Optimizing Solvent-Assisted SAGD in Deep Extra-Heavy Oil Reservoirs: Mechanistic Insights and a Case Study in Liaohe

Abstract

This study investigates the feasibility and optimization of Expanding Solvent Steam-Assisted Gravity Drainage (ES-SAGD) in deep extra-heavy oil reservoirs, with a focus on the Shu 1-38-32 block in the Liaohe Basin. A modified theoretical model that accounts for steam quality reduction with increasing reservoir depth was applied to evaluate SAGD performance. The results demonstrate that declining steam quality at greater burial depths significantly reduces thermal efficiency, the oil–steam ratio (OSR), and overall recovery in conventional SAGD operations. To overcome these challenges, numerical simulations were conducted to evaluate the effect of hexane co-injection in ES-SAGD. A 3 vol% hexane concentration was found to improve oil recovery by 17.3%, increase the peak oil production rate by 36.5%, and raise the cumulative oil–steam ratio from 0.137 to 0.218 compared to conventional SAGD. Sensitivity analyses further revealed that optimal performance is achieved with cyclic injection during the horizontal expansion stage and chamber pressures maintained above 3 MPa. Field-scale forecasting based on five SAGD well pairs showed that the proposed ES-SAGD configuration could enhance the cumulative recovery factor from 28.7% to 63.3% over seven years. These findings clarify the fundamental constraints imposed by steam quality in deep reservoirs and provide practical strategies for optimizing solvent-assisted SAGD operations under such conditions.

1. Introduction

The rapid development of modern society urgently requires the efficient exploitation of unconventional oil and gas resources such as heavy oil, shale gas, and gas hydrates [1]. Deep extra-heavy oil resources represent a strategic development in China’s energy development. Globally, heavy and extra-heavy oils account for roughly 70% of the remaining oil resources [2,3]. In China, proven onshore heavy oil reserves exceed 20 billion tons, predominantly concentrated in the Liaohe, Shengli, and Xinjiang oilfields [4]. Among various enhanced oil recovery methods, Steam-Assisted Gravity Drainage (SAGD) has demonstrated significant success over the past two decades, especially in the development of massive, block-type extra-heavy oil reservoirs. A representative case is the Du-84 Guantao reservoir in the Liaohe Oilfield. However, as conventional shallow (depth < 900 m) extra-heavy oil resources have entered the middle-to-late stages of development, the focus has shifted towards deeper and ultra-deep reservoirs.

Exploiting such deep extra-heavy oil reservoirs presents significant challenges, primarily due to severe heat losses along extended wellbores, resulting in low-quality steam at reservoir depth. Furthermore, high reservoir pressures at great depths suppress steam dryness, limiting steam chamber growth. Consequently, conventional thermal recovery methods, such as SAGD, exhibit poor energy efficiency and marginal recovery in these deeper formations.

Conventional SAGD relies on forming a steam chamber between two co-planar horizontal wells, where heat diffuses upwards, and mobilized oil drains downwards via gravity to the producer well. Under ideal conditions (reservoir thickness > 20 m), SAGD can recover approximately 60–70% of the original oil-in-place (OOIP), provided that high-pressure enthalpy steam and good vertical mobility are available. However, thin pay zones and deep burial significantly diminish these favorable conditions. In extra-heavy oil reservoirs, the steam chamber rises slowly, and premature steam condensation occurs, causing only about 40–50% of the reservoir heat to effectively contribute to oil production. Practical experience with SAGD in China’s deep heavy oil zones has frequently shown elevated injection pressures, truncated steam chambers, and rapid pressure declines. Minakov et al. (2023) noted that SAGD is “suitable for thick super heavy oil reservoirs,” yet China’s thinner, deeper, and highly heterogeneous reservoirs present substantial difficulties for traditional SAGD designs [5]. Similar limitations also apply to steam flooding or cyclic steam stimulation (CSS or huff-n-puff) in deep formations, where high injection pressures and significant heat losses contribute to the overburden and further reduce effective oil zone heating.

