Traditionally, pipeline health is determined by periodical run-throughs of sensor tools of the pipeline. The resulting data is then used to determine actions on the pipeline such as exploration digs and repairs. These sensor tools can be of varying technologies such as in-line magnetic sensors, ultrasonic tools, as well as indirect electrical surveys. With no standardized format nor spatial component analytics for these tools provided me with an awesome opportunity to provide deeper insights with FME. Using FME to standardize the data and then spatialize the data in both 2D and 3D allows our pipeline integrity team to analyze these pipelines in a much more detailed fashion to observe pipeline health. Not only are engineers able to utilize GIS to observe if pipeline anomalies are caused by environmental factors, the engineers are now able to layer many vintages of various tools to observe anomaly growth and target problematic issues far in advance away from catastrophic events happening.

1. Temporal Analysis on Pipeline
Health and Risk Assessment

2. 20
22
FME
User
Conference
Patrick Cheng
GIS Analyst
Plains Midstream Canada
UBC B.Sci’13 Physical
Geography
Certified FME Professional

3. 20
22
FME
User
Conference
Challenge:
Rich amount of Pipeline Integrity Data often
sits and dies as spreadsheets, can FME be
used to repurpose and give them deeper
meaning

4. 20
22
FME
User
Conference
I believe…
FME can be leveraged to
create an effective
pipeline integrity
analytical tool that can be
used to perform spatial
and temporal analytics

5. 20
22
FME
User
Conference
We are also convinced that:
FME can be used as a
safety and scheduling
tool in the Oil and Gas
industry

6. 20
22
FME
User
Conference
Agenda
● Introduction to Integrity Data and its
Richness
● Identifying Opportunity in the Data
● What are the Challenges
● Solutioning and Algorithm Run Through
● Results and Learnings
● Organic and Future Growth

7. 20
22
FME
User
Conference
Deeper Dive into the Data

8. 20
22
FME
User
Conference
What is Integrity Data?
Integrity Data is pipeline health reports derived from
various methods of detection such as:
In Line Inspection with Magnetic Flux Leakage
(MFL) Data

9. 20
22
FME
User
Conference
What is Integrity Data? (cont.)
Integrity Data is pipeline health reports derived from
various methods detection such as:
Ultrasonic Crack Detection Data

10. 20
22
FME
User
Conference
What is Integrity Data? (cont.)
Integrity Data is pipeline health reports derived from
various methods detection such as:
Indirect Current (DCVG/ACVG) Inspection

11. 20
22
FME
User
Conference
What is Integrity Data? (cont.)
Integrity Data is pipeline health reports derived from
various methods detection such as:
Cathodic Protection Current Inspections

12. 20
22
FME
User
Conference
Opportunity?
These tools all have similar goals, which is to detect
anomalies & deficiency in pipelines health and have
similar outputting spreadsheets.
Not only is the data from various tools, but also have
different vintages.
How Can We Unify all these formats, tools and vintages
together?

13. 20
22
FME
User
Conference
Challenges?
Every vendor of the data has a slightly different format
for their outputting spreadsheet format
Accounting for inevitable error of wrong positional call
outs, whether by slippage of the tool or misalignment
Results needs to be accessible to non-GIS users

14. 20
22
FME
User
Conference
Solution: Standardize Data & Spatialize
Standardization and
Ingestion to PODS
Spatialize into GIS Data
with FME
Source:
Spreadsheet Format
Non-Standardized
Result:
Platform to visualize
Integrity data and
perform deeper
analytics

15. 20
22
FME
User
Conference
Solutioning & Algorithm Run Through

16. 20
22
FME
User
Conference
Data Standardization
FME is our premier tool for the ETL (Extract, Transform, Load) process into our PODS (Pipeline
Open Data Standard) database
This process was extended to ingest the already existing tables in the PODS hierarchy (ILI_Data &
ILI_Cluster)

17. 20
22
FME
User
Conference
Spatializing into GIS Data

18. 20
22
FME
User
Conference
Account for Slippage and Error
Error in tool runs are accounted for by scaling data to a
master/trusted tool run that creates the geometry of the pipeline
anomalies

19. 20
22
FME
User
Conference
Account for Slippage and Error (cont.)
Scaling is done by running against this in-house algorithm

20. 20
22
FME
User
Conference
Spatialize According to Angle, Clock Position and
Exact Sizing
Originating Position of Anomalies are varied by vendors and technology, this portion of the workbench accounts for
call outs for (center, upper left, center left, and etc.)

21. 20
22
FME
User
Conference
Creating a Welcoming and Easy to Use Environment
Create a Splayed Pipeline: unroll the pipeline to visually see where the anomaly is positioned
Create a Weld Joint Line: Establish visual quick referencing points

22. 20
22
FME
User
Conference
End Product & Key Learnings

23. 20
22
FME
User
Conference
End Result
Snippet of Pipeline with a cluster of anomalies data is overlaid with
two vintages of data and from two different types of tools
(Scale 1:20)

24. 20
22
FME
User
Conference
End Result (cont.)
Immediate ‘Wins’
• Ability to predict causes of anomaly (eg: close to waterbody, in a valley, possible interference from
3rd
party lines) as part of being able to integrate with a visually GIS platform
• Ability to cross reference various tools to observe whether there is anomaly growth due to
compounding pipeline health factors (eg: Cathodic disbondment/Cracking & external and internal
pipeline degradation)
• Ability to rapidly reference data spatially, making using a GIS platform the premier location for
further analytics for the integrity engineering team

25. 20
22
FME
User
Conference
End Result (cont.)
Key Learnings
• Spreadsheet data, or legacy data in general, can contain a wealth of data. With FME there are
countless possibilities to extend traditionally boring data
• By leveraging many vintages of data past and incoming surveys, an opportunity to observe
development temporally has naturally occurred and because a major value statement
• Organic Growth of this

26. 20
22
FME
User
Conference
Organic Growth & On-Going Developments

27. 20
22
FME
User
Conference
End Result: Change in Behavior
Organic Growth and New Use Cases:
• Finding older vintages of integrity data and overlaying with newer data (various differing and similar
tool technology) has enabled temporal analytics to observe growth of possible failure spots
• Using temporal analytics to schedule digs and repair at a greater confidence level and reducing
time for actionable tasks to improve pipeline safety
Current New Growth Use Case:
• Developing an Integration with history dig repairs to ensure work quality and if growth area of
pipeline issues persist
• Further develop algorithms to have FME or possible machine learning software to recognize
troubled or growing issues on a pipeline

28. 20
22
FME
User
Conference
Digging Deeper into
Integrity Data existing in
Spreadsheet format has
Paid Off!

29. 20
22
FME
User
Conference
Resources
PODS: https://pods.org/about/

30. 20
22
FME
User
Conference
Call to Action
Don’t let spreadsheets bore you,
they hold immense amount of
information that can be used to
develop incredible tools.

31. Thank You!
Patrick.Cheng@plainsmidstream.com
linkedin.com/in/patrick-cheng-fmep-66a70056