A numerical method to predict the production and stimulation performance of re-fracturing is introduced in this paper. This prediction approach is based on the dynamic differential modeling method for forecasting single well re-fracturing. In this study, the average production during 30 days and fracture surface area after re-fracturing were chosen as predicted indices. Fourteen parameters were considered as influencing factors which were chosen from geological, reservoir and treatment parameters. The historical data of predicted indices and influencing parameters were collected from the data of 6 re-fractured wells in the Konys oil field. The historical data was input into the dynamic differential model to establish and discretize the relation functions. Three target well were chosen and their corresponding influencing factors were used to calculate the predicted indices. The results showed that for the target wells, the modeling predicted indices were fairly close (accuracy was 80%) to the real numbers. This model is practical for the engineer in the field since the input parameter acquisition is accessible from the common oil field data. It could be considered as a basic application based on the theory of big data concept.


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