How Equinor used AIM
to save $12M in CAPEX

How Equinor used AIM
to save $12M
in CAPEX

The challenge.

In late 2022, Equinor’s development team was gearing up for the next phase of one of the most important oil fields on the Norwegian Continental Shelf: Johan Sverdrup Phase 3.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Multiple conflicting objectives had to be resolved:

How many wells would be required to reach key targets?
Where should templates be placed to balance drilling cost and infrastructure constraints?
Could longer, more complex wells reduce subsea infrastructure?
What was the best combination of layout, routing, and reservoir access?

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.

And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

In late 2022, Equinor’s development team was gearing up for the next phase of one of the most important oil fields on the Norwegian Continental Shelf: Johan Sverdrup Phase 3.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Multiple conflicting objectives had to be resolved:

How many wells would be required to reach key targets?
Where should templates be placed to balance drilling cost and infrastructure constraints?
Could longer, more complex wells reduce subsea infrastructure?
What was the best combination of layout, routing, and reservoir access?

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.

And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

In late 2022, Equinor’s development team was gearing up for the next phase of one of the most important oil fields on the Norwegian Continental Shelf: Johan Sverdrup Phase 3.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Multiple conflicting objectives had to be resolved:

How many wells would be required to reach key targets?
Where should templates be placed to balance drilling cost and infrastructure constraints?
Could longer, more complex wells reduce subsea infrastructure?
What was the best combination of layout, routing, and reservoir access?

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.

And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

Using AI to explore all options.

Using AI to explore
all options.

Enter AIM (Artificial Intelligence for Maturation) — a joint solution developed by WhiteSpace Solutions in collaboration with Equinor’s Subsurface Excellence and Digital unit.

Unlike traditional field development planning tools that require planners to manually iterate through a handful of predefined concepts, AIM was designed to do something radically different:

Explore every possible combination of well targets & trajectories, template configurations, and pipeline routing options, within engineering and economic constraints.
Present a Pareto front of options, allowing teams to clearly see the trade-offs between cost, complexity, and risk.
And most critically, AIM embedded a human-in-the-loop design, giving engineers full control over design rules, constraints, and the final decision.

Enter AIM (Artificial Intelligence for Maturation) — a joint solution developed by WhiteSpace Solutions in collaboration with Equinor’s Subsurface Excellence and Digital unit.

Unlike traditional field development planning tools that require planners to manually iterate through a handful of predefined concepts, AIM was designed to do something radically different:

Explore every possible combination of well targets & trajectories, template configurations, and pipeline routing options, within engineering and economic constraints.
Present a Pareto front of options, allowing teams to clearly see the trade-offs between cost, complexity, and risk.
And most critically, AIM embedded a human-in-the-loop design, giving engineers full control over design rules, constraints, and the final decision.

Trajectory planning.

AIM uses user-defined drilling and subsurface constraints to explore all viable well paths.

Template optimisation.

AIM then uses well path options to create campaigns including SURF drivers and constraints.

Planning with the full picture.

Planning with the
full picture.

Field development planning often suffers from fragmentation. Subsurface teams propose targets. Drilling engineers push back on inclination. SURF tries to minimize flowline length. Each group optimizes in a silo — and planning becomes sequential, political, and slow.

AIM flips that script by enabling simultaneous, constraint-driven optimization across disciplines. Engineers aren’t just handed a computer-generated plan — they defined the rules, review the outputs, and select the concepts that best balance their needs.

This approach leads to a step-change in planning efficiency:

Months of iteration compressed into days
Manual layout testing replaced by scenario screening
Stakeholder alignment driven by dataAfter two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Field development planning often suffers from fragmentation. Subsurface teams propose targets. Drilling engineers push back on inclination. SURF tries to minimize flowline length. Each group optimizes in a silo — and planning becomes sequential, political, and slow.

AIM flips that script by enabling simultaneous, constraint-driven optimization across disciplines. Engineers aren’t just handed a computer-generated plan — they defined the rules, review the outputs, and select the concepts that best balance their needs.

This approach leads to a step-change in planning efficiency:

Months of iteration compressed into days
Manual layout testing replaced by scenario screening
Stakeholder alignment driven by dataAfter two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Field development planning often suffers from fragmentation. Subsurface teams propose targets. Drilling engineers push back on inclination. SURF tries to minimize flowline length. Each group optimizes in a silo — and planning becomes sequential, political, and slow.

