Announcer:
Bringing you our latest series on Navigating the Energy Transition, a podcast series where RBC Capital Markets experts and guest speakers share their insights on the latest trends and opportunities in energy transition.
Biraj Borkhataria:
Good morning and good afternoon, everyone. Thanks for joining the session today. So this is our ninth in our Navigating the Energy Transition series. My name is Biraj Borkhataria and I cover the integrated energy names for RBC. For those who haven't listened in or watched before, once a month you'll hear from RBC analysts on relevant discussions around the energy transition. We've talked about carbon capture. We've talked about hydrogen, the future of grids, et cetera, and a bunch of other topics. Today we have Dan Jeavons from Shell, who's the General Manager of Data Science for a conversation on digitalization in the energy industry and its impacts on both the way Shell does business, but also how it influences and impacts Shell's approach to the energy transition.
Biraj Borkhataria:
It's going to be a wide-ranging discussion and we do want to make it as interactive as possible. So if you do have a question, please submit it online and then we'll try and get to a few at the end of the conversation. So, Dan, thank you. Thank you very much for joining today. I want to start with a fairly broad question just to set the scene and conceptualize some of the things we're going to talk about. But could you talk a bit about some of the mega trends in the digital world currently and how these are driving or impacting the energy transition?
Dan Jeavons:
Yeah, of course. So firstly, just to say, thank you so much for having me. It's great to be with you, and look, really excited to talk a little bit about what we're doing in the digital and AI space within Shell. I think hopefully everyone is aware that if you look at the overall mega trends, there are really two that are worth calling out. The first is of course energy transition, the topic of this series, which is I think changing a lot of things very, very quickly in the energy transition as investor pressure as also societal pressure, and frankly also the compelling need to transform the energy system becomes clear to all of us. And also, I think at the same time we see an acceleration of digital technology happening very, very quickly within the energy industry.
Dan Jeavons:
And actually I think it's a very exciting time to be in the energy industry because these two things are coming together very, very quickly. I'm a big believer that digital technology is one of the core levers that is going to help us navigate the energy transition and can have a material impact on what we're doing. You will see in Shell one of the things that we are talking about is this strategy around powering progress, which is our approach to navigating these challenging waters. And I think within that, you will have seen that digital plays a very key role in many different areas.
Dan Jeavons:
And I guess the two that I want to call out, which you talked about the mega trends. I think the first thing is digital is going to be key in terms of helping customers navigate this energy transition, which is a core part of our strategy. But it's also going to be key to making the existing energy systems more effective and efficient. So let me just give you a couple of examples of that and then I'll go into how we are doing at Shell on this journey.
Dan Jeavons:
I think the first thing I want to say is that we are already working with our customers to leverage some of the digital technologies that we're developing within Shell to help them accelerate their decarbonization journey. And I get very, very excited about this. I think it's just a fantastic piece of work that we've started. And you will have seen maybe some of the things we've published recently, working with customers like Dalmia Cement. They're one example where we're starting to apply Shell's deep knowledge of energy systems and the way in which energy is consumed in industrial process and apply that now to customer operations to help them to decarbonize their operations. So just one example of how we're working with customers.
Dan Jeavons:
But we're also doing it to ourselves. So it's not just about customers, we also need to apply this to our existing operations. And I'm very pleased to being allowed to talk about this one for the first time. One of the things we've been able to do is work with some technology we developed a while ago called realtime production optimization. And we have the hypothesis that we could apply this technology not just to improve production, but actually to decrease CO2. So we've been working with one of our L&G facilities, and one algorithm was able to deliver around 70% reduction in boil off gas associated with flaring, which reduced CO2 for that facility at around 130 kilotons per annum roughly. And so that gives you a real sense of an example of the sort of impact that AI can have on CO2 when it's applied to our own operations.
