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8 - OGE-R - Oil & Gas - Reservoir Engineering
OG-R 136 - Geostatistics, Data Integration and Reservoir Modeling (10 Days)
| Code | Start Date | Duration | Venue | |
|---|---|---|---|---|
| OG-R 136 | 12 May 2025 | 10 Days | Istanbul | Registration Form Link |
| OG-R 136 | 07 July 2025 | 10 Days | Istanbul | Registration Form Link |
| OG-R 136 | 01 September 2025 | 10 Days | Istanbul | Registration Form Link |
| OG-R 136 | 27 October 2025 | 10 Days | Istanbul | Registration Form Link |
| OG-R 136 | 22 December 2025 | 10 Days | Istanbul | Registration Form Link |
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Course Description
Geostatistics, Data Integration and Reservoir Modeling is a course that focuses on the application of geostatistical techniques to integrate various types of data in reservoir modeling for the oil and gas industry. The course covers topics such as spatial data analysis, variogram modeling, kriging, simulation, uncertainty analysis, and reservoir characterization. Participants will learn how to use geostatistical methods to analyze and interpret subsurface data, build reservoir models, and make informed decisions in reservoir management. The course also emphasizes the importance of data integration and quality control in the modeling process.
Course Objectives
- Learning depositional and petrophysical facies modeling
- Building structural and stratigraphic models
- Building up a complete model for simulation
- Learning stochastic and deterministic modeling methods
- Learning uncertainty mitigation by generating realizations or scenario, ranking
- Learning how to apply reservoir description and modeling to support reservoir management
- Recognizing the limitations and opportunities of reservoir modeling.
- Learning the Kriging techniques
- Understanding the statistics and probability
Who Should Attend?
- Petroleum
- Geophysicists
- Geologists
- Reservoir Engineers
- Processing Engineers
Course Details/Schedule
Day 1
- Overview of reservoir modeling and its significance in oil and gas
- Understanding geostatistical concepts in reservoir modeling
- Data types and sources (well logs, seismic, production data)
- Data quality control (QC), editing, and statistical analysis
- Introduction to software tools used for reservoir modeling
Day 2
- Well correlation techniques and interpretation
- Fault modeling: Identification and structural impact
- Pillar gridding: Constructing the framework for reservoir models
- Horizoning: Defining key reservoir surfaces
- Layering strategies for detailed reservoir subdivision
Day 3
- Geometric modeling and its role in reservoir studies
- Well logs and data point upscaling for accurate property distribution
- Introduction to facies modeling techniques
- Depositional environment interpretation for facies mapping
- Impact of facies variations on reservoir performance
Day 4
- Concept of variograms and covariance functions
- Variograms modeling techniques for subsurface property estimation
- Directional variograms analysis for anisotropic reservoirs
- Data clustering and stationarity assumptions in geostatistics
- Variograms interpretation and validation
Day 5
- Interpolation algorithms: Deterministic vs. geostatistical methods
- Volume calculations and property distribution
- Property quality control (QC) and validation techniques
- Reservoir workflow development: Step-by-step model building
- Uncertainty mitigation strategies through scenario ranking
Day 6
- Principles and theory of kriging in geostatistics
- Types of kriging: Ordinary, universal, and indicator kriging
- Cross-validation and error assessment in kriging models
- Advanced kriging techniques: Co-kriging and sequential Gaussian simulation (SGS)
- Application of kriging in petro physical property estimation
Day 7
- Integration of geostatistical and geological models
- Structural modeling principles for complex reservoirs
- Estimation of properties at well locations using geostatistical tools
- Case studies on integrated spatial modeling
- Workflow automation and efficiency improvements in modeling
Day 8
- Building reservoir models using integrated geostatistical data
- Estimation of permeability, porosity, and saturation distributions
- Uncertainty assessment and model validation techniques
- Reservoir connectivity analysis for production optimization
- Decision-making in field development based on modeling results
Day 9
- Application of geostatistical techniques in a real-world project
- Building and refining a reservoir model using course concepts
- Presentation of project results and key learnings
- Peer review and feedback session on modeling approaches
- Discussion on industry challenges and future trends in reservoir modeling
Day 10
- Conditional simulation techniques for property modeling
- Facies and rock type modeling using stochastic methods
- Petro physical properties simulation for reservoir performance forecasting
- Ranking of realizations for risk assessment
- Construction of simulation-ready reservoir models for future predictions
