Kantzas, ApostolosSkripkin, Evgeny2015-09-292015-11-202015-09-292015http://hdl.handle.net/11023/2557In this thesis an integrated approach to predict the effective thermal conductivity of porous media is presented. A pore scale level heat transfer model is developed and used to generate a custom mixing rule for thermal conductivity prediction. The novel mixing rule is developed based on particle size distribution data for unconsolidated porous media. The fluid and solid phase are considered, with fluid phase being stagnant. The point contact between the grains and spherical shape of the grains are also assumed. The model and mixing rule are validated and sensitivity analysis is performed. The question of upscaling the results of pore scale level modeling is also addressed. Two approaches are presented: equivalent network model and upscaling using computer tomography images. Equivalent network model was validated using model-by-model validation approach. Computer tomography images upscaling approach was applied to predict the scaled up thermal conductivity of oil sand core samples.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Engineering--PetroleumEffective thermal conductivityoil sandsSpherical packingsVirtual porous mediaMixing rulesSigmoid rulePore Scale ModelingUpscalingNetwork modelingmaster thesis10.11575/PRISM/25789