AI Refineries: Mexico’s Export Drop, Kazakhstan’s Edge

Mexico’s crude exports have fallen from ~1.1 million b/d (2020) to ~665,000 b/d (2025)—a drop of roughly 40%. December 2025 was even harsher: exports hit 503,000 b/d, the lowest level this century. The steepest slide is in Maya, Mexico’s flagship heavy crude: 253,000 b/d in December, down ~86% from 2020.

Most headlines treat this as a simple supply story—declining upstream capacity, aging fields, politics. That’s only part of it. The more interesting angle is operational: refining is “reawakening,” absorbing barrels that used to leave the country. When domestic refining finally runs steadier, exports shrink by design.

For Kazakhstan’s energy and oil-gas leaders, Mexico is a useful case study: when the balance between refining, exports, and product demand shifts quickly, the winners are the companies that can plan, optimize, and reroute fast. And right now, the fastest way to build that agility is AI and data analytics—not as a buzzword, but as a daily operating system for crude selection, refinery optimization, maintenance, and export logistics.

What Mexico’s export collapse really signals

The direct answer: Mexico’s export decline signals a structural reallocation of crude from export to domestic refining, not just a shortage of production.

Mexico’s numbers are too sharp to ignore. A move from ~1.1 million b/d to ~665,000 b/d over five years suggests more than normal volatility. The December 2025 trough at 503,000 b/d—and Maya’s collapse—highlights how quickly the export system can shrink when:

• Refineries increase utilization (or at least stabilize after long underperformance)
• Domestic product needs and politics prioritize local processing
• Crude quality constraints (heavy sour grades like Maya) make some export destinations less flexible

Exports vs. refining: the hidden trade-off

Here’s the operational trade-off many markets underestimate:

• Exporting crude is mainly a logistics and marketing problem.
• Refining crude is a constraints problem—feedstock quality, unit limits, catalysts, hydrogen, energy costs, reliability, and product slate targets.

When refining “reawakens,” the constraint moves inside the fence line. That’s where AI delivers real value because it can continuously compute the best decision under changing constraints.

Snippet-worthy point: When exports fall because refining absorbs crude, the question isn’t “Where did the barrels go?” It’s “Can the system optimize the barrels it kept?”

Why refinery optimization is an AI problem (not a spreadsheet problem)

The direct answer: refinery optimization needs AI because decisions are multi-variable, real-time, and constrained by equipment health and crude quality.

Refining looks stable from the outside—big assets, long cycles. In reality, daily operations involve thousands of interacting variables. A small change in crude blend, unit temperature, or hydrogen availability can ripple into:

• product yields (diesel vs gasoline vs fuel oil)
• sulfur specs compliance
• energy consumption and emissions
• unplanned downtime risk

Where AI improves refining outcomes

AI doesn’t replace process engineers; it gives them better “reflexes.” In practice, the most bankable use cases are:

1. Crude blending & feedstock selection

   • Models learn which crude mixes maximize margin while respecting unit constraints.
   • Useful when heavy grades (like Maya) fluctuate in availability or quality.

2. Advanced process control (APC) + ML overlays

   • ML can sit on top of APC to adapt faster to disturbances.
   • Goal: reduce variability, keep units closer to optimal setpoints.

3. Predictive maintenance for rotating equipment and critical assets

   • Failures in compressors, pumps, furnaces, and heat exchangers are margin killers.
   • AI forecasts failures earlier, allowing planned interventions.

4. Energy management and emissions optimization

   • Refineries are energy-intensive; fuel gas systems and steam networks are complex.
   • ML can reduce energy intensity and stabilize operations under carbon constraints.

If Mexico’s refining truly is stabilizing, the next bottleneck won’t be crude availability—it’ll be operational excellence. And that’s increasingly a data problem.

Export logistics: AI is the difference between “sold” and “delivered”

The direct answer: AI improves export performance by forecasting demand, optimizing cargo scheduling, and reducing demurrage and quality disputes.

