Optimization of EOR-CCS Core Flooding Experiments Using Taguchi DOE and Regression Analysis

Tariq Ali Chandio 1,2✉ Emailtariqalichandio@graduate.utm.my

M. N. A. M. Norddin 1,3

A. A.A. Rasol 1,4

Mansoor Zoveidavianpoor 5

Department of Petroleum Engineering, Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia (UTM) 81310 UTM Skudai, Johor Bahru Malaysia

2 Department of Petroleum and Gas Engineering Dawood University of Engineering & Technology (DUET) 74800 Karachi, Sindh Pakistan

3 Advanced Membrane Technology Research Centre (AMTEC) Nanostructured Materials Research Group (NMRG) - MD - Frontier Materials, Universiti Teknologi Malaysia Johor Bahru 81310 UTM Skudai, Johor Malaysia

4 Malaysia Petroleum Resources Corporation Institute for Oil and Gas (MPRC IFOG-UTM), Universiti Teknologi Malaysia Johor Bahru Malaysia

5 Department of Energy, Environment and Climate Action Senior Environmental Assessment Officer (Petroleum) East Melbourne Victoria Australia

Tariq Ali Chandio^{1,2, ✉}, M. N. A. M. Norddin^1,3, A.A.A. Rasol^1,4,✉, Mansoor Zoveidavianpoor⁵

1 Department of Petroleum Engineering, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Skudai, Johor Bahru, Malaysia.

2 Department of Petroleum and Gas Engineering, Dawood University of Engineering & Technology (DUET), Karachi 74800, Sindh, Pakistan.

3 Advanced Membrane Technology Research Centre (AMTEC), Nanostructured Materials Research Group (NMRG) - MD - Frontier Materials, Universiti Teknologi Malaysia, Skudai, Johor Bahru, Johor 81310 UTM, Malaysia

4 Malaysia Petroleum Resources Corporation Institute forOil and Gas (MPRC IFOG-UTM), Universiti TeknologiMalaysia, Johor Bahru, Malaysia.

5 Senior Environmental Assessment Officer (Petroleum), Department of Energy, Environment and Climate Action, East Melbourne, Victoria, Australia.

^✉ Corresponding Author: tariqalichandio@graduate.utm.my

ABSTRACT

Enhanced Oil Recovery (EOR), when aligned with Carbon Capture and Storage (CCS) objectives, increases oil production while addressing climate concerns through geological CO₂ storage. Water Alternating-Gas (WAG) injection is an effective enhanced oil recovery (EOR) technique in oil mobilizing and CO₂ storage. In this study, we conducted laboratory-scale experiments to investigate the impact of variable operating parameters on both oil recovery and CO₂ storage efficiency. This study focuses on balancing recovery and storage output by identifying optimum conditions. The experimental design included four control variables — injector rate, pressure, WAG ratio, and brine salinity —each tested at three levels: 0.15–0.50 mL/min, 650–850 psi, 1:1–2:1, and 20,000–30,000 ppm, respectively. The Taguchi design approach was employed for the experiments, facilitating a structured analysis of key variables with fewer experimental runs. The regression analysis, Signal-to-Noise (S/N) ratios, and an Overall Performance Index (OPI) were employed to evaluate oil recovery and CO₂ storage, both as separate responses and in combination. The findings outlined a clear trade-off between the two

objectives

higher injection rates increased oil recovery, whereas lower rates favored CO₂ storage. The WAG ratio had a limited effect on oil recovery but consistently enhanced CO₂ storage, particularly at a 1:2 ratio. Salinity demonstrated a secondary yet positive influence, improving the overall performance, while pressure showed the least noticeable impact. Statistical ranking identified the injection rate as the most influential parameter, followed by salinity, WAG ratio, and pressure. Both the injection rate and WAG ratio were found to have a strong impact on responses. According to the OPI-based optimization results, the most favorable operating conditions were an injection rate of 0.15 mL/min, pressure of 850 psi, salinity of 30,000 ppm, and a WAG ratio of 1:2. In addition, predictive regression models forecasted oil recovery and CO₂ storage across different operating conditions, offering valuable guidance for future experiments and field applications. Overall, the findings highlight the significance of injection rate and WAG ratio in determining immiscible WAG performance, offering practical insights for enhancing EOR-CCS optimization.

Keywords:

Enhanced Oil Recovery (EOR)

Carbon Capture and Storage (CCS)

Water-Alternating-Gas (WAG) injection

CO₂ sequestration

EOR-CCS optimization

1. Introduction

The population growth, industrial expansion, and urbanization have resulted in rising global energy demand. To meet these growing needs, countries have historically relied on a combination of renewable and non-renewable energy sources. In recent years, however, there has been a clear shift towards renewables while efforts are being made to reduce the carbon footprint of conventional energy systems. As part of clean-energy commitments, fossil fuel–dependent nations are enhancing existing technologies to achieve lower or net-zero emissions. One promising pathway involves capturing carbon dioxide from the atmosphere and either utilizing it in industrial applications or storing it permanently in deep geological formations, a process known as Carbon Capture and Storage (CCS). The integration of carbon capture and storage with enhanced oil recovery is referred to as CCS-EOR [1]. When integrated with Enhanced Oil Recovery (EOR), this approach not only boosts hydrocarbon production but also provides a practical means of reducing CO₂ emissions, creating a valuable synergy between energy production and climate mitigation [2, 3]. Among the various EOR techniques, Water-Alternating-Gas (WAG) injection is preferred due to its effectiveness in improving sweep efficiency, delaying gas breakthrough, and maintaining reservoir pressure. WAG injection is the cyclic process involving alternating injections of water and gas [4, 5].

