Comprehensive Investigation for CO₂ Flooding Methodology in a Reservoir with High Water Content

Abstract

In response to the development challenges caused by the high initial water saturation, low porosity, low permeability, and strong heterogeneity in C tight sandstone reservoirs, a comprehensive study was conducted on the optimization of development methods using a fuzzy model, core flooding experiments, and reservoir numerical simulations. The initial evaluation indicates the good adaptability of CO₂ flooding for improving oil recovery in a C reservoir; the experimental result of the CO₂ displacement method also performs the best, with a recovery rate of 68.38% at a connate water saturation of about 30%, compared with surfactant flooding and water flooding. However, higher water saturation inhibits the CO₂ development effect. The oil recovery factor of pure CO₂ huff-n-puff is 32.24% lower than the CO₂ displacement method, while surfactant-assisted CO₂ huff-n-puff can increase the recovery rate by 0.85% compared to pure CO₂. Based on actual geological models, numerical simulations were conducted on Well Block A and B. The results showed that the optimized production pressure is above the Minimum Miscibility Pressure (16.44 MPa); with consideration of the fracture pressure limitation, the CO₂ injection rate in Block A should be less than 3000 m³/d, and the recovery rate after 10 years is only 0.48% (oil change ratio is 0.07 t/t), while the CO₂ displacement rate of Block B should not exceed 7500 m³/d, and the recovery rate after 10 years can reach 27.39% (oil change ratio is 0.2 t/t). CO₂ displacement is an effective development method for a C reservoir, but due to a high water content the oil change ratio is very low, indicating a low potential for further development. The research provides important references for the development of similar oil reservoirs.

1. Introduction

Unconventional resources possess substantial reserves, primarily consisting of shale oil, sandstone oil, heavy oil, and tight oil globally [1]. Tight sandstone reservoirs are a key focus in current oil and gas exploration and development and are a vital component of China’s current energy strategy [2,3,4,5]. However, conventional development methods are difficult to implement economically and effectively for tight sandstone reservoirs, which are characterized by low porosity and low permeability [6]. A C reservoir is a typical high-water-cut tight sandstone reservoir in China, with an average oil saturation of 31.6%, an average porosity of 8.32%, and permeability of 0.78 mD, indicating a low hydrocarbon enrichment. Additionally, the average crude oil viscosity is 4.18 mPa·s. According to the theory proposed by Aladasani and Bai [7], it is preliminarily concluded that CO₂ miscible flooding is the most suitable development approach for reservoir C. The mechanisms of CO₂-enhanced oil recovery primarily include the following [8,9,10,11,12,13,14]: (1) Microscopic effects: extraction and vaporization effects, molecular diffusion, and convective dispersion (molecular diffusion being the primary factor [15]). (2) Macroscopic effects: dissolution-induced viscosity reduction, interfacial tension reduction, improved fluidity, and macroscopic dispersion.

In 2014, Zhang Li [16] pointed out that, during CO₂ miscible flooding, CO₂ can extract light components, achieving miscibility between CO₂ and crude oil. This effectively reduces capillary forces and lowers oil viscosity, thereby enhancing the recovery efficiency. In 2018, PVT experiments performed by Guo Maolei et al. [17] demonstrated that, at a constant pressure, the reservoir fluid density decreases with increasing CO₂ injection concentrations, the volumetric expansion coefficient gradually increases with increased injected CO₂ concentrations, and the reservoir fluid viscosity decreases with higher CO₂ concentrations. In 2021, Gu Yinlong et al. [18] demonstrated through core flooding experiments that CO₂ flooding significantly enhances the production capacity in ultra-low-permeability wells, improving the oil recovery factor with broad application prospects. In 2023, Wang et al. [19] discovered that the CO₂ flooding efficiency in tight sandstone reservoirs positively correlates with reservoir permeability and CO₂ injection pressure. Flooding efficiency rapidly increases from near-miscible to miscible flooding stages, while it decreases with further increasing injection pressures afterward miscible pressure is reached.

