AI Project Management for Offshore Expansion Projects

A deepwater subsea expansion can look “routine” on a press release. Then you see the numbers: 2,100 metres of water depth, offshore campaigns stretching across 2026 and 2027, and a “sizeable” contract banded at $50m–$150m. That’s the reality behind Subsea7’s newly awarded work for LLOG’s Buckskin South Expansion—a reminder that offshore growth isn’t slowing down; it’s getting more complex.

For energy leaders in Trinidad and Tobago, this kind of project matters even when it’s happening in the Gulf of Mexico. Not because we need to copy the exact field development, but because the execution challenges are the same: long supply chains, expensive vessel time, strict HSE controls, and constant pressure to deliver barrels and gas molecules at a lower operating cost.

Here’s my stance: most offshore expansion projects don’t fail because the engineering is impossible; they struggle because coordination breaks down. That’s where AI in oil and gas stops being a buzzword and becomes a practical operating advantage—especially for teams managing subsea packages, offshore campaigns, and multi-contractor schedules.

What Buckskin South tells us about execution risk

Answer first: Buckskin South is a classic example of how “simple” scope (umbilical + flowline) becomes high-risk when you mix deep water, long schedules, and multiple interfaces.

Subsea7’s scope includes the transportation and installation of a subsea umbilical and a rigid flowline at water depths up to 2,100m. Even if you’ve delivered similar work before, deepwater installations create a narrow margin for error: weather windows, vessel availability, equipment readiness, and offshore coordination can turn small variances into expensive downtime.

The other detail worth paying attention to is when work happens. Project management and engineering starts immediately (from Houston), while offshore execution is planned for 2026–2027. That gap is where projects are won or lost. Teams either build a clean line-of-sight from design to procurement to offshore readiness—or they spend 18 months managing surprises.

For Trinidad and Tobago operators and service companies, the parallel is clear:

• Offshore work is increasingly campaign-based (bundle tasks to reduce mobilisation costs).
• Supply chains are increasingly global, with longer lead times and more exposure to disruption.
• Stakeholders are increasingly impatient—and they want proof, not promises.

AI can’t change the ocean. It can reduce the number of times your team is surprised.

Where AI fits in offshore project management (and where it doesn’t)

Answer first: AI is strongest in offshore project execution when it’s used for forecasting, coordination, and decision support—not as a replacement for engineering judgment.

The best results come from applying AI to the messy parts of delivery: the emails, PDFs, progress updates, vendor delays, and shifting priorities that quietly derail schedules.

1) AI schedule risk forecasting (before the delays hit)

Traditional project controls often tell you you’re late after you’re late. AI models can estimate schedule risk earlier by learning patterns from:

• historical activity durations
• vendor performance (lead time reliability)
• weather downtime patterns
• non-conformance trends
• interface density (how many dependencies an activity has)

A practical output isn’t a fancy chart—it’s a weekly “risk heatmap” that says:

• Which 10 activities are most likely to slip in the next 4–6 weeks
• Which vendors are trending off-plan
• Which interface decisions need escalation now

For subsea work (umbilicals, flowlines, SURF packages), this matters because late material readiness has a nasty habit of becoming late vessel time, and late vessel time becomes a budget headline.

2) AI for document intelligence (the hidden time sink)

Offshore projects run on documents: specs, ITPs, procedures, drawings, change requests, inspection records, punch lists. In practice, teams lose time because information is hard to find, inconsistent, or stuck in email.

AI-enabled document intelligence can:

• extract requirements from specs and align them to checklists
• flag inconsistencies across revisions (scope creep that sneaks in)
• summarise vendor documentation and highlight missing evidence
• auto-classify RFIs and route them to the right owner

In Trinidad and Tobago, this is a high-ROI move because many organisations have capable technical teams but too much operational friction—the work is happening, but it’s not flowing.

3) AI-driven progress verification (less arguing, more acting)

Progress disputes are common: “We’re 92% done.” “No, you’re 84% done.” The argument burns time and trust.

AI can improve progress verification by combining:

• planned schedule logic (what should be done)
• daily reports and timesheets (what people say is done)
• quality records and inspections (what’s accepted)
• optional: imagery from ROV/inspection footage where relevant

The goal isn’t surveillance. The goal is a single source of operational truth that reduces rework and accelerates decisions.

Where AI doesn’t help much

AI won’t fix:

• unclear scope
• weak contracting strategies
• poor leadership habits
• a culture where bad news is punished

If you don’t address those, AI becomes a dashboard that politely reports failure.

AI in subsea operations: the practical use cases T&T can borrow

Answer first: The most transferable AI use cases for Trinidad and Tobago offshore work are equipment reliability, logistics optimisation, and HSE monitoring.

