Introduction

Fuel derivatives from oil and gas, synonymously known as fossil fuels, are still the major contributors to the world’s energy supply [1, 2]. Practically in the oil industry, two approaches were used to meet the world energy demand: either exploring new fields or maximizing oil recovery through the application of enhanced oil recovery (EOR) methods [3,4,5,6]. However, EOR is a relatively fast approach to achieving incremental oil recovery from brown-producing fields as compared with the risky and time-consuming approach of exploring new oil fields. Besides, EOR is generally proposed to unlock the bypassed or the residual oil left in the ground reservoir after both primary and secondary recoveries [4, 6]. Polymer flooding is considered one of the most efficient and mature chemical-enhanced oil recovery (CEOR) methods, which entails injecting polymer solution into the reservoir to minimize the water–oil mobility ratio (MR) and thereby improve sweep efficiency [7,8,9]. In the context of practical applications, the most often utilized polymer in EOR field projects and conformance improvement treatments is hydrolyzed polyacrylamide (HPAM), due to its well-known physiochemical properties, good viscosifying qualities, and reasonably inexpensive cost [7, 8, 10, 11]. Nevertheless, HPAM can be damaged by extreme reservoir conditions, like high temperatures. and high salinity, which significantly affects their performance in EOR [8, 12, 13]. However, researchers are continuing to enhance the performance of synthetic polymers or develo** new composite polymers that would outperform conventional polymers [11, 14,15,16,17,18,19].

The breakthrough development of nanotechnology and promising experimental results have noticeably shifted researchers’ perceptions toward using nanomaterials in EOR applications [20,21,22,23]. Recently, scholars have demonstrated that the ideal integration of nanoparticles (NPs) into polymer architecture can enhance polymer properties like rheological, thermal stability, and chemical resistance to an extent beyond that of traditional polymeric materials [13, 24,25,26]. The reported NPs with the polymeric solutions were silica (SiO2) [27,28,29,30], titanium dioxide (TiO2) [31], graphene oxide (GO) [32, 33], zinc oxide (ZnO) [34], and alumina (Al2O3) [35].

Moreover, the combination of nanoparticles and polymers reduced interfacial tension (IFT) [31, 33, 36], changed wettability [12, 31], improved organic pollutants adsorption from produced water [37, 38], and enhanced polymer viscoelasticity [39, 40]. All these mechanisms contributed to an increase in incremental oil recovery. Polymeric nanofluid, also synonymously known as nanoparticle-assisted polymer flooding or nanocomposite polymer, is the name of this recently invented class of enhanced oil recovery [26, 41].

In the light of polymeric nanocomposites, Lashari et al. [32] investigated GO-HPAM polymeric nanocomposite using core flood experimental through CFD modeling. Their results showed that 19.67% of the OOIP could be recovered after injecting GO-HPAM polymeric nanocomposite as an EOR approach. In another experimental investigation, Kumar et al. [42] compared the performance of two different polymeric nanocomposites: GO-HPAM and SiO2-HPAM. The researchers observed that incorporating GO NPs and SiO2 NPs into HPAM resulted in significant enhancements in rheological characteristics, reduction in fluid-fluid interfacial tension (IFT), and decrease in contact angle. Additionally, it was discovered that GO NPs exhibited superior rheological characteristics and reduced interfacial tension (IFT) compared to SiO2 NPs. Conversely, SiO2 NPs demonstrated better wettability modification performance compared to GO NPs. Cao et al. [39] executed core floods with polymeric nano-silica. Their results exhibited incremental oil recovery of 17.3% of OOIP for polymer-nano silica versus 5% of OOIP for conventional polymer solution. Hu et al. [43] in their investigation on polymeric nanofluid using the sand pack model as the porous media, reported an incredible improvement in recovery of 58% for traditional polymer flooding, 63% for polymer-Al2O3, and 67% for polymer-SiO2 as compared to 49% for water flooding. In their study, Gbadamosi et al. [35] conducted oil displacement experiments on sandstone cores using two types of polymeric nanofluids: SiO2 PNF and Al2O3 PNF. The results showed that Al2O3 PNF demonstrated a greater oil recovery compared to SiO2 PNF. Specifically, Al2O3 PNF achieved a cumulative oil recovery of 65.3% of the original oil in place (OOIP), while SiO2 PNF yielded a cumulative oil recovery of 60.81% OOIP. Furthermore, Bera et al. [27] examined the effectiveness of a combination of silica nanoparticles (NPs) and guar gum in enhancing oil recovery. They conducted core displacement tests using a mixture consisting of 4000 ppm of guar gum and 2000 ppm of silica NPs. The results demonstrated an increase in oil recovery ranging from 10.7 to 27.3% of the original oil in place (OOIP). This improvement can be attributed to the enhanced viscosity of the polymer and the modification of wettability in the sandstone cores, transitioning them from an intermediate wet state to a water-wet state. Agi et al. [44] conducted a comparative study to assess the effectiveness of Al2O3-polymeric NF and SiO2-polymeric NF in comparison to crystalline starch NF. The experimental investigations revealed that crystalline starch NF outperformed Al2O3 polymeric NF and SiO2 polymeric NF in terms of enhancing viscosity, reducing interfacial tension (IFT), and modifying wettability. The results showed incremental oil recovery percentages of 23%, 17%, and 13% for crystalline starch NF, Al2O3 polymeric NF, and SiO2 polymeric NF, respectively. Furthermore, Hamdi et al. [45] evaluated the effectiveness of polymer-grafted graphene nanoplatelets (PG-GNPs) in comparison to polymer-dispersed graphene nanoplatelets (PD-GNPs). The study reported that the incremental recovery factor (RF) using PD-GNPs was 5% of the original oil in place (OOIP), while PG-GNPs exhibited a higher incremental RF of 15% OOIP. These findings highlight the potential of these nanoparticles as promising options for chemically enhanced oil recovery (EOR) in challenging reservoir conditions.

