Affiliation:
1. The John and Willie Leone Department of Energy and Mineral Engineering, The Pennsylvania State University, The EMS Energy Institute, The Pennsylvania State University, University Park, PA, USA
Abstract
Abstract
A successful surfactant flood maximizes oil recovery by achieving ultralow oil/water interfacial tension at the optimum salinity (S*). Optimum salinity, among other parameters, is dependent on the equivalent alkane carbon number (EACN) of the oil pseudocomponent. This paper compares common EACN determination methods used for dead crude at ambient pressure and then proposes a third more consistent and reliable method that simultaneously fits data from both methods. The first method is based on a linear plot of S* and EACN of pure alkanes, where the dead crude EACN is linearly interpolated using the measured lnS* of the crude. The second method determines the crude EACN by iteration until the measured lnS* of the dead crude and all dilution measurements become nearly linear. For live oil, the EACN is based on the common linear EACN mixing rule but corrected for pressure.
The results show that inconsistencies in estimated crude EACN using the common two methods are resolved when regression is made on all data simultaneously and when an unbiased estimate of optimum salinity is made using HLD-NAC theory, where the inverse of three-phase solubility is linear with lnS*. No nonlinear behavior is observed when fit this way and using the simple graphical approach, as has been reported in the literature using the same data. The graphical approach determines the optimal salinity based on the intersection of the linear regressions of inverse oil and water solubility with lnS*. This approach has the advantage that the optimum is unbiased, and its uncertainty is easily estimated. Using a combination of ambient and high-pressure data, we also show that the EACN of the live oil can be estimated using a methane ACN of 1.0, as it should physically be, when the effect of pressure is properly included.