From machine learning to computer vision, deep learning to virtual assistants and autonomous vehicles to robotics, Shell has been focused on a range of technologies that have supported advances in AI.
Businesses that harness new data sources and use AI and machine-learning technology to provide insights will be in a strong position to shape future commercial development and influence how society changes. Many of the algorithms behind AI and machine-learning systems are not new but limited volumes of accessible data have hampered their application. The recent explosion in data volumes and availability has led to a step change in the training of algorithms and provided important new insights: easy access to vast data volumes is making AI algorithms smarter.
Examples of AI delivering value in Shell
Shell scales AI with the Microsoft DevOps platform
Title: Shell scales AI with the Microsoft DevOps platform
Duration: 02:15 minutes
By adopting DevOps practices and the Microsoft DevOps platform, with Azure and GitHub, Shell is able to scale through rapid growth in their data science network, while changing their development cycle from months to weeks.
Rhythmic medium-tempo music swells
[Voice of Yuri Sebregts]
In the energy industry we have a vast amount of data, from exploration to how we engage with our customers.
Timelapse footage of an oil rig at sea at sunset
Front view of a large ship sailing on the sea, large waves crashing over the front.
Two male Shell workers on-site wearing red jumpsuits, hard hats and gloves
Close-up of mobile phone in a man’s hand with a map of Rotterdam on the screen. The phone is in search mode.
Close-up of a computer tablet, scrolling up through a collage of Shell images.
Side view of a Shell worker on-site wearing a hard hat and glasses.
Two work colleagues in an office in front of a number of computer screens showing lots of different data.
Aerial view of an oil rig out at sea
Timelapse of a petrol forecourt. Lots of Shell V-Power branding – flags and signs
Close up of the forecourt sign with the fuel prices.
[Disclaimer text at the bottom of the video footage]
The views expressed in this video are a testimonial based on current experience, not an endorsement. Others may not have the same experience/results.
Chief Technology Officer
Historically, many of those data went unused.
[Voice of Yuri Sebregts]
Now machine learning and digital technologies make it possible to unlock those insights and transform our business. Driving common ways of working across the enterprise is essential for us to get to the full scale impact.
Two work colleagues viewing video footage of part of an oil rig on a large screen, with computer generated squares pin pointing certain areas.
Close-up of power buttons. Some of the green ‘on’ and red ‘off’ lights are switched on
Close-up head shot of a Shell worker looking at his computer screen with a colourful 3D graph visible.
Medium head shot of Yuri Sebregts talking to camera
Three work colleagues in a white office having a meeting in front of laptops. Camera moves to a panning close-up shot of the workers
DevOps as a methodology allows us to bring teams together and to move quickly and generate tremendous value particularly through the energy transition as we move from molecules to electrons.
Four colleagues in an office having a meeting, view changes to a close-up of one of the colleagues, then to another colleague presenting next to a large screen that’s being screen shared and is showing computer code.
Close-up from behind of someone wearing a ‘Shell.ai Making Energy Smarter’ red T-shirt
Vice President – IT Engineering
Medium head shot of Kayoor Gajarawala talking to camera. Sitting in an office by a window
A fuel nozzle being inserted into a vehicle
A charger being inserted into an electric car. Computer graphics overlay the video footage
[Voice of Daniel Jeavons]
We’ve been on an exponential growth curve. We have a data science network of around 2,000 people.
Woman walking down a corridor and walks into the ‘Digital Facility’. Camera pans up to the sign above the door
Medium head shot of Daniel Jeavons talking to camera, sitting in an office.
General Manager – Data Science
[Voice of Daniel Jeavons]
With that sort of scale, we need to standardize the way we work, and that’s why DevOps is so important.
View from behind a helicopter on a helipad on an oil rig out at sea. Sunset in the background.
Close-up of a driver at a steering wheel while stationary, using a computer tablet.
