AI + Geothermal: Kazakhstan’s Next Energy Advantage

Geothermal is having a quiet moment globally—and that’s exactly why Kazakhstan should pay attention. While parts of the world argue loudly about which renewables “count,” geothermal often slips through the political crossfire because it doesn’t look like wind farms or solar parks. The recent U.S. example is telling: even with a pro–fossil fuel political mood, geothermal has largely avoided being targeted in the same way other clean energy sectors have.

Here’s the stance I’ll take: geothermal is one of the most practical “bridge” renewables for a hydrocarbon-heavy economy—especially when paired with artificial intelligence in energy operations. It can coexist with oil and gas, share skills and subsurface expertise, and deliver steady output that makes the grid easier (not harder) to run.

This post is part of our series «Қазақстандағы энергия және мұнай-газ саласын жасанды интеллект қалай түрлендіріп жатыр». The through-line is simple: Kazakhstan doesn’t need to choose between traditional energy strengths and cleaner generation. AI can help the country run a hybrid system—more efficiently, more safely, and with better economics.

Why geothermal keeps surviving political pushback

Answer first: Geothermal tends to avoid political targeting because it behaves more like “reliable infrastructure” than a symbolic climate policy.

In the U.S., the RSS article highlights that geothermal is one of the few renewable sectors President Donald Trump has not tried to quash in favor of fossil fuels. That’s not because geothermal is invisible. It’s because its value proposition is less ideological and more operational:

• Firm power: geothermal can generate 24/7 (where resources are proven), unlike intermittent wind/solar.
• Industrial fit: it aligns with drilling, reservoir engineering, and subsurface modeling—skills oil and gas already has.
• Local energy security: geothermal projects are domestic, site-based, and less exposed to fuel price swings.

The European Commission’s framing is also useful for non-specialists: geothermal energy is renewable energy harnessed from thermal energy stored in rocks and fluids. Translation: we’re tapping heat that’s already there.

For Kazakhstan, this matters because the energy transition conversation often gets stuck on “either/or.” Geothermal is “and.” It can complement gas, support district heating, and provide stable generation that reduces the need for expensive balancing.

Kazakhstan’s geothermal opportunity isn’t theoretical

Answer first: Kazakhstan can treat geothermal as a subsurface development problem—something its energy sector already knows how to solve.

Kazakhstan has long operated in environments where geology, drilling risk, and production uncertainty are normal business conditions. That makes geothermal culturally compatible with the existing oil and gas ecosystem.

Where geothermal fits best in Kazakhstan

Geothermal doesn’t have to start with grid-scale power plants. In fact, the fastest wins are often heat-first projects:

• District heating for cities: geothermal heat can stabilize winter heating costs and reduce dependence on coal or gas in peak periods.
• Industrial process heat: food processing, chemicals, and mining operations often need steady heat more than electricity.
• Co-produced geothermal: using hot produced water from oilfields (where feasible) for heating or low-temperature power.

A practical point: heat demand peaks in winter, exactly when solar is weakest and when energy systems feel the most stress. If you’re thinking like a grid operator, geothermal heat is not a “nice-to-have.” It’s a stress reliever.

The hard part: exploration and economics

Most geothermal projects globally are still in early stages because the challenge is front-loaded:

• You spend money to confirm the resource before you can earn revenue.
• Drilling is expensive and uncertain.
• Subsurface data is incomplete until you drill.

This is where Kazakhstan’s established subsurface capability becomes a strategic edge—and where AI in oil and gas can be repurposed.

What AI actually does for geothermal (beyond buzzwords)

Answer first: AI reduces uncertainty in subsurface decisions, improves uptime, and helps integrate geothermal output into a hybrid grid.

A lot of “AI in energy” talk is vague. For geothermal, the wins are concrete and measurable because the process has clear pain points: exploration risk, equipment reliability, scaling/corrosion, and dispatch planning.

1) Better subsurface targeting with ML + physics

Geothermal exploration relies on geological, geochemical, and geophysical signals. The decision you’re trying to make is straightforward: where should we drill to find productive permeability and sustainable heat flow?

AI can help by:

• Fusing data sources: seismic, magnetotelluric, gravity, well logs, temperature gradients, satellite data.
• Ranking prospects: machine learning models can prioritize zones with higher probability of commercial flow.
• Updating in real time: as drilling proceeds, models can be updated using new measurements (rate of penetration, mud logs, downhole temperature/pressure).

The strongest implementations combine ML with domain constraints—often called physics-informed approaches. In plain terms: don’t let the model hallucinate geology.

Snippet-worthy truth: In geothermal, AI’s job isn’t to “predict the future.” It’s to reduce the cost of being wrong.

2) Predictive maintenance for geothermal plants and wells

Geothermal operations face equipment stress: pumps, turbines, heat exchangers, and pipelines can suffer from scaling, corrosion, and vibration issues.

