Macro- and Microscopic Study of "Smart Water" Flooding in Carbonate

Jul 11, 2019 - Macro- and Microscopic Study of "Smart Water" Flooding in Carbonate Rocks—An Image-based Wettability Examination ...
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Article Cite This: Energy Fuels 2019, 33, 6961−6970

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Macro- and Microscopic Studies of “Smart Water” Flooding in Carbonate Rocks: An Image-Based Wettability Examination Hongna Ding, Yuzhu Wang, Arthur Shapoval, Yuyun Zhao, and Sheik Rahman* School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia

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ABSTRACT: To date, numerous studies have shown that “smart water” flooding can enhance the oil recovery of carbonate reservoirs by altering the rock wettability. In particular, the Ca2+, Mg2+, and SO42− ions in smart water play important roles in altering the wettability of carbonate rocks, although their symbiotic effects are still under debate. In this study, we employ both macro- and microscopic methods, including ζ-potential measurements, contact angle measurements, and micro X-ray computed tomography (μ-CT) scanning to examine the effects of several “smart waters”, e.g., increasing SO42− concentrations or decreasing Ca2+ and Mg2+ concentrations in seawater, in changing the wettability of Austin chalk. ζ-potential results confirm that the surface potentials of chalk samples become more negative in smart waters than in seawater. Contact angle results suggest that smart waters are more effective in making the chalk surface more water-wet than seawater. However, seawater with four times the SO42− concentration (SW4SO) and seawater with one-fourth of the Ca2+ concentration (SW0.25Ca) show more potential in enhancing the alteration of chalk wettability compared to other smart waters. The μ-CT images offer a microscopic view of the fluid distribution in the porous media of chalk samples after flooding with seawater and followed by SW4SO or SW0.25Ca, which shows that SW4SO contributes to the increase in water-wetness in nanopores (or subscale porous structure), whereas SW0.25 contributes to the increase in water-wetness in micropores. In addition, the “effective contact angles” of chalk samples decreases approximately 10° after SW4SO and SW0.25Ca flooding, resulting in an increase in the “microscopic oil recovery” by 18.6 and 20.2%, respectively. Thus, this result suggests that SW0.25Ca is more effective in enhancing the waterwetness of chalk samples than SW4SO.

1. INTRODUCTION In recent years, “smart water” flooding has attracted considerable attention in the petroleum industry. Numerous laboratory studies and several field tests have confirmed its potential in enhancing the oil recovery of carbonate reservoirs.1−8 Smart water flooding is also favored for its low cost and environmental benefits compared to other enhanced oil recovery (EOR) methods such as polymer or surfactant flooding. Studies have shown that both the salinity of smart water and the presence of Ca2+, Mg2+, and SO42− in specific concentrations can influence the oil recovery of carbonate reservoirs.9,10 Particularly, it has been reported that the oil recovery in carbonate reservoirs can be greatly enhanced by increasing the concentration of SO42− 11,1−7,12,13 or manipulating the concentrations of Ca2+ and Mg2+ in smart water.11−15 In fact, the increased oil recovery by smart water flooding can be ascribed to the symbiotic effects of Ca2+, Mg2+, and SO42− on carbonate wettability.8,10 Schematic descriptions of the wettability alteration are shown in Figure 1, where the oil-wet

carbonate surface is altered to water-wet by the adsorption of SO42− to the positively charged carbonate surface, inducing a co-adsorption of Ca2+ and/or Mg2+ (>90 °C), which can react with the acidic groups in crude oil and promote their desorption from the rock minerals.2,5,6 This mechanism is usually termed multicomponent ion exchange (MIE) or surface-charge change, and additionally, the Ca2+, Mg2+, and SO42− ions are termed potential determining ions. It is worth mentioning that the MIE mechanism suggests that SO42− alone cannot alter the wettability of calcite unless it cooperates with Mg2+ and/or Ca2+. It also suggests that neither Mg2+ nor Ca2+ is effective in altering the calcite wettability without the presence of SO42−.2,52,5 However, the MIE mechanism is partly refuted by many other studies.16−24 For example, Rashid et al. have proposed that Mg2+ or SO42− alone can act as a wettability modifier for the carbonate surface without the presence of other ions. Thus, the symbiotic effects of SO42− with Ca2+ and/or Mg2+ in the MIE mechanism are refuted. Moreover, the authors proposed that calcite dissolution also played a role in altering the wettability of carbonate rocks; thus, the requirement of a temperature window (>90 °C) in MIE is also rejected.22 Although the mechanisms of wettability alteration are still under debate, all studies have shown that Ca2+, Mg2+, and SO42− in the smart water play important roles in altering the wettability of carbonate rocks. Received: March 4, 2019 Revised: May 30, 2019 Published: July 11, 2019

Figure 1. Schematic diagram of wettability alteration on the carbonate rock surface by smart waters.2,22 © 2019 American Chemical Society

