About this Training Course
This course provides a comprehensive understanding of the principles and practical workflow of Integrated Reservoir Modeling (IRM). It focuses on how geological, petrophysical, and reservoir engineering data are systematically integrated to construct robust static reservoir models. These models serve as the foundation for reliable dynamic reservoir simulation and informed field development planning.
Participants will learn how 3D static reservoir models are developed by integrating geoscience and petrophysical datasets while maintaining consistency with the geological framework, including facies distribution and rock types. The course emphasizes the generation of key reservoir properties such as porosity, permeability, and fluid saturation, ensuring alignment between geological interpretation and petrophysical characterization.
Through structured workflows and real field case examples, the training introduces uncertainty evaluation, sensitivity analysis, and upscaling techniques. The workflow and examples will be demonstrated using PETREL applications to illustrate industry practices. For organizations with access to PETREL licenses, the course can be adapted to include practical sessions using the software.
1. What is Integrated Reservoir Modelling (IRM)?
Integrated Reservoir Modelling (IRM) combines geology, geophysics, petrophysics, and reservoir engineering into one process. As a result, it gives teams a clear view of the subsurface. It also helps them understand reservoir structure, properties, and fluid flow.
2. Why is integration of subsurface data important in reservoir modeling?
In reservoir modeling, teams combine subsurface data to keep results consistent. For example, Integrated Reservoir Modelling (IRM) links seismic data, well logs, and geological data. Therefore, teams reduce errors and improve accuracy. In addition, this process helps them make better predictions.
3. What is the difference between static and dynamic reservoir models?
Static reservoir modeling shows the structure of the reservoir. It maps properties like porosity and permeability. As a result, teams get a clear picture. In contrast, dynamic reservoir modeling shows how fluids move over time. It also predicts reservoir performance. Because dynamic models depend on static models, teams must first build accurate static models. Therefore, both models work together to guide decisions.
4. How does uncertainty affect reservoir modeling?
Uncertainty affects reservoir modeling at every stage. It comes from limited data and different views. As a result, it can change results and decisions. Therefore, teams test different cases and review results to reduce risk and improve confidence.
5. What are the main challenges in Integrated Reservoir Modelling (IRM)?
Integrated Reservoir Modelling (IRM) uses many data types. However, these data sets may not match. Therefore, teams must check and align them. At the same time, teams must keep models consistent. In addition, they must manage uncertainty. As a result, teams run regular checks during the workflow. This process helps them build reliable models.
6. How does Integrated Reservoir Modelling (IRM) support field development planning?
Integrated Reservoir Modelling (IRM) provides a clear view of the reservoir. Therefore, teams can make better forecasts. In addition, teams use this approach to place wells and improve recovery. They also support both technical and cost decisions. As a result, it helps increase field value and performance.