Fracturing effect prediction based on sparrow search algorithm and BP neural network

The accuracy of the existing engineering methods for predicting fracturing effects is generally not high, which is likely to cause economic losses. For this reason, an algorithm model was proposed to optimize the artificial neural network by using sparrow search algorithm (SSA), so as to improve the prediction accuracy of fracturing effect. First, the BP neural network model was used to predict the fracturing effect, and then the prediction model was proposed after optimizing the weight of the BP neural network by the sparrow search algorithm. The latter has higher prediction accuracy and can solve the problems, such as slow convergence, easy falling into local optimal solution, and prone to overfitting, in BP neural network. The comparison results show that the average relative accuracy of the results evaluated by BP neural network model whose weights are adjusted by the sparrow search algorithm reaches 93.85%, which is not only higher than the accuracy predicted by the engineering method, but also higher than 90.91% of the BP neural network model before optimized by the sparrow search algorithm. This algorithm model has more stable performance and higher accuracy in actual tasks.