Sensitivity analysis is typically required to screen unwanted history matching parameters so that computational cost can be reduced. Random Forests is a well-known statistical learning tool that maps a list of input parameters onto a predicted response. A constructed Random Forests model ranks these input parameters efficiently even for highly nonlinear data. The aim of this study is to investigate the capability of Random Forests in assisted/automatic production history matching in reservoir engineering. A history matching case study has been chosen to rigorously compare the capability of Random Forests and one-parameter-at-time (OPAT) in sensitivity analysis. The capability of Random Forests as a sensitivity analysis tool was investigated and compared with OPAT. By implementing the optimal number of decision trees, which the Random Forests' configuration parameters, and the appropriate design of experiments method Fractional Factorial Design (FFD), the Random Forests-based history matching results have shown to be relatively better than OPAT-based results in this case. Here, Random Forests combined with FFD helped in improving the average error by 23.1, along with the reduction of the standard deviation of errors by 28.9 relative to OPAT. Combining Random Forests with FFD is relatively better than OPAT because of the rigorous permutation tests of each parameter. In addition, this combination requires only 16 samples, where OPAT requires 21 samples. The combination of Random Forests and FFD as a sensitivity analysis tool successfully selected the top parameters which are horizontal permeability-related. Such parameters are well known in their direct relationship with water production as described by the well-known fractional flow theory. To conclude, this study found there is a strong indication that Random Forests can be a more reliable sensitivity analysis tool in production history matching, given the right configuration and design of experiments strategy. © 2019 Elsevier B.V.