Apart from experimental investigations, experimental design methods, and response surface methodology provide efficient approaches to optimize polymer characteristics by identifying the key factors, modeling their effects, and finding the optimal conditions [46,47,48]. Moreover, numerical simulations are a key aspect of modeling and optimization of the flooding performance of polymers and chemical EOR agents on the lab and field scale [14, 49,50,51,52].

This research aims to separately scrutinize the influence of three NPs: SiO2, Al2O3, and ZrO2 on the polymer flooding process by experimentally analyzing polymer rheology, contact angle, IFT, and oil displacement tests. Moreover, validation and simulation modeling of sand-pack flooding using CMG STARS simulator. These NPs were selected due to their key advantages such as being environmentally friendly and relatively reasonably priced rather to other NPs [12]. In addition, the key Advantages of such selected NPs towards EOR are summarized in Table 1. Despite their potential, zirconium oxide (ZrO2) nanoparticles have gotten the least consideration in EOR research. Notwithstanding the promising features of ZrO2 NPs, this nanomaterial is not perceived as an EOR candidate material, hypothetically because it is not widespread in the upstream oil industry. Since ZrO2-NPs are uncommon in the upstream oil industry, they may not be considered an EOR agent despite their intriguing qualities and a few studies have employed ZrO2 NPs as an EOR agent.

The previous research was mostly directed only at silica-based nanoparticles since silica NP shows positive results toward enhanced oil recovery. Besides, the literature on the utilization of ZrO2 in polymeric nanocomposites EOR investigations is very limited, and accordingly, limited findings in the literature have been reported. Therefore, the comparison was meant to give an insight into the potential use of HPAM-ZrO2 in improving oil production. Previous studies focused on the use of silica polymeric nanocomposite. However, there is a lack surrounding the synergy between ZrO2 in EOR application. Therefore, this systematic study aimed to compare the enhanced oil recovery between HPAM-SiO2, flooding, HPAM-Al2O3, HPAM-ZrO2, and traditional HPAM.

Although numerous studies have explored the utilization of nanoparticles in polymer injection, there has been limited investigation into comparing the efficacy of silica, alumina, and zirconia nanoparticles in enhanced oil recovery (EOR) approaches. Therefore, a secondary objective of this article is to comparatively analyze the impact of silica versus alumina and zirconia NPs on the rheological, interfacial tensions, wettability, and oil displacement of HPAM. Initially, nanoparticles were selected and characterized. Secondly, Polymeric nanocomposites were prepared to serve as EOR agents in further investigations. Then, the rheological performance of the native and polymeric nanocomposite was investigated in a variety of temperatures and salinity. Moreover, Wettability alteration and IFT reductions induced by polymeric nanocomposites were tested. Additionally, sand-pack flooding was implemented to assess the oil recovered. Finally, the polymer nanocomposite was numerically modeled at the core scale using CMG STARS.

Table 1 The advantages of the selected NPs in the EOR process
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Materials and Methodology Procedures

The experimental investigation is summarized in Fig. 1 The details of the material, apparatus, and procedures are presented in the following subheading.

Fig. 1
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Flow chart shows the experimental works

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Materials

A commercial synthetic polymer of hydrolyzed polyacrylamide (HPAM), the polymer was purchased from Guanru Chemical Co, China. Moreover, three different sorts of NPs, Silicon dioxide NPs (SiO2), Aluminum oxide NPs (Al2O3), and zirconium oxide NPs (ZrO2) were obtained from Nano Gate Egypt company, and synthetic sodium chloride (> 98%). A magnetic stirrer (premium hotplate stirrer) was utilized to prepare polymeric nanocomposites to obtain a homogenous distribution of nanoparticles in polymer solutions. The packed sandstone model employed in flooding tests was obtained from real core data, in Egypt. Crude oil was obtained from the Western Desert of Egypt and used in the investigation of interfacial tension, contact angle, and sand-pack flooding. Typically, Table 2 reports the description of crude oil utilized in the proposed investigations of the current study.

Table 2 Oil properties used in the investigation understudy
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Methods

Selection and Characterization of Nanomaterials

In this research, three distinct nanoparticles (NPs) were used specifically, SiO2, Al2O3, and ZrO2 to formulate HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively. Therefore, two different tests were used to characterize these NPs and to verify they exist in nano-size form: X-ray diffraction (XRD) and Transmission Electron Microscopy (TEM). Herein, the main objectives of XRD and TEM were to determine the sizes and morphologies. First, XRD analysis was conducted with Empyrean Malvern Panalytical (Netherland) Powder Diffractometer system with Cu Kα radiation at the 2-θ angle to determine the sizes and compositions of the NPs. Next, TEM was accomplished on JEOL JEM-2100 high-resolution at an accelerating voltage of 200 kV.

Preparation/Formulation of Polymeric Nanocomposites

The formulation of polymeric nanocomposites is somewhat significant since the agglomeration of particles is controlled by the formulation process of a nanofluid. In the literature, there are two techniques commonly used for the preparation of polymeric nanocomposites: the polymer nano-grafting method and the polymer nanosuspension method [24, 41, 45]. In general, ease and simplicity of formulation make this method more desirable to researchers. In the current work, the polymer nanosuspension method was used. Nanoparticles are blended into the polymer using sonication and high-pressure homogenization. It should be highlighted that the mixing process was gently performed to achieve a stable and uniform dispersion of nanoparticles. Three formulated nano-polymers were prepared: HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 to investigate their capability toward HPAM concerning polymer rheology, IFT, and wettability alteration and oil recovery.

