Hydrocarbon Displacement Efficiency by Water and Polymer and Optimization of Multiple Parameters in Porous Media: Experiments and Numerical Simulation

Abstract

Polymers are effective agents for EOR due to their water solubility, which improves water viscosity, sweep volume, and displacement efficiency. To elucidate their mechanisms in EOR and optimize polymer–water synergistic flooding parameters, this study combined core and core network experimental research with numerical simulations. Experimental flooding results demonstrated that polymer–water synergistic flooding reduces residual oil saturation by 13.79% compared to water flooding. Key parameters such as well pattern, well spacing, bottom-hole pressure, polymer viscosity, and injection slug size were optimized through numerical simulation of a conceptual model based on actual oilfield data. A bottom-hole flowing pressure of 10.6 MPa, well pattern density of 84 wells/km², staggered line drive pattern, and polymer viscosity of 21 cp are recommended for EOR. Numerical simulation data showed that polymer–water synergistic flooding enhances displacement efficiency by 5–11% over conventional water flooding. The findings from the experimental research and numerical simulations indicate that the total recovery factor may be increased by implementing the recommended parameters in an actual oilfield.

1. Introduction

Mature oilfields developed in China have contributed to over 70% of the country’s crude oil production, with most now in the high or ultra-high water-cut stage [1,2]. Due to reservoir heterogeneity and the implementation of various development methods, the distribution patterns of oil and water within these reservoirs have become more diversified compared to their original conditions [3,4]. The potential tapping targets for remaining oil have gradually shifted from previously relatively contiguous enrichment areas to scattered and more challenging residual oil reservoirs [5]. Once oilfields enter the high water-cut stage, the produced fluids contain extremely high water content, which sharply increases treatment costs and causes continuous declines in oil production, significantly reducing economic returns [6,7]. Therefore, further development of mature oilfields is essential for EOR [8].

However, as oilfield development advances into more mature stages, reliance on single technologies becomes insufficient to meet the requirements for stable production and enhanced recovery [9,10]. With the growing difficulty and diminishing returns associated with continuous innovation in individual EOR methods, the integration of multiple technologies emerges as an essential pathway toward sustaining stable production in mature fields [11,12].

In oilfield development, synergistic flooding involves the integrated application of key techniques, such as water flooding, chemical flooding, and well pattern optimization, throughout the entire development lifecycle [13]. This paper discusses polymer–water synergistic flooding involving the coordination of displacement media (polymers and water) and well pattern modifications that alter flow pathways. The aim of this combination is to increase sweep efficiency and improve both the oil displacement efficiency and recovery factor during the ultra-high water-cut stage [14,15]. Chemical flooding effectively mobilizes residual oil within rock pores, leading to its localized enrichment [16]. However, due to the complexity of reservoir flow fields and significant formation heterogeneity, the zones of enriched residual oil often do not coincide entirely with the areas swept by the chemical flooding system [17]. Residual oil synergizes with the well network in EOR. The enrichment of residual oil forming an “oil wall” serves as the foundation for well network adjustments, while the well network provides the necessary conditions for efficient recovery of the “oil wall” [18]. Polymer–water synergistic flooding between chemical agents and the target oil efficiently removes residual oil from pore spaces, promotes the uniform advancement of [19] the displacement front, increases flow resistance, improves dynamic reservoir heterogeneity, and expands the sweep volume of the displacement phase [20].

The polymer solutions injected into porous oil reservoirs are influenced by shear rate during polymer–water synergistic flooding [21]. After a second critical shear rate, shear-thickening behavior emerges. The onset shear rate for this regime decreases with reducing permeability and increasing polymer molecular weight or concentration, while the apparent viscosity of the polymer increases with rising shear rate [22]. The shear-thickening behavior of polymers adversely affects injection capacity. Around the wellbore, the velocity and shear rate of the polymer slugs are relatively high, causing injection pressure to exceed the formation fracture pressure and induce fractures [23]. Several researchers have developed pore-scale viscoelastic models for quantifying the altered injectivity and recovery factor because of the polymer’s viscoelastic effects. However, the main limitation with viscoelastic polymers is the empirical reliance on coreflood data to predict the shear-thickening efforts in porous media [24]. In the Sheng-tuo Oilfield, with a current recovery factor of 37.3%, polymer–water synergistic flooding has been implemented. Compared with old wells in adjacent areas, the oil saturation in new wells has increased by 9.0% to 14.2%, verifying the secondary enrichment of remaining oil during the process. Through polymer–water synergistic flooding, numerical simulation predicts that the recovery factor of this block will be further enhanced by 7.5%, with a cumulative increase of 15.6%, ultimately reaching a final recovery factor of 60.5% [25]. A comparison of different displacement methods is shown in

Table 1. Comparison of different displacement methods.