To address these technical challenges, researchers have explored multimedia-enhanced SAGD approaches. One approach involves co-injecting non-condensable gases (NCGs), such as CO₂ or N₂, alongside steam. Laboratory and field studies indicate that adding small fractions of NCGs to the steam chamber can improve thermal sweep efficiency and reduce steam requirements. For instance, co-injecting CO₂ lowers the steam partial pressure, thus inhibiting excessive condensation and promoting broader, more uniform steam chamber growth. Field pilots in the Liaohe Oilfield have tested N₂ injection in SAGD wells, reporting increased oil-to-steam ratios, suppressed chamber vertical expansion, and significantly reduced temperatures at the chamber apex. Similarly, Xie et al. (2024) demonstrated that pre-injecting CO₂ could create preferential flow channels within the reservoir, reducing injection pressures by approximately 1–2.4 MPa and slowing down steam chamber interference [2]. Additionally, CO₂ dissolves into the oil, lowering its viscosity and providing the benefit of carbon sequestration. Nevertheless, NCGs alone have a limited impact on heavy oil viscosity reduction, as inert gases dissolve poorly compared to hydrocarbon solvents, making the mobilization of highly viscous bitumen challenging.

An alternative and promising method is solvent-assisted SAGD, also known as Expanding Solvent SAGD (ES-SAGD) [6]. This technique involves co-injecting light hydrocarbon solvents (such as propane, butane, or dimethyl ether) with steam. The mechanism of ES-SAGD is twofold: as the steam chamber expands, the injected solvents dissolve into adjacent cold oil, dramatically reducing viscosity even beyond the thermal front. Consequently, solvents act as mobilizing agents, enhancing oil mobility. Early research and laboratory experiments demonstrated that adding light solvents can significantly reduce steam–oil ratios (SORs) and accelerate the startup of the steam chamber [7,8]. Recent studies have further validated these results. For instance, Esmaeili et al. [6] conducted sand-pack experiments using propane/butane solvents in ES-SAGD under heavy oil conditions, observing how solvent co-injections substantially enhance oil production and decrease steam requirements compared to pure SAGD. Importantly, increasing the butane fraction (up to 80%) in the solvent mixture increases both the system’s temperature and production rate; propane alone results in the lowest chamber temperature. These findings suggest that although butane is more expensive, it more effectively expands and heats the chamber compared to propane, which is cheaper but less effective at viscosity reduction. Hence, optimizing the solvent blend is critical to balancing cost and performance. Other studies have similarly confirmed that multi-component solvent co-injection enhances oil production per unit of injected steam [9]. Field pilots, such as those conducted in Cold Lake, Alberta, demonstrated that adding 5–20% hydrocarbon diluents with steam resulted in noticeable improvements in oil production rates, consistent with laboratory observations [10,11,12].

The current study specifically targets the abundant deep-block-type extra-heavy oil resources in the Liaohe Oilfield, which exhibit very low oil recovery under existing production methods, making significant technological upgrades essential. Currently, these reservoirs are primarily developed using cyclic steam stimulation. However, as development has entered the middle-to-late stages, the recovery pace has significantly slowed, and overall recovery factors remain at around 24.6%. There exists substantial potential for increasing recovery through methodical upgrades. The Shu 1-38-32 block, the focal reservoir of this study, is situated at 950 m depth with an oil viscosity of around 100,000 cP. Due to significant challenges with conventional SAGD, there is a pressing need to investigate the feasibility of solvent-assisted SAGD at this site.

In this study, a feasibility assessment of a pilot-scale ES-SAGD scheme in the Shu 1-38-32 block of the Liaohe Oilfield was conducted. The primary objective was to evaluate, through numerical simulation and pilot design, the technical feasibility and economic viability of solvent-assisted SAGD under conditions specific to deep, high-viscosity reservoirs. In particular, the research investigated the effects of solvent co-injection on steam-chamber development, oil recovery efficiency, steam–oil ratios, and operating costs compared to conventional SAGD. The numerical model was carefully calibrated to reflect the geological and fluid characteristics of the Shu 1-38-32 block. Sensitivity analyses were carried out to identify optimal solvent concentrations and injection strategies tailored specifically to this reservoir. Additionally, the study assessed projected economic outcomes for the proposed ES-SAGD pilot. The findings of this research provide valuable insights into the proposed pilot implementation in the Liaohe Oilfield and offer practical guidance for the future development of analogous deep extra-heavy oil reservoirs globally.

2. Theoretical Analysis of the Effect of Depth on SAGD Production

This section initially describes the geological characteristics of the target reservoir. Based on the geological setting and burial depth of the reservoir, the classical Butler SAGD productivity prediction model was employed to theoretically analyze the feasibility of developing the target block through conventional SAGD technology. By performing a theoretical evaluation of this model, the critical constraints associated with reservoir depth were identified and assessed, providing a theoretical basis for the selection of solvent-assisted SAGD as an alternative recovery method.

