By the end of this course, participants will:
- Understand the principles and applications of geostatistics in reservoir characterization.
- Apply variogram analysis and modeling techniques to quantify spatial variability.
- Integrate geological, geophysical, and petrophysical data into geostatistical models.
- Utilize geostatistical simulation methods for reservoir property prediction.
- Develop robust workflows for uncertainty quantification and risk assessment.
DAY 1
Introduction to Petroleum Geostatistics
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- Overview of geostatistics and its importance in petroleum exploration and production
- Fundamentals of spatial statistics and data analysis
- Geostatistical concepts: stationarity, spatial continuity, and anisotropy
- Exercises: Exploratory data analysis and visualization
DAY 2
Variogram Analysis and Modeling
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- Variogram concepts: structure, range, sill, and nugget
- Variogram modeling: isotropic and anisotropic cases
- Practical variogram fitting techniques
- Exercises: Building and interpreting variograms for subsurface data
DAY 3
Geostatistical Estimation Techniques
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- Kriging: Simple, ordinary, and cokriging
- Advantages and limitations of geostatistical estimation
- Combining hard and soft data in geostatistical models
- Exercises: Property estimation using kriging methods
DAY 4
Geostatistical Simulation Methods
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- Introduction to stochastic simulation
- Sequential Gaussian and indicator simulations
- Multi-variate simulation techniques for reservoir modeling
- Exercises: Simulating reservoir properties using multiple data sources
DAY 5
Uncertainty Quantification and Risk Assessment
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- Integrating geostatistics into reservoir risk analysis
- Probabilistic resource estimation and uncertainty management
- Case studies: Real-world applications of geostatistics in reservoir studies
- Final exercises: Developing workflows for uncertainty quantification and model validation
