 I will be talking about some of the outcomes of my PhD. So my PhD thesis looked at some of the key issues still impeding the large scale deployment of carbon capture and storage today. And hopefully by the end of this talk, you'll get a sense of what these are and why it's important to address these and how I try to do this in this work. So I defended my thesis in June successfully. But one of the first questions that I was asked when coming into my examination was what brought you here today? And that totally took me by surprise because I imagined being in a closed room with two of the biggest efforts in my field that we would just dive right into the technical stuff. But they wanted to know why I was working and what I was working and a bit about who I was. So for today's presentation, I decided that I would tell you a bit about what that is. So I think that the first time that I really made the strong decision to work on environmental sustainability and climate change mitigation was when I first heard about COP 15 that took place in Copenhagen. That was one of the first UN Framework Convention on Climate Change's conferences, at which the expectation was that some kind of agreement would come at the end of it and which nations would make a decision on reducing their greenhouse gas emissions as a whole. And that conference being hosted in Copenhagen, the country of which I'm partly from, I was rather disappointed because I thought that Denmark was kind of the holy grail for sustainability. And it was mostly second nature, whether it was recycling for generations, but they weren't able to get to that kind of outcome. So going from there, I decided to study chemical engineering at Imperial College in London. I worked for a few summers in a row in hazardous waste treatment at Vile environmental services. And that's when I first or mostly came across something called the Montreal Protocol, which banned the use of ozone depleting substances. And that gave me hope for a similar type of policy around greenhouse gas emissions. Following that, with some time spent at Columbia University where I gained some backbone in economics, I then went on to do a PhD at Imperial in carbon capture and storage. CCS was something that I saw throughout my undergraduate period working on one of the first small pilot plans that the university had looking at CO2 absorption. And then following that, I got the chance to spend some time here at Stanford where I worked with Adam and Mohamed and met some great faculty here, which also brought me to California and an inspiring state in its ambitions towards cleaner greenhouse gas emission goals, and finally brought me to my current job as a consultant at Energy Environmental Economics, where I work on a lot of resource planning for a future world in which or a current world for California, but a future world for more states and more countries around the world, where renewables are a large part of electricity generation, how do you plan for that kind of world and the policies that come into play, integrating resources, batteries and storage capacity that's needed to help those renewable resources and so on. So that's a bit about me diving into what my talk is going to be about. So first the motivation, and then I'm going to be outlining four key questions that made up my thesis. So they may not mean much right now, but I'll just introduce them. So one, how does the value of financial risk impact the costs of CCS? Two, what's the value of financial and commercial incentives to enable large scale deployment of carbon capture and storage? Three, will varying and gradual deployment of CCS have any impact on storage, on CO2 storage? And finally, how could we potentially, looking on a large scale, how could we reduce the cost of CO2 purification? So to start out with, what is carbon capture and storage? I'm sure most of you are familiar with this, but just a quick background. So CCS is a means by which you can capture CO2 from its point source emissions. So these point sources can be power plants or industrial sources. And when capturing that CO2, you need to strip it from other gases, purify it, and compress it to a level at which it's possible to transport it, typically via pipeline, and then store it in deep geological reservoirs that use the type of trapping mechanisms that have trapped oil and gas for millions or centuries and would provide safe storage for CO2 over centuries to come. Another one of the uses for CO2 is in CO2 enhanced oil recovery, which I'll be introducing a bit later in my talk. So just to point out, one of the most expensive part of CCS is here on the capture side. And finally to note, in terms of industrial emissions, so whether it's steel and iron production or cement production, CCS currently is the only way to decarbonize those sources in addition to improved energy efficiency. That's yeah. And why is that important? So Sally touched on this. The IPCC, just the Intergovernmental Panel on Climate Change, just released their 1.5C global warming report that has the objective of telling us what the impact of 1.5 degrees C is and how would we get to limiting our temperature rise by that amount. And this graph here shows on the y-axis the current CO2 emissions per year. So we're