NLNG puts AI to work across its value chain: efficiency, reliability, and HSE at the core
AI has moved from concept to execution at Nigeria LNG (NLNG). Speaking at Gastech 2025 in Milan, Deputy Managing Director Olakunle Osobu said the company is embedding AI across its operations to strengthen efficiency, reliability, and sustainability while reinforcing its Goal Zero safety ambitions. The message was clear: AI is now part of day-to-day operations, not a side project.
What NLNG announced at Gastech 2025
During a panel on "Operational Excellence through the Application of Artificial Intelligence Technologies," NLNG outlined a practical roadmap for AI integration. Osobu noted that AI is improving safety and business performance across the company's value chain. The company is prioritizing use cases that deliver measurable outcomes and fit into existing systems.
Where AI is already in use at NLNG
- Workforce enablement: Virtual reality tools and AI agents are accelerating onboarding and strengthening knowledge retention.
- Safety and compliance: Smart cameras and satellite feeds deliver visual analytics to monitor operations and enforce safety standards.
- Asset performance: Process automation and predictive maintenance are improving uptime and asset reliability.
- Environmental performance: AI-driven optimization is improving emissions monitoring to support the company's energy transition commitments.
The operating model: integrate first, then scale
- Integrate AI into existing platforms: Tie models into control systems, historians, CMMS, and analytics stacks. This enables predictive, preventive, and corrective maintenance to keep plants running at peak performance.
- Value-first approach: Prioritize use cases with clear ROI and minimal disruption. Kill or iterate fast based on outcomes.
- Upskill from within: NLNG is developing talent through a Centre of Excellence to ensure solutions fit real operational needs.
- Safety by design: Every use case is evaluated against Goal Zero-no harm to people, assets, or environment.
Metrics operations leaders should track
- Unplanned downtime, MTBF, MTTR, and OEE impacts from predictive maintenance
- Safety indicators: near-misses, compliance violations detected by computer vision, and incident rates
- Training time to proficiency and first-time-right rates after AI-assisted onboarding
- Emissions intensity, flaring events, and reporting accuracy
- Model performance: false positives/negatives, drift, and alert fatigue
90-day playbook to mirror NLNG's approach
- Week 1-3: Inventory data sources (DCS/SCADA, historians, CMMS, imagery). Baseline current KPIs.
- Week 4-6: Select three pilots: one asset-critical predictive maintenance use case, one safety/computer vision use case, and one emissions-monitoring model.
- Week 7-9: Integrate models into existing dashboards. Define human-in-the-loop review, escalation paths, and intervention thresholds.
- Week 10-12: Validate impact, tune models, and prepare SOP updates. Lock in cybersecurity controls and access governance.
Why this matters for operations
AI is becoming foundational to how LNG facilities plan, run, and maintain assets. NLNG's message is straightforward: connect AI to current systems, focus on high-value use cases, upskill teams, and let the results guide scale-up. That's how you reduce downtime, improve safety, and meet environmental commitments-without overhauling the plant.
For broader context on digital and energy systems, see the International Energy Agency's work on digitalisation in energy: IEA report.
If you're building an operations-focused AI upskilling plan, explore role-specific learning paths here: Complete AI Training: Courses by Job.
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