Conventional geothermal energy has long been seen as geographically limited, with most U.S. resources concentrated in the West. However, a new wave of AI-powered exploration, led by Zanskar, challenges this view by identifying vast, previously missed geothermal resources using legacy geological data. The company argues that the U.S. possesses up to 10 times more undiscovered potential than the U.S. Geological Survey estimated—possibly reaching terawatt-scale capacity. A key validation came from the Lightning Dock site in New Mexico, where a reactivated geothermal field produced 10 to 40 times more energy than originally predicted. Zanskar’s AI models, trained on scattered historical data including well logs, surface observations, and geophysical surveys, have evolved significantly, improving predictive accuracy over time. The company emphasizes that natural permeability in hot rock formations is far more cost-effective than artificial permeability in enhanced geothermal systems. They have secured a 100 MW power purchase agreement with Envy Energy in Nevada, using a portfolio-based approach to manage risk and provide firm, carbon-free power. The technology also suggests that deeper drilling—especially in arid and geologically active regions—could uncover new resources, with evidence of water in deep wells indicating hidden permeability. This shift not only redefines geothermal’s potential but also positions it as a scalable, low-cost, baseload energy source that could expand beyond the Western U.S. in the future.
all right greetings hello everyone this is volts for september 9th 2026 is there way more
conventional geothermal than we thought i'm your host david roberts conventional geothermal power
depends on the subterranean presence of hot permeable rock which tends to cluster around
tectonic and volcanic activity that makes it a geographically limited resource in the u.s
it's almost all in the west but how much is there the last comprehensive assessment by the u.s
geological survey in 2008 identified about 9 000 megawatts of discovered
potential and estimated that there were about another 30 000 megawatts still undiscovered
that is a theoretical ceiling of around seven or eight percent of current u.s electricity demand
nothing but not a game changer that is why most of the excitement in geothermal over the past
five years has been about enhanced or advanced geothermal various attempts to make
the technology less dependent on geography here at volts we have talked with fervo which drills
horizontal wells and fractures the rock between them to build its own hot rock reservoirs and
quays which uses microwaves to drill super deep on the theory that all rock is hot if you go far
down enough a company called zanskar is making a different bet its founders carl hoyland and
joel edwards argue that the u.s gs numbers rest on old crude data from an industry that stopped
seriously exploring in the 1980s they think there are roughly 10 times more undiscovered sites than
the usgs estimates and that each one can produce roughly 10 times more energy than it estimates
that's true we're no longer talking about tens of gigawatts we're talking about terawatts and that's
true but we're talking about solar panels we're talking about solar panels we're talking about solar panels
enough to supply all u.s electricity demand all of it reachable with old-fashioned easy to finance
conventional geothermal drilling techniques hoyland and edwards say their ai tools can help them find
that undiscovered geothermal last year they found a blind resource in nevada with no surface
signs and no prior drilling repowered a written off plant in new mexico
and signed a 100 megawatt power purchase agreement with nevada's utility to help power amazon data
centers so the question is no longer whether they can find heat it's whether there are really
terawatts of it to be found i can't wait to drill down on it with them all right so with that
wretched pun carl hoyland and joel edwards welcome to volts thank you so much for coming
thank you it's great to be here yeah thanks david i should have
perhaps mentioned in the intro carl you're the ceo joel you are now the cto of zanskar so maybe
joel let's start with you this is easiest technical question in history but i think maybe it will
help orient new listeners who are new to geothermal so just briefly when you're looking for geothermal
resources what are you looking for what are the qualities features that you're trying to find
that make something a good geothermal resource there's really just two ingredients you need
exceptionally hot rocks that have open pathways for fluids to flow and so actually we found that
these two ingredients exist fairly commonly in the western united states iceland these other people
these other places that lots of people are familiar about these systems themselves are
sort of on the smaller scale in those broader regions so you kind of have to find these
sort of chocolate chips in the chocolate chip cookie so to speak so it does take some exploration
it takes lots of data collection and you're looking for these little chocolate chips where
you have exceptionally hot rocks and you have rocks that are basically they have large open
pathways in them and you get these little active convection cells where you have hot water that's
sort of circulating through that system right heat and permeability is what you're looking for
so my second question is how have they traditionally been found i did a pod a few years ago with a
company called cobalt metals i don't know if you've heard of them but they have a strikingly
similar pitch to yours except they're going after metals rather than geothermal resources but it's
the same thing they're using ai to find metals and one of the things they described to me on that pod
is the way traditional metals deposits have been found just amounts to a dude walking around
on the surface looking for sort of visible manifestations of metals it's sort of
astonishingly analog so i'm sort of curious is that the same way that geothermal resources
have traditionally been found like how have they traditionally been located yeah that's right there
is a lot of geologic like walking along the surface looking at different sort of relationships
across rocks to try to find systems that's historically how they used to do it but now
used to do it in particular the early explorers they figured out hey we need you know those open
pathways we need permeability so in theory if we find you know in the western u.s fault zones
which fault zones in the western u.s are pretty common across utah nevada california and so forth
particularly if we look for young fault zones we think that there will probably be geothermal
systems there so in the early days they would sort of drill just sort of sequentially along
mapped fault zones thinking that there was going to be geothermal systems along those and
unfortunately they found that the geothermal systems were a little bit rare and they were not
very predictable along those fault zones so in other words the vast majority of fault zones
they don't have geothermal systems necessarily in a pattern along those that these geothermal
systems they kind of crop up in sort of non-unique or non-obvious places so it gave the perception
from the 1970s and 80s that exploration work that these systems were going to be geothermal systems
were rare that perception has fed all the subsequent modeling i see yeah it was a similar
thing with the metals just like that traditional way of finding them had an astonishingly low
hit rate and thus an astonishingly high sort of like per deposit discovery cost because there's
just a lot of mist a lot of dry wells a lot of wells that are mines that didn't find anything
so carl there's a lot of dust there's a lot of dirt there's a lot of dust there's a lot of dust
there are about 36 operating geothermal fields in the u.s roughly four gigawatts total that is only
up about a gigawatt in the last decade so explain to us why this technology has been conventional
geothermal has been kind of stuck in place for so long why has it been so moribund you know maybe
the simplest answer i'd have to that is that we stopped looking that idea that it was so
hard so challenging that we had so many failures led to a belief that there weren't that many out
there left to find and as a result investment really shifted into other areas into other ways
of maybe engineering these systems etc and maybe on that last point i i like to point out you're
right it's an analog industry in many ways the way that exploration has been done but it's not
unsophisticated it's not unscientific it's not deeply technical it's an industry that joel and
i i think have been passionate about since we were kids really humans have been exploring for natural
resources for thousands of years and it turns out it's actually just really hard to apply some of
these modern data science or statistical methods to it because it's a sparse data problem it's a
messy data problem there's conceptual understanding that needs to go into it and so really the timing
of the work that we're doing is driven a lot by some of the changes in modern neural networks and
the ability to apply much bigger models to the problem and so when we went back and looked at
some of these early estimates you mentioned the ones that we're doing right now we're looking at
that are almost 20 years old now and based on data that's even older than that i think like
the estimate is 20 years old but the data the estimates are using i think dates back to like
the 70s and 80s exactly right and so as we started looking at the underlying data sets and the
assumptions being made assumptions about how many undiscovered systems there were assumptions about
how deep you could drill into them assumptions about how productive each system could be
we started to realize that technology had come a long ways in those 20 years and we were probably
significantly underestimating the resource potential maybe by orders of magnitude yeah
both that you're using ai everybody's using ai for everything these days you guys are both
years yeah but one of the things that they were doing cobalt is doing with ai is just taking all
these old records which turn out to be just this wildly kind of disparate data like old
field records and just notes from explorers and different databases and using ai to sort of
rationalize and systematize that information is that the same thing you're doing is that your main
use of ai there's a few different uses one of them yes is that taking all this existing legacy
information
transformation in pre-training these models. But maybe ours is a little bit different. The
geothermal industry is much smaller than the minerals industry. And as a result, we have much
less data. You mentioned those 36 operating fields, and we often like to point out that half of them
were almost found by accident. The initial indication that there was an exceptional heat
resource was maybe a farmer drilling for water or a gold company drilling for gold. And the other
half of those sites had some seeping hot water at the surface, boiling mud pots, fumaroles, et
cetera. And so when you sort of hear this message of, oh, it's all tapped out and there's only a
little bit left, it reminded us of messages that were communicated in the late 1800s about oil and
gas. They sort of declared peak oil, get ready, Pennsylvania, it's all ending.