currently at around 40 billion tons of CO2 per year. And in order to what this compilation of studies has shown is that in order to limit this temperature rise, we need to essentially get to net zero carbon emissions by the middle of the century. And those four graphs on the right illustrate how this net zero carbon emissions, how we would get there and with what technologies. And in all of these and all four pathways with the exception of P1, which essentially assumes completely drastic behavioral change and economic change, these all include some form of carbon capture and storage. Or as well as negative emissions, mostly to make up for the industries in which you can't replace the processes by renewables in which you can't mitigate for CO2 apart from with CCS. So that's to put into context why CCS is important. But so given this, that's pretty straightforward. We should be starting to deploy CCS on a scale that meets these gigaton levels of emissions reductions. CCS today is barely deployed at all. In fact, there are two CCS with power plants deployed in the world, and there are one in Texas and one in Canada. And then 21 projects in operation or under construction. The rest of these being on industrial emissions or natural gas processing, which is the cheapest way in which or the cheapest form of CO2 capture. And depending on where you decide to deploy CCS, and presumably that will be a global problem, the costs will be different. And depending on different levels of technological advancement and learning. So here I'm showing a table that gives you a range of these levelized costs of CCS, so meaning when you annualize the capital investment that you would require in CCS, how much does it cost? So in this first column are the first of a kind costs for CCS. So that means the first few plants, depending on what country you're in, they'll typically be the most expensive. And then as you gain learning, learning by doing, you would presumably start reducing the cost of CCS by 10% to 50%, depending on the type of point source that you're applying your CCS to. So that leads me on to my first question. So technological learning has been widely studied and the impacts of that and the impacts of learning by doing, but the levelization and the levelization factors or factor required to get to this cost of CCS are less well understood. And so what I wanted to look at is how does the financial risk of investing in this technology have an impact on its cost? So in order to get to the answer for that, taking a few steps back, I looked at what the typical economic model would be to get to this levelization factor. And so the capital asset pricing model, CAPM, is typically what's used to inform the return on equity that an investor would require before making an investment in the energy sector, for example. And that equation relates the risk-free rates. So that would be the rate that you would get when investing in a government bond, for example. So the most risk-free type of investment. And then your systematic risk, which is beta here. And that essentially applies a risk value to your asset and how that compares to and how volatile that asset would be compared to the market in which you could be investing. And then the rate of return on that market minus the risk-free rate, which is considered the market risk premium. And with that equation, you get the return on equity. And that return on equity is typically what informs an investor's internal rate of return, depending on how much debt that investor decides to take on or not. And then subject to inflation, that gives you a discount rate, DR. And then depending on how long the length of your annualization, you get your capital recovery factor. And that capital recovery factor is what will inform your levelized cost of CCS and get to this dollar per tonne of CO2 yearly value that is kind of thrown around, but the assumptions behind it are sometimes less clear. And so here, I'm showing a graph of the expected equity return on the y-axis as a function of that beta, so that risk and the systematic risk that you consider your asset to have. And depending on the market risk premium that you take, so within the energy sector that's typically within this range, you would extrapolate to get an expected equity return and have that inform your capital recovery factor. So here, I've assumed two on the high risk side, a 20% return on equity, and on the low risk side, a 10% return on equity. And what that informs is this levelized cost of CCS on the y-axis. And then what I'm showing here are the first bar shows what this levelized cost would be in the high risk scenario for a first of a kind CCS plant, the most expensive prior to learning by doing. And the next one is the same high risk, but for an enthivokind type plant in which a lot of learning has been achieved and the capital cost of investing CCS has reduced. Here, you have the low risk for a first of a kind plant. So this 10% IRR. And then finally, what a low risk levelized cost of CCS would be for an enthivokind plant. And what I want to show with these arrows is that by de-risking essentially your investment, you can achieve something like between 43% to 48% reduction in the cost of CCS when you look at it this way. When comparing