In April 2025, Weatherford International, a US multinational oilfield services company, signed a memorandum of understanding with AIQ, a leading Abu Dhabi-based artificial intelligence company that develops innovative solutions for the energy sector, to deeply integrate artificial intelligence (AI) systems into global oil and gas operations, promoting a transformation in production methods.
In March 2025, Abu Dhabi National Oil Company (ADNOC) signed a landmark $340 million contract with AIQ to implement ENERGYai, a company specializing in providing advanced solutions that leverage AI and machine learning to optimize various aspects of the energy sector, and related AI solutions across its upstream value chain. These initiatives indicate that international energy companies are collectively moving towards a new development paradigm through generative AI, which is evolving from an auxiliary tool to a fundamental driver of industrial transformation.
As digital transformation progresses, international oil and gas companies are increasing their investments in artificial intelligence. The driving force behind this process is not only technological innovation, but also a deep understanding of the future competitive landscape: the use of intelligent tools to promote coordinated advances in efficiency, cognitive upgrades, risk control, and green development, and to create a value system that combines efficiency, agility, and sustainability.
Improving efficiency and reducing costs are the main motivations for implementing artificial intelligence. In key areas such as production, operations, and investment, AI is helping companies achieve extraordinary results in terms of cost optimization and efficiency. ExxonMobil, one of the world’s largest oil and energy companies, formed in 1999 from the merger of Exxon (formerly Standard Oil) and Mobil, expects its cloud oilfield project, which involves the use of a range of IT services such as data storage, software, and analytics provided over the Internet by global networks of remote servers to the oil and gas industry to improve operational efficiency, developed in collaboration with Microsoft, will generate billions of dollars in net cash flow for the company between this year and 2029.
Saudi Aramco is also steadily increasing its investment in technological upgrades, investing more than $3.5 billion annually in research and development for cutting-edge technologies such as artificial intelligence and drones, with a focus on increasing oil and gas production, intelligent equipment maintenance, and risk reporting.
Strengthening decision support and knowledge management is another key driver behind the in-depth development of artificial intelligence applications. The oil and gas industry has long faced the challenge of highly intensive data and fragmented information. The advantages of large-scale models in natural language processing allow them to quickly organize and interpret unstructured information scattered across reports, logs, and documents, thus providing more efficient support for business and research and development.
British oil multinational Shell considers large-scale models to be “research assistants” and uses them to extract decades of research results accumulated within the company. Previously, researchers had to consult multiple sources to find key information; now they can obtain it quickly by talking to AI assistants. This technology significantly improves R&D efficiency, shortens decision-making cycles, and accelerates the flow of knowledge within the organization. Today, AI is no longer just an auxiliary tool, but a driving force for cognitive evolution and structural innovation within the organization.
Safety management and carbon emission reduction are other important areas of AI application in the oil and gas industry. French company TotalEnergies SN has effectively improved its accident prevention capabilities by intelligently monitoring construction sites and conducting independent and systematic real-time verification and evaluation (audits) of safety processes through the application of computer vision and large-scale pattern analysis. Saudi Aramco has introduced AI into the monitoring of subsea pipelines and oilfield equipment, enabling early fault identification and automatic alarm activation, ensuring production safety and reducing environmental risks.
As we have already mentioned in relation to carbon emission reduction, the contribution of AI is increasingly significant. Shell uses AI models to simulate site selection for carbon capture and storage projects to improve the geological compatibility and economics of projects; TotalEnergies SN uses AI to analyze equipment operating data, intelligently identifying anomalies in energy consumption and emissions deviations and making timely adjustments to reduce its carbon footprint.
AI is being integrated into key areas, redefining the fundamentals of the oil and gas industry. The introduction of large-scale AI models is a crucial approach to reshaping the core competitiveness of the oil and gas sector and restructuring business processes and organizational models. Research conducted by Ernst & Young, a global network of professional services firms providing advisory, auditing, tax, transaction, and training services, shows that over 92% of global oil and gas companies are investing or plan to invest in AI in the next two years. Market research firm Future Market Insights, an expert in market research based in Pimpri Chinchwad, India, predicts that by 2034, the commercial value of artificial intelligence in the global oil and gas market will reach $13 billion, demonstrating enormous growth potential and increased confidence from the industry.
In business management and knowledge scenarios, AI tools are being rapidly implemented, helping companies automate administrative processes and streamline knowledge flow. For example, British Petroleum, a British company operating in the energy sector, particularly in the oil and natural gas sector, where it is one of the four largest operators worldwide (along with Shell, ExxonMobil, and Total), was one of the first global energy companies to test Microsoft’s Copilot smart office tool. It is developed on the basis of GPT technology: Generative pre-trained transformer, a type of large language model and an important framework for generative artificial intelligence. It is an artificial neural network used for natural language processing by machines. Copilot offers automatic email composition, meeting minutes generation, and administrative report generation, significantly reducing the time employees spend on daily document processing and information organization. An internal BP assessment report indicates that since Copilot’s full launch in 2024, the company’s administrative efficiency has increased by nearly 30%, and employees have significantly increased the time spent on core activities (a company’s main activities) and innovative research.
In contrast, Shell has taken a more cautious and pragmatic approach to its AI strategy, focusing on close collaboration with leading technology companies and prioritizing the adoption of mature, industry-proven technology solutions to mitigate the risks and costs associated with in-house development of large-scale models.
