The biggest obstacle to the refinery of the future is a mindset shift

Posted: May 06, 2026

Autonomous operations in oil and gas are no longer a figment of the imagination. Companies now have all the tools, and the business case, to take the industry into the future.  

So why, across the global refining industry, does the vast majority of plants still operate much the same as a decade ago? Bar a few exemplary cases, refineries continue to rely heavily on manual interventions, reactive maintenance and periodic optimization reviews—leaving enormous value locked inside their own processes.  

For operations leaders, these facts hide an uncomfortable truth: the biggest barrier to autonomous refining is not technology, but mindset.

Autonomous operations: Key to relieving pressure on oil and gas

As anyone in the industry knows, refining faces pressure from multiple directions. On one side, the business is squeezed by thin and cyclical margins, crude price volatility, shifting product demand and relentless competition. On the other, companies face a growing sustainability imperative: carbon pricing and emissions regulations, but also investors and customers demanding progress on decarbonization.

What makes autonomous operations so strategically compelling is that they address both pressures at once. Energy is both the largest single operating cost in refinery operations, and the primary source of the industry’s direct carbon emissions. Every unit of energy saved is therefore simultaneously a margin improvement and an emissions reduction.

And the most powerful tool to optimize energy use at modern refineries is autonomous operations, with the potential to raise efficiency by at least 11%.

How autonomous refining is evolving

True autonomous operations go far beyond conventional process control, and require the ability to look ahead and address challenges proactively. These capabilities are now enabled by the convergence of predictive analytics, process simulation and real-time optimization.

But one of the most demanding tests is managing transient processes outside of steady-state operations—think startups, shutdowns or feedstock switches. The challenge becomes even more acute as refineries move toward higher levels of autonomous operations maturity. The broader the scope of autonomous control, the more critical it becomes to handle transient behavior reliably and safely across multiple interconnected units at the same time.

Evidence for the benefits of autonomous operations is mounting among early adopters. Take ADNOC, one of the world's largest energy companies, which has deployed Neuron 5, an advanced AI-powered autonomous operations platform co-developed with AVEVA and AIQ, across its upstream and downstream facilities.  

Initially tested at a crude field and gas compression plant, Neuron 5 not only autonomously monitors thousands of critical equipment assets—like compressors, valves and generators—but also monitors and predicts process performance across ADNOC’s facilities. The results from the pilot phase are striking, promising to reduce unplanned shutdowns by 50% and extend planned maintenance intervals by 20%.

Separately, a new generation of technology is redefining what is possible: Deep Reinforced Learning (DRL) combines the predictive power of dynamic process simulation with the adaptive, experience-driven training of machine learning. DRL systems learn optimal control strategies through continuous interaction with a simulated process environment, developing the ability to navigate complex and evolving operational conditions in ways that static models cannot.  

The potential upside is impressive: early deployments demonstrate that DRL-driven control policies can stabilize industrial processes twice as fast as manual operators during large feed changes and process upsets—exactly the kind of transient conditions where traditional control falls short.

From operator to orchestrator: the mindset shift in autonomous refining

For these critical tools to find broader adoption, however, requires a fundamental mindset shift.

The refining industry has always attracted deeply skilled professionals who take pride in hard-won operational expertise. But that same pride, combined with organizational inertia, is increasingly becoming the obstacle preventing refineries from adopting meaningful automation—and achieving improvements across the two dimensions that define success in modern refining: margin performance and environmental responsibility.

Historically each refinery shift had its own philosophy for running things, a way of working that is upended by automatic, standardized processes. Operators and supervisors are also rightfully worried about taking responsibility for autonomous processes they do not understand, fearing they will bear the consequences if something goes wrong. Finally, the adoption of autonomous operations can appear to threaten their very livelihood—or, at the very least, require a substantial rethink of their role.

Companies need to be conscious of these concerns and seek to replace them with an alternative message: that autonomous systems do not remove human control, but elevate it. After all, when AI monitors process control, or predictive systems anticipate future incidents, experienced operators are freed from reactive firefighting and instead evolve to become empowered orchestrators.

Of course this evolution relies on next-generation data capabilities, since autonomous operations cannot function on fragmented information. Refinery orchestrators require supervisory platforms that provide a 360-degree operational view of a plant’s processes, assets, energy flows, cost drivers and emissions profile at any given moment.  

Advanced analytics sit at the heart of this supervisory layer, extracting meaningful intelligence from the vast data streams modern refineries generate. Without this comprehensive picture, optimization remains partial—meaning margin opportunities are missed and environmental management remains reactive.

The cost of standing still on autonomous refinery operations

It’s clear that the question is no longer whether autonomous operations work in refining, but whether operations leaders are ready to lead the transformation.

The refinery of the future is being built today, by organizations that recognize autonomous operations as a genuine inflection point — as ADNOC and others show, this is now a measurable operational reality delivering real value. Those who wait will find that the performance gap, both economically and environmentally, widens to a point where it becomes very difficult to close.

The good news is that succeeding in this transition only requires operations leaders to actively champion the change and effect a mindset shift among the skilled workers who will continue to be crucial to orchestrate and optimize performance.

They need to embed both economic and sustainability targets into their optimization frameworks, define governance that clearly delineates where autonomous control operates and where human judgment is required, and measure success by whether people and systems together deliver outcomes that neither could achieve alone.

The technology is here and the business case is clear. All the industry needs now is operations leadership with the vision to see the opportunity and the courage to seize it.