Buckskin South highlights subsea installation complexity. For T&T, the most useful question is: Which AI applications reduce offshore days and prevent integrity incidents?

Predictive maintenance for critical equipment

When subsea campaigns depend on a narrow set of high-value assets (vessels, winches, lay systems, hydraulic power units), reliability becomes the schedule.

Predictive maintenance uses sensor data and maintenance history to:

• detect early failure signatures
• recommend maintenance timing that avoids offshore stoppage
• reduce “just in case” maintenance that wastes time

Even small improvements matter. When vessel day rates and offshore spread costs are high, avoiding one unplanned downtime event can pay for the analytics work.

Logistics optimisation and materials readiness

Offshore teams often get trapped in a loop: missing parts → expedite → errors → more delay. AI helps break that loop by improving materials readiness planning:

• forecasting which items are likely to miss required-on-site dates
• recommending reorder points for spares with long lead times
• identifying substitutions that meet spec without triggering re-approval chaos

This is especially relevant for Trinidad and Tobago, where procurement may span local suppliers, regional distributors, and OEM channels. AI can bring coherence to that reality.

AI for HSE: leading indicators, not lagging reports

HSE reporting is typically lagging: what happened last month. AI can surface leading indicators:

• rising near-miss frequency in a specific task category
• procedural non-compliance patterns (e.g., repeated PTW issues)
• fatigue risk signals (shift patterns + overtime)

Used properly, this supports supervisors and safety leaders with earlier interventions—not more paperwork.

The deal activity angle: why consolidation increases the need for AI

Answer first: M&A and asset transfers make project execution harder, and AI helps standardise operations across changing organisations.

This contract award landed shortly after news that Harbour Energy agreed to acquire LLOG Exploration Company for up to $3.2bn (cash plus shares). When ownership and portfolios shift, projects don’t pause. But governance, reporting lines, systems, and decision rights often change midstream.

In my experience, that’s when execution gets messy:

• new leadership asks for new dashboards
• reporting templates multiply
• approvals slow down
• “how we do things” becomes unclear

AI-enabled project management platforms can reduce this disruption by:

• standardising data ingestion from multiple tools n- creating consistent project health metrics across assets
• capturing institutional knowledge (what decisions were made and why)

For Trinidad and Tobago’s energy ecosystem—operators, service companies, EPCs—this matters because partnerships and joint ventures are common. You need an execution model that doesn’t collapse every time stakeholders change.

A practical 90-day AI plan for offshore project teams in Trinidad and Tobago

Answer first: Start with one project pain point, one dataset, and one decision workflow—then expand.

AI programmes fail when they start as “digital transformation.” They succeed when they start as “reduce late materials” or “stop schedule surprises.”

Here’s a 90-day approach I’d actually back.

Days 1–15: Pick a single execution outcome

Choose one measurable outcome, such as:

• reduce RFIs aging over 14 days
• cut expediting events by 20%
• improve forecast accuracy for schedule milestones
• reduce repeat non-conformances in a top 3 workpack

Days 16–45: Build a usable data pipeline (not a perfect one)

You probably already have the data, just scattered:

• planning tool exports
• procurement logs
• vendor progress reports
• emails and meeting minutes
• NCR and inspection registers

The target is a weekly refreshed dataset that supports one decision.

Days 46–75: Deploy an AI assistant inside the workflow

Examples of what “inside the workflow” looks like:

• a weekly risk briefing generated from schedule + constraints
• an RFI classifier that routes queries and drafts responses
• a materials readiness predictor that flags likely late items

If it doesn’t save time for a project engineer or planner, it won’t stick.

Days 76–90: Prove ROI with hard numbers

Track before/after metrics. Offshore teams respect results when they’re specific:

• fewer schedule slips
• fewer urgent shipments
• fewer overdue actions
• fewer rework loops

Then scale the same pattern to the next pain point.

What to do next if you’re planning expansions in 2026

Offshore execution is heading into 2026 with the same pressures we’ve been living with: tight supply chains, high stakeholder expectations, and increased scrutiny on safety and emissions performance. The difference is that AI tooling is now mature enough to be useful—if you implement it with discipline.

If you’re leading an operator, EPC, or service company in Trinidad and Tobago, take the Buckskin South lesson for what it is: deepwater projects reward teams that manage uncertainty better than their competitors. AI is a force multiplier for that—particularly in project controls, documentation, and materials readiness.

The next question isn’t “Should we adopt AI in oil and gas?” It’s: Which part of your offshore project system is most expensive when it fails—schedule, quality, or readiness—and what would you automate first?