Efficient reservoir simulation requires not only accurate input data but also careful numerical tuning to achieve stable and reliable results. CMG’s simulation suites (IMEX, GEM, STARS) offer powerful capabilities for modeling complex reservoirs, yet challenges such as non-convergence, long run times, and numerical instabilities often arise. This training course focuses on advanced techniques for grid design, time-step control, solver optimization, and model parallelization to improve performance and ensure robust simulation results across conventional, unconventional, and thermal recovery projects.

Participants will learn practical strategies to debug convergence issues, optimize solver settings, and balance computational efficiency with accuracy. The program emphasizes hands-on exercises that demonstrate how tuning decisions affect model stability, speed, and predictive quality. By the end of the course, attendees will have the skills to manage large-scale simulation models effectively, reduce run times, and maximize the potential of CMG simulation software for decision support in field development and reservoir management.

Duration: 10 Days

Target Audience

• 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

Objectives

• Understand the fundamentals of numerical tuning in CMG simulation suites
• Design grids effectively to balance resolution and run time
• Apply time-step control techniques for stability and accuracy
• Optimize solver settings to improve convergence rates
• Implement parallelization strategies for faster runs
• Debug common convergence and instability issues
• Conduct sensitivity analysis on numerical parameters
• Apply workflows to handle large-scale and complex models
• Improve computational efficiency without compromising accuracy
• Integrate numerical tuning with field development planning
• Visualize and analyze numerical performance results
• Apply best practices for simulation optimization and stability

Course Modules

Module 1: Introduction to Numerical Tuning in CMG

• Importance of numerical tuning in reservoir simulation
• Common numerical challenges in IMEX, GEM, and STARS
• Performance bottlenecks and optimization strategies
• Balancing model accuracy and run time
• Case examples of tuning impacts

Module 2: Fundamentals of Grid Design

• Principles of grid construction in CMG simulators
• Cartesian and corner-point grids
• Balancing resolution and computational cost
• Handling complex geology with grids
• Grid refinement strategies

Module 3: Advanced Grid Optimization Techniques

• Local grid refinement workflows
• Nested grids for fracture and well modeling
• Impact of anisotropy on grid design
• Upscaling for efficiency
• Practical grid optimization exercises

Module 4: Time-Step Control Basics

• Role of time-step in simulation stability
• Automatic vs manual time-step control
• Identifying problematic time-step sizes
• Time-step adjustment strategies
• Case studies in time-step control

Module 5: Advanced Time-Step Management

• Time-step constraints for multiphase flow
• Thermal and compositional model considerations
• Adaptive time-step algorithms
• Handling fast-changing reservoir conditions
• Practical applications in CMG

Module 6: Solver Fundamentals

• Overview of CMG solver algorithms
• Linear and nonlinear solver settings
• Iteration strategies for convergence
• Identifying solver-related instabilities
• Default vs advanced solver options

Module 7: Solver Optimization Techniques

• Tuning solver tolerances and thresholds
• Improving convergence rates
• Handling ill-conditioned problems
• Managing large, complex systems
• Case examples of solver tuning

Module 8: Parallelization in CMG Simulations

• Introduction to parallel computing in CMG
• Strategies for partitioning models
• Scaling simulations across processors
• Reducing run times through parallelization
• Best practices in distributed computing

Module 9: Debugging Convergence Issues

• Identifying signs of non-convergence
• Systematic debugging workflows
• Common sources of numerical instability
• Tools for troubleshooting CMG models
• Case studies of convergence solutions

Module 10: Handling Large-Scale Models

• Challenges in scaling models
• Efficient workflows for large datasets
• Optimizing memory usage
• Balancing complexity and stability
• Real-world large-scale examples

Module 11: Sensitivity Analysis on Numerical Parameters

• Importance of sensitivity testing
• Key numerical parameters in CMG
• Running parameter variation studies
• Analyzing results for optimization
• Applying findings to workflows

Module 12: Workflow Integration and Automation

• Automating tuning workflows in CMG
• Using scripts and batch runs
• Linking with CMOST for optimization
• Workflow efficiency tips
• Case examples in automation

Module 13: Visualization of Numerical Performance

• Tools for monitoring convergence
• Visualizing time-step and solver performance
• Generating performance reports
• Identifying patterns in failures
• Communicating performance metrics

Module 14: Case Studies in Numerical Optimization

• Industry examples of numerical tuning
• Successes and lessons learned
• Applications in conventional reservoirs
• Applications in unconventional and thermal models
• Peer-reviewed group discussions

Module 15: Future of Simulation Optimization

• Advances in solver technologies
• Role of AI and machine learning in tuning
• Cloud computing for performance optimization
• Integration with real-time data
• Next-generation approaches to simulation

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.