micro2588

Fervo uses engineered reservoirs in granitic basement rock so this is less of an issue. Hot rock in a working fluid can still dissolve silicates out of the granite and lead to scaling / degradation of the flow rates through the reservoir and that is a risk but chemical anti scaling treatments are used to reduce this.

CA has the worlds largest geothermal power complex in the Geysers. That one field produces an equivalent amount of power as all the geothermal in Iceland and there are others.

In geothermal there is still a lot of interest in efficiency and exploring different working fluids because binary systems now have efficiencies of 10-20%. That is why you see companies like Sage Geosystems working on developing / deploying supercritical CO2 turbines to try and boost practical power densities.

They do a good job of publishing their results in technical industry publications (advancing the field overall in a surprisingly open way) but I agree can be misleading in their marketing.

It will be interesting to see the results of the Cape project once they do multi-well laterals from a single pad power plant with larger diameter wells. That is really more a demonstration of power plant economics beyond the technical feasibility of creating a horizontally fracked reservoir that can be operated for a year.

Supercritical geothermal is similar to talking about the economics of fusion. There is a DOE enhanced geothermal test site near the Newberry Volcano in central Oregon which has temperatures close to this range at reachable depths. That is more of a demonstration site for drilling technology.

I think as a tech demonstration project it was successful because they were a bit conservative in some ways that will make the economics look worse. I agree it's far from "geothermal everywhere" which seems to be the hype. You can't extrapolate that from one successful EGS well literally right next to an existing geothermal power plant.

There is no way to tell yet what the longevity of the resource will be as it's too early. In fracked resources the main issue is "short circuiting" where increased flow rates travel along preferential paths between the doublet wells as the source rock cools and cooling rate of the source rock in general. This causes the MWt of the resource to decline per injection / production well. Fervo is getting around this by drilling extra wells per pad to be turned on in response. Many geothermal resources decline over time as heat is slowly extracted and these declines are somewhat manageable by tuning the injection production well rates and drilling new wells. They are built into the economics of existing plants. Geothermal is kind of extractive and not "renewable" in this way over medium term time scales, you need to continuously keep drilling at a certain rate. Rock is a good insulator and it takes a long, long time for it to heat back up.

You are right there is no getting around that relatively low grade heat in geothermal is a big barrier for scaling in terms of energy production. Binary /organic rankine cycle geothermal plants used for these low / medium temperature resources operate at ~10% efficiency. Dry / flash steam resources are higher but produce waste in terms of emitted GHG and / or crap in the geothermal brine.

Ormat (NYSE ORA) is a publicly traded geothermal company and they are profitable.

Fervo's initial demonstration project was next to an existing power plant in Nevada which previously failed to produce at it's stated capacity over time (Battle Mountain) so they were able to tie in extra MWt capacity to an existing ORC turbine. Fervo's technology has to be located somewhat near existing traditional hydrothermal geothermal resources because it's the convection along an exiting fault for hundreds of thousands of years that produces an above background thermal gradient near enough to the surface for it to be economical. That is true for their demonstration area in Utah which is located near the existing Blundel geothermal power plant in Milford Utah.

Julia has been designed for single core performance fullstop. Functional collections may work well with a state of the art GC, with Julia's not so much. The fact that Julia can interop seemlessly with C code (easily) kind of bounds the design of the GC.

I think it is a little disingenuous to say that a Julia programmer does not have to worry about types. Type inference alleviates many burdens, but correct typing of arguments is essential (and hidden promotions or casting can kill performance). So while you can write correct programs easily, for efficient programs you end up worrying about this quite a lot.

Will this eventually solve the "Julia does not like Pizza" issue (https://github.com/JuliaLang/julia/issues/3721)?

Hi Patrick, can you point to a paper / resource that describes this recent work?

You can do this using the SymPY.jl package. As a good chunk of matrix functionality is parametric on element type, you can do matrix operations on SymPY's symbolic variables.

See example 6 in: https://github.com/andreasnoack/andreasnoack.github.io/blob/...

I think this comment is referencing the fact that as of right now, slicing a julia array creates a copy and not a lightweight "view" of the sliced data. This makes vectorized operations on slices of arrays sometimes faster in numpy than in julia. See https://github.com/JuliaLang/julia/issues/3701.