Process mining can build process model according to event logs generated by enterprise information management system. There always exist some deviations between the process model and event logs when the actual business process changes. At this time， the process model needs to be repaired. For the process model with concurrent structures， the precision of some existing repairing methods will be reduced because of the addition of self-loops and invisible transitions. Therefore， a method for repairing process models with concurrent structures was proposed on the basis of logic Petri net and token replay. Firstly， according to the relationship between the input-output places of the sub-model and event logs， the insertion position of the sub-model was determined. Then， the deviation positions were determined by a token replay method. Finally， a method was designed to repair the process models based on logical Petri net. The correctness and effectiveness of this method were verified by carrying out simulations on ProM platform， and the proposed method was compared with Fahland’s and other methods. The results show that the precision of this method is about 85%， which is increased by 17 and 11 percentage points respectively compared with those of Fahland’s and Goldratt methods， In the terms of simplicity， the proposed method does not add any self-loop or invisible transition， while Fahland’s and Goldratt methods add some self-loops and invisible transitions. All of the fitting degrees of the three methods are above 0.9， and the fitting degree of Goldratt method is slightly lower. The above verifies that the model repaired by the proposed method has higher fitness and precision.