Evolution of CO2 Storage Mechanisms in Low-Permeability Tight Sandstone Reservoirs

Xiangzeng Wang; Hong Yang; Yongjie Huang; Quansheng Liang; Jing Liu; Dongqing Ye

doi:10.1016/j.eng.2024.05.013

Engineering ›› 2025, Vol. 48 ›› Issue (5) :114 -127. DOI: 10.1016/j.eng.2024.05.013

Research Carbon Capture, Utilization, and Storage—Article

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Evolution of CO₂ Storage Mechanisms in Low-Permeability Tight Sandstone Reservoirs

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Abstract

Understanding the storage mechanisms in CO₂ flooding is crucial, as many carbon capture, utilization, and storage (CCUS) projects are related to enhanced oil recovery (EOR). CO₂ storage in reservoirs across large timescales undergoes the two storage stages of oil displacement and well shut-in, which cover multiple replacement processes of injection–production synchronization, injection only with no production, and injection–production stoppage. Because the controlling mechanism of CO₂ storage in different stages is unknown, the evolution of CO₂ storage mechanisms over large timescales is not understood. A mathematical model for the evaluation of CO₂ storage, including stratigraphic, residual, solubility, and mineral trapping in low-permeability tight sandstone reservoirs, was established using experimental and theoretical analyses. Based on a detailed geological model of the Huaziping Oilfield, calibrated with reservoir permeability and fracture characteristic parameters obtained from well test results, a dynamic simulation of CO₂ storage for the entire reservoir life cycle under two scenarios of continuous injection and water–gas alternation were considered. The results show that CO₂ storage exhibits the significant stage characteristics of complete storage, dynamic storage, and stable storage. The CO₂ storage capacity and storage rate under the continuous gas injection scenario (scenario 1) were 6.34 × 10⁴ t and 61%, while those under the water–gas alternation scenario (scenario 2) were 4.62 × 10⁴ t and 46%. The proportions of storage capacity under scenarios 1 and 2 for structural or stratigraphic, residual, solubility, and mineral trapping were 33.36%, 33.96%, 32.43%, and 0.25%; and 15.09%, 38.65%, 45.77%, and 0.49%, respectively. The evolution of the CO₂ storage mechanism showed an overall trend: stratigraphic and residual trapping first increased and then decreased, whereas solubility trapping gradually decreased, and mineral trapping continuously increased. Based on these results, an evolution diagram of the CO₂ storage mechanism of low-permeability tight sandstone reservoirs across large timescales was established.

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CO₂ storage mechanism / Evolutionary patterns / Oil reservoir / Low permeability / Tight sandstone

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Xiangzeng Wang, Hong Yang, Yongjie Huang, Quansheng Liang, Jing Liu, Dongqing Ye. Evolution of CO₂ Storage Mechanisms in Low-Permeability Tight Sandstone Reservoirs. Engineering, 2025, 48(5): 114-127 DOI:10.1016/j.eng.2024.05.013

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1. Introduction

Since the Industrial Revolution, the atmospheric CO₂ concentration has been rising rapidly. In 2021, global CO₂ emissions reached 3.63 × 10¹⁰ t [1], and the global surface average temperature increased by approximately 1.1 °C compared with that in 1850–1900, due to the average CO₂ concentration exceeding 410 parts per million (ppm) [2]. This has resulted in frequent extreme climate disasters across the globe, intensifying the urgent need to reduce greenhouse gas emissions [3], [4], [5], [6]. As an emerging technology developed in response to the call to reduce greenhouse gas emissions, carbon capture, utilization, and storage (CCUS) is anticipated to play an important role in achieving large-scale CO₂ emission reduction [1], [7], [8], [9]. A study conducted by the International Energy Agency (IEA) showed that, under a sustainable development scenario, CCUS technology could contribute up to 15% of cumulative CO₂ emission reduction [1]. The geological utilization and storage of CO₂ is one of the most widely used among CCUS technologies and has the highest carbon emission reduction potential [10], [11], [12]. Moreover, it can be applied to saline aquifers, oil reservoirs, gas reservoirs, and coal beds [13], [14], [15], [16], [17], [18]. According to the Global CCS Institute, there were a total of 41 commercial carbon capture and storage (CCS) projects in operation around the world by the end of 2023 [19], with deep saline formations and oil reservoirs being the most common types of CO₂ storage containment.

