 Hello everyone, my name is Banana Vaheem and I'm a product manager from Microsoft working with the Microsoft Cloud for Energy team. I'm so excited to be here connecting with you all and planning for the next six months. Today, Kadri and I will be kicking off a series of Microsoft presentations with an overview of the Microsoft Cloud implementation of OSDU platform. I'd like to start by highlighting Microsoft's commitment to the OSDU community and how from day zero we recognize the promise of common standards and the hyperscale power of cloud computing and we knew that together they could solve the data problem. We also made a commitment to learn from our customers through every step of the process. And that's exactly what we've done. We have been hearing customer needs since the R3 launch and we found that the common denominator is the need to improve the user experience and to ensure that the OSDU instance is enterprise ready and able to handle production support. And that is why we delivered Azure Data Manager for Energy, which is, or add me for short as you've heard it being referred to as throughout the sessions, which was very nice to see, which is a managed OSDU offering that provides enterprise grade service and support. To make this happen, we dedicated over 100 developers to work on add me and we made sure that it is easy to deploy and support. We also made sure to thoroughly test it for stability so that you can be confident that it is a reliable product. And to put it simply, as far as reliability goes, add me is a Microsoft product. So similar to Azure SQL database, it comes with the same level of enterprise grade security, scalability, and performance, making sure that it is a robust offering that is not just a framework or an auto deployment script. So don't just hear it from me. We do have a couple of videos that I do hopeful work, because I haven't tested this yet, to showcase some of the key functionalities of add me. Yeah, it works. So the first one is the ease of deployment. So our team worked tirelessly to make sure that the deployment of add me, your OSDU instances is easy and quick. So this is just a two-click deployment process that you can see here. And if you do want to see it kind of on a bigger screen, you can find me or anybody from Microsoft and we'll be happy to walk you through it. So it went from a very lengthy and challenging process to just a few clicks. It was a huge improvement in the user experience. So this second video shows you how add me transforms how you update version and visualize your data. So with all of your data in one place, you can now easily integrate it with your productivity workflows. For example, and you can, I hope you can see this here, you can ingest your well logs and send alerts via Microsoft Teams and also visualize your data using Power BI. And in this last video, it shows how you can use add me to integrate your domain data onto compatible business applications such as Petrel, making it a seamless experience to integrate with your business workflows. And that is what you can see here on the screen. And so that's what I had to share today. And I will, before I hand it off to Cudry to provide you all with a deeper dive on really what goes into add me, I do just want to say that if you or any company that you're working with is looking for production ready OSTU instances, then Azure Data Manager for Energy is the solution for you. Thank you. And I will hand it off to Cudry. Thank you so much. Is this? Okay. Cool. I hate talking behind this thing, but anyways, no worries. Thanks. Yeah. So first of all, it's just super, super exciting to see so many people in the room. When we first started the OSTU, it was, you were able to fit in a meeting room in Shell's Wood Creek campus and we have some friends from there still holding on and bringing this to the state. So it's great to see the expansion here, the interest. Thank you very much. Especially this is one of the last sessions. So thank you for being with us. This is Cudry. So I'm a proud elected member of the OMC and then I'm also doing some work with the OPC Foundation and OSTU liaison around the streaming data. So it's very, very exciting to be here. Let me start with one thing that we've been hearing loud and clear is that we just don't want a data platform. We want beyond that. We want to have out of the box workflows implemented and we've been hearing this from our customers and our partners for the last five, six years where we keep on working with them closely. So we've been working with OSTU since release zero and release zero was being deployed into actual customer environment. So since then we've been working closely and you heard some of those presentations and you will hear some of them in the next couple of days. But one of the things that I think we're doubling down on is the out of the box workflows. So you're getting your business, getting the applications that you need to run your business, whatever is needed between that and running those end-to-end workflows on OSTU. So that's our number one thing and we're basically focusing on three more very important things as bananas alluded to. One is the first thing is the, I call it the fundamentals, but it's really having the platform scalable, secure, deployable, manageable, et cetera. That's the basic promise, but as a consumer, as an end user, when you get the platform you should make sure that it performed, you should be able to monitor it, run it, and you shouldn't spend weeks or months trying to deploy the platform on your environment. So that's the first thing that we are looking at. The second one, as I mentioned, is the workflows. So you should be able to get your business workflows and run this on this environment. You shouldn't spend again weeks and months trying to get them. And I think the third thing that we're trying to achieve together with OSTU on Azure is the data piece. So the data should be, it should be findable. You should be able to find your data. It should be scalable. It should be secure, et cetera. So, and it should be liberated. So the data should be accessible from any application and also applications like Power BI or Grafana, whatever the application environment