They were doing peak oil stuff right up until the early 2000s.
Right. And so we've seen this pattern before of underestimating how much is actually beneath us.
And I think the fact that the entire industry globally is still mostly at sites that were
either found by accident or had hot water seeping out of the surface gave us a lot more confidence
also to follow the statistics, follow the data sets towards this possibility of much larger
undiscovered potential. What is the information? Like, what is the data that you do have? You say
it's scarcer than in the mining industry, but what is there? Yeah. On the data side, there are a few
prior waves of exploration. So the seventies and eighties wave, there was a handful of oil and gas
that got into the thermal game and they spent billions of dollars exploration dollars, you know,
adjusted to today, inflation adjusted during that timeframe. And then in the 2008, 2012,
the DOE funded a bunch of exploration campaigns. So there's a few small data sets and those data
sets generally cover single digit percentages of total prospective land in the Western U.S.
So a very small sample size. And then, of course, there's Jason data sets like people drilling for
water wells. There's data in those people drilling for water wells. There's data in
those people drilling for oil and gas or mineral wells. Those are kind of harder to access, but
there's data about the subsurface there. The data is mostly well field. That's sort of your direct
indicator if a geothermal system's at that location or not. And then there's indirect data
sets that come mostly from public sources like the USGS or from universities or national labs.
And these are folks that go out and they do geologic mapping or they collect different
types of geophysical or geochemical data. And they're usually doing that in the context of
scientific studies. And they're usually doing that in the context of geophysical or geochemical data.
And those are sort of indirect type data sets. There's data coming from now satellites that are
circling the earth and they have instruments on the belly of those satellites that are recording.
There's a broad mix of data sets that basically they're measuring some property of the earth at
that location over some region. And none of those properties in isolation will tell you if a
potentially combinations of those data sets can tell you if they're there. If there are training data
sets from wells that you can train models on top of. Carl said earlier, like there's a lot of sort of
messy historical or sort of adjacent, not directly related type data as part of this sort of treasure
hunt problem of finding systems. But your secret sauce, the secret sauce of your company is that
you feed all that existing data into the AI and the AI basically produces models that have
probabilities. Basically, it's not deterministic since you have not done the actual exploration,
but you can produce models that say, based on the data we do have, it's likely there's something
here, right? Yeah. Is that the basic idea? And producing models, probabilistic models is not a
unique thing to Zanskar, right? There's national labs and universities that do this. And there's
even other companies that do this. Anybody can build a model, right? You could go into
cloud right now and probably build a probabilistic model.
Of your own. The hard part is pairing that model with the field piece of it. Like you build a model,
it makes predictions. That's great. Now you need to go out and you need to test those predictions.
And then you need to feed all of that data back in and you need to make a new set of predictions.
So you need this iterative loop between model building teams and field data collection teams.
And when you say collect field data, one of the reasons people got scared off this in the first
place is that it's quite expensive to drill.
Well, to find out if there's something down there, are there cheaper ways of doing that in
the field to gather data in the field that are short of actual like full on deep drilling?
What you said is true, but there's a lot of variance in that truth. So whether you drill
to a thousand meters or whether you drill to 10,000 meters, there's a huge difference in cost.
So if you can figure out a way to basically test your prediction, it might require drilling, but
not all drilling costs the same. So basically, you want to find the cheapest cost of drilling you can to test that prediction at that location. And that's where Zanskar spent a ton of time is figuring out how to drive down those drilling costs over the last five to six years.
So I think anyone would find it just intuitively plausible that if they haven't explored since the 70s and 80s in earnest, and they had this old techniques, they didn't have AI back then, they couldn't crunch these sort of notes, that there's more than they thought.
That there's more than the USGS thought. You're not just saying that there's more than they thought. You're saying that they have underestimated by something like a factor of 30. You are saying they are off by an enormous amount. Like you're claiming terawatts where they have tens of thousands of megawatts. They have a couple of gigawatts. So that is, I think, an extraordinary claim. What is the evidentiary basis?
For that claim, not just that there's more than we thought, but that there's that much more.
In the state of Idaho, there is something called the Snake River Plain, and it's this large historical volcanic province. And basically, the old models had a really hard time understanding if there were geothermal systems there. Up in the Northwest, there's this large history of volcanism up there as well. And those models had a really hard time understanding if there were systems there. So there's geographic diversity across the Western US that the models were having a hard time accounting for. And then also, just that frequently in this five years of exploration, we keep finding systems in the Midwest that are not geothermal systems.
In spots that surprise us, and we're the people building the models. It's sort of, we have a gap in our conceptual understanding of where these systems form, how long they live there, and how they die, in the sense that's just impacting our total model estimates.
Yeah, and maybe the one thing I'd add to that is, in addition to just more undiscovered sites than previously estimated, we've also come to realize that each one of those sites can produce much more power than our previous estimates were attributing to them.