Additionally, numerous scholars have conducted research on CO₂ flooding in tight sandstone reservoirs. In 2017, Liu Jianyi et al. [20] demonstrated that CO₂ flooding enhances recovery primarily through diffusion, dissolution, and expansion using a self-developed high-temperature, high-pressure microscopic visualization apparatus. This process involved alternating conversions between velocity potential and concentration potential, liberating crude oil trapped in small pores and dead pores. In the same year, Zuloaga, Pavel et al. [21] indicated that, for a permeability between 0.001 mD and 0.01 mD, the incremental recovery from CO₂ flooding grew due to the expanded fracture contact area. In 2019, Ma Quanzheng et al. [22] analyzed residual oil distribution across different pore sizes using nuclear magnetic resonance (NMR) technology. The results indicated that the cumulative recovery increment becomes slower with increasing cycle numbers. In 2022, Song Yilei et al. [23] employed NMR technology to demonstrate that CO₂ flooding is better than N₂. In the same year, Samuel Afari [24] employed the response surface methodology (RSM) to investigate the impact of five key operational parameters—injection rate, injection cycle, steaming cycle, production cycle, and production wellbore pressure—on oil and gas recovery efficiency. The study revealed that the wellbore pressure and production cycle were the most influential parameters affecting recovery rates.

This study compared the displacement efficiency of different displacement media (CO₂, water, surfactants) through core displacement experiments. After determining CO₂ as the optimal displacement agent, the effects of injection rate, backpressure, water saturation, and other factors on displacement efficiency were also analyzed. Furthermore, based on huff-n-puff experiments, CO₂ is also the optimal medium, though the huff-n-puff recovery rate was significantly lower than the displacement recovery rate. Finally, using CMG 2021 numerical simulation software on an actual geological model, CO₂ displacement was simulated to assess the feasibility of this method and to optimize production parameters. The findings for the development method of the C reservoir can guide field development practices.

2. Adaptability Analysis of CO₂ Flooding Based on the Fuzzy Hierarchical Comprehensive Evaluation Method

The methods for screening and evaluating the potential of CO₂-enhanced oil recovery reservoirs mainly include the binary comparison method, proxy model method, and fuzzy comprehensive evaluation method [25,26]. The fuzzy comprehensive evaluation method is based on fuzzy mathematics and hierarchical analysis theory, which can weigh the influence of various parameters. This method requires calculating the suitability of the main evaluation indicators in the CO₂ miscible flooding process according to the screening criteria based on fuzzy membership functions.

The latest developed fuzzy analytic hierarchy process is used to obtain weights. In order to make the evaluation results more reliable and comparable, three successful CO₂ flooding reservoirs [27], together with reservoir C, were evaluated, as shown in

Table 1. Reservoir parameters.

Block	Viscosity/mPa·s	Density/(g·cm−3)	Oil Saturation/%	Temperature /°C	Permeability/ (10−3 μm2)	Porosity/%	Depth /m
1#	3.5	871	40	88	20	8	1676
2#	3.5	873	72	18	16	18	336
3#	3.2	876	83	39	1.2	6	579
C	4.18	832.7	50	90	0.26	10.13	2673

, with the membership degree calculation shown in

Table 2. Membership degree calculation result.

Block	Viscosity/mPa·s	Density/(g·cm−3)	Oil Saturation/%	Temperature /°C	Permeability/ (10−3 μm2)	Porosity/%	Depth /m
1#	0.9836	1	1	1	1	0.9846	1
2#	0.9836	1	1	0.9972	1	1	0.6183
3#	0.9990	1	1	1	0.0907	0.8	0.9987
C	0.8918	1	1	1	0.0602	1	1

. The computed membership matrix R is obtained, leading to the construction of the fuzzy judgment matrix P. Subsequently, normalization is performed, and, after calculating the weights W, the fuzzy comprehensive evaluation index Z = R × W is obtained (Equations (1)–(4)).

