Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models
Erschienen am 28. September 2017
368 Seiten
978-1-119-30259-9 (ISBN)
Beschreibung
Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data
Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration.
Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data.
* Apply data-driven modeling concepts in a geophysical and petrophysical context
* Learn how to get more information out of models and simulations
* Add value to everyday tasks with the appropriate Big Data application
* Adjust methodology to suit diverse geophysical and petrophysical contexts
Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.
Weitere Details
Weitere Ausgaben
Personen
KEITH R. HOLDAWAY is advisory industry consultant and principal solutions architect at SAS. He holds seven patents and is the author of Harness Oil and Gas Big Data with Analytics.
DUNCAN H. B. IRVING is a practice partner for oil and gas consulting at Teradata. He publishes regularly on big data analytics applied to the upstream domain.
Inhalt
- Cover
- Title Page
- Copyright
- Contents
- Foreword
- Preface
- Acknowledgments
- Chapter 1: Introduction to Data-Driven Concepts
- Introduction
- Current Approaches
- Is There a Crisis in Geophysical and Petrophysical Analysis?
- Applying an Analytical Approach
- What Are Analytics and Data Science?
- Meanwhile, Back in the Oil Industry
- How Do I Do Analytics and Data Science?
- What Are the Constituent Parts of an Upstream Data Science Team?
- A Data-Driven Study Timeline
- What Is Data Engineering?
- A Workflow for Getting Started
- Is It Induction or Deduction?
- References
- Chapter 2: Data-Driven Analytical Methods Used in E&P
- Introduction
- Spatial Datasets
- Temporal Datasets
- Soft Computing Techniques
- Data Mining Nomenclature
- Decision Trees
- Rules-Based Methods
- Regression
- Classification Tasks
- Ensemble Methodology
- Partial Least Squares
- Traditional Neural Networks: The Details
- Simple Neural Networks
- Random Forests
- Gradient Boosting
- Gradient Descent
- Factorized Machine Learning
- Evolutionary Computing and Genetic Algorithms
- Artificial Intelligence: Machine and Deep Learning
- References
- Chapter 3: Advanced Geophysical and Petrophysical Methodologies
- Introduction
- Advanced Geophysical Methodologies
- How Many Clusters?
- Case Study: North Sea Mature Reservoir Synopsis
- Case Study: Working with Passive Seismic Data
- Advanced Petrophysical Methodologies
- Well Logging and Petrophysical Data Types
- Data Collection and Data Quality
- What Does Well Logging Data Tell Us?
- Stratigraphic Information
- Integration with Stratigraphic Data
- Extracting Useful Information from Well Reports
- Integration with Other Well Information
- Integration with Other Technical Domains at the Well Level
- Fundamental Insights
- Feature Engineering in Well Logs
- Toward Machine Learning
- Use Cases
- Concluding Remarks
- References
- Chapter 4: Continuous Monitoring
- Introduction
- Continuous Monitoring in the Reservoir
- Machine Learning Techniques for Temporal Data
- Spatiotemporal Perspectives
- Time Series Analysis
- Advanced Time Series Prediction
- Production Gap Analysis
- Digital Signal Processing Theory
- Hydraulic Fracture Monitoring and Mapping
- Completions Evaluation
- Reservoir Monitoring: Real-Time Data Quality
- Distributed Acoustic Sensing
- Distributed Temperature Sensing
- Case Study: Time Series to Optimize Hydraulic Fracture Strategy
- Reservoir Characterization and Tukey Diagrams
- References
- Chapter 5: Seismic Reservoir Characterization
- Introduction
- Seismic Reservoir Characterization: Key Parameters
- Principal Component Analysis
- Self-Organizing Maps
- Modular Artificial Neural Networks
- Wavelet Analysis
- Wavelet Scalograms
- Spectral Decomposition
- First Arrivals
- Noise Suppression
- References
- Chapter 6: Seismic Attribute Analysis
- Introduction
- Types of Seismic Attributes
- Seismic Attribute Workflows
- SEMMA Process
- Seismic Facies Classification
- Seismic Facies Dataset
- Seismic Facies Study: Preprocessing
- Hierarchical Clustering
- k-means Clustering
- Self-Organizing Maps (SOMs)
- Normal Mixtures
- Latent Class Analysis
- Principal Component Analysis (PCA)
- Statistical Assessment
- References
- Chapter 7: Geostatistics: Integrating Seismic and Petrophysical Data
- Introduction
- Data Description
- Interpretation
- Estimation
- The Covariance and the Variogram
- Case Study: Spatially Predicted Model of Anisotropic Permeability
- What Is Anisotropy?
- Analysis with Surface Trend Removal
- Kriging and Co-kriging
- Geostatistical Inversion
- Geophysical Attribute: Acoustic Impedance
- Petrophysical Properties: Density and Lithology
- Knowledge Synthesis: Bayesian Maximum Entropy (BME)
- References
- Chapter 8: Artificial Intelligence: Machine and Deep Learning
- Introduction
- Data Management
- Machine Learning Methodologies
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Deep Learning Techniques
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
- Deep Neural Network Architectures
- Deep Forward Neural Network
- Convolutional Deep Neural Network
- Recurrent Deep Neural Network
- Stacked Denoising Autoencoder
- Seismic Feature Identification Workflow
- Efficient Pattern Recognition Approach
- Methods and Technologies: Decomposing Images into Patches
- Representing Patches with a Dictionary
- Stacked Autoencoder
- References
- Chapter 9: Case Studies: Deep Learning in E&P
- Introduction
- Reservoir Characterization
- Case Study: Seismic Profile Analysis
- Supervised and Unsupervised Experiments
- Unsupervised Results
- Case Study: Estimated Ultimate Recovery
- Deep Learning for Time Series Modeling
- Scaling Issues with Large Datasets
- Conclusions
- Case Study: Deep Learning Applied to Well Data
- Introduction
- Restricted Boltzmann Machines
- Mathematics
- Case Study: Geophysical Feature Extraction: Deep Neural Networks
- CDNN Layer Development
- Case Study: Well Log Data-Driven Evaluation for Petrophysical Insights
- Case Study: Functional Data Analysis in Reservoir Management
- References
- Glossary
- About the Authors
- Index
- EULA