And that's actually the work we did in New Mexico, where we acquired really a failing geothermal field called the Lightning Dock geothermal field. And it had been underproducing for years, had run into economic challenges, and we were able to acquire it because our models were looking at the same data sets that everybody else was looking at, but were coming to a very different conclusion about how much power potential it had. And when we came within a year of acquiring it and drilled into a deeper resource on the site, just that first well that we drilled was enough to free up a lot of power.
We were able to bring the plant back to its full nameplate capacity, make it profitable as a facility, but it also proved out a much larger potential that could be as much as 10 to 40 times more than the initial industry estimates were for that site. And we've been finding that applies across many other fields as well.
It's not just the Lightning Dock site, then there's other sites where you found that there's way more power potential in the site than had been previously assessed. That's why you're generalizing it out.
Do you have any expectations about what the new assessment will say? Or are you working at all with the people doing it? Are you in touch at all with the people doing that new assessment? Do you have any sense of whether they are going to come around to your way of seeing things in the new assessment?
Yeah, we've met with those folks and are familiar with their approach that they're taking to the problem. The hard part for the updated assessment is that you're still working with the same legacy data set functionally that you worked with in 2008.
You do have new tools, right? Obviously, you have improved modeling frameworks, and you have some improvement in the conceptual understanding of the system since then. But fundamentally, your benchmark data sets are more or less still the same. The industry hasn't grown that much. It still will be a challenge for them to make predictions that are outside of the benchmark or what they call the prior space. We'll see where it lands. And unfortunately, I wish we could share with them all our data. And then they could build a much more robust assessment.
updated model and go from there. But obviously, like they have a mandate that whatever data they have in the model space, it needs to be published. So if we were to share, you know, then that would make all our sites public.
Are you going to share your data at some point?
Of course, as we, you know, the tricky part is we make a discovery.
We go through a process where we lease the geothermal rights for that discovery.
And so the leasing program sort of dictates when we can make things public, obviously.
There's sort of a sequence to it.
But yeah, hopefully over time, we would love to publish, you know, as much as we can.
All right.
So let's then talk about Lightning Dock.
This is this existing geothermal well.
It had, as drilled, the extant well had been really declining rapidly, more rapidly than
the people who drilled it thought it would and was not even keeping up enough to power
the plant, more or less dead.
You assessed it and decided there's more on that site.
There is more power in that site than they're getting out in that well.
Is that just through your standard testing and modeling that you like?
Why did you identify this?
Why did you decide to take this particular site?
This is not necessarily standard modeling, but this is sort of core to what we do at Zanskar.
Going back to sort of this idea of how AI helps to solve these problems.
One of the founding insights for the company was seeing how quickly neural networks were evolving.
The ability, Joel and I, I think we're in graduate programs in the 2010s when AlexNet came out in 2012, Transformer Architecture in 2017.
And obviously models just kept getting better and better, but what really impressed us was their ability to deal with these problems.
We had to deal with massive unstructured datasets, at the time it was mostly images and image classification, and then to be able to interpret something about it.
And so kind of a really simplistic dummy concept I hold in my own head is if you think of a geothermal system, like a cat or a dog underground, and all you're seeing are the ears of that system at the top.
Do you know enough just based off of what you're seeing in the ears to give a sense of how big is that cat?
Where might its tail be today?
It's like a captcha.
It's like one of those stupid online captcha things.
Which of these are the ears?
Which of these images has a bicycle in it?
Right.
And so even though you only have a little bit of the data, it's actually powerfully predictive about what might be down there.
And so we were looking at the same datasets as others, but getting a lot more visibility and fidelity into how the true potential of that system might be and where we might need to drill within it.
And so, as you mentioned, you also like the drilling technology used in that extant well.
A lot of new drilling technology has come along since then, you know, a lot of it borrowed from oil and gas.
Could you just briefly sort of. Give us a sense of what are the advances?
What are the new things that you brought to that well?
Is it like new kinds of drill bits?
Is it new diggers?
As you can see, I know nothing about drilling technology, but like what are the new bits?
Yeah, quite a bit here.
And some of this you're hearing from across the geothermal industry is how exciting it is to be able to pull modern oil and gas drilling tech into our space and see cost declines come from that.
So we used PDC bits.
It's directional drilling assemblies, mud motors.
A lot of the same technology you're hearing that's driving down the cost curves in EGS are also helping us in conventional geothermal to drive down cost.
And so, Joel, maybe you could speak to how good those results were.
Oh, yeah.
Relative, you know, we bought a legacy site.
So there was legacy wells that had been drilled there over the last, you know, 10 to 15 years.
Right.
So you did have some data.
You had more data.
You had more than none.
We certainly had more than none.
That's right.
Yeah.
Relative, we generally know the rigs that were used.
And the tooling and so forth that was used on the prior wells that were drilled at the site.
We just had a lot more success with this new well.
Part of that is, you know, the fleet of drilling rigs that the oil and gas industry continuously funds.
That fleet today is so much better than the fleets that geothermal has used historically.
Like so much of that drilling rig floor is automated today in a way that it wasn't.
So that making connections, you know, as you go deeper, you have to connect in new rods.
All the connections are faster.
So tripping in and out of the hole.
Obviously, as Carl mentioned, the downhole drilling assemblies, how they steer these bits and hit targets at depth is unreal.
It's a taxi service underground.
And my head, you know, even has a hard time understanding how they're communicating with the bit at that depth, telling it where to go.
And then obviously the drilling speed improvements and the drilling bit length, the run length improvements by using PDC bits had a huge impact, right?
We didn't have to trip in and out of the hole as much because the bits lasted longer and so forth.
Right.
This is a big thing where you're.
You're like, you drill down with the bit, your bit gives out and you have to drag the whole rig up out of the hole, change the bit, put the whole rig back down.
This has been a big, a big cost center previously.
Exactly.
Yeah.
If you're truing through a bit every couple hundred feet, you're tripping every couple hundred feet and you basically lose a half day to a day replacing that bit.
And so you add that up.
That's how you'd end up with well fields.
You know, a well would take 40, 50, 60 plus days to drill in geothermal.
And now those timelines are collapsing down to, you know, weeks.
That's just like standard oil and gas.
Oil and gas has been funding this technology development for especially the last 15 years with all the shale gas development.
That's coming into EGS.
It's also coming into conventional well fields.
And you just went deeper, did you not?
I mean, just another of my questions, like one of the things I've heard is that as you go deeper, the costs do not rise linearly.
It gets a lot more expensive quickly, the deeper you get.
So I'm wondering how you make that bet, that calculation.
How deep to go?
How much of that expense is justified?
How do you make those calculations?
It's a mix of a few things.
There's rig time, right?
So you have basically a small city that's on site and every day that that city operates, there's a cost.
So there's that burn rate.
You have to construct a well, right?
It's not just making a hole in the ground.
You have to put cement and steel and you have to construct it.
So those are fixed costs that go into a well.
The deeper you go, the more you have to construct.
So it's both the time of the rig that you have.