When CO₂ is injected into a formation, owing to the influence of the geological structure, the viscosity, the capillary pressure, and the interaction between the dissolved CO₂ and the formation’s fluids and rocks, most of the CO₂ is trapped in the formation [20], [21], [22], [23], [24]. However, as the CO₂ storage mechanisms change over time, the storage state of the CO₂ within the formation at different stages can differ [25], [26]. Understanding the evolution of CO₂ storage mechanisms is an important prerequisite for verifying the actual CO₂ storage capacity at storage sites and assessing the safety of CO₂ storage [27], [28], [29], [30], [31], [32], [33].

Researchers at the Carbon Sequestration Leadership Forum (CSLF) [34], US Department of Energy (DOE) [35], and US Geological Survey (USGS) [36], in addition to other scholars [37], [38], [39], [40], have proposed various evaluation methods for the storage potential of CO₂ storage mechanisms in saline aquifers, analyzed the main factors affecting storage conditions and efficiency [41], [28], [42], [43], [44], and determined the ranges for the effective coefficients and other key parameters of CO₂ storage [45], [46], [47], [48], [49]. Notably, based on a study of the CO₂ storage mechanism in saline aquifers, the CSLF was the first to specify an evolution diagram of CO₂ storage [34].

Because CO₂ storage in oil reservoirs has the dual benefits of enhancing oil recovery and reducing carbon emissions, it is considered an ideal choice for reducing CO₂ emissions in the current economic and technological situation [50], [51], [52], [53], [54]. Compared with CO₂ solubility in saline aquifers, CO₂ has higher solubility in the crude oil in oil reservoirs; it also has a strong extraction effect on light hydrocarbon components [55], [56], [57], [58].

CO₂ storage in reservoirs across large timescales undergoes the two storage stages of oil displacement and well shut-in. These cover multiple replacement processes of injection–production synchronization, injection only with no production, and injection–production stoppage, which result in complex changes in the conditions of CO₂ storage. The evolutionary pattern of CO₂ storage over time, including stratigraphic trapping, residual trapping, solubility trapping, and mineral trapping, has not been clarified [33], [34], [35], [36], [37], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68]. According to experimental and theoretical analysis, a low-permeability tight reservoir can be effectively developed using CO₂ flooding [69], [70], [71], [72]. In China, low-permeability tight reservoirs provide stable and increased hydrocarbon production over a certain period, offering vast CO₂ storage potential [73], [74], [75], [76]. Nevertheless, understanding the CO₂ storage capacity mechanism is a prerequisite for the industrial application of CO₂ flooding and storage.

In this work, taking a low-permeability tight sandstone reservoir in the Xingzichuan Oilfield in the Huaziping region as an example, a mathematical model considering stratigraphic trapping, residual trapping, solubility trapping, and mineral trapping is established to estimate the CO₂ storage capacity based on oil–water and gas–water relative permeability, CO₂ phase, and water–rock interaction experiments. Well test results are used to calibrate the established geological model. Based on a dynamic simulation of CO₂ storage over the entire reservoir life cycle under two scenarios of continuous gas injection (scenario 1) and water–gas alternations (scenario 2), the effects of CO₂ flooding and storage and the evolution of the CO₂ storage mechanisms are clarified at different stages. An evolution diagram of CO₂ storage in a low-permeability tight sandstone reservoir over time is constructed.

This study provides theoretical support for calculating the carbon emissions reduction of the entire process of reservoir CO₂ storage and promotes the measurement, monitoring, and verification (MMV) method of actual emission reduction in CCUS projects for oil reservoirs. The CO₂ storage capacity of this low-permeability tight sandstone reservoir throughout its life cycle provides a reference for the dynamic evaluation of the actual CO₂ storage capacity of a similar type of reservoir.

2. The CO₂ storage mechanism

2.1. Residual trapping

In the process of CO₂ flooding and storage, with CO₂ injection, the reservoir CO₂ saturation increases, the water saturation decreases, and the change in the relative permeability of the fluids follows the drainage process. Simultaneously, as the formation water retreats and re-saturates the pore space, fluid seepage in the porous medium enters the imbibition process. Under the same saturation condition, due to the non-wetting phase, the relative permeability of CO₂ in the imbibition process is always lower than that in the drainage process. This relative permeability “hysteresis effect” causes CO₂ to be trapped in the form of a discontinuous phase in the microspores, meaning that it is trapped in the form of residual gas. Residual trapping is a relatively stable storage method that is mainly affected by the reservoir rock wettability, pore-throat structure, and fluid properties [77], [78], [79], [80]. Compared with medium- and high-permeability reservoirs, low-permeability reservoirs have smaller pores and more complex pore-throat structures, and CO₂ storage is controlled by capillary force. Taking the Chang 6 formation in the Huaziping oilfield as an example, the reservoir’s pore-throat structure is bimodal and skewed, with an average pore-throat radius of 0.07–0.52 μm.

