SPE 142305

Factors Governing Distance of Nanoparticle Propagation in Porous Media Federico Caldelas*, SPE, Michael J. Murphy, SPE, Chun Huh, SPE, and Steven L. Bryant, SPE The University of Texas at Austin

Copyright 2011, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Production and Operations Symposium held in Oklahoma City, Oklahoma, USA, 27–29 March 2011. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright.

Abstract With a number of advantages hitherto unrecognized, nanoparticle-stabilized emulsions and foams have recently been proposed for enhanced oil recovery (EOR) applications. Long-distance transport of nanoparticles is a prerequisite for any such applications. The transport of the particles is limited by the degree to which the particles are retained by the porous medium. In this work, experiments that quantify the retention and provide insight into the mechanisms for nanoparticle retention in porous media are described. Sedimentary rock samples (Boise sandstone and Texas Cream limestone) were crushed into single grains and sieved into narrow grain size fractions. In some cases, clay (kaolinite or illite) was added to the Boise sandstone samples. These grain samples were packed into long (1 ft–9 ft) slim tubes (ID = 0.93 cm) to create unconsolidated sandpack columns.

The columns were injected with aqueous dispersions of silica-core nanoparticles (with and without surface coating) and flushed with brine. The nanoparticle effluent concentration history was measured and the nanoparticle recovery was calculated as a percentage of the injected nanoparticle dispersion. Fifty experiments were performed in this fashion, varying different experimental parameters while maintaining others constant to allow direct comparisons between experiments. The parameters analyzed in this work are: specific surface area of the porous medium, lithology, brine salinity, interstitial velocity, residence time, column length, and temperature.

Our results indicate that retention is not severe, with an 8% average of the injected amount, for all our experiments. Of the parameters analyzed, specific surface area was the most influential, with a linear effect on nanoparticle retention independently of lithology. Larger salinity increased nanoparticle retention slightly and delayed nanoparticle arrival. Velocity, residence time and sandpack length are coupled parameters and were studied jointly; they had a minor effect on retention. Temperature had a marginal effect, with two percentage points greater retention at 80°C compared to 21°C. Both surface coated and bare silica nanoparticles were successfully transported, so surface coating is not a prerequisite for transport for the particle and rock systems studied.

Introduction Nanoparticles are finding their way into various branches of the petroleum engineering industry. In production applications, Huang et al. (2008) coated hydraulic fracture proppant with nanocrystals to control fines migration without decreasing productivity. Huang and Crews (2008) used nanoparticles to reduce the leakoff of viscoelastic surfactant stimulation fluids at high temperatures for completion applications. In drilling, Sensoy et al. (2009) showed that adding nanoparticles to water-based mud decreases the mud invasion in shale, and thus avoids swelling and wellbore instability.

Reservoir engineering and EOR have also attracted attention for nanoparticle applications. By modifying the surface coating, silica nanoparticles have been used to stabilize both water-in-oil and oil-in-water emulsions for conformance control applications (Zhang et al., 2010). CO2-in-water foams have been created using these same particles by Espinosa et al. (2010), at a range of temperatures (up to 95°C). Remarkably, in both cases, emulsions and foams were created without the aid of surfactants.

Besides modifying the coating, nanoparticles can also be manufactured using different core materials. Yu et al. (2010) employed iron-oxide particles with paramagnetic properties. The behavior of injected fluids can potentially be controlled by imposing an external magnetic field. Prodanovic et al. (2010) investigated the motion of multiphase fluids that have

* Now with DeGolyer and MacNaughton

2 SPE 142305

paramagnetic particles on their interfaces. This would allow for more accurate measurements of saturations for large volumes of reservoir.

Pourafshary et al. (2009) list these and many other applications and prioritize them in terms of their impact in the industry. The highest priority they find is chemical EOR. Even though all of these applications sound very interesting and exciting, they cannot be implemented at the field scale unless the nanoparticles can travel for interwell distances. Being able to predict the retention of these particles as they travel through the porous media is necessary to develop many of the applications discussed above.

Nanoparticle Retention in Porous Media. The transport of nanoparticles in porous media for oil recovery has been scarcely investigated at this point. However, extensive work has been done on colloidal transport for environmental purposes. Colloids in natural subsurface environments include silicate clays, Fe and Al oxides, mineral precipitates, humic materials, and microorganisms (McCarthy and Zachara, 1989). They are generated from various processes (reviewed by Bradford and Torkzaban, 2008) that are mostly unintentional and occur naturally.

For the transport of colloidal particles in porous media, a controlling parameter is their retention. The main retention mechanism is the irreversible attachment to the rock grain surfaces. To predict rock grain surface retention, a constant first-order attachment rate coefficient is usually used. When the colloids and the porous media surface have repulsive forces the predicted coefficients have been found to be underestimated (Bradford et al., 2003). Bradford also mentions other factors that affect the attachment rate coefficient, such as the surface roughness, heterogeneity in the charge, and colloid variability.

While the earlier colloidal transport studies are useful, other factors need to be investigated before applying colloidal transport knowledge to the use of nanoparticles for oil recovery, even under conditions when filtration and aggregation are no longer important. These factors include the differences in surface charges and lower permeabilities of the relevant formations (Kaya and Yuklesen, 2005). Also, colloid concentrations have been reported to range from 108 to 1017 particles per liter (Kim, 1991), whereas the concentrations used for our study, for example, are much larger, from 1019 to 1020 particles per liter.

The retention of nanoparticles from concentrated dispersions in sedimentary rocks has recently been investigated by Rodriguez et al. (2009). In their experiments, Rodriguez et al. injected surface-coated nanoparticles (same surface coating as the experiments in this work) into 3-inch long by 1-inch diameter cores of Texas Cream limestone, Berea sandstone, and Boise sandstone. The retention they observed after post-flushing the cores ranged from 12% (in Boise sandstone) down to as little as 2% (in Texas Cream), with a mean of approximately 9%. These modest retentions are encouraging for oil recovery applications, but a more systematic approach to characterize retention in terms of varying parameters is needed, which is carried out here.