The membership degree matrix is shown below:

$$R=\left|\begin{array}{ccccccc}0.9836& 1& 1& 1& 1& 0.9846& 1\\ 0.9836& 1& 1& 0.9972& 1& 1& 0.6183\\ 0.9990& 1& 1& 1& 0.0907& 0.8& 0.9987\\ 0.8918& 1& 1& 1& 0.0602& 1& 1\end{array}\right|$$

(1)

A fuzzy judgment matrix is constructed as follows:

$$P=\left[\begin{array}{ccccccc}0.5000& 0.5000& 0.7000& 0.6262& 0.7930& 0.8542& 0.8786\\ 0.5000& 0.5000& 0.7000& 0.6262& 0.7930& 0.8542& 0.8786\\ 0.3000& 0.3000& 0.5000& 0.4262& 0.5930& 0.6542& 0.6786\\ 0.3738& 0.3738& 0.5738& 0.5000& 0.6668& 0.7281& 0.7524\\ 0.2070& 0.2070& 0.4070& 0.3332& 0.5000& 0.5613& 0.5856\\ 0.1458& 0.1458& 0.3458& 0.2709& 0.4387& 0.5000& 0.5243\\ 0.1214& 0.1214& 0.3214& 0.2476& 0.4144& 0.4757& 0.5000\end{array}\right]$$

(2)

The normalized weight is as follows:

$$W=\left[\begin{array}{c}\begin{array}{c}0.198051325\\ 0.198051325\end{array}\\ 0.140905436\\ 0.161996351\\ 0.114336679\\ 0.096792891\\ 0.089865993\end{array}\right]$$

(3)

The fuzzy comprehensive evaluation index Z is calculated as follows:

$$Z=R\times W=\left[\begin{array}{c}0.9953\\ 0.9620\\ 0.8763\\ 0.8711\end{array}\right]$$

(4)

The closer the index Z is to 1, the more suitable the reservoir is for CO₂ injection. The Z of reservoir C is 0.8711, slightly lower than that of reservoir 3# in the literature, indicating a strong adaptability for CO₂ injection development to enhance the recovery efficiency for targeted reservoir C.

3. Laboratory-Scale Core Experiment Research

3.1. Experimental Equipment and Methods

(1)

Cores: All cores are natural cores collected from the oilfield. They all have a diameter of 2.5 cm and length of about 5–6 cm.

(2)

Gas: CO₂, with a purity of 99.99%.

(3)

Formation water: Water-type CaCl₂; the total salinity is 23,546 mg/L. The crude oil viscosity at 90 °C is 4.18 mPa·s.

(4)

Surfactants: 0.1% Sodium dodecylbenzenesulfonate (SDBS) at mass concentration, 0.1% Tween 80, and a mixture of the two (SDBS to Tween 80 is 2.5:1).

(5)

Fracturing fluid: Collected from the oilfield.

The experimental flowchart is shown in Figure 1.

3.2. Experimental Procedures

3.2.1. Experimental Procedure for Core Flooding

① The cores are cleaned, dried, and their porosity and permeability are measured. The core data are presented in

Table 3. Core data from displacement experiments.

Core Number	Porosity/%	Gas Permeability/mD	Length/cm	Diameter/cm	Displacement Medium	Pressure /MPa	Constant Speed/ (min·mL−1)	Back Pressure /MPa	Water Saturation /%
N1	9.09	0.69	5.759	2.505	water	35			32.12
N2	9.49	0.75	5.371	2.506	Surfactant	35			30.81
N3	9.79	0.64	5.667	2.509	CO2		0.2	25	29.02
N4	9.53	0.63	5.683	2.508	CO2		0.1	25	27.44
N5	12.35	0.66	5.813	2.503	CO2		0.2	25	52.26

.

② The cores are saturated with formation water.

③ The cores are saturated with oil. The connate water saturation is then calculated.

④ The oil is displaced by different media according to the designed parameters. Data are recorded for calculating relative permeability using the unsteady-state method.

As the Minimum Miscible Pressure (MMP) between oil and CO₂ is 16.44 MPa (provided from the oilfield), the back pressure is set to be 25 MPa to maintain miscible CO₂ flooding.

3.2.2. Experimental Procedure for Huff-n-Puff

① Core cleaning: The core parameters and experimental scheme are shown in

Table 4. Core data for huff-n-puff experiments.