Category	Reservoir Type	Injection Method	Reported Gain
Water flooding	Sandstone/carbonate reservoirs	Injection low-salinity engineered water	Low-salinity waterflooding commonly adds 2–6% OOIP
Polymer flooding	Medium/high viscosity oil reservoirs	HPAM (high-MW partially hydrolyzed polyacrylamide)	Typically 7–10% OOIP, sometimes higher with advanced polymers
Polymer–water synergistic flooding	Medium permeability reservoirs	Copolymers with alternating water slugs	Typically 4–7% OOIP

OOIP: Original Oil-In-Place.

[26,27,28].

The enhancement of sweep and displacement efficiencies is the primary mechanism of the EOR method [29,30]. In fact, the recovery factor is affected by two factors, namely sweep efficiency and displacement efficiency; the product of these two values is the recovery factor [31,32]. Researchers used a 3D physical model to investigate sweep efficiency and displacement efficiency in water flooding, polymer flooding, and functional polymer flooding through nuclear magnetic resonance methods [33]. They observed that in the case of limited sweep efficiency, the recovery factor can be enhanced by improving the displacement efficiency [34].

All of the above shows that polymer–water synergistic flooding can effectively enhance displacement efficiency. However, current research in this field still faces the following three challenges: (1) There is a scarcity of literature on displacement efficiency through polymer–water synergistic flooding and the extent of improvement in displacement efficiency achieved by polymer–water synergistic flooding remains unclear. Research on the quantification of displacement efficiency during high and ultra-high water-cut stages, along with related experimental studies and numerical simulations, is relatively scarce. (2) Research on the distribution patterns, formation mechanisms, and related experimental studies of microscopic residual oil remains insufficient. (3) The synergistic mechanisms and flooding modes involved in polymer–water synergistic flooding have not yet been systematically elucidated.

As shown in Figure 1, this work aims to enhance displacement efficiency by proposing polymer–water synergistic flooding. Through core experiments and core network experiments, the types of remaining oil were qualitatively analyzed and polymer–water synergistic flooding for enhancing displacement efficiency was quantitatively analyzed. Numerical simulations were conducted at the reservoir scale to verify the polymer–water synergistic flooding’s ability to enhance displacement efficiency, providing a basis for the subsequent coordinated development of oilfield production for enhancing displacement efficiency.

2. Methodology

2.1. Materials

Agents: HPAM was purchased from Beijing Hengju Reagent Co. (China). The polymer had a molecular weight of 12 million g/mol, with a hydrolysis degree of 25%. Polymer solution was prepared at a concentration of 1000 mg/L and was stirred slowly for 24 h at 50 rpm. Sodium chloride (NaCl), magnesium chloride (MgCl₂), sodium carbonate (Na₂CO₃), Sudan III, etc., were purchased from Shanghai Aladdin Reagent Co. (China) and were chemically pure.

Crude oil and brine: Deionized water was prepared using Watsons Water 4.5 L. Inorganic salt was added to the deionized water according to the proportional formula shown in

Table 2. The formula of formation water.

Ions	CO32−	HCO3−	Cl	Mg2+	K+ + Na+	Ca2+	Total
Concentration, mg/L	255	2332	816	10	1549	33	4995

to prepare the simulated formation water, and the total salinity was 4995 mg/L. The crude oil (light oil) was degassed and dehydrated with a viscosity of 21.5 cP at 25 °C, Shengli Oilfield, China.

Core model: The core samples for the experiments were obtained from the simulation core model.

Table 3. The properties of core samples and the experiment.

Property	Value
Core lithology	sandpack
Diameter, mm	12.51
Length, mm	98.45
Pressure, MPa	0.1–0.2
Permeability, mD	391
Polymer concentration (HPAM), mg/L	1000
Polymer viscosity, cP	21
Polymer slug size, PV	0.10

presents the core physical properties.

