Mixed neural network-based tight gas well single well yield prediction method

• Main Authors: LIU QIGUO, HU YANG, ZHANG JUN, LIU YUXIN, LIAO ZHOUYANG
• Format: Patent
• Language: Chinese; English
• Published: 18.08.2023
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses a tight gas well single well yield prediction method based on a hybrid neural network. A sparrow population is initialized through a sparrow search algorithm, and an operation result is obtained through iterative screening; initializing hyper-parameters of the LSTM neural network based on the operation result of the sparrow search algorithm to obtain an LSTM model optimized by the sparrow search algorithm; training the model to generate a prediction model; and inputting prediction data into the prediction model and obtaining a result. According to the method, the hyper-parameters of the LSTM neural network are optimized by using the sparrow search algorithm, and the LSTM optimized by using the sparrow search algorithm can accelerate the convergence of the model and improve the prediction precision of the model, so that the problem of insufficient prediction precision in the prior art is solved. 本发明公开了一种基于混合神经网络的致密气井单井产量预测方法，通过麻雀搜索算法初始化麻雀种群并迭代筛选获取运算结果；基于麻雀搜索算法运算结果初始化LSTM神经网络超参数，得到麻雀搜索算法