en.Wedoany.com Reported - SLB and Qualcomm Technologies have announced a collaboration aimed at deploying edge artificial intelligence capabilities at oilfield sites, enabling real-time decision-making for wells, production facilities, and energy infrastructure.
According to a memorandum of understanding announced by both parties, SLB will integrate its Agora edge AI and IoT platform, developed for remote and complex energy environments, with Qualcomm Technologies' low-power edge computing and AI processing technologies. This collaboration comes as operators accelerate the adoption of automated and autonomous workflows in production systems, driving demand for deploying AI applications directly at the asset level rather than relying solely on centralized computing resources.
Rakesh Jaggi, President of Digital Business at SLB, stated that the two companies are committed to helping operators apply AI more effectively across the entire energy infrastructure. Many energy operations rely on real-time decision-making in remote environments, where connectivity and response speed directly impact performance. According to the companies, deploying AI closer to field equipment helps improve operational response times, reduce latency, and support more resilient operations in connectivity-constrained environments.
The collaboration will focus on integrating AI applications into production operations by combining SLB's digital production technologies and energy expertise with Qualcomm Technologies' edge processing capabilities. Nakul Duggal, Executive Vice President and Group General Manager of Automotive, Industrial and Embedded IoT, and Cloud Computing at Qualcomm Technologies, noted that many industrial environments require AI systems operating under limited power, constrained connectivity, and real-time operational requirements. This collaboration aims to bring low-power AI processing capabilities closer to energy operations and operational assets.
SLB stated that the initiative aims to help operators modernize legacy operational environments, enhance cybersecurity for operational technology systems, and drive the adoption of autonomous workflows. The agreement reflects the energy industry's trend of deploying AI directly to field operations, aiming to improve efficiency, enhance decision-making capabilities, and support increasingly autonomous production systems.
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