2.1. Reservoir Description

Figure 1 presents the top view and vertical cross-sectional view of the target block. Figure 2 illustrates the viscosity versus temperature relationship of the heavy oil within the reservoir. According to these data, the Shu 1-38-32 reservoir has a central burial depth of approximately 950 m. Five pairs of horizontal wells were deployed within the block, each situated roughly 2 m above the reservoir bottom and extending approximately 300 m horizontally.

The reservoir parameters listed in

Table 1. Parameters for calculating wellbore heat loss.

Name	CMG Keyword	Unit	Parameter Value
Wellbore Depth	DEPTH	m	Calculated from measured well data
Wellbore Length	WLENGTH	m	Calculated from measured well data
Inner Diameter of Tubing	RTUBIN	m	0.0635
Inner Diameter of Casing	RCASIN	m	0.14
Well Radius	RHOLE	m	0.3
Thermal Conductivity of Cement Sheath	CONDCEM	J/(m·day·°C)	7.89 × 104
Thermal Conductivity of Formation Surrounding Wellbore	CONDFORM	J/(m·day·°C)	4.92 × 105
Surface Temperature	SURFACE_TEMP	°C	10

indicate that the initial reservoir pressure is significantly elevated due to its considerable burial depth. Furthermore, the reservoir porosity, at approximately 25.6%, is notably lower than typical shallow-to-medium-depth SAGD reservoirs, which generally exhibit porosities above 30%. The reservoir also contains extra-heavy oil, characterized by an extremely high viscosity of approximately 109269 cP under reservoir conditions. The combined challenges posed by substantial burial depth, lower reservoir porosity, and the extraordinarily high viscosity of oil significantly increase the technical difficulty and complexity associated with achieving commercial-scale production in the target block.

2.2. Wellbore Steam Quality Simulation

Based on the analysis of the reservoir conditions and oil properties of the target block, this section initially discusses the critical challenge associated with the reservoir’s significant burial depth. A detailed steam quality simulation was conducted using CMG software 2024.20, calibrated with steam quality monitoring data from actual wells within the target block. A detailed steam quality simulation was conducted using CMG, calibrated against field-measured steam quality profiles. By tuning the thermal conductivities of the cement sheath and formation to match the observed steam quality decline along the wellbore, we developed a heat-loss model that accurately reproduces steam quality variation with depth (Table 1). This model precisely predicts changes in steam quality as a function of wellbore depth and different steam injection rates, as shown in Figure 3.

Figure 4 illustrates the simulated bottom-hole steam quality at various injection rates and reservoir depths. The results indicate a substantial influence of well depth and injection rate on the steam dryness reaching the bottom hole. To maintain steam quality greater than 70% at depths of around 1000 m, the required steam injection rate per well must exceed 132 t/d. Consequently, the critical constraint for developing deep extra-heavy oil reservoirs stems primarily from reduced steam quality with increasing burial depth. This diminished steam quality leads to a substantial reduction in the effectiveness of conventional steam injection processes.

2.3. Analytical Model on the Effect of Depth

The influence of steam quality is rooted in the fundamental difference between the latent heat (${\lambda }_{fg}$) of vaporization and sensible heat ($C_p∆T$). Only dry steam effectively carries latent heat, which significantly contributes to reservoir heating. Conversely, wet steam predominantly contains sensible heat, and thus, the latent heat available for heavy oil mobilization is considerably reduced. During SAGD operations, only the latent heat from injected steam contributes effectively to reservoir heating and oil mobilization. In contrast, condensed water injected with wet steam exits the production wells at nearly the same temperature as injected, thereby providing negligible reservoir heating benefits and imposing additional burdens on artificial lift systems.

Based on this, the present study attempts to separate the heat terms and incorporate steam quality into the classical Butler SAGD productivity prediction model. Using the theoretical model, this section investigates how steam quality affects SAGD productivity. In Butler’s classical SAGD productivity model, it is assumed that steam possesses sufficient dryness (i.e., 100% steam quality) to form a steam chamber [13]. Subsequent productivity derivations are premised on and validated under this assumption. However, if the steam quality deviates from 100%, the primary effect on productivity can be attributed to changes in residual oil saturation within the steam chamber. Following this logic, the saturation change can be divided into two parts:

$${\Delta S}_o={\Delta S}_{sens}+{\Delta S}_{lat}$$

(1)

where ${\Delta S}_o$ is the overall oil saturation change; ${\Delta S}_{sens}$ is the saturation change resulting from the sensible heat released by steam cooling; and ${\Delta S}_{lat}$ is the saturation change driven by the latent heat of vaporization.

