A thorough understanding of fluid transport in ultratight shale reservoirs is crucial for designing and optimizing cyclic solvent injection processes, known as huff ’n’ puff (HnP). We develop a two-phase multicomponent numerical model to investigate hydrocarbon and solvent transport and species mixing during HnP. Unlike the conventional modeling approaches that rely on bulk fluid (advective) transport frameworks, the proposed model considers species transport within nanopores. The chemical potential gradient is considered the driving force for the movement of nonideal fluid mixtures. A binary friction concept is adopted that considers friction between different fluid molecules and between fluid molecules and pore walls. After validating the developed model against analytical solutions and experimental data, the model examines solvent HnP enhanced oil recovery (EOR) mechanisms by considering four-component oil and Eagle Ford crude oil systems. The impacts of injection pressure, primary production duration, soaking time, and solvent type on the oil recovery are examined. The results reveal that the formation of a solvent-oil mixing zone during the huff period and oil swelling and vaporization of oil components during the puff period are key mechanisms for enhancing oil recovery. Furthermore, the incremental recovery factor (RF) increases with injection pressure, even when the injection pressure exceeds the minimum miscibility pressure (MMP), implying that MMP may not play a critical role in the design of HnP in ultratight reservoirs. The results suggest that injecting solvents after a sufficient primary production period is more effective, allowing reservoir pressure depletion. Injecting the solvent without enough primary production may result in significant production of the injected solvent. The results show that the solvent-oil mixing zone expands, and the solvent recycling ratio decreases as soaking time increases. However, short soaking periods with higher HnP cycles are recommended for improving oil recovery at a given time frame. Finally, CO2 HnP outperforms CH4 or N2 HnP due to the higher ability of CO2 to extract a larger amount of intermediate and heavy components into the vapor phase, which has higher transmissibilities as compared with the liquid phase.

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