Comparative study of VOF, LS, and VOSET on pore-scale immiscible waterflooding modeling

Abstract

The pore-scale mechanism of the waterflooding contributes to enhancing oil recovery, which has been widely emphasized in the petroleum industry. In this paper, the performances and accuracy of three tracking interface algorithms, including VOF, LS, and VOSET, are compared and analyzed through two-phase flow in the conceptual model of pore-throat. The results show that the VOSET method combines the advantages of the other two methods, which not only satisfies the mass conservation but also improves the continuity of the physical quantities near the interface. Then, based on the binary image of the pore, the two-dimensional micro pore model is reconstructed by extracting image contour. The grid independence of the reconstructed pore model is verified by the single-phase flow simulation. The waterflooding process in the reconstructed pore model is simulated using the VOSET method, and the effects of displacement speed and wettability on the oil recovery are analyzed. The morphologies of residual oil under different conditions of wettability are investigated and analyzed. The study provides a basic theory for modeling the pore-scale oil-water flow and optimizing the scheme of the water injection.

Keywords

Waterflooding / Oil recovery / VOSET / Displacement speed / Wettability

Credit author statement

Jiajun Peng: Conceptualization, Data curation, Investigation, Methodology, Writing -original draft. Rui Song & Yao Wang: Supervision, Writing -review &editing. Hanmin Xiao: Writing -review & editing.

All authors participated in the review and discussion of the revised manuscript.

Availability of data and material

All the data and materials used in this paper are available from the corresponding author upon request.

Declaration of competing interest

The authors declare that there are no conflict of interests with any other people or groups regarding the publication of this manuscript.

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (Grant Number 51909225); National Major Science and Technology Projects of China (Grant Number 2017ZX05013001-002); Open research fund of CNPC (grant No. 2018A-0908), and the Natural Science Foundation of SWUST (Grant Number 20zx7129).

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