A model for interpretation of nanoparticle-assisted oil recovery: Numerical study of nanoparticle-enhanced spontaneous imbibition experiments

Autor: M. Chahardowli, Amin Keykhosravi, Razieh Khosravi, Mohammad Simjoo
Rok vydání: 2021
Předmět:
Zdroj: Fuel. 292:120174
ISSN: 0016-2361
DOI: 10.1016/j.fuel.2021.120174
Popis: Oil-wet reservoirs suffer from an incomplete oil recovery. It is possible to use nanoparticles (NPs) to alter the reservoir rock wettability to a more water wet condition, and thus increase and accelerate oil recovery. Numerous experimental studies reported the proper potential of NPs as an oil recovery agent. In addition, several modeling studies investigated the physics of nanoparticle injection into porous media. However, rarely the proposed models were validated using experimental nanoparticle-assisted oil recovery data. This paper introduces a novel workflow to interpret nanoparticle-enhanced oil recovery processes. The workflow uses a dynamic wettability alteration approach to mimic the transition of the rock wettability from an oil-wet condition to a more water-wet condition. This approach implements a concentration dependent weighting factor, which updates residual oil saturation, capillary pressure and relative permeabilities at each grid block in different time steps. The model equations are solved numerically using a finite difference scheme. Afterwards, the numerical model is implemented to interpret a series of nanoparticle-assisted spontaneous imbibition experiments. An excellent agreement between numerical and experimental results is achieved, which verifies the performance of the proposed workflow. The results reveal that the presented approach is valid enough to be used for the modeling of nanoparticle-assisted oil recovery processes. In conclusion, the findings of this study could provide a better understanding of the performance of nanoparticles in both forced and spontaneous enhanced oil recovery processes.
Databáze: OpenAIRE
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