That city that you have to operate.
And then it's also all the materials.
And yes, at Lightning Dock, we did go deeper.
That new production well is roughly four times deeper than the first set of production wells.
But we also, you can't just drill deeper anywhere, right?
You have to have the right targets.
You have to hit the system in the right locations.
Otherwise, you know, you'll have a, you know, maybe a tight hot well or a tight cold well.
So it was the combination of going deeper into the system and also targeting that system at that depth.
It's worth saying there's a
lot of places where there's hot rock, but no permeability.
And there are places where there's lots of permeability, but no heat.
So it's a fine target.
And so now this new well you've dug here on this site, you claim is the most productive pumped geothermal well in the US.
Is that based on this brief flow test you did, or is that an actual measured claim at this point?
It's an actually very well measured claim at this point.
We've been operating.
We've been operating that well at full capacity, powering the power plant for over 12 months now.
And we saw no temperature decline in that period of time, which is very exciting.
And we're really measuring it by both the total amount of thermal energy that that well delivers, which is over a hundred megawatts of thermal energy at the wellhead.
And I mean, if you think about it, this is a well you can wrap your arms around.
It's not that big.
And that's as much heat as a small modular nuclear reactor would create.
And then you're pulling that into the power plant where you're going to
extract that heat and drive a power cycle, where we're also generating more electricity for that well than Joel, I think still to this day, than any other well of its type in the country.
Yeah.
So that metric, David, the benchmark data set in the state of Nevada, they actually publish all the operating production wells in the state.
So there's over 200 wells and most of those wells are pumped.
So between that data set and then the other pumped wells in the country that we know about, we're able to compare this New Mexico well production well.
To that existing fleet of operating pumped wells.
And this well in New Mexico delivers more thermal energy to the power plant than any other pumped well in the country.
Interesting.
And so is the existence of that excellent resource that you found de facto evidence that there's other excellent resources in that area?
In other words, are you done with that site or are you drilling more wells on that site?
I think the short answer is it sort of does the opposite.
It strongly confirms that there is additional upside there regionally and really at any other field that looks somewhat similar to it,
which is not just in New Mexico where we find these types of fields all over the western United States.
Let's talk about Pumpernickel then.
It's a fun word to say.
It's this Pumpernickel site.
The first well you drilled there was dry, did not turn out well.
And then you drilled a second and got a big hit.
I'm curious, what is your hit rate?
A big problem in early exploration was this low.
Hit rate and it's very expensive to drill these wells.
What is your hit rate?
Is that measurable?
Is that a metric that you can report?
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Is that measurable? Yeah, I could jump in here as well. There's many ways to measure hit rate. One way we measure it
is we make model predictions, and those model predictions have a range of probabilities.
And those model predictions, they're predicting temperatures and permeability as a function of
depth for your planned target. And so one thing we track is for every deep well we drill,
did the outcome of that well fit within the range of probabilities,
and how close were we to the median probability? So that's one way. Another way is to drill the well
and then obviously measure the performance of that well. So for certain types of wells,
this is a fun thing about geothermal that you can't do in oil and gas,
is after you drill a well, if it's a certain type of well, you can open it up and you can let it rip
and do what's called a flow test, where you let that brine come to surface and you flash it into
a pond, a retention pond, and you can measure the flow, the heat content, and the amount of
volume coming out per time. And that'll tell you the performance of the well. Those are two quick
ways to assess your sort of hit rate. At the Pumpernickel site, the first well, it wasn't a
targeting failure. We couldn't complete the well to the planned target depth because the drilling
conditions were so difficult. So when you drill into really sort of productive geothermal resources,
you have actually really difficult drilling conditions. It's actually
sort of fairly correlated that the better the resource, the harder the drilling,
Is that because the rocks are hot? Because they have gaps in them? Like what makes it so hard?
Yeah, exactly. You have gaps. So you have a plumbing network. You can imagine like pipes
under your house where you have this convecting hot resource that's moving through the subsurface.
And when you drill into gaps with a drilling rig, it makes it really challenging for the
drilling rig. And of course, the irony is that that's our resource. In this case, we ended up
hitting shallower resource that made the drilling harder to get to our deeper target. So
we ended up moving to the second site where we were able to get to the targeted depth and show
basically, yeah, this has commercial conditions. It has the permeability, you know, the open space
that we want. It has the temperature that we want. Speaking of permeability, I was reading about the
Pumpernickel site. And one of the things you really emphasize on your substack about that is that
permeability is the main thing that in some sense, heat is kind of easy to find and it's permeability.
It's good permeability that, you know, it's going to be a lot easier to find and it's going to be a lot
easier to find and it makes all the difference because that's where you get your flow rate.
And in the course of that discussion, you express some skepticism about enhanced geothermal because,
of course, the thing about enhanced geothermal is that it is creating its own permeability
through fracturing. I'm curious, do you have generalized skepticism towards enhanced geothermal
for that reason? Tell us a little bit about how you think about permeability.
Yeah, I can maybe comment on that a little bit. I think it's less of a skepticism of whether EGS works
in what scale or places. It's really just where will it fit on the cost curve relative to
conventional geothermal? Because so far, the type of permeability that we've been able to create
artificially in engineered systems is an order of magnitude and sometimes multiple orders of
magnitude less than the type of natural permeability that we find in the earth.
Just smaller gaps, basically, for people. If people are trying to imagine this,
like if you're doing the fracturing they do in fracking, you're just creating hairline cracks as
I'm guessing bigger natural gaps you find in these areas.
Right. And maybe you're injecting, you know, some silica beads or things in there that will hold it
open. But those are rarely, actually so far never, have they been as large as some of the natural
spaces that you find in the earth, which you can imagine can be cavern size. And so when you have
these cavernous openings, large aperture, we call them faults with permeability, you end up just
getting flow rates and prices, really the cost of producing that type of resource is much cheaper.
That's what we get really excited about is finding out that that type of natural permeability
exists in many more places than we previously thought. And in some cases, temperature is a
leading indicator of it, sort of a proxy for, oh, you're getting close to permeability. But in other
cases, it's really hard to identify it without the data or the models leading you there.
So if I could summarize that and tell me if you would endorse this,
a really good natural geothermal resource is going to be cheaper to produce from
than any conceivable enhanced geothermal site that creates its own permeability. Do you think
that's true? Definitely true today. And we think probably indefinitely into the future. There's a
similar comparison or analog here when you look at the oil and gas industry, that we are decades
into the shale revolution. And the conventional fields like the Gawar oil field in Saudi Arabia
still produce oil at a significantly lower cost per barrel than even our best unconventional
shale fields. Same thing there with natural permeability versus,
induced permeability, basically. The natural permeability is still better.
