AI, Rigs, and Rules: What T&T Should Copy Next

Rig counts make headlines, but regulation and automation decide who stays profitable.

That’s why the latest Offshore Technology Focus issue (released this week) is more than a magazine drop—it’s a signal. The themes it highlights—US policy shifts, rig productivity, autonomous robotics, drones for methane detection, and what to do with end‑of‑life infrastructure—map neatly onto the decisions Trinidad and Tobago’s energy sector has to make in 2026.

In this series on how AI is transforming the energy and oil & gas sector in Trinidad and Tobago, I keep coming back to one truth: most companies over-invest in tools and under-invest in the operating model that makes those tools pay off. AI works when your data, governance, and frontline workflows are ready. The magazine’s “rigs and regulations” framing is a timely reminder that technology adoption is usually pulled forward by rules, not hype.

Regulations are pushing AI faster than budgets are

Regulation doesn’t just add compliance tasks—it forces standardization. Standardization is exactly what AI needs.

The magazine’s focus on policy and productivity in the US highlights a pattern Trinidad and Tobago should pay attention to: when governments signal stronger production ambitions, operators respond by driving efficiency per rig, per crew, and per maintenance dollar. In practice, the fastest way to do that now is AI-assisted operations, because it scales decisions—maintenance timing, inspection prioritization, production optimization—without scaling headcount.

Here’s the stance I’ll defend: if you wait for a “perfect” AI strategy before aligning compliance, data, and reporting, you’ll end up doing AI twice—once as a pilot, then again as a real program.

What “AI-ready compliance” looks like offshore

For offshore operators and service companies in Trinidad and Tobago, AI adoption becomes far less risky when compliance and operational reporting are designed to feed the same pipeline. Practically, that means:

• One asset hierarchy (tags, equipment naming, locations) shared across CMMS/EAM, historians, and inspection logs
• One source of truth for work orders (failure codes, cause codes, downtime categories)
• Audit-friendly data lineage (who changed what, when, and why—especially for safety and emissions reporting)
• Model governance for AI (approval, monitoring, drift checks) treated like any other operational change process

If you want AI in oil and gas to survive procurement and HSE review, this is the path.

Autonomous robotics: start with the boring jobs, not the flashy demos

Autonomous robotics gets attention because it’s visual—robot “dogs,” subsea vehicles, remote crawlers. The magazine references real deployments (like robotic platforms used for inspection and monitoring). The lesson for Trinidad and Tobago isn’t “buy a robot.” It’s choose a repeatable inspection workflow and automate it end-to-end.

AI’s role in robotics offshore is usually three things:

1. Perception: computer vision to detect corrosion, leaks, missing insulation, gauge readings
2. Decision support: prioritizing anomalies, recommending follow-up actions
3. Autonomy assist: route planning, obstacle avoidance, and safe operation constraints

A practical T&T use case: inspection backlog reduction

Many offshore assets accumulate inspection debt—deferred checks, overdue thickness measurements, “we’ll do it next shutdown” items. That’s not just an integrity risk; it becomes a cost trap.

A strong starting point is a robotics + AI inspection program focused on:

• High-frequency areas (handrails, topside piping supports, flare booms, known corrosion circuits)
• Repeatable capture (same angles, same routes, same timestamps)
• AI-assisted anomaly detection (flag changes versus last inspection, not just “is there rust?”)

The immediate metric to chase is simple and board-friendly: reduce inspection backlog by 30–50% within two planned cycles by shifting humans from routine walkdowns to exceptions and repairs.

That’s how robotics becomes an operations win, not a PR video.

Drones and methane: AI turns surveys into decisions

The magazine also points to drone technology and methane detection campaigns. This matters because methane isn’t just an environmental topic anymore—it’s increasingly a commercial and reputational constraint, especially for LNG-linked value chains.

Drones collect images and sensor readings. AI makes them useful by answering three questions fast:

• Where is the leak most likely? (probability map)
• How big is it? (estimated rate or severity band)
• What should we do first? (ranked repair list with expected impact)

What to implement in Trinidad and Tobago in 90 days

If you’re an operator, midstream facility, or service provider trying to move quickly, a 90-day methane AI sprint can work if it’s tightly scoped:

1. Select 1–2 facilities (or one offshore installation plus one onshore tie-in)
2. Standardize flight plans and sensor calibration procedures (consistency beats “more data”)
3. Build a labeled anomaly library (known vents, known fugitive points, historical repairs)
4. Deploy an AI triage dashboard that produces a weekly “fix list” tied to work orders

The key is the work order connection. AI that can’t trigger action becomes a report. Reports don’t reduce methane.