WAG injection has been widely explored as an Enhanced Oil Recovery (EOR) technique for improving oil recovery under both miscible and immiscible conditions. In miscible WAG processes, the injected CO₂ dissolves into the crude oil, lowering its viscosity and increasing displacement efficiency. Conversely, in immiscible WAG operations, recovery depends more on fluid flow dynamics and capillary forces to mobilize trapped hydrocarbons. The success of immiscible WAG largely relied on optimizing several operational parameters, including the WAG ratio, injection rate, reservoir pressure, and fluid salinity. An appropriate WAG ratio helps balance gas mobility and water sweep efficiency, preventing early gas breakthrough. Similarly, the injection rate must be carefully managed, as it influences the stability of the displacement front and affects gas channeling. Reservoir pressure plays a key role as it governs gas phase behavior and CO₂ solubility in water, which is key in oil mobility CO₂ storage. Additionally, the salinity of the reservoir and injectant impacts recovery efficiency by altering wettability, interfacial tension, and overall oil mobility [6]. Therefore, evaluating and optimizing the influence of these parameters is crucial for improving the efficiency and sustainability of EOR-CCS operations. Numerous studies have employed both experimental and numerical simulation approaches to investigate and refine WAG injection performance under varying reservoir conditions. The aim of the research previously had been to identify the most effective combinations of operational parameters that maximize oil recovery while simultaneously enhancing CO₂ storage potential [7–9]. A simulation-based study conducted in the North Sea examined the effects of both miscible and near-miscible WAG injection, which outlined the potential of simultaneous recovery enhancement and improved storage efficiency [10]. While experimental core flooding tests are commonly used to evaluate WAG injection performance but in problems like that of CCS-EOR, the increased control variables make the evaluation time, labour, and cost intensive. A structured optimization approach, such as the Design of Experiments (DOE), helps minimize experimental runs while maximizing the reliability of results.

To bridge this gap, the present study applies the Taguchi Design of Experiments (DOE) approach to evaluate the effects of injection rate, WAG ratio, reservoir pressure, and salinity on both oil recovery and CO₂ storage with lower resource allocation. An L9 orthogonal array is employed to minimize the number of experimental runs while ensuring a comprehensive assessment of all factors. Core flooding experiments are conducted to obtain response data, which is then analyzed using regression fitting to develop predictive models. Minitab software is used for statistical analysis to determine the significance of each parameter and to optimize the WAG process for maximum efficiency.

2. Literature Review

Enhanced Oil Recovery (EOR) combined with Carbon Capture and Storage (CCS) has attracted considerable attention as an effective strategy to maximize hydrocarbon production while mitigating atmospheric CO₂ emissions. With growing global emphasis on reducing carbon footprints and ensuring energy security, CO₂-EOR has emerged as a promising dual-purpose approach. In this process, captured CO₂ from industrial sources or environmental sources is injected into depleted oil reservoirs, where it not only enhances oil displacement but also becomes stored within geological formations permanently or for long periods. Research indicates that when properly managed, CO₂ storage during EOR operations can achieve long-term sequestration, thereby contributing significantly to greenhouse gas reduction and climate change mitigation [11–13].

2.1 Previous Studies on CO₂-Based WAG Injection

Water-Alternating-Gas (WAG) injection using CO₂ has been extensively investigated as an effective Enhanced Oil Recovery (EOR) technique. When implemented properly, it improves sweep efficiency and addresses the limitations of conventional water or gas flooding alone. Researchers have examined both miscible and immiscible CO₂-WAG schemes under a range of reservoir conditions.Masalmeh et al. (2025) analyzed the effects of various WAG injection strategies in carbonate reservoirs, emphasizing the critical role of injection sequencing and slug size[14]. Another study outlines the importance of WAG intervention timing, outlining that early implementation helps suppress gas channelling, leading to improved recovery[15]. Additionally, Kulkarni & Rao (2005) comprehensively analyzed WAG injection mechanisms [16], whileBennion & Bachu (2006) examined the influence of CO₂ properties on WAG performance in saline aquifers [17]. These studies emphasize the need for further research, with a focus on operational variables in immiscible WAG injection with a dual objective approach.

2.2 Mechanisms

Depending on the interaction between the injected CO₂ and reservoir oil, WAG injection is classified as either miscible or immiscible.

Miscible WAG Injection: Miscibility occurs when injected CO₂ dissolves into the crude oil, lowering its viscosity and enhancing its mobility within the reservoir. This process significantly improves displacement efficiency but can only be achieved under specific pressure conditions, referred to as the Minimum Miscibility Pressure (MMP) [18]. Miscible WAG is particularly effective in light oil reservoirs; however, its application can be constrained by insufficient reservoir pressure and challenges associated with high gas mobility.

Immiscible WAG Injection: Under immiscible conditions, CO₂ does not dissolve significantly into the crude oil, and displacement primarily occurs through the combined movement of gas and water. This approach is particularly suitable for reservoirs where the Minimum Miscibility Pressure (MMP) cannot be achieved. Although immiscible WAG improves sweep efficiency and helps maintain reservoir pressure, its overall effectiveness largely depends on the careful optimization of key operating parameters such as injection rate, WAG ratio, and salinity [19, 20]. Hence, in immiscible injections, parametric control becomes necessary to achieve optimum conditions.

