Solution

Decommissioning Strategy

Decommissioning Strategy

Balancing Cost, Risk, and Flexibility in a Large Onshore Gas Asset
Balancing Cost, Risk, and Flexibility in a Large Onshore Gas Asset
Upward view from inside an oil drilling rig structure with metal pipes and platforms, sunlight shining through the framework against a blue sky.
Upward view from inside an oil drilling rig structure with metal pipes and platforms, sunlight shining through the framework against a blue sky.

Exploring hundreds of thousands of decommissioning sequences — balancing cost, seismic risk, and flexibility to create a robust close-out plan.

Client(s)

NAM

Sector

Oil & Gas

Challenge

Decommissioning

Decision lens

Activity Planning

Product

-

Geography

Europe

The challenge

The challenge

As production from a large onshore Dutch gas asset phased out, the operators faced the challenge of designing a robust footprint reduction plan.


The task was highly complex:


  • Production must continue at required annual and daily volumes until cessation.

  • Operating costs depend heavily on the number of active clusters and pipelines.

  • Seismic exposure varies across production locations, making some areas riskier than others.

  • Stakeholders have competing priorities - from minimising cost and seismic risk to maximising flexibility and free cash flow.


Traditional methods for sequencing the decommissioning of wells, clusters, and pipelines are manual and time-consuming. They struggled to account for the evolving rules of the game, multiple value drivers, and deep uncertainties such as capacity profiles and execution pace.

As production from a large onshore Dutch gas asset phased out, the operators faced the challenge of designing a robust footprint reduction plan.


The task was highly complex:


  • Production must continue at required annual and daily volumes until cessation.

  • Operating costs depend heavily on the number of active clusters and pipelines.

  • Seismic exposure varies across production locations, making some areas riskier than others.

  • Stakeholders have competing priorities - from minimising cost and seismic risk to maximising flexibility and free cash flow.


Traditional methods for sequencing the decommissioning of wells, clusters, and pipelines are manual and time-consuming. They struggled to account for the evolving rules of the game, multiple value drivers, and deep uncertainties such as capacity profiles and execution pace.

Solution

Flow diagram with multiple nodes connected by curved blue arrows, showing a path starting at “EKL” (marked in red as start) and ending at “SLO” (marked in green as end). The arrows illustrate a looping, complex route between various labeled nodes.
Flow diagram with multiple nodes connected by curved blue arrows, showing a path starting at “EKL” (marked in red as start) and ending at “SLO” (marked in green as end). The arrows illustrate a looping, complex route between various labeled nodes.
Flow diagram with multiple nodes connected by curved blue arrows, showing a path starting at “EKL” (marked in red as start) and ending at “SLO” (marked in green as end). The arrows illustrate a looping, complex route between various labeled nodes.

We designed an AI-assisted decision support approach that framed the decommissioning challenge as a “game” to be mastered.


The tool allowed the asset team to:


  • Explore hundreds of thousands of possible decommissioning sequences.

  • Compare strategies across multiple value drivers (OPEX, seismic exposure, reinstate cost, flexibility, capacity).

  • Test robustness under key uncertainties such as timing of nitrogen plant start-up or maximum execution pace.

  • Visualise the impact of different stakeholder priorities through scenario analysis.


By using self-play, the AI uncovered optimised decommissioning strategies that respected all constraints while making trade-offs transparent.

Results

Clearer trade-offs

transparent comparison of cost, risk, and flexibility across strategies.

Optimised sequences

AI-derived plans that outperformed existing manual schedules.

Stakeholder alignment

scenarios provided a common ground for decisions in a highly complex, politically sensitive environment.

Results

Clearer trade-offs

transparent comparison of cost, risk, and flexibility across strategies.

Optimised sequences

AI-derived plans that outperformed existing manual schedules.

Stakeholder alignment

scenarios provided a common ground for decisions in a highly complex, politically sensitive environment.

Results

Clearer trade-offs

transparent comparison of cost, risk, and flexibility across strategies.

Optimised sequences

AI-derived plans that outperformed existing manual schedules.

Stakeholder alignment

scenarios provided a common ground for decisions in a highly complex, politically sensitive environment.

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Want to learn more about this solution? Get in touch and ask for a demo.

Contact us

Want to learn more about this solution? Get in touch and ask for a demo.

Contact us

Want to learn more about this solution? Get in touch and ask for a demo.

Contact us

Want to learn more about this solution? Get in touch and ask for a demo.

Contact us