The approach adopted in this work is to construct long columns of unconsolidated material obtained from crushing samples of sedimentary rock. The primary motivation for using sandpacks is that transport across large distances (tens of feet) can be studied in the laboratory. Using sandpacks instead of cores also makes it convenient to investigate the effects of certain parameters on retention. For example, choosing different grain sizes for packing causes the specific surface area of the sandpack to vary without changing other parameters. Changing the amount of clay in the packing varies the lithology and the specific surface area.

The main disadvantage of a sandpack is that it is no longer an intact piece of rock. By crushing the grains and rearranging them in an unconsolidated manner, the pore geometry and grain surface area are drastically different than in the original rock. Thus the experiments reported here are not intended to predict field performance. Instead we seek insight into the mechanisms that cause or mitigate retention.

Experiments on Retention of Silica Nanoparticles Materials.

Surface-Treated Nanoparticles. Silica nanoparticles with a polyethylene glycol (PEG) coating were received from 3M (St. Paul, MN) as 23.04 wt% aqueous dispersions, and diluted to the desired concentration. Most experiments were performed injecting 5 wt% aqueous nanoparticle dispersion (1019 particles per liter); although in some initial experiments 18.64 wt% nanoparticle dispersion (1020 particles per liter) was injected. The silica core is 5 nm in diameter and the PEG coating brings the particle to a nominal 10 nm diameter. The PEG has about 7 EG units and is covalently attached to the silica core via Si-O-Si bonds.

Uncoated nanoparticles. NexSil silica nanoparticles with no surface-coating were received from Nyacol (Ashland, MA) in 30 wt% or 40 wt% dispersions. Four dispersions (NexSil 8, 12, 20, and 20K) were received; the numeral in the name of the dispersion indicates the size of the silica core (for example, 8 nm diameter for NexSil 8). These particles are charge-stabilized with sodium ions (except for NexSil 20K, stabilized with potassium) and tend to aggregate and gel in saline (NaCl) environments. Thus, they were diluted with de-ionized water (DI) to reach the desired concentration. The pre- and post-flush for the transport experiments with these non-coated particles were also done with DI water to avoid any contact with salt.

Laboratory grade sodium chloride (NaCl, Fisher Scientific) and calcium chloride (CaCl2, Fisher Scientific, Pittsburg, PA) were used to obtain the desired mixture salinity, as well as de-ionized water (filtered with NANOpure water systems, Apple Scientific, Chesterland, OH). Two different brines were prepared, 3 wt% NaCl and API brine (8 wt% NaCl and 2 wt% CaCl2). Boise sandstone and Texas Cream limestone were received in large blocks. Rock fragments left over from the drilling of cores were crushed and sieved to separate the different grain sizes. Two clay types were used: kaolinite and illite (Green

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Shale) from Ward’s Scientific (Rochester, NY). Kaolinite was used as received in powdered form and illite was received in rock fragments, which we crushed and sieved to 75–105 µm diameter.

The surface area of different sand and sand/clay mixtures was measured via Brunauer–Emmett–Teller (BET) adsorption with a Quantichrome Instruments (Boynton Beach, FL) Nova 2000 series surface area analyzer using nitrogen as the adsorbate gas. Seven points were taken over a range of relative pressures from 0.05 to 0.35. In all cases, correlation coefficients were greater than 0.99, indicating good linear fit with the BET equation; the results are shown in Table 1. Adding any amount of clay significantly increases the specific surface area. Sand/illite mixtures have smaller specific surface area than that sand/kaolinite because the illite particle size is larger (it was crushed and sieved whereas the kaolinite was received in powdered form.) The surface area for sandpacks constructed from these materials was calculated as the product of mass of sand packed (from the weights pre- and post-packing) and the corresponding specific surface area in Table 1. For Boise sandstone with grain sizes other than the ones measured, the value for the BET surface area was interpolated as a function of the inverse of the average grain size.

Experimental Method. The apparatus used was designed specifically for experiments that allow long-distance (up to a few meters) transport. It can handle different sandpack lengths, from 0.3 m to ~10 m, scale; the longest column reported here was 3 m. The steel tubing was cut to the desired length and coiled to fit conveniently within the oven. The sand was dry-packed into the tubing using a combination of air pressure, gravity, and vibration. Figure 1 shows a schematic of the apparatus.

The pump used is a Beckman 100-A HPLC pump capable of injecting 10 ± 0.01 ml/min. Brine solution was injected directly into the sandpack. Pumping brine into an accumulator previously filled with nanoparticle dispersion displaced the internal piston, thereby pushing the dispersion through the sandpack. This keeps nanoparticles from contacting internal surfaces of the pump, which would damage the pistons. The pressure drop across the sandpack was measured using Validyne pressure transducers (Northridge, CA) calibrated for different pressure ranges, depending on the experiment. The effluent is collected in discrete samples (typically 2-4 mL) using a Teledyne (Thousand Oaks, CA) ISCO RETRIEVER 500 fraction collector.

The pressure measurements were used to calculate the apparent viscosity. This requires first injecting water through the sandpack to establish a baseline pressure drop for the water viscosity of 1cp. Pressure drops measured during the nanoparticle injection are normalized by the baseline pressure drop.

The apparatus was modified to add the capacity to perform transport experiments at elevated temperatures. The elements inside the dashed rectangle in Fig. 1 were placed inside a Blue M oven (Thermal Product Solutions, White Deer, PA). A Newport (Irvine, CA) temperature measurement device was plumbed into the flow lines; this device measures the temperature of the fluids just before entering the sandpack. To heat the fluids before they enter the oven, the inlet flow line was coiled and placed inside a water bath (Julabo, Vista, CA).

