US Rig Counts Fell in 2025—AI Lessons for Kazakhstan

US drilling data can look like a simple scoreboard: rigs up, rigs down, end of story. But the late‑2025 numbers from Baker Hughes tell a more useful story for operators and decision-makers—especially if you’re in Kazakhstan and you’re trying to squeeze more output, reliability, and safety from the assets you already have.

Here’s the hard fact: the US ended 2025 with 546 active oil and gas rigs, down 43 rigs year-over-year. Oil rigs specifically were 412, down 70 from a year earlier. Gas rigs were 125, down 2 on the week but up 22 year-over-year. (Baker Hughes weekly rig count data, as cited in the RSS summary.)

And yet—US production stayed close to highs. That combination (fewer rigs, still-high production) is the real signal. It says the winners aren’t “drilling more.” They’re drilling smarter, completing wells more efficiently, and operating with tighter feedback loops.

This post is part of our series «Қазақстандағы энергия және мұнай-газ саласын жасанды интеллект қалай түрлендіріп жатыр». The core idea is simple: AI in oil and gas isn’t a shiny innovation project; it’s how you turn volatility into a planning advantage.

What the US rig count really tells you (and what it doesn’t)

Answer first: Rig counts are a leading indicator of activity, but they’re not a direct measure of production. AI helps convert this noisy indicator into actionable forecasts.

A rig count is basically “how many drilling machines are active.” It says something about near-term investment appetite and drilling cadence. But it doesn’t fully capture:

• DUCs (drilled but uncompleted wells): production can rise even if rigs fall when companies complete backlog.
• Laterals getting longer: fewer wells can still mean more reservoir contact.
• Completion intensity: stage counts, proppant volumes, and design tweaks can move production more than rig changes.
• Operational efficiency: faster drilling, fewer non-productive hours, better geo-steering.

This is why the US can see rig counts down while production stays near highs. It’s not magic; it’s operational math.

Kazakhstan parallel: don’t overreact to “counts,” build a signal

In Kazakhstan, we often watch “big” indicators—capex, export constraints, maintenance cycles, and field-level KPIs. The mistake is treating those indicators as if they are the outcome.

A better approach is to build an AI-assisted early warning system that learns what actually predicts production, downtime, or cost overruns in your specific asset base:

• rigs and workovers (activity)
• completion schedules (near-term flow)
• ESP and compressor health (sustaining output)
• power quality and grid constraints (hidden downtime)
• weather and logistics (winter reality)

If the US story is “fewer rigs, steady production,” the Kazakhstan version can be “fewer interventions, steadier uptime”—but only if data is connected and decisions are fast.

Efficiency beats intensity: why production can stay high with fewer rigs

Answer first: Production stays high when operators improve cycle time, drilling accuracy, completion quality, and uptime. AI accelerates each of those.

When companies get serious about efficiency, they usually focus on four levers.

1) Faster, more consistent drilling performance

Most companies get this wrong: they track “average days per well,” but they don’t explain variance. The variance is where money disappears.

AI models can flag what human reporting misses:

• which BHA configurations correlate with stick-slip in a specific formation interval
• which mud properties precede torque spikes
• which crews and shifts have repeatable performance patterns

For Kazakhstan’s operators, this matters because crew rotation, remote locations, and winter logistics amplify the cost of inconsistency. A two-day slip in drilling isn’t just two days; it ripples into services, trucking, and completion windows.

2) Better well placement and completion design

If you can keep production high with fewer rigs, it usually means your wells are doing more work.

AI supports:

• geo-steering recommendations using real-time MWD/LWD + offset well outcomes
• completion optimization (stage spacing, fluid volumes) based on production response patterns
• type curve segmentation so you stop averaging unlike wells into one misleading forecast

In practical terms: fewer “pretty wells” on paper, more wells that actually hit the plan.

3) Reducing non-productive time (NPT)

NPT is often treated like an inevitability. It isn’t.

AI-based detection can identify early signatures for:

• stuck pipe risk
• lost circulation onset
• pump efficiency degradation
• vibration conditions that predict tool failure

These are classic cases where minutes matter. Humans are good at noticing problems; they’re not great at noticing the earliest weak signals across 50 tags and 6 dashboards.

4) Higher uptime in production operations

The “rig count” conversation is mostly upstream drilling, but the real production story often sits in operations: compressors, pumps, separators, power, corrosion, sand, water handling.

This is where AI in energy operations pays back fast:

• predictive maintenance on rotating equipment
• anomaly detection on pressure/temperature/flow patterns
• optimization of lift settings to reduce failures and stabilize rates

If you’re trying to defend production during a year of tighter budgets, uptime is the first place I’d look.

