 A warm open group in Dublin, welcome please for Johan Krebbers. Thank you, thank you. Good morning. So we're going to talk about really what is ODE all about. Why do we need something like an open sub-surface universe in the oil and gas industry. And also what's all about the role of open group and where we are and how we're moving forward. So it's really give a state from a background and where we are and how we're moving forward in this space. Let's first give a little bit of history. If you look at a typical oil and gas company, especially in the subsurface space. Subsurface is really the space where you try to find your oil and gas futures. Subsurface is really where you go out there shooting your seismic, collecting that data, going to the development phase and try to get wells out there to start producing it. So that whole part today is very much really very much in a siloed environment over there. So you've got data linked to applications, the first one. So you've got many, the data is very intrinsically part of an application out there. Therefore data stored in many silos. So there's not really a lineage out there. What does that mean? A lineage means if you're in a well space, I want to understand why we decide to drill a well at a certain place. They go all the way back to the exploration phase. It's almost impossible nowadays because the data sits all in these silos. It's very hard to figure out, okay, I've now decided to drill a well out there, but what is all the information that led to that decision? That's very hard to find out nowadays. Sometimes you wonder how we still do find oil and gas because of all these limitations out there. So data stored in silos, very important. And then metadata stored with the data. Metadata, of course, is data about data, but often we have millions of files out there in the subsurface space with many different file formats, but very poor with the metadata. It's very hard to find out what data we actually have. If you look at your seismic software, if you look at your interpretation software, what actually is the data we have out there? So we have lack of content information. If you sit, for instance, in case Michelle in Australia, and you want to know what we're doing in the US, it's today's impossible to find out. Limit to no search capabilities. It's very hard to say, okay, because there's no metadata. It's also very hard to say, okay, but what's the search, what data do I have, what kind of fields do I have, what kind of common fields do I have around the world? Again, very hard to figure that one out. And the last one, which is very important, data is not ready for a data-driven world, because we're really moving from, of course, a physics world to physics and data-driven world out there. Because data, because if you look at our data today, only maybe 10% of our data is actually being used in the subsurface space. The rest is not being used. The data is there. It's not being used because the people are not around to make it happen. But with machine learning and AI, it becomes possible that all the data is being used all of a sudden. And that's why this whole part of data application is very important. So this is the world of Shell today. This is the world of Shell. And maybe it may be extra mobile and BP or to tell whatever they're called, do a much better job out there. But based on experience with the OSU experience, we all have that same problem out there. Lots and lots of data. If you look at a company like Shell, in this space, you find by far most of our data. In our case, it's about 200, over 200 to 300 petabytes of data sits in this particular area. And many different data types. And not all structured data. If you go to all the parts of a company, often very structured data driven, here it's all over the place. You've got structured data, unstructured data, binary data, many different formats out there. There's quite a complex environment out there. The most complex environment we have. But the current reality is also that we're not in a very good shape out there. Therefore, we cannot just continue because we are moving to a data-driven world out there. And as long as you have the data sitting out there in your silos, it's very hard to bring the data together and say, oh, now we start applying machine learning to help interpreter what the focus is at. That's the first point, because we move from physics to data, physics and data world. The timelines we have to reduce. Times today are far too long to go from finding, shooting your seismic and really drilling a well out there. Data quality is not always very good. Needs to be improved. And of course, the access to metadata. Metadata is highly underrated but so important because it gives you information about data. Metadata is very hard to find out what information you actually have. What makes it a challenge out here, that you have so many different file tabs out there that you need the metadata to extract from every file tab out there. So it's quite a complex environment. So we cannot control like this. So the biggest change we have to approve is the first one. It's really separate data from application. It starts from a data point of view. It's not about the applications for now, but first get your data store in order. So put all the data into a separate data platform and separate the data totally from your applications out there. And such that any application can access any type of data. But start from a data point of view. And look at all data. That goes from shooting your seismic to a pre-stack, the post-stack, your drilling, your well logs, your borehole data management. That type of data out there will go into that single data platform out there. And the scope we're talking about first, and that will change over time. We're talking first of exploration. Exploration is really the first space you're going out shooting your seismic and do something with that seismic data. Then development, you've decided that you're going to do something. You're going to start developing now your field out there. It's development phase. And well, especially with the phase, we start drilling your wells to start producing out there. We're talking about exploration, development of wells. Together we call it subsurface and a well space. So data descent and support for metamaster data. Making sure that when you're loading your data into a data