Abstract
Pore scale modeling method has been widely used in the petrophysical studies to estimate macroscopic properties (e.g. porosity, permeability, and electrical resistivity) of porous media with respect to their micro structures. Although there is a sumptuous literature about the application of the method to study flow in porous media, there are fewer studies regarding its application to thermal conduction characterization, and the estimation of effective thermal conductivity, which is a salient parameter in many engineering surveys (e.g. geothermal resources and heavy oil recovery). By considering thermal contact resistance, we demonstrate the robustness of the method for predicting the effective thermal conductivity. According to our results obtained from Utah oil sand samples simulations, the simulation of thermal contact resistance is pivotal to grant reliable estimates of effective thermal conductivity. Our estimated effective thermal conductivities exhibit a better compatibility with the experimental data in companion with some famous experimental and analytical equations for the calculation of the effective thermal conductivity. In addition, we reconstruct a porous medium for an Alberta oil sand sample. By increasing roughness, we observe the effect of thermal contact resistance in the decrease of the effective thermal conductivity. However, the roughness effect becomes more noticeable when there is a higher thermal conductivity of solid to fluid ratio. Moreover, by considering the thermal resistance in porous media with different grains sizes, we find that the effective thermal conductivity augments with increased grain size. Our observation is in a reasonable accordance with experimental results. This demonstrates the usefulness of our modeling approach for further computational studies of heat transfer in porous media.
Highlights
The effective thermal conductivity is an important factor to accurately estimate oil production rate for the thermal recovery methods such as Steam Assisted Gravity Drainage (SAGD) process.[1]
There is a sumptuous literature about the application of the method to study flow in porous media, there are fewer studies regarding its application to thermal conduction characterization, and the estimation of effective thermal conductivity, which is a salient parameter in many engineering surveys
We develop a computational algorithm to reconstruct 2D granular porous media by which the thermal conduction behavior and the effective thermal conductivity can be studied in pore scales
Summary
The effective thermal conductivity is an important factor to accurately estimate oil production rate for the thermal recovery methods such as Steam Assisted Gravity Drainage (SAGD) process.[1]. Using computational simulation for grain packing process, we can create numerous realizations of porous media for a given grain size distribution to provide a reasonable correlation among different macroscopic parameters (for example, porosity versus effective thermal conductivity). For these reasons the pore scale modeling approach is greatly utilized in different petophysical studies such as the predictions of relative permeability, electrical conductivity, and capillary pressure.[42]. We reconstruct a 2D granular porous media based on a real grain size distribution of Alberta unconsolidated oil sand sample and measure its effective thermal conductivity with respect to different roughness values. We hope that our work will be an incentive to initiate the application of pore scale modeling in those studies
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