石油钻采工艺2025，Vol.47Issue(2)：207-217,11.DOI:10.13639/j.odpt.202504025
麻雀算法与长短期记忆网络在三相流流型预测中的应用
Application of sparrow search algorithm and long-short term memory network in three phase flow pattern prediction
摘要
Abstract
Accurate prediction of flow regimes in vertical gas-liquid slug flow is vital for understanding the flow characteristics of gas-liquid bubble three-phase systems and enhancing oil and gas production efficiency.This study presents a KPCA-ISSA-BiLSTM model for classifying and predicting gas-liquid slug flow regimes.The model considers factors like gas and liquid flow rates,foaming agent concentration,and pressure,using experimental or real production data.Feature extraction and preprocessing are applied before training the model.The results show that the KPCA-ISSA-BiLSTM model achieves 99.69%accuracy on the training set and 98.33%on the test set,with the highest accuracy for foam slug flow prediction.In contrast,BP,CNN,ELM,and LSTM models yield accuracies between 84%and 90%.The proposed model outperforms these alternatives,offering an effective tool for predicting flow regimes and providing valuable support for optimizing gas-liquid three-phase flow applications in engineering.关键词
垂直井/气水泡三相流/流型预测/机器学习/多算法对比Key words
vertical well/gas-water-foam three-phase/flow pattern prediction/machine learning/comparison of multiple algorithms分类
石油、天然气工程引用本文复制引用
石书强,王亚宁,许梅,王珍,张永才,王鑫..麻雀算法与长短期记忆网络在三相流流型预测中的应用[J].石油钻采工艺,2025,47(2):207-217,11.基金项目
重庆科技大学研究生创新计划项目"泡沫排水采气中泡沫破裂、运移、携液机理及模型建立研究"(编号:YKJCX2420144). （编号:YKJCX2420144）
