1. Introduction
Due to the trapping effect caused by the dominance of capillary forces over viscous forces, roughly 60–70% of oil is still left behind after primary and secondary recovery operations [
1,
2]. Enhanced oil recovery (EOR) technologies are commonly used to boost oil production by efficiently increasing sweep efficiency and thus the recovery factor.
For light oils in carbonate deposits, gas injection has been frequently employed as an EOR approach [
3,
4,
5]. Oil recovery in depleted formations is often enhanced when the injected gas is miscible with the reservoir oil. When part of the gas components condense into the reservoir oil, this causes swelling and lowers oil viscosity [
4]. As a result, the subsequent phase pushes the oil to the surface, where it can be produced.
Although gas injection is extensively utilised and very efficient, it has some drawbacks that result in gas wastage and poor sweep efficiency [
6]. Gravity override, viscous fingering, reservoir heterogeneities, and injectivity problems are some of these challenges [
7,
8,
9]. These limitations are majorly attributed to the high viscosity and density difference between the injected gas and the reservoir oil. Since gas is less dense and viscous than oil, it rises to the top of the formation, missing significant areas of the reservoir. This is especially challenging in situations where the reservoir has a wide range of porosity and permeability since the displacing fluid will struggle to reach low permeability zones [
10].
By improving the volumetric sweep efficiency and lowering gas mobility, foam increases the efficiency of gas EOR. This has led to the active development and optimisation of foam EOR technology [
11,
12,
13]
Foam, a gas-and-liquid mixture, has been found to improve sweep efficiency during gas injection, increase gas storage in the reservoir and lower the gas-to-oil ratio [
14,
15]. However, due to foam instability and the risk of pore blockage, it is not frequently employed in field conditions. There are many challenges regarding foam generation and stability in reservoir conditions. These include harsh reservoir conditions which may result in poor foam generation and which directly affect foam stability, such as the reservoir temperature, pressure, salinity, and surfactant adsorption onto reservoir rocks. If not appropriately screened and studied, these factors can cause poor foam stability and low oil recovery.
Various studies have demonstrated that using surfactants alone is insufficient for generating stable foams [
16,
17]; hence, nanoparticles have been tested to improve foam stability over time [
18,
19,
20]. When compared to surfactants, the efficiency of nanoparticles in enhancing oil recovery has been linked to their buildup at the gas–liquid interface of foams, which directly enhances stability by minimising liquid–gas contact and by preventing liquid drainage and gas diffusion [
19,
21,
22]. Furthermore, because of their small size (<100 nm), they are less prone to adsorption on rock surfaces and may be efficiently carried through rock pore spaces during the oil displacement process without being confined in the porous medium [
23,
24,
25,
26].
Core flooding tests using foams with and without nanoparticles have recently been conducted in several types of porous media. According to Horozov et al. [
27], hydrophilic nanoparticles maintain foam stability by penetrating the liquid film and by functioning as a single layer of bridge particles. This causes a network to form inside the film, aggregating the particles into a tightly packed double layer, and strengthening the foam lamella. Nonionic surfactants were utilised by Kim et al. [
28] to examine the ability of various
nanoparticles to stabilise the foam. The authors found that since smaller nanoparticles have a greater interface and larger diffusivity, they generate more stable foams.
In Kristiansen et al. [
29], the authors investigated the spreading properties of a foaming composition in Berea sandstone core samples. This effect was demonstrated by changing the oil in the porous media. Their findings demonstrated that the stability of foams was frequently influenced by the spreading properties of the phases in a porous medium. In Zitha et al. [
30], foam generation and its subsequent flow were examined using X-ray computed tomography in a granular porous medium.
An experimental investigation of foam behaviour in Beintheimer sandstone, together with an X-ray CT scanner, was demonstrated in the work of Farajzadeh et al. [
31]. In the presence of an anionic surfactant,
and
gas foaming behaviours were studied. The authors found that the two gases behave differently when foaming in the porous medium. The
foam developed a smaller pressure drop over the core than
foam at both low and high pressures. Additionally, nitrogen foam propagated in a front-like way, but the propagation front of
foam appears finger-like. As a result, ultimate production by nitrogen foam was higher than that of
.
Xu et al. [
13] studied the stabilities of nitrogen- and methane-generated foams with a liquid system that comprises commercial surfactants and nanoparticles. Their recovery factors were then compared through microfluidic studies. The authors revealed that although methane foam had better foamability than nitrogen foam, its stability was poor, as its half-life was 50% less than nitrogen foam. Nevertheless, its viscosity and stability were improved by adding 1 wt.% silica nanoparticles. This was evidenced by a rise in the recovery factor of more than 30%.
