The oil/gas and utility industries create prodigious amounts of data each and every minute they produce their respective commodities, but where does it all go? What could and should the data do once it’s there, if there is anywhere?
The utility industry has its vision of a smart grid where devices up and down the generation-to-consumer value chain produce data that helps optimize every single utility operation, from the integration of renewable but variable generation sources into the must-run grid to customer service practices that align with regulators’ understanding of electricity as an essential commodity to every living person.
The oil/gas industry has its vision of a smart field where devices down the hole in far-flung places connect to digitalized hubs for exploration and production decision making. But unconventional resources requiring new extraction techniques push that vision to the edge, challenging the industry to deliver expensive extraction operations at lowest possible costs.
But vision is vision, and putting rubber to the road is a different matter.
Questions of performance and progress can be measured in many different ways, but what are they and who is defining them? If the vision works in one place, or one industry, would it work in another? What tools create performance and progress for the smart grid -- and for the smart oilfield? How do companies and the general public define success for pursuit of these visions?
If these questions sound broad, they are intended to. The oil/gas and utility industries are immense, and the diversity of their operations mind-boggling. The role of analytics is shaped differently in each, to say the least. Broad, open-ended questions best suit not only the application of analytics to those industries, but also discussion in blogs like this.
But here are some general statements borne out of my quick observations.
The utility industry has hyped the smart grid heavily over the last six years, but in its earliest days only envisioned collecting and connecting data. No participants really talked about how they'd accomplish that connectivity or the best-practices for doing something with the data. The industry has evolved and it recognizes not only the value of connecting the dots of data through analysis but also its primary importance to the future of the smart grid.
Look at the EnergyCentral list of Webcast events today and notice the focus on analytics, analytics, analytics: "Demystifying Utility Analytics -- What it's all about, where it's going and how to get started," "Customer Analytics Issues, Trends, & Drivers," "Smart Grid Analytics: All that Remains to be Ready is You." A SAS survey conducted last year confirms the utility industry's belief that analytics can improve network reliability, quality, and efficiency and integrate renewable generation onto the grid. The pump is primed in the utility industry, so to say, and I'll explore that progress in this blog.
But is the pump similarly primed in the oil and gas industry? Google "smart oilfield" and you'll see no real sense of momentum for that term. Yes, the digital oilfield is robust, but is anybody really defining a smart oilfield? Check the event's page for Oil & Gas Journal, the industry bible. See anything that's "analytics" or "smart-oilfield" oriented? Is that industry behind in its acceptance of analytics as the next great tool for progress? Maybe so, maybe not. Chevron is busy at work at the University of Southern California Center For Energy Informatics developing a smart-oilfield concept. Is there a similar university-connected smart grid effort in the utility industry? I’ll discuss these sorts of similarities and differences as well in this blog.
Regardless of their packaging, I'll also look at the implementation of analytics in both industries. No doubt I'll see similarities and differences in their implementations and adoption of analytics. Who knows, maybe one can learn from the other? The utility industry is already immensely interested in the use of analytics in the financial services sector to help it with fraud detection. What might the oil/gas industry learn from the manufacturing community about the use of analytics for predictive asset management?
Stick with this blog and we'll drive through these broad questions -- with broad observations. The nature of analytics itself requires us to approach it this way, and we will.