Core pore network model: The core pore network model was constructed using glass plates. The size of the core pore network model fabricated in this experiment is 25 cm × 25 cm, with a depth of 0.5 mm and a porosity of 0.38.

2.2. The Core Model Experiment

Core model experiments were conducted to determine oil displacement efficiency under extra-high water-cut conditions. The core flooding procedure involves several essential steps [35]:

(1)

Injecting brine into the core to achieve 100% saturation (at a normal temperature of 25 °C);

(2)

Displacing most of the brine by injecting crude oil to establish initial oil saturation and injecting brine to simulate the waterflood stage until the water-cut reaches 99.95% (10 PV) and then calculating displacement efficiency;

(3)

Injecting the polymer until water-cut reaches 99.95% (10 PV) and then calculating displacement efficiency;

(4)

Following the injection of 2 PV of water, switching the process to a 0.1 PV polymer slug, then subsequent water flooding until water-cut reaches 99.95% and then calculating displacement efficiency.

The displacement efficiency from polymer flooding, polymer–water synergistic flooding, and water flooding are determined. The experimental procedure is illustrated in Figure 2.

2.3. The Core Network Model Experiment

To investigate the effect of polymer–water synergistic flooding on the displacement efficiency, a microscopic simulation model was established using dyed oil (Figure 3) [36].

The core network flooding procedure involves several essential steps [13,37]:

(1)

Mount the glass-etched model into a specially designed visualization holder.

(2)

Apply an appropriate confining pressure, evacuate the glass-etched model, and saturate it with crude oil with a viscosity of 21.5 cP until the model is fully saturated. Allow the system to stand until the captured images stabilize.

(3)

Start the pump, set the displacement rate to 0.05 mL/min, and inject water with a viscosity of 0.43 cP to initiate the displacement experiment.

(4)

Continue the water flooding until the water-cut reaches 99.95% (10 PV). Throughout the process, record the entire dynamic flooding procedure in real time via dynamic video imaging.

(5)

To investigate the effect of polymer–water synergistic flooding on oil displacement efficiency, following the injection of 2 PV of water, switch the process to a 0.1 PV polymer slug, then continue water flooding until water-cut reaches 99.95% (10 PV), while maintaining identical displacement velocities for both methods. Throughout the process, record the entire dynamic polymer–water synergistic flooding procedure in real time via dynamic video imaging.

2.4. The Reservoir Condition Model’s Numerical Simulation Experiment

For the part of the experiment using real field data from a specific reservoir in the Guantao Formation of the Gudao Oilfield, a model was constructed in Eclipse reservoir simulation software (Schlumberger Simulation Launcher 2011) [38]. Detailed model parameters are provided in

Table 4. The specific parameters of the model.

Parameters	Value	Parameters	Value
Permeability, mD	2000	Crude oil density, kg·m3	925
Porosity, %	30	Crude oil viscosity, cP	21
Residual resistance index	1.5	Polymer viscosity, cP	21.5
Adsorption index	1	Total PV	13.75
Maximum polymer adsorption	0.005	Grid dimensions	101 × 51 × 1
Grid dimension (∆x, ∆y, ∆z), m	2 × 3 × 1	Initial reference depth, m	1250
Oil-Water Contact, m	1250	Rock compressibility, bar−1	0.0003231
Water viscosity, cP	0.43	Reference pressure (PVTW), bar	125
Polymer–rock resistance factor	1.5	Polymer–rock adsorption index	0.16
Initial reservoir pressure, bar	106	Water injection PV	13.65
Injection rate, m3/(d·m)	2	Production rate, m3/(d·m)	2

. The plyvisc and plyads parameters are provided in

Table 5. The plyvisc and plyads parameters of the model.

Polymer Concentration, kg/m3	Polymer Viscosity, cp	Polymer Concentration, kg/m3	Polymer Adsorption, kg/kg-Rock
0.0	1.0	0.0	0.000000
0.5	8.0	0.3	0.000011
1.0	15.0	0.5	0.000012
2.0	21.5	0.8	0.000013
2.5	30.0	1.0	0.000014
5.0	43.0	3.0	0.000015

.

To analyze the impact of different production methods on displacement efficiency, water flooding over 50 years was considered the baseline, and a polymer–water synergistic flooding scheme was employed. The polymer–water synergistic flooding approach comprised an initial 10-year water flood, followed by well pattern adjustment and polymer injection of 0.1 PV, with water flooding continuing until the end of the 50-year period.

