Surfactant Behavior Analysis in Enhanced Oil Recovery Blends Using

Dec 9, 2015 - Offshore reservoirs challenge chemical flooding, e.g., low-tension and foam flooding, because of the combined hardness and salinity ...
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Surfactant Behavior Analysis in Enhanced Oil Recovery Blends Using One-Dimensional Proton Nuclear Magnetic Resonance Griselda Garcia-Olvera,† Teresa Reilly,† Teresa E. Lehmann,‡ Leilei Zhang,† and Vladimir Alvarado*,† †

Department of Chemical and Petroleum Engineering, and ‡Department of Chemistry, University of Wyoming, 1000 East University Avenue, Laramie, Wyoming 82071, United States ABSTRACT: In this work, nuclear magnetic resonance (NMR) spectroscopy is used to investigate surfactant phase behavior relevant to chemical enhanced oil recovery. The effect of the solution electrical conductivity on the NMR signals was corrected using reference spectra of known conductivity. This allowed us to develop a semi-quantitative method to estimate the surfactant concentration by correlating it with either integrated NMR peaks or intensity of selected surfactant signal peaks. A distinct change in the slope of the assumed linear relationship between signal intensity and surfactant concentration was observed as the surfactant concentrations were increased. This was attributed to the progressive surfactant aggregation in solution. This result can be used as an alternative method to estimate the critical micelle concentration (CMC) of surfactants. NMR spectra were collected for individual surfactants and their combinations in a variety of saline aqueous solutions. Our results were compared to estimates obtained through more traditional ultraviolet−visible (UV−vis) spectroscopy and surface tension measurements. Consistency between NMR and surface tension estimates was found. CMC values determined through UV−vis were similar, although not quite the same as those of the other two techniques. Similarly, the NMR signals were used to estimate surfactant adsorption in the rock in the so-called static adsorption experiments, in which ground rock is exposed to a surfactant solution of known initial concentration. The results obtained show that NMR offers a powerful alternative to more frequently used methods to estimate not only CMC but also surfactant adsorption, particularly when multiple surfactants are present in aqueous solution.

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INTRODUCTION A chemical flooding process is commonly evaluated according to at least two important criteria: first, by examining the potential incremental oil recovery factor and, second, by estimating the amount of chemicals required to achieve this increment in oil recovery. In a typical chemical flood, the chemical cost is usually half or more of the total project operating cost. In chemical flooding designs, several factors impact decisions on the necessary concentration of reagents, e.g., polymers and surfactants. Surfactant retention arises from adsorption on the rock surface and phase trapping in porous media, which is an important concern in chemical enhanced oil recovery (cEOR). Surfactant retention depends upon several variables, such as surfactant structure, mineralogy, salinity, and pH. Reservoir mineralogy is also an important consideration in this sense. For instance, because carbonates are positively charged at reservoir conditions, they attract an anionic surfactant and high adsorption is observed. A similar situation occurs when anionic surfactants contact sandstone containing clay. Some investigations have been conducted to reduce surfactant adsorption and turn the projects more feasible. Liu et al.1 and Sheng2 report that this strong adsorption can be mitigated at high pH. Similarly, Hirasaki et al.3 mention that the adsorption of anionic surfactants on carbonate surfaces, such as calcite and dolomite, can be considerably reduced with the addition of sodium carbonate as alkali. Typically, alkalis are avoided when working with carbonates as a result of strong reactivity. Meanwhile, Wang et al.4 present results of a laboratory study where surfactant adsorption is reduced when a polymer is injected by either pre-flushing or co-injecting it. A significant point to note is that the surfactant retention values reported are affected by the physicochemical environment. For © XXXX American Chemical Society

example, aerobic conditions in laboratory tests yield a higher adsorption when compared to field results, where oxidation is not significant. The redox potential of brines may change the rock surface charge density to make a difference in surfactant adsorption.5 This fact can serve to partially explain why the reported surfactant retention differs between these two scenarios. Another consideration is that surfactant retention estimated in laboratory experiments depends upon whether measurements are conducted under static or dynamic conditions. In static tests, crushed rock is used, and therefore, the surface area is generally higher in these tests than in a core, because the disaggregated material is more readily exposed. Additionally, static tests are conducted under single-phase conditions, i.e., in the absence of crude oil; therefore, no barriers between the surfactant and rock exist, and the adsorption estimate might be unreasonably high. Nelson6 reports that the salinity gradient may affect surfactant retention. Therefore, if brines with different salinities are injected into the reservoir, the adsorption could change over time. Consequently, the surfactant retention should be monitored as part of the project evaluation. Surfactants are amphiphilic molecules that possess both hydrophobic and hydrophilic properties. A typical surfactant molecule consists of a long hydrocarbon “tail” that dissolves in hydrocarbon and other nonpolar solvents. Surfactants are compounds that change the interfacial properties sharply. In the oil industry, these are used as emulsifiers to produce oil−water Received: August 12, 2015 Revised: December 9, 2015

