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Oil, Gas, and Data by Daniel Cowles

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Chapter 1. Oil, Gas, and Data

Introduction

When you hear “innovation in oil and gas,” your first thoughts might go to hardware—bigger, faster, deeper drilling; more powerful pumping equipment; and bigger transport—or to the “shale revolution”—unconventional wells, hydraulic fracturing, horizontal drilling, and other enhanced oil recovery (EOR) techniques. But, just like any other industry where optimization is important—and due to large capital investment and high cost of error, it’s perhaps even more important in oil and gas than in most other industries—the potential benefits of predictive analytics, data science, and machine learning, along with rapid increases in computer processing power and speed, greater and cheaper storage, and advances in digital imaging and processing, have driven innovation and created a rich and disruptive movement among oil and gas companies and their suppliers.

The truth is, the oil and gas industry has been dealing with large amounts of data longer than most, some even calling it the “original big data industry.”1,2 Large increases in the quantity, resolution, and frequency of seismic data, and advances in “Internet-of-Things"-like network-attached sensors, devices, and appliances, are being combined with large amounts of historical data—both digital and physical—to create one of the most complex data science problems out there, and a new industry is developing to help solve it.

In oil and gas more than in almost any other industry, efficiency ...

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