Before each nanoparticle transport experiment a passive tracer was injected in the sandpack. This consisted of a 2 PV slug of different salinity brine. Effluent salinity was measured using an Orion 3-Star conductivity probe (Thermo Fisher Scientific, Waltham, MA). These data were fitted to the classical convection-dispersion equation to calculate the dispersivity and Peclet number for the sandpack. After the tracer injection, the sandpack is flushed to the desired initial brine concentration, typically the same value as the salinity of the nanoparticle dispersion. A slug of nanoparticle dispersion is injected into the sandpack while collecting samples of the effluent with the fraction collector (part e in Fig. 1).

To determine the nanoparticle concentration in the effluent, the refractive index (RI) of the effluent samples is measured using a Leica Mark II Plus Refractometer (Reichert, Depew, NY). The relationship between nanoparticle concentration and refractive index was found to be linear for the range used. The nanoparticle concentrations are converted to dimensionless form using the following equation:

0

0

NP

DI

C CC

C C (1)

The nanoparticle injection is followed by a post flush using the same brine composition that originally saturated the sandpack. The nanoparticle concentration in the effluent is measured until it becomes undetectable by RI. This usually happened at approximately 1-2 PVs flushed, at concentrations below about 0.1 wt%. All post flushes were carried out for at least six pore volumes to ensure that we have reached effluent nanoparticle concentration too dilute to measure (earlier experiments have longer post flushes, up to 22 PVs).

Using trapezoid rule, the area under the dimensionless nanoparticle concentration vs. pore volume injected curve was calculated. This area is the total mass of nanoparticles eluted from the sandpack. We divide this quantity by the total pore volumes of injected nanoparticle dispersion. This is the fraction RNP of injected nanoparticles that are recovered from the column. The fraction of injected nanoparticles that remain in the column, 1 RNP, is deemed the fraction of (permanently) retained particles. It is possible that nanoparticles were still being eluted at small concentrations when the postflush was halted. Thus the nanoparticle recovery reported here is a lower bound of the actual recovery, and the reported degree of nanoparticle retention is an upper bound.

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Experimental Results A summary of the theoretical parameters investigated and the experimental conditions that affect them are shown in Table 2. Each subsection below will refer to these parameters for the runs being analyzed as well as to nanoparticle effluent concentration comparison plots in Figs. 2-6 and 9-12 (each plot shows the effluent nanoparticle concentration in blue and the expected effluent for a conservative tracer in red). Of the 54 experiments run to date (Caldelas, 2010), 29 are presented in this paper; their experimental conditions and retention results can be found in Table 3.

Effect of Grain Size.

Surface Treated Particles. Experiments 24 and 25 both utilize a 1 ft long sandpack with a flowrate of 1 ml/min 3M coated particles but have a sand grain size of 63–75 µm and 297–420 µm, respectively. Using the larger grains results in a 30% reduction in specific surface area (surface area per unit bulk volume, S/V), from 21400 cm-1 to 15000 cm-1. Figure 2 shows the effluent nanoparticle concentration for both runs. The nanoparticle recovery (RNP) increases from 92% to 96% with the increase in grain size (decrease in solid specific surface area). This suggests that retention is cut in half (from 8% to 4%) for this 30% decrease in S/V. Although not a one-to-one linear relationship (i.e. doubling the surface area does not double the retention), this trend follows the expected behavior that more surface area gives more sites at which particles can be attached and retained.

Uncoated Particles. Experiments analogous to Exp24 and Exp25 in the previous section were performed injecting NexSil 20 nanoparticles. In this case, the grains used were 150–177 µm and 420–590 µm, for Experiments 28 and 29 respectively. This produces a 21% reduction in surface area per unit volume, from 17100 cm-1 to 13500 cm-1. The trend observed is similar to that of the surface coated nanoparticles, see Fig. 3 for effluent concentration histories. The value of RNP goes up from 91% to 96% with the increased surface area, as observed in coated nanoparticles.

Effect of Lithology.

Clay Content. The large recoveries (over 90%) for transport through crushed Boise sandstone, which is very clean, motivate the question of whether larger changes in surface area would affect retention similarly. In this series of experiments we added clay to the sandpack to increase specific surface area. This method enables larger specific surface areas than are feasible using sieved sand. Because the attraction between the grain surfaces and the nanoparticles used here is primarily due to van der Waals forces, the chemical difference between sand grains and clay grains is not expected to be important. In Experiments 31, 32, and 36, different amounts of kaolinite powder were mixed with 250–297 µm Boise grains. Since sandpacks with clay require salinity in the brine to avoid swelling, the uncoated particles could not be used in these experiments. Three runs were performed with clay content from 5 wt% to 25 wt% (Experiments 31, 32, and 36). Experiment 25 is also included in this section as a reference for the retention in the absence of kaolinite. For this set of experiments, the column length was 1 ft and the flowrate 1 ml/min. Figure 4 shows the effluent concentration histories for these four runs. Experiments 25, 31, 32, and 36 display a clear trend of the nanoparticle recovery decrease as specific surface area increases.

Clay Types. Experiment 43 was performed with 10 wt% illite at the same conditions as the 10 wt% kaolinite run (experiment 32), namely 1 ft length and 1 ml/min flowrate. The illite experiment exhibited 84% nanoparticle recovery, compared to 85% for a 10 wt% kaolinite sandpack under the same operating conditions. The effluent nanoparticle concentration histories for both runs are presented in Fig. 5. Compared to the kaolinite/sandpack, the effluent concentration for the illite/sandpack increases more slowly, reaches approximately the same plateau concentration, and elutes from the sandpack with a longer tail. Such differences in effluent characteristics suggest that the difference in the surface chemistry of kaolinite and illite has some effect on nanoparticle adhesion and detachment from the grains, though the ultimate retention is similar.