To formulate polymeric nanocomposites, three grams of HPAM powder was dissolved in one liter of deionized water using magnetic stirring to obtain a polymer concentration of 0.3 wt%. The polymer concentration was maintained at 0.3 wt% to prevent viscous fingering during the injection process. Regarding polymer nanocomposites, the NP concentration was 0.1 wt%. The required quantity of SiO2, Al2O3, and ZrO2 nanoparticles were weighed separately and blended appropriately in HPAM with a stirring speed of 400 rpm for 4 h to formulate three polymeric nanocomposites, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2. To mimic salinity at reservoir conditions, brine with 4 wt% concentration was blended in all polymeric nanocomposite solutions while stirring was maintained at 400 rpm for 3 h to make a harmonized polymer nanosuspension. More importantly, NPs mixing with HPAM is implemented to create a harmonized nanocomposite to prevent the accumulation of NPs and guarantee the full adsorption of NPs in polymeric solutions. Figure 2 shows schematically the formulation of polymeric nanocomposite.

Fig. 2
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Sketch shows the formulation of polymeric nanocomposites

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Rheology Assessment

Evaluation of the polymeric and polymeric nanocomposite viscosity is a crucial aspect of giving suitable perceptions about the flow behaviors of polymeric solutions. To that end, the MCR 102e Rheometer was employed to comparably measure the rheology of conventional polymeric and nanocomposite polymer solutions. The rheometer had a temperature regulator and a water bath linked to it for elevated temperature measurements. The polymeric solutions were transported to a holder at the investigated temperature. The shear data were attained at a shear rate range of 1–1000 s−1. The rheology of HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were analyzed using MCR 102e rheometer. The measurements were implemented under the shear range 1–1000 s−1, temperature variety of 25–120 °C, and brine series of 1 wt% ppm to 8 wt% to simulate reservoir conditions. To adjust the rheometer before the measurements, tests were conducted at different temperatures using pure water and standard oil. Afterward, the HPAM and the three formulated polymeric nano-suspension solutions were transferred into the coaxial cylinder and measured.

Interfacial Tension (IFT) Investigation

In this part, the pendant drop method was used to estimate the IFT using Attension Theta (Bioline Scientific company; Finland). To mimic the typical reservoir circumstances, the IFT of polymeric nanocomposites, experiments were performed at 25 °C and 80 °C. IFTs were calculated through the Attension Theta tensiometer using the Laplace–Young equation. A small amount of oil is placed in a syringe, which is then used to create the drop. The gravity of the oil sample was 34 API, acquired from the western desert of Egypt. The unit for interfacial tension (IFT) was mN/m which equals dynes/cm as well.

Contact Angle (CA) Investigation

Typically, contact angle (CA) is considered a quantitative evaluation of the wettability of a solid surface or rock by oil liquid. In this section, the sessile drop method was used to estimate CA using Attension Theta tensiometer (Bioline Scientific company; Finland). A glass plate was employed as the substrate for the contact angle measures. It is worth stating that rock wettability is a crucial factor in EOR processes as they exactly influences oil displacement [57]. It is therefore significant to investigate it under the reservoir conditions. To investigate how temperature affects the CA of polymeric nanocomposites, experiments were performed at 25 °C and 80 °C. The droplet was positioned on the solid surface and an image of the drop was recorded. The contact angle is therefore determined by fitting the Young–Laplace equation around the droplet using the Attension software.

Displacement Test by Sand-Pack Flooding

In this section, displacement tests via sand-pack flooding experiments were accomplished to quantitatively assess the oil recovery performance of the formulated polymeric nanocomposites as nanoparticle-augmented polymer flooding. The flooding equipment is schematically displayed in Fig. 3. The flooding apparatus consists of main parts as follows:

  • Displacement pump: It can provide high pressure and an injection rate of up to 50 cc/min. The inlet pressure, injection/flow rate, and temperature were identified through the displacement pump using its control panel.

  • Linear sand pack Model: The model diameter and length are 5 cm and 62.5 cm respectively. It was fabricated from high-grade stainless steel. It was packed by carefully chosen sand size to generate a sand pack model with acceptable permeability and porosity values. Moreover, an aluminum foil sheet serves as a thermal jacket around the stainless-steel sand pack model, trap** heat in an envelope-shaped pattern.

  • Pressure Gauge: Mounted at the culmination of apparatus ending to identify the final pressure and ranges from zero to 1000 psi.

  • Cylinders: To hold the flooding oil, brine, and chemicals.

  • Graduated Collector: The main function is to gather the produced liquids (oil and water) during the flooding runs.

Fig. 3
figure 3

Schematic diagram of flooding apparatus

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First, Sand particles were mechanically rammed into a sand-pack holder. Table 3 summarizes the properties of the sandpack model. Then, pore volume (PV) was calculated by comparing the mass differences of the sand pack before and after water saturation divided by the brine density. Next, the porosity of the sand pack was estimated as the pore volume (VP) divided by the total volume of the sand pack (Vb) (21–22%). Figure 4 explains the flooding procedures. The sand-packed core was initially injected with water at 5, 10, 15, and 25 cc/min, and the corresponding pressure difference was documented to estimate the absolute permeability using Darcy’s law. The flooding temperature and brine salinity for all experiments were set at 80 °C and 4 wt% to mimic the reservoir conditions. Then, the oil flooding was performed by injecting oil at an injection rate of 1 cc/min till it seemed that no more water would be delivered. This process established the initial water saturation as depicted in Fig. 3. Water flooding was then conducted at 4 wt% salinity to simulate the reservoir conditions. The water was injected at 1 cc/min until it appeared that no more oil would be produced by this method. This procedure determined the residual oil saturation as revealed in Fig. 4. Tertiary conventional polymer flooding (HPAM) and three polymeric nanocomposites flooding (HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2) were then conducted to attain incremental oil recovery at the same injection rate (1 cc/min) until it appeared that no more oil would be produced. The duration of each run was almost 8 h. The incremental oil recovery factor is attained by dividing the incremental oil recovered through the EOR process per the initial oil in place.