platform, that you do extract all the metadata out there. Because that is gold for when you start looking for information about the data out there. And of course real-time. Being more and more a real-time company out there. Also in your subsurface space with your reservoirs, we're more of using sensors to real-time collect data about that field. So it's not just a batch environment. More and more comes also a real-time environment out there. But the first point is to put data at the center of what we're trying to do out there. That's the most important point we need to do. And for the rest, applications will fall. I'll come back to that. So data descent and any type of data. If you look at this, the overall high-level picture of what we call then the open subsurface data universe is a data platform and an application platform. And I'll play the calling in a moment. But let's first look at what we call maybe sort of a green calling. It's a send you find that the data platform services. That's where you store all your data. So all the data we're talking about, all structured data, unstructured data, your documents, whatever you have, is loaded via the data ingesting services into your data platform. Your data ingesting services are very important. But that's where you extract metadata. So you have your data ingesting services. That's where you decide for each data type, your file type, what's the metadata I can extract. And the metadata, again, is the information about the data itself. There's a data ingest delay. You extract your metadata. You also look at the data quality and you bring all the data into your data platform service. So all the data you can think about, whether it's your raw seismic to your drilling logs at the other end, will go into that single data platform. Of course, this all sits in a public cloud-based environment. I'll come back to that later. It's a public cloud environment. And of course, you also have your information security out there, which has three layers. First, you use authentication. It's all about who has access to what. Use authorization. If the user logs in, what type of access do the data does that person have? Does it bar rolls and by metadata again? And the third layer is, of course, the protection of the hosting platform itself. And then, of course, you've got the whole support environment. You enter and support the management. You operate your order trails. You're monitoring your CSID development environment, et cetera. So that's very much the green part. And then you also find the green part in country because we also operate environments where we're not allowed to move the data out of country. But if there's a public cloud environment, public cloud is not available in every country in the world. And we have countries out there where we're not allowed to move data outside country. So we have to accommodate also what we call an income dissolution. It means I can put a hardware in country to support that same capability. That's our income dissolution all about. It's that income solution of your public cloud environment. You see here, the green part is the part. So now we talk about the OSU environment as part of the open group. The green part where we as operators do not compete. I do not compete with BP, with Total, with ExoMobil in the green part. We do not compete how we structure data. We will compete about the date itself, but how we structure data, who cares? Nobody would care about it. So the green part is clearly a part we can work well together. And as part of OSU, we are working well together. I'll come back to that where we are on that. The gray part is where we do compete. Those are the applications. Because everybody will have their own set of applications, either developed in-house or budget on the external market out there. And the red part is a clear separation, the API layer, the application program interface. That's a very important thing out there, because that determines the separation. It also tells you what the applications need to do, how the applications will get access to your data platform layer. But what we try to achieve with OSU is a platform capability. We have lots of parties who develop in the gray part, whether you are a startup, whether you are an operator, whether you are a large company, a small company, who cares. But every company will develop applications in the gray part. So we create competition out, I'm going to explain in a moment. Therefore, the API layer is very important, but it gives a well-defined anti-point, which you cannot just change for people who develop applications. And whether we got OSU installed at Exxon, or in Shell, or in BP, or in Woodside, or in Petrobras, we don't care, because that application will run in each of those environments, because the API is always well-defined. Of course, different data out there, but the API is well-defined. So all this is public cloud-based, there is public cloud, public cloud, and public cloud, so no private cloud in this space, apart from the income dissolution. So all data is loaded by the data ingestion services, where we recognize the data types, an exact metadata from the data types. We have the set of RESTful APIs out there. All applications, we support both legacy applications out there and new applications, because our new applications will all, and I talked in a moment, will all be HTML5, Microsoft-based, but of course, we have a large number of legacy applications out there. We still, often, are still Windows, desktop, or Linux desktop-based, and we support these as well, because we have to move the migration forward. Talking security, we're talking about in-country, and of course, we talked about the legacy environment. So if you're talking about OSDU, it's both the data platform services and the application platform services, and it comes for us, really, the single and center of truth over there, because all the data, all our subservice and Rails data will be sitting, will go moving to the single data platform with all the applications sitting on top of that. So that's the overall picture we're creating. So it covers all our guest companies, although there's very little unique to this picture. I mean, if you say I want to use OSDU in production, what we have to change is very much the data ingestion service and make sure that all the data ingestion services do support the data type being