Dan Jeavons:
So just a couple of examples of how these mega trends are coming together and how we're trying to make our existing business more effective and efficient and work with customers to accelerate energy transition. But you also see the acceleration in terms of general impact. And I think it's worth just touching on a slide, which was rather buried in the appendix of our strategy day presentation, which I thought would be worth bringing to the fore in the context of this conversation. And I want to start on the right hand side with the numbers that are quite small, but are actually really significant. In 2019 we delivered about a billion dollars in bottom line impact from digital technology, that's in terms of lower cost, improved production, improved utilization, reduced downtime and increased margins. And I want to say this is delivered value. I see often that with investors delivered and projected get conflated, this is delivered value that we have delivered already in 2019.
Dan Jeavons:
Despite COVID we doubled that number in 2020, and that's something I'm very proud of. It shows the acceleration that we see and it shows sort of some of the things I was able to share with investors previously, and some of the conversations that we had. These kind of leading indicators, the acceleration of deployment, the increase in data volumes, the acceleration in machine learning model deployments, all of these things we now start to see translated into that bottom line impact. And hopefully this starts to give the investor community a sense to what's happening in digital Shell.
Dan Jeavons:
And so just to quote a few other numbers, and I'm just going to update some of these. So we've gone from 1.3 to 1.7 trillion rows of data in the period that we talked about since strategy day. We've gone from 1.7 million to 2.1 in terms of customers using the Go+ loyalty program. We're now over a 100 AI powered applications in production, over 6,000 pieces of equipment being monitored in real time using AI. And so I just give you a sense of that because what I want to give to the audience here is a sense of momentum and a sense of the excitement that we feel around how quickly digital is starting to hit the energy industry, and then hopefully how that's going to translate into thriving through energy transition.
Biraj Borkhataria:
That's really good. So, I mean, strategy was three months ago and obviously some of those numbers are increasing quite dramatically. So I guess following on from that, you gave the example about the LNG facility. I wanted to guess, and a lot of guests talked about how digital could impact the old platform in 10, 15, 20 years time. And the one example you gave was how it is today. So I wanted to focus on today and just get a sense of how things are being done with digital now versus how things were done five or 10 years ago? And I mean, you can touch on wherever you think the lowest hanging fruit is, whether it's expiration, maintenance, you mentioned operations there, but yeah, a couple of examples will be useful there.
Dan Jeavons:
And look, I think for me what's really important here is that we have to recognize that this is happening everywhere, it's happening in every part of our business. But I think maybe just to zoom in a little bit on one other example that I think gives you a sense of how quickly things are changing. It's a example I like to talk about a lot, so seismic processing. It's kind of one of the most fundamental parts of the industry of we take seismic data in its rawest form. We take that through a series of processing steps to generate insight about the subsurface, and there's an analog here, I think with medical imagery. And you see this happening in the medical sector very, very quickly where AI and machine learning is starting to transform the way we process CAT scans.
Dan Jeavons:
And so you can see the same thing happening here. And so what we've been doing is working on, for example, can we use AI to denoise that data. As it comes in, one of the laborious processes you have to go through is to take the noise out from the processing to actually get to the signal. And this can take historically many hours and even several weeks to get to the point where we can get denoise data out. And using some AI we've developed we called SNAP, we can actually develop, we can actually demonstrate that we can reduce that cycle time very, very significantly using machine learning. And to give you an idea, this is now being applied everywhere throughout the seismic processing workflow. So these types of AI modules, it's not just in denoising, it's also in things like rapid velocity model building, automated geo body interpretation, trying to identify features like faults or SALT, automatically using machine learning.
Dan Jeavons:
And to give you an idea of the impact that this is having on the seismic processing workflows, we've seen about a 25% improvement in cycle time using these AI technologies, and one recent example, we were able to complete a rapid analysis of a new opportunity in a large basin with complex imaging requirements in just three months. And I think ultimately that resulted in a successful bid that provides a strong addition to our exploration portfolio. But I think it's a great example of how we were able, I question whether using traditional methods we could have got the level of insight that we had about that play without the machine learning. And so I think it's that increased cycle time that we're seeing happening that's really transforming some of these workflows. I also think we're just getting started. So I think there's a lot more to come in these areas.
