Eliza Strickland: Technology to combat climate change got a big boost this year when the US Congress passed the Inflation Reduction Act, which authorized more than 390 billion for spending on clean energy and climate change. One of the big winners was a technology called carbon capture and storage. I’m Eliza Strickland, a guest host for IEEE Spectrum‘s Fixing the Future podcast. Today, I’m speaking with Philip Witte of Microsoft Research who’s going to tell us about how artificial intelligence and machine learning are helping out this technology. Philip, thanks so much for joining us on the program.
Philip Witte: Hi, Eliza, I’m glad to be here.
Strickland: Can you just briefly tell us what you do at Microsoft Research, tell us a little bit about your position there?
Witte: Sure. So I’m a researcher at Microsoft Research, and I’m working on scientific machine learning in a broader sense and high-performance computing in the cloud. And specifically, how do you apply recent advances in machine learning in the HPC to carbon capture? And I’m part of a group at Microsoft that’s called Research for Industry, and we’re overall part of Microsoft Research, but we’re specifically focusing on transferring technology and computer science to solving industry problems.
Strickland: And how did you start working in this area? Why did you think there might be real benefits of applying artificial intelligence to this tricky technology?
Witte: So I was actually pretty interested in this topic for a couple years now, and then really started diving deeper into it maybe a year-and-a-half ago when Microsoft had signed a memorandum of understanding with one of the big CCS projects that is called Northern Lights. So Microsoft and them signed a contract to explore possibilities of how Microsoft can support the Northern Lights project as a technology partner.
Strickland: So we’ll get into some of these super tech details in a little bit. But before we get to those, let’s do a little basic tutorial on the climate science here. How and where can carbon dioxide be meaningfully captured, and how can it be stored, and where?
Witte: So I think it’s worth pointing out that there are kind of two main technologies around carbon capture, and one is called direct air capture, where you capture CO2 directly from ambient air. And the second one is what’s usually referred to as CCS carbon capture and storage, is more carbon capture in an industrial setting where you extract or capture CO2 from industrial flue gases. And the big difference is that in direct air capture, where you’re capturing CO2 directly from the air, the CO2 content is very low in the ambient air. It’s about 0.04 percent overall. So the big challenge of direct air capture is that you have to process a lot of air to capture a given amount of CO2. But you are actively reducing the overall amount of CO2 in the air, which is why it’s also referred to as a negative emission technology. And then on the other hand, if you have some CCS, where you extracting CO2 from industrial flue gases, the advantage there is that the CO2 content is much higher in these flue gases. It’s about a 3 to 20 percent. So by processing the same amount of air using CCS, you can extract, overall, much more CO2 from the atmosphere, or more accurately, prevent CO2 from entering the atmosphere in the first place. So this is basically to distinguish between direct air capture and CCS.
And then for the actual capture part of the CCS, there’s a bunch of different technologies so you can do that. And they are typically grouped into pre-combustion, post-combustion, and oxy-combustion. But the most popular one that’s mostly used in practice right now is a post-combustion process called the amine process, where essentially, we have your exhaust from factories that has very high CO2 content, and you bring it in contact with a liquid that has this amine chemical that binds the CO2, that you basically suck the CO2 out of the air. And now you have a liquid, this amine liquid with a high CO2 concentration. And because you want to be able to reuse this chemical that binds the CO2, there has to be a second step in which you now separate the CO2 from this amine. And this is actually where now you have to spend most of your energy because now you have to reheat this mixture to separate the CO2 and get a very high content CO2 stream out that you can then store, and then you can reuse the amine. So you have to invest a lot of energy and bring it up to temperature. I think it’s about 250 to 300 degrees Fahrenheit. And once you have extracted the CO2, you have to compress the CO2 so that you can store it in the next step.
