Investigation of an Improved Polymer Flooding Scheme by Compositionally-Tuned Slugs
KAUST DepartmentEarth Science and Engineering Program
Upstream Petroleum Engineering Research Center (UPERC)
Physical Sciences and Engineering (PSE) Division
Energy Resources and Petroleum Engineering
Permanent link to this recordhttp://hdl.handle.net/10754/661474
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AbstractPolymer flooding is an effective enhanced oil recovery technology used to reduce the mobility ratio and improve sweep efficiency. A new polymer injection scheme is investigated that relies on the cyclical injection of low-salinity, low-concentration polymer slugs chased by high-salinity, high-concentration polymer slugs. The effectiveness of the process is a function of several reservoir and design parameters related to polymer type, concentration, salinity, and reservoir heterogeneity. We use reservoir simulations and design-of-experiments (DoE) to investigate the effectiveness of the proposed polymer injection scheme. We show how key objective functions, such as recovery factor and injectivity, are impacted by the reservoir and design parameters. In this study, simulations showed that the new slug-based process was always superior to the reference polymer injection scheme using the traditional continuous injection scheme. Our results show that the process is most effective when the polymer weight is high, corresponding to large inaccessible pore-volumes, which enhances polymer acceleration. High vertical heterogeneity typically reduces the process performance because of increased mixing in the reservoir. The significance of this process is that it allows for increased polymer solution viscosity in the reservoir without increasing the total mass of polymer, and without impairing polymer injectivity at the well.
CitationSantoso, R., Torrealba, V., & Hoteit, H. (2020). Investigation of an Improved Polymer Flooding Scheme by Compositionally-Tuned Slugs. Processes, 8(2), 197. doi:10.3390/pr8020197
SponsorsWe would like to thank the King Abdullah University of Science and Technology (KAUST) for support.
This research was funded by the King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
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