However, Eclipse reservoir simulation software allows only a single set of relative permeability data. Since polymer flooding following water flooding inherently alters relative permeability characteristics, this study developed an adjusted relative permeability polymer–water synergistic flooding curve to enhance simulation accuracy (Figure 4).

The initial water flooding utilizes a conventional oil–water relative permeability curve. Upon transitioning to injecting a polymer slug, the model switches to the relative permeability curve calibrated for polymer–water synergistic flooding through oilfield residual oil saturation and relative permeability data. The oilfield experimental data points from both sets of measurements were fitted to form continuous functions, ensuring a physically consistent and numerically stable representation of the displacement process transition between different flooding stages [39].

Five well pattern configurations were simulated by modifying the production scheme, enabling the selection of the optimal pattern (Figure 5). Using a numerical simulation model, we quantitatively analyzed how injection and production parameters influence displacement efficiency and how well pattern adjustments enhance displacement efficiency. The study employed a controlled variable approach, sequentially evaluating each parameter well pattern density, injection–production intensity, and bottom-hole flowing pressure while keeping other factors constant. Subsequently, simulations varying these injection–production parameters examined their temporal effects on water cut and displacement efficiency [40]. The development strategies’ design schemes are summarized in

Table 6. The parameters of the development strategies.

Injection–Production Intensity, m3/(d·m)	Bottom-Hole Flowing Pressure, MPa	Well Pattern Density, Well/km2
1	8.7	50
2	10.6	83
3	12.5	117
4	14.4	150
5	16.3	183

.

To investigate the synergistic mechanisms of development strategies and their impact on recovery efficiency, the study systematically analyzed interactions between key parameters—injection–production intensity, well pattern density, and relative pressure, (P/P₀, P₀ = 12.5 Mpa, where P represents the bottom-hole flow pressure)—following well pattern optimization. Using a controlled variable method, the study designed separately 25 numerical simulation cases to compare combinations of these parameters while keeping all other factors constant (

Table 7. The parameters of the synergistic mechanisms of development strategies.

P/P0	Well Pattern Density, Well/km2	Injection- Production Intensity, m3/(d·m)
0.70	50	1
0.85	50	1
1.00	50	1
1.15	50	1
1.30	50	1
0.70	84	2
0.85	84	2
1.00	84	2
1.15	84	2
1.30	84	2
0.70	117	3
0.85	117	3
1.00	117	3
1.15	117	3
1.30	117	3
0.70	150	4
0.85	150	4
1.00	150	4
1.15	150	4
1.30	150	4
0.70	183	5
0.85	183	5
1.00	183	5
1.15	183	5
1.30	183	5

).

The RRF is defined as the ratio of water phase mobility before polymer flooding to the water phase mobility after polymer flooding and in the subsequent postflush stage with brine. Various RRF values (1, 1.2, 1.5, 1.8 and 2) were considered in this study. IPV describes the fraction of pore volume that is inaccessible to polymer molecules. Various IPV values (0.1, 0.15, 0.20, 0.25 and 0.30) were considered in this study [41]. The polymer–rock adsorption index refers to the process where polymer molecules, dissolved in the injected water, attach and accumulate onto the surface of the reservoir rock matrix. Various polymer adsorption index values (0.1, 0.15, 0.20, 0.25 and 0.30) were considered in this study.

To investigate the synergistic mechanisms of fluid properties and their impact on displacement efficiency, this study employed a two-factor interaction analysis method focusing on key parameters [42]. By comparing multiple factor combinations, this study selected 25 numerical simulation cases (

Table 8. The parameters of the synergistic mechanisms of fluid properties.

Polymer Transition Timing, Year	Polymer Slugs	Polymer PV	Polymer Viscosity, cP
5	1	0.054	15.0
10	1	0.054	21.5
15	1	0.054	25.0
20	1	0.054	30.0
25	1	0.054	40.0
5	2	0.108	15.0
10	2	0.108	21.5
15	2	0.108	25.0
20	2	0.108	30.0
25	2	0.108	40.0
5	3	0.162	15.0
10	3	0.162	21.5
15	3	0.162	25.0
20	3	0.162	30.0
25	3	0.162	40.0
5	4	0.216	15.0
10	4	0.216	21.5
15	4	0.216	25.0
20	4	0.216	30.0
25	4	0.216	40.0
5	5	0.270	15.0
10	5	0.270	21.5
15	5	0.270	25.0
20	5	0.270	30.0
25	5	0.270	40.0

).

