The Integrity Compliance process within CIM provides users the capability to analyze their assessment data in a variety of ways. Selecting the corrosion growth rate (CGR) methodology is just one of the many factors that is selected when designing an analysis per the user’s needs. In fact, there are 20+ ways to customize the CGR calculation alone. Read on as we explain the dizzying array of CGR methods that await your corrosion anomalies.
CIM’s algorithms automatically match anomalies across all inline inspection data sets, regardless of the ILI service provider or number of assessments. Because of this matching capability, a corrosion growth rate based on matched anomalies can easily be calculated. If the “current” anomaly i.e. the anomaly from the most recent inline inspection matches multiple (one-to-many) historic anomalies, CIM can calculate either the minimum, maximum or average change in anomaly depth based on the user’s selection, as described herein.
Example scenario: An anomaly reported in a 2024 report has a depth of 51%. It has no match in the 2019 assessment but matches two 2014 anomalies of 23% and 49%.
If matching results in a positive growth i.e. the current anomaly has a greater depth than previously reported, CIM takes the change in depth, divides by the time elapsed between the two measurements, and converts that into an annualized growth rate.
During an analysis conducted as part of State of Integrity, it was found that for those clients utilizing the CGR calculation in CIM, 85+% were selecting Pit-to-Pit Minimum, followed by Pit-to-Pit Maximum (4%), Historical Growth Trend (4%), Fixed Growth Rate (2%), User Defined (2%) and Half-Life <2%.
‘Fixed Growth Rate’ is typically utilized as a fall-back from other methods, while ‘User Defined’ allows the user to import CGRs into CIM for each anomaly, each joint, etc. to use in growth-related calculations.
‘Historical Growth Trend’ calculates a growth rate from 3 or more reported depths by fitting a multi-point trend line through the anomaly depths. This can be quite useful, especially if you suspect anomaly growth was active, but a recent ILI may provide evidence that it has since been rendered inactive.
Graph 1: An illustration of the Historical Growth Trend method which calculates the CGR by fitting a multi-point trend line through ILI-predicted depths of one anomaly from multiple ILIs.
Another variable to consider when calculating CGR within CIM is the population from which the CGR is calculated: on an individual basis or on a population basis per the following rules:
The following figure illustrates how a CGR would be calculated if ‘Pit-to-Pit Minimum’ is selected along with ‘Individual’ for the growth method.
Figure 2: Decision tree illustrating how CGR would be calculated when ‘Pit-to-Pit Minimum’ and ‘Individual’ is selected.
CIM’s machine learning classification algorithm identifies clusters during ingestion and displays that information in the standardized ILI report i.e. the Log Features Report. Therefore, each corrosion anomaly is identified as a cluster (parent) or pit (child). The depth of a cluster is typically reported as the depth of the deepest pit. Two or more pits make up a cluster. If pit-to-pit is utilized to calculate CGR, the CGR can differ depending on whether clusters or individual pits are matched.
As presented in our 2024 User Group meeting earlier this month, excluding clusters from corrosion growth rate calculations can make a significant impact. One example showed a doubling of the calculated CGR from 5 mils per year (mpy) to 10 mpy. Therefore, the ability to exclude clusters from the CGR calculation and only derive a CGR from the pits is available as a new(ish) attribute in the Integrity Compliance process / analysis setup. Consult the User Guide or contact your account executive to learn more.
Figure 3: CIM’s “Joint View” of anomalies from multiple inline inspections.
For more information on the entire library of corrosion growth rate methods, consult the CIM User Guide.