The combined effect of hydrophilic nanoparticles and anionic surfactants on foam in Berea sandstone cores was studied in Singh et al. [
32]. Their findings revealed that when the concentration of nanoparticles increased, the mobility reduction factor in the cores also increased. The addition of nanoparticles to the foaming solution resulted in a 10% increase in oil displacement.
In the experiment conducted by Sun et al. [
33], the authors used partially hydrophobic silica nanoparticles to improve foam stability. Although they found that adding nanoparticles to the foaming solution enhanced foam stability, the key mechanisms of stability and interactions between nanoparticles and surfactants were not discussed. Furthermore, the flooding process was carried out at room temperature, which does not indicate actual field conditions because reservoir temperature is a critical parameter that affects foam stability and hence oil recovery. The interaction between nanoparticles and surfactants in the presence of crude oil in a porous medium must be adequately explained to completely understand the flow and displacement process of nanoparticle-stabilised foam. However, previous studies have not thoroughly explored and discussed these issues, indicating a research gap that needs to be investigated.
In order to achieve this goal, we first conducted screening tests in Bello et al. [
34] to determine the optimal types of nanoparticles and surfactants and their concentrations in different salinity and temperature conditions. Therefore, in the present work, we conducted foam stability experiments with
and
foams in the presence and absence of nanoparticles at high pressure and temperature to mimic real oilfield conditions. For this purpose, we used a high-pressure microscope to visualise the foam generation and to investigate the interaction between nanoparticles and surfactants at reservoir conditions. The foam was generated in situ by co-injection with a foam generator built in the lab. The foam was then flowed into a high-pressure microscopy cell at constant pressure and temperature. Bubble size and bubble count were used to assess foam stability. After studying foam stability and its mechanisms, the foam was generated with
, and its ability to increase oil recovery was studied in both artificial and carbonate cores.
3. Results and Discussion
Foam stability under various conditions was analysed by the foam bubble count and average bubble size over a three-hour period in high-pressure microscopy studies. The dynamics of these parameters are presented in the sub-sections that follow.
3.1. Effect of Foam Quality on the Stability of Foams without Nanoparticles
Bubble shape and size are frequently used to characterise and classify foams. Since foam bubbles are not uniform in morphology, their bubble size distribution is commonly used to describe them [
36,
37]. Foam stability is linked to bubble size. Smaller-bubble foams are more stable than larger-bubble foams [
33,
38,
39]. However, more essential is how long they maintain their size and morphology.
In this study,
and
foam were generated at a pressure of 11.7 MPa and at temperatures of 40 and 26 °C, respectively. Moreover, foam qualities of 80% and 50% were used.
Figure 6 shows images of foam bubble evolution over three hours. A graphical representation of the numbers is shown in the
Supplementary Materials.
Foam quality, as reported in other studies [
40,
41], has a direct effect on most foam characteristics. This is because it determines the proportions of the components of the foam.
The graphs presented in the
Supplementary Material support these claims even more. It can be seen that in both
and
foams, 80% foam quality is more stable. At 80% foam quality, the average bubble size is reduced, and the bubble count is increased. We assume that because the gas volume in the foam is smaller in the 50% foam quality, less foam is produced. This results in a lesser number of bubbles, which are completely engulfed by the liquid phase. As a result, the foam lamellae reduces in size, and stability declines as all of the foam bubbles collapse rapidly.
Our findings are consistent with earlier research works. Fu et al. [
42] discovered that when foam quality was increased from 10% to 70%, apparent viscosity increased as well; however, increasing foam quality beyond 70% resulted in a decrease in apparent viscosity. The authors further linked these findings to foam texture and observed that as foam quality increased between 10% and 70%, the generated
foam became finer. They proposed that as foam qualities improve, thus will foam viscosities, and hence foam stability, because the lamellae will provide a better resistance to deformation as the surface tension gradient at the interfaces increases.
3.2. Effect of Nanoparticles on the Stability of Foams at High Pressure
At the parameters previously described, we investigated the effect of nanoparticles on the stability of
and
foam generated at 80% quality. The results can be seen in
Figure 7, which shows a series of images depicting the progression of foam bubble size in the presence and absence of nanoparticles within three hours at a fixed field of view.
Visually, it can be seen that with the addition of nanoparticles, foam bubbles tend to become smaller in size, resulting in an increase in bubble count. To ascertain the numbers underlying the foam bubbles, a quantitative analysis was also performed.