2.5. Mathematical Equations

Displacement efficiency refers to the ratio of the volume of crude oil displaced within the oil-bearing area affected by water, polymers, and other agents to the original oil volume in that area. It is defined as [43]

$$E_D={\displaystyle \frac{S_{oi}-S_o}{S_{oi}}}$$

(1)

where S_o is the current average oil saturation and S_oi is the original oil saturation.

In reservoir engineering, sweep efficiency is a vital indicator for evaluating the effective coverage of displacement within oil-bearing formations. It is quantitatively defined as the percentage of the reservoir volume effectively swept by the displacement fluid relative to the total reservoir volume. It is defined as [44]

$$E_S={\displaystyle \frac{S_{swept}}{S_{total}}}$$

(2)

where S_swept is the volume of reservoir rock in contact with the displacing fluid and S_total is the total volume of the reservoir formation containing oil.

As a vital parameter characterizing fluid flow behavior in porous media, N_c plays a decisive role in synergistic flooding. N_c has long been used in chemical flooding to establish the relationship between the balance of viscous and capillary forces and residual oil saturation. It is defined as [45]

$$N_c={\displaystyle \frac{\mathsf{\mu }v}{\mathsf{\sigma }\mathrm{c}\mathrm{o}\mathrm{s}\mathsf{\theta }}}$$

(3)

where $\mathsf{\mu }$ is the viscosity of the displacing fluid; v is the viscosity of the displacing fluid; $\mathsf{\sigma }$ is the interfacial tension between oil and the displacing fluid; and $\mathsf{\theta }$ is the contact angle between the fluid and reservoir rock.

The recovery factor is defined as [46]

$$R=E_S\times E_D$$

(4)

where $E_s$ is the sweep efficiency and $E_D$ is the displacement efficiency.

Immobilized oil that is swept by the displacing slugs but cannot be displaced is called residual oil. Residual oil saturation is defined as [47]

$$S_{or}={\displaystyle \frac{V_{residual}}{V_p}}$$

(5)

where $V_{residual}$ is the volume of oil remaining in the core sample after extended flooding and $V_p$ is the pore volume of the core sample.

3. Results and Discussion

3.1. The Distribution and Formation Mechanism of Remaining Oil

The distinct oil flooding mechanisms of water flooding and polymer–water synergistic flooding resulted in different microscopic residual oil distributions. These distributions were categorized into five types: network, filamentous, oil film, columnar, and droplet. Figure 6 illustrates the microscopic residual oil distributions after water flooding and synergistic flooding.

Table 9. The distribution and mechanism of different types of remaining oil.

Type	Morphology	Formation Mechanism	Distribution Characteristics
Network		Fingering effect	Narrow pores and throats
Filamentous		Capillary force	Narrow pores
Oil film		Taylor dispersion effect and wettability	Wall of the pores
Columnar		Jamin effect	Narrow throats
Droplet		Marangoni effect (Interfacial tension)	Large throats

illustrates distribution and mechanism of different types of remaining oil.

Columnar residual oil is predominantly generated within H-shaped pore channels and constricted throat zones. This phenomenon arises because crude oil located in pores aligned with the flow direction is more readily displaced by the driving force, whereas oil in perpendicular pores tends to be bypassed, owing to the limited pressure differential across adjacent parallel channels. In terms of oil droplets and oil films, water flooding under low-N_c conditions often results in the entrapment of oil droplets inside pores. Under such conditions, capillary forces dominate, restricting displacement efficiency and impeding the mobilization of the trapped droplets. Filament residual oil predominantly forms when water invades smaller pores and throats. In these confined spaces, elevated capillary forces elongate the oil phase, creating filamentous structures. The restricted geometry of narrow pores and throats impedes oil droplet migration, promoting capillary-induced filament formation. This residual oil morphology typically results in heterogeneous oil distribution, consequently reducing displacement efficiency [45].

Table 10. A comparison of the core network models of water flooding and polymer–water synergistic flooding.

	Type	Network/mm2	Filamentous /mm2	Oil Film/mm2	Columnar/mm2	Droplet/mm2
Regime
Initial condition		104.70	0.00	0.00	1.26	0.16
Water flooding		24.79	8.75	11.08	4.12	1.66
Synergistic flooding		0.00	0.00	14.00	3.17	5.46

presents the distribution of different types of residual oil under various displacement modes.