Two Shell colleagues wearing hard hats and glasses using a computer tablet and a mobile phone
Four colleagues having a meeting in an office
Three colleagues having a meeting in a white office all sitting in front of laptops
[Voice of Daniel Jeavons]
So as we’ve been trying to standardize AI across the enterprise and the way in which we develop new solutions, we’ve been working with Microsoft to leverage products like Azure Boards, Azure Pipelines, GitHub Enterprise, in order to ensure that our engineers and our data scientists are working consistently, that they can share code and they can deploy them to the edge and the cloud easily. That’s changed our development cycles from months to weeks.
Man in orange jumpsuit sitting in front of a number of computer screens and panels with computer graphics overlaying the video footage
Another shot of colleagues in front of rows of computer screens with computer graphics overlaying the video footage showing bar graphs and pie charts
Three colleagues in an office with two laptops, computer graphic of a map overlaps one of the laptops
Close up head on view of one of the three colleagues looking at the laptop and with more computer graphics of maps, graphs and statistics overlaying the video.
3D image on a tablet. A man’s finger changes the view by moving his finger across the screen.
Close up view of a tablet being held by a gloved worker wearing a high visibility jacket. Computer graphic lines overlaying the video
Daniel Jeavons presenting a meeting, pointing to information on a large screen being screen shared from a laptop.
Close-up of information on the computer screen relating to Azure DevOps
Colleagues having meetings in a large office with lots of light
Colleagues sitting at their desks in an open plan office. Camera moves to a close-up side view of one of the female colleagues working at a computer screen.
Close-ups of some male colleagues
Close-up of a computer screen with computer code, screen scrolls down
Close-up of a cursor on a webpage selecting ‘deploy’ button
Close-up of someone using a mobile phone to set up charging for an electric vehicle.
Close-up of behind the mobile phone and computer graphics appear on the left of the screen of a spinning car and graphs.
[Voice of Laura Foulquier]
Having GitHub Enterprise integrated with Azure Boards and Azure Pipelines makes it really simple for any collaborator on the project to follow the life-cycle of the code, knowing where it’s at, where it has been deployed, and how successfully.
Footage of Laura Foulquier in an open plan office sat at a computer screen working. Camera pans to a view behind her head with a close up of the computer screens and then changes position again to end on a close-up of her face.
Close-up of two colleagues chatting by a computer screen, pointing at the screen.
Medium head shot of Laura Foulquier talking to camera, sitting in an open plan office.
Close-up of fingers typing on a computer keyboard
Close-up of two colleagues faces looking at a computer screen
[Voice of Daniel Jeavons]
Containerization has been very much part of this journey, and we are also using Kubernetes to allow us to deploy that at scale.
Colleagues in an open plan office chatting by a desk in front of computer screens
Side close-up view of a man’s eyes behind a pair of glasses.
View of a worker from behind his computer screens and then a side view of him in a meeting.
[Voice of Daniel Jeavons]
We’ve been working with Microsoft in developing the Kubeflow framework on Azure. We see huge innovation in the open source frameworks.
Close-up of a selection of webpages from ‘Kubeflow’. Computer curser moves around the pages clicking on different buttons
[Voice of Daniel Jeavons]
We’re trying to leverage those communities that often gravitate around GitHub and bring that into Shell to allow us to continually innovate.
Daniel Jeavons presenting a meeting about Kubeflow – screen sharing the website from a laptop onto a large screen on the wall.
A group of colleagues in a meeting room with a load of laptops and large computer screens on the back wall. Footage moves to a close-up of one of the colleagues in the meeting.
[Voice of Kayoor Gajarawala]
Support and training for new technologies is really important. I think Microsoft has offered a very good way for our developers to get up to speed and practically apply what they’ve learned
View of a woman’s hands typing on a computer keyboard
Same woman talks to a man who is at another desk, he then points to his monitor, woman joins him looking at his screen up close.
Still shot of Microsoft Azsoft Azure Academy Webpage with a picture of a man at a whiteboard. Shot then scrolls down through the webpage showing the curriculum.
This space is always changing.
Medium shot of Daniel Jeavons talking to camera
[Voice of Daniel Jeavons]
Working together with Microsoft has been absolutely critical and I look forward to that continuing.
Two men walking down a corridor, shot following one man into an office walking past people at desks.
Man and a woman walking through the lobby of a shell building walking towards the steps.