that to what the difference, what the reduction cost would be with technological learning, so going from first of a kind to enthivokind, that reduction is slightly lower and essentially showing that de-risking CCS can go just as far as what we've been looking at in terms of technological learning if this is the way we can consider cost and putting forward the technology. So that's my answer there. What that then brings me to is my second question. So presumably, financial risk would be subsided if an investor knows what kind has a security of the type of incentives that they would have in investing in carbon capture and storage. But what is the value of these commercial incentives and how much of it is required to achieve this gigaton level of CCS deployment that's expected to get an equally gigaton level of greenhouse gas emissions? So one of the main markets for CO2 currently, or one of the largest at least, is using CO2 for enhanced oil recovery. So that's typically a tertiary means of oil production in which you would inject your CO2 at high pressure and that becomes missable with the oil remaining. So that will have gone through primary and secondary production and has a substantial amount of oil that's immobile. But when that CO2 becomes missable with the oil, it changes its properties in order to increase its mobility and producing it on the other end. That CO2 is then recycled back and finally stored long term. And on this end, you have your oil production as a product. And from that, you can gain revenue. And I just wanted to highlight here that the 45Q tax that's now put in place would also compensate this investment with $35 per ton of CO2 tax credit for that CO2 that's sequestered and used for EUR. The credit is higher if it's just sequestered. So in order to answer this question, so what are all these different incentives for CCS and how do they work together? This is the work that I did here at Stanford with Adam and Mohamed, who are here in the audience. And what I wanted to look at was the interplay of multiple of these incentives. So on the one hand, this oil price, thanks to the CO2 EUR and the product there. On the other hand, the carbon price. So I have a little image here of William Nordhaus, who just was awarded the Nobel Prize in Economics, shared Nobel Prize in Economics, and is a big advocate for a global carbon price. And finally, and technological learning as well. So how would, as CCS is deployed over time, how would technological learning enable a larger scale deployment of this technology? So in order to answer that question and answer it in a way that we felt confident about, we developed this model called model of iterative investment in CCS with CO2 EUR, otherwise known as mice. And I'm going to go through some of what goes into that. So apologies for the very busy slide. But there are a number of inputs that go into mice. These include the oil price, the CO2 tax, or CO2 price, rate of oil price change, technological learning, and the capital costs that you assume the CO2 capture to be at. So starting at an initial year, so the iterative nature of it is that you'll go through multiple years. But starting at year one, we generate 1,000 potential fields based on a set of characteristics. And the combination of each of these characteristics, so permeability, porosity, and depth, give a unique kind of stamp to that field. And that field will look like what a typical EUR type field could be. So taking each one of the 1,000 fields, we'll go through a selection of production profiles, coupling that with the CCS capture capacity, and looking at a deployment strategy based on that combination, computing the net present value of that combination, and finally choosing which capture size and oil field combination would give the highest net present value. So imagining that an investor would mainly be driven by the net present value that they can get from that whole combination, from the capture plant, the transport, and the EUR and storage. And then that's repeated for each one of these 1,000 fields. And each of those combinations are then sorted by best to worst. And only the top ones that meet the growth limitations imposed would then go ahead and be deployed. And finally, those fields that are selected in that year are eliminated from that pool of future selections. And then we go on to the next year, and that's done over an iteration of 35 years. The model is open source and can be modified, adjusted, tested, whatever you like. So what are the results from this model? So looking at, we were able to look at 36,000 total combinations, so 14 oil prices, 13 CO2 prices, 11 technological learning rates, six capital cost investments, and then three oil rate changes. And in this graph, I'm showing the combination of two of these input variables. So on the y-axis, the CO2 tax achieved by 2050, assuming a change in the CO2 tax or price. And then on the x-axis, the price of oil. So where we stand today, presumably, at about $70 per barrel and with the 45 Q tax, which gives a $35 per ton of CO2 credit, we still do not get to this gigaton expected or wished for deployment. Looking at technological learning and the impact that that has, so on the y-axis here, going up, you increase the amount of technological