Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

ISBN-13: 9783030178598 / Angielski / Twarda / 2019 / 640 str.

Y. Zee Ma

Spis treści

Preface

1. Introduction and Overview

Part 1: Reservoir Characterization

2. Essential Reservoir Geology and Multi-Scales of Petroleum Reservoir Heterogeneities

2.1 Structural Controls on Petroleum Resources

2.2 Sequence Stratigraphy and Hydrocarbon Resources

2.3 Depositional Environments

2.4 Facies and Lithofacies

2.5 Petrophysical Properties

2.6 Subsurface Fluid Heterogeneities

2.7 Summary

Appendix 1: Large-Scale Tectonic Settings and Their Characteristics

Appendix 2: Hierarchy of Depositional Systems: Fluvial Architecture Example

3. Introduction to Petrophysical Reservoir Characterization

3.1 Porosity Characterization and Estimation

3.2 Clay Volume Analysis and Its Impacts on Other Petrophysical Parameters

3.3 Permeability Characterization and Estimation

3.4 Fluid Saturations Characterization and Estimation

3.5 Uncertainty Analysis in Petrophysical Analysis

3.6 Summary

4. Practical Seismic Reservoir Characterization

4.1 Seismic Data: Resolution and Coverage

4.2 Structural and Stratigraphic Interpretations

4.3 Reservoir Delineation, EOD Mapping and Seismic Facies Identifications

4.4 Reservoir Property Evaluation Using Seismic Data

4.5 Seismic Attribute Analysis and Integration for Reservoir Characterization

4.6 Summary

Appendix 1: Time-Frequency Representation of Seismic Data

Appendix 2: Amplitude Versus Offset (AVO): An Overview

Appendix 3: Overview of Forward Modeling and Seismic Inversions

5. Statistical and Data Analytical Reservoir Characterization

5.1 Common Descriptive Statistics for Reservoir Analysis

5.2 Sampling Bias in E&P and Mitigation Methods

5.3 Multivariate Statistical Data Analysis and Applications to Reservoir Analysis (Statistical Correlation, PCA, and Clustering Analysis)

5.4 Overview of Artificial Neural Networks and Example Applications

5.5 Bayesian Inference for Reservoir Characterization

5.6 Regression for Mapping and Modeling of Reservoir Properties

5.7 Advanced Regressions for Integrated Reservoir Characterization

5.8 Summary

Appendix 1: Lord's Paradox and Reconciling Mathematics and Reservoir Problems

Appendix 2: Impact of Interdependencies/collinearity on multiple linear regressions