Following water flooding, the low N_c and poor crude oil mobility caused oil clusters to become isolated. These clusters were trapped by capillary forces at the oil–water interfaces, forming residual oil zones with a saturation of 23.43% (Figure 7). In contrast, polymer–water synergistic flooding increased the displacement fluid’s viscosity and injection rate while reducing interfacial tension, thereby raising the capillary number. The resulting higher displacement pressure differential and decreased capillary forces enhanced the mobility of residual oil, enabling more effective displacement from small pores and reducing residual oil saturation to 9.64%. This demonstrates that polymer–water synergistic flooding significantly improves displacement efficiency compared to water flooding alone.

3.2. The Results of the Core Model Experiment

Formation water was injected into the core at a constant pressure of 0.1 MPa. After 10 PV, the displacement efficiency reached 73.06%. As polymer slugs (0.1 PV) were injected, reaching a cumulative injection of 10 PV of formation water, a displacement efficiency of 82.50% was achieved. When 10 PV polymer was injected, the displacement efficiency reached 87.15% (Figure 8).

Evaluation results confirm that both polymer flooding and polymer–water synergistic flooding are superior over conventional water flooding. Polymer flooding delivers the highest ultimate displacement efficiency; however, polymer–water synergistic flooding achieves a high displacement efficiency with a much smaller polymer slug volume.

To validate the numerical model, history matching was performed for both water flooding and polymer–water synergistic flooding. Using polymer–water synergistic flooding as an example, the simulation results demonstrate excellent agreement with the experimental core data (Figure 9). The differences in displacement efficiency and water-cut are within 1%, with consistent trends observed throughout the process.

Taking water flooding as an example, the distribution of remaining oil obtained by scaling up the core network model to the reservoir scale for simulation is shown in Figure 10 (simulating 50 years of water injection). Compared with 23.4%, the average residual oil saturation of water flooding is close to 23.43%. The core model and core network model results prove the accuracy of the numerical simulation model.

3.3. The Results of Reservoir Condition Model’s Numerical Simulation

Table 11. The displacement efficiency of water flooding and polymer–water synergistic flooding in different well pattern forms.

Type	Initial Pattern/%	Direct Line Drive/%	Staggered Line Drive/%	Five-Spot (1/2)/%	Nine-Spot (1/2)/%
Water flooding	64.04	69.41	69.72	68.18	69.57
polymer–water synergistic flooding	69.02	74.31	74.76	73.12	73.24

presents the displacement efficiency for both water flooding and polymer–water synergistic flooding across different well pattern types. Well pattern optimization increased displacement efficiency by 4–6% compared to the initial configuration. Additionally, polymer–water synergistic flooding enhanced displacement efficiency by 4–5% over water flooding. The combined polymer–water synergistic flooding approach delivered significantly greater displacement efficiency than water flooding alone [42].

After switching the production strategy, polymer–water synergistic flooding demonstrated a significantly higher displacement efficiency compared to conventional water flooding (Figure 11). The highest ultimate displacement efficiency of 11.8% was achieved by increasing the bottom-hole flowing pressure. The second most influential factor was well pattern density, which contributed up to 7.3% improvement in displacement efficiency. In contrast, the injection–production intensity had the least impact, with a 3.2% increase. Therefore, in subsequent numerical simulations, special attention should be given to the parameterization of bottom-hole flowing pressure and well pattern density.

The well pattern density is adjusted on the basis of the staggered line drive. As density increased, water-cut remained relatively stable, while displacement efficiency initially rose before declining. At the critical density of 84 wells/km², both the water-cut inflection point and peak displacement efficiency coincided. Displacement efficiency variation among different density schemes ranged from 2% to 3%. (Figure 12).

Figure 13 illustrates the impact of injection–production intensity on oilfield development performance. The results indicate that this parameter exhibits low sensitivity for the reservoir under study. Increasing injection–production intensity caused negligible changes in both water-cut and displacement efficiency. At the threshold intensity of 1 m³/(d·m), the system simultaneously reaches the water-cut inflection point and peak displacement efficiency. Displacement efficiency variation across different intensity schemes remains below 1%.