And another kind of visual example of this, Joel mentioned the well that we drilled at
Lightning Dock. It is actually still pump limited. We had a stronger pump that could
pull more fluid out of that. The permeability is sufficient that we could flow it at much
higher rates than it is today. Oh, interesting.
And that's really just giving you a sense of how exceptional the permeability is down there.
And so then let's talk about this third discovery. This is called Big Blind in Nevada. And this is,
I think, the news that made everybody really sit up and pay attention. Because this was,
unlike the previous two we've discussed, this was genuinely new. There were no prior wells
on this site to give you any data. There were no surface manifestations of activity on the surface.
This was a genuine blind site. So how did you find it? In a nutshell, how did you find it?
Yeah, this is really our bread and butter. So Joel, I'll let you kind of talk through
how greenfield perspective exploration works. Yeah, it's that concept we talked about at
the beginning, David, that you build models. Everybody can build models. Zanskar builds
models. But you have to pair your model predictions with tests in the field of those predictions.
And so this is one of those cases where we had made a set of predictions across this
part of the state, and we were out testing those predictions and ran into this system
at this location. And this is a system.
That's west of Tonopah, Nevada. So pretty remote. And like you said, there was no prior data at the
site. And if you or me or anybody were to walk across the site, there would be nothing at the
surface that would clue you into that there's a massive system below. That initial discovery was
made at the end of 2023. And then we went through the permitting processes and we came back with a
larger rig and drilled into that resource in what years announced 2026. I think we drilled into in
2027. And that's when we encountered commercial reservoir conditions at around two to 3000 feet.
The thing that's really exciting about this system in particular is how big it is. This system by the
surface footprint of the heat flow anomaly is much bigger than pumpernickel and much bigger than
lightning dock. Oh, interesting. I guess you've just kind of started exploiting it, right? I mean,
is it commercially operational? Where's the state of play there? No, we're in the wellfield stage of
the project. We're in the wellfield stage of the project. So we're permitting additional exploration wells right now to go back to the site. So we're
still early stages. And yes, still trying to figure out how big the site itself is, how big the
resource itself is. Exactly. We have a sense of sort of the surface area, how big it is. That's
why I can make that claim relative to some of the other projects. But we do not yet know at depth
how big and hot the system is. It seems quite spicy to think that this thing is going to be
sitting out there. There's lots of exploration across the West for minerals and other things.
And to just think that this had been missed for so long, you know, gets us really excited about
the future. So you had your models and they gave you a sort of probabilistic sense. And then you
go out and do some exploration in those probabilistic areas and you just got lucky on this
one. I guess not lucky. The probabilities paid off in this spot. Yeah, maybe the models got lucky.
There's luck in all this. And there's also bad luck when we miss. And to be clear, we've missed
way more than we've hit. Like to make this sound like this thing has been working better, you know,
than the legacy explorers. In the early days, our models were frankly much worse than expert
explorers. And we do benchmark against expert predictions in our modeling space. And it's
really only our models really only in the last year or two have started to outperform. And, you
know, the models get worse. So it's not like it's up into the right. It's like there's a wobble. It's
sort of like the climate, right? Like some years it's wobbling hotter and cooler, but you're
generally up into the right. And that's very true for our modeling space. So you think from now on
over time, the more exploration you do, the more data you feed into the models, the better the
models get. Over time, you think you're going to get better and better at finding these things?
And that's actually quite clear already from the data we've collected. Like Joel said, they started
out much worse. They started out much worse. They started out much
worse than humans, they caught up and they're performing better in many locations.
and areas. And we've seen it improve in two ways. As you put more data into the models,
the same architecture ends up yielding higher precision, better performance. But in other cases,
actual changes to the architecture, the way we've designed the model itself with the same data
yields an improvement in prediction. So the teams are constantly driving improvements in both of
those fronts. Interesting. So maybe not up and to the right for every increment, but over time,
in the fullness of time, you're going up and to the right with your models. Yeah. And really those
sort of sidesteps that Joel's talking about, or what might feel like even a step back, is often
in a certain type of metric, like precision or accuracy. But it usually reflects that we weren't
realizing that there was something less generalizable about the prior model. And so this
step back is getting you to a more accurate representation of the uncertainty. So it's still
a better model. You're just being more correct in your estimation of uncertainty for it. Right.
Interesting. Interesting.
This manifests, it's funny, you would expect our sort of hit rate, like when we find a system to be
consistent and linear. But what I've found is that our hit rate is like, you know, like in sports,
you get into like a streaky zone, like home run after home run, or whatever, you're hitting the
threes, like back to back to back. That's the same thing in our models. Like we go through periods
where bang, bang, bang, like survey after survey after survey, we're hitting, hitting, hitting.
And then we go through like a cold slump for a period of time, and then we push out a new one.
So it's been a very like nonlinear sort of spikiness to the discovery,
is there any way to bring down the cost of drilling those exploratory wells? Or is it just
is what it is like exploring is expensive, and it's going to be expensive? Is there a cost curve
there anywhere? Yeah, we've been focused on a couple sides of that one is just drilling fewer
wells. And so as the models get more predictive, more powerful, it just means we have to drill
fewer of them. Right. But the other side of it is really focusing on what are the most powerful
data types or wells to drill that really vector you into the site faster.
And driving down the cost of those data types or those wells. And so as Joel mentioned,
we've brought a lot of data collection and drilling in house, primarily so that we could
start to focus on how do we make this more efficient? How do we make this safer? How do
we make this faster, so that we can really drive down the cost of data collection during exploration.
And in some areas that we've seen significant cost declines 50 to sometimes 90% cost declines.
Let's pivot a bit. I'm behind schedule here. As always, this stuff is too interesting. Let's
pivot a bit to financing.
Financing has always been a tricky aspect of geothermal, because by definition, if you're
trying to finance your exploration, you don't have a resource to finance yet. You're sort of
financing the possibility of finding resources. And this has always been kind of a problem.
So you guys have worked out this development capital facility, which is this sort of rotating
fund. I'm going to stop talking there before I say something stupid and wrong about it. Why don't
you just describe?
What the structure of that is, because as I understand it, this is something that might
benefit the industry generally and not just you guys. But maybe, Carl, you can tell us like how
that's built.
To your point, historically, geothermal was seen as so risky that it was sort of like wildcatters
out there just taking total random shots. And therefore, anyone who was going to finance a
project wanted you to have completed not only all the exploration, but really the well field
and come with a basically ready to go project. There should be no risk left before we can bring
in traditional project finance to build the power plant.
Which means?
Exploration had to fund itself, which is obviously highly limiting.
Had to fund itself. And when you combine that high cost of capital for exploration dollars,
plus the long timelines in certain geographies for exploration, it ends up adding a lot of soft
costs to the final levelized cost of electricity, which also made the whole industry struggle to
compete. One of the things that I think we were able to uniquely do is by drilling, as Joel said,
so many sites, testing so much acreage, we could build out robust statistics around what our
success rates were.
