Fifteen years ago, while at the Society of Petroleum Engineers (SPE.org) conference, I was introduced to artificial intelligence (AI) tools specific for Oil and Gas use. I was very excited to learn more and build models to optimize production and understand its key influencers for example. I was certain data-driven insights were what this industry needed. What engineer wouldn’t want to use this?
To my surprise though, only a handful of engineers were ready to embrace the technology, and most said their organizations simply weren’t ready for it.
Fast forward to 2017. Data-Driven and AI analytics are reasonably commonplace among engineers. Tools are found in nearly every company – not just the major companies, but also in the independent players and ambitious smaller companies. How did this happen?
This is what happened: Time, technology and people changed.
A widespread of technology is usually a result of ease-of-use, reliability, and usefulness. One needs only look as far as Apple’s iPhone. Apple created an amazingly intuitive, reliable and useful phone, with an ever-growing market of applications.
With each new iteration, more and more people wanted an iPhone. Today not only is every citizen using a smartphone but they are entirely comfortable asking digital strangers named Siri, Alexa or Cortana for directions or to plan their daily activities.
Advancements in smartphones (and subsequent widespread adoption) raised the technological comfort level of the everyday user. Consequently, today’s oil and gas citizens easily embrace new technology and will take the time to experiment with different ideas and tools.
These same consumers are not afraid of change – they expect it now.
Statistical and AI based analytical tools were (and are) perfectly placed to succeed in Oil and Gas. Increased adoption was inevitable. But they are still not at the level I expected 15 years ago. Why?
What needs to happen in Oil & Gas next?
The problem is that while the market is ripe, oil and gas infrastructure and culture must catch up as well. More integrated and better quality data must seamlessly flow to analytical tools so an average company-citizen (and not IT) can easily explore any data, trust it and generate meaningful calculations or reports, faster, efficiently and more insightful than ever.
That vision translates to three actions:
- Prepare a data strategy, architecture, and governance that enable an analytical company. Few advancements in the MDM and Data Lake areas that will put you on a good pathway.
- More intuitive and easier to use analytical tools must infiltrate the organization, the way outlook or excel does. Take advantage of smart searches, NLP (Natural Language Processing), and machine learning to name a few.
- Create and encourage a culture that expects and enforces data-driven decisions across the entire company, for this you will need a clear vision and commitment from the leaders.
Until then, AI and Data Driven analyses remain in the hands of the chosen few ‘nerds’ – thanks to The Bing Bang Theory, being a ‘nerd’ is totally cool.
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