At the North Sea Transition Authority (NSTA) we regulate and influence the oil, gas and carbon storage industries. We help drive North Sea energy transition, realising the significant potential of the UK Continental Shelf as a critical energy and carbon abatement resource. We hold industry to account on halving upstream emissions by 2030, working with government, industry and other regulators to accelerate the move to net zero while meeting the UK’s energy demand and security. We aim to be a value creator in everything we do, encouraging and enabling economic benefit and job creation across the UK. We drive greater efficiency through inclusive leadership, data, analysis, stewardship and the use of our powers. Digital and data has a central role in the NSTA, which has been underpinned by our digital people, skills and platform. The NSTA use FME (Desktop and Server) to power its digital platform with updates to APIs that reach thousands of users every month and automating the quality checking of regulatory data submissions from 50+ distinct industry organisations. In our session we’ll appeal to you whether you’re focussed on business benefits, or more comfortable with the technical aspects of a project, because we’ll highlight the power that FME gives to NSTA and highlight why we can’t now do without it! We’ll then dive into the detail, with some specific examples of a few of our key production FME workspaces, and the transformers that add us our “special sauce”. These workspaces collate and quality check the submission of offshore infrastructure datasets, datasets which have been identified as holding key information for the UK energy transition. FME is one of the key tools to facilitate our digital future and we’d like to show you how.

1. North Sea Transition Authority: Using
FME to Regulate and Influence in the
Energy Transition

2. Presenters
The
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of
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Peter
Birch
Senior Geospatial
Specialist
Bee
Smith
Senior Geospatial Manager
NSTA
Oliver
Morris
Business Director
Tensing, UK

3. The
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Agenda
1. Who we are & what we do and why it matters
2. What we use FME for
3. Our journey to date
4. One tricky workspace we built
5. Incremental improvements
6. A quick overview of our FME Coaching
approach
7. Future plans

4. The
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Introduction

5. The
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6. The
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7. The
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NSTA Digital Strategy

8. The
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9. The
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“We got stuck in straight away, we didn’t start
with best practices but it didn’t matter, FME
saved the day”.

10. The
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Our journey to date...

11. The
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We got stuff done
Cool things it does
• Completely automated: Each
workbench initiates the next.
• How it works – Generates test
attributes, reviews outcomes,
emails operator if failures occur.
• Collects statistics on each
submission – Monitors operator
success, aiming for fewer
submissions over time.
• It works!
Not so cool things
• A large, hard-to-navigate
workbench
• Test parameters may change or
new tests added – ran out of screen
real estate
• Takes a long time to run
• It’s not very well organised
• Hard to decipher

12. The
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Then we improved
An evolving Workspace
• Much easier to read and
understand
• 1829 -> 445 transformers
• Does exactly the same thing!
• Still some room for improvement
for next time (aggregate emails
next…)

13. The
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and refined - Data building workspaces

14. The
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Data building spaces
6 types x 3 CRSs
= 18 datasets
7 workspaces
=126 writers
• Custom attributes in each writer
• Nightmare if the schema changes between submission rounds (highly likely!)
• Dynamic readers and writers
• Dynamic schema links to a document I can just make changes to schema in
one place!

15. The
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Incremental improvements

16. The
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18 individual Readers – 6 datasets in 3 CRS
36 Writers – 6 datasets, 3 CRS, and 2 output formats
All with fixed schemas
Static Workspace
Reader with ‘Exposed’ attributes Writer with ‘Manual’ attribute definition

17. The
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Dynamic Workspace
FeatureReader with single output port
Expose ‘fme_feature_type’
Single FeatureReader – 18 feature types combined through the <Generic> output port
Independent of input data schemas – Reading everything every time

18. The
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Writer with dynamic schema definition
Schema from table
Dynamic Workspace
6 Writers producing 36 outputs – 3 CRS, 2 output formats, and 6 feature classes via ‘Fan Out’
All Writers using dynamic schemas taken from an external lookup table

19. The
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Writer with dynamic schema definition
Schema from table
Schema from table
6 Writers producing 36 outputs – 3 CRS, 2 output formats, and 6 feature classes via ‘Fan Out’
All Writers using dynamic schemas taken from an external lookup table

20. The
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of
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Schema from Table

21. The
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FME Coaching
NSTA got stuck in straight away
Sometimes it doesn’t go to plan!
Coaching – keeping you on the right path
Just in time learning for transformers or techniques
Personalised and tailored to NSTA’s particular challenges
Continuous support – ensuring progress doesn’t stall
Knowledge Sharing – Insights into best practices and tips
Feedback Loop – regular catchups and continuous
improvements

22. The
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A few ideas for the future
● Schema Scanner
● FME Hub Transformer

23. Future Plans
Small but mighty – the SchemaScanner
Extracts schema from data sources
Aids in data documentation and validation
Identifies changes in schemas
Details data structure

24. The
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Future Plans

25. ThankYou!
bethel.smith@nstauthority.co.uk
peter.birch@nstauthority.co.uk
omorris@tensing.com

26. The
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Resources
● FME Hub - https://hub.safe.com/
● SchemaScanner -
https://docs.safe.com/fme/html/FME-Form-Documentation/FME-Transformers
/Transformers/schemascanner.htm
● Schema Drift -
https://engage.safe.com/webinars/database-tips-tricks-strategies-for-detecting-
and-handling-schema-drift/