Particle size distribution measurements can be used for permeability estimation, and it is widely accepted that there exhibits a certain degree of correlation between permeability and porosity. In this paper, an efficient, low-cost, and reliable approach is used to develop an empirical correlation for estimating permeability based on particle size distribution characteristics and porosity in two modes: mode #1 includes 5% ($D_{\mathit{5}})$, 10% ($D_{\mathit{10}}$) and 60% ($D_{\mathit{60}}$) of the cumulative passing particle size distribution curve and porosity for situations where porosity is known, and mode #2 where porosity is unknown. To optimize the coefficient of the proposed relationships, genetic-binary particle swarm optimization algorithm is used. A database consisting of 50 samples collected from four wells drilled in two neighboring pads in Western Canada were used, and their permeability values were predicted successfully. A validation based on a reference study and an application of sand completion design based on the finding of this study are also discussed. The novelties of the proposed approach are examining the effect of fines content, investigating the full range of particle size distribution curve, and using a hybrid intelligent method to optimize the coefficients of the correlations. In addition to sand completion deigns purposes, the proposed method can be used in enhanced oil recovery studies, reservoir management, and reservoir simulation applications.

粒度分布测量可用于渗透率估计，并且普遍认为渗透率和孔隙度之间表现出一定程度的相关性。在本文中，使用一种高效、低成本且可靠的方法来开发经验关联式，用于基于两种模式下的粒度分布特征和孔隙度估算渗透率：模式 #1 包括 5% ($D_{\mathit{5}})$, 10% ($D_{\mathit{10}}$) 和 60% ($D_{\mathit{60}}$) 的累积通过粒度分布曲线和孔隙率在孔隙率已知的情况下，以及在孔隙率未知的情况下的模式 #2。为了优化所提出的关系的系数，使用遗传二元粒子群优化算法。使用了一个数据库，该数据库包含从加拿大西部两个相邻垫层钻探的四口井中采集的 50 个样品，并成功预测了它们的渗透率值。还讨论了基于参考研究的验证和基于本研究结果的砂完井设计的应用。所提出方法的新颖之处在于检查细粉含量的影响，调查粒度分布曲线的全范围，并使用混合智能方法优化相关系数。