Modern inspection techniques offer an unparalleled insight into the location and dimension of corrosion metal loss features within pipelines; however, to estimate useful remnant life of a particular asset the Pipeline Engineer must make an informed estimate of Corrosion Growth Rate.
Two dominant methods of reasonably direct calculation exist, the first is to use internal or external corrosion future predictive models with operational parameters as inputs. Several appropriate methodologies such as the De Waard & Milliams Model are identified within the appropriate NACE documents for direct assessment.
A second method exists to conduct statistical analysis on the comparison of features reported by multiple inline inspections has been previously demonstrated by Penspen and various other sources. Stability of the solution is related to the number of features matched and the time period in between inspections, additionally it is well understood that various different corrosion mechanisms may be acting at different rates in various regions of a particular pipeline. The Engineer therefore has judgements to make on input data validity, in addition to considering whether past operating conditions are likely to be representative of the probable future operating conditions.
The authors have implemented automated feature matching algorithms to optimise the number of features matched below an acceptable false positive threshold. Matched features and associated metadata are subject to further statistical analysis algorithms which aim to quantify the quality of input data at any particular point on pipeline in addition to providing an analysis of corrosion growth rate.
The authors will detail a methodology which calculates data quality and estimated error in the predicted corrosion growth rate all points along the length of a pipeline enabling the Engineer to make informed choices on appropriate corrosion growth rates for use in further calculation.