learning. And so what I forgot to say was that the rate of technological learning basically means that for every doubling of your capacity, how much will that cost be reduced by? And so that's this rate here. And then on the x-axis, what's the CO2 tax? So barring any kind of CO2 price, technological learning can go a long way. But this 20% rate up here is what's considered for renewables and not so much for such a large scale plant like CCS. So when considering something more along the lines of 10%, that gigaton level is still not achievable. So the answer to the second question is CO2UR does help catalyze the deployment of CCS and, in essence, does reduce the cost of capital through the learning that is inherited from this. But additional incentives would be needed. So whether it's a carbon tax price of over $70 per ton or an oil price that's higher than what it is today or very high rates of technological learning. So that leads me to my third model, to my third question. So thinking of a world in which CCS deployment may come gradually or will be varying, we may not need it all the time. Will there be, may there be any effect on a reservoir that's able to otherwise store that CO2? So in order to answer that question, being based in the UK, this is a UK model of one of the main storage things considered for the UK when considering CCS, called the Bunter Sandstone. And so what I wanted to look at here was considering 12 injection sites and using reservoir simulation tools. And yeah, imagining that the UK does take up CCS and would have one of its largest power plants capture its CO2 from that point source, would you still be able to inject at the levels expected? And how would deciding on your infrastructure deployment have an effect on this? So at the top here, what I'm showing is these varying rates of CO2 supply assumed. So they equate to one large coal-fired power plant in the UK, Drax Power Station. And so comparing this varying every five years or every two and a half years to a constant average rate, I looked at the reservoir response. So on the left, this shows the plume migration. And on the right, the average reservoir pressure. And in essence, it seems like the reservoir is pretty resilient. And regardless of these variations or this constant average injection, the plume migration and the average reservoir pressure remain the same. And the CO2 storage supply is achievable. In a second set of injection scenarios, I looked at different types of infrastructure deployment. So on the first looking at if you just start injecting all 12 sites and you inject gradually, would that be any different from choosing only half of those sites or a quarter of those sites or choosing the deepest of sites? And sorry, so the explanation behind choosing the deep sites is that the pressure limitations would be less stringent than in more shallow areas. And turns out there isn't much effect. So this essentially shows that you're able to achieve the rates of injection that you're looking at when looking at this kind of 50 to 100 year timescale and injection rates of 0 to 16 million tons of CO2 per year. So this was the subject of a paper as well. But the answer was mostly not so interesting, but at least reassuring in the sense that the outcome is you could presumably model CO2 storage as a constant or average injection rate, even though you may want to consider varying deployment or even though you would assume varying deployment of CCS and varying capture rates. So if all is good on the storage side, how could we possibly address the CO2 purification side on a holistic kind of systems you approach in order to reduce its overall cost? So in order to answer that question, I looked at a particular type of CO2 capture. So oxycombustion capture is one of three of most advanced and considered capture technologies. So the way oxycombustion works is that instead of burning your fuel in air, you would burn it in almost pure oxygen. And then that flue gas is at higher purity than other types than you would usually from a power plant. But that still needs to undergo an additional purification, dehydration, and compression in order to go to the pipeline system. And this part of the process in oxycombustion takes or still makes up 50% of that capture cost and is currently required to achieve a very high purity of CO2. This doesn't mean much, but what these different process models are are my attempts to model this compression and purification unit at different levels of complexity. And for different levels of complexity, you can achieve different levels of purity. And with different levels of purity, you achieve different levels of cost. So the process achieving the lowest purity is also the one that will be the least cost. And finally, these also have different levels of capture efficiency. What that means is typically when capturing CO2, we assume this 90% capture rate, 10% of it lost to the atmosphere. And that's typically because of the separation process in order to get that CO2 to very high purity. But when considering lower purity, you can achieve a much higher capture efficiency. But currently, the way oxycombustion would be modeled is down here. So the highest purity, the highest cost, and the lowest capture efficiency, which is