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 R
2 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.