Texas Cream Limestone. A set of experiments was performed with Texas Cream limestone and surface coated nanoparticles. The packing was prepared in the same way as the sandstone experiments, crushing the rock and sieving “grains” by their sizes. Because the limestone does not have an intrinsic individual grain size, the grain size fraction chosen for packing is essentially arbitrary. Transport in un-crushed Texas Cream cores has been analyzed by Rodriguez et al. (2009).

Packs with Texas Cream limestone also present a different set of challenges. Most of the crushed rock turns into very small fragments. These cannot be used because when flooded they aggregate and result in extremely low permeabilities. Thus, a representative experiment was performed for each length (1, 3, and 9 ft) with large grain sizes only (>177 µm). The effluent concentrations for the 1, 3, and 9 ft Texas Cream experiments are found in Fig. 6. Experiment 25 with crushed Boise sandstone is included for comparison.

The recoveries are generally consistent with observations for sandstone packs. The smaller specific surface area in the limestone pack would be expected to result in smaller retention. However, comparing Experiments 46 and 25, which apart from grain composition have similar experimental parameters, a larger fraction of the nanoparticles are retained in the limestone pack (10%) than expected (4%). However, this difference is comparable to the differences in recovery between sandpacks with similar specific surface areas, so it cannot be conclusively attributed to the difference in lithology. Figure 7 illustrates this point with a plot of specific surface area vs. nanoparticle retention plot for all experiments.

The 3 ft limestone pack shows anomalously small recovery, the consequence of very early arrival of the post-flush, Fig. 6c. The reason for this early arrival is unclear, and other features of the effluent history (arrival of leading edge of

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nanoparticle bank, plateau concentration, tail in the trailing edge of the nanoparticle bank) are similar to those in the other limestone packs, Figs. 6b and 6d.

Specific Surface Area Comparison. Retentions observed for four different lithologies (Boise sandstone, Texas Cream limestone, kaolinite clay, and illite clay) and a range of specific surface areas, for surface treated and uncoated nanoparticles, are summarized in Fig. 8. All the experiments in Fig. 8 were run for a 1 ft column at 1 ml/min, so this dataset should isolate the effects of surface area and lithology. Some variation in the value of recovery is observed for experiments with similar surface areas. As suggested by Fig. 7, this variation is consistent with retention being controlled by the balance between van der Waals attraction, Brownian motion, and hydrodynamic drag. Thus we interpret Fig. 8 to show that the specific surface area has a first-order influence on the retention of nanoparticles during transport, and that grain lithology has relatively modest influence.

Effect of Brine Salinity. To test the effect of salinity on nanoparticle retention, two brine compositions were prepared: 3 wt% NaCl, and “API brine” (8 wt% NaCl and 2 wt% CaCl2). As in all experiments reported here, both the pre-flush and post-flush use the same brine composition (either 3 wt% NaCl or API brine). Appropriate amounts of the two salts are added to the nanoparticle dispersion to match the salinity of the pre- and post-flushes. Thus the salinity is constant throughout the experiment. This ensures that the variations in refractive index in the effluent samples are due only to variations in nanoparticle concentration. Because the uncoated particles aggregate at these salinities, these runs were carried out only with the surface-treated particles.

Experiments 45 and 48 (API brine) are analogous to Experiments 25 (3% NaCl, sand without clay) and 32 (3% NaCl, sand with clay) respectively. Nanoparticle effluent concentration histories for all four experiments are shown in Fig. 9. Salinity has a noticeable influence on the adsorption/desorption rates (compare shapes of the effluent histories) and on the retention of the nanoparticles (smaller recovery at larger salinity). In API brine, the recovery was almost ten percentage points smaller than in 3 wt% NaCl brine (compare Fig. 9c to Fig. 9a and Fig. 9d to Fig. 9b). The greater salinity caused the retention to almost double in the columns containing kaolinite, and to triple in the columns containing only sand. In API brine the arrival of particles in the effluent was slightly delayed relative to 3 wt% NaCl. This suggests that even though the particles are surface coated, making them uncharged, their transport is still affected by changes in the ionic strength of the medium. There was no visible change in the stability of the nanoparticle dispersion when salt was added. Nevertheless the smaller repulsion between nanoparticles in the more saline medium may have enhanced the attraction between particles and grain surfaces.

Effect of Velocity, Residence Time, and Length. These three experimental parameters are coupled for any column flood such that we cannot keep two constant and modify the third independently. Thus, we systematically chose experimental conditions that would keep constant one parameter and the ratio of the other two (for example, increasing length and flow rate threefold keeps their ratio as well as residence time constant). A detailed discussion of the experiment matrices is presented in Caldelas (2010). The points at ~15000 cm in Fig. 7 are the experiments performed for this section. The variation of the range of recoveries (87–98 %, with an average of 94 %) is consistent with retention being controlled by van der Waals attraction (particle attachment) and Brownian motion and hydrodynamic drag (particle detachment). Under the range of conditions studied here, these forces are apparently almost balanced, and small deviations upset the balance and lead to a few percentage points change in recovery.

Effect of Length. One of the main objectives of this study was to determine whether the transport of nanoparticles at the field scale was feasible. Previous work on nanoparticle retention (Rodriguez et al., 2009) has shown large recoveries for 3-inch cores. In this set of experiments we increased the length to 9 ft sandpacks with both Boise sandstone and Texas Cream limestone (experiments 50 and 51 respectively, see Fig. 10). All of these experiments were performed with API brine, to simulate high salinity environment that can be encountered in the field. An experiment with a 15 ft long column yielded effluent concentration history similar to that in Fig. 11. But pump malfunctions during this experiment introduced large uncertainty in the volume of nanoparticle dispersion injected (and thus in the nanoparticle recovery), so we do not consider this experiment further.

