porous medium

In waterflooding process, the time for breakthrough of injecting fluid into a production well is of great importance. Predicting this time helps in designing reservoir development plan. Due to uncertainties in reservoir characterization, estimating the breakthrough is not easy, so alternative methods to estimate quickly the breakthrough time is useful. The percolation method uses limited available reservoir data to predict the breakthrough time distribution, and it may be used for engineering applications. However, implementation of this to real reservoirs requires some adjustments. The aim of this study is to show how percolation approach can be used to real problems. In particular, the effects of permeability contrast between the reservoir and non-reservoir parts in the model are investigated. In order to use the breakthrough scaling function to more realistic reservoir models, a dimensionless breakthrough time was used. The analysis of the breakthrough time of models with zero permeability background (tk=0) and such time for the case of non-zero permeability background (tk=αk) shows a linear dependency which can be used to find breakthrough time distribution. Hence, this correction extends the applicability of the percolation method for predicting breakthrough time when permeability of the system background is not zero.

In the process of oil recovery, after the initial oil recovery process, a considerable amount of oil remains in oil reservoirs. Enhanced oil recovery methods are used to extract residual oil of reservoirs. Various methods are used to improve oil recovery, one of which is the injection of nanofluids instead of water injection. In this study, a numerical study has been considered to determine the effect of various nanomaterials on the improvement of oil recovery. Various nanoparticles have been included, and their major impacts on the factors affecting oil extraction are also presented. The black oil model has been used to study the numerical effect of the nanoparticles on oil extraction. A mixture of different metal oxides nanoparticles such as Al2O3, TiO2 and SiO2, and water as nanofluids is used as an aqueous phase in solving problems. Mass balance and momentum balance equations of nanofluids are solved numerically. In this study, the effect of temperature changes, nanoparticle concentration, nanofluid density, size and density of solid particles of nanoparticles on oil recovery, interfacial tension, and pore pressure variations have been examined.According to the results presented in this study, the addition of nanoparticles reduces the amount of suction and interfacial tension and also increases the amount of oil extraction by about 10%. By increasing the concentration of nanomaterials in the base solution, the amount of oil extraction increases by average 10%. The effect of the size and density of solid particles of nanoparticles on the amount of oil extraction is considerable, and the variations of these parameters also result in a change in oil extraction and increase the amount of oil recovery by about 5 to 8 percent.

Laboratory and field experiments have shown that dispersivity is one of the key parameters in contaminant transport in porous media and varies with elapsed time. This time-dependence can be shown using a time-variable dispersivity function. The advantage of this function as opposed to constant dispersivity is that it has at least two coefficients that increase the accuracy of the dispersivity prediction. In this study, longitudinal dispersivity values were obtained for the conservative NaCl solute transport in a laboratory porous medium saturated with tap water. The results showed that the longitudinal dispersivity initially increased with time (pre-asymptotic stage) and eventually reached a constant value (asymptotic stage). Four functions were used to investigate the time variations of dispersivity: linear, power, exponential and logarithmic. In general, because of the linear increase of dispersivity during a long time of transport, the linear function with R2=0.97 showed better time variations than the other three functions; the logarithmic function, having an asymptotic nature, predicted the asymptotic stage successfully (R2=0.95). The ratio of the longitudinal dispersivity to the medium length was not constant during the transport process and varied from 0.01 to 0.05 cm with elapsed time.

Stability analysis of miscible displacement has several applications in industries such as oil recovering and ground water tables. In this article an analytical solution is presented based on Tan and Homsy’s results for stability analysis in t = 0. Moreover, a novel semi analytical solution is used, based on weighted residual method, to solve the Fourier space equations. The results are shown as  (disturbance growth rate) – k (wave number); profiles for different values of mobility ratios and times. A comparison with the results of the other researches is also presented. Stability Analysis, Displacement, Miscible Fluid, Porous Medium