Close up shot of a woman's face wearing 3D eyewear
Two women are wearing similar 3D eyewear looking a screen of colourful imagery
Close up shot of woman in a hard hat and protective eyewear looking into the distance
Shell and Microsoft working together
Microsoft logo and the Shell logo next to each other on a white background, logos separated by a thin black line.
Frame changes to the Microsoft Azure logo on a white background
Then the Azure word disappears and leaves just the Microsoft logo on a white background
Rhythmic up-beat music swells and ends.
Title: Shell RechargePlus
Duration: 1:14 minutes
Powering EV Growth
Interview with Angie Boakes
E Mobility Manager
The future shift to electric vehicles is pretty clear.
Shell is launching our RechargePlus solution, and it’s all about really how you optimize charging for their electric vehicle.
Interview with Phillip Villagomez
Business Development Electric Mobility
Our optimization algorithm is pretty smart, that it moves the charge to a time when solar or wind are at their highest and prices are at their lowest, therefore helping the environment and the driver save money as well.
And what we need them to do is basically use our app to therefore tell us what time they need to depart and roughly how many miles or how much energy they need by the end of the day, and then we will do the rest.
Interview with Dana Madrid
RechargePlus has given me the freedom and the peace of mind to park, plug in…
And I’m charged, and I don’t have to come back out.
We are helping people save money. Not only the drivers, but also the site host and building owners.
If you have a bank of ten chargers, you don’t want ten drivers plugging in at the same time. This causes expensive demand charges and also puts a strain on the grid.
We’re helping them to switch to zero emission vehicles, helping the grid to have more renewables on the system. It’s more, cleaner energy.
Title: Predictive Maintenance
Duration: 1:39 minutes
Shell staff talk about predictive maintenance as a way to leverage technologies to forecast issues and predict failures before they arise. Unable to find an off-the-shelf solution, they built one themselves and now have 24/7 coverage. The model is now being replicated in other facilities and offers good insight, as well as allowing engineers more time to do engineering work instead of analysing mountains of data.
Interview with Sankesh Sundareshwar
Senior Process Engineer, Shearwater
Predictive maintenance is leveraging the technologies we have today to forecast issues before they can arise.
Interview with Arnold Hes
Instrument Engineering Team Lead, Pernis
We have to keep our plants running and our biggest problem is when the plants trip due to equipment failures.
If we can predict that, then we can prevent these failures and save the company a lot of money.
Interview with Claudia Zuluaga Giraldo
Principal Digital Product Manager
The more stable operations, the more energy efficiency we are able to obtain, the less emissions we generate.
We looked outside quite extensively if anyone else had a product that we could just take off the shelf and we couldn’t find anything that useful to us, so we decided to build it.
Interview with Kenneth Innes
Before we developed these systems, our people spent a lot of time analysing mountains of data which was very labour intensive.
Shot of Kenneth standing by a row of monitors listening to a male colleague sat down talking. Close-up of Kenneth nodding. Close-up of Neil Forbes speaking to off-screen interviewer at an interior location.
Interview with Neil Forbes
Operations Support, Shearwater
Instead of that being reactive, we’re now proactively going out fixing issues before they become a bigger problem.
The big benefits of this programme is 24/7 coverage.
It looks at about 300 system parameters all the time. Human beings can’t do this kind of thing.
Now we’ve got to think globally and think about replication and replication at pace.
[Arnold Hes Voiceover]
We were able to use and replicate that model in our facilities and gave us a good insight in how our valves are behaving.
[Sankesh Sundareshwar Voiceover]
This technology is going to make us better engineers because we have more time to do engineering work to prevent systems going offline.
Interview with Sander Buhling
Instrument Project Engineer, Pernis
I think the major change, it’s not really the prediction; it’s the scale-able part of this project.
You build a solution for 16 valves; it has the potential to increase for all valves within Shell.
Why not, yes, use it on everything we have?
LNG Shipping Accelerator
Title: LNG Shipping Digitalisation case study
Duration: 1:13 minutes
A case study showing how Shell is using digitalisation to improve the efficiency and lower the emissions of its’ fleet of LNG tankers.
