Predictive Maintenance · Real-time Analytics · Supply Chain Optimization
Aramco needs to turn extremely large and complex exploration, drilling, refining, logistics, and market datasets into faster, safer, and more reliable operational decisions while reducing uncertainty, downtime, and risk across a global energy portfolio.
Reported impact
Aramco reports that AI improves subsurface visibility, well placement, drilling performance, refining and yield planning, turnaround coordination, safety, and equipment reliability while reducing uncertainty, delays, unplanned downtime, operational risk, and costly repairs across its energy operations.
Aramco describes an in-house industrial AI stack built on hundreds of petabytes of exploration, drilling, field development, production, refining, logistics, and market data. Its implementation starts by ingesting seismic data, sensor readings, subsurface models, refinery process data, IIoT telemetry, and supply-chain and market signals into enterprise digital platforms. High-performance computing platforms Dammam-7 and Ghawar-1 accelerate seismic imaging and reservoir simulation, while AI and machine-learning models generate predictive well logs, estimate reservoir properties, forecast equipment health, and optimize drilling, refining, turnaround planning, and logistics. Operations Co-Pilot combines AI-powered digital twins with advanced ML and soft-sensor techniques to surface predictive insights for plant operators. Saudi Aramco Intelligent Rig Optimizer operationalizes drilling improvements across the rig fleet, and purpose-built language models plus agentic AI support engineers with technical guidance, workflow execution, and recommended interventions.
Seismic data, sensor readings, subsurface models, refinery process data, IIoT telemetry, and market and logistics inputs are fed into Aramco's digital platforms and HPC environment. AI and ML models process these data streams to create digital Earth models, predictive well logs, equipment-health forecasts, and plant-performance insights, which are then delivered to engineers, drilling crews, maintenance teams, and trading and distribution operations for real-time decisions and guided interventions.
"Across facilities and infrastructure, AI analyzes data from extensive IIoT (Industrial Internet of Things) sensor networks, and generates synthetic corrosion and well casing logs to detect metal loss and anticipate equipment degradation before failures occur."
"For decades, we have built one of the world's most comprehensive energy datasets, comprising hundreds of petabytes spanning exploration, drilling, field development, and production."
"Generative AI tools now support scientists and engineers by synthesizing technical information, drafting operational plans, and delivering context-specific insights at the point of need."
"Our Operations Co-Pilot uses AI-powered digital twins alongside advanced machine learning models to enhance plant performance under real operating conditions."
"Our intelligent drilling platform, the Saudi Aramco Intelligent Rig Optimizer, extends our capability beyond our own operations."
"Rather than only generating insights, AI agents can execute defined workflows, manage operational parameters, and recommend targeted interventions."
"Supercomputing platforms, such as Dammam-7 and Ghawar-1, further accelerate advanced seismic imaging and reservoir simulation, enabling highly detailed 3D earth models that improve resource recovery and reduce exploration and development risks."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
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