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Energy & Fuels microemulsions to reduce interfacial tension and, consequently, oil saturation. Nelson and Pope7 show that the optimal microemulsion (Winsor III) depends upon the amount of water, oil, and surfactant. Furthermore, the surfactant blend should be designed to maintain a near ideal concentration throughout the reservoir. To meet the requirements for a specific application, such as the temperature, costs, optimum salinity, water hardness, oil properties, and lithology, a wide selection of surfactants has been made available on the market. In low-tension designs, i.e., when the brine−crude oil interfacial tension is below 10−2 dyn/cm, the optimal formulation tends to require a mixture of surfactants at different concentrations to attain optimum salinity behavior. Commercial surfactants are composed of multiple species, and preferential adsorption on the rock surface or partitioning may cause a decrease in the surfactant concentration in the porous media with adverse effects on oil recovery.3 Surfactants can aggregate in a mixture composed of only one phase, such as in the case of micellization. In this sense, an important trait of surfactants is the critical micelle concentration (CMC), which is defined as the concentration of surfactant above which micelles spontaneously form. McBain introduced the word “micelle” into surface and colloid chemistry in the context of the association of surfactant molecules in aqueous solution.8 The number of molecules in a micelle depends upon the surfactant, but generally between 50 and 100 are found.9,10 Several factors affect micellization, such as the temperature, ionic strength, and surfactant nature. For example, for ionic surfactants in an aqueous solution, the minimum CMC value is reached at roughly 25 °C and may be greater when the temperature increases or decreases. Mohajeri and Noudeh11 describe it as a U-shaped behavior. On the other hand, before reaching the CMC, surface tension decreases strongly with the increment of the surfactant concentration. After reaching the CMC, the surface tension remains constant or changes are not important, as shown in Figure 1. The

Methods to determine the CMC include surface tension and optical techniques, such as ultraviolet−visible (UV−vis) spectroscopy, light scattering, fluorescence, and total carbon number.4,13,14 One popular method to measure the surface tension is the pendant drop apparatus, which uses drop shape analysis. While some authors suggest using the mature highperformance liquid chromatography (HPLC) technique to estimate the surfactant concentration and, consequently, the surfactant retention, Solairaj et al.14 show that no surfactant separation is observed in corefloods using the HPLC technique. On the other hand, total organic carbon (TOC) detection has been used but has significant limitations in the presence of other commonly used reagents, such as polymers.4 Nuclear magnetic resonance (NMR) is the most direct and non-destructive spectroscopic technique for identifying the structure of both pure compounds and mixture of solids or liquids and has vast potential in different areas. The possibility of identifying different chemical species with a single spectrum gives this technique a great advantage when compared to UV− vis spectroscopy and surface tension analysis. In this paper, we show the effectiveness of one-dimensional (1D) 1H NMR to estimate the surfactant concentration for surfactants with distinct spectral signatures to be identified in aqueous solution blends. Several applications are illustrated here. First, CMC is estimated for several commercial surfactants using two well-known techniques available in our laboratory, UV−vis and interfacial tension, and compared to the proposed technique using 1D 1H NMR. Another important consideration of enhanced oil recovery (EOR) designs containing surfactants is adsorption on the rock. While this is discussed in several publications, available techniques tend to consider the average adsorption of a surfactant mixture, instead of an individualized analysis for each surfactant in a blend. Because a good relationship between the surfactant concentration and 1D 1H NMR signal exists, this technique is proposed to estimate surfactant retention for each chemical used in the injection. The commercial surfactants, two internal olefin sulfates (IOS) S2 (C15−18) and S3B (C20−24), S13D (C16−17−PO13−SO4),3,17 (Alpha) Foamer (ammonium alkyl ether sulfate),15 and C8 (branched alkyl benzenesulfonate),16 were used in the first part of this paper to show similarities in the CMC results among the four techniques. In the second part of this paper, the behavior of surfactant blends as a function of the surfactant concentration ratio, temperature, salinity, and surfactant adsorption are discussed. Details on NMR measurement errors and detection of the surfactant concentration in emulsions are provided. To this end, we selected S2 and S13D surfactant blends as a result of their suitability for a field application in the Minnelusa formation in Wyoming.17