2.3 Key Factors Affecting WAG Performance

Several operational parameters significantly influence the efficiency of WAG injection, especially when immiscible injection is considered. These include:

WAG ratio

The Water-Alternating-Gas (WAG) ratio, defined as the volume ratio of water to gas injected, plays a crucial role in balancing gas mobility with water sweep efficiency. A higher WAG ratio generally enhances sweep efficiency but can result in water blockage within the reservoir, whereas a lower ratio increases the risk of early gas breakthrough and reduced displacement effectiveness [21, 22].

Injection Rate

The injection rate plays a key role in maintaining the stability of the displacement front and ensuring uniform CO₂ distribution within the reservoir. Selecting an optimal injection rate helps prevent issues such as viscous fingering and gas channeling, thereby enhancing oil recovery and improving the overall efficiency of the WAG process [23].

Reservoir Pressure

Reservoir pressure is a critical factor in determining whether CO₂ injection occurs under miscible or immiscible conditions. In immiscible WAG operations, maintaining an optimal pressure range is essential to ensure effective CO₂ distribution, improved sweep efficiency, and enhanced CO₂ retention within the reservoir [24, 25] .

Salinity

Salinity influences several key reservoir properties, including wettability, interfacial tension, and the solubility of CO₂ in water. Elevated salinity levels tend to reduce CO₂ solubility, which can negatively affect both oil displacement efficiency and the overall capacity for CO₂ storage [17, 26].

2.4 Application of Design of Experiments (DOE) in EOR Research

The Design of Experiments (DOE) methodology is a systematic and efficient framework for optimizing engineering applications while reducing the number of experimental trials required. Among various DOE techniques, the Taguchi method is particularly valued for its robustness in handling multiple factors and their interactions [27–29]. Employing orthogonal arrays enables a structured assessment of parameter effects, allowing researchers to enhance process efficiency and reliability with a limited set of experiments [30, 31]. Several studies have employed the Taguchi method to design and evaluate the parametric performance of various engineering processes. In recent years, this approach has also been applied to the simulation-based design of WAG injection models, enabling the estimation of CO₂ storage capacity during and after the injection period[32]. Furthermore, Romany et al (2018) developed a simulation-based DOE workflow to optimize four different recovery schemes, demonstrating the effectiveness of structured experimental design in evaluating complex reservoir processes [33]. These findings emphasize the potential of DOE approaches for evaluating injection strategies and improving the performance of immiscible WAG operations. The integration of Enhanced Oil Recovery (EOR) and Carbon Capture and Storage (CCS) through Water-Alternating-Gas (WAG) injection is a promising pathway. Although extensive research has been conducted on CO₂-WAG processes, parameter optimization under immiscible conditions is still a major area of investigation. The application of the Taguchi Design of Experiments (DOE) method offers a systematic framework for refining injection strategies, minimizing experimental effort, and improving overall process efficiency.

Therefore, the present study employs a DOE approach to assess the effects of injection rate, WAG ratio, reservoir pressure, and salinity on both oil recovery and CO₂ storage performance, thereby addressing critical gaps in the optimization of immiscible WAG operations. Previous studies on EOR-CCS have investigated the effects of various parameters on oil recovery and CO₂ storage using simulation-based sensitivity analyses and detailed laboratory core-flooding experiments. While Design of Experiments (DOE) methodologies have been applied in reservoir engineering, their use has predominantly focused on single-objective optimization, typically aimed at enhancing either CO₂ injectivity or oil recovery, rather than addressing both objectives simultaneously. For instance, without doing any experiments, Mohamadi-Baghmolaei et al. (2024) used Taguchi design in simulation workflows to evaluate the effectiveness of CO₂ storage[32]. Similarly, Kamali et al. (2015) co-optimized recovery and storage by combining experimental and numerical analysis; however, they lacked a systematic experimental design framework [8]. Regression modeling has been widely employed in EOR studies to establish predictive correlations between experimental factors and recovery outcomes. However, such approaches often rely on full factorial designs, which can be resource-intensive. In contrast, the present study introduces an optimized framework for immiscible WAG core flooding that integrates the Taguchi Design of Experiments (DOE) with regression modeling in a novel way. This combined approach offers a statistically robust and resource-efficient methodology that minimizes experimental runs while generating predictive equations and a unified performance index (OPI). Collectively, these observations address a key methodological gap in dual-objective EOR-CCS optimization research by enabling simultaneous evaluation of oil recovery and CO₂ storage performance.

3 Methodology

This methodology aims to evaluate and analyze EOR-CCS performance using a Design of Experiments (DOE) framework. The Taguchi DOE approach offers an efficient method for conducting multi-variable engineering experiments by reducing resource requirements and experimental time while maximizing the information gained. In this study, major control parameters were systematically arranged into an orthogonal array to ensure balanced and statistically meaningful testing. Core-flooding experiments were then conducted to assess oil recovery and CO₂ storage under different combinations of control variables defined by the DOE matrix. The resulting data were analyzed statistically to determine the significance of each factor, followed by regression modeling to develop predictive relationships and interpret performance trends.

3.1 Experimental Design using Taguchi DOE

The design of the experiment (DOE) methodology was implemented before experimental runs to ensure a scientific approach in designing experiments. The Taguchi Method was considered for experiment design because it is efficient and cost-effective in designing complex multifactorial experiments. This method has been proven to cover extensively the interactions at various intervals of all the factors, which ensures control in improving and optimizing. The design was limited to evaluating the impact of injection rate (cc/min), pressure (psi), salinity (ppm), and WAG ratio on oil recovery and CO₂ storage. The ranges for the control variables are given in the table below.

