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Modeling and Experimental Study of Solid-Liquid Two-Phase Pressure Drop in Horizontal Wellbores With Pipe Rotation

机译:管道旋转水平井筒中固液两相压降的建模与实验研究

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Determining pressure loss for cuttings-liquid system is very complicated task since drill-string is usually rotating during drilling operations and cuttings are present inside wells. While pipe rotation is increasing the pressure loss of Newtonian fluids without cuttings in an eccentric annulus, a reduction in the pressure loss for cuttings-liquid system is observed due to the bed erosion. In this study, cuttings transport experiments for different flow rates, pipe rotation speeds, and rate of penetrations (ROPs) are conducted. Pressure loss within the test section and stationary and/or moving bed thickness are recorded. This study aims to predict factional pressure loss for solid (cuttings)-liquid flow inside horizontal wells using computational fluid dynamics (CFD) and artificial neural networks (ANNs). For this purpose, numerous ANN structures and CFD models are developed and tested using experimental data. Among the ANN structures, TrainGdx-Tansig structure gave more accurate results. The results show that the ANN showed better performance than the CFD. However, both could be used to estimate solid-liquid two-phase pressure drop in horizontal wellbores with pipe rotation.
机译:确定钻屑-液体系统的压力损失是非常复杂的任务,因为钻柱通常在钻井作业期间旋转并且钻屑存在于井内。当管道旋转增加了不带偏心环隙的钻屑的牛顿流体的压力损失时,由于床层侵蚀,观察到了钻屑-液体系统的压力损失减少了。在这项研究中,进行了针对不同流速,管道旋转速度和穿透率(ROP)的岩屑运输实验。记录测试区内的压力损失以及固定床和/或移动床的厚度。这项研究旨在使用计算流体力学(CFD)和人工神经网络(ANN)预测水平井内固(切屑)-液流的派生压力损失。为此,使用实验数据开发并测试了许多ANN结构和CFD模型。在人工神经网络结构中,TrainGdx-Tansig结构给出了更准确的结果。结果表明,人工神经网络的表现优于差价合约。但是,这两种方法都可以用来估计水平井筒中随管旋转的固液两相压降。

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