Biraj Borkhataria:
You talk about pace increasing, and then obviously the imagery tools it provides you. I wanted to talk a bit about mindset. So you work for a very large corporation, and I also work for a large corporation and I feel the same thing. If I come up with a really great idea, there'll be five people standing in my way explaining why things are done a certain way, and this is always a way it's been done, and this shouldn't change. So how do you actually get things done? Do you have license from the top to say, "Digital's a way forward in this. We need to do this." Or how does that actually work in practice?
Dan Jeavons:
I want to say, hopefully going back to what I said at the start. You can see how quickly things are changing. So I see a lot of positive things happening in that sense. So, certainly I'm extremely positive about some of the changes we are seeing. And I've been, but I do want to recognize what you're saying, which I think is really important, which is the fact that if you look at a company like ours, we have to recognize that to be relevant in both energy transition and in digital, we have to change culture. And I've been extremely blessed really with a team of senior executives that got it, that understood this is what had to happen and gave me really top down from our executive committee the opportunity to drive this. So we back in 2017 basically went about setting out a digital strategy, which created, I guess, the space and the vision to say, "We're going to go after this as a strategic initiative, as something that matters for us at group level. That's going to be key to our futures success."
Dan Jeavons:
And I think what came out of that is really top down we call them roadmaps, which was effectively different parts of our organization for sub surface and wells, for asset management, for customer centricity, which includes our new energy business as well, renewables and energy solutions is now. We were trying to make sure that each of those areas had very specific priorities that were very visible at the top where these great ideas that you talked about could be fed in, and indeed blockers could be removed. So I think that's one element of it, that top down component.
Dan Jeavons:
But I think there's another element which is often overlooked. And I think we started with a bottoms up movement as well. So one of the things that we focused on very early was developing a network of change agents. And we call, in my area, we call that the Shell.ai community. We now have an equivalent one for DIY, which is do-it-yourself software developments. And these communities are very much bottoms up self-health capabilities, where we empower the frontline. We give them training through providers like Udacity. We enable them with the tools, things like Power BI, Power Apps, Alteryx, and we enable them to start to develop their own solutions.
Dan Jeavons:
And to give you an idea of the scale of this thing, we've got well over a 1,000 now DIY software engineers, and a similar number of DIY data scientists. So it's really quite a significant thing. And all of these folks are coming up with new ideas, seeing problems in their work processes, and being able to just get on with it. They've got the tools, they can solve the problem for themselves. They don't need to call IT, the data's available, or it's becoming available as we bring together our corporate data landscape. And so that's really accelerating the ideas. And what we're also trying to build alongside that is saying, "If we've got a great idea and you now want to replicate and scale that up, we now have an engineering capability within our IT function that can help us to accelerate the development of those ideas." So it's really both a top down push, these are the big priorities from the senior level, as well as a bottom up movement, which is bringing that together, which is driving that cultural change.
Dan Jeavons:
And I think the other thing that I've been really benefited from is a huge openness, our senior executives want to know, "How can I be more digitally savvy as a leader? How can I help and enable this within my workforce?" And I think that's extremely powerful.
Biraj Borkhataria:
Just a quick follow up on that. But if you have a really great idea and you have the data and you have the solution, and it's a global issue that you find a solution for, how long would it take? I know there are probably not one answer for this, but roughly speaking, how long does it take to implement something that you've found a solution for?
Dan Jeavons:
Yeah, great question. So, I mean, we talk in terms of what we call minimum viable products. So often what happens is someone's built an initial prototype and they come to us and say, "Hey, we want to turn this into a globally replica product." I have this mantra, which is, think big, start small, learn fast. And so I think the key thing is, what we often try and do is lay out the total aspiration for this product. And then effectively also be very clear of what is the minimum thing we can deploy to deliver valuable functionality to the end users today? And then how can we learn and scale that fast towards that end vision? And so that's very much the mantra that we're using. Typically our cycle time is somewhere between six weeks and three months. We've done better than that. We've done worse than that as I usually rightly point out. But the idea is, ideally we're trying to deliver MVPs in kind of that six weeks to three months cycle time.