The nanoparticle effluent concentration histories from these experiments are qualitatively very similar to experiments done in 3 ft and 1 ft columns. The nanoparticle recoveries on these runs are also similar to those observed in columns with similar specific surface areas. The nanoparticle arrival is noticeably delayed for these experiments, and in API brine experiments in a shorter column (discussed in "Effect of brine salinity" section above; cf. Fig. 9). The delay is larger for the Boise sandpacks than for the limestone case.

Another experiment was designed to assess a more likely scenario in a field application, namely, the volume of nanoparticles injected is a fraction of the column pore volume. Experiment 53, a 9 ft Boise sandpack, was conducted injecting a small nanoparticle slug (0.26 PV, whereas ~3 PV are injected in most of these experiments). All other parameters are similar to experiment 50 shown above, except the sand grains were slightly larger (297–420 µm vs. 210–250 µm in Experiment 50). The effluent tracer concentration plot in Fig. 11 shows that hydrodynamic dispersion for this sandpack was small (NPe = 800) allowing the tracer slug effluent to almost reach its injected concentration. The nanoparticle effluent on the

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other hand has a significantly delayed arrival and the maximum concentration reached is less than 0.5 times the injected concentration. The delay in arrival can be attributed in part to the large brine salinity, as with previous observations (Experiments 45 and 48, cf. Fig. 9). The larger retention of nanoparticles in a smaller slug is also consistent with the idea that sandpack grains have a finite number density of surface sites to which nanoparticles can be strongly attached.

We can test this idea as follows. In Experiment 53, 27 ml of nanoparticle dispersion was injected, 50% of the nanoparticles were recovered, and the sandpack surface area was 380 m2. In Experiment 45, the corresponding values were 35.5 ml of nanoparticle dispersion, 89 % recovered and 43 m2. In Experiment 50, the corresponding values were 300 ml, 92

% and 410 m2. From these data we can compute the nanoparticle retention per unit surface area ( R̂ ) for each sandpack as

1ˆ NP NP NP

total

V C RR

S (2)

yielding:

245

250

253

ˆ 4.91 /

ˆ 2.77 /

ˆ 1.63 /

R mg m

R mg m

R mg m

Thus the specific retention is similar magnitude in each experiment. Moreover these values are much smaller than would be observed if the nanoparticles were closely packed on the grain surfaces. This suggests that the number density of “permanent adsorption sites” is not large. Further, once enough nanoparticles have been injected to satisfy the requirement of these sites, subsequently injected nanoparticles will migrate unhindered. The large retention in Experiment 53 is the result of injecting fewer nanoparticles, and the small retentions in the other experiments are the consequence of injecting many more nanoparticles than needed to satisfy the "permanent adsorption capacity."

Effect of Temperature. Two experiments (52 and 54) were performed at elevated temperatures, ~55°C (131°F) and ~80°C (176°F) respectively, compared to ~21°C (70°F) for all other runs. The effluent concentration histories can be found in Fig. 12. For comparison, experiment 45 is included in Fig. 12 as the closest representative of ambient temperature runs, having all parameters the same as Experiments 52 and 54 except for grain size.

Temperature scarcely affects retention in this set of experiments. The recovery was maintained at 89% for the ambient and 55°C experiments and decreased slightly to 87% for 80°C. This would be consistent with the weak temperature dependence for nanoparticle attachment (by means of van der Waals forces) and detachment (by means of Brownian motion). The delay in nanoparticle arrival in the effluent (relative to the tracer) can be attributed to the API brine used in pre- and post-flushing for all these runs.

Conclusions Single-phase flow of concentrated aqueous dispersions of nanoparticles through long columns packed with mixtures of crushed sedimentary rock and clay show that transport across distances of several meters is possible. For suitably engineered nanoparticles (surface treated or uncoated), retention by the porous medium is consistent with competition between adsorption (van der Waals attraction between nanoparticle and solid grain surface) and desorption (Brownian motion and hydrodynamic drag). For the range of conditions studied here, the competition is nearly balanced, so that some variability in retention (a range of about ten percentage points) is observed among experiments with similar conditions. Also consistent with van der Waals attraction, the specific surface area of the porous medium has the greatest systematic effect on retention, and the composition of the grains—quartz, limestone, clay—has only a secondary effect.

Increased salinity (up to 10 wt%, with monovalent (Na+) and divalent (Ca2+) cations) in the pre-flush and post-flush brines as well as in the nanoparticle dispersion increases the nanoparticle retention seven to eight percentage points. A considerable delay in nanoparticle arrival was also observed with increase in salinity. Increased temperature scarcely changed the retention in a series of experiments for which only temperature was varied. Injection of a small slug of nanoparticle dispersion showed a disproportionately larger retention (as a fraction of injected nanoparticles). But experiments with widely differing volumes of injected dispersion still showed similar concentrations (mass per unit surface area) of retained nanoparticles. This behavior suggests that the columns of unconsolidated grains have a small number of sites at which nanoparticles are strongly adsorbed, and that once these sites are occupied, other nanoparticles are transported with minimal retention.