Table 3 Petrophysical properties of the sand pack model
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Fig. 4
figure 4

Sand-pack flooding procedures

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Numerical Simulation

The properties of polymeric nanocomposites solutions from the experimental investigation were fed into a simulator and the numerical simulation was performed to recognize the efficacy of polymeric nanocomposites as an EOR approach. In this section, the CMG STARS 2015 Simulator was employed to model the generated displacement tests. It is worth stating that CMG STARS is widely used by the upstream industry [49, 50, 58]. Besides, CMG STARS has many simulation options and is preferably appropriate for advanced modeling of recovery processes involving such as waterflooding, chemical EOR flooding, and thermal EOR applications [59]. For the current study, the CMG STARS Simulator was selected to model the polymeric nanocomposite at the core scale. The results of the numerical simulation were validated with the results of sand-pack flooding. Moreover, oil saturation profiles were generated using numerical simulation to demonstrate the swee** efficiency of the nanocomposite polymer flooding.

Results and Discussion

Nanoparticles Characterization

The sizes and morphologies of the employed nanoparticles were observed by XRD and TEM tests (Figs. 5, 6, 7, 8, 9 and 10). Remarkably, the XRD diffractogram positively shows the SiO2, Al2O3, and ZrO2 in a crystallinity pure and nanosized form. The average sizes of SiO2, Al2O3, and ZrO2 NPs were determined from TEM and found to be 50, 20, and 50 nm, respectively. As shown from XRD patterns, SiO2 had more purity than both Al2O3 and ZrO2. Furthermore, it was depicted from TEM that the SiO2 shape is amorphous while both Al2O3 and ZrO2 had spherical shapes. SiO2 is typical of amorphous materials because the atoms are randomly dispersed in three-dimensional space. The TEM indicates that the SiO2 NPs lack a regular crystal network structure and have a disordered arrangement of atoms (Fig. 5). Furthermore, SiO2 couldn’t exhibit a well-defined shape, and their atomic arrangement lacks long-range order. In the case of Al2O3 and ZrO2 NPs, the TEM analysis indicates that the Al2O3 NPs and ZrO2 NPs display a spherical shape. This implies that aluminum oxide and zirconium oxide have a well-defined spherical morphology, with a regular layout of atoms forming a crystalline pattern structure. The spherical shape of Al2O3 suggests a uniform distribution of aluminum and oxygen atoms within the nanoparticles, resulting in a symmetrical appearance (Fig. 7). Likewise, the spherical shape of ZrO2 NPs suggests a uniform spreading of zirconium and oxygen atoms within the nanoparticles, resulting in a regular appearance (Fig. 9). The key properties and further information of SiO2, Al2O3, and ZrO2 NPs concluded from XRD and TEM were summarized in Table 4. The analysis conducted using TEM and XRD provided substantial evidence supporting the presence of these nanoparticles in their nano-sized forms.

Fig. 5
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TEM images of the SiO2 NPs. a Scale 100 nm, b scale 200 nm

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Fig. 6
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XRD of the SiO2 nanoparticles

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Fig. 7
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TEM images of Al2O3. a Scale of 20 nm, b scale of 100 nm

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Fig. 8
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XRD pattern of alumina nanoparticles

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Fig. 9
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The TEM images of Zirconia nanoparticles. a Scale of 50 nm, b scale of 100 nm

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Fig. 10
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XRD image of zirconia nanoparticles

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Table 4 General Properties of SiO2, Al2O3 and ZrO2
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Rheological Measurement

Investigation of Shear rate on Viscosity of HPAM and Polymeric Nanocomposites

The apparent viscosity of polyacrylamide and three different polymer nanosuspensions as a function of the shear rate was measured using an MCR 102e Rheometer from Anton Paar. The shear rate range was 1–1000 s−1 to include and mimic the shear rates in the wellbore and oil reservoirs. However, the shear rate of the wellbore and reservoir would be in the range of 0.1–10 s−1 [60].

The viscosity against shear rate curves of HPAM, HPAM-SiO2, HPAM-Al2O3- and HPAM-ZrO2 solutions are presented in Fig. 11. The results indicated that polymeric nanocomposite solutions had better viscosity performance than conventional polymer (HPAM). However, the viscosity performance versus shear rate exposes that the viscosity is reduced by raising the shear rate, signifying the non-Newtonian performance of all tested polymeric solutions. Nonetheless, the results indicated the addition of NP into HPAM didn’t eliminate the shear-thinning behavior which would be advantageous, especially at handling or transporting polymer at the surface before injecting it into the reservoir pores. The viscosity preservation of HPAM-SiO2, HPAM-Al2O3- and HPAM-ZrO2 composites was greater than that of HPAM at the same conditions of shear rate (Fig. 11). Such behavior showed shear-resistance of polymer nanosuspension better than that of traditional polymer. A possible explanation for this shear resistance might be attributable to the inclusion of NPs generated by the formation of the powerful connection between traditional polymer and NP triggered by the configuration of hydrogen bonds [28]. It should be highlighted that SiO2-HPAM polymeric nanosuspension had high viscosities at all shear rates. In the current study, the authors suggest that the SiO2-HPAM polymeric nanocomposite was well dispersed compared with Al2O3-PAM and ZrO2-PAM polymeric nanosuspension. In addition, the adsorption interaction during the mix of the SiO2 with HPAM could be greater than that both of Al2O3 with HPAM and ZrO2 with HPAM.

Regarding ZrO2, Al-Anssari et al. [61] reported that the viscosity of the polyacrylamide was noticeably augmented by adding a low concentration of ZrO2 NPs (less than 0.03 wt). In contrast, they reported that higher concentrations of ZrO2 NPs greater than 0.03 wt% did not suggestively boost the viscosity.

Fig. 11
figure 11

Viscosity of HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 plotted versus shear rate

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Investigation of Temperature on Viscosity of HPAM and Polymeric Nanocomposites

The key output of this section was to get perspective on the temperature investigation on viscosities of nanosuspension solutions and traditional polymers. To this end, the viscosities of polymers were measured at different temperatures (25 °C up to 120 °C). The measurements were performed at a shear rate of 9.32 s−1 which represents the reservoir conditions from a shear rates point of view. Practically, the shear rate of the wellbore and reservoir would be in the range of 0.1–10 s−1 [60].

