Course Objectives:

By the end of this training, participants will be able to:

1. Introduction to Subsurface Synthesis

• Understand the concept of subsurface synthesis, which involves the integration of geological, geophysical, reservoir engineering, and production data to create a unified model of the subsurface.
• Explore the importance of data integration in providing a comprehensive understanding of the reservoir and its behavior over time.
• Study the challenges of integrating various subsurface data types, including seismic data, well logs, core samples, and production history into a coherent model.

2. Fundamentals of Geomodelling

• Learn the basics of geomodelling, focusing on the creation of 3D subsurface models to represent geological structures, reservoir properties, and fluid dynamics.
• Study the key components of a geomodel, including structural models, stratigraphic models, property models, and fluid flow models.
• Understand how geomodelling is used for reservoir characterization, simulation, and production optimization.

3. Geomodelling Workflows

• Explore the workflow for developing a subsurface model, including the following steps:
1. Data collection: Gathering all available geological, geophysical, and engineering data.
2. Data integration: Combining data from different sources to create a cohesive model.
3. Model construction: Building the 3D geological model with appropriate scaling and representation of features such as faults, stratigraphy, and reservoir properties.
4. Model validation: Ensuring that the model accurately reflects real-world reservoir conditions through history matching and sensitivity analysis.
5. Model updating: Incorporating new data from drilling, production, and monitoring into the model to improve its accuracy over time.

4. Geological Interpretation and Data Integration

• Learn how to interpret geological data, including well logs, seismic data, core samples, and geophysical surveys, to build an accurate subsurface model.
• Understand the importance of combining data from different disciplines (geology, geophysics, reservoir engineering) to create a unified interpretation of the reservoir’s structure and properties.
• Study how to manage uncertainty in geological data and the role of statistical methods and probabilistic models in handling incomplete or uncertain information.

5. Geomodel Construction Rules

• Learn the best practices and rules for constructing effective geomodels, including:
• Resolution and grid size: Balancing computational efficiency with model accuracy.
• Fault modeling: Properly representing faults and fractures, which are key to fluid flow behavior and reservoir management.
• Stratigraphy: Accurately representing different rock layers, horizons, and facies distributions in the model.
• Property modeling: Assigning reservoir properties (e.g., porosity, permeability, fluid saturations) based on well data, core samples, and seismic interpretation.

6. Seismic Data Integration

• Understand the role of seismic data in geomodeling, including how seismic surveys are interpreted and incorporated into the subsurface model to provide a 3D representation of the reservoir structure.
• Study the process of seismic inversion, where seismic data is used to estimate reservoir properties such as porosity and permeability at locations where no direct measurements are available.
• Explore the seismic-to-well tie process to improve model accuracy and ensure consistency between seismic data and well log information.

7. Petrophysical Modeling

• Learn the fundamentals of petrophysical modeling, focusing on how to estimate key reservoir properties (such as porosity, permeability, and fluid saturations) and incorporate them into the subsurface model.
• Understand how petrophysical properties are derived from well logs, core analysis, and seismic data.
• Explore how petrophysical models are used to characterize the reservoir’s ability to store and transmit fluids, and how this information is critical for simulation and production forecasting.

8. Reservoir Simulation and Flow Modeling

• Understand how reservoir simulation is integrated with geomodelling to model fluid flow within the reservoir and predict future performance under different production scenarios.
• Learn the principles of reservoir simulation, including the use of black oil models, compositional models, and thermal recovery models.
• Study how to use the subsurface model to conduct sensitivity analyses and optimize development plans by simulating different injection and production strategies.

9. Uncertainty Quantification and Sensitivity Analysis

• Learn how to quantify and manage uncertainty in subsurface models by incorporating variability in data and model assumptions.
• Understand the role of sensitivity analysis in identifying the most critical variables affecting reservoir behavior and field development decisions.
• Study Monte Carlo simulations and other probabilistic methods used to assess model uncertainty and develop risk-based management strategies.

10. Model Validation and History Matching

• Learn how to validate subsurface models by comparing model predictions to actual field data (production rates, pressure data, etc.) through the process of history matching.
• Understand the importance of refining and updating the model over time as new data is collected and the reservoir evolves.
• Study common challenges in history matching and techniques for overcoming them, such as using advanced algorithms and machine learning methods.

11. Practical Applications of Geomodels in Field Development

• Explore how geomodels are used in practice for field development, including well placement optimization, reservoir management, and EOR (Enhanced Oil Recovery) strategies.
• Understand how geomodels inform drilling decisions, production forecasting, and reservoir management strategies.
• Study case studies of successful field developments where geomodelling played a critical role in optimizing reservoir performance.

12. Advanced Geomodeling Techniques and Emerging Trends

• Learn about the latest advancements in geomodelling techniques, including the use of machine learning and artificial intelligence for automating and improving model construction and updating processes.
• Understand the integration of real-time data from smart wells and downhole sensors into subsurface models for better decision-making.
• Study digital twin technology and its potential for creating dynamic, real-time models of the subsurface that evolve alongside field production and development.

Target Audience

This course is designed for professionals involved in subsurface modeling, reservoir management, and field development planning. The target audience includes:

• Reservoir Engineers
• Geologists and Geophysicists
• Geomodellers and 3D Modelling Specialists
• Production Engineers
• Data Scientists and Analysts
• Project Managers
• Asset Managers
• Consultants in Geosciences and Reservoir Engineering