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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:

  • Analytics: The Agile Way by Phil Simon
  • Analytics in a Big Data World: The Essential Guide to Data Science and Its Applications by Bart Baesens
  • A Practical Guide to Analytics for Governments: Using Big Data for Good by Marie Lowman
  • Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian
  • Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst
  • Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs
  • Business Analytics for Customer Intelligence by Gert Laursen
  • Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron
  • Business Intelligence and the Cloud: Strategic Implementation Guide by Michael S. Gendron
  • Business Transformation: A Roadmap for Maximizing Organizational Insights by Aiman Zeid
  • Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner
  • Data‐Driven Healthcare: How Analytics and BI are Transforming the Industry by Laura Madsen
  • Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs
  • Demand‐Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase
  • Demand‐Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain by Robert A. Davis
  • Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments by Gene Pease, Barbara Beresford, and Lew Walker
  • The Executive's Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow
  • Economic and Business Forecasting: Analyzing and Interpreting Econometric Results by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard
  • Economic Modeling in the Post–Great Recession Era: Incomplete Data, Imperfect Markets by John Silvia, Azhar Iqbal, and Sarah Watt House
  • Enhance Oil and Gas Exploration with Data‐Driven Geophysical and Petrophysical Models by Keith Holdaway and Duncan Irving
  • Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan
  • Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data‐Driven Models by Keith Holdaway
  • Health Analytics: Gaining the Insights to Transform Health Care by Jason Burke
  • Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World by Carlos Andre Reis Pinheiro and Fiona McNeill
  • Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset by Gene Pease, Boyce Byerly, and Jac Fitz‐enz
  • Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education by Jamie McQuiggan and Armistead Sapp
  • Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, Second Edition by Naeem Siddiqi
  • Killer Analytics: Top 20 Metrics Missing from Your Balance Sheet by Mark Brown
  • Machine Learning for Marketers: Hold the Math by Jim Sterne
  • On‐Camera Coach: Tools and Techniques for Business Professionals in a Video‐Driven World by Karin Reed
  • Predictive Analytics for Human Resources by Jac Fitz‐enz and John Mattox II
  • Predictive Business Analytics: Forward‐Looking Capabilities to Improve Business Performance by Lawrence Maisel and Gary Cokins
  • Profit Driven Business Analytics: A Practitioner's Guide to Transforming Big Data into Added Value by Wouter Verbeke, Cristian Bravo, and Bart Baesens
  • Retail Analytics: The Secret Weapon by Emmett Cox
  • Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro
  • Statistical Thinking: Improving Business Performance, Second Edition by Roger W. Hoerl and Ronald D. Snee
  • Strategies in Biomedical Data Science: Driving Force for Innovation by Jay Etchings
  • Style and Statistics: The Art of Retail Analytics by Brittany Bullard
  • Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics by Bill Franks
  • Too Big to Ignore: The Business Case for Big Data by Phil Simon
  • The Analytic Hospitality Executive by Kelly A. McGuire
  • The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs
  • The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions by Phil Simon
  • Using Big Data Analytics: Turning Big Data into Big Money by Jared Dean
  • 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

Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models



Keith R. Holdaway
Duncan H. B. Irving






Wiley Logo

Keith Holdaway: To my patient and loving family, Patricia, my wife, and my children, Elyse and Ian.

Duncan Irving: To Sarah, my wife, and my children, Alfred, Edwin, and Ingrid, who have had to put up with less daddy‐time than normal during this creation. Sorry, and thank you!


I vividly remember the first time I met Keith Holdaway. It was 14 years ago, and he was standing in the front row of an analytics conference. He cut a distinctive profile as he challenged the speaker at the podium, asserting quite stubbornly that the oil and gas industry could realize huge returns by using a more data‐driven approach that exploited the full potential of analytics. As a young man (or so I thought of myself at the time), I had been tasked with selling analytical software to upstream oil and gas companies. Coming from a technology background, I realized that this gentleman was the guide I was looking for and made a mental note to seek him out at the cocktail hour.