Interview with Paul McStay
LNG Fleet Performance Manager
Shell has an ambition to drive down its carbon footprint. And within shipping and maritime, our vision is very much aligned with that.
Interview with Samantha Lehel
LNG Commercial Freight Operator
The LNG Shipping accelerator essentially consolidates loads of information in one place.
So that instead of having to consult various different resources for each one of my vessels, I can see everything immediately.
The real value is, sort of, the time savings for myself as an operator, but also the level of accuracy and efficiency that that affords our business.
What this allows us to do is see data live and in real time and therefore be able to proactively intervene when performance starts to drop.
Interview with Grahaeme Henderson
VP Shipping & Maritime
If we can improve our efficiency, we can reduce the amount of time that we’re waiting at ports…
Then we can reduce our fuel usage. And by reducing our fuel usage, we can improve our emissions.
This project allows us to understand where our footprint is and reduce that by setting appropriate benchmarks, and therefore reductions into the future, using data as a means to enable insights that will allow the business to deliver value by driving our vessels more efficiently and enabling us to deliver more molecules of LNG to our customers.
What’s happening at Shell is pretty amazing. They have a very deliberate strategy of using AI, right across their operation… from the drilling operations to safety in… Shell Retail stations.
The new possibilities in working with data over the last few years are unlocking amazing opportunities in all aspects of what we do in the company. In one example, we can now forecast in many cases when a compressor is at risk of failure 24 or 48 hours in advance, which was not easy to do before despite all the instrumentation you have on these large and complex machines.
Shell has “the largest AI deployment that we are aware of anywhere in the world. Everybody else is kind of looking at it. These guys are rolling it out."
“The scale of the Shell.ai initiative is extremely impressive – one of the largest we have been a part of… We are excited at the opportunity that this gives us to help to tackle some of the world’s toughest energy challenges.”
Shell is an integrated energy company going through a significant transition as the world’s need for more and cleaner energy solutions require significant changes in the way in which energy provided. Digital technology, including AI, is a significant enabler for this transformation, from more efficient exploration and production, more reliable manufacturing, more nimble trading, and a more intimate customer experience and new digital solutions for emerging areas such as power and hydrogen. Embedding AI in every part of our organization is crucial—from making our existing businesses more effective, efficient and make us competitive.
Anyone can develop a small-scale proof of concept with a machine learning model that meets a specific local requirement. But, to make an impact and maintain it at scale, we need to develop solutions which can be deployed globally at a rapid pace.
To enable AI at scale across Shell’s businesses, we are standardising approaches and aligning on common data structures, platforms, tools and ways of working. This allows us to share best practice, code and work seamlessly in cross discipline global teams.
Shell.ai is a change programme was formed to drive a common approach to data science platform technology, develop consistent ways of working, and build a community of practice which can demonstrates the art of the possible, and shares best practices across the entire business.
- We have a community (Shell.ai community) of over 5000 people engaged the Shell.ai programme. We have over 160 AI projects in flight. Our Shell.ai Self-Service platform is used by over 800 citizen data scientists.
- We have held over 40 hackathons in the last few years.
- We have over 1.1 trillion rows of curated sensor data in our data lake – used for the purposes of machine learning. Over the last year, we have quintupled the amount of data we have integrated into our central data platforms – handling 850,000 sensors producing data at a frequency of once every minute or higher.
- We are managing thousands of live machine learning models, monitoring over 1500 different pieces of equipment.
Shell.ai Residency programme
In line with its ambition to grow and strengthen our talent pools, Shell has launched its AI Residency Programme, a two-year immersive opportunity designed for students to work on projects across Shell’s business.
The programme offers residents the opportunity to develop deep, technical expertise across the spectrum of AI, learning more about the energy industry and working in agile teams to develop new solutions that can optimise current processes, enable workforce and unlock new business models.
More in Digitalisation
Shell is a pioneer in the development and deployment of many digital technologies.
Shell is built on 125 years of technological innovation. Our grasp of computational technology helped us to lead the way in technological developments in exploration in the 1960s, 70s and 80s. This expertise in mathematics and computing is what gives us such a strong advantage today in developing and adopting digital technology.