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Energy & Fuels Table 1. Compositions of Seawater and Smart Waters (in ppm)a ions

SW

SW2SO

SW4SO

SW0.5Ca

SW0.25Ca

SW0.5Mg

SW0.25Mg

Na+ Ca2+ Mg2+ SO42− Cl− TDS

18 300 650 2110 4290 32 399 57 749

20 356 650 2110 8580 32 399 64 095

24 467 650 2110 17 160 32 399 76 786

18 300 325 2110 4290 31 822 56 847

18 300 163 2110 4290 31 534 56 396

18 300 650 1055 4290 29 278 53 573

18 300 650 528 4290 27 717 51 485

a

TDS is the total dissolved solid in salt solution.

respect to the in situ characterization of “effective contact angles” in porous media.33−39 To date, however, there is a lack of application of the μ-CT technique to evaluate the effects of smart water flooding, which, in turn, indicates an absence of subscale confirmation of wettability alterations in the porous media of carbonate rocks. To bridge the gap between macroand microscale studies, in this study, we employ μ-CT scanning to examine the wettability change in chalk samples by smart water flooding. The rest of the paper is arranged in the following order: first, the ζ potentials of chalk particles and oil droplets in seawater (as a reference) and several formulations of “smart waters” were determined with an electrophoresis method. Several types of smart waters were designed to enhance the negative potentials of chalk and oil by manipulating the concentrations of Ca2+, Mg2+, and SO42− in seawater; second, the contact angles of seawater and smart water droplets were measured on oil-aged chalk surfaces. The results of ζ potential and contact angle were used to preexamine the effectiveness of smart waters in changing the surface wettability of chalk samples; finally, core flooding and μCT scanning were carried out using two types of smart waters, SW4SO and SW0.25Ca, to examine the microscopic changes of wettability in the porous media of chalk samples.

To evaluate the effects of Ca2+, Mg2+, and SO42− on the wettability of carbonate rocks, numerous macroscopic studies have been carried out. In a recently published review paper, the authors have summarized the present findings of smart water flooding by core flooding, spontaneous imbibition, and chromatographic tests (see ref 10). In direct contrast, there are limited microscopic studies in the literature to investigate the impacts of Ca2+, Mg2+, and SO42− on rock wettability. Nonetheless, as the alteration of wettability is related to the electrochemical interactions of a rock−brine−oil system, the ζ potential is typically used to determine the change in surface charges on carbonate and oil surfaces in contact with smart water.25,1,2,5 It has been found that the carbonate surfaces become more negative with a 2−4-fold increase in SO42− concentration in smart water,4,26,27,15 whereas they become more positive with a 2−3-fold increase in Ca2+ and/or Mg2+ concentrations.4,27,15 In fact, the affinities of Ca2+, Mg2+, and SO42− toward the carbonate surface were found to be mutually influenced by each other, e.g., the adsorption of SO42− was positively correlated with the adsorption of Mg2+, but it was less influenced by the adsorption of Ca2+, whereas Ca2+ and Mg2+ competed with each other to coadsorb with the SO42− on the carbonate surface.15,28 The contact angle is also used as a rough estimation of the change in wettability because its result is significantly influenced by surface roughness and heterogeneity.29,30 Studies have shown that increasing the concentration of SO42− in seawater (SW, e.g., 2−8 times) could make the carbonate rocks more water-wet, and four times the SO42− concentration in seawater (SW4SO) was considered to be the optimum composition for wettability alteration.31 In direct contrast, it has been observed that the smart water, which contains four times the Ca2+ concentration in seawater (SW4Ca), made the carbonate rocks more oil-wet,11,12 suggesting that increasing the concentration of Ca2+ had negative effects on wettability alteration, even in the presence of SO42− or Mg2+.13 Meanwhile, some studies reported that the water-wetness of carbonates was enhanced by increasing the concentration of Mg2+ in smart water.14,15 On the other hand, it is also possible to inhibit the adsorption of SO42− by spiking the Mg2+ concentration. Hence, there may exist an appropriate ratio of Mg2+ and SO42− in smart water that generates more surface-charge change and better enhances carbonate waterwetness.15,28 Nevertheless, the presence of Mg2+ in smart water has been deemed crucial in observing a significant alteration of wettability of carbonate surfaces,12 as confirmed by the statistical analysis of numerous published data.32 Micro X-ray computed tomography (μ-CT) is a powerful tool for wettability studies. It fills the void in macroscale studies, such as spontaneous imbibition and chromatographic tests, by providing details of wettability changes in a porous structure. This technique has been used to determine the wettability alterations in carbonate rocks due to scCO2 injection with

