Energy output – and global demand for energy – continues to change dramatically on an international scale. In 2013, U.S. crude oil production rose to its highest level in a quarter century as shale drilling has pushed U.S. output to its fastest pace on record, according to the Energy Information Administration.
The oil boom has placed the U.S. on pace to become the world’s largest producer by 2015, a prospect that was unthinkable just a few years ago.
Meanwhile, global energy demand continues to shift. In 2010, China surpassed the U.S. as the world’s largest energy consumer. Over the next two decades, global demand for energy resources is expected to double while China’s energy needs are expected to grow fourfold, according to Chevron.
Looking ahead, stakeholders of all types – from energy executives to government leaders to business buyers – can use predictive analytics to better forecast energy demand as well as the availability of supplies over the short- and long-term.
For instance, despite a 61 percent increase in natural gas output from the Marcellus shale – in Pennsylvania, New York, Ohio and West Virginia – last year, some prognosticators have expressed concerns that the U.S. should limit exports of natural gas and hold onto its reserves for domestic use in coming years.
Government decision-makers and energy leaders can use predictive analytics to estimate the proved reserves for both crude oil and natural gas, which can then be combined with data regarding domestic and internal energy demand that can be used to help guide policy making and decision-making on exports.
Additionally, big data analytics is expected to improve grid performance and customer engagement, according to an article in Electric Light & Power. But that means energy companies have to revamp their analytics solutions to get the necessary insight from the massive amounts of data they collect from monitoring, control and transactional energy operations.
“Creating a comprehensive road map focused on high-return predictive analytics with clear destinations and achievable milestones is the starting point for better understanding customers, their behavior and their impact on grid operations,” the authors of the article note.
- Please join us on Thursday, October 16, 2014, at 12:00 p.m. EDT, for our complimentary webcast, “Increasing Recoverable Reserves: Leveraging the TERR Engine in Spotfire.” In this webcast, you will learn how Ruths.ai, a data science company, has helped a major Texas-based energy company to optimize and predict infill drilling performance in a mature asset by leveraging the TERR engine within Spotfire.
- Subscribe to our blog to stay up to date on the latest insights on big data and data analytics.