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MATERIALS AND METHODS

Commercial surfactants from the Stepan Company (S2, S13D, S3B, Foamer, and C8) were selected for this study. The solvents were deionized water (DW) and NaCl aqueous solution of analytical-grade. Individual surfactants were analyzed as well as some mixtures with different surfactant ratios. Two well-known techniques are used to estimate CMC, namely, UV−vis spectroscopy and surface tension, using an Agilent 8453 UV− vis spectrophotometer and software Fta32 Video 2.0, respectively. These methods will serve as references for the new proposed technique based on 1H NMR. The conductivity of the aqueous solutions is determined using an Accumet Basic AB30 conductivity meter at room temperature, roughly 20 °C. The meter is calibrated prior to the measurements using

Figure 1. Formation of micelles with an increasing surfactant concentration.

morphology of the micelles is controlled by different factors, such as the surfactant structure and concentration and temperature. For a low surfactant concentration but above the CMC, spherical micelles are commonly reported. However, if the surfactant concentration is increased further, the aggregation number and micelle size are greater, and as a result, spherical micelles turn to cylindrical, which causes a considerable increment in viscosity.12 B

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Energy & Fuels standard solutions, depending upon the conductivity range of the samples. NMR samples are prepared in 90/10% H2O/D2O brine mixtures. The experiments are performed on a Bruker Avance III instrument at 600 MHz. The 1D 1H NMR spectra are collected with WATERGATE incorporated into the pulse sequence to suppress the solvent signal, and 16 scans are collected with a spectral width of 12 ppm. The NMR spectra are analyzed recording absolute intensity, areas for the main peaks, and normalized intensity with respect to the applied gain. The intensity of the NMR signal induced depends upon the sample concentration; therefore, the amount of gain or amplification in the receiver is commonly adjusted automatically for each sample. As a result, more concentrated samples require smaller receiver gain values.18 For this reason, a constant gain or a normalized intensity with respect to the gain is recommended in concentration analysis. The intensity of the NMR signals decreases with the increase of the aqueous phase conductivity;19,20 therefore, a calibration of the NMR spectra is required to be able to compare them even when some of the samples are dissolved in different brines with different conductivities.

Figure 3. Surface tension versus surfactant concentration in DW to estimate CMC.

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RESULTS AND DISCUSSION CMC. The samples were prepared with individual surfactants and some blends with the concentration varying from 0.1 to 1.0 wt % in DW and some of them in 10 000 and 80 000 ppm of NaCl. In this section, the estimation of CMC is reported, first using surface tension measurements using a pendant drop system, UV−vis spectroscopy, and then 1H NMR. The pendant drop method uses a surfactant solution droplet suspended in air. Measurements are conducted at room temperature, roughly 20 °C, because the available equipment does not have good temperature control for measurements in air as the external phase. Figure 2 shows surface tension versus surfactant S2 concentration in DW. It is apparent that, when the surfactant

Figure 4. Surface tension versus surfactant concentration for S2 and S13D in equal proportions in DW at 20 °C.

Some samples used in the pendant drop method were also tested using UV−vis spectroscopy to estimate CMC at room temperature, approximately 20 °C. For this technique, it is preferable to analyze the wavelength where UV adsorption is maximal and the CMC is interpreted as the intersection of the two lines connecting points with different surfactant concentrations, although strictly the points do not align to two straight lines in all of the cases. In some cases, the maximum UV adsorption is not clear, as shown later for surfactant S2. Figure 5 shows the spectra of S2, S13D, and 50:50 wt % blend. For surfactant S2, λmax is between 200 and 250 nm. However, this portion of the spectrum does not exhibit a clean trend, which prompted us to use the signal at 288 nm for the analysis instead. The spectra for S13D is easier to discern; therefore, the signal at 216 nm was used to compare absorbance values. For the blend of S2−S13D, because two signals are involved, we took the sum of the two representative wavelengths chosen for each surfactant, and the area between 200 and 300 nm, obtaining similar results in both cases. In Figure 6, the absorbance of the wavelength and the area versus surfactant concentration are plotted. The CMC for each surfactant is defined by the interception of the trend lines. As seen, the CMC values or these surfactants are roughly 0.5 wt %. The CMC values calculated with this technique are somewhat lower than those obtained with the pendant drop technique.

