Prospects of chemical methods of enhanced oil recovery are currently due to both the deterioration of mature oil fields reserves and chemical industry development, which is able to provide solutions to urgent problems of the oil and gas companies. The initial selection of compositions for enhanced oil recovery includes experiments to assess the physicochemical properties: viscosity, interfacial tension, etc. These parameters are determined under conditions close to reservoir ones. At the next stage of the laboratory selection of the chemical composition, coreflooding experiments are carried out to assess the effectiveness of oil displacement by the selected chemical composition. These experiments can be carried out on model analog cores, as well as on single cores or composite core columns of samples taken from the target reservoir. As a result, the nature of the interaction of the selected composition with oil and rock in conditions close to reservoir ones is determined. The results of coreflooding experiments (increase in displacement efficiency, pressure gradients during flooding) are the basis for making decisions on the further implementation of regarded technology at the field. It should also be noted that these experiments have a high degree of uncertainty due to the complex processes occurring in the pore medium during flooding of chemicals. There are also a number of parameters that cannot be measured during the experiment, but these parameters affect the efficiency of oil displacement by chemicals.
The article considers the features of creating and matching linear models of coreflooding experiments conducted to assess the technological effectiveness of surfactant-polymer flooding. The purpose of constructing a linear model of the coreflooding experiment is to increase the accuracy of predicting the effectiveness of the selected technology by reducing the degree of uncertainty in interpreting the results of the coreflooding experiment. Uncertainty reduction is achieved by reproducing main parameters of the experiment in a linear simulation model. These parameters are adjusted on a linear model by matching the calculated parameters to the actual experimental data. The properties of chemicals estimated on the adapted linear model are used in further to predict the effect of the technology.
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