How to Include Features that Apply to Specific Classes
If I understood correctly, you said that  Pipe Size does not have a correlation with the Predicted Hours. If you are sure that one or more variables do not possess the information about the target then drop them.
Other than that, if a feature (or some of your features) has a relation only with a specific task, then replace the null values with 0.
But in your case, I think you should also try the simpler algorithms like Linear Regression. But be careful about the sample size vs a number of features balance. Since you have few samples, use at most 1 variable for every 15 samples (In rare cases, 10 can be used too, but it is not recommended). As this source mentions:

In summarizing the findings by Schmidt, Green suggested that the minimum number of SPV ranges from 15 to 25.

Also, you need to evaluate your model's performance, but since you may have an unbalanced number of samples for each Task Type, be careful while splitting your data to train and test, make sure your test data has enough samples from each Task Type.
Some other important things:

Try to do bivariate (between the target variable and an independent variable) analysis before modeling, so you can eliminate unnecessary variables.
In the case of using Linear Regression, be careful about multicollinearity if you will use the coefficients to interpret your results. Because if your model has multicollinearity, then your coefficients will be incorrect although your predictions will note be spurious i.e. they will still be trustable.