When imagining a green energy future, many envision a world powered entirely by renewables. While that remains the ultimate goal, it is still a distant one. For now, a hybrid energy landscape—still reliant on fossil fuels—will prevail.
Artificial intelligence (AI) and machine learning (ML) offer petrochemical companies a way to enhance operational efficiency and reduce environmental impact in the interim. Not only can these technologies help boost output, but they can also limit ecological harm on a micro scale.
Speaking to ESG Mena, Jack Jendo, Founder and CEO of Brain Digits, underscores the transformative potential of AI in the Gulf’s petrochemical sector:
“The greatest opportunity AI brings to the Gulf’s petrochemical sector is transformation at the decision-making level. Yes, efficiency and sustainability are critical, but the real game-changer is predictive intelligence—where AI doesn’t just automate processes, but anticipates disruptions, optimizes supply chains in real time, and personalizes energy output based on market demand. For firms in the Gulf, this means not just doing better—but thinking smarter and faster than the global competition.”
Adoption well underway
AI and other advanced technologies are being swiftly adopted across the oil and gas sector to improve productivity, cut costs, and maintain competitiveness. Senior leadership is increasingly recognising their strategic value.
According to DNV’s Transforming Through Uncertainty, 47% of oil and gas professionals surveyed in 2023 said their organisations planned to implement AI by 2024. While few had done so at the time, adoption has since accelerated as capabilities have matured.
That said, widespread deployment remains slow, largely due to concerns about reliability and the industry’s deep-rooted resistance to change. As long as the product flows, profits follow.
“The industry needs assurance that AI can deliver value effectively, efficiently, securely and safely. It needs to be sure it is investing in trustworthy AI, developed in-house or purchased, that can meet stakeholder expectations in a verifiable way. Hence, external developers and suppliers of AI components and AI-enabled systems need to prove through validation and verification that their products can be trusted,” wrote Kjell Einar Eriksson, Vice President for Digital Partnering at DNV.
Research from UAE-based Aitropolis Technologies outlines how AI can enhance exploration, interpret seismic data, and strengthen predictive maintenance. Notably, the Abu Dhabi National Oil Company (ADNOC) boosted production by 3–5% at its Bu Hasa oilfield by using AI to optimise operations.
Similarly, Saudi Aramco leverages ML to anticipate equipment failures and reduce unplanned downtime—saving over $20 million annually with one model alone. Aramco also deploys drones to monitor infrastructure and pipelines. These gains follow earlier successes by Royal Dutch Shell, which heavily invested in ML-driven efficiencies.
AI’s tangible benefits
“AI is not a silver bullet, but it is a tool that can help us to accelerate energy transition and to reduce CO2 emissions,” explained Dan Jeavons, VP of Digital Innovation at Shell.
Indeed, companies are turning to AI and ML primarily for data optimisation. In the oil and gas sector, immense volumes of data are collected—yet often remain underutilised. ML enables companies to extract actionable insights from this data, helping to identify growth opportunities and guide decision-making.
Predictive analytics—powered by ML—can also enhance planning and forecasting. By analysing historic well production or seismic survey results, businesses can anticipate future output and plan operations accordingly. This type of forecasting, successfully employed by both ADNOC and Aramco, enables more strategic long-term investment.
More advanced applications pair AI with specific drilling tools. Algorithms trained on Shell’s drilling data and simulated exploration models help guide drills through subsurface terrain. Drawing on seismic survey results and real-time drill bit data—such as pressure and temperature—the system equips geosteerers with a clearer understanding of their environment. The result: faster, more accurate drilling and less equipment wear. In many ways, it mirrors the challenge of developing self-driving cars, albeit in a far more complex seabed environment.
Beyond drilling, AI and ML offer improvements across refining, transport, and storage. Much of Shell’s digital edge in this arena came from big data analytics firm SparkCognition—now rebranded as Avathon. The company, which counts Qualcomm and Nvidia among its clients, delivers tailored ML solutions poised for expansion in Middle Eastern refineries.
With efficiency gains already in the 2–5% range, broader AI adoption could significantly upscale performance.
Tech convergence and future promise
Given the sector’s strategic significance, oil and gas companies require robust security and traceability frameworks. This is where convergence with blockchain technology comes into play.
As sensor use increases, blockchain can directly store transaction and accounting data, streamlining processes and linking assets to contracts. It also offers a secure method for collaboration and verification.
Experts suggest that the next step is a private, blockchain-enabled ecosystem that spans the entire transaction life cycle—from price discovery and trading to settlements and payments. This will, however, require buy-in from all stakeholders, including petrochemical buyers in both government and private sectors.
A detailed study notes how blockchain could act as a filter, helping to identify legitimate actors in the market and exposing bad faith operations. Its transparency, immutability, and traceability allow authorities and firms to audit royalties, revenues, and contractor performance with greater precision.
Though still in the testing phase, blockchain could save companies significant costs while improving accountability.
Jack Jendo also highlights the challenges faced by smaller firms in adopting AI technologies:
“There’s a widening digital divide. While giants like Aramco and ADNOC are building entire ecosystems around AI, smaller players risk becoming obsolete if they don’t rethink their strategies. But this isn’t a death sentence—it’s a call to collaborate, not compete. By partnering with innovation hubs, adopting modular AI solutions, and investing in digital skills, even the smallest firm can punch above its weight in the age of AI.”
AI and ML are just getting started at an industrial scale. Each successive generation of models is surpassing the last, driving demand for AI professionals across the Middle East’s oil-rich economies.
For leading producers like Saudi Arabia and the UAE, staying ahead of the curve offers both national and collective benefits. It positions them to lead in R&D, enhance OPEC’s technological standing, and influence the future trajectory of the petrochemical sector.
By: Omar Ahmed