Carbonated water injection (CWI) is known as an efficient technique for both CO
2
storage and enhanced oil recovery (EOR). During CWI process
CO
2
moves from the water phase into the oil phase and results in oil swelling. This mechanism is considered as a reason for EOR. Viscous fingering leading to early breakthrough and leaving a large proportion of reservoir un-swept is known as an unfavorable phenomenon during flooding trials. Generally
instability at the interface due to disturbances in porous medium promotes viscous fingering phenomenon. Connate water makes viscous fingers longer and more irregular consisting of large number of tributaries leading to the ultimate oil recovery reduction. Therefore
higher
in-situ
water content can worsen this condition. Besides
this water can play as a barrier between oil and gas phases and adversely affect the gas diffusion
which results in EOR reduction. On the other hand
from gas storage point of view
it should be noted that CO
2
solubility is not the same in the water and oil phases. In this study for a specified water salinit
y
the effects of different connate water saturations (
S
wc
) on the ultimate oil recovery and CO
2
storage capacity during secondary CWI are being presented using carbonate rock samples from one of Iranian carbonate oil reservoir. The results showed higher oil recovery and CO
2
storage in the case of lower connate water saturation
as 14% reduction of
S
wc
resulted in 20% and 16% higher oil recovery and CO
2
storage capacity
respectively.
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references
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Activating solution gas drive as an extra oil production mechanism after carbonated water injection
Machine learning-assisted characterization of oil micro-displacement hydrodynamics by bionanofluid-flooding in microchannel sand-packed porous media towards enhanced oil recovery
Enhanced gas production and CO2 storage in hydrate-bearing sediments via pre-depressurization and rapid CO2 injection
Conformance control by a microgel in a multi-layered heterogeneous reservoir during CO2 enhanced oil recovery process
Gas leakage recognition for CO2 geological sequestration based on the time series neural network
Related Author
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