How AI turns market volatility into a planning advantage

Answer first: AI doesn’t predict the future perfectly; it makes planning less fragile by running scenarios faster and updating forecasts as data changes.

US rig counts are volatile because investment decisions respond to price expectations, service costs, and shareholder pressure. Kazakhstan has its own volatility drivers: export routes, maintenance and turnarounds, regulatory shifts, FX, and service capacity.

AI helps in three planning layers.

Forecasting: from single-number forecasts to scenario sets

A single forecast is comforting—and often wrong.

A stronger approach is probabilistic forecasting:

• baseline production outlook
• downside case (equipment constraints, power interruptions, water cut acceleration)
• upside case (debottlenecking, fewer failures, faster completions)

AI models can refresh these scenarios weekly as actuals come in, which makes leadership discussions more concrete: “If this pump failure rate continues, we’ll miss by X. If we fix root cause A and B, we recover Y.”

Budgeting: connect spend to operational outcomes

Budget cuts usually hit “discretionary” items first. The problem: what looks discretionary (sensor calibration, minor maintenance, integrity checks) often protects uptime.

AI-enabled cost-to-outcome mapping helps you answer:

• Which spend categories reduce unplanned downtime measurably?
• Which interventions improve recovery per tenge spent?
• Where are we paying for complexity that doesn’t move production?

This is where Kazakhstan operators can learn from the US shift: when rigs fall, the industry survives by being brutally clear on what drives output.

Commercial and supply chain: don’t let logistics be the hidden constraint

In Kazakhstan, logistics can quietly become the bottleneck—especially in winter. AI planning tools help by predicting:

• service demand spikes (cement, frac spreads, trucking)
• spare part criticality and lead times
• weather-driven access constraints

It’s not glamorous, but it’s the difference between “plan achieved” and “plan slipped.”

Practical AI use cases Kazakhstan can deploy in 90–180 days

Answer first: The fastest wins come from focused use cases with existing data: equipment health, production optimization, and planning analytics.

If you’re reading this and thinking “We don’t have perfect data,” good. Perfect data is a myth. What you need is enough data, a clear owner, and a narrow scope.

Use case 1: Predictive maintenance for compressors and pumps

Start with a single asset class (say, compressors). Build a model that predicts failure risk 7–30 days out using vibration, temperature, pressure, runtime, and maintenance logs.

What success looks like:

• fewer emergency shutdowns
• shorter repair time because diagnosis is earlier
• measurable reduction in deferred production

Use case 2: AI-assisted production surveillance

Use anomaly detection to flag wells or facilities deviating from expected behavior.

Examples of anomalies worth catching early:

• sudden increase in water cut
• declining tubing head pressure patterns
• unstable separator performance
• ESP current signatures indicating wear

Use case 3: Rig/workover schedule optimization

Even if you’re not running many rigs, scheduling is where value leaks.

AI scheduling can minimize:

• idle crew time
• travel and mobilization costs
• clashes between services and equipment availability

Use case 4: Short-term production forecasting with uncertainty

Build a forecast model that updates weekly and outputs a range, not a point.

This improves:

• export and storage planning
• maintenance timing decisions
• leadership alignment (“what would change our forecast next week?”)

A useful rule: if your forecast can’t explain what would change it, it’s not a forecast—it’s a hope.

“People also ask” questions (quick answers)

If US rig counts are down, why doesn’t production fall immediately?

Because production depends on completions, well productivity, and uptime—not only the number of rigs. Backlog completions and efficiency gains can offset fewer rigs.

What’s the most valuable AI application in oil and gas operations?

For most assets, it’s predictive maintenance + anomaly detection tied to clear action workflows. Models without operational response don’t create value.

Do Kazakhstan oil and gas companies need huge AI teams?

No. A small cross-functional team (operations + data + maintenance) with strong governance usually beats a large “AI department” that’s disconnected from field reality.

Where this leaves Kazakhstan in 2026

US drilling activity ending down in 2025 while production stays near highs is a reminder: activity metrics are not destiny. Efficiency, reliability, and planning discipline win.

For Kazakhstan’s energy and oil‑gas sector, AI is becoming the practical toolset for that discipline—production surveillance, predictive maintenance, schedule optimization, and scenario-based planning. If you’re serious about stable output and safer operations in 2026, you don’t need more dashboards. You need faster decisions that are harder to argue with because they’re backed by data.

If you’re mapping your 2026 priorities, ask yourself one forward-looking question: Which single operational decision would improve the most if you had a weekly model-driven forecast instead of monthly hindsight?