I do remember my first time stepping on a refinery going,
Wow, this is huge.
The dimensions of it are just mind-blowing.
It has thousands of miles of pipeline,
up to ten thousand valves and a million instruments.
All of those things have sensors embedded in them. These vast
data stores create a huge opportunity for digital
transformation.
And that can really lead to some significant new solutions.
Digital Transformation is really about two things. It's about
recognizing that the amount of data we have around our existing
business gives us a big opportunity to transform the way
in which we operate. And digital transformation is also about
finding ways in which we can operate in the world that's
coming.
Companies in the energy industry today are going through a
massive transition. If these companies can't manage the
energy transition, which is really about the world demanding
more energy, but more energy from sustainable sources,
they're gonna have challenges operating both from a social
licence to operate, but also from just the basic economics of
doing business.
Shell has a really rich history in digital technologies and data
science in particular. We developed a statistics group in
the 1970s. And so when I started work in the space in 2013, I was
building on a history that we already had. We saw that things
were changing. And in particular, what was changing
was the ability to take large data sets to store them in a
cloud-based environment, and to use cloud-based computation to
accelerate the development of new software.
In 2015, I was really blessed to be given a chance to lead the
digital transformation team.
Alisa was a phenomenal visionary. She understood where
the technology was going. And she understood the
transformation that that could lead to for the company.
I think I have been working with Dan for about 10 years. Yeah.
When he came in, he was this young punk, with his hair
standing up. Even though today he still looks like a punk.
Yeah. What I really treasure about Dan is his passion, his
passion in using technology to solve problems.
In 2014, we had a big incident where we were lucky that no
people got injured. Our control valve failed.
The plug came loose from the stem, and it starts oscillating
in the process. And at one point, it actually broke the
body of the valve.
We had to repair the plant. It cost us three weeks of unplanned
downtime.
We started thinking about how we could use the data that we have
already collected to prevent future failures.
We contacted the Shell data science department. They had
much more experience in this.
What was fortuitous about the work with Pernis was that it
came at a time where we'd been working in this space for quite
some time, in Shearwater in the UK. And what we've been looking
at was the opportunity to effectively take all of the
telemetry data that we had, and to use that to train data-driven
machine learning models to detect anomalies that humans
otherwise wouldn't detect. And so we very quickly saw the
opportunity to leverage this approach in Pernis.
We have years and years of data. We started with 15 valves.
We're looking at things like the temperature of that valve, the
flow rate of that valve...
It refreshes, once every minute or once every second.
...and we're using that to forecast, if you will, the
normal behavior of that valve and then understand where the
behavior of that valve deviates from normal conditions. And when
that happens significantly enough to generate an alert.
The challenge was how to automate it without a lot of
human effort. As I said, twenty thousand valves in Pernis, but
we have like a hundred thousand valves in Shell.
How do you scale that? To run that globally? We needed a
machine-learning operation solution. We also needed a
solution that would scale on the vast quantities of data that's
generated from these assets. We were looking to say, Well, do we
build this ourselves? And I think what we realized quite
quickly is we don't want to be an AI platform company. Our
focus is around our understanding of the problems
that our industry faces.
We spotted a company that is called C3. They were at the
cutting edge of developing predictive maintenance,
modeling... a lot of algorithms that we saw was appropriate for
us.
We were approached by Shell some years ago. And we demonstrated
that we can identify device failure with very, very high
levels of precision. This is being accomplished through the
application of an entirely new generation of technology called
predictive analytics or AI.
Shell was interested in what C3 AI was building, and whether it
would be relevant to Shell operations. The challenges they
were running into were really around scaling and deploying
their software and their machine-learning algorithms to
the entire footprint of Shell. While the mechanics of the valve
were simple, the context of the valve required that each valve
had to be treated as its own independent piece of equipment,
which meant that you really needed at least one independent
machine-learning model per valve.
Shell has an unusually gifted team of data scientists. They
are professionals, they understand the industry cold.
The C3 AI platform enables the Shell data scientists, using the
tools they've always used before, to immediately put these
applications to work at full enterprise—Shell scale, which is
as large an enterprise as there is in the world.
What C3 demonstrated was that their platform was capable of
managing two million of these models. That was what excited us
because it gave us confidence that they understood the problem
that we have, and that their platform was scalable enough to
be able to deal with it as we put these models into
production.
The types of these successes tend to be somewhat under the
radar because they are all avoided failures, right. So
nothing happened. And that's a good thing.
We have repaired eighty-three valves, basically, that we would
not have found without the analytics. And we have millions
of savings on unplanned downtime.
