Can AI predict the next fuel crisis before it happens?

Procuring fuel is not a simple 'add to cart' action. It requires an understanding of when, how much, what type, and what quality of fuel resources will be required, while also ensuring a proper reserve that prevents a potential energy disruption. This means the fuel ecosystem isn’t just vast, fast-moving, and deeply interconnected, but it’s also bilateral, spanning exploration, refining, logistics and consumption.

The question of whether AI can predict a fuel crisis in such a complex ecosystem may sound futuristic, but it’s closer to reality than we think.

Today, artificial intelligence is already transforming the way industries sense, plan, and respond to disruptions—and the global fuel ecosystem is no exception. With the right data and models, AI can help us move from a reactive approach to proactive forecasting, improving global fuel security and giving stakeholders early visibility into potential risks. But reaching that point requires a continuous flow of relevant, high-quality data.

Lessons from past disruptions

Fuel crises are not new, and realistically, they may never disappear entirely. But they can be anticipated better.

The 1973 Oil Embargo left the US petrol pumps dry. In 2022, the Russia-Ukraine conflict removed nearly 3 million barrels per day from global supply, which was about 3% of world output. This triggered price surges and realignments in supply. US crude stocks have fallen by 6.4 million barrels this year, reminding us how fragile the supply balance can be.

The world isn’t alone in its vulnerability.

India has had its own fuel shocks

In 2025, a damaged natural gas pipeline in Mumbai forced several CNG stations to shut their operations. At that time, what appeared to be a local disruption became a city-wide transport bottleneck within hours, and autos, taxis, and mobility services queued for hours or simply stopped running. 

Similarly, CNG prices across major Indian metros have climbed sharply in recent years because demand grew faster than infrastructure and domestic production. Delhi alone has seen CNG prices rise more than 70% since 2021, driven by supply-demand imbalance.

India has also experienced LPG shortages in the past, where refills were delayed by days or weeks, reminding us that even widely distributed fuels are hostage to supply chain complexity.

And all of these crises could be affected by a bigger truth: India imports nearly 85% of its crude oil. If there is a global shock, it ripples into Indian pricing, distribution, and planning faster than most economies.

These examples reinforce one point: the data existed, the signals existed, but what didn’t exist was the ability to connect and interpret them in time.

The data is already out there

Every link in the energy chain generates data through refinery uptime, tanker movements, storage levels, pipeline flows, weather conditions, and even shipping congestion. Besides that, we also need to understand geopolitical events and trade regulations, and trigger an alarm when things are moving in unexpected ways. AI can do it at an unprecedented rate.

It can process millions of data points in real time, it can detect subtle signs of stress that humans might overlook, and could signal a high probability of a future fuel shortage, weeks or even months in advance. 

In fact, recent real-world incidents reinforce this potential, such as the fire at Hungary’s largest refinery that raised immediate supply concerns across Europe. In a world where every delay or disruption ripples globally, predictive intelligence will not be just a luxury but an insurance.

Turning prediction into prevention

AI thrives on pattern recognition. Historical datasets, covering decades of oil shocks, price fluctuations, freight bottlenecks, and consumption trends, provide a foundation for training predictive models.

As James D. Hamilton’s research on oil shocks shows, crises are often preceded by identifiable signals such as sudden demand surges, export slowdowns, or storage drawdowns.

AI can continuously learn from these indicators, and help governments and companies to:

• release strategic reserves before shortages hit
• secure alternative supply routes
• shift consumption priorities
• communicate proactively and avoid panic behaviour

A smarter future for fuel security

In simple words, AI can’t eliminate the fuel crisis, but it can transform how we anticipate and respond to it.

Without fuel, any country could come to a standstill. And, for a nation like India, where mobility demand is rising, clean fuels are scaling, and infrastructure is still catching up, the difference between reacting and anticipating could define energy resilience.

We can no longer afford to wait for queues to form or prices to spike before acting. The future of fuel security belongs to those who listen, not just to markets, but to the data that whispers what’s coming next.

And if we integrate AI across the entire value chain through data collection, modelling, actionable alerts, and stakeholder response, we can see a fuel crisis coming first.

The author is Co-founder and CEO of Nawgati, a fuel-tech aggregator focused on reducing congestion and inefficiencies at fuel stations.