The first patent for Amazon Alexa was filed in 2012. Since then, there has been an incredible growth of skills to Amazon's voice assistant, and the credit for this growth goes to machine learning. Many of these skills include asking Alexa to tell you about your schedule, to play music, or update you on the daily weather.

We've also been using machine learning (or ML as some people reference it) for years. It's been a factor in our growth to help oil and gas producers on their digital transformation journey to autonomous operations using the exact solutions that will bring them more production, profits, and sustainability.

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In 2011, IBM’s Watson took on two of Jeopardy’s top winners in a contest of knowledge and speed. After three exciting days, Watson had more than tripled the winnings of either human. Let's apply that to oil and gas production data insights enabling you to triple your 'winnings' - by gaining more production, more profits and more sustainability.

In the digital oilfield that lives on data and is powered by computers light years past Watson, end users are starting to get answers to questions they never dreamed of asking before.

Instead of competing with computers, many oil and gas producers are teaming with AI (artificial intelligence) so that everyone wins. And keeps winning.

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The benefits of making oil and gas production autonomous are many. Production increases based on the ability to detect and remedy problems in seconds rather than hours is topmost, followed by safety, efficiencies when physical repairs are needed and more precision in PM schedules are among the others.

We’re not there yet—some would point out that the Oil Patch, due to its boom-and-bust nature, lags behind other industries in this area—but we’ve made big strides since the 2015-16 bust. Necessity being the mother of invention.

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There is a bit of magic in every childhood story we read. Whether it be the Chronicles of Narnia with its enchanted kingdom or Aladdin’s genie in the lamp, we learn very early that supernatural powers often come in pretty handy.

Maybe you could use some telepathic powers to know what is happening in a remote production location. You’ve probably wished to have psychic abilities to know when a wellsite will stop producing before it happens, or if your team is safe working on site.

Most certainly you’d enjoy the gift of telekinesis to restart an artificial lift.

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From 1859, when Colonel Edwin Drake successfully drilled America’s first oil well in Pennsylvania and over the next several decades, the oil and gas business was primarily driven by manual labor. 

This meant high vehicle mileage, and lost production time because pumpers and engineers were constantly needed on location, which resulted in operational inefficiencies and many safety issues. Until the introduction of automation technology into the oil and gas industry.

As automation technology has progressed, from cable tools to rotary drills; from car phones to palm-sized smart phones; from desktop computers for accounting to ubiquitous sensors sending big data through the cloud to smart devices; oilfield efficiency has continued to cut costs, save time, boost production and improve safety.

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For years now we’ve been able to monitor tank levels, compressors, pump operations and flow meters. Most of that is pretty cut-and-dried. The tank is at whatever level it’s at; the pump is either running or it’s not. An alarm tells us what we need to know the minute we need to know it.

But detecting a pipeline leak involves combining a number of data points and deciding which ones, or which combination of points, means there really is a leak. I’m stressing “really” because the industry first believed any anomaly needed to be alarmed because at that point an anomaly equaled leak, and we defiantly know the faster that leak could be stopped, and repaired; the more profits from your production ended up staying in your pocket.

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