AI helps by:

• Detecting anomalies early (temperature, pressure, vibration signatures)
• Predicting remaining useful life for critical components
• Scheduling maintenance when it’s cheapest (not when it’s catastrophic)

For Kazakhstan’s energy companies already investing in industrial AI, this is transferable capability. The same logic used on compressors and rotating equipment in gas facilities can be applied to geothermal assets.

3) Smarter dispatch in a hybrid energy system

Geothermal is often described as “baseload,” but modern grids increasingly value flexibility. With AI-driven forecasting, operators can coordinate geothermal with gas peakers, hydro, wind, and storage.

Concrete AI use cases:

• Load forecasting tied to weather and industrial cycles
• Unit commitment optimization (which plants run, when, and at what output)
• Congestion management in constrained networks

Kazakhstan’s grid modernization efforts benefit from this: AI turns “more generation types” into “more controllable generation.”

Funding signals: what the U.S. geothermal story teaches Kazakhstan

Answer first: Public and private funding tends to follow risk reduction—so Kazakhstan should fund data, pilots, and standards before scaling mega-projects.

The RSS summary notes that geothermal in the U.S. has significant promise even though many projects are nascent, and that both public and private funding are expected to bolster the sector.

That funding pattern is not random. Geothermal attracts capital when early-stage risks are reduced by:

• Government-backed exploration programs (resource mapping, test wells)
• Clear permitting pathways
• Stable offtake contracts (especially for heat)
• Shared infrastructure where possible

A Kazakhstan-friendly investment sequence

If you want geothermal plus AI to produce real outcomes—not conference slides—sequencing matters:

1. Resource screening (6–12 months): aggregate subsurface datasets; identify high-potential basins and heat-demand anchors.
2. Pilot heat projects (12–24 months): small, measurable, locally valuable—district heating or industrial heat.
3. Digital foundation (parallel): sensors, SCADA integration, historian data quality, cybersecurity baseline.
4. AI deployment (after data readiness): predictive maintenance, reservoir monitoring, dispatch optimization.
5. Scale (24+ months): replicate the proven model across regions and industrial sites.

Here’s what works in practice: treat AI as an operational layer on top of disciplined engineering, not as a substitute for it.

“People also ask” (and what I’d answer)

Is geothermal realistic in Kazakhstan compared to wind and solar?

Yes, but it’s not a copy-paste. Wind and solar are often faster to deploy; geothermal can be slower upfront due to drilling and resource confirmation. The win is that geothermal can deliver steady heat and power, which reduces seasonal stress.

Does geothermal compete with oil and gas jobs?

It’s closer to a job “adjacency” than a replacement. Drilling, reservoir engineering, well integrity, and subsurface modeling map well from oil and gas to geothermal. That’s a smoother workforce transition than many renewables.

What’s the fastest geothermal business case?

Heat. District heating and industrial process heat can have simpler economics than electricity-only projects because they replace expensive winter fuels and avoid some grid constraints.

Where does AI deliver the quickest ROI?

Predictive maintenance and operations analytics usually pay back first because they reduce downtime and extend asset life. Exploration AI can be high value too, but it depends on data availability and governance.

How to get started: a practical checklist for energy companies

Answer first: Start with one geothermal pilot, one AI use case, and one clear KPI—then scale only what works.

If you’re in Kazakhstan’s energy or oil and gas sector and you want to test this seriously, here’s a realistic starting plan:

• Pick a site with heat demand (industrial facility or city district heating node)
• Secure subsurface data access (existing wells, temperature logs, geological surveys)
• Define KPIs upfront:
  • cost per GJ of delivered heat
  • uptime/availability
  • maintenance cost per operating hour
  • emissions reduction relative to baseline fuel
• Instrument the system (temperature/pressure sensors, vibration monitoring, flow meters)
• Deploy a focused AI model (anomaly detection + failure prediction) before you attempt full digital twins
• Build a cross-functional team: reservoir + facilities + data engineer + cybersecurity lead

A blunt truth: most AI programs fail because they start with “big transformation” instead of one operational bottleneck.

Where this is heading for Kazakhstan in 2026

Geothermal won’t replace Kazakhstan’s oil and gas sector overnight—and it shouldn’t be framed that way. The smart play is to build a hybrid energy system where hydrocarbons remain important, but operations become cleaner, safer, and more efficient through AI, and where firm renewables like geothermal provide stability.

If the U.S. example shows anything, it’s that geothermal can keep progressing even when politics are messy—because it’s useful infrastructure. For Kazakhstan, the combination of subsurface know-how and accelerating жасанды интеллект adoption is a rare alignment.

If you’re planning your 2026 energy roadmap, consider this a pointed question to end on: Which is riskier—testing geothermal with AI on one pilot site, or waiting until everyone else has already learned the lessons?