Can I arbitrarily eliminate 20% of my training data if doing so significantly improves model accuracy? My dataset contains 2000 records with 125 meaningful fields 5 of which are distributed along highly skewed lognormal behavior.

I've found that if I eliminate all records below some threshold of this lognormal behavior (by combining the fields together then filtering for Nth percentile), my model improves in accuracy from ~78% to ~86%, using a highly tuned random forests classifier. This filter is only done after splitting my data into train, test (which is done after SMOTE).

What makes this particularly odd is that that filter improves results across multiple sampling methods.

Is this filtering acceptable behavior? Why might it be resulting in better predictions?
One flaw in your procedure is the use of SMOTE before splitting in train/test. This should be avoided as you may have synthetic examples in the test data which generation depends on training data and that will be highly close to this data in your feature space (as SMOTE uses Euclidean distance).

Moreover, if most of the minority data belongs to the not-skewed region of your specific variables, these points will be also over sampled and so this reduction in the variables space will produce an overly optimistic performance which does not reflect the real distribution of the data.