AIM flips that script by enabling simultaneous, constraint-driven optimization across disciplines. Engineers aren’t just handed a computer-generated plan — they defined the rules, review the outputs, and select the concepts that best balance their needs.

This approach leads to a step-change in planning efficiency:

Months of iteration compressed into days
Manual layout testing replaced by scenario screening
Stakeholder alignment driven by dataAfter two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

AIM in practice.

In Phase 3, Equinor’s development team used AIM to integrate the full 3D subsurface and seabed of the field and apply constraints such as:

Avoiding shallow hazards and pockmarks
Adhering to anti-collision margins
Cost rules for well architecture, rig moves, SURF routing and template sizes
Engineering preferences from Phases 1 and 2

Once constraints were defined, AIM began generating well paths using evolutionary algorithms. These were evaluated not just on cost, but also on trajectory complexity, feasibility, and flow assurance implications. The next layer used mixed-integer linear programming (MILP) to identify optimal template locations and routing—essentially assembling a full-field layout from the bottom up.

Hundreds of thousands of scenarios were created, evaluated, filtered, and clustered. Then the most promising ones were presented for detailed engineering review.

Human-in-the-loop.

AIM provides engineers with full control over design rules, constraints, and the final decision.

Using AIM, customers in the Middle East and the Nordics could accelerate their Field Development Planning time 10x, from months to days.

AIM provides engineers with full control over design rules, constraints, and the final decision.

The Result: $12mln CAPEX saving

$12mln CAPEX saving

$12mln CAPEX saving.

Among the most powerful outcomes was AIM’s validation of the project team’s conclusion that fewer, larger templates combined with longer, more complex wells could significantly reduce overall CAPEX.

AIM validated that larger size drilling templates reduced subsea cost
AIM confirmed that accepting long-step out well design allowed a CAPEX reduction by 5-18% relative to a layout minimizing drilling risk
A previously overlooked layout — found by AIM — was adopted as the final concept as it improved the production potential. This lead to direct CAPEX savings of $12mln through that improved concept.

Just as critically, AIM gave Equinor the ability to:

Rapidly and rigorously screen millions of options instead of manually testing a few dozen
Build a digital audit trail of trade-off decisions across Drilling & Wells, Subsurface, and SURF (Subsea Umbilicals, Risers, and Flowlines)
Validate or challenge assumptions with data-backed evidence

The result wasn’t just cost savings—it was confidence in the plan and alignment across disciplines in a shorter timeline.

Among the most powerful outcomes was AIM’s validation of the project team’s conclusion that fewer, larger templates combined with longer, more complex wells could significantly reduce overall CAPEX.

AIM validated that larger size drilling templates reduced subsea cost
AIM confirmed that accepting long-step out well design allowed a CAPEX reduction by 5-18% relative to a layout minimizing drilling risk
A previously overlooked layout — found by AIM — was adopted as the final concept as it improved the production potential. This lead to direct CAPEX savings of $12mln through that improved concept.

Just as critically, AIM gave Equinor the ability to:

Rapidly and rigorously screen millions of options instead of manually testing a few dozen
Build a digital audit trail of trade-off decisions across Drilling & Wells, Subsurface, and SURF (Subsea Umbilicals, Risers, and Flowlines)
Validate or challenge assumptions with data-backed evidence

The result wasn’t just cost savings—it was confidence in the plan and alignment across disciplines in a shorter timeline.

Among the most powerful outcomes was AIM’s validation of the project team’s conclusion that fewer, larger templates combined with longer, more complex wells could significantly reduce overall CAPEX.

AIM validated that larger size drilling templates reduced subsea cost
AIM confirmed that accepting long-step out well design allowed a CAPEX reduction by 5-18% relative to a layout minimizing drilling risk
A previously overlooked layout — found by AIM — was adopted as the final concept as it improved the production potential. This lead to direct CAPEX savings of $12mln through that improved concept.

Just as critically, AIM gave Equinor the ability to:

Rapidly and rigorously screen millions of options instead of manually testing a few dozen
Build a digital audit trail of trade-off decisions across Drilling & Wells, Subsurface, and SURF (Subsea Umbilicals, Risers, and Flowlines)
Validate or challenge assumptions with data-backed evidence

The result wasn’t just cost savings—it was confidence in the plan and alignment across disciplines in a shorter timeline.

What's next?

