Reservoir Static Modeling

reservoir static modeling

Course Description:

This intensive course provides a comprehensive foundation in reservoir static modeling,
combining key concepts in statistics, geostatistics, structural and petrophysical modeling, and
uncertainty analysis. Participants will gain practical experience with industry-standard software
and tools, learning how to build and integrate models for reservoir characterization and
volume estimation. The course emphasizes hands-on exercises and real-world case studies,
culminating in the development of a complete reservoir static model with uncertainty analysis.

Duration

10 Days

Target Audience

Reservoir engineers
Geoscientists
Professionals involved in reservoir modeling and characterization

Prerequisites

Basic understanding of geology, geophysics, and reservoir engineering principles

By the end of this course, participants will be able to

1-Understand the Fundamentals of Reservoir Static Modeling

Explain the importance of static modeling in reservoir characterization and its applications.
Identify key components such as reservoir architecture and static properties used in static modeling.

2-Apply Statistical and Geostatistical Techniques in Reservoir Modeling

Understand key statistical concepts such as mean, variance, and standard deviation and their relevance to reservoir data.
Use geostatistical techniques like variograms, kriging, and simulation methods for spatial correlation and uncertainty analysis in reservoir models.

3-Implement Deterministic and Stochastic Geostatistical Approaches

Apply deterministic techniques like kriging for reservoir characterization.
Understand and implement stochastic modeling methods to account for uncertainty in reservoir models.

4-Develop and Refine Structural Models

Create structural models using gridding, interpolation, and 3D modeling techniques.
Incorporate geological and geophysical data into structural models for enhanced accuracy.

5-Create and Integrate Petrophysical Models

Understand key petrophysical properties such as porosity, permeability, and saturation.
Develop petrophysical models using well logs and inversion data, integrating them with structural models.

6-Perform Volume Calculations and Base Case Analysis

Estimate reserves and resources using integrated static models and perform base case volume calculations.
Conduct volume calculations for different reservoir scenarios based on static model outputs.

7-Conduct Uncertainty Analysis in Reservoir Models

Understand the sources and impacts of uncertainty in reservoir modeling.
Use techniques like Monte Carlo simulation and sensitivity analysis to quantify uncertainty in reservoir predictions.

8-Integrate All Aspects into a Comprehensive Reservoir Static Model

Combine structural, petrophysical, and geostatistical models to create a complete static model for reservoir characterization.
Apply uncertainty analysis techniques to assess the reliability of the reservoir model.

By the end of the course, participants will have the practical knowledge and skills to build, refine, and integrate static reservoir models, perform volume calculations, and quantify uncertainty, making them better equipped to support decision-making in reservoir development and management.

SYLLABUS

Day 1: Introduction to Reservoir Static Modeling

Overview of Reservoir Static Modeling: Importance and Applications
Key Concepts: Reservoir Architecture and Static Properties
Introduction to Tools and Software Used in Reservoir Static Modeling

Day 2: Introduction to Statistics

Overview of Classic Statistics: Descriptive and Inferential Statistics
Key Concepts: Mean, Median, Mode, Variance, and Standard Deviation
Practical Applications of Statistics in Reservoir Modeling

Day 3: Introduction to Petroleum Geostatistics

Overview of Geostatistics: Concepts and Applications in Reservoir Modeling
Key Geostatistical Techniques: Variograms and Spatial Correlation
Practical Exercise: Applying Geostatistical Methods to Reservoir Data

Day 4: Deterministic Approaches in Geostatistics

Deterministic Modeling Techniques: Kriging and Its Variants
Understanding the Role of Deterministic Models in Reservoir Characterization
Practical Exercise: Implementing Deterministic Geostatistical Methods

Day 5: Stochastic Approaches in Geostatistics

Overview of Stochastic Modeling Techniques: Simulation Methods
Understanding the Role of Stochastic Models in Uncertainty Quantification
Practical Exercise: Implementing Stochastic Geostatistical Methods

Day 6: Structural Modeling Fundamentals

Techniques for Building Structural Models: Gridding and Interpolation Methods
Understanding Fault Modeling: Types of Faults and Their Impact on Reservoirs
Practical Exercise: Creating a Basic Structural Model from Geological Data

Day 7: Advanced Structural Modeling

Advanced Techniques in Structural Modeling: 3D Modeling and Visualization
Incorporating Geological and Geophysical Data into Structural Models
Case Studies: Successful Structural Modeling in Reservoir Studies

Day 8: Petrophysical Modeling Fundamentals

Overview of Petrophysical Properties: Porosity, Permeability, and Saturation
Techniques for Petrophysical Modeling: Using Well Logs and Inversion Results
Practical Exercise: Developing a Basic Petrophysical Model

Day 9: Volume Calculations and Base Case Analysis

Techniques for Volume Calculations: Estimating Reserves and Resources
Performing Base Case Volume Calculations Using Integrated Models
Practical Exercise: Conducting Volume Calculations

Day 10: Uncertainty Analysis and Final Project

Understanding Uncertainty in Reservoir Models: Sources and Impacts
Techniques for Uncertainty Analysis: Monte Carlo Simulation and Sensitivity Analysis
Final Project: Participants Work on a Case Study to Create a Complete Reservoir Static Model with Uncertainty Analysis
Course Review and Q&A Session