Physical factors affecting the transport and deposition of particles in saturated porous media

2017 ◽  
Vol 17 (6) ◽  
pp. 1616-1625 ◽  
Author(s):  
Xianze Cui ◽  
Quansheng Liu ◽  
Chengyuan Zhang
Keyword(s):  
Porous Media ◽  
Deposition Rate ◽  
Rate Coefficient ◽  
Dispersion Coefficient ◽  
Deposition Process ◽  
Suspended Particles ◽  
Longitudinal Dispersion ◽  
Physical Factors ◽  
Longitudinal Dispersion Coefficient ◽  
Injection Modes

Abstract Saturated sand box experiments were conducted to explore the effect of various physical factors on the transport and deposition of suspended particles in porous media. Red quartz powder and natural quartz sand were employed in the study and acted as suspended particles and porous media, respectively. Particles were injected into the sand box in two modes, i.e., pulse injection and continuous injection. Tests were performed at various particle concentrations, flow velocities, deposition rate coefficient and longitudinal dispersion coefficient by both injection modes. The breakthrough curves were described with the analytical solution of a convection–dispersion equation, in which first-order deposition kinetics were taken into account. Different behavior of suspended-particle transport and deposition in porous media was observed under different injection modes and experimental conditions. The results show that effluent concentration was approximately linear with the initial particle concentration. The deposition rate coefficient depends strongly on particle size and flow velocity, and the transport and deposition process was very sensitive to it. Furthermore, the longitudinal dispersion coefficient increases with increasing flow rate, and particles are easier to transport through pores as the longitudinal dispersion coefficient increases. This study shows the importance of particle concentration, flow velocity, deposition rate coefficient and longitudinal dispersion coefficient in the transport and deposition process of suspended particles.

Taylor's Dispersion in Stratified Porous Media

1981 ◽  
Vol 21 (04) ◽  
pp. 459-468 ◽  
Author(s):  
Larry W. Lake ◽  
George J. Hirasaki
Keyword(s):  
Porous Media ◽  
Velocity Dispersion ◽  
Dispersion Coefficient ◽  
Fluid Velocity ◽  
Longitudinal Velocity ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Bulk Fluid ◽  
Transverse Profile ◽  
Taylor’S Dispersion

Abstract During 1953-54, Taylor showed that if a certain criterion is met the combined effects of the transverse profile of longitudinal velocity and transverse diffusion on a solvent slowly flowing through a tube will manifest themselves as a longitudinal diffusion phenomenon. A similar phenomenon exists in stratified porous media where the transverse profile of longitudinal velocity and transverse dispersion can produce an effective longitudinal dispersion, called Taylor's dispersion in this paper. Since this effective longitudinal dispersion is larger than the corresponding homogeneous longitudinal dispersion, the quantitative description of this phenomenon would be important to dispersion-sensitive EOR processes, such as surfactant or miscible flooding.Taylor's dispersion will occur in two-layer porous media if a suitably defined dimensionless number is much greater than unity. When this condition holds, the effluent history of a constant-mobility equal-density miscible displacement is that of the same displacement in a homogeneous medium with increased dispersion. The resulting effective longitudinal dispersion may be derived analytically and verified numerically as a function of several media properties. The most important of these are system thickness and permeability contrast.In multilayer media, when two adjacent layers have a large transverse dispersion number they behave as a single layer with suitably averaged properties. This observation suggests an algorithm whereby Taylor's dispersion may be extended to multilayer systems. The algorithm, or grouping procedure, loves effluent histories that are in agreement with numerical solutions to the continuity equation and allow properties of the resulting effective dispersion to be investigated. From the results of this work, Taylor's dispersion can offer an explanation for the large field-scale dispersion observed in tracer test studies. Moreover, it appears that the grouping procedure could indicate a method for obtaining layered reservoir models from core data. Introduction In laboratory displacements, longitudinal (parallel to the bulk fluid velocity) dispersion is well characterized as consisting of additive contributions of diffusion and convection:when K is the longitudinal dispersion coefficient, Do is the molecular diffusion coefficient, F is the formation electrical resistivity factor, v is the interstitial longitudinal velocity, and, is the longitudinal dispersity. At a velocity above about 0.1 ft/D (0.35 mu m/s) the convection term dominates Eq. 1, so for practical displacements about 1 ft/D (3.5 mu m/s) - K depends only on the term, in turn, is a function of average particle size and local heterogeneity, and averages 0.05 to 0.2 in. (0.13 to 0.51 cm) for homogeneous laboratory displacements. Similarly, transverse (perpendicular to the bulk fluid velocity) dispersion iswhen Kt is the longitudinal dispersion coefficient and is the transverse dispersivity. Measurements of are much less common than but they indicate that /30.In the scaled differential material-balance equations, both K and Kt become part of Peclet numbers, vL/K and (vH/Kt)H/L, which appear as inverses in the equations. In laboratory displacements, and are of the order of fractions of centimeters and L is the order of centimeters. SPEJ P. 459^

scholarly journals Investigation of a parameter estimation method for contaminant transport in aquifers

2001 ◽  
Vol 3 (4) ◽  
pp. 203-213 ◽  
Author(s):  
Channa Rajanayaka ◽  
Don Kulasiri
Keyword(s):  
Experimental Data ◽  
Hydraulic Conductivity ◽  
Contaminant Transport ◽  
Dispersion Coefficient ◽  
Estimation Method ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
System Noise ◽  
Estimated Parameters ◽  
Comparison Of The Results

Real world groundwater aquifers are heterogeneous and system variables are not uniformly distributed across the aquifer. Therefore, in the modelling of the contaminant transport, we need to consider the uncertainty associated with the system. Unny presented a method to describe the system by stochastic differential equations and then to estimate the parameters by using the maximum likelihood approach. In this paper, this method was explored by using artificial and experimental data. First a set of data was used to explore the effect of system noise on estimated parameters. The experimental data was used to compare the estimated parameters with the calibrated results. Estimates obtained from artificial data show reasonable accuracy when the system noise is present. The accuracy of the estimates has an inverse relationship to the noise. Hydraulic conductivity estimates in a one-parameter situation give more accurate results than in a two-parameter situation. The effect of the noise on estimates of the longitudinal dispersion coefficient is less compared to the effect on hydraulic conductivity estimates. Comparison of the results of the experimental dataset shows that estimates of the longitudinal dispersion coefficient are similar to the aquifer calibrated results. However, hydraulic conductivity does not provide a similar level of accuracy. The main advantage of the estimation method presented here is its direct dependence on field observations in the presence of reasonably large noise levels.

A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers

2011 ◽  
pp. 358-374
Author(s):  
Jianhua Yang ◽  
Evor L. Hines ◽  
Ian Guymer ◽  
Daciana D. Iliescu ◽  
Mark S. Leeson ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dispersion Coefficient ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Artificial Neural ◽  
Input Variables ◽  
Training Examples ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network

In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes Genetic Algorithms (GAs) to identify variables that are being input into a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), which simplifies the neural network structure and makes the training process more efficient. Once input variables are determined, GNMM processes the data using an MLP with the back-propagation algorithm. The MLP is presented with a series of training examples and the internal weights are adjusted in an attempt to model the input/output relationship. GNMM is able to extract regression rules from the trained neural network. The effectiveness of GNMM is demonstrated by means of case study data, which has previously been explored by other authors using various methods. By comparing the results generated by GNMM to those presented in the literature, the effectiveness of this methodology is demonstrated.

Sign in / Sign up

Export Citation Format

Share Document