A New HLD-NAC Based EOS Approach to Predict Surfactant-Oil-Brine Phase Behavior for Live Oil at Reservoir Pressure and Temperature

Abstract

Abstract Surfactant-polymer (SP) floods have significant potential to recover waterflood residual oil in shallow oil reservoirs. A thorough understanding of surfactant-oil-brine phase behavior is critical to design SP processes. Current practices involve repetitive laboratory experiments of dead crude at atmospheric pressure in a salinity scan that aims at finding an "optimum formulation" of chemicals for targeted oil reservoirs. While considerable progress has been made in developing surfactants and polymers that increase the potential of a chemical enhanced oil recovery (EOR) project, very little progress has been to predict phase behavior as a function of formulation variables such as pressure, temperature, and oil equivalent alkane carbon number (EACN). The empirical Hand's plot is still used today to model the microemulsion phase behavior with little predictive capability as formulation variables change. Such models could lead to incorrect recovery predictions and improper SP flood designs. In this paper, we develop a new predictive phase behavior model and introduce a new factor β to account for pressure changes in the hydrophilic-lipophilic difference (HLD) equation. This new HLD equation is coupled with the net-average curvature (NAC) model to predict phase volumes, solubilization ratios, and microemulsion phase transitions (Winsor II−, III, and II+). The predictions of key parameters are compared to experimental data and are within relative errors of 4% (average 2.35%) for measured optimum salinities and 17% (average 10.55%) for optimum solubilization ratios. This paper is the first to use a HLD-NAC model to predict microemulsion phase behavior for live crudes, including optimal solubilization ratio and the salinity width of the three-phase Winsor III region at different temperatures and pressures. Although the effect of pressure variations on microemulsion phase behavior are generally thought to be small compared to temperature induced changes, we show here that this is not necessarily the case. The predictive approach relies on tuning the model to limited experimental data (say at atmospheric pressure) similar to what is done for equation-of-state modeling of miscible gas floods. This new equation-of-state-like model could significantly aid the design of chemical floods where key variables change dynamically, and in screening of potential candidate reservoirs for chemical EOR.