Table 3 and
Table 4 show the average bubble size of foams generated with supercritical
and
estimated with the particle-detecting software embedded in the HPM, while the foam bubble counts are shown in
Table 5 and
Table 6.
The absence of nanoparticles resulted in a lesser bubble count, as shown in the tables above. This can be explained by Ostwald ripening. Due to the pressure differences generated by the Young–Laplace effect [
43], the bubble size increased rapidly over time, and large-sized bubbles merged with neighbouring small bubbles. The pressure within the smaller bubble is higher than the pressure within the larger one, as shown in
Figure 8, and if the diffusing gas is soluble in the liquid phase, gas can be transported from the smaller bubble to the larger one. As a result, the number of large bubbles will continue to increase. When compared to the data collected for foam in the presence of nanoparticles, it can be seen that adding nanoparticles increased the number of bubbles and hence foam stability.
The bubbles were more uniform in the presence of nanoparticles than in the absence of nanoparticles, and the morphology of the bubbles was maintained. This is because
was adsorbed on the bubble surface, and it increased the surface dilational viscoelasticity, which strengthened the foam’s interfacial layer [
44,
45]. Furthermore, the adsorption of
on the bubble surface decreased the surface area accessible for interbubble gas diffusion, which prevents Ostwald ripening [
46].
Nanoparticles do not have the ability to generate foams on their own; however, they can cause an increase in foam volume by forming an extra layer at the gas–liquid interface, which limits the area available for gas diffusion, resulting in the formation of more foam bubbles.
The trends in the graphs in the
Supplementary Material can be used to interpret foam stability. The addition of nanoparticles resulted in an increase in foam bubble count and in a corresponding decrease in average bubble size in all cases. The extent of these changes varies depending on the type of gas used in foaming and the concentration of nanoparticles added. This is why determining the optimal nanoparticle concentration at specific conditions is critical.
In our experiment, it appears that smaller bubbles were produced in the presence of nanoparticles in both nitrogen and
foams. This is due to the nanoparticles’ high adhesion energy at the interface. Nanoparticles can improve foam stability by adsorbing at the interface and as such increase the flow resistance on liquid films and slow liquid drainage [
47,
48]. Binks et al. [
49] claimed that nanoparticle adhesion energy at the interface was hundreds of times more than that of a surfactant molecule.
Nanoparticles in the liquid film form a monolayer structure between the bubbles, slowing drainage by limiting gas mobility and by preventing bubble coalescence. However, foam stability will be reduced at concentrations above the optimum concentration of nanoparticles. This reduction in foam stability is caused by an accumulation of nanoparticles at the foam interface, which can begin to precipitate, thereby lowering the stability.
3.3. Role of the Gas Phase in Foam Generation and Stability
Foams created with gases in their original vapour state and at supercritical conditions are compared in this section. At 26 °C and 3 MPa, both and foams were generated under vapour conditions. Nitrogen foam was produced at 26 °C and 11.7 MPa under supercritical conditions, whereas foam was produced at 40 °C and 11.7 MPa.
It is clear from
Figure 9 that foams generated with vapour gases differ greatly from foams generated with gases at supercritical conditions (
Figure 7). Due to the higher density of the gas at a supercritical state, especially for
, the foam generated was emulsion-like, and hence, more stable foams were generated than when the gas was in the vapour state. Our findings can be supported with the study of Johnston and Shah [
50]. The authors noted that fluids at supercritical temperatures do not exhibit surface tension because they are not in contact with both gas and liquid phases. This absence of gas–liquid contact prevents the attraction of gas molecules towards the interior of the liquid phase [
51,
52]. Consequently, this lack of surface tension is the reason that foams were observed to be more stable under supercritical conditions of gases in our study. Foams are composed of gas bubbles dispersed in a liquid medium, and thus, their stability depends on the balance between various forces, including surface tension. In the absence of surface tension in supercritical fluids, there is no attraction between gas molecules and the surrounding liquid phase. This lack of attraction can result in enhanced stability of foams formed in supercritical conditions.
Figure 9 illustrates that
foam is significantly more stable than
foam even under vapour conditions. Although the stability of foams generated with
in the gas phase appears to be poor, the effect of nanoparticles can still be seen when 0.05 wt.% silica nanoparticles were added, as shown in
Figure 9b. Foams generated with 0.05 wt.% nanoparticles were thicker than foams generated without nanoparticles. This is also the case for the foam generated with
in gas phase (
Figure 9a). The foam bubble size is reduced and is evenly distributed when 0.05 wt.% nanoparticles were added to the
foam.