2. EXPERIMENTAL SECTION 2.1. Brines. Synthetic seawater (SW) was prepared by dissolving designed amounts of salts (Sigma-Aldrich, AU), e.g., NaCl (>99.5%), Na2SO4 (>99%), MgCl2·6H2O (>99%), and CaCl2 (>96%), in Milli-Q water (18.2 MΩ at 22.4 °C). The composition of SW was similar to that reported in Yousef et al.’s study.40 Then, six types of smart waters were prepared by manipulating the concentrations of Ca2+, Mg2+, and SO42− in the SW. For example, SW2SO and SW4SO were prepared by spiking the SO42− concentration in SW by two and four times, respectively; SW0.5Ca and SW0.25Ca were prepared by decreasing the Ca2+ concentration in SW to one-half and one-fourth, respectively; SW0.5Mg and SW0.25Mg were prepared by decreasing the Mg2+ concentration in SW to one-half and one-fourth, respectively. The compositions of SW and smart waters are summarized in Table 1. 2.2. Oil. Light mineral oil (0.868 g/mL, >95%, Sigma-Aldrich, AU) was used as the model oil, and its composition was analyzed by the gas chromatography and mass spectrometry methods. The results showed that the mineral oil was primarily composed of linear and saturated alkanes (C17H36−C28H58) and small amounts of aromatics (C17H28 and C19H30). In core flooding and μ-CT scanning experiments, a mixture of 65% mineral oil and 35% 1-iododecane [CH3(CH2)8CH2I, >98%, Sigma-Aldrich, AU] was used as the oil phase. 1-Iododecane was chosen due to its effects in increasing the attenuation coefficient of the oil phase, easing the image segmentation process. 2.3. Rock. Austin chalk was used as the carbonate rock in this study. The mineral compositions of four cylindrical samples were analyzed by X-ray powder diffraction (XRD). A consistent composition of the Austin chalk includes 96−98% calcite (e.g., the highest peak in Figure 2), 0.5% dolomite, 0.5% halite, and 0.6−2.5% quartz. Two chalk samples, AC4 and AC5, containing fewer quartz minerals (0.6%) were used in the experiments (see Figure 2). Chalk AC5 was cut into 10 thin 6962

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micropores are intergranular pores, whereas the nanopores are intercrystal pores and erosion pores that occur in oolitic layers. 2.4. ζ Potential. A small piece of chalk AC5 was crushed into powders using an 8000M ball mill (SPEX). Chalk−brine mixtures were prepared by adding 10 mg of powder into 20 mL of SW or smart waters: SW2SO, SW4SO, SW0.5Ca, SW0.25Ca, SW0.5Mg, and SW0.25Mg (see Table 1). The pH of each chalk−brine mixture was titrated to 8.0 by adding tiny amounts of 0.1 M HCl or NaOH solution (Sigma-Aldrich, AU) to mimic the pH condition of reservoir fluids (7.5−8.5)41,42 and generate similar charge densities on the chalk surfaces due to calcite dissolution, allowing a comparison between different smart waters. Each mixture was stirred by a magnetic oscillator at room temperature for 2 days to ensure an equilibration between chalk and brine. Then, the upper suspension free of large particles was extracted and used immediately in the experiments. An oil−brine emulsion was prepared by adding 1 mL of mineral oil into 20 mL of SW or smart waters, followed by treatment in an ultrasonic bath at room temperature for 30 min. The pH of each emulsion was also adjusted to 8.0 using 0.1 M HCl or NaOH solution. The emulsion was again treated in an ultrasonic bath for 30 min and used immediately in the experiments. ζ potentials were measured based on the electrophoresis method with a Zetasizer Nano ZS machine (Malvern Instrument). The experimental parameters were optimized to avoid dramatic changes in sample properties on prolonged standing (e.g., precipitation or demulsification):28 each sample was equilibrated at 25 °C for 2 min; 5 runs along with 50 subruns were executed; the applied voltage was alternated in a range of 8−9 V; and ζ potential was derived from particle mobility using the Helmholtz−Smoluchowski equation. After ζ-potential measurements, the pH of each sample was remeasured. It was observed that the pH varied in the range of 8.0− 8.4, indicating that the sample properties remained unchanged during the experiments. 2.5. Contact Angle. Chalk AC5 (length 40 mm and diameter 20 mm) was cut into 10 slices using a Buehler IsoMet Low-Speed Precision Cutter, and each slice had a thickness of 2−3 mm. Each slice was polished with a 4000-grit polish paper using a metallographic grinding and polishing machine to reduce the impact of surface

Figure 2. XRD result of Austin chalk (AC5). slices, each with a thickness of 2−3 nm. Contact angles of water and brine droplets were measured on these thin slices. Two small cylindrical cores, AC4a and AC4b, were excavated from chalk AC4, which were used in core flooding and μ-CT scanning experiments. The physical properties of AC4a and AC4b are given in Table 2. Figure 3 illustrates the inner porous structure of AC4a via μ-CT,