used in the production space. So it's very little unique here to say, well, this can only work in subservice and Rails. The only thing which makes it unique is the support for data types out there. Subservice portfolio includes express and development of Rails, and of course, we expect this to grow, most likely also into the production space. Now folks at this, because it's the most complex part, we need to get it right. All data types, structured and unstructured. Unstructured in this space is very important. Unstructured could be your PowerPoints, could be your Word documents, because often in express and they use, they put images in PowerPoints and in Word documents about areas of the world. Yeah, with sort of coordinates. If you have typical search environment in Microsoft, in SharePoint, it's unable to find these images inside Word documents, just recognize them. That means we lose a lot of data in this space because we're unable to find that. So we're also loading that type of data into this OSDU environment where we can extract the images of those PowerPoints and Word documents because there's a load of information in that environment which today we're unable to absorb. Support for data-driven applications, very important. Machine learning, machine learning, machine learning, machine learning again. Why is it so important? Because the first time now, we can exploit every byte of data we collected. And if you need to realize, whenever we do a survey out there, we're collecting petabytes of data per survey. And we do many surveys every year. So there's too much data for normal human being to comprehend. And therefore, using machine learning to go through all of that data and start steering the interpreter and say, okay, focus here, focus there, and focus there. So start steering, primarily managing, driving that interpreter. Therefore, that data platform for machine learning is also very important. We talked about metadata. Never underestimate the importance of metadata. We talked about the single-cell definitions and, of course, the API layer out there. So OSU coverage. If you now look at how that would look like, take this single-cell environment. So although you could say, well, in principle, you could put OSU in one location. Let's put in Dublin, because quite a few, the Amazon dates and sits over here in Dublin for Europe. So you could say, okay, let's put OSU for shell over in Dublin. Technically, that could work. The problem, of course, we have, we've got many so-called 3D applications, a three-dimensional application. That means that the distance between your data and your user can only be a certain distance because otherwise it won't perform. Therefore, you see the first bullet point over there, the OSU location-driven, makes this between use and application, often 3D data. And so, normally, it's about more than 40 or 50 milliseconds. That means you're talking about between here and maybe 44 milliseconds is well into Europe, so we're into the continental Europe. Therefore, what you see over here, in the case of shell, we will be implementing these OSU employees around the world. That means every system will have the full set of metadata. So metadata is replicated around the world. Wherever you sit in this environment, when you sit over here in Europe, in Norway, or you're sitting in Australia, you know exactly what data is available around the world in this environment. The real data will stay within the region so the European data will sit in Dublin, all the US data will sit in Virginia, where the AWS data is for the US, et cetera, et cetera, et cetera. And where we have teams working together, we will temporarily replicate the data as well. But the reason for this abuse is purely driven by the bullet point one. It's the max this you can have because we've got high-intensity 3D applications with interactive use. They're not reporting applications, they're working with that application. So if that is not performed, you get some very frustrated users out there who will shoot you or kill you, whatever you're going to do with you. Metadata is replicated globally. It's very important. So if somebody in Perth and Australia wants to know, did we ever have this type of field before? Did we have drilling this type of environment before? You can ask that question using search out there, using graph out there, and you get the information anywhere in the world, but replicated around the world is the metadata. In APIs, every system has the same environment out there. And extra data stays in the reach of what's being created, like in the U.S., or in Europe, or in Brazil, or in Australia, wherever this is. And of course, it also includes complete development environments. CSD, development environment for DevOps, so if you want to develop your own application out there, it's a complete system, which includes your data platform, your application platform, but also your full development environment. As part of OSDU, we will go beyond the normal standards. We're not going to say, well, here, this OSDU from a standards point of view, we also are working with both Microsoft and Amazon, as soon as we Google, for them to deliver an OSD implementation on their platform. And the first release will come later this year and we'll come back to it in a moment. So it's important to realize we go beyond just delivering some standards out there. But I don't believe just leaving standards out there, that will be good enough. We need to make sure that people have something they can implement, they can work with, and they can actually make use of. And I'll come back also in a moment how we do that. So this will maybe set up a shell if Exxomo would do something like this, it would most likely be very similar because it depends on where you got your reserves around the world. If you just operate like Authnog in Abu Dhabi, of course, you would only have one system in the Middle East. One system that would have reserves around the world. That really drives this. And everybody will have their own implementation so we're not sharing them. So OSD targets very much. Flexbox has workflows. It's the workflows so far the workflows because the silos were very narrow. They were only worked within exploration or in development or in the well space. Now because I have the data available across the whole spectrum so my workflow can start in exploration and go all the