Table 1

Details of design factor (variable) levels
S. No	Variable	Range
1	Injection Rate (cc/min)	0.15, 0.30, 0.5
2	Salinity (ppm)	20000, 25000, 30000
3	WAG ratio (ratio)	1:1, 2:1, 1:2
4	Injection Pressure (psi)	650, 750, 850

The factorial design (L9(3^4)) for 4 variables with 3 levels outlined, 9 experimental runs as given in the table below. The design allows for the interaction evaluation of each level of the variable with all the levels of the other variables present in the design. Hence, the impact of variable interaction can also be evaluated.

Table 2

Taguchi design for 4 factorials at 3 levels
Exp No.	Inj Rate, c/min	Press, psi	WAG ratio	Salinity, ppm
1	0.15	650	1:1	20000
2	0.15	750	2:1	25000
3	0.15	850	1:2	30000
4	0.3	650	2:1	30000
5	0.3	750	1:2	20000
6	0.3	850	1:1	25000
7	0.5	650	1:2	25000
8	0.5	750	1:1	30000
9	0.5	850	2:1	20000

3.2 Core Flooding Experiments

A homogeneous Buff Berea sandstone core was utilized for the experiments. The core was cleaned, then heated in an oven for 24 hours to ensure the removal of any remaining residues and moisture. The core was saturated under vacuum conditions until no air bubbles were observed, indicating complete saturation. Porosity and brine permeability were measured using a porosimeter and saturation method. The petrophysical properties showed similarity or lower variance due to the similar source. The details are given in the Table below:

Table 3

Properties of the Buff Berea Sandstone core sample
Core	Length (cm)	Dia (cm)	Porosity %	Brine Permeability (md)
Buff Berea Sandstone	7.5	3.6	24.9	137

Multi-accumulator core flooding equipment, along with two injection pumps, was set as shown in the schematic in Fig. 1. Gas was injected into the accumulators per the designed pressure (for each run), with the back pressure regulator (BPR) set at the designed pressure to ensure the system remained pressurized as per the design pressure. The overburden pressure was applied using an injection pump at 1.5 times the system pressure. Brine was first injected at a flow rate of 5 cc/min for 3 pore volumes (PV) to ensure complete saturation. This was followed by the injection of paraffin oil at 2 cc/min until no water was produced, thereby establishing initial oil saturation and residual water saturation. Waterflooding was then carried out at a rate of 2 cc/min for 3 PV to reach irreducible oil saturation, with oil recovery monitored at the outlet using a graduated measuring cylinder. Once no further oil could be recovered by waterflooding, alternating injections of CO₂ and brine were carried out according to the experimental design matrix established using the Design of Experiments (DOE) approach.

Fig. 1

Schematic of the core flooding experimental setup

Gas production was continuously monitored using a wet gas meter, and measurements were manually recorded after every 0.25 pore volumes (PV) of CO₂ injection. The CO₂ storage efficiency was determined by comparing the volume of CO₂ retained within the core to its total pore volume. As mineral trapping was not considered in this study, the evaluated storage mechanisms were assumed to be primarily governed by solubility trapping and residual trapping processes.

3.3 Regression Analysis & Statistical Validation

Minitab 18 was employed to analyze the Taguchi design of experiments, focusing on both the mean responses and the Signal-to-Noise (S/N) ratios to evaluate the influence of various parameters on CO₂ storage and oil recovery outcomes. The S/N ratio analysis provided valuable insight into the relative sensitivity of each factor and its levels, highlighting how variations in the control parameters affected overall performance. In addition to S/N evaluation, main effects plots were generated to identify the contribution and statistical significance of each factor to the total variance. Subsequently, a regression model was developed and validated statistically by comparing predicted and experimental results, ensuring the model’s accuracy, robustness, and predictive reliability.

4 Results and Discussion

The core flooding experiments were conducted according to the experimental design, with systematic variation of key parameters across the test matrix to evaluate their effects on oil recovery and CO₂ storage using Berea sandstone. Light crude oil from Malaysian oil fields was utilized as the reservoir fluid. Each experimental run was structured to isolate and analyze the influence of injection rate, reservoir pressure, salinity, and WAG ratio on the optimization outcomes. A comprehensive evaluation of the experimental data, supported by statistical analyses, was then performed to assess the individual and interactive effects of these parameters on overall EOR-CCS performance.

4.1 Core flooding

The core flooding experiments revealed distinct trends in the influence of injection parameters on oil recovery and CO₂ storage, as shown in Table 4 below. Higher injection rates (i.e., 0.3 and 0.5 ml/min) consistently enhanced oil recovery, likely due to more efficient displacement of resident fluids within the core. In contrast, CO₂ storage was generally more favorable at lower injection rates (i.e., 0.15 ml/min), which can be attributed to reduced viscous fingering and extended gas-rock contact time, promoting enhanced trapping mechanisms.

Table 4

Recovery and storage responses against each run
Exp No.	Inj Rate	Pressure	WAG	Salinity	Recovery % OOIP	Storage %
1	0.15	650	1:1	20000	65.20	65.00
2	0.15	750	2:1	25000	63.15	63.00
3	0.15	850	1:2	30000	66.00	66.50
4	0.3	650	2:1	30000	64.75	64.00
5	0.3	750	1:2	20000	62.30	66.50
6	0.3	850	1:1	25000	63.50	65.00
7	0.5	650	1:2	25000	65.75	59.05
8	0.5	750	1:1	30000	69.20	57.20
9	0.5	850	2:1	20000	68.60	53.50

The effects of injection pressure, salinity, and WAG ratio on both recovery and storage were comparatively less definitive, exhibiting non-monotonic and context-dependent behavior. These findings suggest the presence of complex interactions among reservoir properties, phase behavior, and flow dynamics that require further detailed investigation. For instance, higher salinity seemed to impair recovery in some runs, while in others it had minimal or even a positive impact. This variability indicates that these parameters likely interact in nonlinear ways, and their effects may be sensitive to the specific combination of variables present in each run.

