Transforming Big Data into Actionable Intelligence using a Cloud Based Platform
By Charles Karren, Senior Director, Oil and Gas Industry Strategy, Oracle
The oil and gas industry is again facing one of the most challenging periods in its history (and one that it has seen before in the mid-1980s as well as the late- 1990s)—a new low oil price environment. As oil and gas companies work yet again to operate in this volatile period, operators are looking for ways to cut immediate costs rather than focusing on long-term ROI.
“E&P operators can improve performance by applying modern analytic techniques to legacy data allowing companies to operate more effectively, cut costs and operate safely”
Oil and gas companies have always invested in data, from seismic analysis, drilling and operating information. And these companies are the original big data users. As a leading oil and gas chief executive officer said:
“When you think about an oil and gas company, most people think about the molecule of gas or the oil or gasoline. But if you think about what we really do, we process information. It's just as simple as that. We never touch the molecule of our gas. It's in some pipeline or some tank someplace. What we're doing is taking a bunch of information and using that information to make money; and it happens to be in the oil and gas business”
This data allows the industry to produce 90 million barrels of oil a day in a safe and cost effective manner.
But with this low-oil price environment— there needs to be a paradigm shift by oil and gas executives in uncovering additional hidden knowledge of their companies. According to Bain, only about 4 percent of companies have the capabilities to use advanced data analytics to deliver tangible business value.
With oil prices at this level for the foreseeable future, advanced predictive analytics using
The challenge of big data
Oil and gas companies need to be more nimble and confident in their decision-making cycles as they engage in new ventures, generating new value from a tsunami of data. With the rise of pervasive computing devices—affordable sensors that collect and transmit data—E&P producers are now capturing more detailed data in real time at lower costs and from previously inaccessible areas than ever before.
But not all data is equal and not all analytics require big data solutions. Still, advanced analytics can play an important role in improving productivity in unconventional, deep water and traditional conventional operations in oil and gas.
Predictive analytics in unconventional
A leading super-independent pursuing a significant increase in shale activity was having big data issues as it worked to manage information from 43,000 wells. The issues impacted its ability to effectively deploy and manage personnel in the field. It needed a timelier and more accurate way of managing and updating personal assignments for oil and gas wells, leases and territories to improve analysis and decision-making.
To solve these big data issues, this super-independent deployed a Predictive Analytics solution that allowed it to build teams independently of geopolitical boundaries. Specifically, the solution:
•Delivered metrics and the ability to mine data for needed answer
•Improved the ability to look back at history to assess changes in production, and use that information to explain current performance and plan for the future
•Implemented version controlled data to help explain questions like, “Did that swing in production happen because an asset manager is responsible for more or fewer properties than he did last month?” or “Did that swing in production occur because his wells are not producing at the same level as before?”
But where a predictive analytics software solution really comes in handy is in the weeks or months before an issue—(a major one as there are no minor ones) arises or a question needs to be asked where it has never been asked before. For instance, a Predictive Analytics solution can pick up patterns through modeling and advanced diagnostics of equipment. If there are any abnormalities based collection of disparate data, then an operator can plan downtime to minimize lost productivity, at which point they can make a minor repair rather than replace an absolutely decimated piece of equipment.
E&P operators can improve performance by applying modern analytic techniques to legacy data allowing companies to operate more effectively, cut costs and operate safely. These predictive tools with a range of pattern-recognition techniques can help companies spot trends, intervene early and create repeatable solutions with predictable outcomes.