way to wells. Again, it's very important. We talked about linus before. If you're sitting in wells, how do I know how did I come to that? How did I make a decision that we drilled it particularly well over there. Now I can go all the way back to exploration, okay, these were decisions made on the way to come to that decision in the end. The field doesn't produce as expected. You can go back again and say, okay, what is different in the field than expected was when we did the whole phase before that. Kubernetes support, familiar with that is my file driven means I can run on every type of endpoint. It's a fully browser based environment. Also important collaboration because teams often work around the world. The teams work on the data often do not sit in one location but often work on multiple locations onto one location. So the data, therefore, collaboration virtual reality, it's quite important. Physics and data, we talked about it. AI, very, very important and the reason I gave you. And it supports current applications out there. So if you use Schlumberger, it's very popular in this market. It's a Windows-based environment. Windows desktop-based environment. Also that is supported in our OZU environment. So it's a good way to promote and live them across the OZU space. It won't have all the full set of capabilities as a native application will have but it will run in that environment and people can have a workflow in the old application and probably in the new applications out there. So what's the big impact of doing this? So we have all subsurface which is exploration, development and wealth data in a single application. So silos have gone. So I can go all the way back from left to right, from right to left whatever I want to do. Date is separate from applications. The application no longer owns the data, what's really today. If you go to Petrel, the Petrel is owned by the Petrel application. That also means I'm storing that data in different formats than today to make sure it's more generally accessible out there. So we need to find out what information is available about wealth, about activities wherever sit in the world. Because my metadata is available around the world. And the last one, we're creating a world out there where many companies start using the same APIs out there. So software companies can start developing applications out there which can be used by a wider group because if you look at the subsurface today you have a limited set of companies out there. You've got people like Sloan Mose, you've got Halley Burton, you've got Baker Hughes, you've got a small set of companies and you have some other ones, but a small set of companies active in this space because as I mentioned before it's a very silent space out there. What we do over here, we separate the data from the application so the data is being handled by OSDU and the application is now handled by the software developers out there. We've got a set of APIs being used by all the operators. So if a startup wants to say, I want to develop software for this space it can be used in many different companies out there, like in the past. The last bullet point is important. Thinking behind all of this to create a market or create a platform out there where people develop software for this environment so more users make use of it and if more users make use of it more people start developing software for that environment. Salesforce.com, Salesforce.com you all familiar with has about 3,000 partners out there developing software for that platform. We won't just get 3,000 out there yet but the thinking is important. The thinking behind it is what it is. You will have other people that develop for your platform so your platform gets more popular, etc. That's the thinking behind the last bullet point out there. Make it accessible. Okay, so if you go a little bit let's do a little bit history over here we started Shell with SDU early last year so Shell started with the subsurface data universe and Shell started with the subsurface data universe early last year and said okay for this to be successful it's far better if we can make this an industry solution because we are far better off if many people develop applications for this space so we get more competition out there more innovation out there okay, together with Phil Biong he's my partner in crime sitting out there so you can blame the two of us to start all of this but we said okay let's talk to some other large majors out there we did that in March last year then we started talking open grouping queue to last year we had a meeting with, well we called Steve he said Steve, this is what we are thinking about what do you think and open group do something like that because we need a framework together we can't just work together as companies without a framework and the open group was that framework out there and we had our kickoff meeting in Houston, in the Galleria in September last year so when we started in September last year with about 10, 9 or 10 operators so Exxon BP, Total Devon so it was mainly US based but also European ones out there and we had a kickoff meeting for the OSGU and that's where we are now, so that's where we started formally but if you now see where we are so this is where our operator members are so these are people who produce oil and gas out there and it's not 1% up to date but it's really relatively up to date so these are, you see all the IOCs out there we're now moving into the NOCs and national oil companies you've got reliance on there from India and to get also the NOCs and the smaller non-convencers in the US also in this space and of course we have several supply companies out there, we have about 45 companies now who have joined the OSGU since I would say late last year because we only opened up for the non-operators in Q4 of last year also notice over here the Amazon, Microsoft and Google why is they important, because they are important for us to deliver the solution into the field so that's how we went to we already went in August last year to these three partners to say can you help us creating a solution in the market which is an operate like Exxon or Shell or BP you can come to you and say please implement OSGU for my environment so these are people either service companies who deliver service into oil and gas market, software companies but in total about about 45 companies today