Course Objectives:

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

1. Introduction to Uncertainties in Reservoir Engineering

• Understand the role of uncertainties in reservoir modeling, and how they impact decision-making in reservoir management.
• Learn how to categorize and quantify different types of uncertainties, including data uncertainty, modeling uncertainty, and parameter uncertainty.
• Study the importance of uncertainty analysis in improving the accuracy of reservoir predictions and ensuring more reliable operational decisions.

2. Types of Uncertainty in Reservoir Engineering

• Data Uncertainty: Understand how measurement errors, sampling biases, and incomplete data can lead to uncertainty in reservoir models.
• Modeling Uncertainty: Learn about the inherent limitations and assumptions in reservoir simulation models and how they introduce uncertainty.
• Parameter Uncertainty: Study how unknown reservoir properties, such as permeability, porosity, and fluid properties, create uncertainty in model predictions.
• Environmental Uncertainty: Understand the external factors such as market conditions, regulatory changes, and technological advancements that influence uncertainty in reservoir development.

3. Geostatistics Fundamentals

• Introduction to Geostatistics: Understand the principles of geostatistics and how it is used to describe spatial variability in subsurface reservoirs.
• Learn the key concepts of spatial correlation and how it is applied to model heterogeneity in reservoir properties.
• Study the role of stationarity and how assumptions about stationarity influence geostatistical models and their uncertainty.
• Understand the difference between random fields and deterministic models in geostatistical reservoir modeling.

4. Geostatistical Methods for Quantifying Uncertainty

• Learn the key geostatistical techniques used to quantify uncertainty in reservoir models:
• Kriging: Understand the concept of ordinary kriging and how it is used to estimate unknown reservoir properties based on available data.
• Kriging with uncertainty: Study how kriging techniques can be extended to quantify uncertainty in the spatial distribution of reservoir properties.
• Simulation Methods: Learn about sequential Gaussian simulation (SGS) and sequential indicator simulation (SIS) as methods to create multiple realizations of reservoir models that incorporate uncertainty.
• Monte Carlo Simulation: Understand how Monte Carlo methods can be used to propagate uncertainties through reservoir models and assess the likelihood of different outcomes.
• Probabilistic Methods: Study probabilistic approaches to modeling and forecasting reservoir performance while accounting for uncertainty.

5. Estimation of Reservoir Properties Using Geostatistics

• Learn how geostatistics is applied to estimate reservoir properties (e.g., permeability, porosity, saturation) from available data points.
• Study the use of variograms to quantify the spatial structure of reservoir properties and assess the degree of continuity.
• Understand the importance of data quality and sampling density in improving the reliability of geostatistical estimates.
• Explore methods for integrating well logs, seismic data, and core data into geostatistical models.

6. Geostatistical Models and Reservoir Simulation

• Learn how to incorporate geostatistical models into reservoir simulation for more realistic predictions of reservoir behavior.
• Study how geostatistical realizations are used in history matching and forecasting to evaluate a range of potential reservoir outcomes.
• Understand how uncertainty in reservoir parameters is propagated through dynamic reservoir models and how to interpret simulation results.
• Explore the integration of uncertainty analysis into the decision-making process for well placement, production strategies, and field development.

7. Quantifying Uncertainty in Reservoir Forecasting and Decision Making

• Study the process of uncertainty quantification in reservoir forecasting and how to assess the range of possible production scenarios.
• Learn how to create probabilistic forecasts for production rates, recovery factors, and reserves.
• Understand how to use decision analysis techniques, such as expected value, decision trees, and real options analysis, to incorporate uncertainty into strategic decisions.
• Explore risk management strategies to mitigate the impact of uncertainty on reservoir development plans.

8. Sensitivity Analysis and Uncertainty Reduction

• Learn how to perform sensitivity analysis to identify the most influential parameters in a reservoir model and focus efforts on reducing uncertainty in those areas.
• Study techniques for reducing uncertainty in reservoir models, such as additional data collection, advanced logging techniques, and improved simulation algorithms.
• Understand the role of history matching in reducing uncertainty by calibrating reservoir models against real-world data.

9. Managing and Communicating Uncertainty in Reservoir Engineering

• Learn how to effectively communicate uncertainty to stakeholders, including managers, investors, and regulatory bodies.
• Study methods for presenting uncertainty in a clear and actionable way, such as through probabilistic scenarios, uncertainty bands, and risk assessments.
• Understand the importance of transparent reporting and decision-making in the face of uncertainty.
• Explore tools and techniques for visualizing uncertainty in reservoir models, such as probability maps, histograms, and uncertainty plots.

10. Case Studies and Applications of Uncertainty and Geostatistics

• Analyze case studies where geostatistics and uncertainty analysis have been applied to real-world reservoir modeling projects.
• Learn from examples where managing uncertainty led to better decision-making, improved production forecasts, and enhanced reservoir management strategies.
• Study case examples where poor understanding of uncertainty resulted in significant operational risks, economic losses, or suboptimal reservoir performance.

Target Audience

This course is designed for professionals involved in the development, management, and modeling of subsurface reservoirs who need to address uncertainty and apply geostatistical techniques in their work. The target audience includes:

1. Reservoir Engineers

2. Geoscientists and Geologists

3. Production Engineers

4. Reservoir Simulation Engineers

5. Data Scientists and Reservoir Analysts

6. Wellsite Engineers

7. Project Managers