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.”
The search for new hydrocarbon deposits demands a huge amount of materials, manpower, and logistics. With drilling a deepwater oil well often costing over $100 million, no one wants to be looking in the wrong place. To avoid this issue, Shell uses fiber optic cables (created in a special partnership with Hewlett-Packard for these sensors), and the data is then transferred to its private servers, maintained by Amazon Web Services (AWS). This gives a far more accurate idea to engineers of what lies beneath and saves a considerable amount of time and effort.
New oil drilling locations and new ways to stimulate shale oil are only some of the benefits of applying big-data analytics in the oil industry. Seismic software, data visualization, and pervasive computing devices are some of the modern analytical tools that are currently being adopted by the oil firms.
Oil drilling is a continuous process and machines have to work for long hours under severe temperatures and conditions. Big data is used to ensure that machines are working properly and are not damaged due to breakdowns or failures. Machines are fitted with sensors that collect data about its performance. This data is then compared to the aggregated data ensuring that parts are replaced in an efficient manner and downtime is minimized, further reducing additional expenses.
In a recent survey by Accenture and Microsoft, oil companies and those in the support industries established that 86% to 90% of respondents said that an increase in their analytic capabilities, use of mobile technologies in the field and banking more on Industrial Internet of Things would increase the value of their business. According to the survey, over the next 3-5 years, investment in big data and automation are expected to increase from 56% to 61% and 53% to 65%, respectively. Finding and producing more hydrocarbons, at lower costs in economically sound and environmentally friendly ways can not only add value to the data but also helps in accurate decision-making.
The popularity of big data across various industries has gained momentum with increasing amount of data awareness. Good analytics help managers become more proficient in managing different kinds of data. Such variety of data may include seismic, drilling logs, operational parameters such as drill bit RPMs and weight on bit, frack performance data and production rates. With each function producing vast and variable data, the right data needs to go to the right hands, so as to optimize performance.
Just like other industries, the oil and gas industry needs to understand how big data can be optimally used and what applications are possible. Since not all data is valuable, knowledge of storing relevant information becomes important. As the equation in global oil supply and demand shifts, more and more statistics needs to be mapped. Locations and techniques also need restructuring on a frequent basis and this requires professionals who not only know how to use big data efficiently but also can draw value out of it. The challenge also is about efficiency in the data process (sifting out the important from what is not).
The hired experts need to know when a technology upgrade is required since the oil and gas industry is based on ever-fluctuating demand and supply. They should understand open-source models, cloud technologies, pervasive computing and iterative development methodologies. Shell has about 70 people working full-time in the data analysis department along with hundreds more spread over the world participating on an ad hoc basis.
The gradual transition towards big data implementation may not be easy for many oil companies since many lack the manpower and capabilities for hiring the required personnel that can handle big data. Only about 4% of companies across industries have the talent and skills they need to draw tangible business value from analytics.
Personal and cyber security also need attention since this remains a perceived barrier in realizing the value of big data analytics. Big data real-time analytics surely presents innovative opportunities to establish more efficient oil production, cost and risk reduction, safety improvement, more regulatory compliance and better decision-making. Good expertise and strategic prudence while using big data tools, will not only ensure success but also reduce the margin of error.