Page 67 - Hybrid Enhanced Oil Recovery Using Smart Waterflooding

CHAPTER 3 Modeling of Low-Salinity and Smart Waterflood 59

Zhang & Austad, 2006). Because oil has the negative wettability modification as the underlying mecha-
potential, the negative surface potential of rock surface nism. They interpret the mechanism of wettability
indicates the water-wetness in carbonate reservoir. The modification in terms of the geochemical reactions,
comparison between surface potentials of oil and electrokinetics, etc. Therefore, the models of LSWF
calcite can be used to determine rock wettability. process incorporate the calculations of comprehensive
Before validating the first mechanism using the geochemical reactions in the crude oil/brine/rock sys-
z-potential calculation, the reliability of z-potential tem and/or electrokinetics in the system. Although
calculation is also validated by investigating water sta- the extensive studies provide the simulation results
bility between carbonate and oil. The stability of water of LSWF comparable with the experimental results,
films between carbonate surface and oil indicates the only a few studies have been published for the
wettability of rock. The stability of water films can be LSWF modeling of the field-scaled trials.
quantified by calculating disjoining pressure. The dis-
joining pressure discusses the ability of oil to collapse
the water film andadhere tothe rock surface. In this FIELD-SCALED MODELING
numerical study, the disjoining pressure is assumed Kazemi Nia Korrani et al. (2016) numerically modeled
to be only consisted of van der Waals and double layer the interwell field trials of LSWF in Endicott field on
forces. Using a number of brines, the equivalent the North Slope, which have been reported by Seccombe,
wettability of calcite can be interpreted by both Lager, Webb, Jerauld, and Fueg (2008) and Seccombe
z-potential and disjoining pressure calculations. Then, et al. (2010).In the field test, LSWF is applied into
the z–potential calculation is used to investigate whether Well 3e35 after injection of produced water. The produc-
the change in z-potential or surface potential, i.e., first tion and response from Well 3e37 are monitored. From
mechanism, can explain the wettability modification. the interwell test, successfully, the reduction of residual
Using the brines, used in the experiments (Zhang & oil saturation with 0.14 units is observed. Kazemi Nia
Austad, 2006; Zhang, Tweheyo, & Austad, 2006, Korrani et al. (2016) constructed a multilayer model of
2007), the z-potentials are calculated at increasing tem- the target reservoir using the UTCOMP-IPhreeqc simu-
perature. The experimental results of LSWF in carbonate lator. The effluent ions, water-cut, pH, and alkalinity of
reservoirs show the strong dependency of EOR potential numerical simulations are compared with the historical
on the temperature. If the surface potential change is the data of field tests. Because the simulator of UTCOMP-
main cause of wettability modification, the surface IPhreeqc can model the reactions of the aqueous/rock
potential should be strongly sensitive to the tempera- geochemistry because of soluble hydrocarbon compo-
ture. However, the developed model of z-potential nents and surface complexation with exchanger, the nu-
shows less sensitive to the temperature. Therefore, merical simulation of LSWF accurately reproduces the
Hiorth et al. (2010) proposed the calcite mineral historical data of field tests. The numerical simulations
dissolution as the main mechanism by calculating cover the dissolution of soluble hydrocarbon compo-
calcite dissolution. nentsaswell asCO 2 into water, which influences the
The studies (Bedrikovetsky, Siqueira, Furtado, & pH of brine because of the aqueous and mineral
Souza, 2011; Zeinijahromi, Nguyen, & Bedrikovetsky, reactions. Modeling the reactions leads to the correct
2013) have developed the mechanistic model of LSWF trend for pH of the produced water. In addition, the hy-
using the fines migration. The numerical model of pothetical simulations of LSWF process with and without
two-phase flow incorporates the detachment of fine par- the surface complexation reaction are carried out. The
ticles, fine migration, and their straining. In the model, results of both simulations are compared with the
the maximum concentration of retained particles as a measured data of the field tests. The LSWF model
function of saturation and erosion number, i.e., the ratio without the reaction shows a significant discrepancy
between the detaching and attaching torques at the against the measured data of the field tests, especially
absence of attached fine particles, of porous media, re- in terms of alkalinity and iron concentrations
duces the water permeability. With the assumption of (Fig. 3.14). Measured alkalinity and iron concentrations
large-scaled approximation, the two-phase flow of fines are higher than the results from the simulation neglect-
migration can be similar to the simulation of polymer ing the reactions. However, the LSWF model with the
flood without adsorption. Therefore, the LSWF process reactions results in the alkalinity and iron concentrations
involved with fines migration is simulated by using comparable with the measured data. In the study, it is
the commercial simulation approach of polymer flood. also hypothesized that the underlying mechanism of
However, majority of these studies have developed LSWF process is the wettability modification, which is a
the numerical models of LSWF assuming the function of total ionic strength. The numerical model