 Good afternoon everyone. Oh, it's really loud first off I'd like to thank Dennis For organizing this and of course all the team and especially all of the facilitators it's hard enough to coordinate a half-day workshop on campus much less if there's food involved in everything so I can Imagine the task that this really is and I just want to say thank you to to him and also thank you to all of you who take the time and the opportunity to To come here and to be with us to collaborate together and work on on this hard problem of subsurface data It's really it's really I'm really grateful that you guys are taking the opportunity and the time to work with us So first off, let me introduce myself a little bit. I'm Jacob Jackson. I'm with ExxonMobil and my background at ExxonMobil is actually geophysics and petrophysics and But now I my current role is leading the subsurface data strategy for the Guyana business unit inside of ExxonMobil So I'm not a data architect. I am not an IT person at ExxonMobil I'm actually an end user I'm someone who's been on the other side for years and has all the complaints that everyone else does and so it's interesting that they would That they would put a person like that to lead the data strategy Also think that it's interesting that they put That they that they created a data strategy inside the business unit as well We'll probably talk a little bit more about that in just a second. There's three things that That I want to talk to you about Today we're going to talk about the rock and fluid sample DDMS and how that we're accelerating the OSDU platform Value with our partners. We want to get value out of the OSDU platform And so we chose to accelerate the rock and fluid sample DDMS So first we're going to talk you we're going to talk about The motivation and the business case for why we wanted to do it We'll talk about how we've implemented with our with our partner and then we'll talk about how we're working with the broader OSDU and and Also collaborating with others in in that space as well. So I Hope to demonstrate to you that we are We are operationalizing OSDU we are operationalizing these data types as we're developing it in the forum and We are trying to realize and we will realize value for ourselves and for those who are working with us And I want to send the message that we want to work with all of you as well So let's work on this thing we call OSDU and let's get the value that we need to make the dis and the ability to make the business decisions So Users spend a significant amount of time searching for subsurface data Data can be in vastly different locations and formats depending on the asset the business stage and the vintage I don't think I'm telling you anything here. You don't know This is the same problem. I think that has been described Including yesterday and today as as well. And so we're and this is not unique to rock and fluid sample data This is this is all subsurface data and beyond as well So whenever whenever we're we're here this week at this face-to-face to address this problem Right and all of you who participate in the forums on a daily basis You're all working to address this problem. So this is the base. This is the base problem it impairs our decisions it takes time it costs money and I've really enjoyed this week seeing the passion of each and every one of you as you as you're you're you're working on this problem you're collaborating with each other and Either you really really really like talking about data definitions and architecture diagrams Or it's really painful or maybe it's both. I I don't know which Our vision is to radically modernize our subsurface data with the best technology standards and workflows We're not going to tolerate incremental uplift anymore We want to make a step change here and we want it to be really really valuable so we want to push the boundaries of what is possible with this OSDU platform and So that we can maximum maximize the value that we get from our workflows because in today's environment We need to do more we need to do it faster and we need to do it better than we ever have OSDU platform is the way that we're going to do that. We finally have the technology with the OSDU platform so we want to continue and to work with the industry to get to a point with interoperability and and therefore The ability to integrate without OSDU we continue to suffer with painful and time-consuming workflows So we're leveraging the strength of the industry to tackle challenges bigger than ExxonMobil all of you have the same challenges that we have we all have different challenges that are also unique and We want to to work on this thing. That's bigger than all of us the OSDU platform So our vision is a world where trusted data is at the heart of everything we do and we don't have to move or copy it in order to use it and A lot of people ask me why rock and fluid sample data? And really the the reason is because data governance we wanted you to governance and these data types were ripe for data governance today They exist in a myriad of excel sheets or PDFs or what have you and We wanted to get this much more standardized. We wanted to get it much more operational or operate Interoperable so that we can get better insights faster insights and so forth So the strategy here is not just data management because we can put them into Folders on the land and save their locations. We could transform them into just a standard flat database if we wanted We want to go beyond that and we want to be able to use data