And then in between the capture and the storage, you have, of course, the transportation, because usually you have to transport it from wherever you captured it to where you can store it. The most common ways to transport the CO2 is either in pipelines or in vessels. And then in the final step, when we actually want to store CO2, there’s different possibilities for a storage that has been explored in the past. So people that have looked even at storing CO2 at the bottom of the ocean, which we kind of moved away from that idea now. I don’t think anybody’s really considering that anymore. People have also looked at storing CO2 in old mineshafts, and the approaches that are most seriously looked at now, or already used in practice, actually, is storing CO2 in old oil and gas-depleted reservoirs or in deep saltwater aquifers that are a couple kilometers below the surface. The important factors when you look at storage sites and where should I source CO2 is that, first of all, you have to have a large enough volume so that it’s very impactful that you can store enough CO2 there. Obviously, it has to be safe. Once you store the CO2 there, you’d want to make sure that it actually stays where you injected it. And then just as important as also the cost factor, if you can not store it cost-effectively, then it’s just not going to be used in practice. So like I said, this depleted oil and gas reservoirs in these deep-water saline aquifers are right now the storage sites that pretty much satisfy these three requirements.
Strickland: And as I understand it, carbon capture and storage is looked on as a useful technology for this transition because it can help society move away from fossil fuels like power plants that run on gas and coal and factories that use fossil fuels. Those sort of entities can keep going for a little while, but if we can capture their emissions, then they’re not adding to our climate change problem. Is that how you think about it?
Witte: I think so. There’s a few areas like, for example, the power grid, that we have a good understanding of how we can actually decarbonize it. Because a lot of it now is still using coal and natural gas, but we have kind of a path towards carbon-neutral energy using nuclear power plants, renewable energies, of course. But then there’s other areas where the answer is maybe not that obvious. For example, you release a lot of CO2 and steel production or petrochemical production or cement, construction. So all these areas where we don’t really have a very good alternative at the moment, you could make that carbon neutral or carbon negative by using CCS technology. And then I guess also why CCS is considered one of the main options is just because it’s very mature in terms of technology because the underlying technology behind carbon capture itself and CCS dates back actually to the 1930s where they developed this process that I just described, but it captured the CO2. And then as part of other industrial processes, has been used extensively since the 1970s. That’s why we have this whole network of pipelines that you could use to transport CO2. So I mean, in terms of technology, we have a really good understanding of how CCS works. That’s why a lot of people are looking at this as one possible technology. But of course, it’s not going to solve all the problems. There’s no silver bullet, really. So eventually, it has to just be part of a whole bigger package for climate change mitigation.
And it’s going to have to be part of the package at pretty enormous scale, right? What volume of carbon could we be potentially storing below ground in decades to come?
I have some numbers that I got from listening to a talk from a Philip Ringrose, who is one of the leading CCS experts. Roughly, we are releasing about 40 gigatons of CO2 into the atmosphere every year worldwide. And then one of the first commercial CCS projects that is currently being deployed is the Northern Lights project. And at the Northern Lights project, they’re looking at storing about 1.5 megatons initially, and then 3.5 tons at a later stage. So if you take these numbers and you look at the overall global release of CO2, you would have to have roughly 10,000-ish Northern Lights projects, 10,000 to 20,000 CO2 injection wells. So if you hear that, you might think, “Wow, that’s really a lot. 10 to 20,000 projects. I mean, how would we ever be able to do that?” But I think you really need to put that into perspective as well. Just looking, for example, how many wells we have for oil and gas production just in the US alone, I think in 2014, it was roughly 1 million active wells for oil and gas exploration, and only in that year alone, they drilled an additional 33,000 new wells, only in 2014. So in that perspective, 10 to the 20,000 wells, only for CCS, doesn’t sound that bad, is actually quite doable. But you’re not going to be able to capture all the CO2 emissions only with CCS. It’s just going to be part of it.
Strickland: So how can artificial intelligence systems be helpful in this mammoth undertaking? Are you working on simulating how the carbon dioxide flows beneath the surface or trying to find the best spots to put it?
