SLB Expands Collaboration with NVIDIA to Scale AI Infrastructure for the Energy Industry
Global energy technology company SLB has announced an expanded collaboration with NVIDIA aimed at accelerating the development and deployment of advanced artificial intelligence infrastructure for the global energy sector. The partnership focuses on creating scalable AI systems, specialized models, and high-performance computing environments designed to help energy companies manage massive datasets and improve operational decision-making.
As digital transformation continues to reshape the energy industry, companies are increasingly relying on artificial intelligence to process and interpret the vast volumes of data generated across exploration, production, and energy infrastructure. Through this strengthened partnership, SLB and NVIDIA intend to provide energy companies with the tools and infrastructure needed to industrialize AI applications—moving from experimental deployments toward enterprise-wide adoption.
The collaboration centers on three strategic initiatives: modular data-center design, the development of an AI Factory tailored for energy applications, and the integration of accelerated computing capabilities across SLB’s digital platforms.
Modular Design for Next-Generation AI Data Centers
One of the central elements of the partnership involves the development of modular AI data-center infrastructure. Under the agreement, SLB will serve as the modular design partner for NVIDIA’s DSX AI factories, which are designed to provide the computing power required for large-scale AI training and deployment.
The modular design approach allows major components of data centers to be manufactured offsite and then assembled at the deployment location. This method offers several advantages compared with traditional construction approaches. By shifting much of the fabrication work to controlled manufacturing environments, companies can achieve higher quality standards, improved reliability, and more predictable construction timelines.
In addition, modular data centers can significantly reduce project costs, labor requirements, and supply-chain constraints. Components can be prefabricated and delivered ready for installation, reducing the time required to build high-performance computing infrastructure.
For energy companies increasingly adopting artificial intelligence technologies, speed and scalability are critical. Modular design allows data-center capacity to be expanded quickly as computational demand grows. Organizations can add additional modules or computing clusters without needing to redesign or rebuild entire facilities.
SLB’s experience in industrial engineering, large-scale infrastructure deployment, and energy-sector operations positions the company to play a key role in designing modular data centers optimized for demanding AI workloads.
Creating an “AI Factory for Energy”
A second major focus of the collaboration is the creation of what the companies describe as an “AI Factory for Energy.” This concept refers to a reference environment where advanced AI models, software frameworks, and computing infrastructure are combined to accelerate the development and deployment of artificial intelligence applications across the energy value chain.
The AI Factory will be powered by domain-specific generative AI models trained on energy-industry datasets, along with industrial-scale agentic AI systems capable of autonomously performing complex tasks and analyses. These capabilities will operate within SLB’s digital platforms, allowing energy companies to apply AI directly to their operational workflows.
Generative AI models can help interpret subsurface geological data, simulate reservoir behavior, optimize drilling operations, and improve predictive maintenance for energy infrastructure. Agentic AI technologies extend these capabilities by enabling systems to take autonomous actions based on real-time data inputs.
By providing a standardized AI development environment tailored to energy-sector needs, SLB and NVIDIA aim to reduce the complexity of adopting advanced AI technologies. Energy companies will be able to build, train, and deploy models faster while maintaining compatibility with their existing digital ecosystems.
The AI Factory framework also supports collaboration across organizations. Energy companies, technology providers, and research institutions can contribute models, data sets, and applications within the shared environment, accelerating innovation and improving the performance of AI solutions.
Accelerating Data Processing and AI Workflows
The third pillar of the collaboration focuses on optimizing the processing of large datasets and complex AI models across SLB’s digital platforms. Using NVIDIA’s latest AI computing infrastructure, the companies aim to set new benchmarks for performance and efficiency in energy applications.
Energy companies generate enormous volumes of data every day. Subsurface exploration activities produce seismic datasets and geological models, while production operations generate continuous streams of sensor data from wells, pipelines, and processing facilities. In addition, power generation, grid management, and renewable energy assets create further layers of operational information.
Processing these datasets requires advanced computing capabilities capable of handling complex simulations, machine-learning algorithms, and real-time analytics. NVIDIA’s accelerated computing technologies—including GPUs, AI frameworks, and specialized software libraries—will be integrated into SLB’s digital platforms to enhance processing speed and efficiency.
This optimization will allow companies to run AI models faster, analyze larger datasets, and extract insights more quickly. The result is improved operational decision-making, increased productivity, and the potential for more efficient and sustainable energy production.