Figure 12a displays graphically the viscosity profile for HPAM, HPAM-SiO2, HPAM-Al2O3, and PAM-ZrO2 at varying temperatures of 25, 60, 90, and 120 °C respectively. The viscosities of all tested sample solutions decrease upon the increase in temperature. Notably, all nanocomposite polymer solutions show a higher reserved viscosity than traditional polymer (HPAM), which designates the improvement of temperature tolerance for the three polymeric nanocomposites. As a comparative analysis among the three polymeric nanosuspensions, HPAM-SiO2 exhibited a higher viscosity performance than PAM-Al2O3 and PAM-ZrO2, under all investigated temperatures (Fig. 12a). For instance, at 90 C, the apparent viscosities were 181, 120, 91, and 81 mPa s for HPAM-SiO2, PAM-Al2O3, HPAM-ZrO2 and HPAM, respectively. It can be observed that the addition of SiO2, Al2O3, and ZrO2 significantly enhanced the polyacrylamide viscosity at elevated temperatures by 123%, 48%, and 13% respectively. The author suggests that the effect of SiO2, Al2O3, and ZrO2 on polymer viscosity enhancement were excellent, moderate, and fair respectively. The increment in HPAM viscosity was assigned to the ion-dipole communications between the oxygen of Silica, alumina, and zirconia with Na+ which diminish the charge shielding of polymer’s COO groups, with the consistent insight of Maghzi et al. [29]. Moreover, the ion-dipole links between silica with Na+ were the strongest as compared to Al2O3-HPAM and ZrO2-HPAM. The composite SiO2-HPAM exhibited effective dispersion, leading to superior viscosity enhancement when compared to composites of Al2O3-HPAM and ZrO2-HPAM. Overall, under varied temperatures, the average polymer viscosity is boosted by 107%,45%, and 12% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively as depicted in Fig. 12b.

Fig. 12
figure 12

a Investigation of temperature on viscosity for HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2. The measurements were performed at a shear rate of 9.32 s−1. b Fold increase for the viscosity of polymeric nanocomposite under investigation of varied temperature

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Investigation of Salinity on Viscosity of HPAM and Polymeric Nanocomposites

Typically, the brine salinity can damagingly influence the performance of polymer viscosity in the reservoir [26]. To examine conceivable influences of salinity on the viscosity of HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2, NaCl brine with concentrations of 1, 4, 6, and 8 wt% was inserted and stirred to all the polymer solutions of 0.3 wt% at 60 °C. The impact of salinity on the rheology of polyacrylamide and three formulated polymeric nanocomposites are shown in Fig. 13a. The brine concentrations were 1 wt%, 4 wt%, 6 wt%, and 8 wt% which mimic typical reservoir salinity conditions. The HPAM-SiO2 polymeric nanocomposite had the highest salinity tolerability compared to HPAM-Al2O3 and HPAM-ZrO2 polymeric nanocomposites. At 0.3 wt% polymer concentration, 0.1 wt% NP concentration, the shear rate of 9.32 s−1, and 8 wt% brine concentration, the apparent viscosity of HPAM-SiO2, HPAM-Al2O3, HPAM-ZrO2, and HPAM polymer were 102, 94, 82, and 73 mPa s, respectively. The interlink bond that forms between the nanoparticles and polymer macromolecules is what causes the viscosity increase in the polymeric nanocomposites. It can be observed that the addition of SiO2, Al2O3, and ZrO2 significantly enhanced the polyacrylamide viscosity at elevated salinities by 40%, 29%, and 14% respectively. Typically, the viscosity of examined polymeric solutions decreases as the brine concentration increases. Nonetheless, the HPAM-SiO2 composite demonstrated a superior viscosity than those of HPAM-Al2O3, HPAM-ZrO2, and HPAM solutions for all brine concentrations investigated. At the highest investigation brine concentration (8 wt%), the viscosity of HPAM-SiO2 was 40% higher while HPAM- Al2O3 and HPAM-ZrO2 viscosity were 29% and 14% higher than those of HPAM traditional polymer. As shown in Fig. 13a, HPAM has the least viscosity. The results justified that the cation of the brine strikes the amide and carboxylate group of HPAM, leading to diminishing the electrostatic repulsion within the polymer chains [12, 62]. In contrast, a positive charge will be established in polymeric nanocomposites owing to the existence of NPs and accordingly support electrostatic repulsion among the polymer chain [26].

The addition of NPs in the polyacrylamide improves the polymer viscosity. Moreover, comparing the findings described in Fig. 13a and b exposes that the SiO2 can significantly counteract the reduction in polyacrylamide viscosity induced by the salinity and temperature effect. Moreover, Al2O3 exerts a moderate effect on boosting polyacrylamide viscosity, meanwhile, ZrO2 had slight effects on boosting polyacrylamide. The authors suggest that the composite SiO2-HPAM was well dispersed and resulted in the excellent performance of viscosity boosting compared with both composites of Al2O3-HPAM and ZrO2-HPAM. Generally, as seen in Fig. 13b, under investigation of salinity, the average polymer viscosity is boosted by 73%,48%, and 12% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively.

