A novel numerical simulation of CO₂ immiscible flooding coupled with viscosity and starting pressure gradient modeling in ultra-low permeability reservoir

Abstract

CO₂ immiscible flooding is an environmentally-friendly and effective method to enhance oil recovery in ultra-low permeability reservoirs. A mathematical model of CO₂ immiscible flooding was developed, considering the variation in crude oil viscosity and starting pressure gradient in ultra-low permeability reservoirs based on the non-Darcy percolation theory. The mathematical model and numerical simulator were developed in the C++ language to simulate the effects of fluid viscosity, starting pressure gradient, and other physical parameters on the distribution of the oil pressure field, oil saturation field, gas saturation field, oil viscosity field, and oil production. The results showed that the formation pressure and pressure propagation velocity in CO₂ immiscible flooding were lower than the findings without considering the starting pressure gradient. The formation oil content saturation and the crude oil formation viscosity were higher after the consideration of the starting pressure gradient. The viscosity of crude oil considering the initiation pressure gradient during the formation was higher than that without this gradient, but the yield was lower than that condition. Our novel mathematical models helped the characterization of seepage resistance, revealed the influence of fluid property changes on seepage, improved the mathematical model of oil seepage in immiscible flooding processes, and guided the improvement of crude oil recovery in immiscible flooding processes.

Graphical abstract

Keywords

viscosity / starting pressure gradient / flow simulation / CO₂ immiscible flooding / ultra-low permeability reservoir

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Acknowledgments

Parts of this work were supported by the Dongying Science Development Fund Project (Nos. DJ2022009 and DJ2020003), the Shandong Provincial Higher Education Research and Development Program (Science and Technology A Class) (No. J18KA201), the High-level Talent Research Start-up Fund of Shengli College of China University of Petroleum (No. KQ2019-008), the Chunhui Project of Shengli College of China University of Petroleum (No. KY2017004) and the Research Cultivation Project of College of Big Data and Basic Science of Shandong Institute of Petroleum and Chemical Technology (No. XYPY2201) which supports are appreciated. The authors thank TopEdit for its linguistic assistance during the preparation of this manuscript. We also thank all editors and anonymous reviewers for their comments and suggestions.

Competing interests

The authors declare that they have no competing interests.

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