Equinor saved USD 130 million with AI in 2025 - here's what operations teams can take from it
AI isn't a pilot project at Equinor anymore. It's embedded in day-to-day operations across offshore platforms and land facilities - delivering USD 130 million in value in 2025, and more than USD 330 million since 2020.
For operations leaders, the headline is simple: applied AI is paying off where it counts - uptime, maintenance, throughput, and emissions.
Key numbers at a glance
- USD 130 million in value creation and savings in 2025; >USD 330 million since 2020
- Predictive maintenance across 700 rotating machines and 24,000 sensors
- USD 120 million in value since 2020 from condition monitoring and failure prediction
- 10x increase in seismic interpretation capacity; 2 million square kilometres interpreted in 2025
- AI-driven well and field planning produced a solution in Johan Sverdrup phase 3 that saved USD 12 million
- Target: maintain production on the Norwegian continental shelf at 2020 levels (~1.2 million boe/d) through 2035
Where AI is driving outcomes today
1) Predictive maintenance and equipment health
Monitoring 700 rotating machines via 24,000 sensors lets teams spot failure modes early, plan interventions, and avoid unplanned downtime. The bonus: fewer sudden shutdowns, fewer flaring events, and lower CO2 emissions. This area alone has created USD 120 million in value since 2020.
2) AI-assisted well and field development planning
Algorithms generate thousands of viable scenarios so experts can focus on the best ones. On Johan Sverdrup phase 3, AI surfaced a design no one had proposed - and the partnership banked USD 12 million in savings.
3) Seismic interpretation at scale
AI tools boost interpretation capacity by a factor of ten. That means more square kilometres covered, richer subsurface insight, and a stronger basis for drilling decisions. In 2025 alone, teams interpreted 2 million square kilometres.
What Equinor says - and why it matters for Ops
"AI is a central part of our operations. Moving forward, AI will become even more important for solving industrial tasks safely, faster, more profitably, and at scale," says Hege Skryseth, executive vice president for Technology, Digital, and Innovation at Equinor.
Her team uses primarily traditional machine learning on operational data, while employees also rely on copilots, chatbots, and agentic AI to get work done. The throughline is practical: safer operations, tighter plans, and measurable ROI.
Operational value, in plain terms
- Uptime: Early warnings reduce forced outages and stabilize production.
- Cost: Better planning lowers rework, logistics spend, and maintenance hours.
- Safety: Fewer surprises mean safer interventions and fewer abnormal situations.
- Emissions: Avoided trips and flaring cut CO2 intensity per barrel.
- Throughput: Faster subsurface interpretation feeds more confident drilling schedules.
Practical takeaways for operations leaders
- Prioritize high-impact assets first. Start with critical rotating equipment where failure costs are obvious and data is already flowing.
- Let AI propose, humans dispose. Use algorithms to generate options for planning; keep experts in the loop to validate and select.
- Instrument for outcomes. Tie models to clear KPIs: avoided downtime hours, reduced maintenance spend, fewer flaring events, CO2 per unit produced.
- Tighten data quality. Reliability improves when sensor health, labeling, and feedback loops are treated as part of the work, not an afterthought.
- Scale what works. Standardize successful models across similar assets and sites to compound value fast.
Context: Norwegian continental shelf and future plans
Equinor aims to keep production on the Norwegian continental shelf at 2020 levels through 2035, around 1.2 million boe per day - using AI to interpret more seismic data, plan and drill more wells, run facilities safely and profitably, and optimize energy use to curb emissions. For background on the shelf and activity, see the official overview from Norsk Petroleum.
On the agenda
At the annual conference of Norway's business confederation, NHO, on 7 January, Hege Skryseth will share how Equinor applies AI today, what Norway should prioritize next, and why AI agents will change the way teams work.
Upskilling your Ops team
If you're building similar capabilities - from predictive maintenance to AI-assisted planning - these resources can help your team get practical, fast:
- AI courses by job function to align training with day-to-day roles in operations and reliability.
- AI Automation certification for teams implementing monitoring, alerting, and workflow automation.
Bottom line: treat AI as standard operating equipment. Start where data and payback are clear, measure the results, and scale with discipline.
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