 Welcome. Apologies for a bit of technical glitches now. Good afternoon from Kuala Lumpur. It is now 1117pm. So today I'd like to talk about our upstream digital value chain and journey to our digital transformation. All right. So just wanted to share in terms of, you know, we hear about major energy firms initiating digital transformation, mainly now out of necessity, due to the challenges that we see across the value chain. So, in terms of patronage upstream business that is no exception. So we talk about what is digital transformation. And basically, why is it crucial in building those capabilities towards sustainability. So the aims are various from business improvement to productivity to safer hydrocarbon discovery and subsequently monetization. So hence the question that I would like to pose here is, is there really such thing as what we call a digital strategy. So, in this sense, right, we talk about digital strategy is business strategy. So there shouldn't, from our lens, there shouldn't be any difference between what you say digital transformation should do, and what you say is your business target. So this is where in context we put on the left hand side, we say that digital strategy is about how we entrench digital in our way of work in our day to day operations in our day to day business. And how do we do that is basically by the use of innovative way where information is thus combined with technology to raise human and machine performance. So basically, how do we put in the data that we have to good use so that we elevate how as humans we make decisions. So leading to that aspiration to use digital to make those better decisions and optimize across the value chain and from there, we unlock the resources, we unlock more safe ways of work and more efficiently. So this is in context of how we see, or how, at least from a strategic perspective, we see how digital is pursued as part of the business strategy for patronage upstream. I'd like to go across what we call as the data value chain for that matter. Okay, the data value chain is something that we see, you know, transformation is not a walk in the park, the challenge, the various. Just now one of the presenters rightly mentioned the energy industry is a very capital intensive industry. The key here now in this current price and also a situation is finding that oil hidden in the data. Coming into context of patronage upstream business, at least in the Malaysia friend is we are brown peel. So how do we extract that additional, you know, barrels of oil from the mass data that we have. How do we get that additional barrels of oil without the additional capex that is normally required for field improvement programs. Okay, this is where this chart is actually showing what we talk about. Okay, the capture curate consume and act. So this is how we see the data value chain, just like we have the hydrocarbon value chain across exploration development production and subsequently abandonment. This is how we see that. Okay, when we talk about the initial stages of data, how do we capture the data, how is data transmitted, you know, from, from our field, all the way into the decision point area. So, we, in digital there's a lot of talk about investment investment investment right. So this is where we see that actually the initial phase of capture curate consume. These are all costs. So looking from a digital investment perspective, everything that we put in terms of capturing the data, be it by upgrading our infrastructure, buying the hardware associated software behind it. These are all costs. And then we spend some more money to curate the data to what is normally money spent on curating data. So those are the likes of pay for integration services, we pay to develop solutions, and we pay to manage our data quality do modeling to a certain extent having work flow. So all that is still processing the data. After that, through the likes of, you know, perhaps the consumption layer. So we talk about, okay, how do we consume, you know, the data through getting inside. How do we put in analysis behind it. So all this is still costs. So from the point you capture you curate consume is all costs and normally these are the costs that people talk about, you know, the high cost of ownership for digital transformation. Until the point that you can actually act on the data that you receive. So, from our lens, from the experience that I've been facing also, this is where we kind of like, okay, how can we make the process faster. So that, you know, the data that we have in abundance is something that we can add on because only when you take action on the data that you receive. That's where the value comes in. And this is, if we talk about the business value for upstream, we talk about production unit, we talk about, you know, the cost to run the business, the cost of accepting one barrel of oil, this is where it comes into place. We are also cognizant that, you know, all this is good and great if the data is readily available. But we're quoting the data book report, right. So in any organization, and while I guess there's no exception, most of the data that we are looking at is very much that data. The king far beneath our line of sight, and to extract this is another challenge. So, with this in mind, this is our investments in terms of digital. The earlier years, we are very in front of this. So the earlier years is looking into focusing on basically getting the data out, getting the data out so that we can actually mine it. So that is the ambition. Going to the next slide, I would, I'm very interested in this. These are the very meaningful statistics along the other few months we have been doing this that we found data from our physical entities right be it from offshore facilities, processing plants, ships, even from center itself, which generate business data. We found that at least the data that we need to work on for our digital transformation, about only 45% is coming from the machines. So, the rest are based on people, how people enter the data. So it's already something which is the process. There are multiple fingerprints on the data. And this was something very interesting that we found that a lot of our decision making, we need to rely on at least 55% data which is generated by our people. So, in that sense, when we look into digital transformation, we found that sometimes it's not half of the time it's not about getting the best tools, getting the best sense, getting the senses out there. But we also need to look at people and how people as a 55% of data source keen in input, how they treat that data, how they need to see at the end of the day they are the first in the value chain, and whatever garbage in will be garbage out. So it is part and parcel also for us to look into the cultural aspect of how data is being treated at source by the humans that generate it. That was the realization when we started doing this, is that we have to trust the data, when we have to take action the data so making sure that the people enter that data is also something that they need to have to know and in mind. So, so from there also we realize that consistent standards and work processes at the point of curation is very much required, because being in the, in the, there are multiple ways of how certain work is being done. We face this challenge, we face this challenge of not standardized work process will keep generating similar type of data input, but from multiple types of processes. These are the things that we also realize along the now along the timeframe that we really need to get a grasp on