麻雀算法与长短期记忆网络在三相流流型预测中的应用

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中图分类号：TE377 文献标志码：A 文章编号：1000-7393（2025）-02-0207-11

Abstract：Acuratepredictionofflowregimesivertical gas-lquidslugflowisvitalforunderstandingtheflowcharacteristicsof gas-liquid bubble tre-phase systemsand enhancingoilandgas production eficiency.This study presentsa KPCA-ISSA-BiLSTM modelforclasifingandpredictinggas-lquidsugflowregimesThemodelconsidersfctorslikegasandliquidflowratesfoaming agentconcentratio，andpressureusingexprimentaloalprodctiodata.Feature extractionandpreproceingealdefore training the model. The results show that the KPCA-ISSA-BiLSTM model achieves 99.69% accuracy on the training set and 98.33% onthe testset，withthehighestaccuracyforfoamsugflowprediction.Icontrast，B，CNN，ELM，andLSTmodelsyield accuracies between 84% and 90% . The proposed model outperforms these alternatives， offering an efective tool for predicting flow regimes and providing valuable support for optimizing gas-liquid three-phase flow applications in engineering.

Key words：verticalwell;gas-water-fathre-phase;fowpatemprediction; machineleaing;comparsonofmultiplealgorithms

0 引言

在油气开采领域，气水两相流的流型预测被认为是优化井筒举升效率和控制积液风险的核心问题。（剩余19020字）