Witte: Overall, you can apply AI to all the different three main components of CCS, the capture part, the transport part, whereas I’m focusing mainly on the storage part and the monitoring. So for that, there’s essentially three main questions that you have to answer before you can do anything. Where can I store the CO2? How much CO2 can I store, and how much can it inject at a time? And then is it safe and can I do a cost-efficiently? In order to answer these questions, what you have to do is you have to run these so-called reservoir simulations, where you have a numerical simulator that predicts how the CO2 behaves during injection and after injection. And the challenge of these reservoir simulations is that, first of all, it’s computationally very expensive. So it’s these big simulations that run on high-performance computing clusters for many hours or days, even. And then the second real big challenge is that you have to have a model of what the earth looks like so that you can simulate it. So specifically for reservoir simulation, you have to know what the permeability is like, what the porosity is like, how the different geological layers look like. And obviously, you can’t directly look into the subsurface. So the only information that we do have is from drilling wells, which usually in CCS projects, you don’t have very many wells, so that might only be one or two wells.
And then the second information comes from basically remote sensing, something like seismic imaging, where you get an image of the subsurface, but it’s not super accurate. But then using this very sporadic data from wells and seismic data and some additional ones, you build up this model of what this subsurface might look like, and then you can run your simulation. And the simulation is very accurate in the sense that if you give it a model, it’s going to give you a very accurate answer of what happens for that model. But like I said, the problem is that model is very inaccurate. So over time, you have to adjust that model and kind of tweak the different inputs so that it actually explains what’s really happening in practice. So one of the big challenges there is that you want to be able to run a lot of these simulations with always changing the input a little bit to see if you get the answer that you would expect.
So where we see the role of AI helping out is, on the one hand, providing a way to simulate much faster than with conventional methods, because like I said, the conventional methods, they’re very generic, but oftentimes, I sort of have an idea of what this subsurface looks like. I only want to tweak it a little bit here and there, which is where we think that AI might be helpful. Because you have a lot of data from just running the simulations, and now you can use that simulated data to train a surrogate model for that simulator. And you might be able to evaluate that surrogate model much, much faster, and then use it in downstream applications like optimization or uncertain quantification to eventually answer these three questions that I initially mentioned.
Strickland: So you’re talking about using simulated data to train the model. How then do you check it against reality if you’re starting with simulated data?
Witte: So the simulated data, you would still have to do the same process of matching the simulated data to the data that you measure when you’re out in the field. For example, in the CCS project, the CO2 injection wells has all kinds of measurements at the bottom that measures, for example, pressure, temperature, and then you have these seismic surveys that you run during injection and after injection, and then you can get an image, for example, of where the CO2 is after you inject it. So you have a rough idea of where the CO2 plume is, and now you can run your simulations, and again, change the inputs that the CO2 plume that you simulate actually matches the one that you observe in the seismic data or matches the information from your well logs. That’s something that’s often done by hand, which is very time-consuming. And the hope of machine learning is that you can not only make it faster, you can also maybe automate some of these things.
Strickland: You’re using a type of neural network called Fourier Neural Operators in this work, which seem to be particularly useful in physics for modeling things like fluid flows. Can you tell us a little bit about what Fourier Neural Operators are, what kind of inputs they use, and what the benefit of using them is?
Witte: Fourier Neural Operators is a kind of neural network that was designed for solving partial differential equations, and the original work was done by Anima Anandkumar, a PhD student, Zongyi Li, and I think Andrew Stuart from Caltech was also involved. And the idea is you simulate training data using a numerical simulator where you have a bunch of different inputs that could be, for example, the earth model, what does the earth look like? And then you simulator output would be how does the CO2 behave over time? You have many different inputs, and then typically, you train this in a supervised fashion where I now have thousands of training pairs. And then you would train, for example, a Fourier Operator to simulate the CO2 for a given input. And then you can use that in these downstream applications that require a lot of these simulations.
Strickland: Okay. So to bring this back to the physical world, what happens if carbon dioxide that’s injected into a subsurface aquifer or something like that doesn’t stay put? Is there a safety problem? Could it potentially cause earth tremors, or is it just that it would negate the effect of putting CO2 underground?
