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

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"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"--

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.

Foreword xv
Preface xxi
Acknowledgments xxiii
Chapter 1 Introduction to Data-Driven Concepts
1(33)
Introduction
2(13)
Current Approaches
2(1)
Is There a Crisis in Geophysical and Petrophysical Analysis?
3(1)
Applying an Analytical Approach
4(1)
What Are Analytics and Data Science?
5(3)
Meanwhile, Back in the Oil Industry
8(2)
How Do I Do Analytics and Data Science?
10(3)
What Are the Constituent Parts of an Upstream Data Science Team?
13(2)
A Data-Driven Study Timeline
15(15)
What Is Data Engineering?
18(1)
A Workflow for Getting Started
19(11)
Is It Induction or Deduction?
30(2)
References
32(2)
Chapter 2 Data-Driven Analytical Methods Used in E&P
34(34)
Introduction
35(4)
Spatial Datasets
36(1)
Temporal Datasets
37(2)
Soft Computing Techniques
39(5)
Data Mining Nomenclature
40(3)
Decision Trees
43(1)
Rules-Based Methods
44(20)
Regression
45(1)
Classification Tasks
45(3)
Ensemble Methodology
48(2)
Partial Least Squares
50(1)
Traditional Neural Networks: The Details
51(3)
Simple Neural Networks
54(5)
Random Forests
59(1)
Gradient Boosting
60(1)
Gradient Descent
60(2)
Factorized Machine Learning
62(1)
Evolutionary Computing and Genetic Algorithms
62(2)
Artificial Intelligence: Machine and Deep Learning
64(1)
References
65(3)
Chapter 3 Advanced Geophysical and Petrophysical Methodologies
68(34)
Introduction
69(1)
Advanced Geophysical Methodologies
69(9)
How Many Clusters?
70(2)
Case Study: North Sea Mature Reservoir Synopsis
72(2)
Case Study: Working with Passive Seismic Data
74(4)
Advanced Petrophysical Methodologies
78(21)
Well Logging and Petrophysical Data Types
78(4)
Data Collection and Data Quality
82(2)
What Does Well Logging Data Tell Us?
84(2)
Stratigraphic Information
86(1)
Integration with Stratigraphic Data
87(2)
Extracting Useful Information from Well Reports
89(1)
Integration with Other Well Information
90(1)
Integration with Other Technical Domains at the Well Level
90(2)
Fundamental Insights
92(3)
Feature Engineering in Well Logs
95(3)
Toward Machine Learning
98(1)
Use Cases
98(1)
Concluding Remarks
99(1)
References
99(3)
Chapter 4 Continuous Monitoring
102(38)
Introduction
103(1)
Continuous Monitoring in the Reservoir
104(1)
Machine Learning Techniques for Temporal Data
105(1)
Spatiotemporal Perspectives
106(1)
Time Series Analysis
107(1)
Advanced Time Series Prediction
108(9)
Production Gap Analysis
112(5)
Digital Signal Processing Theory
117(1)
Hydraulic Fracture Monitoring and Mapping
117(1)
Completions Evaluation
118(1)
Reservoir Monitoring: Real-Time Data Quality
119(3)
Distributed Acoustic Sensing
122(1)
Distributed Temperature Sensing
123(6)
Case Study: Time Series to Optimize Hydraulic Fracture Strategy
129(9)
Reservoir Characterization and Tukey Diagrams
131(7)
References
138(2)
Chapter 5 Seismic Reservoir Characterization
140(34)
Introduction
141(1)
Seismic Reservoir Characterization: Key Parameters
141(6)
Principal Component Analysis
146(1)
Self-Organizing Maps
146(1)
Modular Artificial Neural Networks
147(1)
Wavelet Analysis
148(11)
Wavelet Scalograms
157(2)
Spectral Decomposition
159(1)
First Arrivals
160(1)
Noise Suppression
161(10)
References
171(3)
Chapter 6 Seismic Attribute Analysis
174(32)
Introduction
175(1)
Types of Seismic Attributes
176(4)
Seismic Attribute Workflows
180(3)
SEMMA Process
181(2)
Seismic Facies Classification
183(21)
Seismic Fades Dataset
188(1)
Seismic Facies Study: Preprocessing
189(1)
Hierarchical Clustering
190(3)
k-means Clustering
193(1)
Self-Organizing Maps (SOMs)
194(1)
Normal Mixtures
195(1)
Latent Class Analysis
196(2)
Principal Component Analysis (PCA)
198(2)
Statistical Assessment
200(4)
References
204(2)
Chapter 7 Geostatistics: integrating Seismic and Petrophysical Data
206(34)
Introduction
207(4)
Data Description
208(2)
Interpretation
210(1)
Estimation
210(1)
The Covariance and the Variogram
211(3)
Case Study: Spatially Predicted Model of Anisotropic Permeability
214(10)
What Is Anisotropy?
214(1)
Analysis with Surface Trend Removal
215(9)
Kriging and Co-kriging
224(5)
Geostatistical Inversion
229(2)
Geophysical Attribute: Acoustic Impedance
230(1)
Petrophysical Properties: Density and Lithology
230(1)
Knowledge Synthesis: Bayesian Maximum Entropy (BME)
231(6)
References
237(3)
Chapter 8 Artificial Intelligence: Machine and Deep Learning
240(36)
Introduction
241(2)
Data Management
243(1)
Machine Learning Methodologies
243(4)
Supervised Learning
244(1)
Unsupervised Learning
245(1)
Semi-Supervised Learning
245(2)
Deep Learning Techniques
247(4)
Semi-Supervised Learning
249(1)
Supervised Learning
250(1)
Unsupervised Learning
250(1)
Deep Neural Network Architectures
251(17)
Deep Forward Neural Network
251(2)
Convolutional Deep Neural Network
253(7)
Recurrent Deep Neural Network
260(2)
Stacked Denoising Autoencoder
262(6)
Seismic Feature Identification Workflow
268(6)
Efficient Pattern Recognition Approach
268(2)
Methods and Technologies: Decomposing Images into Patches
270(1)
Representing Patches with a Dictionary
271(1)
Stacked Autoencoder
272(2)
References
274(2)
Chapter 9 Case Studies: Deep Learning in E&P
276(38)
Introduction
277(1)
Reservoir Characterization
277(3)
Case Study: Seismic Profile Analysis
280(8)
Supervised and Unsupervised Experiments
280(2)
Unsupervised Results
282(6)
Case Study: Estimated Ultimate Recovery
288(5)
Deep Learning for Time Series Modeling
289(3)
Scaling Issues with Large Datasets
292(1)
Conclusions
292(1)
Case Study: Deep Learning Applied to Well Data
293(5)
Introduction
293(1)
Restricted Boltzmann Machines
294(3)
Mathematics
297(1)
Case Study: Geophysical Feature Extraction: Deep Neural Networks
298(4)
CDNN Layer Development
299(3)
Case Study: Well Log Data-Driven Evaluation for Petrophysical Insights
302(4)
Case Study: Functional Data Analysis in Reservoir Management
306(6)
References
312(2)
Glossary 314(6)
About the Authors 320(3)
Index 323
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.
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