Page 68 - Hybrid Enhanced Oil Recovery Using Smart Waterflooding

60 Hybrid Enhanced Oil Recovery using Smart Waterflooding

(A)
Well 3-37 - Alkalinity Concentration
2,500

UTCOMP-IPhreeqc: High salinity
2,100

1,700 Endicott measured data

1,300
UTCOMP-IPhreeqc: Low salinity

900
UTCOMP-IPhreeqc: Freshening

500
0 0.5 1 1.5 2 2.5
PV
(B)
Well 3-37 - Iron Concentration
6

UTCOMP-IPhreeqc: Low salinity
5
Endicott measured data
4
ppm
3

2 UTCOMP-IPhreeqc: High salinity

1

UTCOMP-IPhreeqc: Freshening
0
0 0.5 1 1.5 2 2.5
PV
FIG. 3.14 The simulated results of (A) alkalinity and (B) iron concentrations against the measured data of the
interwell field trial. (From Kazemi Nia Korrani, A., Jerauld, G. R., & Sepehrnoori, K. (2016). Mechanistic modeling
of low-salinity waterflooding through coupling a geochemical package with a compositional reservoir
simulator. SPE Reservoir Evaluation and Engineering, 19(1), 142e162. https://doi.org/10.2118/169115-PA.)

of LSWF accurately matches the water-cut of the field in a carbonate reservoir and numerical simulations of
trials in Endicott field. the SWCTT and LSWF processes. The numerical simula-
Yousef, Al-Saleh, et al. (2012) and Yousef, Liu, et al. tions use the two important mechanisms of SWCTT test
(2012) also reported the field trials of SWCTT at Well A and LSWF process in carbonate reservoirs. The SWCTT
and Well B to analyze the performance of LSWF process test involves the injection of tracers (ethyl acetate,