Table 2. Physical Properties of Austin Chalk (AC4) sample

AC4a

AC4b

length (mm) diameter (mm) porosity (%) permeability (mD)

10.03 5.94 27.6 11

10.21 6.05 17.9 18.4

scanning electron microscope (SEM) imaging, and mercury injection capillary pressure (MICP) analysis. It is obvious in Figure 3d that the size distribution of the pore-throat has two groups with peaks of 1 and 10 μm, which correspond to nanopores (≤1 μm) and micropores (1 μm < pore size ≤ 10 μm), respectively. In addition, the pore system of Austin chalk is characterized by a dual porous structure consisting of micropores and nanopores (Figure 3b,c, respectively). Most of the

Figure 3. Porous structure of the Austin chalk sample and its pore-throat size distribution. (a) Two-dimensional section extracted from the μ-CT image with a resolution of 2.9 × 2.9 × 2.9 μm3/voxel; (b) is an SEM image with a resolution of 485 × 485 nm2/pixel; (c) is an SEM image with a resolution of 16.2 × 16.2 nm2/pixel; and (d) is the pore-throat size distribution of the Austin chalk obtained by MICP. The pore-throat size distribution shows two groups, with peaks of 1 and 10 μm, respectively. 6963

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Figure 4. ζ potentials of chalk particles and oil droplets in SW and smart water. The error bars represent 95% confidence interval of five measurements. were saturated with SW and allowed to stabilize for 10 days. (b) The two cores were flooded with five to seven PV of oil (mineral oil and 1iododecane) at a rate of 0.03 cc/min to establish irreducible saturations of water. Then, the cores were aged at 60 °C and 250 psi for 7 days. (c) The two cores were flooded with 5−10 PV of SW at a rate of 0.03 cc/min to displace the oil; afterward, they were scanned by μ-CT. (d) The two cores were further flooded with SW4SO and SW0.25Ca, separately, and then scanned by μ-CT. The μ-CT results after SW4SO or SW0.25Ca flooding were compared with those after SW flooding to evaluate the effectiveness of these two smart waters in changing the wettability of chalk samples. 2.7. Micro-CT Scanning. The μ-CT scanning in this study was performed using the Tyree X-ray micro-CT facility in the UNSW. Four different types of images were obtained by μ-CT scanning: (1) dry chalk samplesAC4a and AC4b (Figure 7A), (2) oil-aged chalk samples (60 °C, Figure 7B), (3) wet samples after primary SW flooding (Figure 7C), and (4) wet samples after secondary SW4SO and SW0.25Ca flooding (Figure 7D). The scanning resolution was 3.0 × 3.0 × 3.0 μm3/voxel for both AC4a with SW4SO and AC4b with SW0.25Ca. Hereafter, the images were processed in the following steps to visualize the fluid distributions: (a) Image denoising, which removed the noises such as pepper−salt noise and beam hardening. (b) Image registration, which transformed different sets of image data into one coordinate system to compare different images. (c) Image segmentation, which divided each μ-CT image into multiple segments. In this study, the tomography image of the dry sample was segmented into three phases, pore, oolitic layers, and solid, using the converging active contours (CACs) method.43−46 Note that the oolitic layer was considered a subscale porous structure that consisted of nanopores that were beyond the resolution of μ-CT (Figure 8a). Additionally, the CAC method is preferred for its effectiveness in distinguishing the oolitic layer phase and the boundaries of pore and solid phases due to an intensity transition. For wet samples, the pore phase or oolitic layer phase was subdivided into two separate phases, i.e., water-occupied and oil-occupied pore phases (Figure 8b,c). (d) Image analysis, which accessed the fluid phases in each voxel to quantitatively determine the improvements in water-wetness after smart water flooding compared to SW flooding. The improvement in water-wetness (or oil recovery) is calculated by the following equation

