Middle East manufacturers are leading the way in AI adoption, according to the Rockwell Automation report.

According to Rockwell Automation’s newly-issued 10th annual State of Smart Manufacturing Report, 98% of manufacturing companies in the Middle East are using or planning to use generative AI, the highest rate globally, with 96% committed to broader AI/ML technologies

The study is based on feedback from more than 1,500 manufacturing leaders globally, including representation from the oil and gas (7%), renewables and energy transition (9%) and chemicals (4%) sectors.

The study highlights that Middle East companies are leading the way in deploying AI to improve operations and meet business goals, shifting from broad digital expansion to focused strategies, to achieve measurable outcomes in efficiency, cybersecurity and sustainability.

"Middle East manufacturers are not just experimenting with smart manufacturing technologies; they are applying them to address real operational challenges," said Ediz Eren, regional vice president, Middle East, Türkiye, and Africa, Rockwell Automation. "From cyber resilience to ESG performance and workforce engagement, the data shows a shift toward outcome-driven digitalisation."

Practical use cases

Manufacturers in the region are focusing on practical use cases for AI, with 68% planning to use AI for quality control, 61% for cybersecurity, and 46% for energy management over the next 12 months, significantly above European benchmarks, and 42% applying AI/ML to monitor sustainability targets.

This is paying off in terms of ROI, with 10% of manufacturers citing GenAI as their top-returning technology, and 15% saying the same of cybersecurity platforms.

Cybersecurity has risen up the agenda for manufacturing firms, with 98% of companies having either invested in or planning to invest in cybersecurity platforms, and 44% deploying countermeasures to mitigate rising threats, the highest rate globally. Thirty-six percent now view cyber risk as their top external concern, up from 27% last year.

Technology is also reshaping the workforce, with  43% of regional respondents now prioritising upskilling, up 14% year-on-year and significantly ahead of the European average.

The onshore drop node solution will set a new standard for land seismic data acquisition. (Image source: Viridien)

Viridien has launched the Sercel Accel, an onshore drop node solution which will set a new standard for land seismic data acquisition

Launched at the EAGE Annual Conference and Exhibition in Toulouse, France, Accel is designed to overcome the challenges of today’s complex, high-density seismic operations by accelerating survey deployment, enhancing operational efficiency and consistently delivering high-quality data.

Accel revolutionises onshore seismic data acquisition by eliminating the need for nodes to be buried or planted in the field, thereby providing significant savings in time and labour. With its unique droppable design, compact size, and integrated smart portable deployment system, Accel streamlines logistics, improves in-field agility and helps to reduce operational costs by up to 30% and significantly lower HSE risk.

Accel is powered by Sercel’s QuietSeis® MEMS sensor, a long-standing benchmark of total data integrity, while built-in Sercel Pathfinder QC technology also provides near real-time quality control status monitoring and ensures reliable node retrieval.

Viridien has also introduced modular Accel Solution Packs which combine nodes, software and services. These are designed to meet wide-ranging survey needs, from initial exploration to large-scale mega-crews and can be tailored to customer needs, based on project duration, complexity and strategic goals.

Jerome Denigot, head of Sensing & Monitoring, Viridien, said, “For many decades, our high-end Sercel geophysical solutions have led the industry, ensuring acquisition of the highest-quality seismic data. With the launch of Accel, we have drawn on our expertise to take a bold leap forward - revolutionising how data is captured, managed, and ultimately trusted by our customers for its total integrity and accuracy. Thanks to its seamless integration with our other acquisition systems, our Accel drop node solution enhances both crew productivity and safety. Scalable and supported by our flexible Accel Solution Packs, including software and services, it heralds the start of a new era in fast, high-resolution land seismic acquisition – accelerating projects of any size.”

DUG Elastic MP-FWI Imaging is a unique approach to seismic processing and imaging. (Image source: DUG)

DUG has released the latest results from its elastic multi-parameter full waveform inversion (MP-FWI) imaging technology which it launched in 2022, since when more than 70 successful projects have been completed worldwide

DUG Elastic MP-FWI Imaging is a unique approach to seismic processing and imaging which is not only a complete replacement for the traditional processing and imaging workflows, it also replaces the subsequent inversion workflow for elastic rock properties.

With the traditional processing workflow, projects can take many months to years to complete. It involves the testing and application of dozens of steps such as deghosting, designature, demultiple and regularisation, all designed to overcome the limitations of conventional imaging. These workflows are complex, subjective, and very time-consuming and they rely on many assumptions and simplifications. All of these issues impact the output data quality. The resulting, primary-only data then undergoes a similarly complex model-building workflow to derive an estimate of the subsurface velocity, which is used for depth imaging. Post-migration processing is performed before the pre-stack reflectivity undergoes another workflow to derive rock properties that feed into interpretation, also relying on simplifications of the actual physics.

As well as three-component reflectivity and velocity, DUG Elastic MP-FWI Imaging enables the estimation of fundamental rock properties like P-impedance, density and Vp/Vs from field data, without the need for a secondary amplitude variation with angle (AVA) inversion step. DUG Elastic MP-FWI Imaging simultaneously resolves not only subsurface structural features but also quantitative rock property information while avoiding the need for extensive data pre-processing and (post-imaging) AVA-inversion workflows.

