There’s no shortage of fields and industries impacted by artificial intelligence, it being 2019 and all. We’ve detailed huge advancements in medicine, image recognition and editing, communications, job searching, and on and on, but one field we maybe didn’t expect to see such huge advancements so early was a seemingly analog industry — oil & gas exploration.
Now, there’s no doubt the stakes and industry impact of oil & gas exploration means the industry-leading companies have deployed technology in lots of both advanced and novel ways to help in that exploration and extraction (think complex geological modeling for exploration, etc.). But the way Shell has integrated AI methodologies and technologies into every step of their sourcing and retailing lifecycle is pretty extraordinary.
One of Shell’s primary cost drivers is the process of extraction, so any reduction in operating costs there would mean huge increases in bottom line results because Shell’s gas business is a huge profit center. In a recent article for Forbes, Bernard Marr, who helped Shell implement many of their AI systems and processes, wrote about how Shell was using AI to improve drilling and extraction:
Algorithms designed to guide the drills as they move through a subsurface are trained on historical data from Shell’s drilling records, as well as information gathered from simulated exploration. It covers mechanical information from the drill bit, such as temperature and pressures, as well as data on the subsurface from seismic surveys.
The result is that a Shell geosteerer – the human operator of the drilling machine – is able to understand the environment more accurately they are operating in, leading to faster results and less wear, tear and damage to machinery.
According to Shell’s Daniel Jeavons, Shell’s general manager for data science, introducing AI into the drilling process isn’t about removing humans from the equation, but rather to arm them with better, more real-time information that’s actionable; this will empower one geosteerer to oversee more wells simultaneously while reducing both mistakes and costly damage, wear and tear to the drills and associated machinery.
Shell is also testing AI applications on the polar opposite end of the supply chain at their gas stations. A major risk factor at fill stations are motorists smoking while refueling their vehicles. Shell is testing another initiative in “Singapore and Thailand involv[ing] the use of computer vision at service station forecourts. Computer vision – cameras which can “think” and understand what they are filming – are trained to watch out for the potential hazard of customers lighting cigarettes in the vicinity of pumps and refueling vehicles.”
Instead of requiring the cameras to send their data and conclusions to the cloud and waiting for feedback, this type of AI is defined by ‘edge processing’ in which the calculation and action is made locally to that AI system instead of relying on a huge neural network elsewhere. This has huge implications not just for motorist safety, but also the ability for real-world, edge-processing AI systems across retail and enterprise.
Finally, according to Marr, “while it currently focuses on spotting smokers, in the future the technology could also be trained to detect other hazards such as reckless driving, criminal damage or theft.”
Within the next 3-5 years, it’s hard to imagine an industry that won’t be impacted by artificial intelligence in some way. Looking at ‘old-school’ industries like oil & gas exploration proves exactly that — businesses that want to remain competitive and gain a competitive advantage over their peers are first movers in the AI space.
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