The success of AIM in Johan Sverdrup Phase 3 and other projects has sparked broader adoption across Equinor and interest from other operators. Future AIM deployments will include:

Integration with flow assurance and economic modeling tools
Expanded optimization capabilities for brownfield tiebacks
Further automation of detailed engineering handover

For WhiteSpace Solutions, this project was another validation of the power of AI-assisted planning — not as a black-box solution, but as a strategic co-pilot that empowers engineers to make better, faster, and more defensible decisions.

The success of AIM in JS Phase 3 and other projects has sparked broader adoption across Equinor and interest from other operators. Future AIM deployments will include:

Integration with flow assurance and economic modeling tools

Integration with flow assurance and economic modeling tools
Expanded optimization capabilities for brownfield tiebacks
Further automation of detailed engineering handover

For WhiteSpace Solutions, this project was another validation of the power of AI-assisted planning — not as a black-box solution, but as a strategic co-pilot that empowers engineers to make better, faster, and more defensible decisions.

Better planning. Less guessing.

Better planning.
Less guessing.

Johan Sverdrup Phase 3 proved what’s possible when field development planning is reimagined with AI. AIM didn’t just help Equinor make a better decision—it helped them explore every option worth considering, understand the trade-offs, and choose with confidence.

In today’s world of complex, capital-intensive offshore development, that’s a competitive edge that few can afford to ignore.

Want to learn more or read the detailed SPE Paper on our work for Equinor? Contact us below.

Johan Sverdrup Phase 3 proved what’s possible when field development planning is reimagined with AI. AIM didn’t just help Equinor make a better decision—it helped them explore every option worth considering, understand the trade-offs, and choose with confidence.

In today’s world of complex, capital-intensive offshore development, that’s a competitive edge that few can afford to ignore.

Want to learn more or read the detailed SPE Paper on our work for Equinor? Contact us below.

Johan Sverdrup Phase 3 proved what’s possible when field development planning is reimagined with AI. AIM didn’t just help Equinor make a better decision—it helped them explore every option worth considering, understand the trade-offs, and choose with confidence.

In today’s world of complex, capital-intensive offshore development, that’s a competitive edge that few can afford to ignore.

Want to learn more or read the detailed SPE Paper on our work for Equinor? Contact us below.

Unlock your planning potential.

Unlock your planning potential.

WhiteSpace Solutions

info@whitespacesolutions.eu

The challenge.

The challenge.

The challenge.

In late 2022, Equinor’s development team was gearing up for the next phase of one of the most important oil fields on the Norwegian Continental Shelf: Johan Sverdrup Phase 3.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple. Multiple conflicting objectives had to be resolved:

How many wells would be required to reach key targets?

Where should templates be placed to balance drilling cost and infrastructure constraints?

Could longer, more complex wells reduce subsea infrastructure?

What was the best combination of layout, routing, and reservoir access?

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

In late 2022, Equinor’s development team was gearing up for the next phase of one of the most important oil fields on the Norwegian Continental Shelf: Johan Sverdrup Phase 3.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple. Multiple conflicting objectives had to be resolved:

How many wells would be required to reach key targets?

Where should templates be placed to balance drilling cost and infrastructure constraints?

Could longer, more complex wells reduce subsea infrastructure?

What was the best combination of layout, routing, and reservoir access?

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

In late 2022, Equinor’s development team was gearing up for the next phase of one of the most important oil fields on the Norwegian Continental Shelf: Johan Sverdrup Phase 3.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple. Multiple conflicting objectives had to be resolved:

How many wells would be required to reach key targets?

Where should templates be placed to balance drilling cost and infrastructure constraints?

Could longer, more complex wells reduce subsea infrastructure?

What was the best combination of layout, routing, and reservoir access?

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

Using AI to explore all options.

Using AI to explore all options.

Using AI to explore all options.

Enter AIM (Artificial Intelligence for Maturation) — a joint solution developed by WhiteSpace Solutions in collaboration with Equinor’s Subsurface Excellence and Digital unit.

Unlike traditional field development planning tools that require planners to manually iterate through a handful of predefined concepts, AIM was designed to do something radically different:

Explore every possible combination of well targets & trajectories, template configurations, and pipeline routing options, within engineering and economic constraints.

Present a Pareto front of options, allowing teams to clearly see the trade-offs between cost, complexity, and risk.

And most critically, AIM embedded a human-in-the-loop design, giving engineers full control over design rules, constraints, and the final decision.

