RESUMO
Hydraulic fracturing technology is the main method to develop low-permeability reservoirs. Fracture conductivity is not only the basis of fracture optimization design but also one of the key parameters to determine the effect of hydraulic fracturing. However, current methods of calculating fracture conductivity require a lot of time and labor cost. This research proposes a fracture conductivity prediction model based on machine learning. The main controlling factors of fracture conductivity are determined using the Pearson coefficient method and gray correlation analysis. Example application shows that the R2 values of the BP neural network model based on a genetic algorithm for predicting the fracture conductivity of block A and block B are 0.981 and 0.975, respectively, indicating that the machine learning model can accurately predict fracture conductivity.