Fig. 13
figure 13

a Investigation of salinity on viscosity for HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2. The measurement was performed at 9.32 s−1. b Fold increase for the viscosity of polymeric nonfluid under investigation of varied salinity

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IFT Investigation

The interfacial tension of the polymeric nanocomposite solutions at the oil/aqueous interface is an important parameter in evaluating the efficiency of nanoparticle-assisted polymer flooding at reservoir conditions. The interfacial tension measurements were accomplished for crude oil against conventional polymer (HPAM) and polymeric nanocomposite solutions. All polymeric nanocomposites were at 0.3 wt% polymer concentration and 0.1 wt% NP concentration, and the findings are depicted in Fig. 14a. Distinguished by conventional polymer, the insertion of nanoparticles in polymer solution reduces the interfacial tension. At 25 °C, the IFTs for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were 70.38, 52.02, 62.75, and 54.94 mN/m respectively. Likewise, at 80 °C, the IFTs for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were 63.73, 44.83, 55.16, 47.5 mN/m respectively. At 25 ˚C, the IFT is reduced by 26%, 11%, and 22% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively as presented in Fig. 14b. Similarly, at 80 °C, the IFT is reduced by 29%, 13%, 25% for HPAM-SiO2, HPAM-Al2O3, HPAM-ZrO2 respectively as revealed in Fig. 14b. Therefore, all polymeric nanocomposites display better properties of reducing interfacial tension than conventional polymer (HPAM). The adsorption of silica, alumina, and zirconia at the oil–water interface lessens the IFT of nanocomposite polymer solutions. Moreover, the authors suggest that IFT reduction could be attributable to the decrease of Gibbs energy in the presence of NPs [63]. It can be perceived that the HPAM-SiO2 composite revealed an effective tendency to reduce the IFT amongst oil and water than HPAM-Al2O3 and HPAM-ZrO2 at the equivalent NP concentration (0.1 wt%). It can be seen that the HPAM-SiO2 unveiled a more effective capability to reduce the interfacial tension of the oil/water system under the same conditions. The magnitude ability of polymeric nanocomposite to reduce interfacial tension is as follows: HPAM-SiO2 > HPAM-ZrO2 > HPAM-Al2O3. It was also observed (Fig. 14a), with increasing temperature, a further reduction in IFT. The authors suggest that the interactions of NPs with polymer at high temperatures become more, and accordingly better adsorption at the interface could occur. El-hoshoudy et al. [36] indicated that the enclosure of hydrophobic groups in the PAM’s backbone supports the creation of micelles, which lowers its proximity to water and, accordingly, lowers IFT. However, the results reported by Cao et al. [64] presented no decrease in IFT once the nanoparticle concentrations in polymeric solution surpassed the critical value (0.1 wt% and 0.05 wt%). Overall, such lessening in IFT by the polymeric nanocomposites may promote an attitude concerning the application of nanoparticle-induced polymer flooding which could reduce the residual oil.

Fig. 14
figure 14

a Investigation of IFT for HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2. b Percentage of reduction in IFT of polymeric nanocomposite

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Contact Angle Investigation

The contact angle is considered the most common measurement of the surface’s wettability. As reported in the literature, the contact angle (CA) in the reservoir system is as follows, water-wet (θ = 0°–75°), intermediate-wet (θ = 75°–105°), and oil-wet (θ = 105°–180°) [21]. In this study, the contact angles between polymeric nanocomposite solutions and quartz surface were measured using an Attension Theta tensiometer (Bioline Scientific company; Finland) at temperatures 25 °C and 80 °C to evaluate wettability alteration. The quartz surface was aged with crude oil for 12 h before determining the contact angle to simulate the real reservoir conditions. The oil droplet was positioned on a glass surface submerged in the polymer or polymeric nanocomposite solution. Afterward, the Laplace–Young equation was utilized to compute the contact angles through the formulated solutions using the Attension Theta tensiometer (Bioline Scientific company; Finland). The measurements of contact angles for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were performed at two different values of temperature: 25 °C and 80 °C. as shown in Fig. 15. Figure 15 shows that SiO2, AL2O3, and ZrO2 polymeric nanocomposites decreased the contact angle towards strongly water wet increase. At 25 °C, the contact angle for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were 21.32°, 13.47°, 14.36°, 14.62° respectively. Likewise, at 80 °C, the contact angles for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were 41.45, 34.3, 36.05, and 35.22 degrees respectively. Such results imply that the existence of the NPs in the HPAM polymer altered the contact angle from water-wet to a strongly water-wet state. Hence, the findings suggest that the addition of SiO2, Al2O3, and ZrO2 in HPAM increases the water wetness of reservoir media. The authors suggest that this phenomenon is related to the interaction of the positively charged NPs with the negatively charged glass substrate and is accordingly responsible for the alteration in contact angles. Moreover, a collaboration of polymer and NPs can upsurge the structural disjoining pressure gradient, separating the oil from the glass surface and shifting its wettability [30]. Based on the results presented in Fig. 15, HPAM-SiO2 showed contact angle reduction compared to HPAM-Al2O3 and PAM-ZrO2. This suggests that PAM-SiO2 has a better wettability change compared to HPAM-Al2O3, and HPAM-ZrO2. Furthermore, previous studies have also reported similar findings. Saha et al. [65] indicated that the electrostatic repulsion inside the nanomaterials triggered the particles to distribute along the surface and drop the contact angle. Fan et al. [63] reported no change in contact angle (CA) after optimized NP concentration (3000 ppm). Overall, the results suggest that the addition of SiO2, Al2O3, and ZrO2 nanoparticles to polyacrylamide enhances its hydrophobicity. SiO2 nanoparticles offer better wettability alteration compared to Al2O3 and ZrO2 nanoparticles. In addition, ZrO2 nanoparticles suggest better wettability modification compared to Al2O3 nanoparticles. In particular, the authors suggest that disparity in morphology, chemical composition, and structure of SiO2, Al2O3, and ZrO2 NPs results in variations in their effects on IFT and CA of polymeric solutions.