Enter AIM (Artificial Intelligence for Maturation) — a joint solution developed by WhiteSpace Solutions in collaboration with Equinor’s Subsurface Excellence and Digital unit.

Unlike traditional field development planning tools that require planners to manually iterate through a handful of predefined concepts, AIM was designed to do something radically different:

Explore every possible combination of well targets & trajectories, template configurations, and pipeline routing options, within engineering and economic constraints.

Present a Pareto front of options, allowing teams to clearly see the trade-offs between cost, complexity, and risk.

And most critically, AIM embedded a human-in-the-loop design, giving engineers full control over design rules, constraints, and the final decision.

Trajectory planning.

Trajectory planning.

Template optimisation.

Template optimisation.

Template optimisation.

Planning with the full picture.

Planning with the full picture.

Planning with the full picture.

Field development planning often suffers from fragmentation. Subsurface teams propose targets. Drilling engineers push back on inclination. SURF tries to minimize flowline length. Each group optimizes in a silo — and planning becomes sequential, political, and slow.

AIM flips that script by enabling simultaneous, constraint-driven optimization across disciplines. Engineers aren’t just handed a computer-generated plan — they defined the rules, review the outputs, and select the concepts that best balance their needs.

This approach leads to a step-change in planning efficiency:

Months of iteration compressed into days

Field development planning often suffers from fragmentation. Subsurface teams propose targets. Drilling engineers push back on inclination. SURF tries to minimize flowline length. Each group optimizes in a silo — and planning becomes sequential, political, and slow.

AIM flips that script by enabling simultaneous, constraint-driven optimization across disciplines. Engineers aren’t just handed a computer-generated plan — they defined the rules, review the outputs, and select the concepts that best balance their needs.

This approach leads to a step-change in planning efficiency:

Months of iteration compressed into days

Field development planning often suffers from fragmentation. Subsurface teams propose targets. Drilling engineers push back on inclination. SURF tries to minimize flowline length. Each group optimizes in a silo — and planning becomes sequential, political, and slow.

AIM flips that script by enabling simultaneous, constraint-driven optimization across disciplines. Engineers aren’t just handed a computer-generated plan — they defined the rules, review the outputs, and select the concepts that best balance their needs.

This approach leads to a step-change in planning efficiency:

Months of iteration compressed into days

AIM in practice.

AIM in practice.

AIM in practice.

In Phase 3, Equinor’s development team used AIM to integrate the full 3D subsurface and seabed of the field and apply constraints such as:

Hundreds of thousands of scenarios were created, evaluated, filtered, and clustered. Then the most promising ones were presented for detailed engineering review.

Human-in-the-loop.

Human-in-the-loop.

The Result: $12mln CAPEX saving

$12mln CAPEX saving

$12mln CAPEX saving.

Among the most powerful outcomes was AIM’s validation of the project team’s conclusion that fewer, larger templates combined with longer, more complex wells could significantly reduce overall CAPEX.

Just as critically, AIM gave Equinor the ability to:

The result wasn’t just cost savings—it was confidence in the plan and alignment across disciplines in a shorter timeline.

Among the most powerful outcomes was AIM’s validation of the project team’s conclusion that fewer, larger templates combined with longer, more complex wells could significantly reduce overall CAPEX.

Just as critically, AIM gave Equinor the ability to:

The result wasn’t just cost savings—it was confidence in the plan and alignment across disciplines in a shorter timeline.

Among the most powerful outcomes was AIM’s validation of the project team’s conclusion that fewer, larger templates combined with longer, more complex wells could significantly reduce overall CAPEX.

Just as critically, AIM gave Equinor the ability to:

The result wasn’t just cost savings—it was confidence in the plan and alignment across disciplines in a shorter timeline.

What's next?

What's next?

What's next?

The success of AIM in Johan Sverdrup Phase 3 and other projects has sparked broader adoption across Equinor and interest from other operators. Future AIM deployments will include:

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Multiple conflicting objectives had to be resolved:

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.

And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Multiple conflicting objectives had to be resolved:

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.

And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

After two successful development phases, Phase 3 focuses on unlocking additional reserves in the southern portion of the field. But the task ahead was far from simple.

Multiple conflicting objectives had to be resolved:

This wasn’t just a complex puzzle — it was a planning problem with millions of possible permutations.

And timelines were tight. Equinor needed to move from screening to concept select rapidly while maintaining engineering integrity, cost control, and operational feasibility.

Using AI to explore
all options.

Planning with the
full picture.

Better planning.
Less guessing.