A streamlines based multivariate regression model to quantify the impact of reservoir heterogeneity on ultimate recovery



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In this study, we developed multivariate regression models using streamlines information such as time-of-flight and streamlines density to assist practicing engineers better quantify the impact of reservoir heterogeneity on ultimate hydrocarbon recovery and also easily predict fluid saturation changes during primary or enhanced recovery periods. For this purpose, extensive streamlines simulation runs covering a wide range of permeability distributions, mobility ratios and producers' constraints using a popular streamline simulator were made. These numerical experimentations showed the presence of strong inter-relationships between streamlines simulation (as the independent variables), namely time-of-flight and streamlines density; and fluid saturation changes and oil recovery factor (as the dependent variables). Additionally, most of plotted data points of predicted oil recovery values and simulated values fall close to the ideal 45 line, indicating high degree of predictive accuracy of the models. Other existing classifiers, though yielding reasonable accuracy, they were not nearly as accurate as the new criterion.



Streamlines simulation