Artificial intelligence (AI) is rapidly gaining traction in the energy industry.

Today, it is making operations safer, with automatic hazard detection; faster, by accelerating the characterization of the subsurface; and more sustainable, with autonomous operations that minimize footprint.

As AI capabilities continue to advance, they have the potential to drive even higher levels of productivity, performance, safety and emissions reduction. But in our industry, as in most others, unlocking AI’s full potential requires embracing open and secure data platforms, rapid experimentation, new technology adoption and a commitment to building up an AI-trained workforce.

Operationalizing AI at scale

The energy industry generates enormous amounts of data from characterizing the subsurface, drilling wells and operating production facilities. Unlocking access to this data is critical for AI applications and this will require the industry to transition away from traditional, and often proprietary, data infrastructures.

To operationalize AI at scale, our industry must shift toward modern open data architectures based on common standards that make it easy to discover, clean, enrich, access and consume data.

SLB took a significant step in this direction in 2019 when we open sourced and contributed our data ecosystem to The Open Group Open Subsurface Data Universe™ (OSDU) Forum, helping create our industry's first open and standardized platform for subsurface data. We then worked with digital technology leaders such as Microsoft, Google, Amazon and IBM to make the OSDU platform available worldwide.

Another facet to operationalizing AI is to make it accessible and easy to use. And not just for data scientists and software engineers, but for everyone—from domain experts such as geoscientists, drilling engineers and production engineers to IT, HR and finance professionals.

Ultimately, data scientists cannot scale, but the digital tools that enable data science capabilities can. At SLB, we’ve focused on three key digital elements to make this happen: 1) an open and extensible technology stack to train, deploy, discover, consume and re-train AI models; 2) seamless connectivity to subsurface, operations and enterprise data; and 3) automated AI model management and model operations.

Growing AI capabilities

For AI to become truly ubiquitous in the energy industry, leveraging the latest in AI technologies is key.

Across the world, we have teams of AI experts working to bring new AI capabilities to life. At our Software Technology Innovation Center in Menlo Park, California, we focus on experimenting with cutting-edge technologies as they emerge from Silicon Valley. We rapidly prototype implementations of new AI concepts and technologies and make recommendations for adoption into our products and solutions. At our AI Lab in Paris, we focus on embedded AI solutions that drive the use of AI technologies in products and services offered by our oil and gas services divisions. At our Digital Technology Hub in Houston, we develop and commercialize AI-enabled products for our subsurface and operations technology portfolio.

We have also opened six INNOVATION FACTORI locations across the globe where we collaborate with our customers to deploy the latest AI and machine learning techniques and accelerate the process of transforming AI concepts and experiments into enterprise-scale digital solutions.

Another way we’re accelerating AI adoption is through our unique Domain Data Scientist training program, which we operationalized across our business to transform thousands of domain experts into data scientists through a combination of theory and hands-on real-world projects. This is a concept we are beginning to roll out to our customers to help transform their workforce to leverage AI systemically across the energy industry.

Making AI safe and secure

Unfortunately, AI has opened new opportunities for hackers and bad actors. In building out AI capabilities it will remain critical that the industry implement cybersecurity and regulatory compliance strategies that safeguard intellectual property.

At SLB, we have put in place a state-of-the-art cyber security operations center that continuously monitors activities that are targeted at our systems and systems we deploy and operate for our customers.

Our digital platforms conform to the latest data and infrastructure security standards to govern access to data and ensure privacy of our employees and customers.

Around the world, countries are defining boundaries and implementing strong governance on the use of AI. To keep abreast of these changes, we have implemented strong AI governance within SLB to understand evolving regulatory environments in the EU, US, and elsewhere, and adapt our AI-enabled technologies and services to comply with the latest in regulatory, privacy, IP and TCC compliance.

Writing the next chapter

Beyond the step changes to performance, efficiency and emissions reduction our industry will continue to realize by using AI, new and emerging forms of AI—especially generative AI—will have the potential to have an even greater impact.

Generative AI, which uses large language and foundational models, can extract new insights or create new content based on patterns from existing data. Large language models can enable semantic search and power smart assistants to extract insights from data. Foundational models in subsurface, drilling and production domains can accelerate the automation of well construction and production operations in the remotest of locations, leading to increased productivity, fewer emissions and higher capital returns.

In conclusion, the role of AI in the energy industry cannot be overstated. Powered by the ability to work with data at scale on an open and secure digital platform, an AI-trained workforce can harness AI's transformative capabilities to accelerate the energy transition, paving the way for a more sustainable and prosperous world.