Introduction

Reservoir modeling is a critical process in upstream oil and gas operations, enabling accurate representation of subsurface conditions and supporting effective decision-making for field development and production optimization. Integrating geological, geophysical, and engineering data ensures reliable models that enhance forecasting and reservoir management.

This program, designed by Global Horizon Training Center, equips participants with the knowledge and practical skills required to build, analyze, and integrate reservoir models using multidisciplinary data for improved asset performance.

Course Objectives

By the end of this program, participants will be able to:

• Understand the fundamentals of reservoir modeling and simulation
• Integrate geological, geophysical, and production data into reservoir models
• Develop static and dynamic reservoir models
• Apply data interpretation techniques for model accuracy
• Perform history matching and forecasting
• Identify uncertainties and manage modeling risks
• Optimize reservoir performance using simulation outputs
• Support field development planning and decision-making

Target Audience

This program is designed for:

• Reservoir Engineers and Petroleum Engineers
• Geologists and Geophysicists
• Production and Field Development Engineers
• Data Analysts in upstream oil and gas
• Asset Management and Planning Professionals
• Technical Specialists involved in reservoir studies

Outline

Day 1: Fundamentals of Reservoir Modeling

• Overview of reservoir modeling concepts and workflows
• Types of reservoir models (static vs. dynamic)
• Geological and petrophysical data fundamentals
• Data sources and quality assessment
• Introduction to modeling software and tools

Day 2: Geological and Geophysical Data Integration

• Seismic data interpretation and structural modeling
• Well log analysis and correlation
• Building structural and stratigraphic frameworks
• Petrophysical property modeling (porosity, permeability)
• Data integration challenges and solutions

Day 3: Static Model Construction

• Grid design and model discretization
• Facies modeling and distribution
• Property modeling and upscaling techniques
• Model validation and quality control
• Uncertainty assessment in static models

Day 4: Dynamic Simulation and History Matching

• Reservoir simulation fundamentals
• Fluid properties and flow behavior
• History matching techniques and workflows
• Calibration of models using production data
• Forecasting production performance

Day 5: Optimization, Uncertainty, and Decision Support

• Sensitivity analysis and uncertainty management
• Reservoir management strategies
• Field development planning using models
• Integration with production and economic data
• Case studies and real-world applications