Nomenclature

A = Sandpack cross-sectional area, cm2 ABET = Sample surface area measured with BET, m2/g

C0 = Initial concentration, wt% CI = Injected concentration, wt%

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CF = Flush concentration, wt% CNP = Nanoparticle concentration, #/L CD = Dimensionless nanoparticle concentration CDi = Dimensionless nanoparticle concentration of ith sample Dp = Grain diameter, µm ID = Inner diameter of sandpack tube, cm

L = Sandpack length, ft NPe = Peclet number, dimensionless PVI = Total pore volumes injected PVIi = Pore volumes injected at ith sample

q = Volumetric flow rate, cc/min r = Grain size radius, µm

rNP = Nanoparticle radius, nm RI0 = Refractive index of flush fluid (CD = 0) RI1 = Refractive index of injected nanoparticle dispersion (CD = 1) RNP = Nanoparticle recovery, % S/V = Surface area per bulk volume, cm-1

S = Total surface area, cm2 T = Temperature, °C

tres = Residence time, min u = Bulk or darcy velocity, ft/day v = Interstitial velocity, ft/day

Vp = Pore volume, cc = Porosity, %

µapp = Apparent viscosity, cp µw = Water viscosity, cp = Kinematic viscosity, m2/s

mix = Nanoparticle dispersion density, g/cc sand = Sand grain density, g/cc NP = Nanoparticle density, g/cc

Acknowledgments Dr. Jim Baran of 3M provided samples of the PEG coated nanoparticles used in this study. Ki Youl Yoon of the Chemical Engineering department of The University of Texas at Austin carried out the BET measurements. We would like to thank the Advanced Energy Consortium (BP, Baker Hughes, ConocoPhillips, Halliburton, Marathon, Occidental, Petrobras, Schlumberger, Shell, and Total) for their support. Acknowledgment is made to the donors of the American Chemical Society Petroleum Research Fund for partial support of this research.

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8 SPE 142305

Prodanovic, M., Ryoo, S., Rahmani, A. R., Kuranov, R., Kotsmar, C., Milner, T. E., Johnston, K. E., Bryant, S. L., and Huh, C., “Effects of Magnetic Field on the Motion of Multiphase Fluids Containing Paramagnetic Particles in Porous Media”, SPE 129850 presented at the 2010 SPE Improved Oil Recovery Symposium, Tulsa, OK, 24-28 April 2010.

Rodriguez, E., Roberts, M. R., Yu, H., Huh, C., and Bryant, S. L., “Enhanced Migration of Surface-Treated Nanoparticles in Sedimentary Rocks”, SPE 124418 presented at the 2009 SPE Annual Technical Conference and Exhibition, New Orleans, LA, 4-7 October 2009.

Sensoy, T., Chenevert, M. E., and Sharma, M. M., “Minimizing Water Invasion in Shale Using Nanoparticles”, SPE 124429 presented at the 2009 SPE Annual Technical Conference and Exhibition, New Orleans, LA, 4-7 October 2009.

Yu, H., Kotsmar, C., Yoon, K. Y., Ingram, D. R., Johnston, K. P., Bryant, S. L., and Huh, C., “Transport and Retention of Aqueous Dispersions of Paramagnetic Nanoparticles in Reservoir Rocks”, SPE 129887 presented at the 2010 SPE Improved Oil Recovery Symposium, Tulsa, OK, 24-28 April 2010.

Zhang, T., Davidson, A., Bryant, S. L., and Huh, C., “Nanoparticle-Stabilized Emulsions for Applications in Enhanced Oil Recovery”, SPE 129885 presented at the 2010 SPE Improved Oil Recovery Symposium, Tulsa, OK, 24-28 April 2010.

Table 1—BET Measurements of Surface Area

Sample Grain size fraction / type Added Clay

ABET (m2/g)

63–75 µm Boise - 1.67 297–420 µm Boise - 1.10

297–420 µm Texas Cream - 0.74 250–297 µm Boise 5 wt% Kaolinite 2.66 250–297 µm Boise 10 wt% Kaolinite 3.91 250–297 µm Boise 25 wt% Kaolinite 9.13 250–297 µm Boise 10 wt% Illite 2.89

Table 2—Parameters Theoretically Expected to Affect Nanoparticle Retention in a Sandpack

Theoretical Parameter Section Title Subsection Experimental Parameter(s) Experiments (see Table 3) Surface coated particles Grain Size 24 and 25 Grain Size

Non-surface coated particles Grain Size 28 and 29 Clay content 25, 31, 32, 36, 37, and 38

Clay Clay type 32 and 43

Specific Surface Area Lithology

Texas Cream Limestone Lithology 25, 46, 47, and 51 Salinity Salinity - Salt Concentration 25, 45, 32, and 48

Surface coated particles 25, 27, 30, and 39 With added clay 32, 41, and 42

Velocity, residence time, length

Coupled Parameters

Non-surface coated particles Injection flow rate, length

33, 34, and 35

Length Effect of

length - Length, slug size 50, 51, and 53

Temperature Effect of

Temperature -

Oven and water bath temperature

52 and 54

SPE 142305 9

Table 3a—Summary of Experimental Parameters for Runs 24-30

Exp 24 25 26 27 28 29 30

L (ft) 1 1 1 1 1 1 3

Vp (cc) 12.7 11.7 12.2 11.4 11.6 12.7 33.6 Pack

Φ 45% 38% 40% 37% 38% 42% 37%

Sand Type Boise Boise Boise Boise Boise Boise Boise

Dp (µm) 63-75 297-420 150-177 297-420 150-177 420-590 297-420

S/V (1/cm) 21435 15016 17525 15132 17082 13466 15755 Sand

S (cm2) 6.1E+05 4.3E+05 5.0E+05 4.3E+05 4.9E+05 3.8E+05 1.3E+06

q (cc/min) 1 1 1 3 1 1 3

v (ft/day) 113 131 126 404 132 121 409

Npe N/A 119 107 85 111 124 185

tres (min) 12.7 11.7 12.2 3.8 11.6 12.7 11.2

T (°C) Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22

C0 3 wt% brine 3 wt% brine DI 3 wt% brine DI DI 3 wt% brine

PVI (PVs) 3 3 3.279 3 3 3.0433 3

CI 5.00% 5.00% 40.00% 5.00% 5.00% 5.00% 5.00%

Nanoparticle 5nm PEG Si 5nm PEG Si NexSil 20 5nm PEG Si NexSil 20 NexSil 20 5nm PEG Si

