You might think AI’s role in the energy industry is restricted to exploration — namely, finding the goods. But that’s only a small part of what AI can do.

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“AI is impacting almost every single aspect of the energy industry, and as the industry begins rolling out AI-based solutions and realizes the potential of this technology, we expect AI to expand into every area of the industry in some way or another,” says Chris Cooper, CEO of AIQ, an Abu Dhabi-based technology company taking on sustainability and efficiency across the energy sector.

AIQ is a joint venture between the Abu Dhabi National Oil Co. (ADNOC) and AI specialist G42.

Much of the efficiency AI offers in the industry stems from prevention — in all stages of processes.

AI AS RISK MANAGER

AIs are being used in risk aversion: Keeping track of regular machinery maintenance prevents equipment malfunctions and breakdowns.

Though preventative maintenance has been around for a long time, AI can constantly analyze the current condition of equipment and use historical and current data to determine deviations from the norm. This can bring attention to issues before they happen, reducing or eliminating failures and therefore saving millions of dollars in down time.

Not only does this save money, but AI’s preventive elements are also in charge of finding indications that environmental disasters are on the horizon.

Oil spills are one of the most impactful risks to the environment, especially the oceans. In 2022 alone, 700 metric tons of oil leaked in four oil spills. This damage is catastrophic to the ocean’s wildlife and ecosystems: Animals can die from hypothermia when the fur coats that insulate or the feathers that repel water become coated in oil. Plus, while cleaning themselves these animals ingest poison that can impact reproduction and growth rates in offspring.

Aside from things we cannot control like natural disasters, the main causes of oil spills are equipment failure and human error. AI can manage these.

The cost of human error is high. The 2010 Deepwater Horizon explosion, for example, killed 11 people and is the most expensive water oil spill in history. It cost BP and partners about U.S.$71 billion in legal fees and cleanup costs.

AI is used to monitor pipeline conditions for small amounts of deterioration, cracks, etc., offering critical real-time information. Identifying imperfections can help operators address the issues before leaks occur, accidents happen or machines fail. Predictive maintenance contributes to safety and savings across the industry and reduces unpredicted downtime, typically averaging 27 days at a price of U.S.$38 million.

ADNOC initiated pilot testing of its Centralized Predictive Analytics Diagnostics (CPAD) in 2017, and it remains at the center of the company’s digital transformation path.

“CPAD’s predictive maintenance capability can track any mechanical degradation as well as variations in performance to help maintenance teams to plan required work well in advance with consideration to any production constraints,” the website reads.

“AI technology is contributing to monitoring and maintenance again in many different areas, from computer vision being used to monitor pipelines, to sensor monitoring to detect variations in machine operations, through to chemical analysis to detect corrosion in pipelines and monitoring of remote sites or hard-to-reach locations using drones,” AIQ’s Cooper says.

AI can also monitor pressure and oil-flow rates to identify issues before they become problems, mitigating leaks and ensuring worker safety.

GRAPHICS: Abjad Design

The benefits are ample, protecting the environment, workers and the bottom line.

AI’s ability to predict demand means it is also poised to provide risk assessment for investors.

Investment analysis incorporates political and economic events, trends in oil products across the value chain, historical data, public inclination and so on. AI can collate all the information that contributes to price fluctuations to help investors make well-informed decisions.

EXACTING THE EXTRACTION

Energy companies are also harnessing AI capabilities to process data from seismic surveys, improving the accuracy of drilling locations and the drilling plans themselves.

AI algorithms can come up with new extraction techniques and create reservoir models to anticipate how different extraction approaches will work in multiple conditions. This could mean better operations and more lucrative extractions with less environmental degradation.

“Better reservoir development and planning can result in fewer wells being drilled to extract the same resources,” Cooper says.

The best way to develop and plan is to model. One AI tool that AIQ has in the market to assist with reservoir modeling is the AR360 (Advanced Reservoir 360). The 360-degree model allows for review and all-encompassing digital assessment of existing reservoir simulation models.

“The Reservoir Performance Advisor module takes advantage of machine learning, advanced analytics, petro-technical workflows and business logic,” Cooper adds. The system analyzes the data identifying wells declining in performance then provides solutions. What used to be done manually over months is now completed in minutes, improving strategic production, field development planning, cost reduction and lower emissions.

Once the oil is out of the ground and ready for the refining process, analysis ensures maximum efficiency throughout. Refining the process, not just the oil, reduces offsets and, consequently, the environmental impact.

“Many of the AI solutions that have been developed to address efficiency and productivity will also bring improvements to overall sustainability by reducing waste, eliminating unnecessary processes and so on,” Cooper tells KUST Review.

