Leak Off Testing
Digitize well integrity test records and reviews
Oil & Gas
Operational Intelligence for Offshore Oil & Gas Infrastructure
Featured Use Cases: How Oil & Gas Leaders Configure Focus FS to Achieve Their Vision
Process Safety
Strengthen process safety oversight
Car Seal Management
Digitize and control critical isolation systems
Shift Handover
Improve operational continuity between shifts
Reliability Threats
Identify and act on emerging threats to asset reliability
Critical Experiences
Capture and share high-value operational experience
Incident Reporting
Accelerate incident investigation workflows with AI Suggested Lessons Learned
Lessons Learned
Turn operational experience into actionable knowledge
Measurable Impact
Improved Continuity
Shift-to-shift operational continuity and knowledge transfer.
Faster Response
Incident reporting and investigation cycles accelerated.
Quick Access
Reduced time locating operational records and documentation.
Greater Visibility
Enhanced visibility into operational and safety trends in real-time.
Strong Compliance
Auditable digital records ensure regulatory compliance.
Case Study
Lessons from Offshore AI Deployments with ExxonMobil
At the Digital Offshore Conference, Focus FS President Jeff Brown joined ExxonMobil on stage to discuss the realities of deploying AI in offshore oil & gas operations, including the methodology, risks, and lessons learned from early implementations in safety and operational workflows.
The Challenge
AI dominates headlines, but offshore operators need practical results. Historical records, inspection reports, incident narratives, and operational notes often exist in fragmented formats, leaving large volumes of operational knowledge difficult to access when decisions need to be made.
The Approach
- Start small with specific workflows such as shift handovers, incident management, and lessons learned.
- Collaborate closely between operators and technology providers.
- Treat security and governance as non-negotiable.
The Outcome
By targeting focused workflows first, teams demonstrate immediate operational benefits while building trust in AI across the organization. AI acts as an operational intelligence layer, making institutional knowledge, historical experience, and operational context available when they matter most.
Key Lesson: Data Quality Matters More Than Model Sophistication
“Organizations often chase increasingly sophisticated models while overlooking the state of their underlying data. In reality, a well-configured system operating on clean, normalized data will consistently outperform a more advanced model working with inconsistent or poorly structured information.”
A Pragmatic Path Forward
1. Start with high-value workflows2. Focus on data quality and retrieval3. Embed AI directly into operational processes4. Implement strong security and governance5. Expand incrementally as trust grows