Enhance oil & gas exploration with data-driven geophysical and petrophysical models
"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 dra...
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| Main Authors | , |
|---|---|
| Format | Electronic eBook |
| Language | English |
| Published |
Hoboken, New Jersey :
Wiley,
[2018]
|
| Subjects | |
| Online Access | Full text |
| ISBN | 9781119302599 1119302595 9781119394228 1119394228 9781523115549 1523115548 9781119302582 1119302587 9781119215103 1119215102 |
| Physical Description | 1 online resource |
Cover
Table of Contents:
- 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.