AI Offshore Production Lessons for Trinidad & Tobago

CNOOC’s latest offshore start-up is a reminder that modern production isn’t only about finding hydrocarbons—it’s about running leaner, safer, more automated assets once you’ve found them. On 22 December 2025, CNOOC started production at the Xijiang Oilfields 24 Block development in the Pearl River Mouth Basin, using existing nearby facilities and a new unmanned wellhead platform. It’s aiming for ~18,000 boepd peak output in 2026 from ten development wells, producing light crude.

That specific combination—tie-back to existing infrastructure + unmanned platforms + tight production ramp-up targets—is exactly where AI earns its keep. And it’s also a useful benchmark for Trinidad and Tobago’s energy sector, where the pressure is the same: squeeze more value out of mature assets, keep people out of harm’s way offshore, and make decisions faster with less waste.

Here’s the thing about AI in oil and gas in Trinidad and Tobago: the winners won’t be the companies with the most slides about “digital transformation.” They’ll be the ones that turn day-to-day operational noise—sensor data, work orders, inspection footage, production logs—into repeatable decisions.

What CNOOC’s Xijiang start-up signals about offshore operations

CNOOC’s Xijiang 24 Block story signals a shift toward platform-light production, where automation and remote operations do more of the heavy lifting.

The development is built around three operational choices that matter:

1. Reuse what already exists. The project uses existing facilities at the nearby Huixi Oilfields. That’s a cost and schedule play.
2. Add an unmanned wellhead platform. People don’t need to live on every structure anymore.
3. Engineer for stable flow assurance. CNOOC highlighted that the Xijiang 24-7 platform is China’s first unmanned offshore platform designed for high-temperature fluid cooling and export, with temperature control meant to reduce heat impact on subsea pipelines.

Those choices aren’t “AI projects” on paper. But they create an operating model where AI becomes practical—because unmanned assets and tie-backs demand better prediction, better monitoring, and faster exception-handling.

Snippet-worthy reality: Unmanned offshore platforms don’t reduce risk by themselves—they just shift risk into the quality of your monitoring, alarms, and decision-making.

Why unmanned platforms make AI non-optional (not a nice-to-have)

An unmanned wellhead platform changes the rules. When fewer people are physically present, the operator must detect problems earlier and respond with more confidence.

The minimum AI stack for unmanned offshore assets

For operations teams, “AI” can sound abstract. On unmanned assets, it’s usually very specific:

• Anomaly detection on pressure, temperature, vibration, and flow signals (spot the abnormal early, before a trip or leak)
• Predictive maintenance models for rotating equipment and critical valves (reduce unplanned downtime)
• Computer vision for corrosion, leaks, flare observations, and safety compliance using inspection imagery
• Alarm rationalization and prioritization (reduce alarm floods; raise only what matters)
• Digital twins (targeted, not huge) for flow assurance and constraints (predict slugging, hydrate risk, or thermal stress)

CNOOC’s mention of high-temperature fluid cooling and pipeline heat impact is a perfect example. Temperature control isn’t only mechanical engineering—it’s data. Once you instrument the system, AI can learn what “healthy” thermal behavior looks like and flag drift long before it becomes a throughput problem.

Trinidad and Tobago parallel: fewer heads offshore, more brains onshore

Trinidad and Tobago offshore operations already feel the pinch of:

• aging equipment and constraints
• tighter turnaround windows
• HSE expectations that keep rising
• skills shortages in niche disciplines

AI-enabled remote operations is the sensible response. Not because it’s trendy, but because it reduces dependence on “tribal knowledge” and makes performance repeatable across shifts.

The tie-back strategy: where AI delivers fast ROI

CNOOC used adjacent infrastructure. That’s not just capital discipline—it’s an operations challenge. Tie-backs often introduce complexity: changing flow regimes, new temperature/pressure profiles, new bottlenecks at processing.

For Trinidad and Tobago, tie-backs and brownfield optimization are common realities. AI helps most when it’s aimed at the specific pain points that tie-backs create.

1) Production optimization under constraints

Most offshore facilities don’t have unlimited separation capacity, compression capacity, or export capacity. AI optimization models can recommend setpoints and routing strategies that increase throughput without violating constraints.

