MicrosoftExpanded

Shell revolutionizes oil and gas operations with AI

Shell, one of the world's largest oil and gas companies, undertook major digital transformation to address operational complexity and support a low-carbon future. They deployed over 100 AI applications built with Microsoft Azure and in partnership with C3 AI, Baker Hughes, and SparkCognition. In upstream operations, they use generative AI and reinforcement learning for rapid exploration and drilling optimization. Downstream, computer vision and AI monitor service stations for safety, manage inventory, forecast demand, and optimize energy management. The effort resulted in substantial reductions in exploration time (from nine months to nine days), cost savings, production increases, improved equipment monitoring, and better use of renewable energy. Shell's challenges included integrating AI within legacy environments, ensuring data privacy and security, and aligning solutions with sustainability goals. Their AI-first strategy standardizes platforms and data for scale and impact, setting a benchmark for AI adoption in energy. Shell's comprehensive use of AI spans from drilling to retail, with solutions addressing efficiency, safety, and sustainability. Generative AI, deep learning, and computer vision enable operational enhancements and renewable energy transition across the enterprise.

Organization
Shell
Location
Netherlands
Published
September 2023

Reported outcomes

9 days

timeTime & speed

Strategic outcomes

Speed & agilityAccelerated exploration and discoveryCost efficiencyLowered operational costs and idle timeSustainability & ESGImproved renewable energy useRisk & complianceEnhanced operational safety

Primary read

Use case focus

Showing 3 of 5

  • 1Predictive Maintenance for Industrial Equipment
  • 2Generative AI for Seismic Data Analysis in Exploration
  • 3AI-Powered Drilling Optimization
  • Complex and costly oil and gas operations impacting efficiency and sustainability.
  • Lengthy exploration cycles leading to delayed resource identification (previously up to 9 months).
  • Need for improved safety in hazardous environments.
  • Pressure to meet global sustainability and low-carbon benchmarks.
  • Difficulty integrating new AI-driven solutions with legacy processes and systems.
  • Deployed 100+ AI applications across upstream and downstream operations using Microsoft Azure.
  • Used generative AI with SparkCognition for rapid seismic data analysis and oil discovery.
  • Applied reinforcement learning to optimize drilling procedures.
  • Implemented computer vision to monitor and improve safety at service stations.
  • Leveraged AI for energy management, inventory planning, and demand forecasting.
  • Standardized tools and data structures for scalable impact in partnership with C3 AI and Baker Hughes.
Technologies
  • Reduced exploration time from 9 months to 9 days.
  • Lowered operational costs and equipment idle time.
  • Increased production yields and improved use of renewable energy.
  • Enhanced operational safety and decreased waste.
Architecture

Shell standardized platforms and data structures across business units, enabling scalable AI deployment. Upstream, generative AI and reinforcement learning models on Azure analyze seismic data for exploration and optimize drilling. Downstream, computer vision and AI applications monitor service station safety and manage inventory, demand, and energy use. Collaborations with partners (C3 AI, SparkCognition, Baker Hughes) integrate solutions with legacy systems.

Implementation partners3
Sources & evidence1
ExpandedExpanded

The same organization appears in newer AI deployment evidence.

  • Same organization re-documented as recently as 2025.

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Groundedness: Unavailable

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