Ask Our Expert Blog Series – Francisco Barrera

Know Your ILI Toolkit – Understanding the Benefits and Limitations of Each Solution for Transmission and Midstream Pipelines

 

Question 1: What challenges do you typically see Transmission operators face the most when analyzing ILI data?

I’ve noticed 3 key challenges that many of our clients’ face which includes; i) having the appropriate Inline Inspection (ILI) tools for the project, ii) understanding the ILI tool tolerances and finally iii) how to effectively manage non-piggable lines.

 

It takes more than one ILI tool (smart pig) to provide the full picture of your pipeline
One key challenge that we see happen often is the ideology with having one singe source ILI tool to provide an accurate response for all possible threats for a pipeline asset. Currently, there is not one single ILI tool that can do this. Some examples of common ILI tool types used in combination with each other include:

  • Magnetic Flux Leakages (MFL) Tools: These are the most widely used tools for in-line inspection and can be run successfully in both liquids and gas pipelines. These tools can detect different types of defects, such as missing material and damage.
  • Ultrasonic (UT) Tools: These provide the most accurate assessment, and they are widely used for detecting stress corrosion cracking and other forms of corrosion in liquids pipelines.
  • Electromagnetic Acoustic Transducers (EMAT): This is a technology that can detect cracks, weld characteristics, and wall thickness variations in gas pipelines.
  • Caliper Tools: These utilize a set of mechanical fingers to measure pipeline internal geometry changes.

You could do a combo ILI tool such as an MFL-A/Caliper/IMU combo that will provide an assessment of metal loss, dents, and geometry of your pipelines. However, this leads to other issues when using extensive size ILI combo tools, such as requiring additional batteries to power the tool or making sure the pipeline geometry can handle the long-sized combo tool requirements. Regarding other potential threats such as cracking or strain, an MFL-A tool is not the best tool to detect a crack feature. Instead, you might use an EMAT tool to provide you with ILI crack data.

To sum things up, there are a variety of ILI tools available to you. The key to obtaining the right ILI data for your needs is understanding the tool’s advantages and disadvantages. Furthermore, the ILI data provided by the ILI vendor is only a snapshot of the pipeline condition when the ILI was run. There is a potential that things have changed by the time the ILI report is issued to the operator that might be a few months after the run.

 

Understanding your ILI tool (smart pig) tolerances
Operators can often forget about the ILI tool limitations when reviewing ILI data. For example, an operator runs a typical high-resolution MFL-A tool, the ILI report indicates there is a feature that has an external corrosion depth of 12%. The depth-sizing variability is ±10% wt (wall thickness) with 80% certainty (confidence level). This means the feature could be as high as 22% to as low as 2% deep in reality. The only way to confirm the actual size of the feature is by performing a field dig.

Other potential tolerances to consider when dealing with ILI data outside the tool specifications includes human errors. These can lead to potential uncertainty variables in performing integrity assessments of features for traditional dig programs or other engineering assessment types that require ILI data. Some of these can include:

  • Potential interpreting errors of the ILI signal ways by data analyst or vendor software,
  • Field operators working in challenging environmental conditions (e.g. cold environments),
  • ILI tool calibrated incorrectly,
  • ILI tool overspeed which is when the tool ran too fast in the pipeline to accurately capture the ILI data.

How to effectively manage non-piggable lines
Another key consideration for operators is how to effectively manage non-piggable lines so resources and time are not wasted to keep pipelines operating, while ensuring standards and procedures are in place. There are a variety of reasons why a line is not piggable that may include:

  • Inaccessibility for the ILI smart pig to enter the pipeline because the pipeline cannot have a launcher or receiver installed,
  • Operating conditions such as low-pressure flowing line or temperature conditions,
  • Geometry challenges such as multi-diameter pipelines or bi-directional pipelines (extremes bends, dead legs, crossovers, and laterals)

Some alternative options that I suggest operators can take when assessing a non-piggable line such as:

  • Looking at specialized ILI type technologies such a robotic crawler tools, tethered tools or specialty free-swimming tool,
  • Completing Directs Assessments (DA) such as ECDA, ICDA or SCCDA,
  • Performing advance simulations based on pipeline, products, and environmental conditions.

Question 2: What tips would you have for an operator to improve their current data set?

Some tips that I can suggest to operators to improve their current data set include:

  • Use of free online software like Google Earth as another tool to look for various issues such as crossings, constructions, potential encroachment in your ROW, etc. Another use for Google Earth is the historical imagery feature. One can easily go back in time and see various changes such as new subdivisions, roads, and river morphology changes.
  • ILI Vendors can provide additional services other than just running a tool for you. Additional offerings also include Run Com (corrosion) analysis or Strain analyses at a reasonable cost than trying to complete this yourself as these service providers have access to the ILI signal data.
  • Collaboration with recognized industry associations such as the Canadian Energy Pipeline Association (CEPA) or the PHMSA to collaborate with other Transmission and Midstream operators.

Question 3: How can an operator effectively maximize their ILI Data?

For operators to ensure they obtain the most value from their ILI data, it is important to have an accurate understanding of your ILI program. There are different programs available based on a cost/benefit ratio. An example of this is the option of having a lower cost program, running cleaning pigs and/or chemical to prevent future issues vs. running an expensive technology program such as laser scanning of a dig feature that will also predict and mitigate future problems.

In addition, operators should set and maintain metrics (KPIs) like unity plots and confirm the ILI tools are performing as indicated when compared to the field to verify data. This is valuable information that sometimes operators forget to use, such as calibrate your corrosion algorithm or selecting your next ILI tool for your next run.

Finally, the use of Machine Learning (ML) or software tools like our IRAS ILIAnalyst application provides value for operators to recognize trends of historical ILI data. These computer algorithms predict potential future issues such as growing metal loss features and the associated date that requires a dig with the use of establish corrosion rates. In addition, the use of an algorithm to identify interaction features, such as metal loss and dents, could mitigate potential issues in the future.

To learn more about our ILI solutions and services, please visit our Pipeline Integrity Management Software page or reach out to us directly at info@dynamicrisk.net.


About the Author:

Francisco Barrera, P.Eng, Manager, Integrity Services and Analytics
Francisco is the Manager of the Integrity Services and Analytics team. He leads a team of 12+ professionals responsible for the management of the in-line inspection (ILI) program, data analysis of ILI surveys, and other related activities within Dynamic Risk. Francisco has been with Dynamic Risk for over 14 years working within the Engineering department. Francisco provides guidance to clients on integrity management scope, maintenance planning and overall integrity management. Francisco Barrera is a Professional Engineer and has a technical background in both Engineering and GIS. His education includes a civil engineering degree from Lakehead University, a post-graduate certificate from Sault College for GIS, and a civil engineering technologist diploma from Confederation College. Francisco is a professional engineer registered with the Association of Professional Engineers and Geoscientists of Alberta (APEGA).