“Elastic MP-FWI Imaging accounts for both compressional and shear waves, handling variations in seismic wave dynamics as a function of incidence angle, including in the presence of high impedance contrasts and onshore near-surface geological complexity,” said Tom Rayment, DUG chief geophysicist. “Multiples and converted waves are now treated as valuable additional signal, increasing sampling, resolution and constraining the inverted parameters.”

DUG managing director, Dr Matthew Lamont, added, “We have invested over a decade of R&D to realise this opportunity. Our new Elastic MP-FWI Imaging technology is the product of a multi-year, significant and ongoing R&D effort, which has seen the continuous integration of complete-physics FWI imaging including viscoelasticity, anisotropy and multi-parameter updates. When using the full wavefield for simultaneous velocity model building, rock property inversion and true-amplitude imaging, a multi-parameter solution is a necessity.”

“The fact that DUG MP-FWI Imaging is delivering material imaging uplifts using field-data input is very powerful, but to couple this with high-resolution elastic rock property outputs for quantitative interpretation is even more exciting, providing immediate opportunities for new surveys and maximising the value of legacy datasets,” said Martin Stupel, geophysical manager, Geophysical Pursuit Inc.

NOCs across the region are making tangible progress in applying AI to boost performance.

James Thomas, partner, Shantanu Gautam, principal, and Pavel Evteev, senior manager, with Strategy& Middle East, highlight the importance of culture change for national oil companies (NOCs) to harness the full benefits of AI

The GCC’s national oil companies (NOCs) must put AI to work if they are to keep delivering the world’s lowest cost and lowest carbon footprint barrels. To achieve this, NOCs need organisational cultures that can quickly produce many small, high-impact artificial intelligence (AI) applications.

AI-powered solutions are the next major cost and efficiency frontier in the oil and gas industry. Leading oil majors are already using them to produce oil faster, at lower cost and resource intensity. For example, AI can accelerate subsurface analysis, reduce uncertainty, and optimise capital allocation. Shell partnered with startup Avathon (formerly SparkCognition) and is using AI-powered deep learning to reduce seismic shots by 99%, maintaining image accuracy while cutting exploration time from nine months to just nine days.

Beyond exploration, AI is transforming well planning, automating drilling, predicting conditions, and streamlining workflows. ExxonMobil, collaborating with IBM, used AI to reduce well planning and design time from nine to seven months, and cut data preparation time by 40%.

Drilling optimisation is another area seeing major gains. AI can now analyse real-time downhole data, optimise rate of penetration, and predict failures. Machine learning can adjust drilling parameters dynamically, reducing non-productive time, cutting costs, and improving well economics. ConocoPhillips used three years of drilling data to develop a machine learning model that improved vertical rate of penetration by 20% and reduced premature drilling-motor failures by 65% – saving US$30,000 per well.

Environmental performance is improving too. AI can track emissions in real time, detect leaks, and increase carbon capture. Chevron deployed AI to optimise methane emissions reduction in upstream operations, helping cut methane emission intensity by 60%.

NOCs across the region are also making tangible progress in applying AI to boost performance. Aramco, for example, deployed 40,000 sensors across 500 wells, enabling AI-driven process control that increased production by 15% and halved troubleshooting time. ADNOC’s Emission X tool helped abate 1 million tonnes of CO2 in one year through AI-powered emissions prediction and optimisation.

Building on successes

Now is the time to build upon these successes. The GCC’s NOCs can develop a broad-based, AI-solutions portfolio that drives immediate, incremental gains and mitigates the risk of being outpaced in the AI race. These solutions can drive down costs, support better exploration and investment decision-making, accelerate field development, optimize drilling efficiency, and reduce emissions without cutting production. We estimate that AI applications can reduce the upstream operating costs of the GCC’s NOCs by 10–15%, with approximate annual savings of US$3–US$4.5bn.

The region’s NOCs need a new approach. In the past, they have gone slow-and-steady with large-scale digital initiatives. That meant a robust cycle of requirements definition, vendor selection, and at-scale implementation that took years to come to fruition.

Instead, GCC NOCs need a distributed AI capability as well as a cultural shift that would encourage employees to quickly learn from peers, experiment with applications, and rapidly deploy effective applications. By enabling simultaneous AI development across businesses and functions, NOC’s could realise steady, incremental gains over time.

To build this AI capability, NOCs can forge strategic partnerships with AI startups, collaborate with technology providers, and invest in developing in-house expertise. Importantly, this effort should be business-led, not IT-led. It should teach engineers and analysts to use AI tools efficiently and bring them together into small, cross-functional teams that contain the business and technical knowledge needed to drive improvement.

These teams need a three step, agile approach to AI experimentation. First, a rapid review to identify initial, high-priority pockets of inefficiency in which traditional methods have reached their limits, and AI can unlock productivity gains with minimal complexity. Second, a careful selection of AI use-cases based on their impact and ease of implementation, and to prioritize high value, quick wins. Third, rapid prototyping and scaling to ensure the most effective solutions reach enterprise-wide adoption.

To succeed, such an approach requires a cultural shift. Speed must be prioritised over perfection, while leaders must empower their teams to experiment, learn, and even fail early – all with minimal oversight and bureaucracy.

NOCs that quickly build a capability for AI and successfully foster a culture of experimentation and empowerment will be able to rapidly produce many small, high-impact AI applications that drive immediate gains. Over time, as more solutions emerge, they can increasingly connect like pieces of a larger puzzle, creating a cohesive, AI-driven organisation.

Strategy& Middle East is part of the PwC network.