Flush (PVs) 6 6 6 6 6 6 6

CF 3 wt% brine 3 wt% brine DI 3 wt% brine DI DI 3 wt% brine

Run

Recovery 92% 96% 93% 91% 91% 96% 98%

Table 3b—Summary of Experimental Parameters for Runs 31-39

Exp 31 32 33 34 35 36 39

L (ft) 1 1 1 1 3 1 3

Vp (cc) 10.4 10.9 12 12.3 34.4 11.2 33.7 Pack

Φ 34% 36% 39% 40% 41% 39% 40%

Sand Type Boise +5%Kao Boise

+10%Kao Boise Boise Boise

Boise +25%Kao

Boise

Dp (µm) 250-297 250-297 297-420 297-420 297-420 250-297 297-420

S/V (1/cm) 37721 57768 14669 14823 15361 142818 15703 Sand

S (cm2) 1.1E+06 1.6E+06 4.2E+05 4.2E+05 1.3E+06 4.1E+06 1.3E+06

q (cc/min) 1 1 1 3 3 1 1

v (ft/day) 148 141 128 375 367 128 125

Npe 12 14 85 107 310 4 162

tres (min) 10.4 10.9 12.0 4.1 11.5 11.2 33.7

T (°C) Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22

C0 3 wt% brine 3 wt% brine 0.00% 0.00% DI 3 wt% brine 3 wt% brine

PVI (PVs) 3 3 3.125 3 3 3 3

CI 5.00% 5.00% 5.00% 5.00% 5.00% 5.00% 5.00%

Nanoparticle 5nm PEG Si 5nm PEG Si NexSil 20 NexSil 20 NexSil 20 5nm PEG Si 5nm PEG Si

Flush (PVs) 6 6 6 6 6 6 6

CF 3 wt% brine 3 wt% brine DI DI DI 3 wt% brine 3 wt% brine

Run

Recovery 92% 85% 94% 96% 96% 69% 87%

10 SPE 142305

Table 3c—Summary of Experimental Parameters for Runs 40-36

Exp 40 41 42 43 44 45 46

L (ft) 3 1 3 1 1 1 1

Vp (cc) 33.2 12 32.8 10.7 11.8 11.6 12.5 Pack

Φ 40% 42% 39% 38% 42% 41% 44%

Sand Type Boise Boise

+10%Kao Boise

+10%Kao Boise

+10%Illite Boise Boise Texas Cream

Dp (µm) 297-420 250-297 250-297 250-297 297-420 297-420 297-420

S/V (1/cm) 15545 58180 56979 42292 14978 14978 15286 Sand

S (cm2) 1.3E+06 1.7E+06 4.8E+06 1.2E+06 4.3E+05 4.3E+05 4.3E+05

q (cc/min) 1 3 3 1 1 1 1

v (ft/day) 127 358 385 134 121 123 115

Npe 397 11 11 10 55 30 50

tres (min) 33.2 4.0 10.9 10.7 11.8 11.6 12.5

T (°C) Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22

C0 DI 3 wt% brine 3 wt% brine 3 wt% brine API Brine API Brine 3 wt% brine

PVI (PVs) 3 3 3 3.15 2.5 3.06 3.05

CI 5.00% 5.00% 5.00% 5.00% 4.56% 4.56% 5.00%

Nanoparticle NexSil 20 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si

Flush (PVs) 6 6 6 6 6 6 6

CF DI 3 wt% brine 3 wt% brine 3 wt% brine API Brine API Brine 3 wt% brine

Run

Recovery 98% 89% 86% 84% 108% 87% 90%

Table 3d—Summary of Experimental Parameters for Runs 47-54

Exp 47 48 50 51 52 53 54

L (ft) 3 3 1 9 1 9 1

Vp (cc) 36.3 36.3 10.8 103.5 13 103.6 13.2 Pack

Φ 43% 43% 35% 44% 46% 38% 43%

Sand Type Texas Cream Boise

+10%Kao Boise TX Cream Boise Boise Boise

Dp (µm) 250-297 210-250 210-250 177-297 105-125 297-420 105-125

S/V (1/cm) 15004 56805 17283 18718 17729 16120 17777 Sand

S (cm2) 1.3E+06 1.6E+06 4.1E+06 4.4E+06 5.0E+05 3.8E+06 5.0E+05

q (cc/min) 1 1 3 3 1 3 1

v (ft/day) 116 142 348 346 110 401 116

Npe 70 10 300 50 20 800 70

tres (min) 36.3 10.8 34.3 34.5 13.0 34.5 13.2

T (°C) Amb ~ 22 Amb ~ 22 Amb ~ 22 Amb ~ 22 ~56 Amb ~ 22 ~80

C0 3 wt% brine API Brine API Brine API Brine API Brine API Brine API Brine

PVI (PVs) 3.03 3.00 2.93 2.43 3.38 0.23 3.12

CI 5.00% 4.56% 4.56% 4.56% 4.56% 4.56% 4.56%

Nanoparticle 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si 5nm PEG Si

Flush (PVs) 6 6 6 6 6 6 6

CF 3 wt% brine API Brine API Brine API Brine API Brine API Brine API Brine

Run

Recovery 85% 77% 92% 94% 89% 50% 87%

SPE 142305 11

a

b

c

d

e

f

g

h

Fig. 1—Apparatus schematic: (a) pump, (b) accumulator, (c) pressure transducers, (d) slim tube sandpack, (e) fraction collector, (f) oven, (g) injection temperature measurement, and (h) pre-injection water bath. Pink regions are temperature-controlled.

