  Contamination by polarized foregrounds is one of the biggest challenges for
future polarized cosmic microwave background (CMB) surveys and the potential
detection of primordial $B$-modes. Future experiments, such as Simons
Observatory (SO) and CMB-S4, will aim at very deep observations in relatively
small ($f_{\rm sky} \sim 0.1$) areas of the sky. In this work, we investigate
the forecasted performance, as a function of the survey field location on the
sky, for regions over the full sky, balancing between polarized foreground
avoidance and foreground component separation modeling needs. To do this, we
simulate observations by an SO-like experiment and measure the error bar on the
detection of the tensor-to-scalar ratio, $\sigma(r)$, with a pipeline that
includes a parametric component separation method, the Correlated Component
Analysis, and the use of the Fisher information matrix. We forecast the
performance over 192 survey areas covering the full sky and also for optimized
low-foreground regions. We find that modeling the spectral energy distribution
of foregrounds is the most important factor, and any mismatch will result in
residuals and bias in the primordial $B$-modes. At these noise levels,
$\sigma(r)$ is not especially sensitive to the level of foreground
contamination, provided the survey targets the least-contaminated regions of
the sky close to the Galactic poles.