governance And so we needed a tool that we could influence and that we could And that would work the way we wanted it to work and OSDU is the perfect way to do that So the rock and fluid sample DDMS in the OSDU data platform is an important piece of our subservice data governance strategy We want these all of you know these data types are high value They cost a lot to get they cost a lot to make the analyses and the decisions that the influence are huge So we want to make sure that we are getting the maximum amount of value and that we have the maximum amount of trust in The data because we talked about the pains of can we find it? Where is it that type of thing all of those things ultimately boiled down to trust? I don't want someone redoing analysis redoing a workflow that two other people did years ago Because they don't trust it. They don't know who did it They don't know why they did it and so on and so forth data governance is the next step above data management It's really really important So a lift and shift of data from the land to another data storage location no matter how new and shiny that is It's not going to be what we want unless we are able to have data governance on that. So What does data governance mean well we envision technology standards and workflows that enable these things you could probably think of more But I just put these on this slide to start with so It needs to be the right technology to store subsurface data already said we We don't want to just put it on the land like we've always done the land is great at storing anything you want to put there So if that's your if that's your bar, then that's pretty easy, but we want something that captures We want cap we want something that captures the metadata and the primary records together We want to make sure that that we can set standards around those metadata and enforce that in our Applications and that use the data and that you that users interact with so what is the minimum data? Minimum metadata needed to capture what matters. Why did someone do this? Why did they use this piece of data instead of that piece of data? What should this be used for and so on and so forth? Technical assurance we want the trusted context and technical assurance So I'll go ahead and plug the technical assurance workshop tomorrow over lunch So if you're interested, what is technical assurance? What does it mean? Why are we interested in it? 12 30 tomorrow day three Through lunch you'll be able to I think there's going to be four presentations If I remember correctly be a lot of information in there and discussion around technical assurance um Fourthly we want to accelerate our workflows and this one is really important because I said we already want to go faster We want to do it better. We want to do more and So the best technology is interoperable Which enables integration integration of applications workflows personas and so forth integration enabled by that interoperability that integration enables acceleration and So if we can accelerate our turnaround time then we can come up with more and better scenarios We can come up with new and better innovation. We can work more safely and be more productive So the rock and fluid sample data are a key subsurface data type for us And we're focusing on the development of the rock and fluid sample d dms We want to make sure that we can min maximise the trust and value around these data types So the graphic that you see here, it's obviously oversimplified and it's not meant to be an architectural diagram or anything like that But it's a way to show that the d dms is the is the is the way that these workflows Behind me interact with the data So ingestion curation consumption those are examples of a workflow that the d dms would accelerate and allow us to to leverage the The the value that osdu gives us we'll talk a little bit more about each of these in just a minute But for now i'm going to turn it over to ron cool, thanks jake It's been great working with with everybody in the forum With our partners at exxon and our partner isv groups And all the csps one thing that i think we all Are recognizing is it takes a community to drive value through Now the community that we have all built together Definitely produces a significant amount of value for the industry But working with our partners at exxon what we are really laser focused on is how can we Shorten the time to value Through an organization through all the processes and groups that we have in the osdu forum in the pmc So first we when we're engaging with all of our partners in the in the forum as well as our partners at exxon Is we need to actually go in and discover what? What are the workflows? That's not a quick discovery effort That's looking at what is the data life cycle like for that data asset So in this effort we actually went in and identified How does data move how does value of that data move throughout the organization? And then from that we're able to identify what the white spaces are where the risks are So that those risks and those white spaces can start to be addressed in our implementation So we're first looking at what we need to build and why Especially in rock and fluid data management hasn't really been solved in many organizations Then we look at trying to devise a strategy on how we're going to kind of piece together a prototype a rapid prototype an mvp We create that ddms prototype Make sure that we're at least hitting our minimum viable product here And then within this kind of middle section here, we're