For oil and gas companies, building a foundation is critical for using analytics to gather insight from Big Data. This process took two to three years for Houston-based Noble Energy.
Using data to make decisions is a common practice at oil and gas companies. But the process that companies previously used – engineering and production accountants preparing weekly reports that went up to executives for decision-making, then down the chain for action – was a slow, intensive process, said Mike Maguire, global manager production analysis and optimization at Noble Energy.
What’s needed is the ability of data to execute decisions from repeatable, predictable things, so action happens without intervention, Maguire told attendees at the second annual Data Driven Production Conference USA 2016. As an industry, oil and gas companies are trying to move from tribal knowledge – decisions made based on worker knowledge and gut-feeling – towards this ability. The last piece is taking data and insight from people to make decisions.
“Now that we know there is data, we’re trying to get to the utilized as an asset phase,” said Maguire. “That is the prize we’re after.”
The trick is getting past the silos in companies in which datasets are held. One piece that industry is forgetting in terms of people processes and systems is the data component.
“When you have an app and it puts out bad information, IT gets blamed,” said Maguire.
But the problem is not the app; instead, it’s the data, or ‘garbage in, garbage out.’ Noble had 90 percent of the systems and things it needed; it just needed the data component to tie everything together.
Compiling data to make decisions takes an enormous amount of time. Reluctance to share data sometimes is an issue. But when data is shared, sometimes that data is scrubbed or interpreted to the point that it doesn’t resemble an actual event, said Maguire. While some might argue that ‘one system to rule them all’ is needed to house all of a company’s data, Maguire is not convinced that this is needed.
Instead, different departments can use their apps and groups to do their best-in-breed reporting on one side, then take the best parts of systems and start to integrate them together. Noble’s data scientists then chose a couple of systems that gave context to data, allowing things to be hooked together. Then, programs such as Spotfire were pulled in to analyze data. This process made it easier to get data to become more actionable.
Noble also has sought to continuously improve how it uses analytics and Big Data. This includes improving what people are doing on the front-end and improving system architecture and data sets, Maguire said. The company also has had to shift from a strategy in which IT commanded and controlled ownership of data. Letting go of that control – and greater access to data – is needed to enhance operational success. But this process requires a strategy.
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