Holdaway Harness Oil and Gas Big Data with Analytics

Optimize Exploration and Production with Data Driven Models
1. Auflage 2014
ISBN: 978-1-118-91089-4
Verlag: John Wiley & Sons
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 384 Seiten, E-Book

Reihe: SAS Institute Inc

Use big data analytics to efficiently drive oil and gasexploration and production
Harness Oil and Gas Big Data with Analytics provides acomplete view of big data and analytics techniques as they areapplied to the oil and gas industry. Including a compendium ofspecific case studies, the book underscores the acute need foroptimization in the oil and gas exploration and production stagesand shows how data analytics can provide such optimization. Thisspans exploration, development, production and rejuvenation of oiland gas assets.
The book serves as a guide for fully leveraging data,statistical, and quantitative analysis, exploratory and predictivemodeling, and fact-based management to drive decision making in oiland gas operations. This comprehensive resource delves into thethree major issues that face the oil and gas industry during theexploration and production stages:
* Data management, including storing massive quantities of datain a manner conducive to analysis and effectively retrieving,backing up, and purging data
* Quantification of uncertainty, including a look at thestatistical and data analytics methods for making predictions anddetermining the certainty of those predictions
* Risk assessment, including predictive analysis of thelikelihood that known risks are realized and how to properly dealwith unknown risks
Covering the major issues facing the oil and gas industry in theexploration and production stages, Harness Big Data withAnalytics reveals how to model big data to realize efficienciesand business benefits.

Autoren/Hrsg.

Holdaway, Keith

Holdaway, Keith R.

Inhaltsverzeichnis

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

Über Autor(innen)

KEITH R. HOLDAWAY is Principal Industry Consultant andPrincipal Solutions Architect at SAS, where he helps driveimplementation of innovative oil and gas solutions and products. Healso develops business opportunities for the SAS global oil and gasbusiness unit that align SAS advanced analytics from ExploratoryData Analysis and predictive models to subsurface reservoircharacterization and drilling/production optimization inconventional and unconventional fields. Prior to joining SAS,Holdaway was a senior geophysicist with Shell Oil, where heconducted seismic processing and interpretation and determinedseismic attributes in 3D cubes for soft computing statistical datamining.