Well, from the backprop perspective and theoretical perspective there is no problem with increasing the batch size. However there are some practical issues:

Normalization depends on batch statistics. The bigger the batch size is, the more general statistics are supposed to be. You should wonder if normalization will properly generalize.
Additionally, there exist a relationship (empirically shown) between batch size and learning rate Explanation 6 in a log-scale. If your batch size increases a lot your training may require LR adjustments. In the same way you can find this paper Don’t Decay the Learning Rate, Increase the Batch Size 7 which also gets into this problem.