Research has shown that asphaltenes are the prime stabilizers of water-in-oil emulsions and that resins are necessary to solvate the asphaltenes. Research has also shown that many compositional factors play a role including the amount of saturates and the properties of viscosity and density. These factors can then be used to develop models of emulsion formation. A review of the formation processes of these emulsions and water and oil types is given. This applies to all four water-in-oil types: stable, meso-stable, unstable emulsions and entrained water. The differences among these four types are high-lighted. A number of other techniques have also been used to model emulsions including neural networks. These are noted and compared to the regression models. A data set of more than 400 oils and their water-in-oil mixtures are used for the comparison. Numerical modeling schemes for the formation of water-in-oil emulsions are reviewed. New models are based on empirical data and the corresponding physical knowledge of emulsion formation. The density, viscosity, asphaltene and resin contents were correlated with a stability index. The establishment of an index for emulsion stability enables the use of this value as a target for the optimization of regressions to form a new model. The predictions of the new model are much simpler and better than old models and some that have been in the literature for some time. The new model is more accurate than the old models, although some improvement could still be made. The benefit of the new model is that it is more accurate and simpler than former regression models. The different approaches to these models and older regression models are highlighted.

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