Petroleum Big Data (SAS)

ISBN-13: 9781118779316 / Angielski / Twarda / 2014 / 384 str.

Keith Holdaway

Opis

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.

Spis treści

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

O autorze

KEITH R. HOLDAWAY is Principal Industry Consultant and Principal Solutions Architect at SAS, where he helps drive implementation of innovative oil and gas solutions and products. He also develops business opportunities for the SAS global oil and gas business unit that align SAS advanced analytics from Exploratory Data Analysis and predictive models to subsurface reservoir characterization and drilling/production optimization in conventional and unconventional fields. Prior to joining SAS, Holdaway was a senior geophysicist with Shell Oil, where he conducted seismic processing and interpretation and determined seismic attributes in 3D cubes for soft computing statistical data mining.

Blurb

Praise for Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data–Driven Models

This is a time of great change in the oil and gas industry, and even before embracing real time systems, we were struggling to extract meaningful insights from a large accumulation of data that was right under our noses. Based on his years of global experience, Keith has developed a deep technical understanding of these issues, and in his book Harness Oil and Gas Big Data with Analytics, he combines this understanding with his unique talent for communicating complex issues in a straightforward manner. Reading this book you will begin to see your data as the insight it was intended to be, and not the burden it has become.
Dennis Seemann, Supervisor, Reservoir Management Analytical Division, Saudi Aramco

Keith Holdaway has written an important and timely book addressing the significance of data–driven analytics in our industry. Keith is highly knowledgeable in this area and has a strong command of the important aspects of this subject. This book is a testimony to his dedication to, and depth of understanding of, data–driven analytics as they relate to the exploration and production industry. This is a must–read for anyone interested in this subject.
Shahab D. Mohaghegh, PhD, CEO, Intelligent Solutions, Inc.; Professor of Petroleum & Natural Gas Engineering, West Virginia University

EXPLOIT NEW EFFICIENCIES THAT BIG DATA ANALYTICS CAN BRING TO OIL AND GAS EXPLORATION AND PRODUCTION

From an expert in the field of oil and gas data analytics comes Harness Oil and Gas Big Data with Analytics, 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.

With a unique focus on applying big data analytics to the oil and gas industry, this book provides a roadmap for leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact–based management to drive decision making in oil and gas operations. Starting out with a complete overview of data analysis and oilfield analytics, this resource hits all the high points of big data analytics best practices and challenges before delving into the specifics of oil and gas exploration.

Featuring ten chapters of in–depth information, readers will get a full view of the most important issues for oil and gas data analytics, including seismic attribute analysis, reservoir characterization and simulation, drilling optimization, reservoir management, and production forecasting and optimization. For oil and gas engineers and IT professionals working in the field, this is the resource for making the most of data to forge efficiencies and increase profits from the processes of exploration and production.