Residual oil in sandstone is affected by mineral composition, clay matrix and cementing material. Matrix minerals affect the affinity of a fluid to spread on a rock surface significantly and in turn controls the fluid distribution within the pore spaces. At the interface between the rock surface and the contacting fluid, electrical charges are in the origin of the extent of phase wetness. Available framework grains, a dominant component of rock matrix, affect porosity and, hence, amounts of rock preferential wetness. Cement, clay matrix and quartz overgrowth, which make up for the rest of the grain population in a rock, influence wetness and, therefore, amounts of residual oil. In this paper, spectro-electromicroscopy (SEM) point-count technique in conjunction with neural network analysis were used to determine the effect of certains rock parameters on the amounts of residual oil following waterflooding operations. Using artifcial neural network, the intent was then to predict the extent of residual oil in sandstone rocks given limited information about rock matrix, cementing material and primary porosity.
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