When analyzing deep SAGD reservoirs, the critical component to examine is the portion of steam undergoing vaporization, which can be represented as follows:

$${\Delta S}_o^{eff}={\Delta S}_{sens}+{x\Delta S}_{lat}$$

(2)

where ${\Delta S}_o^{eff}$ is the effective oil saturation change considering steam quality; $x$ is the steam quality, representing the fraction of steam by mass that remains vapor.

Since only dry steam effectively carries the latent heat of vaporization, the effective saturation increment used in displacement calculations is the following:

$${\Delta S}_o^{eff}=x{\Delta S}_o=x\left(1-{\overline{S}}_{or}\right)$$

(3)

where ${\overline{S}}_{or}$ is the average residual oil saturation within the steam chamber.

The total saturation change ${∆S}_o$ is predominantly governed by latent heat and can be estimated using the Cardwell–Parsons relationship for oil saturation in the steam chamber:

$${\overline{S}}_{or}(t)=0.43({\displaystyle \frac{v_s\varphi h}{kgt}}{)}^{0.4}$$

(4)

where ${\overline{S}}_{or}(t)$ is the time-dependent average residual oil saturation; $v_s$ is the steam chamber interface velocity (m/s); $\varphi$ is the reservoir porosity (fraction); h is the reservoir thickness (m); k is the reservoir permeability (m²); g is gravitational acceleration (9.81 m/s²); and t is time (s).

This relationship describes the temporal variation in residual oil saturation under dry steam conditions.

Considering steam quality adjustments in productivity calculations, the productivity equations for different SAGD stages are shown below.

During the vertical rising stage of the steam chamber, the productivity equation is modified as follows:

$$q_{\mathit{oil},\mathit{rising}}=3L({\displaystyle \frac{kg\alpha }{m{v}_s}}{)}^{2/3}(\Delta S_o^{\mathit{eff}}\varphi {)}^{2/3}{t}^{1/3}\approx 3L({\displaystyle \frac{kg\alpha }{m{v}_s}}{)}^{2/3}(x\Delta S_o\varphi {)}^{2/3}{t}^{1/3}$$

(5)

where $q_{\mathit{oil},\mathit{rising}}$ is the oil production rate during the vertical rise stage (m³/s); $L$ is the horizontal length of the steam chamber (m); $\alpha$ is the thermal diffusivity of the reservoir (m²/s); and $m$ is the viscosity–temperature parameter of the oil (m²/s·K).

At this stage, productivity is influenced by the term $x^{2/3}$;

During the horizontal expansion stage, the productivity equation changes to the following:

$$q_{\mathit{oil},\mathit{spreading}}=2L\sqrt{{\displaystyle \frac{1.3kg\alpha \varphi (\Delta S_o^{\mathit{eff}})h}{m{v}_s}}}\approx 2L\sqrt{{\displaystyle \frac{1.3kg\alpha \varphi (x\Delta S_o)h}{m{v}_s}}}$$

(6)

where $q_{\mathit{oil},\mathit{spreading}}$ is the oil production rate during the horizontal expansion (m³/s). At this stage, productivity is influenced by the term $\sqrt{x}$;

Similarly, in the decline stage, productivity is also directly proportional to steam quality via this term.

The analysis above indicates that when steam quality effects are considered, productivity during the steam chamber’s lateral expansion and decline stages is reduced proportionally to $\sqrt{x}$, whereas productivity during the vertical rise stage is reduced proportionally to $x^{2/3}$.

Table 2. The comparison of reservoir and operating conditions for typical SAGD projects.

