Three mistakes the oilfield keeps making about Big Data and what to do about them
I’ve often heard “the upstream industry has handled big data for ever. G&G data is huge, so we are the experts”. Even if the basic premise was correct (it’s not: lots of other industries work with larger datasets), size isn’t everything. My response used to be that we don’t have big data; we have lots of data that lives in silos which limit its value. That’s true, but it doesn’t mean much to a lot of people. Instead, I’ll offer up an analogy.
One of the greatest innovators of the industrial age, Henry Ford is renowned for having had the vision to give people what they didn’t know they wanted – he is famously quoted as saying that if he had asked, people would have told him they wanted a faster horse. As it turns out he didn’t say this, but he did something far more powerful.
His genius lay in the adaptation and integration of existing designs, tools, and processes to revolutionize automobile manufacture. He delivered on something that he actually did say; a vision of a mass-produced car for “the great multitude…constructed of the best material…so low in price that no man making a good salary will be unable to own one.”
Ford’s game-changing idea was not the improvement of the car itself, but that a good, reliable car at a fair price would sell like crazy – and the world was never the same again.
A successful big data story follows this same principle: integrate data, tools and processes to revolutionize business performance. The idea isn’t to just incrementally improve existing systems, but to explore, discover, and implement new ways to deliver value. This is where we make our first mistake.
1: Thinking small about big data
Forget about technology-focused initiatives to give faster answers to known questions. What are the major priorities of your organization? What kind of information is needed to support decisions to achieve those goals? What range of data is needed to create that information? The system and processes that make that data available should be flexible, connected, and extensible to answer the next set of questions that power your business.
It’s what Walmart did when they started to connect store transactions with demographics, weather patterns, trucking, vendor supply chains, and more – to enable them to optimize inventory and logistics, and beat everyone on price.
Closer to home, it’s what ConocoPhillips did when they decided to connect wellhead SCADA, transport logistics, equipment configs, maintenance planning, and more – to optimize Eagle Ford production and boost their balance sheet with a more accurate EUR.
In both of those cases, success demanded a focused effort to implement analytics at scale. It didn’t happen overnight, it wasn’t a single project, but a comprehensive program for which no single part of the organization was wholly responsible.
Accelerating existing processes may get you a faster horse, and that’s probably not a bad thing, but don’t be surprised if you’re beaten by a Ford. It may well require a cultural shift, which is always difficult. Which bring us to our next issue…
Mistake 2: It’s an IT thing. Just give me an easy button
Give me what I want with no effort on my part? I’m in! Buying from Staples may well be as easy as their marketing claims, and the appeal is undeniable. But nothing is ever that simple under the covers – especially making something “easy”.
Lots of technical systems are unnecessarily complicated, not just in terms of software, but in the wider process. Studies show that engineers spend over 75% of their time finding and preparing the data they need to do their jobs because systems are so disjointed. Naturally, this creates huge frustration.
Taking a big data approach can drastically reduce data sourcing time as well as adding analytic insight.
It may seem quicker to sidestep the “official” system and do your own thing on your desktop, or in the cloud, but a bunch of point solutions won’t fix the problem, and will never be an easy button. Oh…and free open source software? Make sure that you understand that we’re talking free speech, not free beer.
To build and maintain an analytic capability at scale is a significant undertaking that can only succeed if all parties are fully committed to its success and trust in their partners. This industry should be very comfortable with this kind of setup, because we see it in other areas of our operations.
Operators stopped doing their own drilling and completions long ago, choosing instead to focus their expertise on how to best develop assets and pay service companies to perform the work. To get their gear to the wellsite, service companies don’t build their own trucks; they extend a base platform from a specialist truck builder. And those trucks are assemblies of subsystems from numerous suppliers, each with their own expert knowledge of brakes, hydraulics, navigation systems, etc.