This course is designed for professionals in the oil and gas industry seeking to understand and apply Artificial Intelligence (AI) and Machine Learning (ML) techniques in operational environments. Participants will gain practical skills in data analysis, predictive modeling, and AI-driven decision-making to optimize exploration, production, safety, and maintenance processes.
The course covers core ML algorithms, data handling in industrial settings, and the application of AI to real-world oil and gas challenges such as predictive maintenance, drilling optimization, reservoir modeling, and energy efficiency.
- Data analysts, engineers, and operators in oil and gas operations.
- Petroleum engineers, drilling, production and maintenance specialists.
- Operations managers and decision-makers interested in digital transformation.
- Anyone seeking to expand knowledge in AI/ML applications within the energy
To equip participants with the analytical mindset, technical skills, and industry knowledge required to leverage AI/ML for smarter decision-making and operational excellence in oil and gas.
By the end of this training course, participants will be able to:
- Understand the fundamentals of AI/ML and its role in oil & gas operations.
- Collect, preprocess, and analyze operational and sensor data for ML applications.
- Understand the key concepts of machine learning and their industrial applications.
- Understand the difference between predictive maintenance & failure detection models and other maintenance concepts.
- Utilize ML for optimizing drilling, reservoir management, and production.
- Evaluate and implement AI-driven safety and risk management systems.
1. Introduction to AI/ML in Oil & Gas
- Evolution of AI/ML and its relevance to the energy sector.
- Data-driven vs. physics-driven approaches.
- Key challenges in implementing AI in oil & gas.
- Data Management and Preprocessing
- Sources of data: sensors, SCADA systems, logs, and seismic data.
- Data cleaning, integration, and feature engineering.
- Handling missing and unstructured data.
- Machine Learning Fundamentals
- Supervised vs. unsupervised learning.
- Regression, classification, clustering, and anomaly detection.
- Model training, testing, and validation.
- Predictive Maintenance & Reliability Engineering
- Failure prediction using ML models.
- Condition-based monitoring and anomaly detection.
- Case studies in equipment reliability.
- ML Applications in Drilling & Production
- Drilling optimization using real-time data.
- Production forecasting and reservoir performance modeling.
- Reducing non-productive time (NPT) with predictive analytics.
- AI for Safety and Risk Management
- Predictive safety analytics.
- Hazard detection and prevention using ML.
- Emergency response optimization with AI-driven simulations.
- Advanced AI Techniques
- Deep learning and neural networks in seismic interpretation.
- Natural language processing for technical reports and logs.
- Digital twins and AI-driven simulations for oil & gas assets.
- Future Trends & Implementation Strategies
- AI ethics and responsible use in oil & gas.
- Integration of AI with IoT and cloud systems.
- Roadmap for digital transformation and scaling AI solutions