Snippet worth repeating: “Methane measurement only creates value when it automatically turns into maintenance prioritization.”

Rig productivity isn’t a rig problem—it’s a data problem

The US “rig count and productivity” storyline is a reminder that more rigs isn’t always the lever. Productivity gains often come from reducing invisible friction: waiting on parts, repeated failures, slow troubleshooting, and inconsistent procedures.

AI in upstream operations targets those friction points through:

• Predictive maintenance on rotating equipment, compressors, pumps, and critical utilities
• Failure prediction and early warning using historian patterns and alarm sequences
• Optimization models that recommend setpoints, choke settings, or lift adjustments under constraints
• Crew decision support using digital procedures plus incident learnings

The underrated quick win: AI for downtime coding and root cause quality

Most organizations want predictive maintenance but don’t have reliable failure labels. A surprisingly effective first step is using AI to improve the quality of downtime and failure records:

• Automatically suggest failure codes from technician notes
• Detect inconsistent categorization across shifts and teams
• Summarize recurring issues by asset and vendor

Once you clean this layer, your next models (predictive maintenance, spares forecasting, reliability optimization) stop feeling like guesswork.

End-of-life infrastructure: AI helps you decide what to keep, repurpose, or remove

The magazine’s look at end-of-life rigs—repurposing ideas and the broader question of “what comes next”—hits close to home. Trinidad and Tobago has mature assets and a real need to manage late-life integrity while staying competitive.

AI adds value here by turning decommissioning and repurposing into a portfolio decision, not a one-off engineering debate.

Three AI-supported decisions that de-risk late-life assets

1. Integrity risk forecasting

   • Combine inspection history, corrosion rates, environmental exposure, and repair records
   • Output: a risk-ranked list of circuits/structures and a costed inspection plan

2. Decommissioning schedule optimization

   • Coordinate vessels, manpower, permits, and seasonal weather windows
   • Output: a schedule that minimizes standby time and rework

3. Repurposing feasibility screening

   • Use structured criteria: water depth, proximity to shore, structural condition, access, community impact
   • Output: shortlist of realistic options (not “rocket launch pads” unless the numbers work)

My opinion: repurposing should be treated like a capital project with a strict kill-switch. AI can speed up screening, but governance must prevent pet ideas from draining budgets.

The AI roadmap that fits Trinidad and Tobago’s reality

Most Trinidad and Tobago energy teams don’t need a giant “AI transformation” banner. They need a roadmap that survives procurement, HSE scrutiny, and offshore bandwidth constraints.

A pragmatic 6-step sequence

1. Pick one operational KPI (downtime, inspection backlog, methane repair cycle time)
2. Confirm data availability (historians, CMMS, inspection logs, drone imagery)
3. Standardize the taxonomy (asset hierarchy + failure coding)
4. Deploy a narrow model (anomaly detection, forecasting, or triage)
5. Integrate with action systems (work orders, permits, shift handover)
6. Add governance (model monitoring, approvals, audit trails)

If you’re trying to generate leads or internal buy-in, this sequence is powerful because every step has a deliverable people can touch.

What to do next (and what to avoid)

If the “rigs and regulations” theme tells us anything, it’s that 2026 will reward operators who can prove three things quickly: safe operations, measurable efficiency, and credible emissions control. AI is now the shortest path to all three—if it’s tied to real workflows.

Here’s what works when you’re serious:

• Start with a single facility or offshore asset, not a company-wide rollout
• Design for frontline adoption (shift routines, maintenance planners, integrity engineers)
• Treat data quality as production equipment—maintain it, monitor it, improve it

Here’s what to avoid:

• Buying tools before agreeing on asset naming and failure codes
• Running pilots that never connect to work orders and budget cycles
• Letting AI sit outside HSE and compliance governance

If you’re building your 2026 plan in Trinidad and Tobago, the question isn’t whether AI in oil and gas is “worth it.” The real question is: which operational bottleneck will you remove first—inspection debt, methane response time, or downtime repeat failures?