so we created September last year the OSGU forum under the open group and we created at that time three subcommittees the enterprise architecture the day definition and if made security that was created September last year where the number faced the face meeting where the enterprise actually started defining okay what is all about so we looked at what Shell would be doing with SDU, we contributed to the OSU environment to give them a kick start and really started finding with these subcommittees the architecture, day definition very important was the first time in this history in this industry that we align definitions if you go to this industry today and ask them all for what's the definition of a well you will get maybe 50 different answers if not more so we're now also aligning that country that's the day definition work team do and if made security we also reached the editor that the business model to say okay how do we make the sustainable model over time this is important also but what you do the first here is far more important how do you sustain is also keep it agile and make sure we we move fast into the changing world out there so these are subcommittees we of course we meet weekly in calls we've got slack very active slack community out there and we've got regular face-to-face meetings the last one we had in January went along for the enterprise art committee we had about 60 people in the room for three days to define the architecture again so it's a pretty active environment out there and we need to deliver something so this is what our plans for this year are first we want to release a demo release of OSDU release one in by mid-June mid to late June what does a demo release mean it's a full copy of an OSDU environment as contributed by Shell a full copy of all cables we talked about and but it is not yet all the members OSDU 4 members can get access to the environment it's a single environment you cannot yet load your own data in that environment we preload with data it's quite unique we found 200 wells in the Netherlands of all places which we loaded in this environment so we loaded 2000 wells sorry in this environment and some demo applications so this being developed by both Microsoft and Amazon so both will offer an environment people can log on to can play with, can try things out in after sometime in June very soon after sometime in Q3 we come with a full release one out there that release one so any operator can say like Devon or Exxon or BP or Total or Equinox can go to either the Amazon or to the Microsoft shop and say please install in my environment under my subscription a copy of OSDU and of course that will be done then and then to get our own environment we first start supporting with wells out there the wells is the first support out there and later this year we also want to add our part of release too, it's not on the shadow over here also seismic to this, that's for late this year but first release coming out sometime in June and release after full blown release for every subscription have their own copy will be later in Q3 so what does that mean what the game says for this industry because this industry has been in the legacy for the last 5000 years and really needs to come out of that because and it has been driven by the move to data we all mean it's time is everything in this world, you know that time is everything when we started when Philip and I started this discussions in March last year all these place in the same space if you speak to BP somewhere ahead, somewhere behind in the same space how do I move from a physics world to a physics data world and make data the centerpiece of all that we do so it's a game changer it's desperately needed also because we really need this environment at least from a shell point of view we need this environment to be successful in the future in this industry people say we still need this industry we still need all especially gas for many many years to come to really move to another world out there it's a platform approach and what's important there is I have a set of APIs which I make available to any company out there startup company, academia we already have like the universe of Oslo member of OSDU because they develop software for the oil and gas market as well so the platform pros is very important to open up that market that's the thing behind the platform platform platform platform data is at the center to support all AI ML based applications out there and it's impossible for this industry whether screw me say or sell to solve the data problem on your own just not possible people tried it, failed and failed and failed what we do with OSDU we make the data story fully owned by OSDU and we need the supplies to focus on the applications not at the data because they cannot solve the data problem we have to solve the data problem jointly it's very important when we're separating for a software company to focus at the application of workflows at the Microsoft's out there we already just started, we started as a means with the first meeting September last year in Houston what is it, I don't know nine months ago whatever it is but we still have a lot of work, we come out with first software this year but still a lot of work in front of us drive, drive, drive so join the club because it's quite exciting we're working well together with both the operators and the companies we have a very focused team going on to make sure we will deliver in June and we will deliver in June, whatever will happen in this world, we will deliver for those of us not so familiar with the oil and gas industry without that background can you say a little about the amounts of money and people and data involved in the subsurface world yeah, in the well, as I said alone, I think 80% of our data sits in this space if you look at the whole of Shell of course we have integrated oil and gas companies so we got the subsurface all the way to retail and of course now also the new energy business, the home delivery of electricity and what have you so in our case it's between 200, 300 petabyte of data so it's about 80% of our data sits in that space also if you look at software development most of the software development work goes into that space, there's a lot of software development still happening in this space because we believe, like other companies believe, that they have skills in this space