Parameter	Xinjiang Qigu Formation	Liaohe Guantao Formation	Shu 1-38-32 Block
Burial Depth (m)	150–500	515–720	875–1015
Porosity	0.302	0.363	0.25
Oil Saturation (%)	0.73	0.69	0.52
Permeability (mD)	2800	5540	1335
Pay Zone Thickness (m)	38	77	44
Initial Crude Oil Viscosity (mPa·s)	182,000	231,900	347,286
Crude Oil Viscosity at Steam Temp (mPa·s)	14.2	4.1	4.2
SAGD Operating Pressure (MPa)	1.25	5.4	4

presents a comparison between the operating conditions and reservoir parameters for the target block and two of the most representative SAGD projects in China: the Qigu Formation SAGD project in Xinjiang and the Guantao Formation SAGD project in Liaohe. Based on the previously derived relationship between steam quality and SAGD production performance, a theoretical analysis was conducted to evaluate the impact of reservoir depth on SAGD productivity and the cumulative oil–steam ratio (cOSR), assuming a constant steam injection rate of 150 t/d across all cases. Figure 5 shows the productivity prediction results. It can be observed that the peak oil production rate for the target block is approximately 58 t/d. Due to the influence of reduced steam quality at greater depths, productivity is significantly lower compared to that of the Guantao and Qigu projects. Figure 6 further illustrates the effect of increasing reservoir depth—and the corresponding decrease in steam quality—on the cOSR. According to the simulation, conventional SAGD projects are expected to achieve a cOSR greater than 0.26. However, for the target reservoir at a depth of 950 m, the predicted cOSR drops to approximately 0.24. If the reservoir depth increases further, for example, to 1400 m, the cOSR is projected to decline to around 0.22. These results reaffirm the earlier theoretical conclusion that the primary challenge in SAGD development for deep extra-heavy oil reservoirs lies in the adverse impact of reservoir depth on steam quality. This degradation in steam dryness directly affects the thermal efficiency and results in a substantial decline in both productivity and the oil–steam ratio compared to conventional SAGD operations.

3. Numerical Simulation Study on ES-SAGD Mechanism

3.1. Model Development

Table 3. Reservoir properties of numerical simulation model.

Reservoir Parameter of Shu 1-38-32
Crude Oil Viscosity, mPa·s	109,269
Crude Oil Density at 20 °C	0.96
Reservoir Depth, m	875–1015
Continuous Pay Zone Thickness, m	44
Porosity	0.25
Permeability, 10−3 m2 (i.e., mD)	1335
Oil Saturation	0.65
Net-to-Gross Ratio	0.8

summarizes the average reservoir properties of the Shu 1-38-32 block. Based on the well configuration of the target block’s pilot, the model was built with a single horizontal production well (at the reservoir base) and multiple vertical injection wells rather than a standard two-horizontal-well SAGD pair. This configuration reflects the field’s development plan, where existing vertical wells from prior cyclic steam stimulations were converted to injectors for the ES-SAGD phase, with a new horizontal well as the producer. This approach allowed us to simulate SAGD-like drainage with the available wells; effectively, it is a hybrid well configuration that has been simplified to one well pair unit in the model for study purposes. The model dimensions are 90 m in width, 300 m in length, and 40 m in thickness, with grid discretization set to 45 cells at 2 m in the i-direction, 60 cells at 5 m in the j-direction, and 20 cells at 2 m in the k-direction as shown in Figure 7. This numerical approach is in line with the methods used in recent studies of coupled thermal–fluid processes in petroleum engineering, lending credibility to our model’s methodology [14]. To investigate ES-SAGD, a compositional PVT model was developed for the solvent–heavy oil system. The solvent selection was guided by steam chamber operating conditions (approximately 4 MPa). Hexane was chosen as the co-injected solvent due to its phase change characteristics being closest to steam under the given reservoir pressure. Hexane’s boiling point and latent heat at operating pressure (~4 MPa) are very close to those of steam, ensuring that it co-condenses with steam in the reservoir [15]. For instance, at 4 MPa, hexane’s saturation temperature (~270 °C) nearly matches that of water (~250 °C). Prior studies have likewise found that among various solvents, n-hexane yields the greatest enhancement in ES-SAGD recovery under similar conditions—outperforming lighter solvents (which remain in the vapor phase and contribute less condensate) and heavier solvents (which condense too early) [16,17]. Figure 8 presents the variation in viscosity and density resulting from the mixing of hexane with heavy oil, providing a solid foundation for the subsequent optimization of the ES-SAGD process. It is acknowledged that prior to heating, extra-heavy oil has negligible mobility. In ES-SAGD, however, the solvent (hexane) is co-injected into the vapor phase along with steam, allowing it to propagate through the reservoir as part of the growing steam chamber. The hot steam quickly creates a high-temperature zone, reducing oil viscosity locally and establishing initial communication. Hexane, being volatile, travels with the steam and condenses at the chamber edges, where it dissolves into the oil and dramatically lowers the oil’s viscosity in situ. This process enables hexane to efficiently penetrate into the deep, cold oil zone by piggybacking on the steam chamber expansion. As the solvent-enriched condensate drains, it improves oil mobility ahead of the chamber, facilitating the further advancement of both the steam and solvent.