Recovery

The recovery profile was almost identical for all nine cases, with the lowest recovery being recorded as 62.3% and the highest as 69.2%. Figure 2 shows the recovery profile of all the injected 5 PVs. The highest recoveries were found at higher rates, whereas other than injection rate, pressure was also observed to influence, as the lower pressure, i.e., 650 psi scenarios, performed better.

Figure 2: Recovery profile (% OOIP vs injected PVs) for all experiments

The recovery profiles across the experiments were generally similar in response; however, the highest ultimate oil recovery (69.2%) was observed at an injection rate of 0.5 cc/min, under the condition of a 1:1 WAG ratio, 750 psi injection pressure, and 3000 ppm brine salinity, as illustrated in Fig. 3 below.

Fig. 3

Recovery profile and Storage profile for the experimental run with the highest recovery

For comparison, the base recovery achieved from waterflooding (2 pore volumes injected) was 52%, indicating a significant enhancement due to WAG injection. This improvement in recovery is attributed primarily to the intermittent CO₂ slugs, where each 0.5 PV of CO₂ injection resulted in incremental oil production. In contrast, the subsequent water slugs contributed relatively less to oil recovery, suggesting that the gas phase played a more dominant role in mobilizing trapped oil.

Storage

The experimental results showed that CO₂ storage decreased with increasing injection rate, indicating that lower flow rates promote more effective trapping mechanisms. Moreover, larger CO₂ slug volumes consistently led to higher storage, underscoring the critical role of gas-phase injection volume in maximizing sequestration efficiency. As illustrated in the storage profiles (Fig. 4), experiments 3, 5, and 7 demonstrated the highest CO₂ storage capacities within their respective parameter ranges. Interestingly, all three experiments were conducted at a WAG ratio of 1:2, despite variations in other operating parameters. This consistency indicates that, after injection rate, the WAG ratio was the second most influential factor governing CO₂ storage performance.

Fig. 4

Storage profiles of all the cases (% OOIP vs injected PVs)

In contrast, injection pressure and brine salinity exhibited minimal or inconsistent effects on CO₂ storage. The top-performing experiments were conducted under varying pressure and salinity conditions, suggesting that these factors may exert only a secondary influence. The response outlines that pressure and salinity likely interact with other variables, such as injection rate and WAG ratio, in more complex and less predictable ways, making their isolated impact on storage efficiency less pronounced.

Fig. 5

Storage and recovery profile of the case with the highest storage

Among all conducted experiments, Experiment No. 3 demonstrated the most favorable conditions for CO₂ storage, as evidenced by corresponding performance outcomes. The results indicate that a lower injection rate, elevated brine salinity, and a higher WAG ratio (1:2) collectively enhanced the storage efficiency, as illustrated in the figure above. A distinctive feature of this experiment was the delayed CO₂ breakthrough, observed after approximately 1.65 pore volumes (PV) of injected fluid during WAG operation. Beyond this point, the CO₂ storage rate gradually declined. The delayed breakthrough suggests more efficient CO₂ entrapment mechanisms, promoting broader gas distribution and prolonged interaction within the pore network before early gas breakthrough. Consequently, this delayed breakthrough is considered a key contributing factor to the higher overall storage efficiency achieved under these operating conditions.

4.2 Statistical Analysis

The statistical analysis employed multiple complementary techniques to evaluate the experimental responses in depth. The Signal-to-Noise (S/N) ratio analysis was used to assess the robustness of oil recovery and CO₂ storage performance against variability, targeting higher efficiency with minimal noise interference. A means analysis was then performed to examine the average influence of each control variable on the measured responses. This is visually represented in the Main Effects Plot for Means, which reveals the directional trends and sensitivity of each factor level. Both individual response analyses (for recovery and storage) and a combined dual-response evaluation were conducted to compare performance outcomes. To integrate these insights, an Overall Performance Index (OPI) was formulated, providing a unified metric that captures the combined impact of all parameters on recovery and storage efficiency. Finally, regression analysis was utilized to model the relationships between process variables and response outcomes, allowing for predictive assessment and identification of the most influential factors. This integrated statistical framework ensures a comprehensive understanding of the experimental behavior, enabling a data-driven optimization of EOR–CCS performance.

4.2.1 Recovery Response Analysis

The Signal-to-Noise (S/N) ratio analysis for oil recovery, derived from the Taguchi DOE framework, was conducted to evaluate the consistency and robustness of recovery performance across three levels of the four key factors: injection rate, pressure, WAG ratio, and salinity. The S/N ratio values for each factor level are: injection rate (36.23, 36.06, 36.63), pressure (36.29, 36.23, 36.39), WAG (36.21, 36.38, 36.32), and salinity (36.30, 36.14, 36.47), with corresponding deltas of 0.57, 0.16, 0.17, and 0.33, respectively.