Figure 2. Surface tension versus surfactant S2 concentration in DW.

concentration is higher than 0.6 wt %, the surface tension remains almost constant; therefore, the CMC value is estimated at 0.6 wt %. A similar plot was prepared for each individual surfactant. While some samples exhibit a less distinctive decrease in surface tension below CMC, it was possible to define the CMC in all cases. For the surfactants analyzed, the CMC varies from 0.4 to 0.7 wt %. Some results are presented in Figure 3. When a blend of two surfactants was analyzed, the CMC for each surfactant in a mixture changed with respect to the results with individual surfactants. For example, the CMC for either S2 or S13D is 0.6 wt %, and when these two surfactants are blended in equal weight fractions, the CMC is 0.6 wt %. This means that micelles are formed with 0.3 wt % S2 and 0.3 wt % S13D, as shown in Figure 4. On the basis of this result, we assume that a hybrid micellization occurs at this surfactant ratio. C

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Figure 5. UV−vis spectra for (a) S2, (b) S13D, and (c) blend of them for different surfactant concentrations in DW at 20 °C.

Figure 6. CMC determined UV−vis absorbance for S2, S13D, and a blend in equal proportions at 20 °C.

For the NMR technique, we recommend that each surfactant be analyzed individually to optimize spectral width and number of scans. This also provides spectra that clearly show which peaks are surfactant-specific and which are shared. Figure 7 shows the spectra for surfactants S2, S13D, and a 50:50 wt % blend of the two in DW at 25 °C. As seen, S2 has two intense peaks at 1.2 and 0.8 ppm, respectively. S13D exhibits four distinct peaks, with the most intense at 1.06 ppm, two small signals at 0.8 and 1.23 ppm, and a series of overlapping peaks between 3.1 and 3.7 ppm. On the other hand, the spectrum of the S2−S13D blend contains all of the peaks from each surfactant in the same position as in their individual spectra. We found the same behavior for different surfactant blends; i.e., all of the NMR signals for each surfactant remain in their original positions when compared to the unblended samples, and no additional peaks appear. Therefore, we can interpret that no chemical reactions occurred in the samples analyzed when two or more surfactants are mixed, and as a result, individual surfactant analysis can be made. At this point, we focus on surfactants S2, S13D, and their blends in DW at 25 °C. First, individual surfactants are analyzed. Absolute intensity and areas for the main peaks are recorded at different surfactant concentrations and their

Figure 7. 1H NMR spectra of S2, S13D, and a blend of them in DW at 25 °C collected at 600 MHz.

corresponding gain. Figure 8 shows the results for S2. The absolute intensity was taken from the main peak at 1.2 ppm, and the area was calculated from 2.12 to 1.6 ppm. Both normalized intensity and area have similar trends. In theory, the intensities of the 1H signals are proportional to the number of equivalent nuclei depicted by those peaks. We observed a greater intensity as the surfactant concentration is increased for all of the samples. At a certain surfactant concentration, two different trends misalign on a plot of signal intensity versus surfactant concentration, when an overlap of the two straight lines is attempted. The breaking point can be interpreted as the CMC, which, in this case, resulted at 0.6 wt %. The CMC values obtained with 1D 1H NMR are similar to those determined using interfacial tension and UV−vis measurements. Micellization is not a concentration-specific process, because a series of aggregation or intermediates can exist before the micelles fully develop. Some molecules aggregate, and this causes a small decrease in the real intensity of the NMR peaks, D

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Figure 8. Normalized intensity and area versus S2 surfactant concentration obtained from the 600 MHz 1H NMR spectra in DW at 25 °C.

potentially producing a change in the intensity−concentration response. When micelles are formed, the NMR trend intensity changes because a new structure is present. This hypothesis is further supported by noticing a slight displacement of the peaks when the surfactant concentration is increased. Some surfactants aggregate differently, and the slope of the intensity−surfactant concentration curve should be different, as noticed in this analysis. For the case of the S2−S13D blend, the intensity value was taken as the sum of the main peaks for S2 and S13D, at 1.2 and 1.06 ppm, respectively, and the area was calculated between 2.25 to 0 ppm. Figure 9 shows the results for the blend of these

Figure 10. Normalized intensity for S2−S13D in a mixture of (a) 80:20% and (b) 20:80% ratio in DW at 25 °C.