We've now managed to deploy multiple applications into
production at scale, in really critical areas. We've developed
together mechanisms to monitor over eight thousand pieces of
equipment every day.
We've been able to expand the scope from just valves. There's
literally hundreds of use cases.
We also now collect quite a lot of data. And we collect data
using robotics.
They can monitor the position of hand valves, they can detect
leaks, gas leaks, but also liquid leaks. They are really
helping the operators doing their daily routines.
We then look at how we can turn this data into patterns.
A lot of the opportunities are in optimization.
Optimization is important. We again look at the data. We can
see where is the best place to put the warehouse, and how's the
best way to get it up to our offshore location.
There will be hydrocarbons for certain industries. So the
question is, How do you produce it in a way that is responsible?
How do you produce it in a way that gets a net zero emissions?
How do you do it in a way that helps support the planet?
We've also developed technology to optimize performance of
things like liquefied natural gas trains. A recent algorithm
we deployed to one of these trains in Nigeria resulted in
the equivalent of taking about twenty eight thousand vehicles
off the road. It's a great example of how AI is starting to
have a big impact.
Shell does a lot to try to understand our customers. And
that's where AI comes in as well, and they can use multiple
sources of data to actually give a much much more holistic
picture of what our customer needs.
When you look at the energy system, everybody has a piece of
the puzzle. The original equipment manufacturer has a
piece of the puzzle. Baker Hughes stepped in with their
deep understanding of how equipment operates. The operator
also has a piece of the puzzle because they understand how that
equipment operates in context. Of course, when you talk about
aggregating large volumes of data, you need to do that in a
cloud-based environment. Microsoft stepped in there to
help us to accelerate the speed of development of our solutions
collectively.
We collaborate with C3, Baker Hughes, as well as Microsoft, to
really look at how we can accelerate our AI journey.
Shell was a long-standing customer of Baker Hughes. So
naturally as our relationship, Baker Hughes' relationship, with
C3 was formed, Shell became a natural party for us to
collaborate on some new innovations on a C3 platform.
We have in C3 AI a deep and committed relationship with
Baker Hughes. Baker Hughes has been our partner and customer
for the last several years in our bid to kick our solution and
our capabilities to the energy field altogether.
The open AI Energy Initiative is a really exciting partnership
between four founding members, Baker Hughes, C3 AI, Shell, and
Microsoft. And really what it aims to do is to start to create
a set of industry standards and references around how we bring
AI technology to the industry.
The whole point of this effort was to take the learnings and
innovation that we have been doing so far with Shell, and
bring the entire industry along.
If we wanted to be really successful transforming the
energy industry, we had to work with a broader ecosystem of
partners. So what the open AI Energy Initiative tries to do is
it tries to say, Let's work together on a common digital
infrastructure from which we can all benefit through fair value
exchange with a common set of open data standards underpinning
it, which can accelerate the digital transformation of an
asset or a site in the energy sector.
And this was really driven by the visionaries at Shell, that
are by far the leaders in applying artificial intelligence
to deliver cleaner, safer, more reliable energy with less
environmental impact.
Nobody can go at this alone. It's a societal challenge that
we're faced with. And with that, we need to collaborate. It takes
all of us to drive this decarbonization agenda.
I think this is an increasingly important part of how we look at
digital, is how we help enable our greenhouse gas ambitions.
Let's think about what Shell is doing. These guys have set the
goal to reinvent themselves as a zero net carbon footprint
company by 2050. This is ambitious.
The demand from green electricity is just growing
across the globe. We couldn't do this without digital solutions.
You look at, for example, electric vehicles. We can look
at ways in which we charge those vehicles to make sure we
maximize the amount of renewables that are on the grid
at any given time. We have the opportunity to optimize the grid
behavior using AI, and to balance all of those new loads
that are coming onto the grid.
If you take the example of the home, where solar panels are
generating solar electricity during the day, but we're
storing them in a battery and using them when people are home
at night, that's a digital solution. So you wouldn't be
able to do that without the right software solutions.
Because at the end of the day, energy transition is urgent.
We're going to have to transform the energy system faster than
we've ever thought possible. And digital is one key lever to do
The companies that do not digitally transform themselves,
that.
with this new generation of technologies, will cease to be
competitive.
My hope is in ten years we have an energy industry that's
producing, certainly an abundance of clean, safe,
efficient energy, and is able to distribute it to parts of the
world that frankly don't have access to it today.
I am very optimistic about the future. I believe technology is
there to help humans solve many problems. And Shell is here to
produce energy, cleaner energy, for the people and for the
planet.