Since September 2020, Shell has entered into a strategic partnership with Microsoft, fully implementing digital tools such as the Azure cloud platform, Microsoft 365, and Power Platform. This enables the company to process billions of data points from global assets each week, quickly integrating and visualizing business operations. In addition, Shell has developed a Microsoft Azure-based carbon emissions monitoring tool to assess supplier emissions, set carbon benchmarks and targets, and establish a scientific carbon management pathway within the company, effectively promoting green transformation and compliance management.
In the more complex stages of exploration and production, Saudi Aramco has explored the in-depth application of AI in key oil and gas industry processes. In March 2024, Saudi Aramco released its large-scale, internally developed model: Aramco Metabrain AI. This model has 250 billion parameters and processed over 7 trillion pieces of data during the training phase, covering over 90 years of engineering, geological, and operational data accumulated by the company. Aramco Metabrain AI has powerful capabilities for understanding and predicting complex data, intelligently analyzing factors such as drilling plans, geological structures, and historical operating costs, and recommending optimal well layout patterns (visual arrangements or structures that organize elements within a space, such as a website, document, presentation, or physical area), significantly improving exploration efficiency and reducing project costs. In downstream operations (all activities that take place downstream of an initial production phase), the model can also predict refined oil price trends and market fluctuations and, in combination with geopolitical dynamics, provide the company with more forward-looking decision support. This marks a crucial step forward for Saudi Aramco in building a smart energy system.
AI is also redefining the supply chain and shaping the future of the oil and gas industry. Global energy giants widely consider AI to be a key factor in redefining the industrial landscape and are actively developing strategic projects. Schlumberger, a leading oilfield services company based in Houston (founded exactly 99 years ago), is a prime example. The company aims to deeply integrate AI throughout its business chain. Its Lumi platform is not just a tool, but a platform dedicated to creating an open, borderless energy cloud ecosystem that releases and connects data, drives the evolution of AI workflows from basic to advanced levels, and accelerates digital transformation.
In the future, Schlumberger will continue to collaborate with technology companies to train models specific to the oil and gas industry, making AI a fundamental driving force, just like electricity. Shell is also integrating AI into its net-zero strategy, which is an integral part of its culture of innovation.
Net-zero is an approach aimed at balancing man-made greenhouse gas emissions with their removal from the atmosphere. This is achieved primarily by minimizing emissions through measures such as energy efficiency and the transition to renewable energy, and offsetting unavoidable residual emissions with carbon removal actions.
Gabriel Guerra, a senior executive at Shell, said that AI will help the industry reduce its carbon footprint while meeting energy needs and enabling safer, more efficient, and more sustainable operations. ExxonMobil is focusing on data standardization and IT environment simplification (specific companies dealing with waste management software), with the intention of creating a unified platform to promote widespread AI adoption and improve front-line decision-making efficiency. BP emphasizes the importance of achieving intelligent operations and real-time assessments through AI, prioritizing AI reliability to ensure that all implementations comply with laws and regulations.
International energy giants are leveraging generative AI to drive performance growth and transformation toward a low-carbon economy. In summary, the oil and gas industry will undergo profound changes in the following four areas.
First, faster and more accurate exploration. Generative models will improve the efficiency of seismic data analysis to measure and record the physical and chemical properties of rocks and fluids within a drilling well and the discovery of resources. For example, Shell is collaborating with big data analytics company SparkCognition to use AI technology to process and analyze large amounts of seismic data, shortening exploration cycles and significantly improving the efficiency and success rate of discovering new oil fields.
Second, smarter and more efficient operations will be implemented. Real-time analysis of sensor data by AI will contribute to parameter adjustment and failure prevention, promoting predictive maintenance as the norm.
Third, more agile and intelligent decision-making: with the integration of large models into corporate databases, engineers can access information through dialogue with AI, breaking down the barriers (silos) that isolate information, expertise, and teams within an organization, preventing collaboration and knowledge sharing, in order to accelerate talent development.
Fourth, more collaborative and optimized supply chains. A unified data platform will promote the integration of exploration, development, and refining and improve the supply chain, which is the set of all activities, processes, and organizations involved in bringing a product or service from its point of origin to the end consumer. This will lay a solid foundation for the large-scale application of AI in the future. For example, ExxonMobil is integrating its global operational data into the cloud, laying the groundwork for future supply chain optimization, market forecasting, and carbon emissions management.
Overall, generative AI is profoundly reshaping the entire oil and gas supply chain, and major energy companies are accelerating the transition from pilot projects to large-scale transformation. In the future, AI will understand oil and gas operations like an expert, helping companies improve quality, reduce costs, and ensure safe production, driving the industry toward a new era of sustainable, low-carbon development. Of course, this process also comes with challenges: data governance and algorithm oversight must be improved simultaneously to ensure that AI outputs are credible and reliable. However, what is certain is that the “intelligence” and “decarbonization” of the oil and gas industry are proceeding at full speed under the impetus of AI.
Author: Giancarlo Elia Valori – Honorable de l’Académie des Sciences de l’Institut de France, Honorary Professor at the Peking University, and President of the Foundation for International Studies and Geopolitics. He plays a leading role in fostering dialogue and cooperation between countries.
(The opinions expressed in this article are solely those of the author and do not necessarily reflect the views of World Geostrategic Insights).
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