15 Topics
5 Days
Programme Overview
Training Description
Reservoir engineers
Petroleum engineers
Simulation specialists
Subsurface modelers
Geoscientists and geologists
Production engineers
Oil and gas consultants
Data scientists in petroleum applications
Field development planners
Research and development professionals
Graduate students in petroleum engineering
Session Objectives
- Understand the fundamentals of CMOST workflows and applications Automate calibration and history matching using CMOST Apply optimization techniques to reservoir development planning Conduct sensitivity analysis on key model parameters Perform uncertainty analysis for risk assessment Run multiple simulation scenarios efficiently Use CMOST for production forecasting and decision support Integrate CMOST with IMEX, GEM, and STARS simulations Interpret outputs and visualize results effectively Enhance decision-making with probabilistic approaches Communicate insights from CMOST analyses to stakeholders Apply best practices in optimization and uncertainty management
About the Course
Advanced reservoir management requires sophisticated decision-support systems to optimize field development, calibrate complex reservoir models, and manage structural uncertainty. Our CMG CMOST Training Course addresses these technical challenges by equipping petroleum engineers and reservoir specialists with a comprehensive understanding of automated CMOST workflows. Guided by reservoir simulation experts, participants explore the mechanics of automated history matching, multi-objective optimization, and probabilistic uncertainty analysis. By combining core theoretical principles with hands-on simulation scenarios, this program prepares engineers to automate model calibration, run high-throughput sensitivity analyses, and evaluate competing field development strategies to deliver reliable, data-driven production forecasts.
Curriculum & Topics
15 Topics | 5 Days
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Subtopic 1.1:
Overview of CMOST capabilities
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Subtopic 1.2:
Role in reservoir simulation workflows
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Subtopic 1.3:
Applications in history matching and optimization
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Subtopic 1.4:
Key features of the CMOST platform
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Subtopic 1.5:
Case study applications
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Subtopic 2.1:
Principles of history matching in reservoir simulation
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Subtopic 2.2:
Importance of calibration for accurate forecasting
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Subtopic 2.3:
Manual vs automated history matching approaches
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Subtopic 2.4:
Introduction to CMOST automatic history matching
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Subtopic 2.5:
Practical examples
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Subtopic 3.1:
Navigating the CMOST interface
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Subtopic 3.2:
Linking CMOST with IMEX, GEM, and STARS
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Subtopic 3.3:
Defining project settings
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Subtopic 3.4:
Input data preparation
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Subtopic 3.5:
Running basic simulations
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Subtopic 4.1:
Steps in automated calibration
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Subtopic 4.2:
Defining objective functions and parameters
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Subtopic 4.3:
Iterative calibration methods
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Subtopic 4.4:
Monitoring convergence and performance
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Subtopic 4.5:
Case studies in automatic history matching
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Subtopic 5.1:
Importance of sensitivity analysis
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Subtopic 5.2:
Identifying key reservoir parameters
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Subtopic 5.3:
CMOST sensitivity analysis workflows
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Subtopic 5.4:
Visualizing sensitivity results
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Subtopic 5.5:
Applications in reservoir development
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Subtopic 6.1:
Principles of optimization in reservoir management
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Subtopic 6.2:
Defining optimization objectives
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Subtopic 6.3:
Deterministic vs stochastic optimization methods
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Subtopic 6.4:
CMOST optimization workflows
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Subtopic 6.5:
Case study applications
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Subtopic 7.1:
Building optimization scenarios
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Subtopic 7.2:
Adjusting operational parameters
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Subtopic 7.3:
Evaluating recovery strategies
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Subtopic 7.4:
Comparing optimization outcomes
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Subtopic 7.5:
Best practices in optimization
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Subtopic 8.1:
Understanding uncertainty in reservoir models
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Subtopic 8.2:
Probabilistic vs deterministic approaches
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Subtopic 8.3:
Monte Carlo simulations in CMOST
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Subtopic 8.4:
Defining uncertainty parameters
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Subtopic 8.5:
Communicating uncertainty
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Subtopic 9.1:
Setting up probabilistic scenarios
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Subtopic 9.2:
Generating multiple realizations
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Subtopic 9.3:
Evaluating production variability
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Subtopic 9.4:
Analyzing probability distributions
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Subtopic 9.5:
Practical forecasting exercises
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Subtopic 10.1:
CMOST as a decision-making tool
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Subtopic 10.2:
Integrating outputs with field planning
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Subtopic 10.3:
Identifying optimal development scenarios
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Subtopic 10.4:
Risk assessment for investment decisions
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Subtopic 10.5:
Case examples in decision support
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Subtopic 11.1:
Linking CMOST with IMEX for black oil models
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Subtopic 11.2:
Integration with GEM for compositional models
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Subtopic 11.3:
Applications with STARS for thermal EOR
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Subtopic 11.4:
Cross-simulator workflows
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Subtopic 11.5:
Best practices in integration
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Subtopic 12.1:
Visualization of sensitivity and optimization results
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Subtopic 12.2:
Creating probability plots and tornado charts
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Subtopic 12.3:
Communicating insights to stakeholders
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Subtopic 12.4:
Preparing technical reports
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Subtopic 12.5:
Case examples of reporting
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Subtopic 13.1:
vReal-world applications of CMOST
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Subtopic 13.2:
Lessons from complex reservoirs
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Subtopic 13.3:
Optimization under uncertainty
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Subtopic 13.4:
Peer-reviewed discussions
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Subtopic 13.5:
Global experiences with CMOST
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Subtopic 14.1:
Advances in optimization algorithms
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Subtopic 14.2:
Role of AI and machine learning in CMOST
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Subtopic 14.3:
Cloud computing for large-scale simulations
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Subtopic 14.4:
Real-time optimization approaches
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Subtopic 14.5:
Emerging trends in decision-support tools
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Subtopic 15.1:
Strategies for efficient CMOST usage
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Subtopic 15.2:
Managing computational costs
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Subtopic 15.3:
Common challenges and solutions
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Subtopic 15.4:
Tips for reliable results
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Subtopic 15.5:
Final project review and recommendations
