Duration: 5 Days (Interactive, Use Case–Driven Sessions)
Delivery Mode: Instructor-led | Hands-on | Scenario-Based Simulations
Course Introduction: The energy sector is undergoing a digital revolution — and Generative AI is at the core of it. From optimizing drilling operations to transforming refinery logistics, the capabilities of Large Language Models (LLMs) and Generative AI are redefining what’s possible in upstream, midstream, and downstream functions. This course is designed to empower technical teams, innovation leads, and decision-makers in the petrochemical and oil & gas industries to harness these next-gen tools for real-world impact.
Target Audience: Digital Transformation Leads in Energy Companies
Reservoir Engineers, Drilling Engineers, and Geoscientists
Data Scientists, ML Engineers, and Automation Experts
Plant Operations Managers and Safety Officers
IT/OT Professionals managing AI integrations
L&D and Innovation Program Leaders
Course Structure
Day 1: Foundations & Industry Lens
- Fundamentals of AI/ML, Generative AI, and LLMs
- Deep dive into the oil & gas value chain
- AI opportunity mapping: operational challenges vs AI use cases
Day 2: Data as the Fuel
- Data ecosystems in oil & gas
- Preprocessing, feature engineering, and synthetic data
- Cloud storage and compute models for industrial AI
Day 3: AI/LLM in Upstream Operations
- Seismic data modeling, geological simulation
- Drilling optimization and anomaly detection
- AI-powered production forecasting and virtual sensors
Day 4: AI/LLM in Midstream & Downstream
- Predictive maintenance and refinery digital twins
- Supply chain forecasting and emissions tracking
- LLMs for compliance, documentation, and risk management
Day 5: Futureproofing with AI
- AI implementation lifecycle and governance
- Digital twins + Generative AI
- Responsible AI, cybersecurity, and regulatory considerations
- Emerging trends: Edge AI, quantum, renewables
🔧 Tools & Platforms Covered
- GPT (OpenAI), BERT, LLaMA
- Azure/AWS/GCP AI Services
- SCADA log analysis
- Data lakes, NoSQL, time-series DBs
- LangChain, Vector DBs, synthetic data tools