Dan Jeavons:
And that might not be, it's quite challenging to do that, because you get into a mentality whereby you're deploying stuff that ain't great to the customer, right? And just being honest about that, and this is the cultural difference is that that has to be okay, because what you're going to have is you're exposing your work to the customer. They're going to tell you that it's rubbish. And they're going to tell you what you need to do to make it better. And that rapid learning that I was talking about follows from that, which gives you a much faster cycle time and gets you to the point of having something valuable more quickly. And it gets away from the alternative, which is wait six months, give it to the customer and realize it's not what they want.
Biraj Borkhataria:
I think there's probably a lesson about writing a research report in there as well, on that waiting six months. But I wanted to focus or hone in on opportunities because, and maybe not costs, also revenues. And we can go through a couple of divisions, but maybe starting with marketing, you have a huge customer footprint and often it's the Starbucks plus McDonald's plus a bit more in terms of customers example is used, but big loyalty scheme. Can you talk about when Shell actually started to use data in that business, where you are in that journey, and what are some of the surprises that have come out of it?
Dan Jeavons:
So funnily, I have a history of this space. So one of my previous jobs was running the landscape that actually generates all of the customer offers for us. And I think we've got a fairly long heritage in doing this. I mean, as you know, Shell's had a driver's club loyalty program for several decades, I think. Not sure exactly when it started, but it's certainly been around a long time. And so to a certain extent we've used data in that space almost from the get go. And I think that's true almost everywhere in Shell. I mean, our statistics team dates from the 1970s. And I think the benefit that I've always had coming into this space is the fact that I very much build on the shoulders of what's gone before.
Dan Jeavons:
And at the same time, of course, the opportunity that you have with digital, particularly when you talk about things like having a mobile app with users interacting with you in real time, gives you a whole new opportunity in terms of generating customer insight. And I want to start with something which is really important, which is we thought long and hard before we started down this road about what are the lines we're not going to cross. Because I think you have to be very careful in dealing with customers' data, not only that you follow the law, which is obviously critical, but that you also make sure that ethically you're comfortable with the way in which you're using that data. So we've been very targeted in how we wanted to do this. And really the focus of this is giving customers a fantastic experience of Shell. That's been the vision of what we're trying to do. So we're trying to enhance the customer's experience using data and AI.
Dan Jeavons:
And the Go+ loyalty program is a great example of that. So what is it and why is it different? At the core of the Go+ loyalty program is two things. One, it has a capability which is around effectively providing visit-based loyalty. So the more you visit Shell and the more you do with Shell, you'll be rewarded for that. But it also has this beautiful random function, which is if you've ever interacted with the Go+ loyalty scheme, you'll suddenly get stuff you didn't expect. So the whole system will provide you with something which we believe you will like. If you don't like it, which means you don't respond to it, or you don't look at it, the system also learns from that. And so effectively what we're doing is we are trying to say, "What do you want from us? We're going to experiment, we're going to give you things that you might be interested in. And then we're going to learn from that using data." And hopefully that enhances your experience of work of visiting a Shell station.
Dan Jeavons:
And I mean, it seems to be working, as I mentioned. There's huge take up for this. As I think I mentioned earlier on it's around now, I think 41 million digital rewards issued, around 2.1 million customers using this. And that's just in the UK by the way. But I think what's really important as well is that we've taken that concept and the core of that concept and we're now trying to extend that to other markets. So it's not the same, because customers in other countries are not the same. But the core engine, the piece of AI that we've generated behind the hood, we're now rolling out to other markets.
Dan Jeavons:
And so we see that not only, you can build something like this once, you can build the underlying technology, the data analytics, the frameworks, and then just like we talked about in some of the other areas, you can then start to roll this out. And the beauty of digital is if you get it right and you build the data models right and you build the platforms right, it can scale very, very quickly. And so, we're going through multiple market roll outs this year of that underlying AI technology because we've built it to scale.