Table 5

Signal-to-Noise Ratio for Recovery Response
Level	Inj Rate	Pressure	WAG	Salinity
1	36.23	36.29	36.21	36.30
2	36.06	36.23	36.38	36.14
3	36.63	36.39	36.32	36.47
Delta	0.57	0.16	0.17	0.33
Rank	1	4	3	2

The delta values, representing the difference between the highest and lowest S/N ratios for each factor, illustrate their relative influence on recovery stability. Among the tested parameters, the injection rate exhibited the greatest effect (Δ = 0.57, Rank 1), followed by salinity (Δ = 0.33, Rank 2), WAG ratio (Δ = 0.17, Rank 3), and pressure (Δ = 0.16, Rank 4). Under the assumption of a “larger is better” criterion for the S/N ratio, higher values such as 36.63 for the injection rate at Level 3 indicate superior recovery performance with reduced sensitivity to noise at that setting. This suggests that optimizing the injection rate at Level 3 and maintaining higher salinity (Level 3, S/N = 36.47) could enhance recovery robustness and consistency.

The Main Effects Plot for Means further supports these findings, illustrating the average recovery performance across the three levels of the four key factors: injection rate, pressure, WAG ratio, and salinity. The mean values for each factor level are: injection rate (64.78, 63.52, 67.85), pressure (65.23, 64.88, 66.03), WAG (64.68, 65.97, 65.50), and salinity (65.37, 64.13, 66.65), with deltas of 4.33, 1.15, 1.28, and 2.52, respectively.

Table 6

Main effects for means data for recovery
Level	Inj. Rate	Pressure	WAG	Salinity
1	64.78	65.23	64.68	65.37
2	63.52	64.88	65.97	64.13
3	67.85	66.03	65.50	66.65
Delta	4.33	1.15	1.28	2.52
Rank	1	4	3	2

The delta values, representing the difference between the highest and lowest mean recovery values for each factor, highlight their relative influence on performance. Among the four parameters, the injection rate exhibited the strongest impact (Δ = 4.33, Rank 1), followed by salinity (Δ = 2.52, Rank 2), WAG ratio (Δ = 1.28, Rank 3), and pressure (Δ = 1.15, Rank 4). Recovery increased notably with the injection rate at Level 3 (67.85), indicating that higher injection rates enhance oil displacement efficiency. Similarly, salinity displayed a positive trend, reaching its maximum recovery at Level 3 (66.65). The WAG ratio achieved its peak performance at Level 2 (65.97), while pressure showed a moderate improvement at Level 3 (66.03). Overall, these results suggest that optimizing injection rate and salinity at their upper levels can substantially improve recovery, with the injection rate emerging as the most dominant factor influencing performance.

4.2.2 Storage Response Analysis

The Signal-to-Noise (S/N) ratio analysis for CO₂ storage, derived from the Taguchi DOE results, was conducted to assess the robustness and consistency of storage performance across three levels of the four key factors: injection rate, pressure, WAG ratio, and salinity. The S/N ratio values for each factor level are as follows: injection rate (36.23, 36.28, 35.05), pressure (35.94, 35.86, 35.76), WAG (36.11, 35.89, 35.56), and salinity (35.76, 35.89, 35.91), with corresponding deltas of 1.23, 0.17, 0.55, and 0.15, respectively. The delta values represent the variability in S/N ratios across the three factor levels, illustrating the relative influence of each parameter on CO₂ storage stability. Among the four factors, the injection rate exhibited the strongest effect (Δ = 1.23, Rank 1), followed by the WAG ratio (Δ = 0.55, Rank 2), pressure (Δ = 0.17, Rank 3), and salinity (Δ = 0.15, Rank 4). This ranking indicates that the injection rate plays the most dominant role in determining storage consistency, as a larger delta value corresponds to greater sensitivity of the response to that factor. Assuming a “larger is better” criterion for the S/N ratio, higher values, such as 36.28 for the injection rate at Level 2, reflect superior CO₂ storage performance with reduced variability. These findings suggest that fine-tuning the injection rate offers the most effective approach for enhancing storage efficiency and operational robustness.

Fig. 6

Main effects plot for S/N ratios for storage response (Larger is better)

The Main Effects Data for the Means analysis of CO₂ storage, derived from the Taguchi DOE, illustrate the average storage performance across three levels of the four key factors: injection rate, pressure, WAG ratio, and salinity. The mean values for each factor level are: injection rate (64.83, 65.17, 56.58), pressure (62.68, 62.23, 61.67), WAG (64.02, 62.40, 60.17), and salinity (61.67, 62.35, 62.57), with corresponding deltas of 8.58, 1.02, 3.85, and 0.90, respectively. The delta values, representing the difference between the highest and lowest mean storage values for each factor, reveal their relative influence on CO₂ storage performance. Among the four parameters, the injection rate exhibited the strongest effect (Δ = 8.58, Rank 1), followed by the WAG ratio (Δ = 3.85, Rank 2), pressure (Δ = 1.02, Rank 3), and salinity (Δ = 0.90, Rank 4). Storage efficiency reached its maximum at injection rate Level 2 (65.17) but declined sharply at Level 3 (56.58), indicating a strong sensitivity of CO₂ retention to injection rate. Salinity showed an increasing trend, while pressure and WAG ratio demonstrated moderate declines across their respective levels. These patterns suggest that lower injection rates combined with higher salinity conditions favor enhanced CO₂ trapping, providing valuable guidance for optimizing storage efficiency under immiscible WAG conditions.