6. Geostatistical Reservoir Characterization

6.1 Variogram and Spatial Correlation

6.2 Theoretical Variogram and Spatial Covariance Models

6.3 Calculating and Fitting Experimental Variograms

6.4 Interpreting Variograms of Reservoir Properties

6.5 Lithofacies Variography and Indicator Variogram

6.6 Cross-Variogram, Spatial Misalignment and Synchronization

6.7 Relationships between Variogram/Covariance Function and Spectrum

6.8 Summary

7. Integrated Facies and Lithofacies Analysis, Identification and Classification

7.1 Geological Interpretation of Facies

7.2 Lithofacies Classification Using Wireline Logs

7.3 Integration of Geological Facies, Seismically Derived Facies, Well-Log-Derived Facies and Core Facies

7.4 Summary

Part 2: Geological and Reservoir Modeling

8. Constructing a Reservoir-Model Framework

8.1 From Structural Elements to a Structural Model

8.2 From Stratigraphic Elements to a Stratigraphic Model

8.3 Building Depositional Geometrics into Geocellular Model

9. Geostatistical Modeling Methods

9.1 Estimation Methods

9.1.1 Simple Kriging, Ordinary Kriging, Nonstationary Kriging

9.1.2 Factorial Kriging and Decomposition of Sub-Processes

9.1.3 Cokriging and Collocated Cokriging

9.2 Stochastic Simulation

9.2.1 Spectral Simulations

9.2.2 Sequential Gaussian Simulation

9.3 Summary

Appendix 1: Ergodicity and Micro-Ergodicity in Stochastic Processes

Appendix 2: Stationarity, Local Stationarity and Intrinsic Random Functions

Appendix 3: Examples of Decompositions of Sub-Processes Using Factorial Kriging

10. Facies and Lithofacies Modeling

10.1 Definition of Lithofacies and Composite Lithofacies for Modeling

10.2 Lithofacies Spatial Trends and Probabilities

10.3 Lithofacies Modeling Methods

10.4 Constructing Facies and Lithofacies in a Reservoir Model

10.5 Multi-Level Modeling of Facies and Lithofacies

10.6 Summary

11. Porosity Modeling

11.1 Statistical Analysis of Porosity Data

11.2 Spatial Characterization of Porosity Distributions

11.3 Modeling Porosity

11.3.1. Using Kriging and Other Interpolation Methods

11.3.2 Using Stochastic Simulation

11.3.3 Trend-Integrated Modeling of Porosity

11.3.4 Seismically Integrated Co-Simulation of Porosity

11.3.5 Depositional-Geometry-Honored Porosity Modeling

11.3.6 Hierarchical Modeling of Porosity

11.3.7 Modeling Porosity with Multiple Constraints

11.4 Summary

12. Permeability Modeling

12.1 Statistical Analysis of Permeability Data

12.2 Permeability Modeling by Regression

12.3 Permeability Modeling by Cloud Transform

12.4 Permeability Modeling by Cokriging and Cosimulation

12.5 Summary

13. Fluid-Saturation Modeling

13.1 Fluid Distributions in a Reservoir

13.2 Data Sources for Fluid Characterization

13.3 Modeling Water Saturation

13.3.1 Using Methods of Sw-Height Functions

13.3.2 Using Cokriging and Cosimulation

13.4 Summary

14. Uncertainty Analysis and Volumetrics Evaluation

14.1 General Issues

14.1.1 Relationship between Variability and Uncertainty

14.1.2 Relationship between Uncertainty and Error

14.1.3 Measurement Uncertainty

14.1.4 Interpretation Uncertainty

14.1.5 Subsurface Fluid Uncertainty

14.1.6 Other Inference Uncertainties Related to Reservoir Analysis

14.1.7 Value of Information in Uncertainty Analysis

14.2 Uncertainty Quantification in Petroleum Resource Volumetrics

14.2.1 Critics on the Classical Volumetric Calculations

14.2.2 Analytical Estimation of Hydrocarbon Volumetrics

14.2.3 3D Model-Based Hydrocarbon Volumetrics

14.2.4 Defining Uncertainties of Input Parameters

14.2.5 Critics of Monte Carlo Simulation for Uncertainty Analysis

14.2.6 Integrated Uncertainty Quantification Workflow

14.2.7 Evaluating Uncertainty Workflow Results

14.2.8 Tranferring Static Uncertainties into Dynamic Uncertainty Evaluation

14.3 Decision Analysis

14.3.1 Known Knowns, Known Unknowns and Unknown Unknowns

14.3.2 Methods for Reducing Uncertainties

14.3.3 Decision Analysis Under Uncertainty in E&P

14.4 Summary

Appendix 1: Probability Distributions for Describing Uncertainties of Reservoir Variables

Part 3: Special and Advanced Topics

15. Naturally Fractured Reservoir Characterization and Modeling

15.1 Faulting and Fracturing in Subsurface Formations

15.2 Characterizing Fractured Formations

15.3 Construction of Discrete Fracture Networks (DFN)

15.4 From DFN to Continuous Reservoir Properties

15.5 Summary

16. Updating a Reservoir Model and Feedback Loop in Reservoir Modeling

16.1 Integrating New Data

16.2 Feedback Loop in Reservoir Modeling

16.3 Production Data Integration

16.4 4D Seismic Data Monitoring and Integration

17. Ranking Reservoir Models

18. Reservoir Model Upscaling, Simulation and Validation

18.1 Upscaling 3D Model Grids

18.2 Upscaling Categorical Variables

18.3 Upscaling Static Reservoir Properties

18.4 Mass-Preservation Upscaling

18.5 Upscaling Dynamic Reservoir Properties

18.6 Reservoir Simulation and Model Validations

19. Common and Uncommon Pitfalls in Integrated Reservoir Characterization and Modeling

20. Planning an Integrated Reservoir Characterization and Modeling Project<

21. Towards a Fully Integrated Reservoir Characterization, Modeling and Uncertainty Analysis for Petroleum Resource Management and Field Development

O autorze

Dr. Ma is a scientific advisor for geosciences at Schlumberger, specialized in reservoir characterization, modeling and resource evaluation. In his over 30 years of experience, he has worked on research and application of statistics, data analytics, and geostatistics to integrated reservoir studies for major oil companies in Europe and the US and has provided technical consultancies and training worldwide. Dr. Ma has published over 100 technical papers or book chapters in petroleum geology, geophysics, engineering, geostatistics, applied statistics and economics, and has received numerous awards, including the Schlumberger's Gold Award and Chairman Award, and the Mathematical Geosciences’ Best Paper. Dr. Ma has earned a PhD in Mathematical Geology and Geoinformatics from Université de Lorraine (previously Institute National Polytechnique de Lorraine), France, and MSc in Geostatistics from École des Mines de Paris, France, and a BSc in Geology from China University of Geosciences.

Blurb

Earth science is becoming increasingly quantitative in the digital age. Quantification of geoscience and engineering problems underpins many of the applications of big data and artificial intelligence. This book presents quantitative geosciences in three parts. Part 1 presents data analytics using probability, statistical and machine-learning methods. Part 2 covers reservoir characterization using several geoscience disciplines: including geology, geophysics, petrophysics and geostatistics. Part 3 treats reservoir modeling, resource evaluation and uncertainty analysis using integrated geoscience, engineering and geostatistical methods. As the petroleum industry is heading towards operating oil fields digitally, a multidisciplinary skillset is a must for geoscientists who need to use data analytics to resolve inconsistencies in various sources of data, model reservoir properties, evaluate uncertainties, and quantify risk for decision making. This book intends to serve as a bridge for advancing the multidisciplinary integration for digital fields. The goal is to move beyond using quantitative methods individually to an integrated descriptive-quantitative analysis. In big data, everything tells us something, but nothing tells us everything. This book emphasizes the integrated, multidisciplinary solutions for practical problems in resource evaluation and field development.