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

机译：基于PSO-RBF神经网络的多级压裂水平井参数识别

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摘要

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.
机译：为了更准确地识别多级压裂水平阱（MFHW）参数并解决储层的异质性以及良好的生产数据的随机性，提出了一种基于PSO-RBF神经网络模型的新方法。首先，GPU并行程序用于计算多级压裂水平井的底孔压力。其次，大多数上述压力数据导入RBF神经网络模型以进行培训。在训练过程中，使用PSO算法的全局最优解的优化功能来优化RBF神经网络的参数，最终建立所需的PSO-RBF神经网络模型。第三，使用剩余数据测试所得到的神经网络。最后，通过使用所提出的PSO-RBF神经网络模型研究了多级压裂水平井的场壳。结果表明，在大多数情况下，所提出的模型比其他模型更好，具有最高的相关系数，最低平均值和绝对误差。这证明了PSO-RBF神经网络模型可以有效地应用于水平阱参数识别。所提出的模型具有提高储层物理参数的预测准确性的潜力。

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