is. So it shouldn't be any property. It should be accessible through open schemas and open API. So that's what we're investing most of our time. And during this journey, as I said, we've been working together with our partners and with our customers, day one, because we want to make sure that the workflows that you need are delivered. So here you see an example with our part of our co-built engagement with SLB. So you see the Petrel and Techlog working together with other data types. And we want to enable these workflows out of the box. Tomorrow, you'll hear from us around some interoperability sessions. So one of them is very interesting. If you look into the agenda, we'll have a session where SLB and Halliburton will be on the stage together with Microsoft and Acre BP. So it's not a typo. Actually, you will see an interoperability scenario in the context of data mesh. So we are really serious with workflows and we are really serious with interoperability. That's what we're achieving, I would say. One of the things, this is interesting. It's a quote from my colleague and partner in crime, Owen. I think the reason why I love this is it kind of like shows. If you ask me what's your vision with OSTU on Azure, I will tell you this is my vision. We as cloud service providers, we should be doing the heavy lifting. We should be doing the application. I call it infrastructure and make it in an enterprise ready way. So that my colleagues like Owen will spend most of their time to find cleaner energy and more energy to the world. So the energy, the world's energy problem. So that's why, thanks for setting the vision statement for Azure Data Manager for Energy, Owen. Thank you. There's also a video on this. So if you look into Microsoft's ADME website and you will see the Equinoa video, the video is much more interesting. So, yeah, it's absolutely worth it. So how do we do that? Again, I'm not going to get into the details. I have to talk for the whole day probably. But we look into OSTU in the middle, which is, yeah. We keep on changing the name so I cannot keep up with the slide, sorry. So we have the Azure Data Manager for Energy on the middle. But it's basically the OSTU engine that does the ingestion, the curation, the discovery, and the consumption. And then we provide and support the OSTU APIs so that our ecosystem can build applications. And then also, we also provide out-of-the-box integration with the productivity stack, like Power Apps and Power BI. I had the pleasure of writing the first Power BI connector, which is pretty still much the same. And also, I had the pleasure of testing it with the four clouds. So it works, there's still one, it should be somewhere still in the GitHub, the version that works with all the clouds. And on the right-hand side, you see the Azure Purview, which is our version of Apache Atlas as a service. And then you see the Synapse, which is our version of Apache Spark. So we're also looking into integrating OSTU with the new models, like LLMs are a big thing nowadays, OpenAI and all these services. But as I said in the beginning, we're doubling down nowadays on getting the fundamentals out so that you have a reliable platform out with enterprise capabilities. And then what that landed us with just a few of the logos that you see here. Again, this is one of the slides I gave up in continuing to try to put the logos. Yeah, just we can talk about it. But I just want to talk about one of them, which is because I just don't have time. I have like seven, eight minutes to talk about all of that. So I just picked one. I do apologize for my other partners here. But with Rock, what they have done is basically they have decades of experience in cleaning log data, well log data. And then they put all of this experience to train an AI model, which kind of like when you feed the well logs and it's runs in massive scale, it basically gets out what are the problems with those logs. So again, it addresses one of the key areas where we address is the data quality availability and findability of the data. And what it does, it does something really interesting stuff. Not only can identify things like here's a piece of missing data or this well header is wrong or whatever. It also does things like, well, this looks like a gamma ray. I mean, you're telling that this is a gamma ray log, but actually it doesn't look like a gamma ray log. So there's something wrong with your tagging or labeling. So it also finds that kind of information. It's really a purposely trained AI model where you can get millions of well logs and then you get quality tested data out of it, which can generate ingestion scripts for OSTU and it gets into OSTU. And they also have developed power BI based data quality dashboards for that manner. But again, as I said, just one example. And where I want to conclude with is the work we're also doing with the rest of the Microsoft. So we're also looking into getting OSTU at the heart and having a robust OSTU which integrates with, we gave the example when I actually showed the video about the power BI that works out of the box. It's pretty good. Then we also looked into things like, okay, so how do we get like 3D models of some assets and put them in the 3D modeler in the digital twin environment and then see how it works? So those are the ones that you see on the right. We got it on a HoloLens and actually it was an interesting experience. We used the HoloLens metaverse application. So, and then there you can have like avatars of different people. And then it was so real that I tried to handshake with the avatar of my friend. And it was, it was, from our team, it was very interesting. And then the last one you see here is, we're demonstrating it. And if you come to our Houston Center of Excellence, we're demonstrating it together with SLB. It's actually a CO2 plume dispersion model in one of the CCS storage projects that we've been doing. Again, this is a view from HoloLens. And I will stop here, otherwise I won't stop. Again, thanks, thanks for being with us with this journey. And then hopefully next year we won't be able to fit here, maybe a bigger. Dennis will be happy with that, he likes to get those challenges. Thank you so much, thanks.