3.2. Comparison of ES-SAGD vs. SAGD

Based on the historical production performance of the target block, the reservoir underwent a 7-year and 9-month period of CSS prior to transitioning into SAGD operations. By the end of the cyclic steam stimulation, the oil recovery factor had reached 30.3%, with an average reservoir temperature of 67.71 °C and an average reservoir pressure of 19.91 kPa. All subsequent SAGD and ES-SAGD simulations were initiated from this post-CSS stage.

Figure 9 presents a comparison of oil production rates and cumulative oil production between conventional SAGD and ES-SAGD, in which 3 vol% of hexane was co-injected from the beginning of the SAGD stage. Figure 10 further compares the cumulative oil production and cOSR between the two processes. The simulation results indicate that the ES-SAGD process with 3 vol% hexane achieved a peak oil production rate of 149.7 t/d, representing a 36.5% increase compared to conventional SAGD. Cumulative oil production improved by 17.3%, and the cOSR increased from 0.137 to 0.218. Also, it can be seen that ES-SAGD provides an initial boost to oil rates (as seen in Figure 9) by more rapidly mobilizing oil with the solvent, leading to the faster depletion of the most readily drainable oil. Consequently, in later years, the ES-SAGD chamber’s growth slows down, and its oil rate declines, whereas the conventional SAGD (which started slower) continues to produce results from the remaining pockets of oil at a steadier rate. This makes the late-stage SAGD instantaneous oil rate appear higher, even though cumulative oil production in the ES-SAGD remains greater. Ultimately, by the end of the 7-year period, ES-SAGD attained a higher recovery factor and cumulative production than SAGD (17.3% more oil), even though its instantaneous rate was overtaken by SAGD in the final stage. The early-time advantages of ES-SAGD translate to higher overall recovery, which is the basis of our conclusions. In addition, numerical simulation results show that the hexane recovery ratio reaches approximately 72%. Assuming that the recovered solvent is reinjected without incurring additional solvent purchase costs, the oil gain per unit mass of solvent can be calculated by dividing the incremental, cumulative oil production of ES-SAGD over SAGD by the amount of solvent consumed. Based on this simulation, the oil gain per unit mass of the solvent is approximately 4.31 t/t, demonstrating favorable oil enhancement and promising economic performance.

In addition to the approaches investigated here, novel low-GHG variants of SAGD have been proposed to improve efficiency while reducing emissions [18], including the use of alternative solvents such as dimethyl ether (DME) [19]. Nevertheless, conventional solvent-assisted SAGD studies have consistently shown benefits: laboratory experiments demonstrated lower steam requirements with butane/propane co-injection, and our use of hexane is similarly supported by sand-pack tests reporting enhanced recovery with C₆ solvents [20]. Field pilots in Alberta have likewise observed higher oil rates when 5–20% hydrocarbon solvents are added to steam. On the other hand, the performance gains can be modest in reservoirs with limited thickness; a solvent-aided process trial in a thin, heavy oil reservoir achieved only minor improvements over steam alone [21]. Overall, thermal methods remain indispensable for heavy oil recovery [22], and our results demonstrate that targeted solvent co-injection can significantly improve SAGD performance under the specific conditions of deep extra-heavy oil reservoirs.

Figure 11 presents the three-phase saturation distribution and concentration profile of the C6 component at a representative mid-stage of the ES-SAGD process. These results provide a detailed basis for analyzing the production enhancement mechanisms of ES-SAGD. As shown in the figure, the horizontal development of the ES-SAGD steam chamber can be divided into four distinct zones: the steam zone (A), the steam condensation zone (B), the oil drainage zone (C), and the cold oil zone (D).

In the steam zone (A), the chamber is fully saturated with steam, and both temperature and pressure are uniformly distributed. The steam condensation zone (B) is characterized by an evident phase change, where the water saturation increases significantly due to steam condensation. The effect and main distribution of the solvent, specifically hexane, are concentrated in the oil drainage zone (C). This zone is distinguished by solvent accumulation, where steam condensation leads to an increased proportion of hexane, a reduction in gas saturation, and a shift in the local phase composition. In the oil drainage zone, the combined effects of the condensed steam and dissolved solvent substantially reduce the effective viscosity of the oil, enhance oil mobility, and increase the oil production rate and overall recovery. The cold oil zone (D) represents the region not yet influenced by the steam chamber. In this area, the temperature remains low and insufficient to mobilize the oil, which, therefore, cannot flow into the production well.