Even when a country exports less, export reliability matters more. With fewer cargoes, each shipment carries higher commercial importance. In crude markets, the cost of being wrong is very measurable:

• demurrage due to port congestion or schedule slips
• penalties from off-spec quality
• missed pricing windows
• suboptimal routing and vessel selection

What an AI-enabled export stack looks like

For Kazakhstan—where export routes, blending points, and geopolitical constraints can shift quickly—AI is practical when it supports decisions like these:

• Demand sensing: short-term forecasts by region and refinery configuration (who can actually run your grade)
• Cargo optimization: batch sizing, timing, and pricing scenarios based on market signals
• ETA prediction: vessel tracking + port queue modeling for better loading plans
• Quality reconciliation: anomaly detection on lab results and custody transfer data to flag disputes early

Snippet-worthy point: In oil exports, the margin isn’t only in the price—you also earn (or lose) margin in the schedule.

Lessons for Kazakhstan: don’t modernize in pieces

The direct answer: Kazakhstan gets the best ROI when AI connects upstream, midstream, and refining into one decision chain.

This post sits in our series on Қазақстандағы энергия және мұнай-газ саласын жасанды интеллект қалай түрлендіріп жатыр, and Mexico’s case reinforces a pattern I’ve seen repeatedly: companies adopt AI in isolated pilots—one dashboard here, one predictive model there—then wonder why results plateau.

The Kazakhstan-specific opportunity

Kazakhstan has a strong incentive to build an integrated, AI-supported operating model because:

• crude quality management and blending are commercially sensitive
• export logistics can be constrained by infrastructure and route dependence
• refineries and product demand planning must react to seasonal shifts

January is a good moment to talk about this because Q1 planning typically locks in budgets, maintenance windows, and trading strategies. If AI is treated as “innovation,” it gets delayed. If it’s treated as “how we plan and operate,” it gets funded.

A practical blueprint: 90 days to operational AI

Most companies get stuck trying to boil the ocean. A better approach is a tight, cross-functional program with measurable outcomes.

Step 1: Pick one margin center and one reliability center

• Margin center examples: crude blending, yield optimization, export scheduling
• Reliability center examples: heat exchanger fouling, compressor failures, furnace efficiency

Step 2: Build a shared data layer (minimum viable) You don’t need a perfect data lake. You need:

• time-series historian access (process tags)
• lab/LIMS quality data
• maintenance/EAM work orders
• shipping/trading records (where relevant)

Step 3: Define “production” as a business process, not an app A model that doesn’t change a decision doesn’t matter. Decide upfront:

• who gets the recommendation
• how often it updates
• what action it triggers
• what KPI proves value

Step 4: Prove value with hard KPIs Examples of KPIs executives actually trust:

• reduced unplanned downtime hours
• energy intensity reduction (GJ/ton)
• improved on-spec rate
• reduced demurrage days
• improved realized margin vs plan

People Also Ask (and the straight answers)

Why would a country export less crude if it produces oil?

Because domestic refining can absorb barrels, and governments often prefer local value-add (fuel, jobs, tax base). When refining becomes more stable, exports drop—even if production doesn’t collapse at the same rate.

Can AI really improve refinery margins?

Yes, when it targets constraints that move daily: crude mix, unit limits, equipment health, and energy costs. The margin uplift usually comes from reducing variability and avoiding downtime, not magic predictions.

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

In many assets, it’s predictive maintenance on a small number of critical machines, plus optimization of crude blending where commercial value is immediate and measurable.

What to do next in Kazakhstan’s energy and oil-gas sector

Mexico’s export slide is a reminder that energy strategy changes faster than most operating models. When refining capacity stabilizes—or when export routes tighten—companies that rely on manual planning get boxed in.

Kazakhstan doesn’t need to copy Mexico’s path. The smarter move is to treat Mexico as a warning: if your refinery, export, and maintenance decisions aren’t data-driven, you’ll react late and pay for it in margin and reliability.

If you’re planning your 2026 initiatives now, I’d start with a blunt question: Which decision in our value chain still depends on “someone’s spreadsheet,” even though the data already exists? That’s usually where AI pays back first—and where operational resilience starts.