Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network

Abstract

<jats:p>In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.</jats:p>

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“…Their results showed that the average recognition rate using PSO-RBF neural network was 97.115%, which was 4% higher than that using RBF neural network. As one of the population-based intelligence algorithms, PSO algorithm is widely used in parameter optimization, neural network optimization and other fields due to its advantages of simple implementation and few adjustment parameters (Dong et al, 2020;Yin et al, 2020). However, the single PSO algorithm has the problem of easily falling into local extremum.…”