Enhance Oil and 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 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.

Auteur

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.

Contenu
Foreword xv

Preface xxi

Acknowledgments xxiii

Chapter 1 Introduction to Data-Driven Concepts 1

Introduction 2

Current Approaches 2

Is There a Crisis in Geophysical and Petrophysical Analysis? 3

Applying an Analytical Approach 4

What Are Analytics and Data Science? 5

Meanwhile, Back in the Oil Industry 8

How Do I Do Analytics and Data Science? 10

What Are the Constituent Parts of an Upstream Data Science Team? 13

A Data-Driven Study Timeline 15

What Is Data Engineering? 18

A Workflow for Getting Started 19

Is It Induction or Deduction? 30

References 32

Chapter 2 Data-Driven Analytical Methods Used in E&P 34

Introduction 35

Spatial Datasets 36

Temporal Datasets 37

Soft Computing Techniques 39

Data Mining Nomenclature 40

Decision Trees 43

Rules-Based Methods 44

Regression 45

Classification Tasks 45

Ensemble Methodology 48

Partial Least Squares 50

Traditional Neural Networks: The Details 51

Simple Neural Networks 54

Random Forests 59

Gradient Boosting 60

Gradient Descent 60

Factorized Machine Learning 62

Evolutionary Computing and Genetic Algorithms 62

Artificial Intelligence: Machine and Deep Learning 64

References 65

Chapter 3 Advanced Geophysical and Petrophysical Methodologies 68

Introduction 69

Advanced Geophysical Methodologies 69

How Many Clusters? 70

Case Study: North Sea Mature Reservoir Synopsis 72

Case Study: Working with Passive Seismic Data 74

Advanced Petrophysical Methodologies 78

Well Logging and Petrophysical Data Types 78

Data Collection and Data Quality 82

What Does Well Logging Data Tell Us? 84

Stratigraphic Information 86

Integration with Stratigraphic Data 87

Extracting Useful Information from Well Reports 89

Integration with Other Well Information 90

Integration with Other Technical Domains at the Well Level 90

Fundamental Insights 92

Feature Engineering in Well Logs 95

Toward Machine Learning 98

Use Cases 98

Concluding Remarks 99

References 99

Chapter 4 Continuous Monitoring 102

Introduction 103

Continuous Monitoring in the Reservoir 104

Machine Learning Techniques for Temporal Data 105

Spatiotemporal Perspectives 106

Time Series Analysis 107

Advanced Time Series Prediction 108

Production Gap Analysis 112

Digital Signal Processing Theory 117

Hydraulic Fracture Monitoring and Mapping 117

Completions Evaluation 118

Reservoir Monitoring: Real-Time Data Quality 119

Distributed Acoustic Sensing 122

Distributed Temperature Sensing 123

Case Study: Time Series to Optimize Hydraulic Fracture Strategy 129

Reservoir Characterization and Tukey Diagrams 131

References 138

Chapter 5 Seismic Reservoir Characterization 140

Introduction 141

Seismic Reservoir Characterization: Key Parameters 141

Principal Component Analysis 146

Self-Organizing Maps 146

Modular Artificial Neural Networks 147

Wavelet Analysis 148

Wavelet Scalograms 157

Spectral Decomposition 159

First Arrivals 160

Noise Suppression 161

References 171

Chapter 6 Seismic Attribute Analysis 174

Introduction 175

Types of Seismic Attributes 176

Seismic Attribute Workflows 180

SEMMA Process 181

Seismic Facies Classification 183

Seismic Facies Dataset 188 <...