In 2006, marketing commentator Michael Palmer had blogged, “Data is just like crude. It’s valuable, but if unrefined it cannot really be used.”
After nine years, the statement still holds true across any industry that depends on large volumes of data. It is true that until and unless, data is not broken down into pieces and analyzed, it holds little value.
As the world becomes more receptive to the advantages of big data, the oil industry does not seem to be far behind. If the huge amount of data is just stored, then it has little worth and so, for it to be useful, it has to be identified, aggregated, stored, analyzed and perfected. The ability to access and draw rich insights from large datasets can make the oil industry more profitable and efficient. A successful oil company will quickly forecast the potential information and keep costs low to actualize its success without losing any discrepancy in the evaluation of the dataset.
Both oil movements and popularity of big data have gradually created a stir over a period of time. Changes in supply and demand of oil have long been related to fluctuations in oil prices. With falling oil prices, oil and gas industry is slowly finding its way towards big data, in order to manage and reduce risk, thereby increasing the overall revenue of a company. Oil prices globally are becoming competitive and as oil-producing economies fight for gaining global market share in oil, big data analytics can help them in identifying areas that require significant improvement.
According to Mark P. Mills, a senior fellow at the Manhattan Institute, “Bringing analytics to bear on the complexities of shale geology, geophysics, stimulation, and operations to optimize the production process would potentially double the number of effective stages, thereby doubling output per well and cutting the cost of oil in half.”
A tech-driven oil field is already expected to tap into 125 billion barrels of oil and this trend may affect the 20,000 companies that are associated with the oil business. Hence, in order to gain competitive advantage, almost all of them will require data analytics to integrate technology throughout the oil and gas lifecycle.
Data volume in the oil industry grows with rapid speed and handling a large amount of data efficiently becomes very important. Oil companies have always been generating extreme volumes of data at a very high rate on a daily basis. Traditionally, large volumes of data can be very expensive for both oil and gas producers. Such huge costs can significantly impact the financial performance of the company.
With the use of big data, companies can not only cut costs but also capture large data in real time. Such use of analytics can help in improving production by 6%-8%. However, the role of big data in the industry of oil and gas goes beyond efficiency and analyzing large volumes of data in real time. Near-real-time visualization, storage of large data sets and near real-time alerts are considered the most important advantages in big data analytics.
Geographically speaking, layers of rocks vary across regions, even though they may be similar structurally. Lessons usually learned from one area can be applied to similar areas. Traditionally, unstructured data is stored in different databases or any storage facility, which requires a lot of time and effort. Data science can help in reducing risk and help in learning more about each subsystem thereby increasing the accuracy in decision-making.
Since oil depends on drilling and oil field exploration, any use of big data analytics in this field is considered a boon. Miller writes, “Big-data analytics can already optimize the subsurface mapping of the best drilling locations; indicate how and where to steer the drill bit; determine, section by section, the best way to stimulate the shale; and ensure precise truck and rail operations.