Core. Number	Porosity/%	Gas Permeability/mD	Length/cm	Diameter/cm	Displacement Medium	Injection Pressure/MPa	Back Pressure/MPa	Soaking Time/h	Cycles
N6	7.4	0.34	5.815	2.503	Fracturing Fluid	25	15	5	4
N7	9.5	0.57	5.919	2.502	Surfactant	25	15	5	4
N8	6.3	0.30	5.960	2.523	CO2	25	15	5	4
N9	14.74	0.92	5.848	2.497	CO2 + Surfactant	25	15	5	4
N10	9	0.54	5.763	2.526	CO2	25	15	7	4
N11	11.6	1.05	5.926	2.501	CO2	25	15	3	4

.

② The apparatus is put into a thermo-tank at 90 °C.

③ CO₂ is injected (fracturing fluid, surfactant, CO₂ + surfactant) into the core at 25 MPa. The soaking time is 5 h.

④ The fluids are allowed flow out from the inlet of the core until the pressure drops to 15 MPa.

⑤ The next huff-n-puff cycle is performed; steps ③ and ④ are repeated. Four cycles are simulated.

3.3. Experimental Results and Analysis

3.3.1. Comparison Between Different Displacement Media and Influencing Factors

(1)

Comparison between Different Displacement Media

The comparison between recovery efficiency and the differential pressure of water flooding (Core N1), surfactant flooding (Core N2), and CO₂ flooding (Core N3) at different injected pore volumes is shown in Figure 2. CO₂ flooding demonstrated the highest oil recovery factor of 68.38%. Surfactant flooding is also good, with a recovery rate of 62.5%, while water flooding is 51.61%. CO₂ flooding also has the advantage of a lower pressure differential needed, which is only about 7.5 MPa, compared to the 35 MPa for water and surfactant flooding. Therefore, CO₂ flooding is more suitable for developing the target reservoir.

(2)

Factors Affecting CO₂ Flooding

The influencing effects of some key production parameters (injection rate and water saturation) on the recovery efficiency of CO₂ flooding are investigated.

① Effect of Injection Rate on Recovery Efficiency

The effects of the injection rate on the recovery efficiency and oil–gas production ratio are shown in Figure 3a and Figure 3b, respectively. At an injection rate of 0.2 mL/min (for core N3), the recovery efficiency exceeds that at 0.1 mL/min (for core N4) by only 0.62%. Furthermore, the gas–oil ratio in 0.1 mL/min is larger than it is in 0.2 mL/min, which is not favorable. The injection rate of 0.2 mL/min is used for the other experiments.

② The Effect of Water Saturation on Recovery Efficiency

Due to high water-cut of the target oilfield, the impact of different water saturations on the recovery efficiency is studied and shown in Figure 4. For Core N5 with a 52.26% water saturation, the resistance for the CO₂ flow is largely increased, resulting in a nearly 20% reduction in the recovery efficiency compared to a lower water saturation (Sw = 29.02%).

3.3.2. Comparison Between Different Huff-n-Puff Media and Influencing Factors

(1)

Comparison between Different Huff-n-Puff Media

Huff-n-puff becomes preferable when the CO₂ source is limited. Using cores N6, N7, N8, and N9, four huff-n-puff experiments were conducted using different media, including fracturing fluid, surfactant, CO₂, and surfactant-assisted CO₂, respectively. The results are presented in

Table 5. The recovery efficiency of different huff-n-puff media.

Core Number	Displacement Medium	First Round/%	Second Round/%	Third Round/%	Fourth Round/%	Cumulative Recovery Rate/%
N6	Fracturing fluid	12.99	6.50	3.13	0.96	23.59
N7	Surfactant	13.68	6.84	3.96	1.80	26.28
N8	CO2	16.87	9.32	5.99	3.11	35.29
N9	Surfactant + CO2	16.26	9.80	6.70	3.38	36.14

. The cumulative recovery rate for CO₂ huff-n-puff reached 35.29%, significantly higher than that from surfactant flooding (26.28%) or fracturing fluid flooding (23.59%). Adding surfactants to CO₂ huff-n-puff further enhanced the recovery rates by 0.85%.