And how they were improving. And we found that for many finance years, they care more about just can
you actually quantify the uncertainty to a robust degree, more so than what the actual accuracy is.
Right.
And so as long as you can quantify the risk profile, then we can price the capital. And that
was something the industry just couldn't do before. And so this facility that we announced,
it's a development capital facility, it comes in prior to the traditional bank style of project
finance, when you're still developing the resource and proving out its full size.
Right. Because there's no projects yet.
There's no projects. But the way we can mitigate the risk there is by pursuing a portfolio of
projects together, where some failures will be made up for by great successes across the
portfolio. And you have the statistics to know how to size and diversify that portfolio.
So you somewhat reduce the cost of capital then for exploration.
Meaningfully reduce the cost of capital.
And it's a rotating fund to talk about that a little bit. It's not just a set amount of money
that people have dumped into this.
Yeah. So ideally use that capital.
To really prove out, get the projects ready for bankable finance, right? Complete the wellfield
and the tests that need to be done. And at that point, you don't need that development capital
facility anymore for that project. You can bring in much lower cost debt financing and tax equity
financing. And so then that capital can go back towards earlier stage projects to prove them out.
And so you're constantly recycling capital in the facility. And as the value of the assets in the
facility grow, you can also draw more and more in an accordion-like manner.
And do you think this tool is going to help you in the future?
Do you think this tool is going to help other geothermal developers? Or is it in some sense
contingent on your specific track record and your specific tools?
Our hope is the former, right? Our hope is that by quantifying not just our performance,
but really the industry, even with legacy tools performance, that you make this a more investable
asset class. And I think many of the learnings that we're taking about the resource sizes,
their potential, their cost structures are helping investors get more comfortable with geothermal.
Because at first glance, it has looked so good.
It's scary to them until they really spend the time to understand it. And it starts to look like
a very attractive opportunity to diversify their investments. So our hope is that, yes,
this will spread beyond Zanskar. Interesting. Okay. You guys have signed a 100 megawatt PPA
with Envy Energy in Nevada. Why with a utility rather than a private offtaker? And is that
100 megawatt PPA for a particular well or set of wells? Or is that one of these probabilistic
things?
Like, we will be able to find 100 megawatts eventually with a set amount of risk. Tell
us a little bit about the deal with Envy Energy.
Yeah. So Envy Energy is a regulated utility in the state of Nevada. And really the benefit of
working with and selling to them is that you're now delivering power onto the grid. In addition
to energy, you're providing all the other ancillary services that geothermal can provide.
Spinning reserves, voltage support, and et cetera.
Firm. It's firm, firm, firm.
It's firm power. But it is important to have customers on the other side of the grid.
Who are asking for carbon-free or who are asking for geothermal. And so it often ends up being more
of a network of commercial drivers that are making this possible. In terms of how we deliver it,
I'll let Joel kind of speak to that portfolio approach of ensuring that we reach the 100
megawatts. Yeah. Like you said, this is, I guess, a probabilistic framework. I hadn't heard it put
that way, but this is what they call a portfolio PPA. So that means there's a small basket of sites
that we've already delivered. And then we have a portfolio of sites that we've already delivered.
And then we have a portfolio of sites that we've already sort of discovered or characterized and
that we have leased positions at and that we can deliver the 100 megawatts from any of those sites
or from a combination of any of those sites. I see. So it's not like we promise we're going
to find new things that we'll be able to deliver this. It's we have found things that are capable
of delivering this. And you also have other geothermal developers, I think, signing PPAs
with NV Energy as well, or Matt, I think, signed up for 150 megawatts. To what
extent is the data center pull behind people's willingness to pursue geothermal in a more
concerted way? Like, is that a big driver for your success? In those two instances that you just
mentioned, it's actually a very large driver. And one of the primary reasons NV Energy was
seeking out geothermal power is because of new hyperscaler demand in their geography,
looking for carbon-free baseload to be able to offset the environmental impacts of what they're
building.
More broadly, though, we're really seeing demand grow well beyond just data centers.
Manufacturing facilities, we're seeing it from utilities themselves that are looking to
decarbonize or just to provide more resilience to the grid. And so really in each market,
it's a different type of customer. In Nevada, the hyperscalers are a big driver of demand.
But we're also selling from our lightning dock power plant to PNM,
a regulated utility in the state of New Mexico. And that's very much just to meet their own needs.
Well, everybody is going to need more firm power. That's practically
the premise of this entire podcast. We're electrifying everything and you need some
firm power if you're going to do that. So I expect there's no worry about like exhausting demand here.
No, certainly not anytime in the near future.
So I guess the skeptical case here against Zanskar would be something like the big obvious fields
have been discovered. So what remains to be discovered is these little pockets here and there,
which might amount to. A successful company, but will not be sort of game changing, will not be amounts that
substantially matter. So what is your pitch to those skeptics? I'm sure you've
made a lot of pitches to a lot of skeptical investors who have asked exactly that question.
What is the pitch that this is not just we can gather a few more small sites that other people
have missed and cobble together a successful company, but this is like something big and
fundamental?
- Yeah, I mean, I often point to one of the most valuable
companies in all of history is a company that was made by an oil discovery a hundred years after
the first peak oil declaration. And that's the Gawar field in Saudi Arabia, right? Aramco today
is still massively valuable. And so the idea that, oh, the best stuff is tapped. We just don't see
that precedent in other natural resource industries. It's more likely that the best
resources are still ahead of us. And even if it all plays out the way you say, even if it's true,
is it not still ultimately kind of a basin and range story here? Because in the West,
you have this unique, unique geography, geology. You have these plates that are pulling apart.
It's letting, it's making that permeability, it's letting heat closer to the surface. So even if you
end up producing hundreds of gigawatts, even if you reach the terawatt someday, it's still coming
out of the U.S. West, right? I mean, still in some sense, geographically limited. Is that not right?
For the next decade or potentially two, that's true, primarily driven by drilling costs.
But as drilling costs get cheaper, you will be able to push into other geographies.
And even in the near term, if you end up having a terawatt of the most cost-advantaged
power source on the planet, we also believe demand will come to where you are.
So the ability to deliver carbon-free baseload power at the lowest price point possible
makes the Western United States exceptional. But that's not to say we won't see geothermal
expand far beyond that.
As the cost of power generation and the cost of drilling continue to come down.
Well, talk a little bit about that, Joel. What other types of geographies? You just mean like
on the edges of this basin and range area or like fundamentally different geographies? Like,
could you find conventional geothermal resources in the East or like in the South where they've
never been found before? Like, how geographically independent is it possible to get if things get
way better?
This is a great arm-waving question.
Maybe the data point I would point to is that across these different geographies that you've
mentioned, I'm from Iowa. Generally, there's a certain depth at which people just haven't
drilled below. And so the conditions below those drillable depths today are totally unknown.
