中文摘要：

基于伞集流涡轮流量计与放射性持水率一密度计组合仪在油气水三相流流动环中的动态测量特性，建立了三相流总流量及分相含率的人工神经网络软测量模型．由于集流伞存在流体非线性漏失，集流后测量通道内流型复杂多变，在软测量模型中考虑了油水流型特性的影响．人工神经网络训练与学习采用了Levenbery-Mar—quardt非线性阻尼最小二乘算法，模型检验结果表明：对泡状流及段塞流流型，利用该模型可以实现较高精度的总流量及分相含率预测，为伞集流油气水三相流测井信息处理提供了一种有效方法．

英文摘要：

Based on dynamic measurement characteristics of turbine flowmeter and radioactive water holdup-densitometer combination tool with basket concentrating flow diverter in oil-gas-water three-phase flow loop, a soft measurement model of artificial neural network （ANN） was established to predict the total flow rate and component flow rate fraction of three-phase flow. The effect of oil-water flow pattern characteristics on the soft measurement model was accounted for by considering the nonlinear leakage of basket concentrating flow diverter and various complex flow pattern variations of the measuring channel after the concentrating flow. Levenbery-Marquardt nonlinear damp least square algorithm was used to train and learn in the model. The model verification results showed that the model could be used to give good prediction accuracy of total flow rate and component flow rate fraction for the bubble and slug flow patterns. It provides an effective information processing method for three-phase flow logging.