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Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley & SAS Business Series include:

  1. Activity-Based Management for Financial Institutions: Driving Bottom-Line Results by Brent Bahnub
  2. Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian
  3. Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst
  4. Branded! How Retailers Engage Consumers with Social Media and Mobility by Bernie Brennan and Lori Schafer
  5. Business Analytics for Customer Intelligence by Gert Laursen
  6. Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund
  7. The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland
  8. Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron
  9. Business Intelligence in the Cloud: Strategic Implementation Guide by Michael S. Gendron
  10. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud
  11. CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel
  12. Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner
  13. Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors by Clark Abrahams and Mingyuan Zhang
  14. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi
  15. The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher
  16. Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs
  17. Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase
  18. Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain by Robert A. Davis
  19. The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow
  20. Economic and Business Forecasting: Analyzing and Interpreting Econometric Results by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard
  21. Executive’s Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose
  22. Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R. Abrahams and Mingyuan Zhang
  23. Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan
  24. Health Analytics: Gaining the Insights to Transform Health Care by Jason Burke
  25. Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World by Carlos Andre Reis Pinheiro and Fiona McNeill
  26. Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset by Gene Pease, Boyce Byerly, and Jac Fitz-enz
  27. Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education by Jamie McQuiggan and Armistead Sapp
  28. Information Revolution: Using the Information Evolution Model to Grow Your Business by Jim Davis, Gloria J. Miller, and Allan Russell
  29. Killer Analytics: Top 20 Metrics Missing from Your Balance Sheet by Mark Brown
  30. Manufacturing Best Practices: Optimizing Productivity and Product Quality by Bobby Hull
  31. Marketing Automation: Practical Steps to More Effective Direct Marketing by Jeff LeSueur
  32. Mastering Organizational Knowledge Flow: How to Make Knowledge Sharing Work by Frank Leistner
  33. The New Know: Innovation Powered by Analytics by Thornton May
  34. Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics by Gary Cokins
  35. Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance by Lawrence Maisel and Gary Cokins
  36. Retail Analytics: The Secret Weapon by Emmett Cox
  37. Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro
  38. Statistical Thinking: Improving Business Performance, Second Edition by Roger W. Hoerl and Ronald D. Snee
  39. Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics by Bill Franks
  40. Too Big to Ignore: The Business Case for Big Data by Phil Simon
  41. The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs
  42. Visual Six Sigma: Making Data Analysis Lean by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens, and Leo Wright
  43. Win with Advanced Business Analytics: Creating Business Value from Your Data by Jean Paul Isson and Jesse Harriott

For more information on any of the above titles, please visit www.wiley.com.

Harness Oil and Gas Big Data with Analytics

Optimize Exploration and Production with Data-Driven Models

Keith R. Holdaway

 

 

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I dedicate this book to my patient and loving family, Patricia, my wife, and my children, Elyse and Ian.

Preface

My motivation for writing this book comes from the cumulative issues I have witnessed over the past seven years that are now prevalent in the upstream oil and gas industry. The three most prominent issues are data management, quantifying uncertainty in the subsurface, and risk assessment around field engineering strategies. With the advent of the tsunami of data across the disparate engineering silos, it is evident that data-driven models offer incredible insight, turning raw Big Data into actionable knowledge. I see geoscientists piecemeal adopting analytical methodologies that incorporate soft computing techniques as they come to the inevitable conclusion that traditional deterministic and interpretive studies are no longer viable as monolithic approaches to garnering maximum value from Big Data across the Exploration and Production value chain.

No longer is the stochastic and nondeterministic perspective a professional hobby as the array of soft computing techniques gain credibility with the critical onset of technical papers detailing the use of data-driven and predictive models. The Society of Petroleum Engineers has witnessed an incredible release of papers at conferences globally that provide beneficial evidence of the application of neural networks, fuzzy logic, and genetic algorithms to the disciplines of reservoir modeling and simulation. As the old school retire from the petroleum industry and the new generation of geoscientists graduate with an advanced appreciation of statistics and soft computing methodologies, we shall evolve even greater application across the upstream. The age of the Digital Oilfield littered with intelligent wells generates a plethora of data that when mined surface hidden patterns to enhance the conventional studies. Marrying first principles with data-driven modeling is becoming more popular among earth scientists and engineers.

This book arrives at a very opportune time for the oil and gas industry as we face a data explosion. We have seen an increase in pre-stack analysis of 3D seismic data coupled with the derivation of multiple seismic attributes for reservoir characterization. With the advent of permanently in-place sensors on the ocean bed and in the multiple wells drilled in unconventional reservoirs across shale plays, coal seam gas, steam-assisted gravity drainage, and deep offshore assets, we are watching a proliferation of data-intensive activity.

Soft computing concepts incorporate heuristic information. What does that mean? We can adopt hybrid analytical workflows to address some of the most challenging upstream problems. Couple expert knowledge that is readily retiring from the petroleum industry with data-driven models that explore and predict events resulting in negative impacts on CAPEX and OPEX. Retain the many years of experience by developing a collaborative analytical center of excellence that incorporates soft skills and expertise with the most important asset in any oil and gas operation: data.

I would like to take this opportunity to thank all the contributors and reviewers of the manuscript, especially Horia Orenstein for his diligent expertise in predictive analytics and Moray Laing for his excellent feedback, expertise in drilling, and contribution with the pictures that illustrate many case studies. Stacey Hamilton of SAS Institute has been an encouraging and patient editor, without whom this book would never have been completed. I would like to acknowledge my colleagues in the industry who have given constructive feedback, especially Mike Pittman of Saudi Aramco, Mohammad Kurdi, David Dozoul and Sebastian Maurice of SAS Institute, ensuring the relevance and applicability of the contents.