Fig. 15
figure 15

Contact angle measurements for HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2. The measurement was accomplished at 25 °C and 80 °C

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Oil Displacement Test

The flooding performance of polymeric nanocomposite as an EOR candidate compared with conventional polymer solution (HPAM) flooding at reservoir conditions. To that end, Flooding tests for conventional polymer (base case) and three different polymeric nanocomposites were carried out to assess the efficacy of nanocomposite polymer on the oil recovery. The results are shown in Table 5; Fig. 16. As revealed in Fig. 16, after injecting one pore volume of brine (4 wt%) the total oil recovery did not increase more than 58% of the initial oil in place. Afterward, polymer injection of HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were separately examined as EOR. The results of the displacement test utilizing HPAM solution caused an ultimate oil recovery of 66.7%. It also was found that using HPAM- SiO2 caused an ultimate oil recovery of 74.8%. The third displacement test was done via HPAM- Al2O3 and caused an ultimate oil recovery of 73.9%. The last displacement test was run by HPAM- ZrO2 with the ultimate oil recovery of 72.3%.

It is observed that 8.6% incremental oil recovery by conventional polymer flooding (HPAM) is obtained, whereas the incremental oil recovery factor of HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 solutions reaches 17.4%, 15.3%, and 13.6% respectively as shown in Table 5; Fig. 17. The HPAM-SiO2 composite has the best performance on enhanced oil recovery concerning the enhanced swept volume and improved viscosity, under reservoir conditions. Figure 16 shows the oil recovery for all polymeric solutions as a function of injected pore volume. Similar findings were reported by Hu et al. [43] where the incremental recovery was 19% and 14% of OOIP for HPAM-SiO2, and HPAM-Al2O3 respectively. However, the results reported by Gbadamosi et al. [35] revealed that the incremental oil recovery by HPAM-Al2O3 was relatively outperformed that by HPAM-SiO2.

Consequently, the efficacy of polyacrylamide solution flooding is lessened and ineffective in harsh conditions of typical reservoirs. It was found that employing NPs in conventional polymer solutions could increase oil recovery. These results reveal that nano-polymer had improved the sweep displacement efficiencies of the injected fluid. Evidence from the former discussion, as stated above advocates these findings and provides insights into the positive effects of nanoparticles on the conventional polymer.

Table 5 Summary of sand-pack flooding findings
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Fig. 16
figure 16

Results of sand-pack flooding experiments for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2. The flooding was performed at 80 °C

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Fig. 17
figure 17

Cumulative oil and incremental oil recovery for HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2

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Numerical Simulation of Polymeric Nanocomposite

In this section, the numerical simulation process of nanocomposite flooding was considered to mimic the flooding performance in the previous section. In other words, the key objective of this section is to validate and match the previous results of sand-pack flooding using simulation. During model building, it is worth stating that the bulk volume of the ascertained simulation model should be alike the bulk volume of the core plug used for experimental flooding. Besides, the constraints of wells, well locations, fluid properties, and injection patterns should be inputted in the model simulation correctly. Moreover, a cartesian system grid was selected to simulate the sand-pack. The grids were divided into hundred cellblocks in the I-direction each of 0.625 cm length. The number of blocks in the j and k directions was set equal to one to simulate one-dimensional flow at the core scale [1]. Figure 18 shows the Cartesian grid of the sand-pack core model for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2. The sand pack properties were taken from the above-mentioned polymer nanocomposite flooding and fed into the simulator. Porosity, permeability, and initial oil saturation (Soi) during sand pack experimental studies for nanocomposite polymer flooding were determined as 21%, 428 mD, and 78% respectively. The injector and producer wells with bore radius of 0.3 were located at [1 1 1] and [100 1 1] nodes respectively. In numerical simulation, numerous parameters like the rheological properties of polymers and other petrophysical properties of the sand-pack were considered from the laboratory data. Some key values were assumed and taken from the literature as presented in Table 6. More importantly, modifying and tuning the relative permeability curves was a crucial aspect of accomplishing a good history match for the flooding during the EOR process [50, 66, 67]. Combining the experimental data measurements of relative permeability with Corey’s correlations defaulted in the CMG STARS simulator to model the relative permeability curves as depicted in Fig. 19. Using CMG-STARS, Corey’s correlations were employed to generate relative permeability curves as presented in Eqs. 1 and 2. Experimentally using the sandpack model, the water–oil relative permeability curves were generated using the production data of polymeric nanocomposites, sandpack model properties, and crude oil properties. The endpoints for residual oil saturations (Sor) for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 from experimental works were 0.26, 0.2, 0.21, and 0.22 respectively. In that way, it is observed that there is an evident reduction in residual oil saturation in the presence of nanoparticles. It should be highlighted that straight-line relative permeability curves have been achieved in some experiments which also gave similar trends to the works of Helmi et al. [68].

$${k}_{rw}={k}_{rwiro}{\left(\frac{{S}_{w}-{S}_{wcrit}}{1-{S}_{wcrit}-{S}_{oirw}}\right)}^{{N}_{w}}$$
(1)
$${k}_{row}={k}_{rocw}{\left(\frac{{S}_{o}-{S}_{orw}}{1-{S}_{wcon}-{S}_{orw}}\right)}^{{N}_{ow}}$$
(2)

where krw: water relative permeability for water–oil; krow: oil relative permeability for water–oil; Swcon: Connate water saturation; Swcrit: critical water saturation; Soirw: irreducible oil saturation; Sorw: residual oil saturation; Nw: Water exponent; and Now: Oil–Water exponent.

