As Stanley Pawlukiewicz said: even under ideal circumstance, you can gain 3 dB of SNR per doubling of recordings. I.e., to increase SNR by, say, 15 dB, you'd need to average $$ 2^{\frac{15}{3}} = 2^{5} = 32$$ recordings. That alone shows that the whole thing isn't really practical: it just doesn't do much unless you use a crazy-high number of recordings.
“Ideal circumstances” are: you know exactly all the phase relations of the desired signals, so they'll properly add up 6 dB. If you don't know the phase relations, then the average gain of both signal and noise will be only 3 dB, i.e. you'd gain no SNR at all. Worse, the phases of all frequencies won't add up in the same way: some may indeed add up for 6 dB gain, but other parts may actually cancel, thus giving an uneven frequency response.
Provided that either you've recorded only a single source or both microphones were at the exact same spot, it's technically speaking possible to infer the exact phase relations from the recordings: first find the highest peak in the cross-correlation of the signals† to get the time alignment. Then divide the (complex) Fourier transforms of the aligned signals to get an IR that fine-adjusts the frequency and phase response of one signal to the other. It's reasonably simple to do that with a small e.g. Python script, but again: just not worth it. Maybe there's software available that has this feature, but I wouldn't bet on it.
†If you're unlucky (likely!) and the signals drift or have very different phase response even in the low frequency range, then this may not be enough, you may need to cross-correlate something like a windowed RMS in chunks to get a time-dependent alignment reconstruction.