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Zeta potential measurements and microscopic surface characterization and imaging were conducted on calcite and dolomite crystals aged in stearic acid model oil and exposed to different synthetic brines representing different potential scenarios of injected seawater from the Arabian Gulf. Calcite particles were negatively charged in deionized water and maintained negative surface charges in all tested brines, except in diluted Arabian Gulf seawater that contained higher concentration of Ca2+ and Mg2+ ions. Dolomite particles were positively charged in deionized water as well as in all tested brines, except in diluted Arabian Gulf seawater that contained four times higher concentration of SO4 2– ions. Scanning electron microscopy and atomic force microscopy experiments on cleaved calcite and dolomite chips showed different morphological changes when both samples were aged in model oil and then treated with brines. Calcite surface dissolution was observed in addition to stearic acid deposition. Surface elemental analysis using energy-dispersive spectroscopy showed Mg2+ and SO4 2– ions adsorb preferably on locations where stearic acid is deposited. The finding that stearic acid was adsorbing more strongly on dolomite than on calcite could indicate why the tested brines were less efficient to change the zeta potential of the dolomite systems. The current study concludes that manipulating the concentration of potential-determining ions present in the Arabian Gulf seawater, especially Mg2+ and SO4 2– ions, will alter the surface charges of aged calcite and dolomite samples as well as their surface morphology.

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Summary Capillary pressure plays an essential role in controlling multiphase flow in porous media and is often difficult to be estimated at subsurface conditions. The Leverett capillary pressure function J provides a convenient tool to address this shortcoming; however, its performance remains poor where there is a large scatter in the scaled data. Our aim, therefore, was to reduce the gaps between J curves and to develop a method that allows accurate scaling of capillary pressure. We developed two mathematical expressions based on permeability and porosity values of 214 rock samples taken from North America and the Middle East. Using the values as grouping features, we used pattern-recognition algorithms in machine learning to cluster the original data into different groups. In each wetting phase saturation, we were able to quantify the gaps between the J curves by determining the ratio of the maximum J to the minimum J. Graphical maps were developed to identify the corresponding group for a new rock sample after which the capillary pressure is estimated using the average J curve of the identified group and the permeability and porosity values of the rock sample. This method also provides better performance than the flow zone indicator (FZI) approach. The proposed technique was validated on six rock types and has successfully generated average capillary pressure curves that capture the trends and values of the experimentally measured data by mercury injection. Moreover, the proposed methodology in this study provides an advanced and a machine-learning-oriented approach for rock typing. In this paper, we provide a reliable and easy-to-use method for capillary pressure estimation in the absence of experimentally measured data by mercury injection.

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