roughness on the contact angle measurements. However, the chalk surface was found to be very heterogeneous under microscopic examination. The heterogeneity was also confirmed in μ-CT scanning, as both macro- and micropores could be observed in the μ-CT images (to be discussed in a later section). To reduce the impact of heterogeneity on the contact angle results, the chalk pieces were first inspected under an optical microscope. Second, the similar sites on the same piece of chalk and on other chalk pieces were marked. Finally, the contact angles of water and brine droplets (SW2SO, SW4SO, SW0.5Ca, SW0.25Ca, SW0.5Mg, and SW0.25Mg) were measured on these marked sites by the sessile drop method with an Attension’s Theta machine (Biolin Scientific). Prior to the experiments, the thin chalk slices were “aged” in mineral oil at 60 °C for 2 days, which aims to make the slices oil-wet to simulate the reservoir rocks that are in contact with oil. After measuring the contact angles of water droplets on several aged chalk slices, it was found that the water contact angles range from 121 to 130°, suggesting that the chalk slices were successfully changed to oil-wet by the aging process. The oil-wetness of the aged chalk slices was also confirmed by the μ-CT scanning as the tomography images of the aged chalk slices were very blurry (to be discussed in a later section). The contact angles of each brine droplet were measured at three to five similar sites on the same piece of chalk surface to ensure reproducibility. The contact angles for different brine droplets were measured on the similar sites of different chalk pieces to obtain comparable results. The contact angle was measured in the following steps: (a) The chalk surface was placed in a glass cuvette (50 × 50 × 58 mm3, Jingrui Technology, China), and mineral oil was gently filled into the cuvette to two-third volume to immerse the chalk surface. (b) The glass cuvette was placed on the sample stage of the Theta machine. Meanwhile, a water or brine (SW or smart water)filled microsyringe (50 μL, Trajan Scientific and Medical, AU) with a plain needle was mounted onto the manual dispenser. (c) The syringe assembly was moved downward to immerse it into the oil phase and right above the calcite surface, and then a water/brine droplet of ∼30.0 μL was manually created by gently squeezing the syringe. (d) The contact angle was calculated by fitting the profile of a water/brine droplet by the Young−Laplace equation to the installed software of the Theta machine. As the chalk surface was very porous, the contact angle was found to decrease with time; therefore, the contact angle was recorded immediately after the droplet was released off the needle. This contact angle was denoted the initial contact angle (θ0), and its decreasing rate (R) was defined by dividing its value by time (i.e., θ0/ t). It was found that the R values for different chalk surface−brine systems were similar in magnitude (see Figure 6); thus, it can be concluded that the chalk surfaces used in the contact angle experiments possess similar porous structures, and the differences in contact angle results can be solely ascribed to the effects of smart waters. The final contact angle of a brine droplet was obtained by averaging the results of three to five measurements of one piece of the chalk surface. Additionally, the 95% confidence interval in contact angle measurements was presented in the final result. 2.6. Core Flooding. In this study, the core flooding experiments are designated for the μ-CT scanning. Core flooding was carried out in the secondary mode, i.e., chalk samples AC4a and AC4b were first flooded with SW, followed by SW4SO and SW0.25Ca, respectively. The experimental procedures can be found in the study of Alhammadi et al.,35,38,39 which can be summarized as follows: (a) AC4a and AC4b

I=

(wpi − wps) + (woi − wos) × ϕo wps + wos × ϕo

where I is the improvement of smart water (SW4SO or SW0.25Ca) against that of SW flooding. wpi and wps are the voxel number proportions of the pore phase occupied by water after smart water and SW flooding, respectively. woi and wos are the voxel number proportions of the oolitic layer phase occupied by water after smart water and SW flooding, respectively. wps and wos are the voxel number proportions of the pore phase and oolitic layer phase after SW flooding, respectively. ϕo is the porosity of the oolitic layer phase (ϕo = 29.7%).

3. RESULTS AND DISCUSSION 3.1. ζ Potentials. The change in the surface charge of chalk and oil by smart waters in comparison with that by SW is 6964

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Figure 5. Snapshots of the dynamic contact angle of an SW4SO droplet on the chalk surface.

quantified by ζ-potential (ζ) measurements. The ζ potential of the chalk is denoted ζchalk, and that of the oil is denoted ζoil; both results are shown in Figure 4. It is observed that ζchalk becomes more negative in smart water than in SW owing to an increase in SO42− concentration (two and four times) or a decrease in Ca2+ and Mg2+ concentrations (one-half and onefourth). In addition, the decrease in ζchalk is more significant in SW4SO, especially in SW0.25Ca and SW0.25Mg. Therefore, ζchalk is more sensitive to the concentrations of Ca2+, Mg2+, and SO42− in smart water, and its surface charge can be made more negative by reducing the concentration of cations (i.e., Ca2+ or Mg2+) than increasing the concentration of SO42−. On the other hand, ζoil becomes more positive in SW2SO and SW4SO (−9.7 and −12.4 mV, respectively) compared to that in SW (−13 mV), indicating that increasing the concentration of SO42− in SW has a negative effect on ζoil due to a promotion of the ionic screening effect.47 In contrast, ζoil is made more negative with a decrease in Mg2+ concentration, particularly, to one-fourth, due to a reduction of the ionic screening effect.47 However, ζoil decreases marginally in SW0.5Ca and SW0.25Ca, which can be explained by the fact that the concentration of Ca2+ in SW is only 650 ppm. With regard to ζchalk and ζoil results, it has been found that the ζ potential can be significantly reduced (toward more negative) by reducing the concentration of Ca2+ or Mg2+ to one-fourth or less by increasing the concentration of SO42− to four times. 3.2. Contact Angles. In contact angle measurements, it is found that the initial contact angle (θ0) decreases with time because the chalk sample is porous in structure. As shown in Figure 5, the contact angle of SW4SO decreases sharply from 98.6 to 67, 30, 15, and 0° in approximately 45 min. Hence, the decreasing rate of contact angle (R) in SW4SO is calculated to be 0.0028°/s. This dynamic change in contact angle is analogous to the process of the imbibition test; the chalk sample favors the imbibition of SW4SO because the oil-wet chalk (θ0 = 126° with a water droplet) becomes more water-wet in SW4SO (θ0 = 98.6°). As shown in Figure 6, the R value in pure water is the largest (R = 0.0172°/s), whereas R decreases in SW and smart waters as a result of the ion diffusion effect. It is also observed that the R values in SW and different types of smart waters are similar in magnitude, suggesting that the impact of surface heterogeneity

