This paper proposes a two-stage multi-system architecture for forecasting post-fracturing responses in a tight oil reservoir using historical fracturing data. The first stage predicts the 180-day cumulative liquid (oil + water) production directly, and the second stage uses differential correction to predict the prediction error resulting from the first stage. The final prediction is a combination of the two stages. 5-fold cross-validation is used in each stage, resulting in five forecasters for each stage. The average of the five predictions is taken as the output of the corresponding stage. Each of the five forecasters in each stage consists of three independent subsystems (Location, Completion and Fracturing), whose inputs are subsets of the well properties. The Location subsystem is constructed by a weighted average, whereas Completion and Fracturing are constructed by fuzzy logic systems. The parameters of the three subsystems are optimized simultaneously using simulated annealing. The final design achieved over 70% prediction accuracy for more than 96% of the testing wells. The main advantages of our approach are that 1) it does not require a large training dataset; 2) it can cope well with incomplete data entries and uncertainties; and, 3) the redundancy in the input parameters is used to improve accuracy.