Reducing Risk in the Petroleum Industry

Machine Data and Human Intelligence

Reducing Risk in the Petroleum Industry

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The oil and gas industry was one of the first aggregators of what we now call “big data,” but the amount of information these companies currently collect is truly unprecedented. In 1990, one square kilometer yielded 300 megabytes of seismic data; in 2015, it was 10 petabytes—33 million times more. This report features highlights from recent Strata+Hadoop World conferences to demonstrate how the petroleum industry uses data science in their operations today.

Oil companies use machine learning to mitigate short-term operational risk and to optimize long-term reservoir management. But, as author Naveen Viswanath explains, machine learning models alone can’t distinguish between good and bad data or reasonable and unreasonable results. Human intelligence—including a deep understanding of how data sources fit into business use cases—is crucial for making these distinctions.

With this report, you’ll learn the challenges these companies face when collecting a variety of data for seismic research, drilling, mechanical maintenance, worldwide logistics, and even gas station retail.

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Naveen  Viswanath

Naveen Viswanath

Naveen Viswanath has been solving problems in the hard disk drive industry since 2000. At the intersection of data, hardware engineering, and control software, he finds that the best challenges are interdisciplinary. Hailing from Chennai, India and living in Colorado, he loves mountains and the outdoors and is calmed by visits to the ocean. He finds ideas plentiful during cold morning dog walks.