Controlled/Low Salinity Waterflooding (LSWF) is an augmented waterflood with well-reported improved displacement efficiency compared with conventional waterfloods. Physical mixing or dispersion of the injected low-salinity (LS) brine with the formation high-salinity (HS) brine substantially reduces the low-salinity effect. Numerical dispersion often misrepresents this mixing in conventional LSWF-simulations, causing errors in the results. Uncertainty in the reservoir description further makes the evaluated performance questionable. Existing studies have suggested optimal amounts for the injected LS-brine to sustain its displacement stability during inter- well flows with physical mixing, but with poor or no consideration of uncertainty. This work focuses on optimizing the injected LS-brine amount considering reported flow uncertainties while ensuring adequate correction of the erroneous influence of numerical dispersion on physical mixing. We investigate the impacts of flow uncertainties on the optimal LS slug-size. The sensitivity of the optimal slug-size to heterogeneity is examined under uncertainty. We evaluate how the interaction between physical mixing and geological heterogeneity influences slug integrity and performance.

We propose an improved ‘effective salinities’ concept to evaluate appropriate effective salinities to characterize the desired representative physical mixing supressing the large numerical dispersion effects usually encountered in coarse-grid LSWF-simulations. This ensures reliable representation of physical dispersion in such grids. We consider different models with characterized levels of heterogeneity and essential variables that control the impact of mixing on LSWF performance based mainly on reported data. New indicators are defined to evaluate the displacement stability and performance of injected LS-brine thereby relating its technical and economic performance. Slug performance is evaluated at different injection times to examine the sensitivity of recovery to LS injection start-time. Performance uncertainty is assessed through a designed four-stage computationally-effective approach: Parameter-space sampling to design representative experiments; Proxy modelling; Proxy validation and verification; and Monte Carlo simulation to provide a wider representative sample for the parameter-space.

We can now reliably represent physical dispersion in LSWF-simulations of current commercial reservoir simulators. Recovery is observed to be relatively insensitive to LS injection start-time until breakthrough of preinjected HS-brine. This is important for LS injection designs as they need not commence immediately for secondary-mode. The potential favourable influence of the spatial distribution of heterogeneity is seen, with links to transverse dispersion. The evaluated optimal sizes from existing studies are observed to be, at best, only suitable as displacement stability thresholds for slug injection considering uncertainty. We find an optimal slug-size of at least 1.0 HCPV to reduce risk under uncertainty.


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