Table 1. Estimated CMC in Weight Percent Using Pendant Drop, UV−Vis, and 1D NMR S2 in DW S13D in DW S2−S13D in DW (50:50%) S2−S13D in DW (20:80%) S2−S13D in DW (80:20%) C8 in DW S3B in DW Foamer in DW

surface tension

UV−vis

1D NMR

0.6 0.6 0.6 0.5 0.5 0.7 0.4 0.5

0.5 0.5 0.5 0.4 0.4

0.6 0.6 0.6 0.6 0.6 0.7 0.4 0.5

0.4 0.4

some samples show the CMC higher with the surface tension technique. Even though the temperature used in each technique is slightly different, 25 °C for NMR and around 20 °C for surface tension and UV−vis, we run the S2 surfactant samples at 20 °C in NMR. The results showed the same CMC at 0.5 wt % for 25 and 20 °C. From the anteceding discussion, we demonstrate that different results are obtained not because of the slight difference in the temperature but because of the technique used. Surface tension evaluates the average of the whole sample, while NMR and UV−vis measure at the molecular level and light dispersion, respectively. Effect of the Temperature in the NMR Signals. The temperature affects the NMR results. Our available spectrometer can be run at up to 150 °C. However, at a high temperature, the NMR samples have to be vacuumed and tubes sealed to avoid boiling and evaporation. To analyze temperature dependence, a new series of experiments were conducted at 40 and 70 °C with surfactant S2. At first, the same “shape” and the same number of peaks were observed and the results were overall similar in trend. However, high temperature elicited an increase in intensity and a shift in peak locations, as Figure 11 shows.

Figure 9. Absolute intensity versus S2−S13D surfactant concentration in DW at 25 °C.

surfactants in equal proportions; as in the previous case, the normalized intensity and areas increase as the concentration increases and two lines are found, below and above the CMC. The CMC value for this blend is 0.6 wt %, corresponding to a 0.3 wt % concentration for each surfactant. Two additional blends of S2 and S13D with ratios of 80:20 and 20:80 wt % were analyzed to compare their CMC values. The normalized intensity for each main surfactant peak and their sum were plotted for both blends (Figure 10). Consistent data are found when individual peaks or both peaks are analyzed in 80:20 wt % (panel a) and 20:80 wt % (panel b); i.e., CMC is 0.6 wt %. Table 1 shows the CMC results using the three techniques selected for this work: surface tension (pendant drop), absorbance (UV−vis), and signal intensity and area (1D NMR). As observed, the CMC values are very similar but not exactly equal. In some cases, the CMC values are similar, but E

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them all. We found that this correction factor is slightly different for each surfactant in NaCl, but they have a similar trend. Figure 13 presents the correction factors that should be applied to S2, S13D, and their 50:50 wt % blend.

Figure 11. S2 surfactant at 0.5 wt % concentration in DW at 25, 40, and 70 °C.

However, when the intensity is normalized, the points almost overlay for the three different temperatures (Figure 12).

Figure 13. NMR conductivity correction factor for the surfactant S2, S13D, and their blend in equal proportions.