Biraj Borkhataria:
Can we talk about, again, going back to sort of ways of working. So what we see now more than ever across the energy industry and other industries is alliances, alliances, shipping, aviation, hydrogen, everything. In most cases actually you see a company like Shell, you are one of your peers with a couple from industry, or independent companies. And you have partnerships with the various tech firms out there, Microsoft, Google, et cetera. So can you talk about the role of these alliances and what exactly do you bring to them?
Dan Jeavons:
Love the question. One of my favorite topics. So you may have to bring me in on this one. But look, I think in every part of our business it's built on partnerships. Not just talked about the loyalty program. One of the opportunities in a loyalty program is of course, and this is not new to Shell, but not new to anyone else, but actually to bring in other offers in an integrated way, and to start to accelerate that. So across the nine or so markets that we're planning to roll out, that offer strategy is key. But in those relationships that we have with those other companies, what's interesting is the dynamics of those relationships in the mega trends I talked about earlier on, in energy transition is changing.
Dan Jeavons:
And what I mean by that is, if you're a consumer goods manufacturer, or if you're a cement producer, or if you're a steel producer, your business needs to change very, very dramatically in the next few years. And actually these deep relationships that we've built up through our traditional businesses means that often these customers are coming to us for help. And often the place where that starts is digital. So I just want to link this back to the overall strategy, which is, it's not only about partnerships, it's also about customers. And I think maybe it's only fair to start with the Microsoft relationship, which I think is one that has been very public and it was very visible for sure. And it's been certainly something I've been very heavily involved in.
Dan Jeavons:
But if you think about what we're trying to do with Microsoft, it's really about three things. It's about us deepening our relationship with them, for sure, in the digital space, that's in terms of innovation and in terms of cloud services. But it's also about Microsoft becoming a customer of Shell, buying renewal power, buying carbon offsets, working with us in carbon capture and storage and becoming a customer of the Northern lights program. So it's really a deepening of the relationship where both energy transition, digital and traditional business models all become very fundamental.
Dan Jeavons:
And so you ask, "What do we bring?" Well, I think, I think what we bring is a few things. I think we bring firstly a very deep knowledge of the energy industry. Secondly, I think we bring a unique capability, which is that combined with, I think a sizable and credible and capable AI team, which can work on some of these really tough problems and translate those traditional capabilities into digital products. And I think as a result what we're trying to do is to find effective partnerships where we can also build on other strengths. So the example with a Microsoft is they bring a very, very robust software engineering and cloud framework, which we can build on top of, and it allows us to accelerate the development of our own solutions.
Dan Jeavons:
And so what we're trying to do really is work with many of these partners and customers to figure out joint opportunities in the digital space where we can accelerate things. Another example working with Kongsberg, as you probably know, we've been working hard on trying to reduce the CO2 footprint from our vessels. And we developed a technology called JAWS, which we showed can reduce CO2 emissions by about 7% on our own ships. And we've licensed that now to Kongsberg, who are now helping to accelerate the development of that technology and take it to market. And that's very exciting because it means it accelerates the deployment of our ships. It accelerates the learning cycle. It has a CO2 impact. It generates licensing revenue, and it also builds a deeper relationship with Kongsberg, which touches many parts of our organization.
Dan Jeavons:
One more example, you may have seen something which we talked about recently, the OAI, the Open AI Energy Initiative with Baker Hughes, Microsoft and C3. Another classic example. We now talked about the vast volumes of sensor data that we have and the predictive maintenance algorithms, which we've deployed now to over 6,000 pieces of equipment. And I think the point about that is, what do we bring? We bring that data. Nobody else has got an aggregated data store outside of other operators perhaps, which can tell you how your machines work in context holistically.
