Energy enterprises (specifically, oil and natural gas companies) are witnessing a monumental shift in the global economy. North America is ramping up production, which is raising a number of health, safety and environmental concerns among United States and Canadian citizens alike.
It's easy to view big data analytics as a cure-all for the challenges faced by the energy industry, but using the technology doesn't automatically solve those problems. As I've repeatedly said, data visualization merely provides finished intelligence to its users – people are responsible for finding out how to apply this newfound knowledge to their operations.
"The ultimate goal of the modern energy company is to optimize production efficiency."
What's the end? Affordability
If energy companies can find efficient methods of extracting and refining larger amounts of fossil fuels without increasing the amount of resources they use, economics would suggest the price of the oil and natural gas would decrease. Ultimately, affordability is dictated by supply and demand, but I digress.
From the perspectives of McKinsey & Company's Stefano Martinotti, Jim Nolten, and Jens Arne Steinsbø, the ultimate goal of the modern energy company is to optimize production efficiency without sacrificing residential health, worker safety and the environment. Based on McKinsey's research, which specifically scrutinized oil drilling operations in the North Sea (the water body located between Great Britain, Scandinavia and the Netherlands), the authors discovered that oil companies with high production efficiencies did not incur high costs. Instead, these enterprises made systematic changes to existing operations by:
- Eliminating equipment malfunctions
- Choosing assets based on quality and historic performance data
- Aligning personnel and properties with the market to plan and implement shutdowns
Analytics as an enabler of automation
The McKinsey authors maintained that automating operations was a key component to further improving existing oil drilling operations. This is where you get into the analytics applications and use cases associated with network-connected devices. Many of the North Sea's offshore oil extraction facilities are equipped with comprehensive data infrastructures composed of network assets, sensors and software.
The authors noted such platforms can possess as many as 40,000 data tags, not all of which are connected or used. The argument stands that if unused sensors and other technologies can be integrated into central operations to create a smart drilling facility, such a property could save between $220 million to $260 million annually. The possibilities and benefits go beyond the bottom line:
- Automation could extend the lifecycle of equipment that is slowly becoming antiquated
- New uses for under-allocated assets could be recognized
- Equipment assessments could be conducted by applications receiving data from radio-frequency identification tags, enabling predictive maintenance
"A smart drilling facility could save between $220 million to $260 million annually."
Resolving industry challenges
From a holistic standpoint, the oil and natural gas sector will use data analytics to effectively handle a number of industry challenges, some of which are opposed by internal or external forces.
One of the obvious challenges is the low tolerance people have for health, safety and environmental accidents. Think of how the BP oil spill of 2010 impacted consumer sentiments toward the energy industry. Technologies and processes associated with data analytics can resolve this issue by monitoring asset integrity, accurately anticipating when failures are about to occur and regularly scrutinizing how operations are affecting certain areas.
Generally, use cases expand as data scientists, operators and other professionals flex their creative muscles. There's no telling how analytics will be applied in the near future.