Estimation of Surfactant Adsorption Using NMR Spectroscopy. On the basis of the good correlation between NMR signal normalized intensity and surfactant concentration, NMR spectroscopy can be used as an alternative tool to estimate surfactant adsorption. In other words, if a decrease in the NMR signal intensity is observed after the sample was in contact with the rock, it can be interpreted as a loss of surfactant. The results in this section ignore surfactant partitioning with other phases that would be present in the reservoir. This corresponds to maximum adsorption bound associate with the rock. To show the potential of this technique, three static, i.e., in the absence of flow through porous media, surfactant adsorptions on ground rock were conducted. A solution of 70 mL of 20 000 ppm of NaCl brine with 0.3 wt % surfactants S13D, S2, and S2−S13D was placed in contact with 30 g of crushed Berea sandstone. The aqueous solutions were mixed for 2 days and then kept for 2 additional days under static conditions to allow for settling of solids. Conductivity was measured, and aliquot samples were subjected to NMR analysis. The same test was run on brine without a surfactant and was used to verify the change in the brine salinity as a result of contact with the rock. The increment in conductivity was found to be insignificant, changing only 0.30 ms/cm, from 33.15 to 33.45 ms/cm in a 20 000 ppm of NaCl, without and with contact with the rock, respectively. It should be emphasized that this is not the case for all of the experiments, and sometimes, the rock−fluid interaction becomes important enough to alter the original brine composition considerably. The spectra of surfactant solutions before and after contacting the rock were compared. The former exhibits higher NMR signal intensities; therefore, we assume that a decrease in intensity is an effect of the surfactant adsorption on the rock. Figure 14 shows an example, for the case of S13D. The signal intensity for the solution without contact with the rock is higher than the signal intensity that is in contact with the rock, and it is easier to see this difference in the peak, around 1.06 ppm. The surfactant adsorptions were estimated using the conductivity correction, even though the change in conductivity for the original solution and the solution in contact with the rock was minute. On the basis of Figure 13, the correction

Figure 12. Normalized intensity for S2 main peaks versus surfactant concentration in DW at 25, 40, and 70 °C.

Effect of the Salinity in the NMR Signals. As aforementioned, NMR signals are attenuated as a result of solvent conductivity; namely, when the brine used is saltier, the conductivity is greater and the NMR signal is smaller, correspondingly. Therefore, a conductivity-dependent correction factor has to be applied to each spectrum if different samples with distinct conductivities are to be compared. This can occur when a surfactant is injected with a specific brine composition into the reservoir. While this batch flows through the porous media, it is in contact with the rock and connate water, and as a result, the effluent may have a different conductivity than the injection batch or the injection strategy implies a salinity gradient. For this purpose, a series of NaCl brines containing a known surfactant concentration below CMC, spanning a salinity range from DW to 80 000 ppm, was prepared. It is important to note here that some surfactants are not soluble at high salinity, as the case of S2 and S13D above 30 000 ppm of NaCl, but when they are blended, the solubility is improved, even at a salinity higher than 80 000 ppm NaCl. After the spectra are analyzed, it is confirmed that the peak intensity decreases as conductivity increases. However, the peaks become wider when the conductivity increases. The normalized intensity from samples in DW is taken as a reference, and the rest is corrected by conductivity to compare F

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and the points to estimate adsorption for S13D, S2, and S2− S13D, respectively. The main advantage of this technique is to do analysis of the peaks representative of surfactants either alone or in mixtures; therefore, we can estimate the absorption of each of the surfactants present in a mixture, as shown for the case S2−S13D (Figure 17). For the blend S2−S13D, the adsorption estimation was performed for each surfactant and the whole sample. The surfactant adsorptions for S2, S13D, and a blend S2−S13D are presented in Table 2. Figure 14. Amplification of the 1H NMR spectra for the 0.3 wt % surfactant S13D in 20 000 ppm of NaCl in (blue) and without (red) contact of Berea sandstone at 25 °C.

Table 2. Estimated Surfactant Adsorption in a Static Test Using 1D NMR for S2, S13D, and a Blend S2−S13D with Berea Sandstone

factor in 20 000 ppm of NaCl was estimated and the spectrum were exaggerated to compare the intensities to the calibration data in DW. Figures 15, 16, and 17 show the calibration data

adsorption (wt %) sample

S2

S2 S13D S2−S13D

7.67 6.07

S13D

S2−S13D

6.67 12.93

9.0

We show now that lower frequency instruments can also be used; therefore, the highest resolution is not always needed. For example, in a sample of 0.5 wt % S2 in DW in a 400 MHz instrument, the peaks agree completely with 600 MHz. When the same sample was tested on a 300 MHz spectrometer, the peaks shifted slightly compared to 600 MHz but the signals preserved their shapes in all three spectra, regardless of resolution, as shown in Figure 18. Additionally, the area of the

Figure 15. Calibration for S13D in DW and adsorption estimation.