Back then, in 1989, the digital oilfield was the topic of the day, promising impressive returns. As the industry embraced the concept more fully over the next decade, I observed companies making significant investments in specific data solutions to automate and solve a broad range of problems. Thought leaders eagerly embraced the application of data‐driven analytics, but the adoption was not necessarily as widespread as one would have thought. Scattershot adoption created its issues, with companies sometimes running hundreds of disparate applications and ending up with silos of data across their organizations. The promise remained.

Fast forward to 2014 and Keith's first book, Harness Oil and Gas Big Data with Analytics, which arrived just before crude plunged to historic lows. In retrospect his book seems almost prescient as the industry's enthusiasm for data‐driven analytics has been driven in part by the potential to generate greater value from its assets in the face of a much lower price per barrel. Many of the leading players—and several influential thought leaders among smaller oil companies—have made substantial investments in this area, and there is more to come. Increasingly, I am contacted by clients looking for data scientists, asking for training, and seeking guidance on how best to implement advanced analytics programs. We often point them to Keith's book, among other resources at SAS and elsewhere, to help them validate the best path forward.

Hence the genesis of this new book. Interest in his first book has been consistent enough that colleagues implored Keith to write a second volume: a more particular text that digs deeper into applying data‐driven approaches across the exploration sector. Keith and his colleague, Dr. Duncan Irving, have written an invaluable book, exploring the data‐driven methodologies in the disciplines of geophysics and petrophysics. And the timing is right. We are witnessing an unprecedented convergence of big data and cloud technology with massive increases in computing power at a time when a climate of low prices has made driving efficiencies an absolute requirement. Add to that the influx of technology‐attuned Millennials into the workforce, and oil and gas practitioners are on the verge of a new era of opportunity to transform their business.

I have no doubt that this volume will be a valuable addition to the growing body of resources focused on this exciting area. Over years of working at the nexus of energy and technology, Keith has become a mentor and friend. His colleague is a globally recognized geophysicist working in the field of data analytics and brings innovative ideas to the evolving science of data‐driven and soft‐computing technologies. This new and important book is the result of years of deep work in this area and a real passion for the topic, approached with the same determination I saw at the front of that conference room many years ago. I am honored to introduce this book: Enhance Oil and Gas Exploration with Data‐Driven Geophysical and Petrophysical Models.

Ross Graham,
Director, O&G Americas
Calgary, June 2017


The oilfield is one of the most data‐rich industries in the world, and concerning real information (as opposed to virtual data generated by the web and other virtual environments) can lay claim to the most data intensive industry. Most organizations, if they are honest with themselves, rarely capitalize on the potential of analytics and ‘big data.’ The authors of this book address the most common pitfalls that beset analytics and provide a comprehensive framework and roadmap, from the exploration and production perspective, to achieve the real goal of analytics—simplifying, expediting, or making possible the translation of data into profitable and sustainable outcomes.

To unleash the power of analytics, one must first understand what they are and are not. Analytics are data‐centric processes that, if designed and executed properly, will lead to insights and outcomes. Each aspect of the process must receive due diligence, and the focus of the endeavor should always be to add value to the organization.


The most common mistake when understanding analytics is to confuse the sizzle with the steak—that is to conflate the perception of a thing with the substance of the thing. Many managers and even technical professionals accept the misconception that analytics is the collation and visualization of data using colorful charts and graphs. This is not only incorrect, but there is a tacit danger in this assumption because it can significantly limit future analytic endeavors that do not, per se, yield an attractive visual. It must be understood, therefore, that dashboards and reports are one of many results of analytics and, while they are the most visible, they may not be the most valuable.