Dan Jeavons:
And if we can do that and then train machine learning models on top for that, provide real-time insights, that's pretty unique. And obviously, partnering there we can accelerate the development of predictive maintenance more broadly across the industry, which has huge benefits, not just for us, but also for partners, also for joint ventures. And of course, again, there's licensing revenue for Shell in that if we're licensing that technology to C3, which we now are through the OAI. So I just give you some examples of some of the things that we're thinking about and how these business models and partnering models are evolving. But also, I hope I convey a sense of excitement because [inaudible 00:27:38], but I think you can see the potential in this and the capability that we have within Shell to actually be highly relevant in this space.
Biraj Borkhataria:
So I guess in a very simple sense, in the digital revolution there's a huge advantage of scale here because more data, more feedback, more learning and so on and so forth. And that's where more data have hugely benefit. What you mean?
Dan Jeavons:
Yeah, exactly, exactly. I mean, I think the key thing [inaudible 00:28:11] is that if you look at what Shell has, we have a phenomenal global footprint, which leads to phenomenal insight. In a data-centric digital world very few people have the level of insight about the physical operation of the energy system that we have. And if we can turn that into a digital asset, it's a huge business opportunity for us.
Biraj Borkhataria:
And just sticking with the sort of alliances discussion. Is there a significant difference in the way you and your team work versus the data team or AI and Microsoft or Google work?
Dan Jeavons:
What's really important is we want to learn from them. And I think, I'm not going to claim that we're necessarily better than them. I think they learn things from us about the energy industry, but we certainly learn things from them because they're dealing, take a Microsoft, they're dealing with vast, vast amounts of data in the Azure platform, in the SharePoint environments, in some of the things they do with Bing. That's not something we're familiar with, not that scale of global data, not the ... We don't ingest the whole World Wide Web and try and run machine learning on it, whereas Microsoft do. And I think there's things we learn from them about how we can unlock that data asset that we have.
Dan Jeavons:
And so I think what's great, and this is what I've really enjoyed about the Microsoft relationship in particular is that it's a partnership. We work together with them every day. We're working on innovation ideas with them every day, we're leveraging their expertise to help us accelerate our development. And I think that's also core to their philosophy, which is great. So I think it's a win-win actually. And of course what we help is they understand the energy system better and they need to understand the energy system better because they're also trying to decarbonize.
Biraj Borkhataria:
Also big energy consumers. So I'm going to ask one more question, and then we can open up to the audience. If you do have a question, please do submit it and we'll try and get to them. So my last question would be around skills and recruitment. So when you're hiring these AI engineers or data scientists, there's presumably more demand than supply I'm assuming at this point. So, what is your proposition to get all these young, bright graduates to work for a fossil fuel company?
Dan Jeavons:
I love the question. The first thing you have to overcome is say that we're an energy company. And I think that's a really important point, which is at the end of the day to your point, one of the propositions that we offer is Shell is trying to lead through the energy transition. And we believe that digital is a part of that. And I know that might sound trite, but actually it's really important in recruitment, because when it comes down to it these people want to make a difference. They want to be a part of solving the problem. And I think it's not for everybody, but for people who maybe are a bit like me, I fundamentally believe. And one of the reasons I work for Shell is I believe that what I do can have a material impact on the energy sector.
Dan Jeavons:
And so it starts with that unique purpose, can we use digital technology to transform the energy system? And actually I find that people find that very motivating because they get it that Shell, if we can make this change happen, we can have a material impact, not just on Shell, but global. And I think that is motivating for people.
Dan Jeavons:
I think the other thing is it comes down to culture. We work very hard on the culture we create, we try and model the culture. I mean, you can probably tell that I'm not the conventional person in terms of the way that I do things. I really try and focus on embracing digital culture and bringing that into Shell and trying to make sure that we are bringing the sort of technical environment, technology environment in the digital space that they expect.
Dan Jeavons:
So, that's another key element. And that goes to things like dress code and office environment, when we had offices. But also, the way we manage code, all of those kind of things become very, very important, because there's an expectation from these people we're trying to recruit about what good looks like. I think the final thing is also it's about leveraging the assets that we have. So not everything's going to be solved through recruitment and we focus very hard on the reskilling aspect of things. So we focus really on trying to say, "Look, we want to also take some of the fantastic technical knowledge we have with deep industry experience and make that relevant through energy transition, but also to this digital world."
