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Oil and gas companies have been dealing with large amounts of data much longer than most industries, and some energy analysts even refer to it as the "original big data industry." Now, with massive increases of seismic data, advances in network-attached devices, and a vast quantity of historical data on paper, the oil and gas space also presents one of today's most complex data science problems.
As this O'Reilly report reveals, the industry is working to add machine learning and predictive analytics in all phases of its exploration, production, refinement, and delivery operations. But it's still in the early adoption phase. While oil and gas has embraced the "digital oilfield" concept, it's a cautious IT culture, with many companies waiting to see what others do first.
In this report, you'll learn how:
- Big data solutions from other industries can't be easily applied to oil and gas
- Much innovation is in the discovery and exploration phase, where risk and uncertainty are high
- Outside companies such as Hortonworks, SparkBeyond, and WellWiki are making a difference
- Oil companies now run some of the largest private supercomputing facilities in the world
- Security tools such as rapid detection are important to an industry with memories of the Stuxnet worm and Shamoon virus
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