Executive viewpoint

The modern smartphone is, for all intents and purposes, an inseparable part of our lives now. As of March 2017, Android commanded a larger market share of the global operating systems than did Windows. Hard to believe? Perhaps your child received his/her first smartphone before a laptop?

There are several drivers behind smartphones, and consumer technology in general, permeating into our lives. I will focus on two that relate specifically to the proliferation of artificial intelligence (AI) in consumer technology: decision intelligence and virality.

Decision intelligence. Google Maps provides a well-understood example of consumer intelligence. When you pull up directions in this service, it doesn’t just give you directions to your destination, the app gives you three alternative routes, each with a corresponding estimate of drive time. Roughly 90% of the time, one takes the fastest route, but 10% of the time, one may decide to take a different route. Google acknowledges that it doesn’t know whether the situation is a leisurely weekend drive or someone late for a meeting. So, it hands over the final decision to the consumer, but it has armed people with the decision intelligence, to make the right decision, with a few finger strokes.

AI is also providing novel ways of delivering business intelligence to the oil and gas industry. For example, a standard part of developing shale plays is examining public and private data sets, to see where nearby wells have been drilled, what direction the laterals were oriented, and how much proppant went into the frac job. This workflow may be called traditional data analytics for Business Intelligence 1.0; however, the latest AI tools deliver important enhancements and lower cycle times. They can generate production predictions on an individual well basis, with a customized decline curve based on the well location (latitude, longitude and true vertical depth), and the types and volumes of proppant and fluids utilized, aka Business Intelligence 2.0.

A reservoir or frac engineer no longer needs to spend hours-to-days looking for trends in Spotfire and guessing whether a certain dot that doesn’t fit a trend line is simply an outlier or an important insight that can save the company millions. Business Intelligence 2.0 tools deliver more accurate results, and in a fraction of the time. Like Google Maps, Business Intelligence 2.0 tools can deliver results within a few clicks, leaving an oil company to run design and cost sensitivities to find the right scenarios that match its drilling and production objectives.

Virality. There is a popular notion that going viral is a phenomenon that is hard to predict and when it happens, we don’t really know why. However, technology companies spend hundreds of millions of dollars on mastering the tools behind virality. How well it is embedded into the product delivery and user experience can mean the difference between becoming Facebook or the start-up you never heard of. Good examples of AI and virality include when LinkedIn sends an email about who is looking at your profile or when Facebook recommends friends with whom you should connect. Both venues foster connectivity between users and repeat visits to their platform, and both drive user growth.

In oil and gas, most C-suites work hard to promote communications and collaboration across business units. Two months ago, I was in a meeting with a major operator, where the completion engineer stated that the formation was geologically benign, so the frac design can be uniform. Then the drilling engineer stated that the well tortuosity might cause undulations out of zone. It is one thing to recognize the problem, but how can AI promote a virtuous cycle (or a viral loop) of information sharing and insight creation among and across teams?

The implications of virality, due to Business Intelligence 2.0 being utilized for production modeling, are significant. Using a common platform, business development professionals can work with GGRE and completions in one simple, intuitive web interface. In an acreage valuation exercise, assumptions about formation properties and frac design can be cross-referenced, to generate powerful predictions of decline curves that are customized for a myriad of subsurface scenarios.

Once the acreage is acquired, the development teams can utilize those A&D models to get a head start on where to drill the wells and find suppliers for the specific type of proppant to use for optimal production. As wells start to be delivered, the production teams can identify explicit sources of variance between that initial business development exercise and actual well performance. But modern-day digital initiatives are able to make the AI operational, so data-driven insights from one team can be captured digitally and subsequently leveraged by other teams, months to years afterward.

If you are a large public company, chances are that Wall Street is asking about your data strategy. If you are a private E&P, chances are that your private equity sponsors are asking the same—even the limited partners of private equity groups are increasingly asking these questions. The winners in this digital race are working hard in our industry to embed lessons learned from everyday consumer applications, be it consumer intelligence tools, or virality, or other forms of technology tradecraft. Oil companies that proactively foster such AI technologies, to fill in the cracks of daily operations, will separate themselves from their competitors and deliver more alpha for shareholders.