Flow of dispersed particles through porous media - Deep bed filtration
Flow of dispersed particles through porous media - Deep bed filtration
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DOI：
10.1016/j.petrol.2009.06.016
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发表时间：
2009-11-01
影响因子：
--
通讯作者：
Maini, Brij
中科院分区：
文献类型：
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作者：
Zamani, Amir;Maini, Brij
在许多工业过程中，人们普遍认为液体中分散的颗粒通过多孔床进行输送。从胶体悬浮液中析出颗粒通过多孔介质的过程通常称为深床过滤。该过程的目标可以是颗粒介质对颗粒的过滤，也可以相反，避免颗粒过滤。悬浮颗粒与介质（色剂）颗粒之间的物理和化学力，颗粒大小，流体速度和粒度在悬浮液中去除颗粒方面起着至关重要的作用。颗粒沉积可以改变孔隙形态，从而改变多孔介质的孔隙率和局部压力梯度。这可能会导致渗透率下降，从而导致产能或井的注入能力下降。本文对深床过滤理论的相关文献进行了综述。在过去的几十年里，人们提出了不同的数学模型来评估颗粒去除的初始阶段和瞬态阶段。轨迹分析或对流扩散方程已用于微观建模或所谓的基本建模来计算初始去除效率。虽然这些可以预测有利条件下的过滤器性能，但它们低估了不利条件下的去除效率。因此，建立了半经验方程来预测不利条件下的去除效率。宏观或现象学模型已被用于预测深床过滤过程的瞬态去除效率。用这种方法预测滤波器的性能需要了解滤波器系数的泛函性。根据出水浓度变化历史，采用搜索优化技术获得过滤系数。综述了用于评价颗粒去除过程初始阶段和瞬态阶段的不同数学模型。(C) 2009 Elsevier B.V.版权所有
Transport of dispersed particles in liquids through porous beds is widely recognized to occur in many industrial processes. The process of particle deposition from a colloidal suspension flowing through a porous medium is usually called deep bed filtration. The goal of the process can be either filtration of the particles by the granular media or, on the contrary, avoiding the particle filtration, Physical and chemical forces between suspended particles and grains of the media (colledors), particle size, fluid velocity and grain size play vital roles in the removal of particles from a suspension. Particle deposition can change the pore morphology and consequently the porosity of the porous medium and the local pressure gradient. This can cause permeability decline and therefore, loss of productivity or injectivity of wells. This article presents a comprehensive review of the literature related to deep bed filtration theories.Different mathematical models for evaluating both initial and transient stage of particle removal have been proposed during last decades. Trajectory analysis or convective diffusion equations have been used in microscopic modeling or so-called fundamental modeling to compute initial removal efficiency. Although these could predict the filter performance under favorable conditions but they underestimate the removal efficiency under unfavorable conditions. Hence, semi-empirical equations were developed for predicting removal efficiency under unfavorable conditions. Macroscopic or phenomenological modeling has been used to predict transient stage removal efficiency of deep bed filtration process. Predicting filter performance by this method requires the knowledge of functionality of filter coefficient. Filter coefficient can be obtained by using search optimization technique along with effluent concentration history. A review on different mathematical models for evaluating both initial and transient stage of particle removal process is presented. (C) 2009 Elsevier B.V. All rights reserved.