Analytics are multi‐step processes which transform data from one or more sources into information which leads to changes in actions and behaviors; and, if an organization is unwilling to do either, investment in analytics should be reconsidered. This book, more than any other before it, details a simple, yet robust, approach to developing an analytics plan that will lead to success. Though analytics methodologies vary depending on query most processes should contain at least the following:

  • Data Modeling. Analytics planning should ensure, within practical limits, that necessary and sufficient data are identified beforehand.
  • Data Gathering with a focus on quality. Identification and management of adverse data are often far more resource intensive and problematic than data that is missing. Acquiring real data often involves rigorous technical and contract specifications that include detailed definitions of data properties.
  • Data Management—how data will be transferred, stored, secured, transformed, and distributed.
  • Analysis—Understanding which analytical methods are most appropriate based on types of data and questions asked as well as the speed and accuracy of the desired results.
  • Communication—Determining the most efficient and influential modes in which to communicate data to those who should, or could, consume it—whether it is formal reports, presentations, email, social media, audiovisual, or combination of these and other forms.
  • Management of Change. Perhaps the most important, yet sadly overlooked, part of an analytics project involves: identifying, before work begins, who all relevant stakeholder (or customers) are, clearly documenting their needs, and agreeing in advance on if, or how, changes to process might occur based on the results of analyses.

Nathan Zenero
Verion Applied Technologies


Our motivation for writing this book comes from the professional curiosity and experience we have accumulated over recent years in the Oil and Gas industry. We have noted and continue to witness the struggles between geoscientists and their multiple spatial and temporal datasets. Traditional interpretation can provide certain answers based on Newtonian physics and the fundamental laws of nature, but with so much data being amassed with sensors in this digital age, it is necessary to marry deterministic interpretation with data‐driven workflows and soft‐computing models.

Owing to the cyclical nature of the Oil and Gas industry, we have seen historically depressed crude prices since 2015. This last downturn, like previous historical downturns, shook the industry to the point of an overreaction: people losing their livelihoods, reduction in OPEX, and cancellation of projects, particularly in exploration. It is at these transition points that oil and gas companies seek more efficient work processes and best practices. This invariably results in the adoption of technologies not necessarily new in other industries. Today we see more adoption of soft‐computing and data‐driven analytics to complement the traditional interpretation.

Given these cyclical‐downturn scenarios, we ask ourselves, being in the trough of a current downturn: What's happening in the Oil and Gas industry today?

We are aware of the dramatic drop in crude oil prices that is a driver behind the industry's march toward adopting new technologies such as analytical and soft‐computing workflows. Oil and gas companies realize the climb from the bottom of the cycle is a slow process and has many global and local influences. Too much supply and weak global demand play into a dynamic scenario.

Oil and gas companies are currently contemplating serious near‐term investments to develop global assets, but it behooves the industry to move gingerly. We shall witness an inexorably slow increase in oil prices, with global supply bound by the reduction in reserve development projects over the past few years.

Many talented engineers have left the industry, and the internal organizational vagaries, coupled with inflexible and complex systems, processes, and attitudes could put the breaks on any innovative and evolving methodologies and best practices. IOCs and NOCs are looking seriously at a digitization environment using advanced analytics for the new daily workflows. Service companies, analytics vendors, and in‐house capabilities are emerging to address these needs. This will enable oil and gas companies to weather current and future industry downturns.

We see this book as a contribution to enabling upstream geoscientists in data‐driven analytics in geophysics and petrophysics. We hope it serves to bring together the practitioners of conventional upstream computing workflows with the new breed of data scientist and analyst and generate overlap and common ground so they can understand each other's perspectives, approaches, and role in this new computing landscape.


We would like to acknowledge and thank all the contributors to and reviewers of the manuscript, especially Dan Whealing of PGS for running his expert eye across the seismic data portions of the book. Stacey Hamilton of SAS Institute has been an encouraging and patient editor without whom this book would never have been completed. We would like to acknowledge our colleagues in the industry who have given constructive feedback, especially Kathy Ball of Devon Energy and Steve Purves of Euclidity, for ensuring the relevance and applicability of the contents. We wish to recognize the research by Dr. Alexander Kolovos for a section of Chapter 7 (“Knowledge Synthesis”) and by Vipin P. Gupta, Dr. E. Masoudi (Petronas), and Satyajit Dwivedi (SAS Institute) for a section of Chapter 4 (“Production Gap Analysis”).