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.

中文翻译：

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基于PSO-RBF神经网络的多级压裂水平井参数辨识

为了更准确地识别多级压裂水平井（MFHW）参数，解决储层非均质性和井产量数据的随机性问题，提出了一种基于PSO-RBF神经网络模型的新方法。首先，利用GPU并行程序计算多级压裂水平井井底压力。其次，将上述压力数据大部分导入RBF神经网络模型进行训练。在训练过程中，利用PSO算法全局最优解的优化函数对RBF神经网络的参数进行优化，最终建立所需的PSO-RBF神经网络模型。第三，使用剩余数据测试生成的神经网络。最后，利用所提出的PSO-RBF神经网络模型，对多级压裂水平井的现场案例进行了研究。结果表明，在大多数情况下，所提模型的性能优于其他模型，相关系数最高，均值最低，绝对误差最小。这证明了 PSO-RBF 神经网络模型可以有效地应用于水平井参数识别。该模型在提高储层物性参数预测精度方面具有很大潜力。这证明了 PSO-RBF 神经网络模型可以有效地应用于水平井参数识别。所提出的模型对于提高储层物性参数的预测精度具有很大的潜力。这证明了 PSO-RBF 神经网络模型可以有效地应用于水平井参数识别。所提出的模型对于提高储层物性参数的预测精度具有很大的潜力。