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Full Description
Use big data analytics to efficiently drive oil and gas exploration and production Harness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets.
The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages:
Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data
Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions
Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks
Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits.
Contents
Preface xi
Chapter 1 Fundamentals of Soft Computing 1
Current Landscape in Upstream Data Analysis 2
Evolution from Plato to Aristotle 9
Descriptive and Predictive Models 10
The SEMMA Process 13
High-Performance Analytics 14
Three Tenets of Upstream Data 18
Exploration and Production Value Propositions 20
Oilfield Analytics 22
I am a. . . 27
Notes 31
Chapter 2 Data Management 33
Exploration and Production Value Proposition 34
Data Management Platform 36
Array of Data Repositories 45
Structured Data and Unstructured Data 49
Extraction, Transformation, and Loading Processes 50
Big Data Big Analytics 52
Standard Data Sources 54
Case Study: Production Data Quality Control Framework 55
Best Practices 57
Notes 62
Chapter 3 Seismic Attribute Analysis 63
Exploration and Production Value Propositions 63
Time-Lapse Seismic Exploration 64
Seismic Attributes 65
Reservoir Characterization 68
Reservoir Management 69
Seismic Trace Analysis 69
Case Study: Reservoir Properties Defined by Seismic Attributes 90
Notes 106
Chapter 4 Reservoir Characterization and Simulation 107
Exploration and Production Value Propositions 108
Exploratory Data Analysis 111
Reservoir Characterization Cycle 114
Traditional Data Analysis 114
Reservoir Simulation Models 116
Case Studies 122
Notes 138
Chapter 5 Drilling and Completion Optimization 139
Exploration and Production Value Propositions 140
Workflow One: Mitigation of Nonproductive Time 142
Workflow Two: Drilling Parameter Optimization 151
Case Studies 154
Notes 173
Chapter 6 Reservoir Management 175
Exploration and Production Value Propositions 177
Digital Oilfield of the Future 179
Analytical Center of Excellence 185
Analytical Workflows: Best Practices 188
Case Studies 192
Notes 212
Chapter 7 Production Forecasting 213
Exploration and Production Value Propositions 214
Web-Based Decline Curve Analysis Solution 216
Unconventional Reserves Estimation 235
Case Study: Oil Production Prediction for Infill Well 237
Notes 242
Chapter 8 Production Optimization 243
Exploration and Production Value Propositions 245
Case Studies 246
Notes 273
Chapter 9 Exploratory and Predictive Data Analysis 275
Exploration and Production Value Propositions 276
EDA Components 278
EDA Statistical Graphs and Plots 284
Ensemble Segmentations 290
Data Visualization 292
Case Studies 296
Notes 308
Chapter 10 Big Data: Structured and Unstructured 309
Exploration and Production Value Propositions 312
Hybrid Expert and Data-Driven System 315
Case Studies 321
Multivariate Geostatistics 330
Big Data Workflows 332
Integration of Soft Computing Techniques 336
Notes 341
Glossary 343
About the Author 349
Index 351