actually working within the forum So we're working on canonical schema reviews in the data definition group We're also collaborating with everybody that's working in that around business cases case studies Why we're making different decisions we're documenting I'm sure you guys have seen daniel pernes documentation we're documenting You know all of the Attribute data definitions, you know to a TA like we would expect in a standards organization We're going to start our ddms schema development for review We're going to try to accelerate that as quick as we can Through the channels in the data definition group We're going to collaborate with the ea and pmc And what we're going to try to do and what we are we have started to do is Create a pmc project To where we can actually get this mvp out contributed and adopted And actually get it into a normal release cycle of osdu In parallel to that we are Not waiting to collaborate With all of our software partners our isv partners because in order to make this capability Successful it has to be used So we're collaborating with our all of our isv partners In conjunction with our partner exxon To make sure that what gets produced Will actually serve the business needs of those isvs and those workflows So kind of a quick look at The high-level architecture what we're looking at one of the challenges that jake alluded to That's very different about rock and fluid data compared to well logging that's in a lot of ways semi-structured Rock and fluid data is very wide There is so many ways you can measure way test the permeability of the conductivity of a rock sample and so And you're dealing with many vintages every lab For even the same company has their own format So we're dealing with many different parsing problems When capturing or when dealing with the unstructured data that is rock and fluid So we are leveraging A series of the framework is leveraging a series of external parsers that we can plug in to actually build Or supply data to the ddms Again it just like many other ddms's is to maiden driven api microservice-based modular and reasonable vendor and technology agnostic And it's deployable by the industry And it's about we are really working hard to balance performance and cloud resource cost With a lot of our experience so far building on osdu We're trying to get the kind of best of both worlds there But basically what we're looking to do With all of you Is see how we can start accelerating Time to value For particular business cases workflows and starting to fill in the capability gaps Within osdu so we can try to bring values quick as possible Thank you ron So with the last few minutes that we have In this talk i'm gonna Now shift gears to how we're actually operationalizing it and how we envision doing that So right now all of these data are scattered across the land Millions of files for just one or two of the assets inside of giana. It's it's um It's very complicated for people to find exactly what they want Exactly what they need to make the decisions that they That they need to make so this slide was given to me by cdg the and the data hub team with with um with uh tom hewitt and ragged and Tom downport and jeff and matt We're we we are We have asked them to take the data that we have And figure out what's there And figure out what the gaps are between the ddms at this time And figure out Between between what's important in those files and and what the where the ddms is and help us to to enrich The the forum with those things with those gaps and close those gaps So first we need to understand, you know, what's in there? What are all in those files and using the discovery that was mentioned earlier? We understand the workflows that we need to tackle Yesterday in the keynote the question was asked You know, do you curate first or do you load first and curate later? And the answer is it probably depends on the data type and the situation and so on so for But the the reality is at some point You need to curate you you have to do something we like I said If you're going to put governance on it You need to make sure that you understand what's there and how to use it So as we do this the main goal is to ingest into osdu We can clean it up, but we want to get it into osdu as quickly as possible So we're we're thankful to to the to that team and all the hard work that they have that they've done Of course, you know, it was also mentioned that you need to visualize it So here we have some some routine core analysis data plotted on the log tracks and then plotted on the On the on the cross plot here and this is routine core analysis working in in ivab So we want to make sure that the first thing the first thing we can do is visualize it If you ask if you go to an SME and say, I think there's a problem with my data. They're going to say show it to me If you Once we load it into into osdu we need to be able to look at it and see what's there And then finally we need to make business decisions With the data that we have and so we'll be Working with collaborating with aspen geologue to to implement the rock and fluid ddms the same way that they've implemented the other dms And we're really really thankful for the the effort that they've gone to and the support that they've given us in In this as well So with that, I think That's it Thank you to all of these To all of these guys who and more who have really helped us aspen tech cgg Epam int and of course all of the internal folks at xl mobile some of which are here So any questions? Thank you