment.
process accounts for any interaction between metal loss flaws without the
requirement for interaction rules. Since an effective area calculation uses all
of the critical thickness values to determine the reduced pressure capacity, it
is less sensitive to any inaccuracies in overall depth prediction.
can be focused upon areas of interest as opposed to strict reporting criteria.
For example, the deepest locations can be identified on a per metal loss flaw,
per pipe joint, per defined length or any combination of the three. This
flexibility in reporting can allow for more informative run comparisons. One of
the difficulties in performing run comparisons based upon a comparison of ILI
spreadsheets is matching up metal loss flaws which, due to growth, may have
combined. Being able to compare deepest locations within defined lengths can
provide a more meaningful picture of metal loss growth.
more information, visit www.QuestIntegrity.com/services/inspection-services/pipeline-in-line-inspection or call (281) 786-4700.