Figure 16. Calibration for S2 in DW and adsorption estimation. Figure 18. S2 peaks obtained using the 300, 400, and 600 MHz spectrometers.

taller peak compared to the area of the smaller peak gives the same ratio for all spectra, impacted slightly by the decrease in resolution and difference in probes in each instrument. Further tests were run to confirm the correlation between the instruments. As shown in Figure 18, the 600 MHz spectrum is processed with a line broadening factor of 2.15 and the intensity is cut in half, showing near perfect congruity with the 400 MHz spectrum, and both are closely related to the 300 MHz spectrum, whose peak is shifted a mere 0.003 ppm. This indicates that essential data are not lost with the drop in sensitivity and resolution, because reliable surfactant concentration information can still be acquired with less sensitivity and resolution. Future advances in NMR low-resolution systems

Figure 17. Calibration for S2−S13D in DW and adsorption estimation.

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Energy & Fuels might provide even more inexpensive options to conduct these experiments. Possible Errors in the NMR Technique. We consider two possible sources of error in this technique: first, errors in sample preparation and representativeness of aliquots from a given sample. For this case, three samples were prepared for the S2 surfactant at 0.8 wt % concentration (1, 2, and 3) and five samples were measured for each sample to have a total of 15 measurements (T). A histogram of the normalized intensity for the 15 measurements is plotted in Figure 19. An error plot is shown in Figure 20 for the 15 samples analyzed (T) and for the five samples analyzed in each batch (1, 2, and 3).

Figure 21. Signal intensity and area for a S2 concentration in the DW range from 0.001 to 1.0 wt % at 25 °C and 300 MHz.

a dispersed phase, such as droplets. To test this, we analyzed two type 1 crude oil/water microemulsions, containing S2 and S13D. The NMR spectra for the microemulsion samples show the same surfactant peaks as the individual surfactant analyzed and a large additional peak around 3.3 ppm. The location for this new peak correlates with methylene, which is the most abundant of the saturated alkanes. We assume that the new peak is due to the oil present in the sample and the surfactant peak can be used to estimate the surfactant concentration in the samples. Figure 22 shows NMR spectra for a 0.4 wt % emulsion

Figure 19. Histogram of the S2 surfactant at 0.8 wt % concentration, with three sample preparations and five measurements for each sample.

Figure 22. NMR spectra for a 0.4 wt % emulsion S2 in DW and crude oil and 0.4 wt % S2 in DW. Figure 20. Error bars using standard deviation for the S2 surfactant at 0.8 wt % concentration, with three sample preparations and five measurements for each sample.

S2 in DW and crude oil and 0.4 wt % S2 in DW. In comparison of the normalized intensity to the calibration curve in Figure 8, we estimate that, for the emulsion S2−DW−crude oil, the surfactant remaining in the aqueous phase is 0.34 wt % and, therefore, 0.06 wt % is in the oil phase. On the other hand, for the emulsion S13D−DW−crude oil, 0.16 wt % of the surfactant is in the aqueous phase and 0.24 wt % is in the oil phase. Significant additional work is necessary to determine how quantitative these estimates are, but the results here indicate the potential of estimating the concentration of surfactants in the oleic phase.

On the basis of the multiple measurements, the estimated error in concentration amounts to 1.25%. This includes both sample and reproducibility errors. To show the potential of the technique at a low surfactant concentration associated with an effective use of surfactants in EOR operations, a set of S2 surfactant solutions at several concentration values in DW was analyzed using the 300 MHz spectrometer, with the concentration ranging from 0.001 to 0.1 wt %. The normalized intensities and areas are plotted in Figure 21. A good trend is seen in the whole concentration range, including the lowest surfactant concentration tested (0.001 wt %). Surfactant Concentration in Emulsions. Because 1D NMR is not affected by opacity of the samples, it can distinguish molecules regardless of whether they are present in

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CONCLUSION From the previous discussion, the following conclusions are drawn: (1) NMR resulted in an excellent tool to estimate the surfactant concentration in aqueous solution as low as 0.001 wt %, being significantly powerful when surfactant blends are present. The concentration can be estimated from NMR H

