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Partition Perforation Optimization for Horizontal Wells Based on Genetic Algorithms

机译:基于遗传算法的水平井分区射孔优化

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摘要

Because of heterogeneity in permeability along horizontal wellbores, early water breakthrough and rapid water-cut increase are always observed in high-permeability completion intervals when a uniform perforating scheme, as a common practice, is applied throughout the wellbore. Optimization on perforation parameters along horizontal intervals helps homogenize the inflow-velocity profile and thus is critically important for enhanced oil recovery. This paper derives a coupled reservoir/wellbore flow model that is based on inflow-velocity-control theory. Genetic algorithms are applied to solve the model because they excel in obtaining the global optimal on discrete functions. The optimized perforating strategy applies a low perforation density in high-permeability intervals and high perforation density in low-permeability intervals. As a result, the inflow-velocity profile is homogenized and idealized.
机译:由于沿水平井眼的渗透率存在非均质性,因此,在整个井眼中采用统一的射孔方案(通常的做法)时,总是会在高渗透率完井间隔中观察到早期的水突破和含水率的迅速增加。沿水平间隔优化射孔参数有助于使流入速度分布图均匀,因此对于提高采油率至关重要。本文基于流入速度控制理论推导了储层/井筒耦合流模型。遗传算法用于求解模型,因为它们擅长获取离散函数的全局最优值。优化的射孔策略在高渗透率层段采用低射孔密度,在低渗透率层段采用高射孔密度。结果,流入速度分布被均化和理想化。

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