Turning Energy Data into Actionable Insights
According to SLB leadership, the companies that succeed in the AI era will be those that combine high-quality data with deep domain expertise and scalable computing infrastructure.
Demos Pafitis, chief technology officer at SLB, emphasized that the collaboration with NVIDIA reflects the growing importance of AI in transforming industrial operations.
He noted that by combining SLB’s energy-sector expertise and digital platforms with NVIDIA’s advanced computing technologies, the partnership aims to help the industry move beyond isolated AI experiments and toward full-scale deployment across operations.
Vladimir Troy, vice president of AI infrastructure at NVIDIA, highlighted the broader significance of artificial intelligence in the global industrial landscape. He described AI as a central driver of a new industrial revolution and emphasized that the energy industry plays a critical role in enabling this transformation.
By building specialized AI infrastructure and models for energy applications, the collaboration aims to turn massive volumes of operational data into actionable insights that can improve efficiency, reliability, and sustainability across energy systems.
Integrating Advanced AI Technologies
A key component of the initiative involves combining NVIDIA’s AI software technologies with SLB’s existing digital platforms.
The collaboration will integrate NVIDIA Omniverse libraries and NVIDIA Nemotron open models with SLB’s digital and AI platforms. These technologies enable the creation of advanced simulations, digital twins, and generative AI models that can interpret complex industrial data.
NVIDIA Omniverse provides a platform for building physically accurate digital environments where engineers can simulate operations and test scenarios before implementing changes in real-world systems. In the energy sector, such simulations can help optimize drilling operations, evaluate reservoir performance, and improve the design of energy infrastructure.
Meanwhile, Nemotron models provide powerful open AI frameworks capable of generating insights from large datasets. When combined with SLB’s domain expertise and energy-industry datasets, these models can help uncover patterns and opportunities that might otherwise remain hidden.
The collaboration will also explore emerging agentic AI technologies. Unlike traditional AI systems that simply analyze data, agentic AI systems can take actions based on insights, enabling more automated and adaptive operations.
Supporting More Efficient and Lower-Carbon Energy Systems
Beyond improving operational performance, the companies believe that AI can play an important role in advancing sustainability in the energy sector.
Artificial intelligence can help optimize energy production, reduce waste, and improve the efficiency of infrastructure. For example, AI models can analyze production data to identify opportunities to reduce emissions, optimize resource utilization, and extend the life of energy assets.
AI-driven predictive maintenance can also help prevent equipment failures, reducing downtime and minimizing environmental risks. By detecting early signs of mechanical problems, companies can address issues before they lead to major operational disruptions.
Additionally, advanced modeling and simulation capabilities can support the development of new energy technologies and accelerate the integration of renewable energy resources into existing energy systems.
A Long-Standing Technology Partnership
The expanded collaboration between SLB and NVIDIA builds on a technology partnership that dates back more than a decade.
The relationship began in 2008 when NVIDIA’s accelerated computing technology was first used to enhance SLB’s subsurface visualization and seismic imaging software. High-performance GPUs enabled faster processing of complex geological data, allowing energy companies to generate more accurate subsurface models and improve exploration outcomes.
Over time, the partnership evolved as both companies expanded their focus on artificial intelligence and digital transformation.
In 2024, the two companies announced plans to develop generative AI solutions specifically designed for the energy sector. That initiative involved integrating NVIDIA’s AI software capabilities with SLB’s Delfi™ digital platform and Lumi™ data and AI platform.
The latest announcement represents the next phase of that collaboration, focusing on the infrastructure and models required to support large-scale AI deployment across the industry.
Moving from AI Experimentation to Industrial Deployment
The energy sector has been experimenting with artificial intelligence for years, using machine learning and advanced analytics to improve exploration, drilling, and production operations. However, many of these initiatives have remained limited in scope.
The expanded partnership between SLB and NVIDIA reflects a broader shift toward enterprise-scale AI deployment. Instead of isolated pilot projects, companies are now seeking to integrate AI across their entire operations.
By combining modular data-center infrastructure, domain-specific AI models, and high-performance computing platforms, the collaboration aims to provide the foundation needed for large-scale AI adoption.
As energy companies face increasing pressure to improve efficiency, reduce emissions, and manage complex global operations, the ability to harness AI effectively could become a key competitive advantage.
Through their expanded collaboration, SLB and NVIDIA aim to help the energy industry unlock the full potential of artificial intelligence—transforming vast amounts of operational data into insights that drive smarter decisions, improved performance, and more sustainable energy systems.
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