Artificial intelligence (AI) and machine learning (ML) are gaining acceptance quickly in the oil and gas industry. Between 2018 and 2020, the percentage of companies that had deployed these technologies more than doubled – from 13% to 32%.[1] Today, 50% of oil and gas executives say they have already begun using AI to help solve challenges at their organizations, and 92% are either currently investing in AI or plan to in the next two years.[2]
While these technologies have mostly been operationalized for specific use cases or particular processes, they are increasingly being incorporated into a whole host of systems and software to improve efficiency, productivity and profitability. And the potential impact is significant: International Data Corporation estimates that the benefits enabled by AI and ML can reduce an organization’s total costs by up to 20%, improve asset availability by 20% and extend the lives of machines by years.[3]
Here we explore four key industry applications for these technologies, as well as the critical role private cellular networks play in leveraging them successfully.
Technologies enable transformation
It’s not AI and ML on their own that make a difference. It’s the capabilities they unlock when applied in combination with other technologies, as illustrated by the following applications.
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Safety monitoring
High-resolution video drones and robotic devices can use AI to conduct site inspections and recommend actions for oil platforms, pipelines and other hazardous work sites with more speed and accuracy while keeping humans out of potential danger.
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Proactive asset maintenance
By applying ML-enabled asset condition monitoring to pumps and compressors, operators can detect equipment failure before it happens, eliminating unplanned downtime and prolonging the life of expensive machinery.
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Upstream automation:
Envision smart drill bits with sensors behind the cutting wheel that capture real-time data about the formations being drilled, sending that data to the Edge Cloud for comparison and interpretation against huge seismic datasets – all preceding drilling jobs – to help direct drilling operations. We have implemented this solution in the mining industry, where we launched autonomous mining with connected Pit Vipers.
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Process automation
From upstream applications such as drill modeling and geospatial data analysis to mid- and downstream applications like inventory management and hazardous emissions monitoring, the opportunities for AI- and ML-powered automation in oil and gas are nearly limitless.
Connectivity enables technology
Just as the above applications rely on AI and ML to work, AI and ML can’t operate in a vacuum, either. They require vast amounts of high-quality, near-real time data to fuel innovations. And advanced cellular connectivity is the fastest, most reliable and most cost-effective way to capture and manage that data.
Furthermore, the low latency and high capacity of 5G allows AI processing to be distributed among the device, edge cloud and central cloud – enabling flexible system solutions for better efficiency, enhanced privacy, improved performance and new levels of automation.
Leveraging our leading 5G private network technology, Ericsson works with industry ecosystem partners to bring AI and ML applications to life in oil and gas companies, creating agility, improving efficiencies and unlocking intelligence. Download our case summary to see how we’re helping major industry leaders harness the power of cellular connectivity to optimize business operations through data-driven insights.
[1] “2021 CIO Agenda: An Oil and Gas Perspective.” Gartner. December 2020. https://www.gartner.com/en/documents/3994056/2021-cio-agenda-an-oil-and-gas-perspective
[2] “Applying AI in oil and gas.” Ernst & Young. https://www.ey.com/en_om/applying-ai-in-oil-and-gas
[3] Manish Chawla. “Applying IIoT and AI to midstream asset management.” Oil & Gas Engineering. September 2021. https://www.oilandgaseng.com/articles/applying-iiot-and-ai-to-midstream-asset-management/