that 90%. So taking an economic standpoint, assuming that an investor would want to invest in one of these CPUs and it would be a revenue-driven investment, what would their, and in order to meet their required rate of return, what would the price of that CO2 need to be in order for them to go ahead and invest in that CPU? So what I'm showing here is the projected CO2 price per ton on the y-axis here. And depending on those four CPU processes, I get a different CO2 product stream purity. So for that highest purity process, the price on CO2 needs to be at its highest in order for the investment to go ahead. And that would be up at $32 per ton. But if you go down to this very low, much lower product purity, you can reduce that cost by about 40%. But the CO2 purity requirements currently at injection point would stand somewhere around here. But then the question would be, why design for something that's all the way up here and would still mean a 15% cost difference? One of the things historically, the way CCS has been designed has been for the CO2 source to go to one CO2 sink and assume that the transport network is from one to one. But in reality, as we assume CCS deployment to develop, you would have multiple point sources going to one injection sink. So given this injection sink requirement on purity, what kind of optimization can we do to get these purities here at minimum cost? In order to answer that question, I looked at a case study in the UK of 20 point sources that would all lead to the same storage sink and achieve this 96% purity. And looking at the system as a whole and minimizing the total capital costs and total operating costs, what would the costs of your system be when still achieving that final purity goal? So in this graph on the y-axis, is this price of CO2 at the trunk line similar type or the same type of methodology that I explained earlier in terms of an investor or a group of investors wanting to get their internal rate of return? What would that price of CO2 need to be in order to match that? And on the x-axis, is this CO2 mass fraction or CO2 purity? And achieving this 96% as opposed to this prescriptive 99.9% can reduce that cost by about 18% in this case study. But the idea is that this is an example for other similar types of case studies. Looking at how if you imagine the transport network, why not imagine that multiple CO2 point sources could share their compression and purification unit and capture system, then what would the implications mean in terms of capital cost if you'd have a much larger compression purification unit four times the size rather than four much smaller ones? And modeling that in, I forgot to mention, those models were done in Aspen, so process design models. You can reduce the capital cost by about 16% and reducing the operational cost as well. So in summary, there are a substantial amount of cost reduction that can be achieved from looking at CO2 purification deployment as a wider system rather than just an individual capture plant and point source. And so sharing that infrastructure and having a more holistic systems you can be very beneficial in achieving this cost reduction and breaking this barrier of high capital cost CCS investment. So I promise that at the end of my talk, I would be giving you some answers to some of the key issues impeding large scale deployment. So the first being that financial risk has a large impact on the cost of CCS considered, on a levelized basis. Two, the substantial amount of commercial incentives and financial incentives that still need to be put in place in order to catch the deployment of large scale CCS of the CO2 price of the order of $70 per ton, even in the presence of CO2 enhanced oil recovery and the market incentive from oil production. Finally, varying CO2 injection does not have much in effect on storage. And that we can eliminate that from the modeling process, presumably. And finally, providing incentives for multiple systems and shared infrastructure can go a long way to reducing the cost of CO2 capture as a whole. So with that, I invite you to four questions. OK, so I will start asking questions. So like normal, we'd like to start with the students. So anyone who's a student? Yeah, please. So on the slide set, you showed all these different projects going on throughout the world in the CCS. And it was very noticeable that most of them were in the US. So can you comment a little bit why Europe is less interested in deploying more of this approach? And how Europe can be incentivized to do so? That's a good question. So yeah, so I think one part of the answer would be that North America has a lot of experience with CO2 transport, CO2UR, first of place in the US several decades ago. There is that the graph does show that Norway has had one of the longest-standing CO2 injection sites at Sleipner. The UK had a CCS competition that was canceled in 2015. I can't comment on EU climate policies, unfortunately. I don't know why they're not doing more about it. The EU as a whole is looking towards more renewables. The UK still is planning on having some deployment of CCS. But really, they haven't made substantial progress to achieve any of the IPCC targets with or without CCS, really. So does that answer a bit? It's probably