Now that the oil is produced, it’s time to get it to its final destination.

AI AS SUPPLY-CHAIN MANAGER

Supply chains around the world are improved by machine learning. Behaving proactively, rather than reactively, will not only get goods from A to B efficiently and safely, it can balance out supply and demand, saving money across the entire production process.

There are many factors built into predicting oil prices, and while some believe it’s too complex for AI to accurately predict, that isn’t stopping researchers from trying.

A team from China’s Shenzhen University found that rather than using single-model machine-learning methods for price prediction, combining multiple models coupled with specific Google Trends for the online data shows promise. This structure, the team says, results in more accurate anticipation of crude-oil price fluctuations.

The team’s AI analyzes large amounts of historical data, looks for patterns and trends and then combines this with current market information to predict demand.

Supply-chain improvement also means better navigation for ships — shorter routes, safety monitoring en route and recommendations for changing course, should the need arise. This is also a sustainability issue.

GRAPHICS: Abjad Design

Paul McStay, performance manager for oil giant Shell’s liquefied natural gas fleet, says improving efficiency can help the industry reduce carbon emissions. “If we can improve our efficiency, we can reduce the amount of time that we’re waiting at port. Then we can reduce our fuel usage. And by reducing our fuel usage, we can improve our emissions,” he says on Shell’s website.

Route planning and fleet management using operations research, mathematics and optimization techniques aren’t new, says Mohammed Omar, who chairs management science and engineering at Khalifa University.

But amplified efficiency and accuracy provided by AI across the board reduce risk, save money and lower the carbon footprint

According to AIMMS, an analytics software company that has been optimizing mathematics to help companies become more efficient since 1989, supply chains can no longer live without AI. Conversely, AI cannot live without supply chain planners.

WHAT’S NEXT?

In a 2023 survey from EY, a leading auditor of oil and gas companies, 50 percent of oil companies reported using AI in some way and 92 percent are planning to begin or add AI applications within the next five years.

With those statistics, it might be understandable for industry workers to worry about their jobs. But many developmental reports and articles insist AI will work in conjunction with humans, not replace them. They say a shift will definitely occur in human roles, but it is unlikely to result in significant job loss.

According to a 2019 EY report, “The AI revolution is already here for some, and for others, such as oil and gas, it’s just around the corner. AI and ML techniques applied to the sector have the potential to take large amounts of structured and unstructured data with a processing power far greater than a company’s workforce, creating transformative impact. What’s more, when AI and ML are coupled with human workforce capabilities, the combined collective intelligence impact has the potential to create lasting competitive advantage.”

Five years on, it seems EY’s foresight was on the money.

Cooper uses the example of AIQ’s suite of AI-enabled applications for borehole image data analysis, WellSight. The analytics are a time-saver for petrophysicists, freeing them up for more complex functions, and the system offers information to enhance planning for drilling operation.

It all sounds positive, but what’s the catch?

WE’RE STILL LEARNING

We’re still on the cusp of understanding AI’s full capabilities in any industry. That means we need people skilled in AI and machine learning. There will also be significant investment in training — training throughout the digital transformation but also retraining those shifting to different roles.

Plus, big data is what AI needs in order to perform, but if the data isn’t relevant, the AI will not meet expectations, and all that money will be wasted.

Finally, the main challenge is getting the buy-in in the first place. Some businesses simply aren’t ready.

Wael William Diab, from the International Organization for Standardization, says it’s all about the mindset: “An AI-positive future is possible, but we need to actively pursue it. If we approach AI with a positive mindset, placing societal needs such as ethics and sustainability at the heart of its development, then we can unlock its full potential,” he says on the non-governmental organization’s website.

“If it is developed ethically and responsibly, AI could help to usher in a new era of innovation and inclusion,” he adds. “This cutting-edge technology could be used to make our world safer and better, opening up possibilities that seemed like science fiction just a few years ago.”

BUT SHOULD WE OR SHOULDN’T WE?

Some of the big concerns surrounding AI rollout globally are ethics and trust. But in the energy industry, buy-in concerns can typically be mitigated with transparency and education.

“If an engineer doesn’t understand how an AI draws the inference it does, then they are less likely to trust the outcomes, and won’t be able to understand any errors that might occur. So, it is important that the users have a good understanding of how the solution works,” says Cooper.

AI understanding is one aspect, but when something goes wrong, the first question is usually: Who did it? Who is accountable when an autonomous AI system is at the helm?

Cooper says it all boils down to finding balance: Balance between rules, framework and AI decision-making and when a human is required to step in.