Abstract: Domestic and foreign scholars have carried out the research of shale gas fracturing production prediction and fracturing parameter optimization based on machine learning on the premise of a large number of foreign shale fracturing sample data. With the continuous development of F Gas Field in recent years, a large number of data from fracturing operation, production dynamic and interpretation results have been accumulated. In view of the fact that these data are not fully utilized in the design of fracturing operation parameters at present, the Bayesian neural network model is established to optimize the fracturing operation parameters by using history data of the fracturing operation parameters and reservoir physical parameters from 200 wells. The reservoir physical parameters, completion parameters and fracturing operation parameters which have an impact on the fracturing effect are selected. The correlation of 11 parameters is analyzed by Pearson correlation coefficient method. Principal Component Analysis (PCA) is used for further dimension reduction. The principal components are used as input parameters of Bayesian Neural Network model. The validity period is used as output parameter. Bayesian method is introduced to adjust regularization coefficient adaptively to avoid neural network overfitting. And then, a three-layer Bayesian neural network prediction model is generated. The model is trained by using 90% of the data of 200 wells as training set and 10% as test set. The experimental results show that the mean relative error of the model prediction results after training is within 5%, which can be used to optimize the fracturing operation parameters.

Key words: fracturing, PCA, Bayesian neural network, operation parameters, optimization