So for example, everybody knows it gets deep, it gets hotter as you go deeper.
What they don't know is how the rocks respond to all that heat at depth. We think that rocks
go through like phase transformations, you know, the minerals change and things like that.
But I will say for the few wells that we have drilled that are deep, so think deep scientific
wells like the one in Germany that went to 10 or 11 kilometers, the one in Russia that went a little
bit deeper. And all of those cases where they went deep, they all ran into conditions that they
didn't expect. Particularly, those wells ran into water. They ran into well stability issues they
did not expect to run into. So how can there be water? How can there be open pathways, open cracks
at 10 kilometers down? Like intuitively, it just doesn't make sense.
Right. Because we know there's heat. I mean, it's just logical that there's heat down there. We know
that through basic geography. It's the permeability that is the question, right? Is whether there's
enough permeability down in those depths. And we just don't know.
Exactly. Yeah. Will the well flow? But I will say in all those deep wells, the two I'm thinking off
top of my head, and I think the others are true too as well, but they ran into water. Like water
in the well bore when. Their teams thought that the wells would be dry.
If you find water, that means you've found permeability.
Exactly. You've drilled a hole and there's water seeping through the well bore walls
into your hole and it's creating drilling stability issues. There's permeability down there. We see
this also true sort of in the Western U.S. where the climate today is very dry. We obviously live
in a desert, but geothermal well fields or when you build underground mines or when your open pits
get deep enough, you run into like massive volumes of brackish water. That brackish water is very old,
but they have to install these huge pumping systems to dewater mines so that they can mine
at those depths, right? So there's a lot about like fluid circulation in the deeper crust that
we have very few data on and that we poorly understand. And so it's very hard for me or
anybody to sit here and say, we will never find a geothermal system in Iowa, right? Because we've
only drilled down to a certain depth in Iowa and we just don't know the rocks below that depth.
So theoretically, go deep enough, you could find stuff that we don't know about yet. You could
even say it's likely to find stuff we don't know about, likely to find some permeability, some
depth. How deep can conventional geothermal wells go? I mean, this is a whole premise for
Quaze that we discussed is that once you get deep enough, traditional oil and gas drilling equipment
has never really needed to go that deep and has never really been able to go that deep. So I think
the question is, how deep can conventional oil and gas drilling equipment go that deep? How deep can
conventional oil and gas drilling equipment go that deep? Just the conventional tools you're using,
how deep can they get? Yeah, maybe Carl would have a little bit of a different answer, but
I think the oil and gas wellfields are a good analog. There's oil and gas wellfields down to
20,000 feet right now. And those are conventional wellfields, like using conventional drilling
technologies. I know those scientific wells that they drilled down to 30,000, 35,000 feet,
those wells took forever. They took like years of drilling in the same hole to get to those depths.
And it's because, you know, like you said, you'd get down to 35,000 feet, you would drill a couple
hundred feet or less, your bit would die. You'd have to come back out of the hole, like just huge
issues and the hole would be collapsing. Right. And so I'm really excited about what Quaze is doing
because I agree with that thesis that there are certain depths where it's hard to imagine
conventional technologies working, I guess, unless there's new stuff coming down the pipe
that I'm not aware of. Interesting. Okay, Carl,
right now, in our current political circumstances, wind and solar are kind of getting
hammered, not treated well, but geothermal has kind of remained a bipartisan
love child. Everyone seems to love it still. In one sense, that's great, still getting the tax
credits, there's the DOE is engaged, et cetera, et cetera. Does that, on the flip side, ever make
you a little nervous though? How policy dependent are you at this stage in your development? And how
confident are you in ongoing policy support? You're exactly right that there's very strong
bipartisan support, which is very exciting to see, especially at a time when it feels like very few
things manage to secure that. It is partly because, like we said at the beginning, geothermal kind of
has everything you want in an energy source, the small footprint, it's baseload, it's carbon-free,
plastic, doesn't rely on foreign supply chains that are at risk, and on and on and on. Our goal,
really, while we're sort of in this infancy as an industry proving out these new technologies,
is to come down the cost curve as fast as possible, so that it no longer requires any sort of
factor other than just a simple cost comparison of you're the cheapest source of power, period.
And we know that's possible for geothermal, because even with current off-the-shelf
technologies, and even our own cost of capital,
if you just knew exactly where to drill every time and you never missed,
it would already be cheaper than all of those other power sources on an unsubsidized basis. So
we see the prize, and that's what we're driving towards. Maybe let Joel comment a little bit on
what are the risks in the meantime of getting from here to there?
Yeah, in terms of driving down the costs?
And really, policy support shifts, is there a risk that we're getting too much attention? I mean,
these are internal conversations we're having.
Yeah, geothermal has both the risk and the opportunity in the sense that it has
one very, very, very large landowner called the federal government.
Yeah, I meant to say that 90% of identified geothermal resources are on public land. So in
some sense, there's no way around policy.
Yeah, for geothermal to scale, it needs access to public lands. And the majority of
those public lands are not national parks, right? These are Bureau of Land Management lands. So
these were lands that you could have homesteaded, but people didn't want to homestead. And so they
were the sort of the federal government came in and took ownership of it.
And they took ownership and management. It's a risk and an opportunity. The opportunity is you have one
landowner, you have one counterparty to negotiate with and to operate with. And that one landowner,
if they're sort of in agreement, and they want you to scale, like, you could scale. The risk is
the inverse of that, obviously. And so we've seen it, like the Biden administration, they put out,
you know, improvements to the permitting stuff right towards the tail end of their administration.
And then the Trump administration has done the same thing in the early days. So right now,
we've got tailwinds. If we can, the frustrating part for us is we've kind of been ignored or a
nobody for so long that the way in which our landlord regulates us has been kind of slow and
outdated and frustrating. And if we can get that streamlined, the industry will boom. It will take
off because there's plenty of hot rocks. There's plenty of projects to go after right now.
So like the permitting, the soft costs, all the same things that all the other
energy sources are struggling with. You guys have that problem too.
Yeah, that's right.
I guess it's hard for me to envision a circumstance in which the U.S. is not going to need and want
more firm, carbon-free electricity. But I've not seen a lot of political things coming
in the last decade. So I wouldn't want to make any categorical statements about that. I'm sure
you guys have some level of nervousness about it too. All right. Final question. What is next?