0

0.2

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1

0 1 2 3 4 5 6

Dim

enti

on

les

s C

on

ce

ntr

ati

on

, CD

Pore Volumes Injected, PV

RNP = 92%

(a)

0

0.2

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0.8

1

0 1 2 3 4 5 6

Dim

en

tio

nle

ss

Co

nc

en

tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 96%

(b) Fig. 2—Effluent concentration of 3M PEG nanoparticles from columns of sand grains of different size ranges: (a) Exp24: 63–75 µm and (b) Exp25: 297–420 µm.

12 SPE 142305

0

0.2

0.4

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1

0 1 2 3 4 5 6

Dim

en

tio

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Co

nc

en

tra

tio

n, C

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Pore Volumes Injected, PV

RNP = 91%

(a)

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0.2

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Dim

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tio

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Co

nc

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tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 96%

(b) Fig. 3—Effluent concentration of NexSil 20 nanoparticles from columns of sand grains of different size ranges: (a) Exp28: 150–177 µm, (b) Exp29: 420–590 µm.

0

0.2

0.4

0.6

0.8

1

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Dim

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tio

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nc

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Pore Volumes Injected, PV

RNP = 96%

(a)

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Dim

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Pore Volumes Injected, PV

RNP = 92%

(b)

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Dim

en

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Co

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Pore Volumes Injected, PV

RNP = 85%

(c)

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1

0 1 2 3 4 5 6

Dim

en

tio

nle

ss

Co

nc

en

tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 69%

(d) Fig. 4—Effluent concentration of 3M PEG nanoparticles from columns containing different amounts of kaolinite: (a) Exp25: No kaolinite, (b) Exp31: 5 wt% kaolinite, (c) Exp32: 10 wt% kaolinite, (d) Exp36: 25 wt% kaolinite.

SPE 142305 13

0

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Dim

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Co

nc

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Pore Volumes Injected, PV

RNP = 85%

(a)

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0.6

0.8

1

0 1 2 3 4 5 6

Dim

en

tio

nle

ss

Co

nc

en

tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 84%

(b) Fig. 5—Effluent concentration of 3M PEG particles in columns containing clay: (a) Exp32: 10 wt% kaolinite and (b) Exp43: 10 wt% illite.

0

0.2

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Dim

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Pore Volumes Injected, PV

RNP = 90%

(a)

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Dim

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Pore Volumes Injected, PV

RNP = 96%

(b)

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Dim

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Co

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Pore Volumes Injected, PV

RNP = 85%

(c)

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Dim

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tio

nle

ss

Co

nc

en

tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 94%

(d) Fig. 6—Effluent concentration of 3M PEG particle from limestone columns: (a) Exp46: 1ft Texas Cream, (b) Exp 25: 1 ft Boise SS, (c) Exp47: 3 ft Texas Cream, and (d) Exp51: 9 ft Texas Cream.

14 SPE 142305

60%

65%

70%

75%

80%

85%

90%

95%

100%

10000 12000 14000 16000 18000 20000 22000

Nan

oparticle Recovery R

NP (%

)

Specific Surface Area  S/V (1/cm) Fig. 7—Specific surface area vs. nanoparticle recovery for all experiments without added clay (see Table 3; Experiment 54 is not included). Large variations in recovery are observed within a narrow range of specific surface areas, suggesting that the experimental conditions nearly balance the rather weak forces of nanoparticle attachment and detachment to grain surfaces.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 20000 40000 60000 80000 100000 120000 140000 160000

Nanoparticle Recovery, RNP(%

)

Surface area per unit volume measured by BET,  (S/V)BET (1/cm)

KaoliniteIlliteBoise SSTx CreamBoise w/ NexSil

Fig. 8—Comparison of retention of uncoated NexSil particles (blue squares) and surface-coated 3M particles (other symbols) in columns of varying lithologies and specific surface areas for a fixed flow rate (q = 1 ml/min) and column length (L = 1 ft).

SPE 142305 15

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Pore Volumes Injected, PV

RNP = 96%

(a)

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Pore Volumes Injected, PV

RNP = 89%

(b)

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Dim

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Pore Volumes Injected, PV

RNP = 85%

(c)

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Dim

enti

on

les

s C

on

ce

ntr

ati

on

, CD

Pore Volumes Injected, PV

RNP = 77%

(d) Fig. 9—Effluent concentration of 3M PEG nanoparticles for column floods at different salinities: (a) Exp25: 3 wt% brine-No clay, (b) Exp45: API brine-No clay, (c) Exp32: 3 wt% brine-10 wt% kaolinite, and (d) Exp48: API brine-10 wt% kaolinite.

0

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Dim

entio

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Pore Volumes Injected, PV

RNP = 92%

(a)

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0.2

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Dim

en

tio

nle

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Co

nc

en

tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 94%

(b) Fig. 10—Effluent concentration of 3M PEG particles on 9 ft columns with API brine: (a) Exp50: Boise SS and (b) Exp51: Texas Cream.

16 SPE 142305

0

0.2

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1

0 1 2 3

Dim

enti

on

les

s C

on

cen

tra

tion

, CD

Pore Volumes Injected, PV

RNP = 50%

Fig. 11—Effluent concentration of 3M PEG particles for a slug injection (0.26 PV): Experiment 53.

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Pore Volumes Injected, PV

RNP = 89%

(a)

0.00

0.33

0.67

1.00

1.33

1.67

0

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Ap

pa

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isc

osi

ty, µ

app

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)

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enti

on

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s C

on

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on

, CD

Pore Volumes Injected, PV

RNP = 89%

(b)

0.00

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0.67

1.00

1.33

1.67

0

0.2

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Ap

pa

ren

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isco

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y, µ

app

(cp

)

Dim

en

tio

nle

ss

Co

nc

en

tra

tio

n, C

D

Pore Volumes Injected, PV

RNP = 87%

(c)

Fig. 12—Effluent concentration for 3M PEG particles for different temperatures: (a) Exp 45: 21°C (Ambient), (b) Exp52: 55°C, and (c) Exp54: 80°C.