Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models

Name: Enhance Oil and Gas Exploration with Data-Driven Geophysical and Petrophysical Models
Brand: Wiley
Price: 73.99 EUR
Availability: OnlineOnly

Keith R. Holdaway Duncan H. B. Irving(Autor*in)

Wiley (Verlag)

Erschienen am 28. September 2017

368 Seiten

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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.

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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

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