Dan Jeavons:
And so we've worked with, I mentioned Udacity, we've worked on targeted reskilling programs. We've also developed DIY data scientist programs to help people who are in the business to generate these new skills and to start potentially a new career path, which I think is really, really exciting.
Dan Jeavons:
I guess I'll say one final thing, which is the other thing is flexibility. We've developed unique programs, which are not conventional. So for example, the AI residency program is a two year fixed term contract with Shell. Because we recognize that not everyone wants to stay with one company for the rest of their life anymore. They want to be able to come, particularly as a data scientist, experience new problems, learn, and then potentially they may want to go somewhere else. Obviously we'd like some of them to stay, but we're also okay if they want to say, "I'll do years and then I'll move on." And so I think some of this thinking of trying to do things perhaps a little bit differently to provide different employee value propositions to a different type of person has been really important and part of why I think we've managed to build a strong capability so far.
Biraj Borkhataria:
No, that's all very clear. I've got a couple of questions that came in via email. The first one is, could you talk about blockchain and its role or uses in what you do, whether it is useful and just some more details around that?
Dan Jeavons:
Yeah, for sure. I mean, let me start by saying, just like with AI in blockchain I think we started pretty early. I will say, I'm not the blockchain expert, but I know it a bit and so I'm happy to talk about it a little bit. I think one of the things that we've been trying to develop in the blockchain space is we've been trying to think about where is it going to apply and where is it going to disrupt traditional ways of doing things? The tough thing is that crypto is obviously transforming the world, and yesterday people's bank balances as well. But anyway, I think the key point is that you have to recognize that there's been a killer use case in crypto, but we expect that blockchain is going to go way beyond that.
Dan Jeavons:
But I think it's also to say it's been a nascent technology and it's now only, I think really just coming of age. A couple of things. I mean, I think we've had a number of people recognized through the contributions they've made. Sabine Brink has been really leading a lot of the dialogue in this space for Shell, and if you haven't looked her up and you want to know more, look up Sabine Brink and have a look at some of the things she's putting out there.
Dan Jeavons:
What I would say is a couple of areas where we have focused. One is trying to look at spare part providence. It's a great use case. If you look at some of the problems, and I'll use this an example of where I think blockchain's going to really disrupt. If you think about where blockchain helps, it's often in cross business plays. So where you have multiple different players who need some sort of authentication, but also some sort of confidentiality in the process. And spare parts is a great example of that. Because for those that aren't familiar, we have these spare parts on the shelves. They need to be maintained. They need to be certified. In many cases they need to go back to the manufacturer to ensure that they're appropriately certified before they're deployed into the production environment. And all of that can get very, very messy in the supply chain. And historically you had whole documents being passed back and forwards with long service histories on spare parts.
Dan Jeavons:
And if you can put all of that on the blockchain, it can make a huge impact in making that whole process much more effective and efficient. I think the other one that I'm really excited about which I think is going to be the next generation of blockchain is low carbon energy product tracking. So it's a perfect use case for blockchain, which is, can you understand the provenance of a particular energy product through the whole life cycle?
Dan Jeavons:
Same, if you the spare part analogy and you link it to an analog, it's actually the same problem. You've got multiple different parties involved in the value chain of creating a energy product. And actually as a consumer or as a customer you want to know that what you're getting is green, and that's going to be an increasing pressure. And so looking at how blockchain can help enable that through the value chain is going to be really, really important. So those are just a couple of examples. And I think one area that we're looking at in just initial focus for that sustainability use case is sustainable fuels in the transport sector. I think it's a great example of how you could envisage this being applied. So lots of excitement, I think more to come in that space, but certainly a key area focus for us and one that we're investing in.