Witte: There’s definitely a risk. It’s not risk-free, but I initially overestimated the risks because kind of the mental picture that I had is that there’s a big, empty space in the subsurface: You inject CO2 as a gas, and then you only need the tiniest leak somewhere and the whole CO2 is going to come back out. But when you actually inject the CO2, it’s not a gas anymore because you have it under very high pressure and very high temperature, so it’s more like a liquid. It’s not an actual liquid. It’s called a supercritical state, but essentially, it’s like a liquid. Philip Ringrose said, “Think of it as olive oil.” And then the second aspect is that in the subsurface where you store it, it’s not an empty space. It’s more like a sponge, like a very porous medium that absorbs the CO2. So overall, you have these different mechanisms, chemical, and mechanical mechanisms that trap the CO2, and they’re all additive. So the one mechanism is what’s called structural trapping, because if you inject CO2, for example, in these saltwater aquifers, the CO2 rises up because it has a lower density than the salt water, and so you need a good geological seal that traps the CO2. You can kind of think of it maybe as an inverted bowl in the subsurface, where the CO2 is now going to go up, but it’s going to be trapped by the seal. So that’s called structural trapping, and that’s very important, especially during the early project phases. But yes, you have these different trapping mechanisms that are additive, which generally, I mean, even if you would have a leak, the CO2 would not all come out at the same time. It would be very, very slow. So in the CCS projects, they have measurements that measure the CO2 content, for example, so that you could easily or very quickly detect that.
Strickland: And can you talk a bit more about the Northern Lights project and tell us about its current status and what you’re working on next to help that project move forward?
Witte: Yeah, so Northern Lights describes itself as the world’s first open-source CO2 transport and storage project. It doesn’t mean open-source in the sense like in software. What it means in this case is that they essentially offer carbon capture and storage as a service so that if you’re a client, for example, you’re a steel factory and you install CCS technology to capture the carbon, you can now sell it to Northern Lights, and they will send a vessel, pick up the CO2, and then store it permanently using geological storage. So the idea is that Northern Lights builds the transportation and storage infrastructure, and then sells that as a service to companies like— I think the first client that they signed a contract with is a Dutch petrochemical company called Yara Sluiskil.
Strickland: And to be sure I understand, you said that the companies that are generating the CO2 are selling the CO2 to the Northern Lights project, or is it the other way around?
Witte: How I think about it more as they pay for the service that Northern Lights picks up the CO2 and then stores it for them.
Strickland: And one last question. If I remember right, Microsoft was really emphasizing open-source for this research. And what exactly is open-source here?
Witte: So the training datasets that we create, we’re planning to make those open-source, the code to generate the datasets as well as the code to train the models. I’m actually currently working on open-sourcing that, and I think by the time this interview comes out, hopefully it will already be open-source, and you should be able to find that at the Microsoft Research industry website. But yeah, we really want to emphasize the open-sourceness of not just CCS itself, but the technology and the monitoring part, because I think in order for the public to accept CCS and have confidence that it works and that it’s safe, you have to have accountability and you have to be able to put that data, for example, the monitoring data out there, as well as the software. Traditionally, in oil and gas exploration, the data and also the codes to run simulations and to do monitoring are. I mean, the companies keep it very tight to the chest. There’s not a whole lot of open-source data or codes. And luckily, with CCS we already see that changing. Companies like Northern Lights are actually putting their data on the web as open-source material for people to use. But of course, the data is only part of the story. You also need to be able to do something with that data, process it in the cloud using HPC and AI. And so we work really hard on making some of these components accessible, and that does not only include the AI models, but also, for example, API suppresses data in the cloud using HPC. But eventually, we were really hoping to-- once we have all the data and the codes available, that it’s really helping the overall community to accelerate innovations and build on top of these tools and datasets.
Strickland: And that’s a really good place to end. Philip, thank you so much for joining us today on Fixing the Future. I really appreciate it.
Witte: Yeah, thanks, Eliza. I really enjoyed the conversation.
Strickland: Today on fixing the future, we were talking with Philip Witte about using AI to help with carbon capture and storage. I’m Eliza Strickland for IEEE Spectrum, and I hope you’ll join us next time.