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From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell\u2019s AI priorities and initiatives.<\/p>\n
Current initiatives include deploying\u00a0reinforcement learning\u00a0in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues.<\/p>\n
Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day. It has also installed computer vision-enabled cameras at service stations, which are capable of detecting customers lighting cigarettes \u2013 a severe hazard.<\/p>\n
During the data strategy development, I worked with Daniel Jeavons, Shell’s general manager for data science. Jeavons talked to me about Shell’s\u00a0 AI-first strategy and said “What it means in practice is that we as a data science team are in a great position because we can make our current business more effective, more efficient, more reliable, safer \u2013 by applying AI into those settings.<\/p>\n
“But we can also play a role in creating some of the new business models that we want to create, and that’s really exciting because we’re playing our part in taking Shell into the next generation of energy sources, new fuels, and new sources of revenue.”<\/p>\n
Precision Drilling<\/strong><\/p>\n Shell is involved in the entire oil and gas supply chain \u2013 from mining raw hydrocarbons from the earth to refining them into fuel and various other products, to retailing them to businesses and individuals. AI is being rolled out or trialed at each step of this process. Recent developments include the adoption of\u00a0reinforcement learning\u00a0\u2013 a form of \u201csemi-supervised\u201d\u00a0machine learning, to control its drilling equipment.<\/p>\n While machine learning can work with either labeled data (supervised learning) or unlabelled data (unsupervised learning), reinforcement learning takes a middle-ground approach by incorporating a reward system, dependent on the outcome of the AI’s “choices.”<\/p>\n As Jeavons says, \u201cThe key thing is you\u2019re giving the [AI] agent the autonomy to make the decision. But you\u2019re providing input into the model, so you\u2019re providing reward or penalty functions on the basis of what\u2019s happening in the model, and how the model responds to the set of conditions that you give it.\u201d<\/p>\n Algorithms designed to guide the drills as they move through a subsurface are trained on historical data from Shell\u2019s 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.<\/p>\n The result is that a Shell geosteerer \u2013 the human operator of the drilling machine \u2013 is able to understand the environment more accurately they are operating in, leading to faster results and less wear, tear and damage to machinery.<\/p>\n In many ways the challenge was similar to those faced by developers working on self-driving cars \u2013 only instead of navigating hazards a vehicle might encounter on the road, the drilling machinery must autonomously adapt to changing conditions under the ground.<\/p>\n Jeavons says \u201cWe talk a lot about augmented intelligence, and the reason is that this isn\u2019t about removing people from the operation \u2026 what we\u2019re trying to do is help the people who make the decisions to make those decisions with additional support from the intelligence that we\u2019ve created.<\/p>\n \u201cWhat we expect is that this will probably never fully replace geosteering as a discipline, but it will allow a single geosteerer to support many more wells.\u201d<\/p>\n Charging efficiency<\/strong><\/p>\n Encouraging motorists to switch to an electric vehicle is seen as key to reducing the Co2 emissions caused by humanity, and limiting their effect on climate change. But it involves something of a chicken-and-egg problem. Motorists are put off making the switch due to a lack of public charging terminals, and forecourt operators may be slow to adopt them due to a lack of demand.<\/p>\n Shell\u2019s answer to this problem involves deploying AI to monitor and predict the demand for terminals throughout the day, enabling power to be supplied more efficiently.<\/p>\n \u201cIf you think about it,\u201d says Jeavons, “as a grid operator you’re operating many, many electric charging posts \u2026 if all the cars plug in at the same time and automatically start charging, you create a big load on the grid t, by the way, can’t be filled by solar, because it’s 7 am or 8 am in the morning.”<\/p>\n \u201cSo, what we can do by understanding people\u2019s charge profiles is we can spread the load during the day, which basically means we can save the consumer money.<\/p>\n \u201cBut also, more renewables are used \u2013 because if you can charge more people at lunchtime, there\u2019s going to be more solar on the grid at that point.<\/p>\n \u201cIt\u2019s an example of where we see the role of artificial intelligence playing a key part \u2013 thinking about not just how we can make things more efficient, but also how we can change energy consumption patterns to take more advantage of renewable sources.\u201d<\/p>\n