You could figuratively say that Tom Moroney and his technology team swim in big data. For the deepwater technology deployment manager of crude oil and natural gas company Shell Upstream Americas, big data analytics is a very important part of his job to find ways to make the most of the business’s deepwater assets in North and South America.
Take a look at these number:
- 20,000 instruments taking well and platform measurements
- 6,000 calculations performed for each measurement, with measurements taken continuously throughout the day
- 300,000 calculations made each day
- 430 million data points collected each day
His team has been using predictive analytics tools for two years now to see ahead of events, identify patterns and head off problems before they occur. However, in a recent interview with writer and editor Beth Schultz, Moroney said one of his key challenges is to convince operational managers to act on the data his team provides them.
He said engineers and operational managers are still reluctant to trust the models he provides. For instance, Moroney said, if they are told that there is a chance than in 10 to 20 hours an event in going to occur, managers are like to ask: “Is that a 100 per cent certainty of a 90 per cent certainty?”
He said he understands their caution because there are costs and potential risks in intervening. Intervention, Moroney said, could mean spending money on “increasing the amount of foamer sent downhole, but not knowing if it’s absolutely needed.”
There is also the fear of causing an unintended event.
Some managers, he said, would rather wait for something to happen before acting on it.
For Moroney and his team, patience is key when dealing with such situations.
There’s still a lot of learning needed for organizations to accept that insights provided statistics and analytics can create business benefits if they are acted on, he said.