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    For Shell, AI and data are just as crucial as oil

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    At Shell, there are plenty of reasons to use AI and data to transform their business.

    The oil and gas industry is at a crossroads, from increased energy needs and disconnected environments to mounting pressures to fight climate change. Energy companies like Shell can either stick to the status quo or embrace the idea of ​​a low-carbon energy future.

    The transition to a more distributed, diverse and decentralized energy system means optimizing end-to-end processes and maintaining them at scale. This means that solutions that can be deployed globally at a rapid pace are crucial. And it means Shell had to become an AI-powered technology company.

    Accelerate digital transformation

    For example, last November Shell founded the Open AI Energy Initiative (OAI) alongside Baker Hughes, Microsoft and enterprise AI company C3 AI to help accelerate the digital transformation of the energy sector.

    According to Dan Jeavons, vice president of computational science and digital innovation at Shell, the OAI offers industry leaders the opportunity to collaborate openly, honestly and transparently. It enables them to create interoperable standards between AI applications and accelerate the adoption of digital technology and achieve net-zero emissions in the future.

    “We are committed to being net zero by 2050 or sooner and achieving a 50% reduction in scope one and two emissions by 2030,” he said.

    While digital technology may not be the panacea, it is one of the key levers Shell is using to accelerate the energy transition. Jeavons added, “While we need to transform a lot of hardware to change the energy sector, we can also leverage the data we have today and use it to transform the system.”

    AI plays a vital role in Shell’s business strategy

    Shell has implemented several AI initiatives over the years, including deploying reinforcement learning in its exploration and drilling program; roll out AI at public charging stations for electric cars; and installing computer vision cameras at gas stations.

    The company also recently launched the Shell.ai Residency Program, which allows data scientists and AI engineers to gain experience working on various AI projects across all Shell companies.

    Shell currently puts more than 100 AI applications into production every year. They have also developed a central community of more than 350 AI professionals who design AI solutions using massive amounts of data available in the many companies within Shell.

    AI helps Shell with predictive maintenance

    “Reliability and safety are absolutely fundamental,” says Jeavons. “It is a priority for us to be able to identify when something goes wrong and to intervene proactively.”

    AI has enabled Shell to use predictive monitoring to extend the monitoring techniques they already had.

    To put that into perspective, Jeavons claims it currently has more than 10,000 devices currently controlled by AI — from valves and compressors to dry gas seals, instrumentation and pumps, while AI also provides predictions about potential failure events. To monitor all that equipment, 3 million sensors collect 20 billion rows of data every week, while nearly 11,000 machine learning models enable the system to make more than 15 million predictions every day.

    Historically, Shell relied on physics-based models to make these predictions. Prior to the advent of a C3 AI predictive maintenance program, the company would typically replace parts after a period of time. Due to this approach, parts were often replaced while still in good condition. An alternative strategy was to wait for something to go wrong. Equipment outages forced assets to be temporarily shut down for repairs, impacting production.

    AI-based predictive maintenance has enabled the company to reduce equipment and maintenance costs by using resources more efficiently, reducing production downtime and avoiding unplanned downtime.

    Tom Siebel, CEO of C3 AI, explained that there are numerous infrastructure and orchestration issues surrounding AI.

    “It’s not that hard to build machine learning models,” he said. “What’s difficult is getting two million machine learning models into production, in one application.”

    However, with a proactive approach to technical monitoring, Shell’s data scientists can analyze thousands of data points simultaneously and empower engineers and others to extract insights from that data.

    “Our team uses that data to understand what normal behavior in our asset base looks like in certain cases, including equipment such as compressors, valves and pumps,” says Jeavons. “We then make projections of what we think will be normal in the coming periods. Based on that prediction, we can determine when normal circumstances no longer occur and then link that back to historical events.”

    AI for optimization is the next step for Shell

    Now Shell has commercialized its predictive maintenance AI applications built with C3 AI software. Going forward, Jeavons says the company is now laser-focused on optimization.

    “This means we can identify ways to produce more efficiently, generate more output for the same cost and, most importantly, we can also look at the carbon footprint of these processes and start optimizing accordingly,” said Jeavons.

    In the near future, he added, Shell is also exploring how AI could be used to monitor carbon capture, storage facilities and methane levels.

    “These ventures relate to making our existing operations more effective and efficient, but also play a key role in our energy transition strategy,” he said.

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