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A New Mechanistic Model to Predict Boosting Pressure of Electrical Submersible Pumps ESPs Under High-Viscosity Fluid Flow with Validations by Experimental Data

机译:一种新的机制模型,通过实验数据预测高粘度流体流动的升压电力膨胀泵的压力

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As the second most widely used artificial lift method in petroleum production (and first in produced amount), electrical submersible pumps (ESPs) maintain or increase flow rates by converting kinetic energy to hydraulic pressure of hydrocarbon fluids. ESPs are generally characterized under water condition and the water performance curve is provided by manufacturers. However, properties of hydrocarbon fluids are very different from water and significantly alter the pump performance. Most of existing methods to estimate ESP pump boosting pressure under viscous fluid flow involve strong empirical nature by correlating the experimental data with correction factors. A universally valid mechanistic model is not available yet. In this paper, a new mechanistic model accounting for viscosity effects of working fluids on ESP pump hydraulic heads is proposed, which is validated with a wide range database collected from different pump types. The new model starts from Euler equations for centrifugal pump, and introduces a conceptual bestmatch flowrate QBM, at which the outlet flow direction of impeller matches the designed flow direction. The mismatch of velocity triangles, resulted from various liquid flow rates, is used to derive recirculation losses. Other head losses due to flow direction change, friction, and leakage flow etc. are incorporated in the new model. QBM is obtained by matching the predicted performance curve with the catalog curve with water. With the determined QBM, the ESP hydraulic head under viscous fluid flow conditions can be calculated. The model predictions of the boosting pressure among various ESPs are compared with an experimental database, which is composed of more than 170,000 data points. The specific speeds (NS) of ESP pumps in this study are ranged from 1600 to 3448, among which one ESP pump is radial type, and the others are mixed types. As baseline, the model-predicted ESP pump water performance well matches to the catalog curves. With viscous fluid medium, the boosting pressures predicted by the model agree well with the experimental data. For most calculation results with medium to high flow rates, the overall prediction error is less than 15%. Unlike the empirical correction factors that use experimental data points as inputs, the mechanistic model proposed in this study does not require any experimental data input, it could predict ESP pump performance with an acceptable accuracy.
机译:作为石油生产中第二最广泛使用的人工升力方法(以及首先生产量),通过将动能转化为烃流体的液压来维持或提高流量速率。 ESPS通常在水状况下表征,并且制造商提供水能曲线。然而,烃流体的性质与水非常不同,并且显着改变泵性能。大多数估算粘性流体流动下泵浦升压压力的现有方法涉及强大的经验性质,通过将实验数据与校正因子相关联。尚未获得普遍有效的机制模型。在本文中,提出了一种新的机制模型,用于ESP泵液压头的工作流体对液压头的粘度效应,这是用不同泵类型收集的宽范围数据库验证。新模型从离心泵的欧拉方程开始,引入概念最佳流量QBM,其中叶轮的出口流向与设计的流动方向相匹配。由各种液体流速导致的速度三角形不匹配,用于导出再循环损耗。在新模型中包含由于流动方向变化,摩擦和泄漏流量引起的其他头部损失。通过将预测的性能曲线与具有水的目录曲线匹配来获得QBM。利用所确定的QBM,可以计算粘性流体流动条件下的ESP液压头。将各种ESP之间提升压力的模型预测与实验数据库进行比较,该实验数据库由超过170,000个数据点组成。本研究中的ESP泵的特定速度(NS)为1600至3448,其中一个ESP泵是径向型,其他ESP泵是混合类型。作为基线,模型预测的ESP泵水性能良好匹配到目录曲线。利用粘性流体介质,模型预测的升压压力与实验数据相一致。对于大多数计算结果,媒体高流量率,总预测误差小于15%。与使用实验数据点作为输入的经验校正因子不同,本研究中提出的机械模型不需要任何实验数据输入,它可以以可接受的精度预测纯泵性能。

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