Image analysis for estimating ultimate oil recovery efficiency by waterflooding for two sandstone reservoirs

The objectives of this study are to consider the influence of pore structures obtained from image analysis on ultimate oil recovery and to incorporate the results of these analyses into generalized stochastic models for waterflooding performance predictions. one samples from a North American and a North Sea oil reservoir were used. The pore systems of the two reservoirs were quantified by image analysis of thin sections and a pattern recognition algorithm was used to classify the porosity objectively into pore types using size and shape information. The thin sections used in the image analysis were obtained from the ends of the core plugs used in the laboratory experiments to obtain recovery efficiency. Regression analysis was used to develop stochastic models to estimate recovery efficiency. alyses of data indicate that increasing volume of well-connected pores and increase in pore size uniformity improve recovery efficiency. The models explain 72% and 88% of the variance in measured recovery efficiencies for samples with similar laboratory test procedures. The expected deviations between estimated and measured recovery efficiencies are 6.2% and 2.3%, respectively, for the two reservoirs. alyses also indicate that relationships between initial water saturation and residual oil saturation are related to pore size distributions. Residual oil saturation correlates positively with initial water saturation and a direct relationship exists with degree of non-uniformity of the associated pore systems. rosity classification method used in this study is shown to be useful for understanding the effects of pore structure on oil recovery by waterflooding under conditions of constant fluid properties and rock wettability. This technique could be used to evaluate recovery efficiency of large numbers of samples including samples too small for conventional core testing.