Table 7

Main Effects Plot of Means for Storage
Level	Inj Rate	Pressure	WAG	Salinity
1	64.83	62.68	64.02	61.67
2	65.17	62.23	62.40	62.35
3	56.58	61.67	60.17	62.57
Delta	8.58	1.02	3.85	0.90
Rank	1	3	2	4

4.2.3 Analysis for Dual Response

The Signal-to-Noise (S/N) ratio analysis for the dual response of recovery and storage, derived from the Taguchi DOE results, was conducted to evaluate the combined robustness and stability of both performance indicators across three levels of the four key factors: injection rate, pressure, WAG ratio, and salinity. The S/N ratio values for each factor level are: injection rate (36.23, 36.16, 35.76), pressure (36.10, 36.02, 36.03), WAG (36.15, 36.11, 35.89), and salinity (35.98, 36.01, 36.16), with corresponding deltas of 0.47, 0.08, 0.26, and 0.18, respectively. The delta values, representing the range of S/N ratios across factor levels, highlight the relative influence of each parameter on the stability of the dual response (recovery and storage). Among the four factors, the injection rate exhibited the strongest effect (Δ = 0.47, Rank 1), followed by the WAG ratio (Δ = 0.26, Rank 2), salinity (Δ = 0.18, Rank 3), and pressure (Δ = 0.08, Rank 4). This ranking indicates that injection rate plays the dominant role in determining the overall robustness of both recovery and storage performance, underscoring its importance as the primary control parameter for optimizing immiscible WAG operations.

Table 8

Signal-to-Noise ratio for dual response
Level	Inj Rate	Pressure	WAG	Salinity
1	36.23	36.10	36.15	35.98
2	36.16	36.02	36.11	36.01
3	35.76	36.03	35.89	36.16
Delta	0.47	0.08	0.26	0.18
Rank	1	4	2	3

For the dual response, the S/N ratio typically follows the “larger-is-better” criterion to achieve a balance between recovery and storage, where higher S/N values signify improved combined performance with reduced variability. In this study, the injection rate at level 1 exhibited the highest S/N value (36.23), indicating superior overall robustness. Similarly, a salinity level of 3 yielded a relatively high S/N value (36.16), suggesting a positive contribution to response stability. The analysis confirms that injection rate and WAG ratio are the most influential factors, underscoring that maintaining a lower injection rate and higher salinity can simultaneously enhance the robustness of both recovery and storage.

Fig. 7

Main effects plot for S/N ratio for dual response (Larger is better)

The Main Effects for Means analysis for the combined response of recovery and storage, derived from the Taguchi DOE data, examines how the average overall performance varies with different levels of the four key factors: injection rate, pressure, WAG ratio, and salinity. The mean values for the dual response at each level are: injection rate (64.81, 64.34, 62.22), pressure (63.96, 63.56, 63.85), WAG (64.35, 64.18, 62.83), and Salinity (63.52, 63.24, 64.61), with corresponding deltas of 2.59, 0.40, 1.52, and 1.37, respectively.

Table 9

Main effects for means data for dual response
Level	Inj Rate	Pressure	WAG	Salinity
1	64.81	63.96	64.35	63.52
2	64.34	63.56	64.18	63.24
3	62.22	63.85	62.83	64.61
Delta	2.59	0.40	1.52	1.37
Rank	1	4	2	3

The delta values, representing the difference between the highest and lowest mean responses for each factor, highlight their relative influence on the combined recovery and storage performance. Among these, the injection rate shows the greatest impact (Δ = 2.59, rank 1), followed by WAG ratio (Δ = 1.52, rank 2), salinity (Δ = 1.37, rank 3), and pressure (Δ = 0.40, rank 4). The highest combined performance is achieved at injection rate level 1 (64.81) and salinity level 3 (64.61), while a noticeable decline occurs at injection rate level 3 (62.22). This trend suggests that lower injection rates and higher salinity favor both enhanced recovery and improved CO₂ storage. The WAG ratio performs best at level 1 (64.35), whereas pressure shows a minimal effect, with only a slight peak at level 1 (63.96). Overall, the analysis indicates that maintaining a lower injection rate and higher salinity can effectively optimize both recovery and storage outcomes.

Fig. 8

Main effects for the mean graph for dual response

The bar chart in Fig. 9 compares the relative impact of four key factors, injection rate, pressure, WAG ratio, and salinity, on the dual response of oil recovery and CO₂ storage. The analysis is based on the delta Signal-to-Noise (S/N) ratio, which measures the difference between the highest and lowest S/N ratios across three levels of each factor. A higher delta value indicates a stronger influence on the consistency and robustness of the response. For oil recovery (blue bars), the delta values are: injection rate (0.57), salinity (0.33), pressure (0.17), and WAG (0.16). For CO₂ storage (green bars), the deltas are: injection rate (1.25), WAG (0.58), pressure (0.25), and salinity (0.20). Among all factors, the injection rate exerts the most significant influence on both responses, particularly on CO₂ storage, where its effect (1.25) is more than double that on recovery (0.57). This highlights its crucial role in governing storage stability and efficiency. The WAG ratio ranks second for storage (0.58) but has a relatively minor effect on recovery (0.16). Salinity demonstrates a moderate influence on recovery (0.33) and a lesser effect on storage (0.20), while pressure shows the smallest overall impact on both (0.17 for recovery and 0.25 for storage). This indicates that injection rate is the most critical factor to control for optimizing the dual response, particularly for CO2 storage, while pressure has a minimal overall impact.

Fig. 9

Factorial Influence plot for dual response

Overall Performance Index (OPI) using Taguchi Multi-Response Optimization

Traditionally, the Taguchi Method is applied for single-response analysis, and the Overall Performance Index (OPI) was introduced to ensure that the analysis of multi-response problems is easily conducted as a single unified metric. Signal-to-noise (S/N) ratios for recovery and storage were normalized using the larger-the-better criterion and then combined with equal weighting to reflect their balanced importance. The OPI for each experiment was calculated using the equation:

This composite index facilitated integrated analysis and identification of optimal factor levels for an optimum trade-off between EOR and CCS performance. In EOR-CCS scenarios, where you have a dual objective trade-off, OPI analysis helps us determine the optimised trade-off and rank experimental runs based on our designed combined desirability of prioritising the outcome or balancing. A higher OPI value means better overall performance. Hence, OPI analysis for our case results in identifying experiment number 3 as the optimized case with a better trade-off, as shown in the figure below.