You've got a well that is commercially producing
You've got a couple of other wells that are in sort of the well development
stage. Is it just steady as she goes? More exploration, more wells? Like what is the next big
challenge?
step here? And how fast do you think you'll be able to scale up? We might have different sides
of this to speak to. For mine, it's a yes and all those things you mentioned. But also, it's been
about 10 years since anybody has turned on a Greenfield geothermal power plant in the United
States, right? Which just hasn't been growing, especially in Greenfield settings. And so our
real goal now is to drive forward as many Greenfield projects as possible and get them
to commissioning. We plan to do that in the next few years with half a dozen and to grow from there
quickly. And I think that is really where you get to that point of this is a real industry. It can
really scale and the costs are what we think they are. And I think it really hits that runaway
inflection point. Joel, from your side? I'm really excited. The projects you mentioned, David,
Big Blind, Pumpernickel and Lightning Dock are really great, exciting projects. Those are
the only projects we've made publicly known. We obviously have many projects behind that,
that we've been working on for years. Maybe we discovered them in some, our first discoveries
were back in the 80s. We've been working on them for years. I mean, I'm assuming that when you make a discovery, you don't go out and wave a flag
about it. You want to get it, you want to get it permitted and whatever lockdown and
exactly whatever else before you announce it. Yeah, that's the biggest sort of bottleneck to
the process of publicly announcing it because many of the discoveries, they've got federal lands,
they've got state lands, they've got maybe a little bit of private lands. And so it takes
a little bit of land work to put together, you know, the position and so forth. So our first
discovery is back towards the tail end of 2021. And so we're going to be working on that. And so
we're still finding system. I, there was a system we found this weekend. I'm really excited to be
more public about these things in the future and get people excited about this huge. We've been
talking about this for a few years that there are a lot of sites out there to discover. There's a lot
of systems out there and we've only announced, you know, one true greenfield blind. I'm excited to
start announcing these and really getting everybody excited about geothermal because there's other
groups doing really exciting stuff. The EGS stuff is super exciting. And I think this discovery piece
is equally as exciting.
So you've announced one true greenfield blind discovery, but you've made others.
Yes. I wish you could see internally. The excitement is real intangible every time. Like
it just doesn't get old every time a new discovery comes through the pipeline.
And in terms of the costs coming down over time, this is my actual final question. This is
the cost of exploration getting cheaper.
With better models, better data, et cetera. Is it the cost of
drilling itself getting cheaper? Where are the real opportunities for cost
declines as you sort of attempt to outrun the need for subsidies? You know, this is what every new
technology is doing, trying as best they can to escape that need as fast as they can. Where are
the areas where you're looking for real cost declines? Oh, and some of these we've already
seen. So finding better and bigger and lower cost resources, you're just starting with a better
resource. Finding better and bigger and lower cost resources, you're just starting with a better resource. Finding those where they're closer to infrastructure, closer to the grid, which reduces your development costs. Drilling faster and cheaper, both with these technologies from oil and gas, but also applying our own AI and data science to the data sets to improve operations. And then also deploying software and AI throughout development and throughout operations of those assets. We're sort of seeing opportunities to lower cost across that entire spectrum.
Interesting. Well, what an exciting time for geothermal, I will say. I wrote an introduction to the
introductory article about geothermal. 2017? Wow. 2018? Yeah, not to pat myself on the back, but like, nobody was talking about it then. And I went through all the conventional stuff, the advanced stuff and everything. I think a lot of people sort of clued into geothermal from that. But man, even in those articles, I underestimated the flood of interest and how fast it would all take off. So really exciting stuff. Thanks, guys, for coming on and walking us through it. Thanks, David. Thanks so much, David. Great to be here. Appreciate it.
Thanks, everybody. You've been listening to Volts, founded and hosted by me, David Roberts, produced by Nate Peavy, and supported entirely through the generosity of listeners like you. If you value conversations like this, please consider joining our community of paid subscribers at volts.wtf. See you next time.
Thank you.
Podcast Summary
Key Points:
Conventional geothermal energy is geographically limited, primarily in the western U.S., but new AI-driven exploration suggests vast, previously overlooked resources.
Zanskar uses AI to analyze legacy geological data, identifying hidden geothermal systems with high heat and permeability, even in blind sites with no surface signs.
The company claims the U.S. has 10 times more undiscovered geothermal potential than historically estimated, potentially reaching terawatt-scale capacity.
A key discovery at the Lightning Dock site in New Mexico revived a failing plant, proving that individual sites can produce 10 to 40 times more energy than previously predicted.
Zanskar’s AI models improve over time as more data is collected, leading to better predictions and a rising hit rate despite early model inaccuracies.
Natural permeability in existing rock formations is significantly more cost-effective than artificially created permeability in enhanced geothermal systems.
The company has signed a 100 MW power purchase agreement with Nevada’s Envy Energy, leveraging a portfolio-based approach to manage risk and secure firm power.
Geothermal’s future growth is not limited to the West, as deep drilling reveals unexpected permeability and water in extreme depths, suggesting potential in other geographies.
Summary:
S. resources concentrated in the West. However, a new wave of AI-powered exploration, led by Zanskar, challenges this view by identifying vast, previously missed geothermal resources using legacy geological data.
S. S. Geological Survey estimated—possibly reaching terawatt-scale capacity.
A key validation came from the Lightning Dock site in New Mexico, where a reactivated geothermal field produced 10 to 40 times more energy than originally predicted. Zanskar’s AI models, trained on scattered historical data including well logs, surface observations, and geophysical surveys, have evolved significantly, improving predictive accuracy over time. The company emphasizes that natural permeability in hot rock formations is far more cost-effective than artificial permeability in enhanced geothermal systems.
They have secured a 100 MW power purchase agreement with Envy Energy in Nevada, using a portfolio-based approach to manage risk and provide firm, carbon-free power. The technology also suggests that deeper drilling—especially in arid and geologically active regions—could uncover new resources, with evidence of water in deep wells indicating hidden permeability. S.
in the future.
FAQs
A good geothermal resource requires exceptionally hot rocks and open pathways for fluids to flow, known as permeability. These two elements create active convection cells where hot water circulates, making the system viable for energy production.
Traditionally, geothermal resources were found through surface-based geological surveys, such as walking along fault zones in the western U.S., looking for signs like hot springs or seeping water. This method was often low-precision and led to high failure rates.
Conventional geothermal has stagnated due to past exploration failures, leading to a belief that viable resources were rare. Additionally, the industry lacked modern data science tools, and exploration relied on outdated, sparse data from the 1970s and 1980s.
Zanskar uses AI to analyze legacy data—including field notes, geological surveys, and well records—to identify patterns and predict the presence of geothermal systems. The AI builds probabilistic models that suggest where hot, permeable rock systems may exist.
Evidence includes finding new systems in unexpected locations (like the Midwest), discovering high-output sites like Lightning Dock that produce 10–40 times more energy than estimated, and identifying widespread natural permeability in volcanic provinces like the Snake River Plain.
Modern drilling technologies such as PDC bits, directional drilling, and automated rig systems have significantly reduced drilling time and costs. These advances allow deeper, more efficient drilling and have made it feasible to drill into resource-rich zones previously considered too expensive or difficult.
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