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Energy & Fuels

(13) Obasi, D.; Ghosh, B. Proceedings of the SPE Enhanced Oil Recovery Conference; Kuala Lampur, Malaysia, July 2−4, 2013; SPE165219-MS, DOI: 10.2118/165219-MS. (14) Solairaj, S.; Britton, C.; Kim, D. H.; Weerasooriya, U.; Pope, G. A. Proceedings of the 18th SPE Improved Oil Recovery Symposium; Tulsa, OK, April 14−18, 2012; SPE-154247-MS, DOI: 10.2118/154247-MS. (15) Farzaneh, S. A.; Sohrabi, M. Chem. Eng. Res. Des. 2015, 94, 375− 389. (16) Sagi, A. R.; Thomas, C. P.; Bian, Y.; Kwan, J. T.; Salehi, M.; Hirasaki, G. J.; Puerto, M. C.; Miller, C. A. Proceedings of the SPE International Symposium on Oilfield Chemistry; The Woodlands, TX, April 8−10, 2013; SPE-164062-MS, DOI: 10.2118/164062-MS. (17) Gregersen, C.; Kazempour, M.; Alvarado, V. Fuel 2013, 105, 368−382. (18) Jacobsen, N. E. NMR Spectroscopy Explained: Simplified Theory, Applications and Examples for Organic Chemistry and Structural Biology; John Wiley & Sons: Hoboken, NJ, 2007. (19) Kelly, A. E.; Ou, H. D.; Withers, R.; Dötsch, V. J. Am. Chem. Soc. 2002, 124 (40), 12013−12019. (20) Moradi, M.; Topchiy, E.; Lehmann, T. E.; Alvarado, V. Fuel 2013, 112, 236−248.

measurements with an error as low as 1.25 wt %. (2) The CMC and the estimates are comparable to other more traditional techniques. The value depends upon the surfactant ratio in a blend, and this was confirmed by the three techniques used in this research. A more in-depth study involving surfactant titrations is underway in our lab. (3) The 1D 1H NMR spectrum for a surfactant blend is the “sum” of each surfactant spectrum; thus, there is no chemical reaction when surfactants are blended, for the cases analyzed. (4) The NMR signal intensity is attenuated as brine salinity is increased, but it can be corrected using aqueous phase conductivity. (5) Surfactant adsorption can be evaluated by NMR without limitation in opacity, particles, or emulsions. (6) A surfactant blend, with two or more surfactants, can be analyzed using NMR, either individually if there are distinctive peaks for each surfactant or all together if the peaks are overlaid. (7) A 1D NMR can be useful to know the surfactant injection−production patterns using the surfactants as tracers by themselves.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the Enhanced Oil Recovery Institute (EORI) at the University of Wyoming for financial support. The authors thank the Stepan Company for providing the surfactants used in this work.

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NOMENCLATURE CMC = critical micelle concentration cEOR = chemical enhanced oil recovery DW = deionized water HPLC = high-performance liquid chromatography NMR = nuclear magnetic resonance TOC = total organic carbon UV−vis = ultraviolet−visible

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REFERENCES

(1) Liu, S.; Zhang, D.; Yan, W.; Puerto, M.; Hirasaki, G. J.; Miller, C. A. SPE J. 2008, 13, 5−16. (2) Sheng, J. Modern Chemical Enhanced Oil Recovery: Theory and Practice; Gulf Professional Publishing: Burlington, MA, 2010. (3) Hirasaki, G. J.; Miller, C. A.; Puerto, M. SPE J. 2011, 16, 889− 907. (4) Wang, J.; Han, M.; Fuseni, A. B.; Cao, D. Proceedings of the SPE Middle East Oil & Gas Show and Conference; Manama, Bahrain, March 8−11, 2015; SPE-172700-MS, DOI: 10.2118/172700-MS. (5) Wang, F. H. L. SPE Reservoir Eng. 1993, 8 (2), 108−116. (6) Nelson, R. C. SPEJ, Soc. Pet. Eng. J. 1982, 22 (2), 259−270. (7) Nelson, R. C.; Pope, G. A. SPEJ, Soc. Pet. Eng. J. 1978, 18 (5), 325−338. (8) McBain, J. W. Trans. Faraday Soc. 1913, 9, 99−101. (9) Johnson, K. A.; Westermann-Clark, G. B.; Shah, D. O. J. Pharm. Sci. 1987, 76 (4), 277−285. (10) Swanson, L. M.; Droske, J. P. Proceedings of the 210th American Chemical Society National Meeting; Chicago, IL, Aug 20−24, 1995; 163CHED. (11) Mohajeri, E.; Noudeh, G. D. E-J. Chem. 2012, 9, 2268−2274. (12) Kamranfar, P.; Jamialahmadi, M. J. Mol. Liq. 2014, 198, 286− 291. I

DOI: 10.1021/acs.energyfuels.5b01840 Energy Fuels XXXX, XXX, XXX−XXX