This article originally appeared in GulfBase.
Big Data analytics may be new to some industries, but oil and gas companies have dealt with huge quantities of data for decades in their quest to learn what lies below the surface. Seismic software, data visualisation and now a new generation of pervasive computing devices—sensors that collect and transmit data—continue to open new possibilities.
With these new tools and advanced analytic capabilities, oil and gas producers can capture more detailed data in real time at lower costs, which can help them improve oilfield and plant performance by 6% to 8%, according to research by Bain & Company.
Those numbers are typical across industries. Our recent survey of more than 400 executives in many sectors revealed that companies with better analytics capabilities were twice as likely to be in the top quartile of financial performance in their industry, five times more likely to make decisions faster than their peers and three times more likely to execute decisions as planned.
Our research also found that few companies are really ready to make the most of all this data: Only about 4% of companies across industries have the talent and skills they need to draw tangible business value from analytics. Although some oil and gas companies have invested in building up their capabilities, many struggle to get their arms around this powerful new opportunity.
Our conversations with senior executives suggest that they are keenly aware of the promise of advanced analytics, but their teams have difficulty realising the potential. Too often, companies delegate analytics to the IT department. But in practice, it belongs in the business, under the watchful eye of the CEO or another top executive, to make sure the effort delivers value to the business.
As executives assess the potential in analytics, the first question they should ask is, where can analytics deliver the most value for the company? We see opportunities in unconventional and conventional production, as well as in midstream operations. For example, good analytics can help producers collect and analyse data on subsurface and geographic characteristics to get a more detailed view of shale basins. In conventional production, producers can move beyond measurement into predictive tools with a range of pattern-recognition techniques that help them spot trends and drill with more predictable outcomes.
In midstream, data analytics can help monitor pipelines and equipment, enabling a more predictable and precise approach to maintenance.