The method of calculation of relative phase permeability functions based on the empirical interfacial interaction function

About the authors:

Mikhail A. Zagorovskiy, Master Student, Department of Fundamental Mathematics and Mechanics, University of Tyumen; Specialist, Tyumen Petroleum Research Center; mazagorovskiy2@tnnc.rosneft.ru

Sergei V. Stepanov, Senior Expert, Tyumen Petroleum Research Center, Tyumen, Russia; Dr. Sci. (Tech.), Professor, Tyumen Petroleum Research Center Specialized Department, School of Natural Sciences, University of Tyumen, Tyumen, Russia; svstepanov@tnnc.rosneft.ru

Aleksandr B. Shabarov, Dr. Sci. (Tech.), Professor, Honored Scientist of the Russian Federation, Professor of the Department of Applied and Technical Physics, Institute of Physics and Technology, University of Tyumen; a.b.shabarov@utmn.ru; eLibrary AuthorID, ORCID, ResearcherID, ScopusID

Abstract:

The article describes the results of research about the creation of an empirical method for calculation relative phase permeability functions (RPP). The method based on calculation of interfacial interaction function (IIF) from the experimental SCAL data and searching multi-parameter dependences for the parameters of the approximation dependence of IIF. It is proposed to use a function defined on two segments of the domain of definition, for approximation of IIF. The research had been carried out on laboratory data for group of terrigenous and carbonate samples. It had been established that the pressure losses due to interfacial interaction of oil and water during the joint flow of the water-oil mixture are at the maximum for the considered core samples from 60 to 90% of the total pressure losses. Multi-parameter dependences for IIF parameters were found for both groups of data. It was defined that using of multi-parameter dependences for IIF parameters provides the quality of RPP forecast with deviation by 30% for terrigenous samples and by 22% for carbonate samples. There was conducted the study of the influence of data set amount for multi-parameter dependences (training set) on the quality of RPP forecast (test set). It had been established that increasing of data set amount for multi-parameter dependences of IIF parameters has a positive effect on the quality of RPP functions forecast. At the same time, the increasing of data set amount in 2 times leads to decreasing of the average relative error of RPP calculation from 25.5 to 20.9% for terrigenous samples and from 70.3 to 23.6% for carbonate samples.

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