Figure 6. Contact angles of water, SW, and smart waters on oil-aged Austin chalk surfaces. The error bars on the contact angle results represent the 95% confidence interval of three to five measurements. The yellow marked line presents the decreasing rate of contact angles in these solutions.

is marginal in this study. In addition, the R values in SW0.25Ca (0.0061°/s) and SW0.25Mg (0.009°/s) are found to be larger than in other waters, which may indicate that these two smart waters possess higher efficiency in changing the chalk wettability. Also shown in Figure 6, the average initial contact angle (θ0) of the water droplet on the chalk surface is 126.0°, which indicates that the chalk surface is changed to oil-wet by “aging” in mineral oil. The contact angle decreases by approximately 7.0° in SW (θ0 = 118.0°), although the chalk surface is still oil-wet. In comparison with SW, the oil-wet chalk surfaces become more water-wet because of the smart waters (θ0 < 100°), especially by SW0.25Ca (θ0 = 92.3°) and SW0.25Mg (θ0 = 85.3°). This finding is consistent with the ζpotential results, showing that ζchalk and ζoil are very negative in these two solutions (SW0.25Ca and SW0.25Mg), suggesting that the change in surface-charge plays an important role in changing the surface wettability. However, it is observed that SW2SO and SW4SO change the chalk surface to equally waterwet (θ0 = 97.8 and 98.6°, respectively); moreover, these two smart waters are slightly less efficient than other waters. As discussed previously, the changes in ζchalk and ζoil in response to increasing SO42− concentration are not significant (see Figure 4), resulting in similar macroscopic contact angles in SW2SO and SW4SO. Moreover, the lesser changes in ζchalk and ζoil in 6965

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Figure 7. Tomography images of AC4a scanned with a resolution of 3.0 × 3.0 × 3.0 μm3/voxel. (A) Image of the dry sample, (B) image of the oilsaturated sample after aging, (C) image of the wet sample after SW flooding, and (D) image of the wet sample after SW4SO flooding. Images (a)− (d) present the subset (2.25 × 2.25 mm2) of images (A)−(D).

Figure 8. Segmented images of AC4a scanned with a resolution of 3.0 × 3.0 × 3.0 μm3/voxel. (A) Image of the dry sample, (B) wet image after SW flooding, and (C) wet image after SW4SO flooding. Images (a)−(c) present the subset (2.25 × 2.25 mm2) of Images (A)−(C).

able to make the surface charge more negative and thereby change the chalk surface to more water-wet, even to a degree better than that of SW4SO, although SW4SO has been reported to be a promising smart water in the literature.9,10 To further

SW2SO and SW4SO than in other waters can produce smaller contact angles when compared to the values in other waters. In summary, the contact angle results agree well with the ζpotential results, suggesting that SW0.25Ca and SW0.25Mg are 6966

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Figure 9. Segmented images of AC4b scanned with a resolution of 3.0 × 3.0 × 3.0 μm3/voxel. (A) Image of the dry sample, (B) wet image after SW flooding, and (C) wet image after SW0.25Ca flooding. Images (a)−(c) present the subset (2.25 × 2.25 mm2) of images (A)−(C).

containing nanopores (beyond the resolution of μ-CT) can be observed (Figures 3 and 8a). Thus, the micropores are segmented into a phase termed the pore phase, whereas the subscale porous structure is segmented into another phase termed the oolitic layer (Figure 8a). The porosity of the oolitic layers is obtained with high-resolution SEM images and determined to be approximately 29.7%. On wet images, both the micropores of the pore phase and the nanopores of the oolitic layer phase can be occupied by invaded brine; hence, the pore phase or the oolitic layer phase are subdivided into two separate phases, i.e., the water-occupied and the oil-occupied pore phase or the oolitic layer phase (Figure 8b,c). In Figure 8b, it is observed that after flooding chalk AC4a with SW, water occupies the center as well as some parts of the micropore boundary (black), whereas the residual oil presents as oil layers covering the micropore boundary (green), indicating that the micropores are changed to moderately water-wet. However, in nanopores, the oil layers cover the nanopore boundary, whereas water occupies the center, suggesting that the nanopores are still in an oil-wet state after flooding with SW. In direct contrast, the micropores are changed to strongly water-wet after flooding with SW4SO because the amount of residual oil that covers the micropore boundary reduces significantly, as shown in Figure 8c. Moreover, SW4SO is found to invade the nanopores and cover the nanopore boundary, suggesting that the nanopores are changed from oil-wet to water-wet. In conclusion, SW primarily increases the water-wetness of micropores, whereas SW4SO can significantly enhance the water-wetness of both micropores and nanopores. This microscale evidence confirms the macroscale contact angle results: the contact angle in SW4SO solution is 20° smaller than that in SW (see Figure 6). In addition to SW4SO flooding, the segmented images of AC4b after SW0.25Ca flooding are presented in Figure 9. It is