Table 6 Key parameters of polymer used in numerical simulation, assumed based on average data from [69]
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Fig. 18
figure 18

1D cartesian grid model for HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2

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Fig. 19
figure 19

Modeling of relative permeability using CMG STARS (a) HPAM (b) HPAM-SiO2 (c) HPAM-Al2O3 (d) HPAM-ZrO2

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Prediction of Oil Recovery from Numerical Simulations

After building models of polymer flooding at the core scale, the simulator was run to evaluate the oil recovery. The results of the oil recovery factor for the four EOR scenarios were presented using the CMG STAR (Fig. 20). The oil RF% for the HPAM flooding (base case) was about 67.1%. The recovery factor obtained by HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 was 74.8%, 73.9%, and 72.3% respectively. The highest oil recovery was 74.8% (HPAM-SiO2 flooding). A comparison of oil recovery by HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 in cases of flooding experiments and numerical simulation has been presented in Table 7. As observed, oil recovery by the simulation model matches the results of oil recovery by sand-pack flood experiments. Moreover, in consistency with the results of sand-pack flooding, the incremental recovery by the three nanocomposite polymer solutions was outperformed by that of conventional polymer flooding (HPAM). Consistent with the experimental flooding, this phenomenon is related to the effect of nanocomposite on the piston-like movement reducing the mobility ratio and improving the sweep efficiency [34]. It was visibly found that employing NPs in conventional polymer solutions could increase oil recovery. These results reveal that nano-polymer had improved the sweep and displacement efficiencies of the injected fluid. Among the three polymeric nanocomposites, the HPAM-SiO2 composite had more remarkable capability for EOR than both the HPAM-Al2O3 and HPAM-ZrO2 composites. The results justified that the nanoparticles’ addition modified the polymer viscosity. The nanocomposite polymer flooding was able to sweep more oil than conventional polymer.

Table 7 Oil recovery from simulation runs and compared with that of sand-pack flooding
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Fig. 20
figure 20

Oil recovery factor from numerical simulation (CMG STARS simulator) for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2

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Oil Saturation Profiles

Simulation of Core-scale displacement tests was built to qualitatively assess the potential of secondary and tertiary recoveries. As an illustration, Fig. 21 presents oil saturation contours for the flooding of HPAM-SiO2 at different periods, while other saturation profiles were provided in supplementary materials. At the initial time, the porous rock model was saturated with oil. Typically, initial oil saturation (soi) was measured as 78% for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 and this corresponds to the initial oil in place. In each EOR scenario, an identical water solution having 4 wt% of NaCl was injected at a constant flow rate (1.0 cc/min) as a procedure of secondary recovery. Throughout the water injection, the porous model’s oil saturation progressively dropped. As depicted in Fig. 21 and supplementary materials, the change in the color of the cartesian grid pictures from red to yellow or green implies a decrease in oil saturation. However, respective oil saturation reduction was also observed after the injection of polymeric nanocomposite and chase water. In other words, the oil-swept area of nanocomposite polymer flooding is greater than that of conventional polymer (HPAM). The final residual oil saturation values corresponding to HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were determined from history-match results as 24%, 18.6%, 19.6 and 20%, respectively as presented in Fig. 22; Table 8. The residual oil saturation (Sor) reduced from 78 to 24%, 18.6%, 19.6%, and 20% for HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively. The results reveal that the lowest Sor recorded was for HPAM-SiO2 flooding. The reduction of oil saturation for the three polymeric nanocomposites was significantly better than conventional polymer flooding. In other words, the reduction of oil saturation for nanocomposite polymer injection was significantly better than conventional polymer flooding, particularly HPAM-SiO2. Such results could be defended that the NPs-assisted polymer flooding boosted the polymer flood viscosity and accordingly improved the sweep efficiency. Oil saturation map** demonstrates that polymeric nanocomposite improves the sweep efficiency of oil from the injection area to the production one [1].

Fig. 21
figure 21

Oil saturation contours from CMG STARS simulator, showed by 3D Cartesian grids at different periods for the flooding of HPAM-SiO2

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Fig. 22
figure 22

Oil saturation performance during HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2

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Table 8 Residual oil saturation (Sor) after HPAM, HPAM-SiO2, HPAM-Al2O3 and HPAM-ZrO2
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Conclusions

This research established new insights into the EOR performance of traditional polyacrylamide and three composites of polymeric nanocomposites through a comprehensive investigation. Laboratory investigations were implemented to assess the efficacy of these polymeric nanocomposites under the frame of EOR. The results have shed light on the crucial role performed by the combined presence of HPAM and SiO2, Al2O3, and ZrO2 nanoparticles resulting in improved polymer viscosity, IFT reduction, wettability modification, and enhanced oil displacement.

The SiO2 nanoparticles demonstrate more influence on polymer rheology, wettability alteration, and IFT reduction of polymeric nanocomposite solutions compared to Al2O3 and ZrO2. Regarding the investigation of polymer rheology, the polymer viscosity is boosted by 107%, 45%, and 12% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2, respectively, under varied temperatures. Besides, the average polymer viscosity is improved by 73%, 48%, and 12% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2, respectively, under investigation of a range of reservoir salinities. The results suggest that SiO2 NPs tend to display a more considerable influence on wettability alteration and IFT reduction of polymeric nanocomposite solutions compared to Al2O3 and ZrO2. The findings indicate that the addition of ZrO2 NPs to HPAM tends to exhibit a more considerable influence on wettability change, and IFT reduction of polymeric nanocomposite solutions compared to Al2O3 NPs. Conversely, Al2O3 NPs tend to have a more significant impact on the polymer rheology of polymeric nanocomposite solutions compared to ZrO2 NPs. Among the three polymeric nanocomposites, the HPAM-SiO2 composite had a more remarkable ability for EOR than both the HPAM-Al2O3 and HPAM-ZrO2 composites. At flooding reservoir conditions, the oil displacement experiments confirm that the incremental oil recovery (EOR recovery) by HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2, is 8.6%, 17.4%, 15.3%, and 13.6% of OOIP, respectively. Finally, sand-pack flooding tests of the polymeric nanocomposite were modeled and validated using numerical simulations. The residual oil saturation values corresponding to HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 were determined from simulation results as 24%, 18.6%, 19.6%, and 20%, respectively.

This work’s findings afford new insight into enhancing oil recovery by polymeric nanocomposite and confirm the promising outlook of such a new EOR process at the field scale. As a recommendation and outlook, it will be interesting to apply the experimental work on the adsorption behavior of polymeric and polymeric nanocomposites, to show the effects of NPs on the reduction of polymer adsorption through porous media.