Fig. 10

Ranking of experiments based on OPI analysis

The recovery rate of 66.0% and storage efficiency of 66.5% were considered the optimum trade-off between recovery and storage. The parametric combination of 0.15 cc/min of injection rate, 850 psi pressure, 30000 ppm salinity, and 1:2 WAG ratio resulted in the above-stated dual response. Hence, the parametric levels are consistent with the observed parametric influence from the S/N ratio and mean analysis.

4.2.4 Regression analysis

Regression analysis is employed to model the relationships between the four experimental factors, i.e., Injection Rate, Pressure, Water Alternating Gas Ratio, and Salinity, and the dual responses of recovery and storage within the framework of a Taguchi Design of Experiments (DOE). In the experimental design, each factor is tested at three levels to capture a wide range of operational conditions. The fit regression model was utilized to quantify the impact of each factor on recovery, storage, and the combined dual response, providing predictive equations that can estimate response values under varying conditions. The regression model developed for oil recovery is expressed as:

$\:\text{Recovery}=55.71+9.46\left(\text{Injection\:Rate}\right)+0.00400\left(\text{Pressure}\right)+0.40\left(\text{WAG\:Ratio}\right)+0.000128\left(\text{Salinity}\right)$

This equation highlights that the injection rate has the strongest positive influence on recovery, followed by the WAG ratio and salinity, while pressure exerts a minimal effect. These results are consistent with the earlier findings from the Signal-to-Noise (S/N) ratio and main effects analyses, reinforcing the conclusion that optimizing the injection rate is the most effective strategy for maximizing oil recovery. To enhance recovery, one might prioritize increasing the injection rate and salinity. The graph below shows the actual experiments' recovery response against the predicted values from the regression equation. Hence, the close fit and lower variance validate the predictive capability of the equation in the variable’s experimental ranges.

Fig. 11

Actual vs Predicted Oil Recovery for all the experimental runs

The regression equation for storage was derived using a fit regression model and is given below, using the Taguchi design of experiments and the storage response for each experiment. The equation confirms that the injection rate has the most significant negative impact on storage, followed by WAG, whereas the pressure has a negligible effect, and salinity has a slight positive influence.

$\:\text{Storage}=74.5\:-\:24.62\left(\text{Injection\:Rate}\right)-\:0.0051\left(\text{Pressure}\right)\:-\:2.52\:\left(\text{WAG\:Ratio}\right)+0.000090\:\left(\text{Salinity}\right)$

This substantiates earlier analyses, such as the S/N ratio for storage (where the injection rate had the largest delta of 1.23) and the means plot (showing a sharp drop in storage with a higher injection rate). To optimize CO₂ storage, the results suggest focusing on lowering the injection rate and WAG ratio while slightly increasing salinity. Figure 12 below compares the predicted storage values from the regression model against the experimental results, demonstrating the model’s accuracy. The close alignment between predicted and observed values, along with low variance and minimal deviation, confirms the predictive capability of the regression equation for evaluating storage performance under varying operational conditions.

Fig. 12

Actual vs Predicted CO₂ Storage for all the experimental runs

5 Conclusion

This study demonstrates that immiscible WAG injection can simultaneously enhance oil recovery and enable long-term CO₂ storage, confirming its dual potential for EOR-CCS applications. The experimental results have clearly validated that a clear trade-off exists: higher injection rates favor oil recovery, while lower rates improve CO₂ storage efficiency. Among the tested parameters, injection rate consistently emerged as the most influential factor across all performance metrics, with salinity playing a secondary role. The WAG ratio showed limited impact on recovery, but a ratio of 1:2 yielded the most favorable storage outcomes, whereas pressure had minimal effect.

Statistical evaluation using Taguchi-based analysis, regression modeling, and Signal-to-Noise (S/N) ratios confirmed these observations and highlighted injection rate as the dominant driver of system behavior. The Overall Performance Index (OPI) identified the optimal operating conditions as an injection rate of 0.15 mL/min, pressure of 850 psi, salinity of 30,000 ppm, and a WAG ratio of 1:2, providing the best compromise between maximizing oil recovery and enhancing CO₂ storage efficiency.

Furthermore, the predictive regression models developed in this study validated the relationships between operating variables and dual responses, offering a practical framework for estimating performance under varying conditions. Collectively, these insights enhance the understanding of immiscible WAG processes, provide actionable operational guidelines, and lay a foundation for scaling dual-objective optimization strategies to field-level EOR-CCS projects.

Funding Details:

This research received no external funding.

Declarations

Clinical Trial Registration

This study is not a clinical trial; therefore, trial registration is not applicable.

Consent to Participate

Consent to Participate declaration: not applicable.

Consent to Publish

Consent to Publish declaration: not applicable.

Ethics Approval

Ethics declaration: not applicable.

Data Availability

The experimental data and the analyzed data from the current study are available from the corresponding author upon reasonable request.

Author Contribution

Tariq A. Chandio: Worked on experimental works, design of experiments, and manuscript draftingM.N.A.M: Supervision, Guidance, Conceptualization, AnalysisA.A.A. Rasol: Supervision, Experimental Design, Draft FormattingMansoor Z.: Conceptualization, Supervision, Methodology

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