We focus on key aspects related to the quantification of the uncertainty associated with modeling of Enhanced Oil Recovery (EOR) through Low Salinity (LS) water injection in a reservoir. Low salinity waterflooding is an emerging EOR technique in which the salinity of the injected water is controlled to improve oil recovery, as opposed to conventional waterflooding where brine is usually used. Several mechanisms have been proposed to underpin the processes leading to additional oil mobility, but none of them has been conclusively identified as the key driving cause. Literature results suggest that LS water causes an alteration of the wettability of the porous medium, leading to more favorable conditions for oil recovery. In this context, simulation models that represent the process using salinity-dependent relative permeabilities have been developed. Here, we consider a tertiary coreflood experiment performed at Eni laboratory facilities through LS water injection, following sea water flooding. Oil and water relative permeability curves are parameterized through the Corey model. Model parameters and their uncertainties are estimated within a stochastic inverse modeling approach, upon relying on a classical reservoir simulator to simulate the measured oil recovery. The likelihood function is maximized through a joint use of the Latin hypercube sampling and the Metropolis Hastings algorithm, while the process model is coupled with a universal Kriging technique. The posterior sample of model parameters is then employed to quantify uncertainty propagation to a sector model of a selected North-East African sandstone reservoir. This enables us to quantify the impact of parameter uncertainty on the expected oil production resulting from a field scale application of the technique under study. The reservoir simulation reveals the potential of the LS water injection technique to improve the recovery in the considered field.


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