Reservoir Simulation Uncertainty and Sensitivity Analysis Training Course

Reservoir Simulation Uncertainty and Sensitivity Analysis Training Course

Introduction

Reservoir Simulation Uncertainty and Sensitivity Analysis Training Course is designed to equip oil and gas professionals with advanced technical knowledge and practical skills for evaluating subsurface uncertainties, optimizing reservoir models, and improving hydrocarbon recovery forecasts. This comprehensive training course explores reservoir modeling, uncertainty quantification, sensitivity analysis, probabilistic forecasting, Monte Carlo simulation, history matching, reservoir characterization, and production forecasting. Participants will learn how to identify critical uncertainties in geological models, petrophysical properties, fluid characteristics, reservoir connectivity, aquifer support, and operational parameters, while understanding how these variables influence reservoir performance and long-term asset value. The course also introduces modern workflows for integrating seismic interpretation, well logs, pressure data, production history, and dynamic simulation results to develop more reliable reservoir development strategies.

The programme emphasizes probabilistic reservoir simulation, global sensitivity analysis, uncertainty propagation, ensemble modeling, decision analysis, and risk-informed field development planning. Participants will explore how uncertainty-aware workflows can support well placement, infill drilling, enhanced oil recovery (EOR), production optimization, and reserves forecasting under different operating conditions. The training also examines the application of automated simulation workflows, data analytics, and emerging digital technologies to improve model efficiency and technical decision-making. By the end of the course, participants will be better equipped to transform uncertain subsurface data into actionable engineering insights, reduce forecasting risks, prioritize data acquisition, and support economically robust reservoir management decisions throughout the field life cycle.

Course Duration

5 days

Course Objectives

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

1. Understand reservoir simulation fundamentals and their application in uncertainty-aware field development planning.
2. Identify and classify geological, petrophysical, fluid, and operational uncertainties affecting reservoir performance.
3. Develop structured reservoir uncertainty quantification (UQ) workflows for static and dynamic models.
4. Apply local and global sensitivity analysis techniques to identify influential reservoir parameters.
5. Perform Monte Carlo simulation to generate probabilistic production forecasts and uncertainty distributions.
6. Develop and evaluate low-, mid-, and high-case reservoir scenarios using consistent modeling assumptions.
7. Apply history matching and ensemble-based modeling to reduce uncertainty in reservoir forecasts.
8. Evaluate the impact of porosity, permeability, net-to-gross ratio, and connectivity on recovery performance.
9. Assess uncertainty in relative permeability, capillary pressure, PVT properties, and fluid contacts.
10. Use design of experiments (DoE) and screening methods to improve simulation efficiency.
11. Interpret probabilistic forecasts using P10, P50, and P90 metrics where appropriate.
12. Integrate sensitivity results into well placement, production optimization, and reservoir development decision analysis.
13. Communicate uncertainty, forecast confidence, and technical risks through decision-ready reports and visualizations.

Target Audience

1. Reservoir engineers and reservoir simulation specialists.
2. Petroleum engineers and production optimization professionals.
3. Geoscientists, geologists, and reservoir characterization specialists.
4. Petrophysicists and formation evaluation professionals.
5. Reservoir modelers and history-matching engineers.
6. Field development planners and asset management teams.
7. Reserves evaluation, petroleum economics, and investment analysis professionals.
8. Technical managers, subsurface consultants, and energy project decision-makers.

Course Modules

Module 1: Fundamentals of Reservoir Simulation and Uncertainty Management

• Principles of reservoir simulation and dynamic reservoir modeling.
• Overview of deterministic and probabilistic simulation approaches.
• Types of reservoir uncertainty: geological, petrophysical, fluid, and operational.
• Sources of uncertainty in static models, dynamic models, and production forecasts.
• Case Study: Comparing deterministic and probabilistic production forecasts for a mature oil field.

Module 2: Reservoir Characterization and Uncertainty Identification

• Geological model uncertainty, including facies distribution and structural interpretation.
• Petrophysical uncertainty involving porosity, permeability, saturation, and net-to-gross ratio.
• Fluid property uncertainty involving PVT data, viscosity, and formation volume factors.
• Uncertainty in faults, fractures, reservoir connectivity, and fluid contacts.
• Case Study: Assessing the effect of uncertain permeability distribution on water breakthrough in a heterogeneous reservoir.

Module 3: Uncertainty Quantification and Probabilistic Modeling

• Fundamentals of uncertainty quantification in reservoir engineering.
• Defining probability distributions for uncertain model parameters.
• Assigning realistic parameter ranges and assessing data quality.
• Building low-, base-, and high-case reservoir model realizations.
• Case Study: Developing probabilistic recovery estimates using alternative porosity, permeability, and aquifer-strength assumptions.

Module 4: Sensitivity Analysis Techniques and Parameter Ranking

• Fundamentals of one-at-a-time and local sensitivity analysis.
• Global sensitivity analysis using variance-based and screening methods.
• Application of tornado charts, scatter plots, and correlation analysis.
• Ranking parameters according to their impact on recovery factor, cumulative production, and water cut.
• Case Study: Identifying the most influential variables affecting oil recovery in a waterflood simulation.

Module 5: Monte Carlo Simulation and Uncertainty Propagation

• Principles of Monte Carlo sampling and probabilistic reservoir forecasting.
• Selecting probability distributions and generating model input samples.
• Propagating uncertainty through reservoir simulation workflows.
• Interpreting production profiles, recovery distributions, and probabilistic reserves indicators.
• Case Study: Using Monte Carlo simulation to evaluate uncertainty in ultimate recovery and field production performance.

Module 6: History Matching, Ensemble Modeling, and Model Calibration

• Role of history matching in reducing dynamic model uncertainty.
• Calibration against oil production, water production, gas production, and pressure data.
• Introduction to ensemble-based history matching and assisted history-matching workflows.
• Evaluating non-uniqueness, parameter compensation, and model plausibility.
• Case Study: Comparing multiple history-matched models to quantify uncertainty in future production forecasts.

Module 7: Uncertainty-Informed Reservoir Development and Optimization

• Evaluating well placement and infill drilling under subsurface uncertainty.
• Assessing sensitivity to well spacing, completion design, and operating constraints.
• Evaluating waterflood performance, injection strategies, and enhanced oil recovery options.
• Integrating uncertainty analysis with economic evaluation and decision trees.
• Case Study: Selecting an infill drilling strategy by comparing expected recovery, investment requirements, and downside risks across alternative reservoir realizations.

Module 8: Advanced Workflows, Reporting, and Decision-Making

• Automating simulation runs and managing large ensembles of reservoir models.
• Applying design of experiments and response surface methods to reduce computational costs.
• Using uncertainty visualization, probabilistic plots, and sensitivity dashboards.
• Translating simulation results into development recommendations and risk management plans.
• Case Study: Preparing an uncertainty-based field development recommendation using P10, P50, and P90 forecasts, sensitivity rankings, and economic scenarios.

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.