The course introduces engineers, geologists and geoscientists to both the fundamental theory and practice of reservoir description and modeling for reservoir management. This course covers the fundamental concepts of reservoir description and modeling using geostatistical techniques. It is an introductory course with emphasis on the principles and practice of integrated studies and uncertainty analysis. Upon completion of this course, you should be able to (l) make decisions on when and where to apply reservoir description and modeling to support reservoir management, (2) understand the methodology of integrated studies (3) select the appropriate reservoir description and geostatistical modeling tools (4) analyze and QC data for reservoir modeling, (5) understand variogram analysis, kriging and stochastic simulation, integrated studies, uncertainty analysis; and (6) recognize the limitations and opportunities for reservoir modeling.
Reservoir Modeling Fundamentals
- What is reservoir characterization and modeling?
- What is reservoir heterogeneity?
- Scales of heterogeneities
- What are the strengths and limitations of different reservoir modeling techniques
Overview of Statistics and Probability
- Basic definitions
- Histograms, frequency distribution and probability
Data analysis, QC and Preparation
- Data analysis and QC tools
- What is it? What is the information to be derived?
- How to calculate variograms
- What is kriging?
- Different types of kriging
- Applications and limitations of kriging
Stochastic Simulation and Modeling Case Studies
- How to conduct a geostatistical reservoir modeling study
- Conditional simulation. Use of hard and soft data
- Sequential indicator simulation and Sequential Gaussian simulation
- Case Studies of geostatistical modeling
Overview of Uncertainty Analysis and Integrated Studies
- What is Uncertainty? Integrated Studies?
- How to account for uncertainty in Integrated reservoir studies
- Case Studies