unique to that market so also a lot of our software development is going into this space and I can't speak for all of them but that's very similar for the other ICs out there right, right big stuff let's see can you say a little about the business model that supports the oil and gas digital transformation that was created by LSDU the business models are all about a number of things the first one of course is freeing up the data because this world like every other world is becoming part of the digital world out there digital world is driven by data and you have to make sure that the data is available to you and what OSDU does is freeing up the data to make it available to an machine learning environment out there which today when you have all in small silos it's very hard to do because it was ML about being data together and extracting knowledge of that data coming together so it supports the move from this space, from a physics world to the digital world out there this is basically about getting more value for the money that you're investing in there and as I mentioned today most of the data we collect we do not read, we do not look at all the data out there with this we can look at all the data and then start focusing on an interpreter and say okay look at all this data you focus here, here and here you start helping the SME the expert where to focus at okay, thank you next question are you making any provisions to ensure the right quality of the data in developing this new standard? I always say quality, just load the data first because but what's important that you don't spend your time trying to solve your data quality issue get the data loaded because you will never solve your data quality issue by trying to do in the background in the dark out there, it won't happen we tried it for 500 years, it doesn't work so we load the data as is of course whilst loading it we have somebody about the quality of data will highlight poor quality the environment, even the poor quality data when you start using it of course you know what type of data you have and if the results are poor or not what it should be you know the quality of data you can also point to who is accounting for that data because you only make data quality better if you can point to people who are accounting for that data if you don't and if you try to do it all at first improve all the data out there you will never ever get there right has the forum considered the use of the open groups open data element framework standard ODEF is that a familiar term to you as the foundation starting point for the metadata framework if not there are some people in the room who would like to talk to you let's talk about it today let's talk about it today so okay let's see the OSTU data platform would have appeal beyond oil and gas are there any thoughts at this stage about other industries that might fit or find this useful or obviously an evolution of it useful yeah well we had I spoke to people in the mining industry for instance and I said the whole model is not unique to this industry what makes unique is the recognition of the data types and data system layer if you extend that okay I can also recognize production data mining data our focus first to get this problem solved we will our plan is to extend also the production in the same environment so then we go all the way from exploration to producing it but the use of the industry is not inhibited this purely is the recognition of data first the whole development the model is nothing in there the industry I smiled when I saw this one come in what's been the reaction of the incumbent vendors in the marketplace positive or otherwise mixed of course because if you look at the market today there's one company that offers a similar solution with the Delphi platform but there are no other people offering these types of solutions the only one is what I meant to say but you really want to make you have more competition you really don't want to depend on one supplier out there I guess the bigger company like the Hellebertas will be less enthusiastic at the same time these big companies are not able to solve the problem for us, the data problem and in a way we're helping all these companies by saying okay the data we make part of the OSDU environment but the application on top of that you as a company can develop at your own less, at your own space and what have you okay thank you and the last question OSDU enables sharing data access but typically who are the source data providers and what is their motivation for sharing the data most of the data we're talking about is data we either procured or created ourselves because you really talk about the beginning of the seismic environment you either procured data out there or you created data yourself out there of course we also use external data sources and that has so far not been issue at all but the majority of the data we're talking about is either we procured ourselves or we create ourselves part of seismic studio of seismic out there we've just had a flood of about six or seven questions all arrive at the same time so we don't have a chance to take them but I'll pick one at random can you elaborate on how you determine what the API is to support the data models in OSDU we've defined the API search because the APIs cannot change over time anymore because you have a contract with those APIs otherwise an application developer will have a problem that the application works today but not tomorrow anymore so the APIs have defined such that we can keep them pretty static and it really changes in the parameters how you call the APIs but we cannot afford to change APIs in a year from now or half a year from now because then your application developer will have a problem and will kill you I would expect I'm going to squeeze one more in because I can has the front end source has the front end source will well head data standard POSC, POSC, CISA by energetics being of any help to your efforts we look at energetics from a data exchange point of view and this is also a member of our OSDU and the open group and also part of the OSDU of the OSDU forum so we look at energetics as a data exchange format not as a data storage format but as a data exchange format okay, all right Johan we'll leave it there and from everyone at the open thank you for your passion and commitment we look forward to seeing the results