(2)

Factors Affecting CO₂ Flooding

① Effect of Soaking Time on Recovery Efficiency

The soaking stage is when the wells are shut in, allowing CO₂ and crude oil to fully interact. Figure 5 shows the decline in cyclic recovery efficiency under different soaking times based on Core N9, N10, and N11.

The cumulative recovery rates after 3 h, 5 h, and 7 h of soaking time are 32.40%, 35.29%, and 35.56%, respectively, meaning an increase in recovery efficiency from 3 h to 5 h of 2.89% and from 5 h to 7 h of 0.27%. This indicates that a longer soaking time can enhance crude oil recovery rates, but the effect gradually diminishes after the soaking time reaches a certain value.

② Effect of Cycle on Recovery Efficiency

The recovery rates at different cycles decrease as the number of huff-n-puff cycles increases (as in Figure 5). When the soaking time is 5 h, the recovery rate achieved from the first two cycles accounts for 76.56% of the total recovery rate, which is 35.29%.

However, the recovery rates for CO₂ huff-n-puff are significantly lower than for CO₂ displacement, which is 68.38%, as shown in Section 3.3.1. So, the reservoir simulation for Well Block A and B was later conducted using CO₂ displacement.

4. Reservoir Numerical Simulation and Analysis

The experimental study proved that the target reservoir is more suitable for the CO₂ injection method. A numerical simulation was conducted based on the relative permeability curves obtained from experiments, providing a basis for simulating the behavior of CO₂ flooding in the target reservoir.

The 3D Field geological model was established by Petrel and then imported to CMG 2021 software to analyze the CO₂ flooding effectiveness and optimize the production parameters. Two blocks named Well Block A and Well Block B were intentionally selected, which were representative wells for the whole oilfield. To make it clearer, Well A is a single existing production vertical well with a fracture half-length of 188 m and a conductivity of 10D·cm. Four injection wells are arbitrarily added in the simulation process to make a five-spot well pattern and check the development effect; this is called Well Block A. To enhance computational efficiency, layer merging was used, and, for example, the geological model for Well Block A was merged from 30 layers into 6 layers (Figure 6).

To check this layer merging accuracy and efficiency, daily and cumulative oil production data before and after layer merging were compared (Figure 7), with a very good consistency, showing that the layer merging is necessary and efficient.

4.1. Numerical Simulation for CO₂ Flooding

Based on the simplified geological model for Well Block A, fluid composition fitting was performed first, followed by a parameter optimization for CO₂ flooding using a five-point well pattern (with Well A as the production well and four injection wells at the corner, with a similar well distance 230 m), which was employed for the CO₂ flooding method. Then, the effects of the gas injection rate on the recovery efficiency were investigated.

With the production pressure being at the minimum miscibility point, gas injection rates of 1000 m³/d, 3000 m³/d, 4000 m³/d, 5000 m³/d, 7500 m³/d, 10,000 m³/d, and 15,000 m³/d were separately set, with all other parameters held constant for simulation (Figure 8). Similarly to the experimental results, lower injection rates resulted in delayed oil production and reduced recovery rates; therefore, the injection rates should be maximized if possible. The recovery rates at different injection rates are shown in

Table 6. Recovery rate and injection pressure of injection wells at different gas injection rates.

Well Block A			Well Block B
Gas Injection Rate (m3/d)	Recovery Rate (%)	Injection Pressure (MPa)	Gas Injection Rate (m3/d)	Recovery Rate (%)	Injection Pressure (MPa)
5000	14.92	36.8	3000	0.48	32.2
7500	27.39	44.0	4000	1.87	36.1
10,000	37.25	50.7	5000	4.57	39.8

. Field data indicate that Well A has a fracture pressure of 44.2 MPa, meaning that the injection rate should not exceed 7500 m³/d. As the simulation results show, the recovery rate is below 27.39% after 10 years. Using the same methodology, Well Block B is also evaluated using the CO₂ flooding development method. Well B has a fracture pressure of 31.7 MPa, showing that the injection rate should not exceed 3000 m³/d. The recovery efficiency of Well B is only 0.5% after 10 years, which is significantly lower than Well A.