4. Operational Optimization and Pilot Performance Forecasting for ES-SAGD

4.1. Operational Parameters Optimization on ES-SAGD

This section presents a sensitivity analysis and optimization study of key operational parameters for solvent-assisted SAGD, including solvent concentration, injection timing, operating pressure, injection strategy, and optimal OSR. The evaluation methodology considers multiple performance indicators, including the incremental oil recovery per unit mass of the solvent, the solvent recovery factor, cumulative oil production, and cOSR.

Figure 12 compares the performance of ES-SAGD under varying hexane injection concentrations (1 vol%, 3 vol%, and 5 vol%). Among the tested scenarios, the 3 vol% hexane concentration yields the most favorable results, achieving a solvent recovery factor greater than 70%, a cumulative oil production of 142,000 m³, a cOSR of 0.218, and an incremental oil gain of 4.31 t per ton of the solvent injected. Although the 5 vol% hexane case achieved the highest instantaneous oil production rate, its advantages over the 3 vol% case were marginal in terms of the cumulative oil recovered. Meanwhile, the higher solvent concentration incurred diminishing returns and a lower efficiency of solvent usage. In fact, the 3 vol% scenarios provided nearly the same cumulative oil production as 5 vol% (differing by only ~1%) but with a significantly higher solvent recovery factor (>70% vs. ~60% for 5 vol%) and a greater oil gain per unit solvent (4.31 t oil per t solvent for 3% vs. lower for 5%). Therefore, the 3 vol% offered the best trade-off between enhanced oil recovery and solvent utilization. Beyond 3%, additional hexane led to only minor oil gains while substantially increasing solvent requirements, which is why we identified 3 vol% as the optimal concentration in our study. Figure 13 illustrates the steam chamber development and solvent concentration distribution for two different injection timings: early injections during the chamber’s rising phase and mid-term injections during the lateral expansion phase. Here, early injections refer to solvent co-injection during the initial rising phase of the steam chamber (immediately after SAGD startup), whereas mid-term injections refer to those introduced into the solvent during the chamber’s lateral expansion phase (after the steam chamber reached the top of the reservoir and spread outward horizontally). The results indicate that injecting the solvent during the lateral expansion phase significantly enhances steam chamber growth and accelerates oil production. Compared to early injection, the mid-term solvent injection scenario shows a higher solvent recovery factor (77.3%), the highest incremental oil gain (9.78 t/t), and the greatest improvement in cOSR. Additionally, a sensitivity analysis was conducted on the impact of operating pressure. As shown in Figure 14, varying the steam chamber pressure leads to notable differences in recovery performance. At operating pressures of 2.5 MPa and 4 MPa, cumulative oil production reaches 136,000 m³ and 145,900 m³, respectively. The solvent recovery factor increases from 70.39% at 2.5 MPa to 73.04% at 4 MPa. The cOSR at 2.5 MPa and 3 MPa is 0.203 and 0.197, respectively, while at pressures above 3 MPa, the cOSR stabilizes above 0.21. These results suggest that maintaining an operating pressure above 3 MPa is recommended for optimal performance. Figure 15 compares three different solvent injection strategies under a 3 vol% hexane concentration: continuous injection, a 3-month cyclic injection, and a 6-month cyclic injection. While all three strategies result in similar cumulative oil production and peak oil rates, the 6-month cyclic injection scenario demonstrates superior efficiency. It achieves the highest cOSR (0.221) and the greatest oil gain per unit of the solvent (4.34 t/t), indicating a better balance between solvent utilization and recovery efficiency.

In summary, a comprehensive sensitivity analysis and optimization of the key operational parameters for ES-SAGD were completed. The recommended operational strategy is to inject n-hexane at a concentration of 3 vol% using a cyclic injection approach during the initial stage of lateral steam chamber development while maintaining the chamber pressure above 3 MPa.

