This book explores methods for managing uncertainty in reservoir characterization and optimization. It covers the fundamentals, challenges, and solutions to tackle the challenges made by geological uncertainty. The first chapter discusses types and sources of uncertainty and the challenges in different phases of reservoir management, along with general methods to manage it. The second chapter focuses on geological uncertainty, explaining its impact on field development and methods to handle it using prior information, seismic and petrophysical data, and geological parametrization. The third chapter deals with reducing geological uncertainty through history matching and the various methods used, including closed-loop management, ensemble assimilation, and stochastic optimization. The fourth chapter presents dimensionality reduction methods to tackle high-dimensional geological realizations. The fifth chapter covers field development optimization using robust optimization, including solutions for its challenges such as high computational cost and risk attitudes. The final chapter introduces different types of proxy models in history matching and robust optimization, discussing their pros and cons, and applications.
The book will be of interest to researchers and professors, geologists and professionals in oil and gas production and exploration.
Author(s): Reza Yousefzadeh, Alireza Kazemi, Mohammad Ahmadi, Jebraeel Gholinezhad
Series: SpringerBriefs in Petroleum Geoscience & Engineering
Publisher: Springer
Year: 2023
Language: English
Pages: 141
City: Cham
Preface
Contents
List of Figures
1 Uncertainty Management in Reservoir Engineering
1.1 Introduction to Uncertainty Management in Reservoir Engineering
1.2 Types and Sources of Uncertainty
1.2.1 Reservoir Characterization Uncertainty
1.2.2 Economic Uncertainty
1.2.3 Operational Uncertainty
1.2.4 Surface Facility Uncertainty
1.2.5 Information Reliability
1.2.6 Modeling Uncertainty
1.3 Uncertainty Management in Different Phases of Field Development
1.3.1 Uncertainty Management in History Matching
1.3.2 Uncertainty Management in Field Development Optimization
1.4 Challenges in Reservoir Management Under Uncertainty
1.4.1 High Number of Uncertain Parameters in History Matching
1.4.2 Dimension of the Reservoir Models in History Matching
1.4.3 Non-linear and Non-Gaussian Distribution of the Reservoir Properties
1.4.4 High Computational Costs
1.4.5 Initial Ensemble of Realizations in History Matching
1.5 Methods of Reservoir Management Under Geological Uncertainty
1.5.1 Forward Uncertainty Management
1.5.2 Inverse Uncertainty Management
References
2 Geological Uncertainty Quantification
2.1 Geological Uncertainty Scales
2.2 Stratigraphic and Structural Uncertainties
2.3 Geological Prior Information
2.3.1 How to Use and Analyze Geological Prior Information
2.4 Use of Seismic and Petrophysical Data in Uncertainty Quantification
2.5 Exploring the Range of Scenarios
2.6 Geological Parametrization
2.7 Geological Realizations
2.8 Geostatistical Methods of Generating Geological Realizations
2.8.1 Kriging-based Methods
2.8.2 Object-based Methods
2.8.3 Multiple-Point Geostatistics (MPS)
2.8.4 Challenges in MPS Methods
References
3 Reducing the Geological Uncertainty by History Matching
3.1 Introduction to History Matching
3.2 Different Data Types and their Scale in History Matching
3.3 Using Seismic, Static, and Production Data in History Matching
3.3.1 Two-dimensional Seismic Data
3.3.2 Three-dimensional Seismic Data
3.3.3 Four-dimensional Seismic Data
3.3.4 Application of Static and Production Data
3.4 Challenges in History Matching
3.4.1 High-dimensional Model Parameters
3.4.2 Non-Gaussian and Non-linear Distribution of Reservoir Parameters
3.4.3 Three-dimensional Distribution of Model Parameters
3.5 Different Approaches to Reservoir Management Under Geological Uncertainty
3.5.1 Open-Loop Reservoir Management (OLRM)
3.5.2 Closed-Loop Reservoir Management (CLRM)
3.6 History Matching Methods
3.6.1 Ensemble-based Methods
3.6.2 Ensemble-Smoother Methods
3.6.3 Stochastic Optimization Algorithms
References
4 Dimensionality Reduction Methods Used in History Matching
4.1 Conventional Dimensionality Reduction Methods
4.1.1 Pilot Points
4.1.2 Gradual Deformation
4.1.3 Principle Component Analysis-based (PCA-based) Methods
4.1.4 Higher-Order Singular Value Decomposition (HOSVD)
4.2 Machine Learning-based Methods
4.2.1 Introduction to Machine Learning and its Applications
4.2.2 Introduction to Deep Learning and its Applications
4.3 Deep Learning Methods used for Dimensionality Reduction of Geological Realizations
4.3.1 Autoencoders
4.3.2 Variational Autoencoders (VAEs)
4.3.3 Convolutional Variational Autoencoder (C-VAE)
References
5 Field Development Optimization Under Geological Uncertainty
5.1 Fundamentals of Robust Field Development Optimization
5.2 Challenges in Robust Field Development Optimization
5.2.1 High Number of Reservoir Simulations
5.2.2 Risk Attitudes
5.2.3 Proper Selection of Representative Realizations
5.3 Methods of Uncertainty Management in Robust Field Development Optimization
5.3.1 Robust Optimization Approaches in Green and Brown Fields
5.3.2 Different Approaches to Selecting Representative Realizations
5.3.3 Decreasing the Computational Cost by Constrained Optimization
References
6 History Matching and Robust Optimization Using Proxies
6.1 What is a Proxy Model?
6.2 Different Kinds of Proxies
6.2.1 Physics-based Proxies
6.2.2 Non-physics-based Proxies
6.2.3 Hybrid Proxies
6.3 Application of Proxies in History Matching and Robust Optimization
6.3.1 Proxies used to Substitute Reservoir Simulators
6.3.2 Proxies used to Assist Reservoir Simulators
6.4 Pros and Cons of using Proxies in History Matching and Robust Optimization
References