Biraj Borkhataria:
Yeah. I mean, I can see it being utilized quite heavily in carbon offsets. Because if you sell an energy cargo and the customer offsets it, or you offset it and sell it to the customer there's going to have to be some kind of tracking system to make sure there's no double accounting in the system.
Dan Jeavons:
Yeah. Yeah, exactly.
Biraj Borkhataria:
So the next question, this was a little bit difficult to answer because you talked about cycle time being much shorter, but looking on a five year view, most impactful technology improvement you can see today on the business?
Dan Jeavons:
It's a great question. Obviously, this is sort of slightly speculative, as you can imagine in that it's more about what I'm most excited about, rather than a forward looking view. So I need to be clear on that one. But I think where I'm excited is optimization. If you look at the last generation of technologies, optimization has shaped the energy industry, whether that be in refinery planning, whether that be in supply chain management, we've seen huge gains from optimization. What we see with the next generation of technology with things like deep reinforcement learning as an example of that, is we can see that deep RL is going to really make a huge impact in terms of, and it already is in things like autonomous vehicles.
Dan Jeavons:
And the ability to optimize using those sorts of technologies, I think will take the whole optimization approach to the next level. And an example is autonomous operations. To a certain extent many operations today are automatic, but there's many challenges to making them fully autonomous in many areas. And I think we'll see some of these technologies, some of these new advanced methods starting to make it possible to do more things autonomously.
Dan Jeavons:
The other one, I think that I'm really excited about is digital twin. So the vast array of sensor data that we're now aggregating in an integrated way together with digital P&IDs and three dimensional scans of the assets is really starting to give you a real-time picture of what's actually happening on any given facility. And we've seen that, again, if you want to go on the website, there's an example we've published around Nyhamna, around how we've already done that. And we see that accelerating very, very quickly.
Dan Jeavons:
Now, this is where it gets a bit mind bending. If you think about that, if you have a real-time picture of the asset at all times consistently and accurately, you can actually start to simulate, you can start to model, you can start to predict, you can start to create outliers. You can effectively do in the virtual world all of the what if question that you want to ask, and that will provide a new level of insight around the operations. So, and then if you combine that with what I said about optimization, those two things coming together again will move the needle even further. So I think that's the area that I'm most excited about right now, and you can probably tell.
Biraj Borkhataria:
Definitely. So, just a follow up on that, digital twin, how widely used is that today? How many examples do you have?
Dan Jeavons:
It's pretty broad. I think, I mean off the top of my head, I mean, so we've said we're rolling this out globally. So we started with Nyhamna and we've partnered with Kongsberg and we're in the middle of a global rollout. The plan is to do that over the next few years. But to give you an idea, we've got about three deployments live right now, and that's accelerating really fast. But as I said, it's a global program and we'll be going asset by asset over the next few months and years.
Biraj Borkhataria:
Right, right. So we've got one last question. And you brought this on yourself in your initial comments, because you talked about $2 billion delivered. So can you talk about a projection? Can you talk about the potential for digital going forward? How significant could this be?
Dan Jeavons:
I thought you have to try it, you know what I'm going to say. I mean, look, what I'm trying to do is find a way of giving enough of the sense of what's happening to the market by leading indicators, understanding the acceleration and understanding the trajectory as you've seen from the one to two billion. I think what we see is those indicators give you an impression of what's happening within Shell, but I'm not going to give any sort of projection go forwards. But hopefully you can share my excitement of what's been delivered.
Biraj Borkhataria:
I had to ask, as soon as you mentioned it I was thinking about it. But no, that's great. And this is a fantastic, really insightful conversation. Obviously there's a lot going on, very fast paced environment and you're going to be busy in the next few years. So thank you very much for joining us today. Really appreciate it.
Dan Jeavons:
Thanks for having me. I really enjoyed it.
Biraj Borkhataria:
Great. So the next session we're going to host as part of this series is actually going to be on the insurance sector, so effectively, how do you price in climate change risk and things like that. So that will be in your inboxes soon, but thank you all for joining us today.
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