4.2. Pilot Performance Forecasting for ES-SAGD

Based on the optimized operational parameters described above and using a typical ES-SAGD productivity forecasting model, the performance of the entire Shu 1-38-32 block—comprising five SAGD well pairs—was evaluated through numerical simulation and statistical trend analysis. The forecast assumes an operating pressure of 3.5 MPa, a steam injection rate of 300 m³/day, a 6-month solvent injection cycle, and the co-injection of 3 vol% hexane over a 7-year production period. The results are shown in

Table 4. Performance forecast for Shu 1-38-32 Block (ES-SAGD). (Note: 1 t = one metric ton (1000 kg); C₆ injection is reported on a mass basis.).

Year	Annual Steam Injection (104 t)	Annual C6 Injection (104 t)	Annual Liquid Production (104 t)	Annual Oil Production (104 t)	Annual Water Production (104 t)	Water Cut (%)	Oil-Steam Ratio	Steam-Oil Ratio	Recovery Factor (%)
1	39.26	0.38	44.17	6.64	37.53	0.85	0.17	1.2	32.5
2	43.97	1.36	54.92	10.32	44.6	0.81	0.23	1.2	39.3
3	43.97	1.36	55.84	10.94	44.9	0.81	0.24	1.2	46.1
4	43.97	1.36	57.87	9.42	48.45	0.84	0.21	1.3	52.3
5	43.97	1.36	56.62	7.73	48.89	0.86	0.18	1.2	57.3
6	43.97	1.36	58.44	5.82	52.62	0.9	0.13	1.3	61.2
7	26.38	0.51	37.84	3.35	34.49	0.91	0.13	1.4	63.3
Total	285.51	7.64	370.7	53.77	–	–	0.19	1.3	–

, which indicates that the ES-SAGD process achieves an OSR of 1.3. The recovery factor at the end of the initial CSS stage reaches 28.7%, while the final cumulative recovery factor improves up to 63.3%.

Figure 16 presents the annual oil production and OSR projections for the ES-SAGD pilot area in comparison with conventional SAGD performance. As shown in the figure, the ES-SAGD approach yields a cumulative oil production increase of approximately 58,000 tons compared to conventional SAGD, corresponding to an improvement of 3.3 percentage points in the ultimate recovery factor. This enhancement is attributed to the solvent’s ability to increase oil production rates, which results in a higher instantaneous oil–steam ratio and reduced water cut in the produced fluids. Statistical analysis further shows that the cumulative oil–steam ratio of the Shu 1-38-32 block is expected to increase from 0.13 under conventional SAGD to 0.19 under the optimized ES-SAGD scenario.

5. Conclusions

In this study, a feasibility assessment of a pilot-scale ES-SAGD scheme in the Shu 1-38-32 block of the Liaohe Oilfield was conducted. These findings provide strong technical and economic justification for the field deployment of ES-SAGD in deep, viscous reservoirs and offer a blueprint for similar applications in other heavy-oil fields worldwide. The conclusions arising from this study are the following:

• Depth-induced steam quality degradation is identified as the primary limitation to SAGD efficiency in deep extra-heavy oil reservoirs. Theoretical modifications to the Butler model reveal that declining steam quality with increasing burial depth significantly reduces productivity and the cumulative oil–steam ratio.
• Numerical simulations demonstrate the technical feasibility of ES-SAGD using 3 vol% n-hexane, achieving a 36.5% higher peak oil production and a 17.3% increase in cumulative oil recovery compared to conventional SAGD. The solvent recovery factor exceeds 72%, with an oil gain per unit of the solvent mass of approximately 4.31 t/t.
• Sensitivity analysis shows that optimal ES-SAGD performance is achieved when using the 3 vol% solvent concentration, employing cyclic injections during the lateral expansion phase, and utilizing operating pressure maintained above 3 MPa. Under these conditions, the process achieves a cOSR of 0.218.
• Pilot-scale forecasting confirms the economic viability of ES-SAGD in the Shu 1-38-32 block. The cumulative oil recovery factor improved from 28.7% to 63.3% over a seven-year development period, with the projected cOSR rising from 0.13 to 0.19.

It should be noted that large-scale solvent co-injection introduces economic and environmental considerations. Solvent purchase and recycling costs may impact the economic feasibility, although our analysis assumes solvent reinjection to mitigate costs. Environmentally, n-hexane is a volatile organic compound with potential health and safety hazards; thus, effective solvent recovery (>70% in our case) and emission control are crucial for field implementation. Future pilot tests should evaluate solvent losses, toxicity management, and the overall eco-efficiency of ES-SAGD.