OG90
Course Contents
Module 1: Digital Reservoir Analytics Foundations
Key Topics
- Digital reservoir concepts and value drivers
- From integrated modelling to analytics-led decisions
- Reservoir data types, sources and common limitations
- Static, dynamic, production and surveillance data links
- Data quality, validation and engineering consistency checks
- Analytics workflow design for reservoir management
- Common pitfalls in data-driven subsurface analysis
Module 2: Reservoir Surveillance and Performance Diagnostics
Key Topics
- Production, pressure and injection surveillance workflows
- Well, pattern, sector and field performance indicators
- Decline trends, water cut, GOR and pressure behaviour
- Injection response, sweep indicators and voidage balance
- Detecting anomalies, outliers and changing reservoir behaviour
- Exception-based surveillance and opportunity identification
- Linking surveillance insights to operational actions
Module 3: Machine Learning and Production Forecasting
Key Topics
- Machine learning concepts for reservoir engineers
- Feature engineering from production and surveillance data
- Forecasting oil, gas, water and injection performance
- Decline curve analytics and hybrid forecasting approaches
- Clustering wells, patterns and reservoir behaviour types
- Model training, testing, overfitting and validation checks
- Interpreting analytics results with reservoir physics
Module 4: Integrated Models, Uncertainty and Digital Twins
Key Topics
- Analytics links with static and dynamic reservoir models
- Assisted history matching and model calibration concepts
- Ensemble models and uncertainty-based decision workflows
- Proxy models and surrogate models for faster screening
- Digital twin concepts for reservoir and production systems
- Scenario analysis for development and optimisation choices
- Communicating uncertainty for management decisions
Module 5: Dashboards, Governance and Implementation Roadmap
Key Topics
- Reservoir analytics dashboards and visualisation principles
- KPI design for surveillance, forecasting and recovery tracking
- Data governance, ownership and workflow discipline
- Collaboration between subsurface, production and digital teams
- Model lifecycle management and analytics performance review
- Scaling pilots into repeatable reservoir analytics workflows