What this looks like in practice:

• recommend choke adjustments to stabilize rates
• balance wells to minimize water handling or sand risk
• reduce flaring by forecasting upsets early

2) Flow assurance forecasting (the quiet profit killer)

CNOOC’s thermal-control focus is a clue: flow assurance issues rarely announce themselves politely. They show up as gradual efficiency loss, then sudden downtime.

AI-assisted flow assurance typically combines:

• real-time sensor feeds (temperature/pressure)
• historical operating envelopes
• simplified physics models

That hybrid approach works well for operators that don’t want to build a massive “everything twin,” but do want to reduce hydrate risk, wax build-up risk, and thermal stress.

3) Faster commissioning and ramp-up

CNOOC plans ten wells and a peak of ~18,000 boepd in 2026. Ramping production is where small mistakes get expensive.

AI supports ramp-ups by:

• detecting unstable wells early
• learning the “normal” for newly commissioned equipment
• guiding operators with decision support when new behavior appears

Practical stance: If your commissioning depends on a few experts being available at the right time, your ramp-up plan is fragile. AI doesn’t replace experts; it scales them.

What Trinidad and Tobago can learn—and apply in 90 days

Global examples like Xijiang can feel far away from the Gulf of Paria or the Columbus Basin. The operational patterns are similar, though. The difference is execution speed.

Below is a realistic 90-day plan I’ve seen work in asset-heavy industries (and it translates cleanly to oil and gas).

Step 1: Pick one “money leak” and one “risk leak”

Don’t start with a giant AI roadmap. Start with two use cases:

• Money leak example: frequent minor trips, repeated downtime on one equipment class, chronic production instability
• Risk leak example: corrosion monitoring gaps, inspection backlogs, alarm floods that hide true emergencies

Write down how you’ll measure success in numbers: downtime hours, deferment barrels, maintenance cost, inspection cycle time.

Step 2: Fix data plumbing before fancy models

Most companies get this wrong. They buy tools before making data usable.

Minimum data requirements for offshore AI:

• consistent tag naming and time synchronization
• clean historian data access
• structured maintenance/work-order data (even if imperfect)
• clear event labels (trips, shutdowns, failures)

If your team can’t reliably answer, “What happened last time?” your model won’t either.

Step 3: Build a “human-in-the-loop” workflow

AI that emails a graph isn’t operations. The goal is a workflow where:

1. the model flags an issue
2. the operator validates it quickly
3. a standard response is triggered (adjust setpoint, schedule inspection, create a work order)
4. the outcome is logged to improve the model

That loop is how you turn AI from a pilot into a habit.

Step 4: Bake governance into the rollout

In Trinidad and Tobago’s energy and oil & gas sector, governance is where AI programs either scale or stall.

You need decisions on:

• who owns the model (operations vs IT vs reliability)
• change management for setpoint recommendations
• cybersecurity boundaries between OT and IT
• audit trails for AI-driven actions

This isn’t bureaucracy for its own sake. It’s how you keep AI from becoming an “extra dashboard” nobody trusts.

“People also ask” (and straight answers)

Will AI reduce headcount offshore?

AI usually reduces exposure hours more than it reduces headcount. The better outcome is shifting work from reactive call-outs to planned interventions.

Is an unmanned platform only for big budgets?

No. The concept scales down if you focus on remote monitoring, condition-based maintenance, and clear operating envelopes. The high-cost mistake is trying to automate everything at once.

What’s the fastest AI win for offshore oil and gas?

Predictive maintenance on a repeating failure mode is often the fastest win—because it’s measurable and doesn’t require changing reservoir strategy.

Where this fits in the Trinidad and Tobago AI-in-energy story

This post sits in our broader series on how AI is transforming the energy and oil & gas sector in Trinidad and Tobago. CNOOC’s Xijiang 24 Block start-up isn’t important because it’s China, or because it’s new. It’s important because it shows the operating direction the industry is committing to: automation-first design, reuse of infrastructure, and stable production through better control.

If you’re leading an asset, a department, or a transformation program locally, the next move isn’t to copy CNOOC’s hardware choices. It’s to copy the logic: build operations that assume fewer people offshore and fewer surprises in the process.

If you want leads, here’s the most useful next step: define one unmanned/remote-ops use case (like anomaly detection on high-temperature production flowlines, or predictive maintenance for critical valves), then map the data you already have to make it real.

The question worth ending on: Which part of your offshore operation still depends on “someone noticing” before it depends on data—and what’s that costing you every month?