if it really serious about, you know, having the data that we make for decision making. And from there, when we consume you use, when we use the data for just to go inside in a consumption layer. And we realize all this is not something that it can be a zone without having a proper enterprise architecture in place infrastructure and also managing the parameters to the state. So those that was a realization of ours of how you know to put the structure. And all this, when we look at digital investment are still cost. So these are the challenges that we face when we talk to our management teams that, okay, we say that, you know, for the next one or two years, we have to spend, you know, a bit of money investment in the fundamental right. So the notion is, when you're doing digital transformation, you want to see these are tangible and you will PNL impact for that matter, for example. So this is, but happy to say, through our efforts and journey selected business case pain point, we have managed to then be on the outside. So data driven insights using our predictive analytics using our foresight based on analytics. We have managed to, you know, go beyond the traditional first principle engineering models to more data driven simulations, which actually had given us value. So prediction and value and then based on the prediction and value when we should act on, you know, the, the data recommendations that we will see. But of course, all this would require aggressive adoption measures like I mentioned just now, when we talk about the data, we talk about humans give me 55% of the information. So this is where it is part of our culture is the transformation is to talk in a manner of how, how would change for the people on the ground, how would change for those who are contributing data to the business processes. So, so essentially, in a nutshell, our transformation kickstarting it has always been to look across the data value chain. So as much as we talked about, again, the upstream oil and gas value chain data value chain is something that runs concurrently with it, because only from there, then we are able to effectively translate the data that we have curated into actionable, and give value to the organization. So essentially, that's my key sharing with the open group today. Happy to take in any questions from the audience. Ask me anything. Okay, thank you. I'll ask you anything. That's great. Thank you very much for that. And you brought you brought us back on time, despite the hiccups. So I appreciate that. And the late hour there. Thank you. It's interesting. The different times of day that people are participating in this. So, some of the questions that came in for you, and what are the tools or what tools do you adopt to catch up capture the data from the diversified data sources, and how do the tools handle the complexities of the data. All right. So to answer that question, I would like to go back to this slide here. So yes, in terms of tools. So basically, again, it goes back to the process, business process area. So, for the more mature operations of sites which are instrumented, we have the lights of the pie instrumentation. And so those are the traditional instrumentation tools. So move across where not all our fields, I mean, being brown things, not all is to be instrumented. So this is where we have started to dabble into IOT, IOT census, due to the lower investment required. And for fields which we, you know, don't require so much of human movement to actually go and check the, check the data. So, so it's a very, very my rat of tools that we also use. But on top of that, again, like I mentioned, people still contribute about 65% of the data. So maybe in terms of business data, interpreted interpreting financial data. Yes, we have FAP financials, but then interpreting it as a certain way, you know, to suit the business metric. So I would say to answer this question. It is actually a mixture where you have the traditional instrumentation of short tools offshore and onshore facilities. You have the IOT that analytics, edge analytics. And you also have going back to people feeding up forms, operations report. So that is actually a source of data as well. And what you do is ingest those multiple, multiple, you know, templates that you have. Yeah. So we are, it is, unfortunately, you can't really 100% get it from. Understood. Understood. Thank you. So, another question. And can you, can you speak to the role you headed on your chart of an enterprise architecture going across the bottom, or pathway across the bottom. And can you speak to the role of the enterprise architects in your company in your digital transformation. We are very, we are very much at the infancy stage of enterprise architecture. However, in terms of business architecture, this is something that we are quite, I wouldn't say infant mature in a sense that due to the regulations. In terms of capacity, we are both host authority and operator. And as a host authority goes into operator there are, you know, guiding principles in place and among them is having, you know, business processes available. So what we feel is, you know, the first step of having a very good enterprise architecture is having that basically intact and part of driving consistent standard and work processes. We are very much experimenting a lot on it is a very big ocean to explore. So what we have actually done is to focus on top critical business processes, or by top critical business processes, and then work from there, the infrastructure layer, and then the subsequent data information flow and so we have packet in that manner. Else, else it would be just where do we start. Well, there's a whole community at the open group that can can certainly share experiences on on how to embrace EA and get the true value from it. As well as earlier speaker, Pedro Vieira from Petrovas, about two of our other standards activities in inside the open group for the open process automation forum and the open subsurface data universe forum are both working on things that would certainly I think be of interest to your organization. So one, one final question. And if I may, are you using public cloud offerings for data analysis. And if yes, are you able to say which one. Okay. Let me take this question. I'll take the number one. I mean, moving forward, I mean cloud, whether it be public or ring fans towards, you know, our internal cloud is still something that I mean that is for economics of scale is definitely for economics of scale and then, you know, being towards everything as a service. You know, leveraging on the industry co creation. That is basically, in my opinion is got to go on that angle. And when you talk about pace being fast to most of the, you know, platform services already on cloud. And it is no point for you as an organization to develop it by yourself when, you know, when it is, it is a, that's why we have open group so you have all the you know my red solution, which is generally offered on the public cloud. And I'm not going to answer that. No, no. And that, and that's fine. And that's fine. I understand. And that you've, you've, you've kept the time, despite the challenges. Thank you for your insight. I should, I should, I should say that one of the messages that came through the chat channel while you were while you were speaking was a great compliment to your slides. And I read it now. Brilliant visual presentation clear and communicate so much in an easy to digest way. So, thank you for communicating clearly and and for your participation at this late hour. Thank you for random applause.