The Big Challenges of Big Data for Oil, Gas

The Big Challenges of Big Data for Oil, Gas

Rigzone Looks Back: The industry’s need to better understand subsurface has driven oil and gas companies to collect a greater volume of data overall, more types of data, and is gathering data more frequently and at higher speeds. As a result, oil and gas companies are grappling with how to leverage Big Data to improve their business strategies.

Now that the oil and gas industry has addressed the challenge of storing Big Data – the collection of data sets so large and complex that processing it with traditional data applications is difficult – oil and gas companies are seeking how to leverage Big Data to improve their business strategies.

The desire to collect more data for informed decision-making is driving the industry’s need to address Big Data. The oil and gas industry’s need to better understand subsurface has driven oil and gas companies to collect more data, more types of data, more quickly and at higher frequencies, said Dale Sperrazza, director of marketing at Landmark Software & Services, a division of Halliburton, in an interview with Rigzone.

“Geoscientists in particular are always looking for more data – consequently, the amount of data being gathered in the well planning and drilling process has skyrocketed,” said Sperrazza.

The Big Challenges of Big Data for Oil, Gas


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Karen Boman has more than 10 years of experience covering the upstream oil and gas sector. Email Karen at


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Thomas Speidel | Mar. 5, 2014
I enjoyed reading the article since it closely aligns to how I have envisioned analytics can help oil and gas companies. However, its a myth that the more the data the closer one gets to some objective measure. It is not the size of data that matters. Most data isnt big and, frankly, does not need to be. "Risk" or "probability of success" are not a function of the size of data. In fact, notwithstanding regulatory and compliance obligations, too much information present many challenges, not just to store and manage, but also to analyze (in statistics we call it the curse or large numbers). Oil and gas is capital intensive and risk prone. Hence, organizations in this industry ought to be cautious before applying the learning, methods and strategies of large internet based organizations. To a company like Google, serving the wrong ad to a user has a non-consequential irrelevant cost. To an oil and gas the cost of improper decision making can have a severe negative impact on profit, safety or productivity. What we need is just the right amount of data and that may mean throwing away some data (do we really need temperature data by the minute? Do we really think it is going to improve our state of knowledge?). We also need to approach problem first, rather than data first, which for some bizarre reason, appears to have been the modus operandi. We also need to rethink how we are going to use data. Simply finding ways to do more of the same, that is, overwhelm users with even more data is hardly going to create any meaningful change. And creating even more summary metrics will not help us understand an outcome. I echo the author when she writes that we need predictive tools to look forward. We also need explanatory models to better help us understand the various contributors to a problem and if and how we can control those. All of this is nothing new, by the way. We have been using them successfully for many decades in other fields. It has just taken that long for organizations to catch up.

James Baird | Dec. 31, 2013
This is exciting article especially about storing big data in order to meet audit/ and regulatory needs.


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