the path of the wellness. Also, they are cold-sweet. You can tell the politics to the contractor. OK, more questions. Students, questions. OK, over there. I have a question on your 0.3 in your simulation. You're sure that it doesn't have an impact, I guess, the rate at which you inject. But what were you looking at? Was it related to the ability of the reservoir to store? Did you look at the geomechanics, the frag radiants, or the seismicity induced? So no, not the seismicity induced. But with every injection site, you have a pressure limitation, which is based on a percentage of that fracture pressure. So in order to ensure safe injection, what I was looking at was the plume migration, the average reservoir pressure, and not the geomechanics. No. And the time scales also were over looking at several years, so 50 to 100 years of injection. And I guess, not any hourly observations, but more in terms of the long term, could that reservoir actually meet the capacity that it was expected to at those subject to varying deployment of CCS? OK, more questions. OK, over here. You talked today about three of us and I'm sure you can see with the trade-offs between those, whether or not they're related. Is there values to it before, or is there one to prioritize? OK, so yeah, I guess I didn't talk about what the trade-offs are in terms of purity. So one of the main thing is that you want to get rid of the water that results from combustion, because you want to avoid corrosion, whether in your transport pipeline or in your casing upon injection. And additional thing is that the limiting inert gases, so the amount of nitrogen, oxygen, and argon that you have is because you want to limit the volume of your pipeline, or limit the amount of compression that you need in order to get the density that you want to be transporting your CO2 at. So is there a trade-off? I mean, the trade-off being the less you capture, the more CO2 you're emitting, the higher purity you expect, the higher the cost. So yeah, until there is a price on carbon, then yeah, then that trade-off will be a different conversation, I guess. OK, we'll have another student question, then we'll go over there and then over here. OK, back to the back. You mentioned that the large-scale EOR deployment would be a carbon price over $70 from carbon from the outside, which kind of seems like a lot to expect from policy makers. So do you think that we can basically deploy EOR to an extent that meets our climate change mitigation goals, or do you think that time and effort would better be looked at and invested into other areas of CCS or whatever it is? OK, let me give that a quick thought. So you're saying that the CO2 price is very high, the $70 per ton. OK, so what I also wanted to show is that CO2 EOR does, and I have some slides here, does help catalyze the deployment of CCS. So that is a benefit in terms of coupling the two and taking into account the revenue from oil. Whether $70 per ton of CO2 is a very high price, I mean, given the IPCC report and the implications of not limiting our greenhouse gas emissions and not putting a price on carbon, that doesn't seem that high to me. But I mean, and then in terms of your point on renewables, yes, we need to be investing more in renewables, which what California is very progressive about, but the US needs to be as a whole. But CCS is key because it also addresses other types of emissions, industrial emissions, but also presumably some type of dispatchable generation or a small amount of gas that you may still want online. And with that, I didn't mention countries where they may not necessarily want to strand their assets and things like that, in which case CCS is still the only way to decarbonize those sources. OK, we'll go over here. I think you had a project off of the English Coast, the reservoir of the winter sandstone. If I saw the graph right, I thought I saw that the maximum amount of CO2 injection was 14 million tons of CO2. No. Up in the left hand on the y-axis, in any one year. Oh, that's just the amount that I tested. OK. 14 million tons of CO2 sounds like a lot, but the IPCC report that just came out said, we're going to need to store, cut out 1 million gigatons of CO2. And that would meet, we'd need around 71 million projects like that in order to store that amount of CO2. So I'm wondering if we could find a way where the urgency of our problem is taken more into account rather than the financial incentives. Absolutely. I completely agree. But currently, it seems that financial incentives and economics still seem to remain the main driver for any type of climate change, or most types of climate change mitigation measures. While our PS policies are very high currently, there's also a big backbone of economics that's pushing that. Renewables have come to a point where they're cheap enough that you need less subsidies to get them going. So yes, I think that it's an urgent problem and that it shouldn't just be the financial incentives, but that seems to be the current reality of how to get to these solutions. Yeah. Do you just want to say a little bit, because it's not millions of times more. So if you were injected 14 million, do you want to? So you're testing 14 million. You do 14 million, and if you wanted a