examine our results, we choose SW4SO and SW0.25Ca to perform μ-CT scanning to evaluate the wettability change offered by smart waters as well as to distinguish the effects of SW4SO and SW0.25Ca. 3.3. Wettability Change. The tomography images of the chalk sample AC4a scanned by μ-CT at a resolution 3.0 × 3.0 × 3.0 μm3/voxel are shown in Figure 7. Four types of tomography images are obtained sequentially. First, the dry chalk sample is scanned to capture the porous structure of the chalk (Figure 7A). Second, the sample is saturated with oil and aged at 60 °C to change it to oil-wet (θ0 = 126°); its image is displayed in Figure 7B. The attenuation responses of oil and solid on the image are similar, and the tomography image is very blurry; hence, the aging process successfully changes the chalk to oilwet. Third, a wet image is taken after flooding with SW (Figure 7C). Finally, another wet image is taken after SW4SO flooding (Figure 7D). The wet images are taken to observe the distribution of water and remained oil in the pore space. It is clearly observed in Figure 7c,d that the oil is displaced after SW flooding and subsequent SW4SO flooding; thus, the oil-wet chalk sample is made more water-wet by these two waters. Moreover, comparing Figure 7c,d, it is found that these two images show noticeable differences in tomography, indicating that there is an incremental wettability change, first by SW and then by SW4SO. Similar findings are also observed in comparing the images of SW and SW0.25Ca (see the Supporting Information). To further assess the difference in fluid distribution after SW and SW4SO flooding, the tomography images are segmented, and the results are shown in Figure 8. The image of the dry sample, including the one presented in Figure 3, reveals that the porous structure of the chalk is very heterogeneous; despite the presence of microscale pores, some subscale porous clusters 6967

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Energy & Fuels

Figure 10. In situ contact angle distributions in chalk samples (A) AC4a and (B) AC4b after seawater and smart water flooding (SW4SO and SW0.25Ca, respectively, for AC4a and AC4b).

interesting to observe that the nanopores in the oolitic layer phase of chalk AC4b are not filled with the water phase (blue) after SW0.25Ca flooding (Figure 9c) in comparison with SW flooding (Figure 9b); therefore, the water-wetness enhancement in nanopores by SW0.25Ca appears to be marginal when compared to SW. In contrast to the nanopores, the water saturation in micropores (black) is remarkably increased after SW0.25Ca flooding (Figure 9c), suggesting that the waterwetness of micropores is much increased by SW0.25Ca compared to SW. Thus, SW0.25Ca primarily increases the water-wetness in micropores, which is different from the increased water-wetness in nanopores after SW4SO flooding. This difference may be attributed to the difference in the mechanism governing the wettability changes in micropores and nanopores. More specifically, the surface-charge change due to a spike in SO42− concentration (SW4SO) may dominate the wettability in nanopores, whereas calcite dissolution due to a reduction in Ca2+ concentration may enlarge the micropores and hence promote the occupation of SW0.25Ca. To quantify the enhanced water-wetness by smart water flooding, the effective contact angles in the porous media of chalk samples are determined with the automatic algorithm developed by Blunt’s research group.36 More specifically, a subvolume of 3.375 mm3 comprising 125 × 106 voxels was analyzed to obtain the in situ effective contact angles. The distributions of contact angles after flooding the chalk AC4a with SW4SO and the chalk AC4b with SW0.25Ca are presented in Figure 10A,B, respectively. In addition, the contact angle distribution was fitted to a normal distribution, and the statistics of the contact angle results is shown in Table 3. For both chalk samples, the in situ contact angles are mostly distributed in the range of 100−140° after flooding with SW, whereas the distribution curves move to the left after flooding with smart waters, and now the contact angles are mostly distributed in the range of 90−130°, suggesting that the chalk samples are made