The stability difference between the
and
foams at vapour conditions is primarily due to their solubility differences [
53,
54]. This effect is more pronounced in
foam due to its greater solubility, which resulted in a decrease in foam stability, as seen in
Figure 9b. This is consistent with the findings of Szabries et al. [
55]. The authors asserted that
foams are stronger and more stable than
foams, because
has a high solubility in water, which is about 60 times greater than that of
.
dissolves more quickly in the liquid layers of the foam, resulting in a higher rate of gas diffusion between the bubbles. Pressure has a similar influence on average bubble size for both
and
in the experiments conducted in this study. With an increase in pressure from 3 to 11.7 MPa, the average bubble size of the foam bubbles was reduced, and the bubble count was increased.
Foam stability is better improved at greater pressures, regardless of the gas type, according to the findings of this study. At high pressures, gases become denser, which improves intermolecular interactions between the gas and the hydrophobic part of the surfactant molecules. With , stable foams may be produced at both low and high pressures, while with , only high pressure is favourable for foam stability.
3.4. Oil Recovery Performance of Core Flooding Experiments
The sequence of flooding in core samples is summarised in
Figure 10.
3.4.1. Core Flooding in an Artificial Core Sample (Set 1)
Oil Recovery Factor
Figure 11 illustrates oil recovery at a reservoir temperature of 38 °C and injection pressure of 20.8 MPa as a function of injected pore volumes.
was injected into the artificial core sample at an initial flow rate of 0.2 mL/min. The initial oil saturation of the core sample was 91.25%. The total oil recovery with
injection was 48.9%. Pre-generated nanoparticle-stabilised
foam of 80% foam quality was then injected at a total flow rate of 0.2 mL/min after almost 11 PV of
injection. There was no additional oil produced as a result of this. It was believed that the differential pressure across the core was not high enough and that the injected surfactant in the liquid phase was used up by rock adsorption, resulting in insignificant oil production. Therefore, to counteract capillary end effects, the flow rate was gradually increased up to 2 mL/min. During foam flooding, an additional 40.8% of oil was recovered.
The oil recovery from the core sample was 28.6% when 2 PV of
was injected, as it can be observed in
Figure 11. Oil recovery increased to 40.5% after injecting 5 PV of
. After that, it gradually grew until it reached 48% after 10 PV was injected. Following gas injection, nanoparticle-stabilised foams were injected. As 2 PV of foam was injected, 24.3% of residual oil was recovered (
Figure 11), bringing the total oil recovery from 48.9% to 73.2%. After 5 PV foam was injected, it climbed to 80.3%. The cumulative oil recovery reached almost 90% after about 11 PV of gas injections and 21 PV of foam injections.
During flooding, gas breakthrough occurred as soon as 0.14 PV of was injected. The density difference between the displacing and displaced fluids, which caused gravitational segregation, is a typical problem during most field operations. To put this into context, the viscosity of gas at thermobaric conditions in this experiment was 0.086 mPas. Oil, on the other hand, had a viscosity of 3.13 mPas.
When foams were injected, the gas breakthrough was not noticed. This was a substantial improvement over the much earlier gas breakthroughs seen in flooding. This implies that the nanofoam has a higher sweep efficiency than .
To limit gravity override and viscous fingering, the injected foam must have a viscosity of several orders of magnitude higher than pure , so as to lower gas mobility and to mitigate gas channeling, as described in previous sections.
Mobility Control
It is necessary to characterise the mobilities of injected fluids in the porous media in order to understand the displacement process and the superior oil recovery performance of foam in comparison with
injection.
Figure 12 below shows the dynamics of differential pressure during the filtration experiment.
The results during foam injection (
Figure 12) demonstrate a rapid foam-generation process shortly after injection, indicating favourable foam behaviour. Although at some point, gradual changes in pressure drop response are noted, this can be explained by the high permeability and initial low injection rate. High permeability suggests that the porous medium has relatively large and interconnected pores to enable fluid movement. This means that foam can quickly flow and propagate through the pathways created by the existing porosity or fractures. We believe that this rapid movement led to rapid foam generation shortly after injection. Furthermore, a low injection rate allows the foam to gradually penetrate and displace the existing fluid within the medium. As the foam displaces the fluid, it fills the pore spaces and creates a foam front that propagates through the porous medium. However, during this initial stage of low injection rate, the foam may encounter resistance, especially if there are narrow or constricted pathways. The gradual changes in pressure drop response observed during this stage could be attributed to dynamics of foam flow through these pathways.