gigaton. I mean, that's only 14 million per year, and that's only the UK. Yeah. But whether it was in the UK or around the world, you'd make 71 million projects like that, and what's the score? No, you don't love it. It's like 60 projects, or 71 projects. Yeah, so it's not millions of projects. It's just a, yeah. I mean, your point is taken scale up is important, but it's not that big. OK, Lynn. So your separation scheme had an air separation unit upstream of burning something with pure oxygen. That is the sort of scheme that the net power, methane, private methane, naturally aspirated power point with a supercritical CO2 turbine is applying. And I know you didn't look at that, because it didn't exist at the time if you were working on all this. But do you think that, do you buy their cost estimates, and do you think that looks like a viable pathway forward for doing more CCS, at least in Texas, where they have lots of work to do? Why not? You can probably comment on that better than I can. I guess I'd ask, what's your feeling? What do you think will happen over, say, the next 20 years? Do you see a reasonable amount of CCS, or does it even if we haven't gotten to the digital time scale, or does it kind of, you know, dribble along the way it has in the past? I'm hopeful in the sense that I, I mean, even with California, who are very anti-CCS, they've just signed for their zero carbon electricity generation. That's not 100% renewable, and to get to zero carbon, there's something that you're, you know, there's something that you need to make up for. This whole negative emissions discussion, bioenergy with CCS is still the main proponent of that. And for that, you need CCS technology to be deployed and at a scale and cost that you can swallow. So yes, I think that, and particularly with this report, that is quite shocking in terms of the, in terms of the implications for humanity and nature. The urgency should be there, and CCS is definitely what would get us to that net zero in any, in any way that's not drastically changing the way we live. Okay, wow, now lots of hands up. We're only gonna take a few more questions. I say something bad, and we had one over here. Was it you? No, it was you, yeah, or yeah, and then we'll come back to someone in the middle. Yeah, sure, you, yeah, no, you, you will, yeah. What's the total capacity for CCS technology for global? Oh, so different estimates, but for example, there's a study that looked at 54 of the largest oil basins. And if you consider just that, you'd get between 140 to 300 gigatons of CO2 stored. And then looking into saline aquifers that adds about another 1,000 gigatons of CO2 storage. So storage is given the rates at which we assume CCS to be deployed, which is not mitigating for all CO2 emissions by any means. The CO2 storage capacity globally seems to be sufficient to meet that. Okay, actually, we're gonna go to Adam. For the last question, sorry everybody. But you'll hang around after this for a moment. So Claire, it's fun to see the other chapters you're working on. I had a question about the purification. So it strikes me that there's like a variety of, or there are a variety of constraints that lead you to something like a 96% purity. So you had mentioned corrosion with moisture or with water, as well as wanting to limit your sort of overall volume of the systems that we're living in. It strikes me that those are pretty different kinds of constraints. So did you look at, for example, loosening one while keeping the other one tight? So for example, I can imagine a cheaper system would involve deep dehydration, but for example, you could leave, relax leave to maybe 10% or 100% hydrogen and argon to sort of cut down on that more expensive separation or my understanding is that it's more expensive than the dehydration. Because you would get sort of a new kind of match where you sort of maintain one stringing and loosen the other. Yeah, so I mean, I didn't look at it from a chemical perspective, but that least purity, least cost model, in fact, all of them considered dehydration. So they'll all be, all of the streams will be dehydrated. That's kind of considered as the minimum requirement. So these all achieve dehydration? These all achieve dehydration, yeah. And then it's the amount of inert. So that one up there is something like 15% inerts. And then I think that one was like seven. This is four and this is 0.01. I'm sorry, one other thing, did you, once you did that, do you have the coupling? So this less pure separation, do you then grow the size of the remaining infrastructure in order to accept a higher throughput? I guess I'm saying, so you have the less deep separation is cheaper, but I guess do you debit against that the increased cost of, let's say, pipe diameter and number of wells are required to inject a larger volume or mass to it? So no, so the quick answer is no, but this, so looking at this multiple point sources to get to that considers a few or two to three of them being from that least cost one, and that, yeah, so it's an assumption that by, in the transport network, these different streams mix and they would achieve purity at which you'd be able to transport and trade off, yeah. Okay, I think we need to wrap up. So thank you very much. Appreciate it.