more water-wet. In addition, the mean contact angle after SW flooding in either chalk sample, AC4a or AC4b, is approximately 121°. This value decreases to 110.1° for the chalk AC4a after SW4SO flooding and to 114.5° for the chalk AC4b after SW0.25Ca flooding. Furthermore, the proportions of oil and water in the pore phase and oolitic layer phase are calculated from the segmented images (Figures 8B,C and 9B,C), and the results are listed in Table 3. It is obvious from the table that the proportions of water in the pore phase, especially in the oolitic layer phase after smart water (i.e., SW4SO and SW0.25Ca) flooding, are much greater than those after SW flooding. Thus, we can conclude that the water-wetness of the chalk samples is enhanced by smart waters. Moreover, the enhancement in chalk waterwetness is more appreciable in nanopores of the oolitic layer phase by SW4SO (3.3%), whereas the increased water-wetness is more noticeable in micropores in the pore phase by SW0.25Ca (7.76%). In addition, the improvements in waterwetness or incremental oil recoveries I are calculated to be 18.55 and 20.21% for SW4SO and SW0.25Ca, respectively (Table 4), indicating that the enhancement in chalk waterTable 4. Improvement in Water-Wetness after SW and Smart Water Flooding (%) AC4a

AC4a

AC4b

SW

SW4SO

volume size (voxels) contact points measured average contact angle (deg) standard deviation (deg)

125 × 10

125 × 10

125 × 10

125 × 106

3000

3000

3000

3000

120.97

110.11

121.06

114.47

17.99

18.05

18.14

19.16

6

SW 6

SW0.25Ca 6

parameter

SW

SW4SO

SW

SW0.25Ca

water in pores (wp) oil in pores (op) water in the oolitic layer (wo) oil in the oolitic layer (oo) solid oil improvement (I)

4.77 1.39 6.23 23.27 64.34

5.02 1.14 9.53 19.97 64.34 18.55

8.04 5.95 12.15 11.53 62.32

9.80 4.19 14.15 9.53 62.32 20.21

wetness is more favorable to SW0.25Ca over SW4SO because the micropores are the primary media of oil and water saturation in the chalk samples. It is worth mentioning that the values of I fall into the general range of enhanced oil recovery by core flooding and spontaneous imbibition experiments (5− 20%); see ref 48. Additionally, the observation from the μ-CT is consistent with our macroscale contact angle results, where the contact angle is 6° smaller in SW0.25Ca than in SW4SO (see Figure 6).

Table 3. Statistics of the Effective Contact Angles after SW and Smart Water Flooding

parameter

AC4b

4. CONCLUSIONS In this study, we applied ζ-potential measurements, contact angle measurements, and microscale analysis based on μ-CT imaging to evaluate the effects of smart water in changing the wettability of chalk samples. The results sequentially confirm 6968

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Energy & Fuels the changes in the surface charge, surface wettability, fluid distributions, and effective contact angles in the porous media after smart water flooding in comparison with seawater flooding. The main conclusions of this study can be summarized as follows: (1) ζchalk becomes more negative in smart waters because of an increase in SO42− concentration or a decrease in Ca2+/ Mg2+ concentration in seawater, whereas ζoil is marginally changed by varying the concentrations of Ca2+, Mg2+, and SO42− in seawater. The most obvious changes in ζchalk and ζoil are observed in SW4SO, SW0.25Ca, and SW0.25Mg. (2) When contact angle measurements by the sessile drop method are used to evaluate the wettability, it is found that the oil-wet chalk surfaces are made more water-wet by smart waters (θ0 < 100°) than by SW, especially by SW0.25Ca and SW0.25Mg. (3) The proportion of the water phase in micropores and nanopores is increased by smart water flooding following SW flooding, as visualized from the segmented μ-CT images. The in situ characterization of effective contact angles derived from segmented μ-CT images shows enhancement of the water-wetness of chalk samples by SW4SO and SW0.25Ca. The expected distribution of effective contact angles shows a decrease from seawater to smart water flooding of approximately 10°. (4) The improvements in microscopic oil recovery by SW4SO and SW0.25Ca are 18.55 and 20.21%, respectively. Moreover, SW4SO is found to be effective in enhancing the water-wetness of nanopores, whereas SW0.25 is effective in promoting the water-wetness of micropores. The primary mechanism governing the wettability changes in micropores and nanopores can be described as follows: a change in surface charges due to a spike in SO 42− concentration (SW4SO) is responsible for the wettability change in nanopores, whereas calcite dissolution due to a reduction in Ca2+ concentration (SW0.25Ca) may enlarge the micropores and promote the occupation of smart water.

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Notes

The authors declare no competing financial interest.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.9b00638. Tomo images of Austin chalk AC4b by μ-CT scanning; pressure curves in the core flooding experiments (PDF)

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Hongna Ding: 0000-0003-4526-5507 Author Contributions

H.D. conducted the contact angle and ζ-potential measurements and prepared this manuscript. A.S. performed the core flooding experiments. The μ-CT image processing and the in situ characterization of the effective contact angles were carried out by Y.W. The in situ characterization of effective contact angles was partly conducted by Y.Z. This project was supervised by S.R. 6969

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