During
gas flooding, the pressure drop increased sharply until gas breakthrough. The pressure drop was significantly reduced after the gas breakthrough, and it was stabilised with a further injection of
. This is similar to the results in Ghoodjani et al. [
56]. According to the authors, the high compressibility of gas increases the pressure prior to gas breakthrough. Following breakthrough, gas has an open path to flow into, and as gas saturation rises, gas relative permeability rises, resulting in a drop in differential pressure.
Mobility control performance of foam injection can also be characterised using the mobility reduction factor (MRF). Using Equation (
3), the mobility reduction factor was determined as 12.6. This implies that in our experiment, nanoparticle-stabilised foam was able to reduce the mobility of
in the porous medium by over 10 times.
3.4.2. Core Flooding in a Real Core Sample with an Injection Pressure of 20.8 MPa (Set 2)
The sequence of flooding in the real core model is the same as that of the artificial core earlier shown in
Figure 10. The baseline injection flow rate of
was also 0.2 mL/min. The initial oil saturation was 66.15%.
As seen in
Figure 13, the oil recovery from the core sample quickly increased to 74.4% after almost 1 PV of
was injected. After that, oil recovery grew gradually to 91% after two pore volumes. From the graph, it can be seen that the last increase in oil recovery was noted between 2 and 2.5 PV. Hence, the flooding process was stopped when there was no significant increase in cumulative oil production. This brings the total oil recovery to 98.8% with a total
injection of three pore volumes. Nevertheless, the foam was injected at 80% quality at a total flow rate of 0.2 mL/min. As expected, there was no oil production. This is mainly due to the fact that
injection proved to be very effective in recovering almost all the oil in the porous medium at such pressure. This can be attributed to the absence of gas breakthrough and a stable pressure drop (
Figure 14).
However, there were some other fascinating findings from this. Comparing the results of
and
foam injections from the artificial core (
Figure 11) with those of a real core (
Figure 13), it should be answered why foam injection was successful in the artificial core and unsuccessful in the real core.
At this point, this is not a question of foam stability, as foam viscosity was determined in a capillary tube as 0.54 and 0.58 mPas. Foam stability in the porous medium during filtration can be evidenced by this, as there was no significant change between foam viscosity before and after filtration.
Additionally, we believe that the permeability differences between the real and artificial cores played a big role in their oil recovery outcomes. From
Table 1, it can be seen that the permeability of the artificial core is higher than that of the real core by several orders of magnitude. Another petrophysical property that could have influenced the disparity of results is the length of the porous medium. The length of the artificial core was 7.58 cm and that of the real core model sums to 9.23 cm. This suggests a shorter propagation distance for the injectant in the case of the artificial core.
Furthermore, we propose that the injection pressure during
injection (20.8 MPa) is much higher than the minimum miscibility pressure (MMP) of
with the oil model in the core sample. In order to verify this, an MMP simulation was made using CMG-WINPROP. Further details of the simulation process can be found in the
Supplementary Material.
MMP was determined as 8.63 MPa. This indicates that the injection occurred at a pressure more than twice its MMP, which makes
instantly miscible with reservoir oil upon first contact and which tends to recover all the oil in place. This is in agreement with the results in Li et al. [
57] where an investigation of the influence of
injection pressure when the pressure is below and above MMP was conducted.
3.4.3. Core Flooding in a Real Core Sample with an Injection Pressure of 8.5 MPa (Set 3)
To support our claims from the previous section, the third set of core flooding experiments was conducted. As with the other sets, the core flooding sequence was the same. However, the injection pressure was reduced to 8.5 MPa, which is closer to the minimum miscibility pressure. When planning gas injections, injection pressure is crucial because, to some extent, it determines the miscibility of the injected gas with the reservoir oil.
Figure 15 and
Figure 16 show that after injecting about 19 pore volumes of
, 37.69% of the oil was recovered, and an additional 28.91% was recovered after injecting roughly 40 pore volumes of nanoparticle-assisted
foam at a ratio of 4:1 of the gas and liquid phases.
These results further confirm our claims in the previous section that the injection pressure during injection plays a significant role in the implementation of foam injection in the field. It would be pointless to inject foam for EOR in circumstances where the injection pressure is significantly higher than the MMP and where a high recovery factor is obtained.
These arguments are supported by the claims in Shyey-Yung J. [
58], who suggested that recovering residual oil by injecting
at pressures close to MMP may be enhanced by a potential improvement in the mobility ratio of the reservoir oil and
. Additionally, Song et al. [
59] compared oil recovery factors at different
injection pressures. According to their findings, the optimum injection pressure was close to the minimum miscibility pressure. Even though they asserted that a higher injection pressure led to a higher oil recovery, it was not ideal because it led to an early breakthrough of gas.