标题:
垂直井筒中多相流动的Beggs-Brill压力梯度预测模型的改进Modification of Beggs-Brill Pressure Gradient Predicting Model for Multiphase Flow in Vertical Wells
作者:
董勇, 李梦霞, 廖锐全, 罗威
关键字:
多相流, 压力梯度, 预测, Beggs-Brill模型;Multiphase Flow, Pressure Gradient, Prediction, Beggs-Brill Model
期刊名称:
《Journal of Oil and Gas Technology》, Vol.38 No.1, 2016-03-14
摘要:
Beggs-Brill模型是具有代表性的压力梯度计算模型，但存在预测的多相管流压力梯度偏差过大的问题。通过对比Beggs-Brill模型的预测误差与试验设置的参数，认为Beggs-Brill模型预测误差与气液比参数关系密切，建立了Beggs-Brill模型预测误差关于气液比的二次回归模型，并结合该回归模型与Beggs-Brill模型，建立了一种新的压力梯度预测方法，即BBM模型。对90组试验数据的处理结果表明，BBM模型的预测平均相对误差为6.07%，而Beggs-Brill模型的预测平均相对误差为21.56%。以试验中含水率30%、90%的情况为已知数据，采用BBM模型预测含水率60%时的压力梯度，与试验测试压力梯度比较的平均相对误差为15.86%，比Beggs-Brill模型提高了3.7%。BBM模型提高了多相管流压力梯度预测的精度，有助于提高油气井设计和分析的可靠性。
Beggs-Brill Model was a representative one for calculating pressure gradient. There was a large difference between the pressure gradient calculated by Beggs-Brill method and the pressure gradients measured in experiments of multiphase flow in vertical tubing. By comparing the predicting deviations between Beggs-Brill model and experimental apparatus, it was considered that the predicted deviation using Beggs-Brill Model was closely related with the parameters of gas-liquid ratio. And then combining the correlation method with Beggs-Brill Model, a two-regression model based on the deviation of Beggs-Brill Model was established, and in combination with the 2 models, a new pressure gradient predicting method named BBM model was built. The results for 90 groups of experimental data show that the average relative error of BBM method is 6.07% and the one of Beggs-Brill method is 21.56%. Based on the known data with 30% and 90% water contents, BBM model is to predict the pressure gradients at 60% water content, and compared with the BBM gradients with the experimental pressure gradient, the average relative error of BBM modle is 15.86%, which is 3.7% higher than that of Beggs-Brill model. The results show that the BBM model improves the predicting precision of pressure gradient for multiphase flow in vertical wells; it is beneficial for improving the reliability of design and dynamic analysis of oil and gas wells.