Leveraging a rich amount of data in our corporate SDE to create, both tabular and spatial, to create text-based models for SPS used for pipeline design, optimization and leak detection. A lot of list building and converting it to text lines, resulting in a sizable text file from spatial data.

1. Spatial to Text:
FME Outside of
GIS
By: Patrick Cheng

2. Objectives
Create models for simulating fluid
in pipelines used in Synergi
Pipeline Simulator [SPS]
● Automate data integration for
pipeline design, optimization,
and leak detection.
● Keep up-to-date with business
demands of using hydraulic
profiling/pressure models.

3. Challenge
Automate a previously month-long
model creation process.

4. Solution
FME Workflow
Integrate data and
convert into a text-
based format
SPS
Fully functioning system
GIS spatial data
+
PODS attribute data
START FINISH

5. B b
Input

6. FME Workspace

7. Basic Breakdown: Part 1
Objective:
• Obtain attributes from PODS
database and As-built/ILI generated
pipeline geometry
• Calculate length weighted averages
for attributes such as wall thickness
and operating pressures
• Prepare geometry to be segmented
by lateral (smaller pipelines)

8. Basic Breakdown: Part 2
Objective:
• Convert spatially projected data
back to a Cartesian coordinate
system
• Apply new adjusted coordinates to
all aspects of the text attribute to be
further used: valves, start mid and
end points of pipeline, facilities.

9. Basic Breakdown: Part 3
Objective:
• Provide elevation profile that can be
used by SPS
• Combine entire elevation profiles
into one attribute feature (attributes
with over 100 lines of text)

10. Basic Breakdown: Part 4
Objective:
• Establishing trigger to create a SPS
ready file
• Putting everything in text format into
a basic text file
• SPS takes a specific file type
[.inprep] but in reality is a just a text
file so outputting .txt is renamed to
.inprep

11. Output

12. Output
• File can be directly imported
into SPS
• Geometrically similar to real
world placement of assets
• Text formatting allows for cut
and paste into existing
models without modifications
to create much complicated
systems

13. Output

14. Results
● A fully automated system that has replace
manual building of models
● File format that can be imported directly into the
software without further modifications
● 1 month of effort has been reduced to 1-2 hours.

15. Benefits
● Increased standardization and accuracy of
pipeline attributes.
● Automation of model creation.
● Quick turnaround modelling allowing for further
analytics for proposed new builds and new
pipeline to be added to already existing models
● Create models that are visually relatable to real
world placement of assets.
● Time is saved to free up resources and staff
workload.

16. Tips
● ListBuilder, Sorter, and
Concatenator are helpful for
creating multiline attributes.
● ExpressionEvaluator is helpful
for creating scaling factors.
● StringConcatenator is helpful
for generating script syntax.

17. Thank you!
Questions?
- Patrick Cheng, Plains Midstream Canada