Compared to modern digital electronics, the human brain is "very wide but very slow". The equivalent clockspeed is just tens of hertz, maybe a few hundred for some areas. High fidelity simulations of the signal waveforms might require only a few thousand samples per second! Contrast that to a typical modern computer, which has clocks a million times faster than that.
Similarly, simulating a brain may not need high precision at all, and is likely amenable to various forms of compression. For example: a simulator could have a library of the behaviour of a million synapses, and simply interpolate between them to closely match each synapse in a brain scan. Hilariously, for extra efficiency, this kind of interpolation in a high dimensional space is something artificial neural networks are quite good at!
I envision a path where ordinary "machine learning" is used to automatically model small sections of the brain, such a ganglia, axons, synapses, etc... Similarly, ML techniques can be used to match scans to the previously learned library of these models. The final thing might just be a hundred 8-bit "parameters" per synapse, and there's 125 trillion of those, so about 12.5 petabytes. That's a lot of data, but you can buy a server right now with 12 TB of memory! Assuming a 30% increase in capacity yearly (thanks Mr Moore), the goal is just 26 years away.
Make an entry in your calendar: 2047 is the year we'll have human-equivalent AI...