Figure 14 demonstrates the impact of bottom-hole flowing pressure on oilfield development. The results indicate that bottom-hole flowing pressure is a highly sensitive parameter for this reservoir. While water-cut remains stable as bottom-hole flowing pressure increases, injection–production intensity initially rises before declining. At a bubble-point pressure of 10.6 MPa, the system simultaneously reaches the water-cut inflection point and peak displacement efficiency. Displacement efficiency variations across different well patterns range from 3% to 4%.

The comparison between well pattern density and injection–production intensity reveals that the trend surface for well pattern density is steeper, indicating a greater influence on production performance, while injection–production intensity has a comparatively smaller effect. A well pattern density of 84 wells/km² combined with a bottom-hole flowing pressure of 10.6 MPa yields the highest trend surface. Similarly, a well pattern density of 84 wells/km² combined with an injection–production intensity of 1 m³/(d·m) yields the highest trend surface; a bottom-hole flowing pressure of 10.6 MPa combined with an injection–production intensity of 1 m³/(d·m) yields the highest trend surface (Figure 15).

Numerical simulations of well pattern configurations demonstrate that synergy maintains consistent sensitivity characteristics and peak values across different parameter combinations. The sensitivity ranking remains unchanged: bottom-hole flowing pressure has the strongest influence on production performance, followed by well pattern density, with injection–production intensity exerting the least effect.

The effects of IPV, RRF, and polymer adsorption on displacement efficiency and water cut were investigated: at various RRFs (1, 1.5, 1.8, and 2) and a constant IPV of 0.05; at various IPVs (0, 0.05, 0.15, 0.20 and 0.25) and a constant RRF of 1.5; at various polymer adsorption indices (0, 0.05, 0.15, 0.20, and 0.25). These results show almost the same displacement efficiency and water-cut (Figure 16).

The optimal conditions occur with a single slug combined with early-stage polymer injection, yielding the highest trend surface. Both higher polymer viscosity and larger injection volume correspond to increased trend surfaces. The results demonstrate that under two-factor synergy, viscosity has the strongest impact on recovery, followed by injection volume, while slug transition timing and slug size have relatively minor effects (Figure 17).

4. Conclusions

This paper aims to enhance oilfield recovery efficiency through an integrated approach combining theoretical analysis, experimental studies, and numerical simulations. The principal findings are

(1)

The core model experimental data demonstrate that polymer–water synergistic flooding increases the displacement efficiency by 9.44% compared to water flooding. Polymer flooding created the highest ultimate displacement efficiency; however, polymer–water synergistic flooding achieves a high displacement efficiency with a much smaller polymer slug volume, rendering it a more cost-effective and operationally feasible alternative for field-scale projects.

(2)

The core network model experiments qualitatively identified five distinct types of residual oil—network, filamentous, oil film, columnar, and droplet—and elucidated the underlying mechanisms governing their behavior. Initially, 99% of the crude oil was present in the network phase. Following water flooding, network residual oil continued to dominate; however, polymer–water synergistic flooding effectively dispersed both network and filamentous residual oil. As a result, the majority of the oil was recovered, with only minor fractions remaining as oil films, droplets, and columnar residuals.

(3)

This study quantified residual oil saturation and displacement efficiency under different flooding methods, showing that polymer–water synergistic flooding reduced residual oil saturation by 13.79% compared to water flooding.

(4)

Numerical simulations evaluated parameter sensitivities related to development strategies and fluid properties, identifying optimal parameter ranges. Polymer–water synergistic flooding achieved recovery factors of up to 10.72% higher than water flooding, closely matching scaled experimental results.

(5)

For the Guantao Formation of the Gudao Oilfield, optimal ranges for key sensitivity parameters were established: a bottom-hole flowing pressure of 10.6 MPa, a well pattern density of 84 wells/km², a staggered line drive well pattern, and a polymer viscosity of 21 cp. Other parameters should be optimized within their operational ranges based on specific field requirements and economic considerations, providing a theoretical foundation for field implementation.

(6)

Through experimental and theoretical analysis, the types and formation mechanisms of microscopic residual oil in pores were revealed, along with their characterization. This innovation is primarily reflected in Figure 6. Based on reservoir physics and numerical simulation, a multi-factor optimization method for influencing polymer flooding efficiency at the reservoir scale was established, providing a reliable basis for the design of water-soluble polymer flooding schemes in oilfields. This innovation is primarily reflected in Figure 15.