Prediction of relative and absolute permeabilities for gas and water from soil water retention curves using a pore-scale network model

Functional relationships for unsaturated how in soils, including those betw een capillary pressure, saturation, and relative permeabilities, are often described using analytical models based on the bundle-of-tubes concept. The se models are often limited by, for example, inherent difficulties in predi ction of absolute permeabilities, and in incorporation of a discontinuous n onwetting phase. To overcome these difficulties, an alternative approach ma y be formulated using pore-scale network models. In this approach, the pore space of the network model is adjusted to match retention data, and absolu te and relative permeabilities are then calculated. A new approach that all ows more general assignments of pore sizes within the network model provide s for greater flexibility to match measured data. This additional flexibili ty is especially important for simultaneous modeling of main imbibition and drainage branches. Through comparisons between the network model results, analytical model results, and measured data for a variety of both undisturb ed and repacked soils, the network model is seen to match capillary pressur e-saturation data nearly as well as the analytical model, to predict water phase relative permeabilities equally well, and to predict gas phase relati ve permeabilities significantly better than the analytical model. The netwo rk model also provides very good estimates for intrinsic permeability and t hus for absolute permeabilities, Both the network model and the analytical model lost accuracy in predicting relative water permeabilities for soils c haracterized by a van Genuchten exponent n less than or similar to 3. Overa ll, the computational results indicate that reliable predictions of both re lative and absolute permeabilities are obtained with the network model when the model matches the capillary pressure-saturation data well. The results also indicate that measured imbibition data are crucial to good prediction s of the complete hysteresis loop.