4.2. Oil Change Rate of CO₂ Analysis

The oil change rate of CO₂ refers to the mass of crude oil displaced per unit mass of injected CO₂, indicating the displacement capacity of CO₂ during oil recovery. The oil change rate for CO₂ flooding at Well Block A and Well Block B was calculated as a function of the daily CO₂ injection rates, with the results shown in Figure 9.

Well A exhibits a higher oil change rate of CO₂ than Well B, though both remain below 0.2 t/t—lower than other tight reservoirs as reported from the literature (

Table 7. Oil change rate statistics of some tight oil reservoirs.

Oilfield Type	Porosity (%)	Permeability (mD)	CO2 Oil Change Rate (t/t)
Changqing Oilfield	6–12	0.1–10	0.3–0.5
Daqing Oilfield	8–10	1–5	0.2–0.4
Xinjiang Oilfield	7–12	0.1–10	0.25–0.45
C Reservoir	6–12	0.1–10	0.01–0.2

).

Increasing the injection rates significantly boosts the displacement efficiency; when the daily injection volume rises from 3000 m³/d to 7500 m³/d, enhanced recovery becomes markedly evident. Increasing the daily gas injection rate helps to improve the formation pressure, favor CO₂ miscibility, and thereby enhance the recovery efficiency. However, excessive injection rates may induce a severe turbulent flow, which should be avoided. Therefore, in the design of CO₂ flooding, the injection rates should be carefully selected based on the reservoir characteristics to achieve an optimal flooding performance.

The CO₂ miscible flooding technology is relatively mature and feasible for this type of oil reservoir. However, from an economic perspective, due to the low oil displacement ratio and economic considerations such as drilling new wells and adding gas injection facilities, the input–output ratio is relatively high.

5. Conclusions

(1)

A Fuzzy Hierarchical Comprehensive Evaluation method is firstly applied for the adaptation analysis of CO₂ flooding, and the results are compared with the published data for other similar reservoirs. The fuzzy comprehensive evaluation index Z is 0.8711 for target reservoir C, showing the good adaptation of CO₂ flooding, and thus more confidence is obtained in determining the development method.

(2)

Core flooding experiments demonstrate that CO₂ exhibits the highest oil displacement efficiency, outperforming surfactants and formation water. At an injection rate of 0.2 mL/min, the core recovery slightly exceeds that at 0.1 mL/min. An increased water saturation elevates oil displacement resistance, so the recovery rate of CO₂ significantly decreases.

(3)

Under identical CO₂ flooding conditions, surfactant-assisted CO₂ huff-n-puff demonstrated the highest recovery rate, 0.85% higher than the recovery rate of only CO₂ huff-n-puff. Considering the cost of the surfactant, CO₂ is the optimized medium for huff-n-puff. The experimental study also shows that the recovery factor increases when the soaking time increases, while the increment rate drops, and 5 h in lab conditions is the optimized soaking time. Similarly, the cumulative recovery rate increases with huff-n-puff cycles, while the cyclic recovery rate decreased with increased flooding cycles.

(4)

Based on the geological models of Well A and B, a CO₂ flooding simulation was conducted. Layer merging significantly enhances computational efficiency, while maintaining calculation accuracy. For reservoir C, CO₂ miscible flooding using a five-spot well pattern can effectively enhance the oil recovery rate if an optimal injection rate can be achieved. However, constrained by the formation fracture pressure, the CO₂ flooding injection rate for Well A should not exceed 7500 m³/d, resulting in a 10-year recovery rate of 27.39% and an oil replacement rate of 0.2 t/t, with an oil change rate of 0.2 t/t. For Well Block B, the CO₂ injection rate should be less than 3000 m³/d, corresponding to a recovery rate of only 0.48% after 10 years and an oil change rate of 0.07 t/t.

(5)

The C reservoir’s tight sandstone formation exhibits a very low initial oil saturation, which is 30–50%. Although various methods have demonstrated the effectiveness of CO₂ flooding compared to other methods, the oil recovery rate remains low. Considering the cost in drilling new wells and expanding gas injection facilities, the future development of the C reservoir is highly challenging.