The quantitative characteristics of the CO₂ residual gas saturation are key parameters for studying the reservoir residual mechanism and evaluating the residual CO₂ storage capacity. Previous studies have mainly analyzed the formation mechanism of residual gas and its influencing factors [10], [11] but have not evaluated the residual storage capacity based on the residual gas saturation under actual reservoir conditions. To characterize the CO₂ displacement–imbibition process, the oil–water and gas–water relative permeability curve of the CO₂ displacement–imbibition process of the Change 6 formation in the Huaziping Oilfield (Fig. 1) was measured according to the national standard, the “GB/T 28912–2012 Test method for two phase relative permeability in rock” [81]. The residual CO₂ gas saturation at any saturation level during the CO₂ flooding and storage process was calculated using the Killough method [82], as shown in Eqs. (1), (2), (3). The maximum gas saturation under the displacement state is 0.33, and the residual gas saturation under the imbibition state is 0.24. The land coefficient C value is 1.14, as calculated using Eq. (2).

(1)

S_{ncrt} = S_{ncrd} + \frac{S_{g} + S_{ncrd}}{1 + C (S_{g} - S_{ncrd})}

(2)

C = \frac{1}{S_{ncri} - S_{ncrd}} - \frac{1}{S_{nmax} - S_{ncrd}}

(3)

S_{ncrt} = \frac{S_{g}}{1 + 1.14 S_{g}}

where S_g is the gas saturation, S_ncrd is the residual gas saturation in the displacement curve, S_ncrt is the residual gas saturation, S_ncri is the residual gas saturation in the imbibition curve, S_nmax is the maximum gas saturation, and C is the land coefficient.

The residual CO₂ storage capacity in the reservoir is calculated as follows:

(4)

M_{residual} = 1.96 \times 10^{- 3} \cdot \frac{A h ϕ S_{g}}{B_{g} (1 + 1.14 S_{g})}

where M_residual is the residual trapping capacity (t), A is the reservoir area (m²), h is the reservoir net thickness (m), ϕ is the porosity, and B_g is the gas volume factor (m³·m⁻³).

2.2. Structural and stratigraphic trapping

After injection into the reservoir, CO₂ mainly exists in the form of free and dissolved gases and undergoes migration and transformation. Part of the free CO₂ is trapped in the form of residual gas by capillary force, while another part is trapped in the reservoir in the form of free gas owing to the barrier effect of the upper overburden—that is, structural or stratigraphic trapping. With the continuous injection of CO₂ and the flooding and storage processes, this part of the CO₂ migrates laterally on a large scale. Therefore, based on the defined residual CO₂ gas saturation, the equation for calculating CO₂ storage capacity due to structural trapping M_free is as follows:

(5)

M_{free} = 1.96 \times 10^{- 3} \cdot A h ϕ (S_{g} - \frac{S_{g}}{1 + 1.14 S_{g}}) / B_{g}

2.3. Solubility trapping

Solubility trapping refers to CO₂ storage that is achieved by dissolving the injected CO₂ in the reservoir’s crude oil and formation water. Unlike saline aquifers, a significant amount of hydrocarbon is contained in the oil reservoir. Because CO₂ has a higher solubility in crude oil than in formation water, under the same formation conditions, the CO₂ storage capacity is larger in oil reservoirs than in saline aquifers. Taking the Chang 6 formation in the Huaziping Oilfield as an example, under the reservoir conditions of 40 °C and 8.9 MPa, the solubilities of CO₂ in crude oil and formation water are 129.62 and 19.01 m³·m⁻³, respectively.

Solubility is a key parameter for calculating the CO₂ storage capacity using solubility trapping. It is primarily controlled by factors such as the reservoir temperature, pressure, formation water salinity, and crude oil composition. In general, CO₂ has higher solubility in formation fluids in low-temperature, high-pressure, low-salinity, and light crude oil environments [83], [84], [85]. By fitting the experimental results of CO₂ solubility in formation water at different pressures (Fig. 2), an equation for calculating the CO₂ storage capacity via solubility trapping in formation water in the target area was constructed.

(6)

R_{sb} = 0.0043 p^{3} - 0.2038 p^{2} + 3.5359 p + 0.5933 (2 \leq p \leq 20)

(7)

M_{solution} = 1.96 \times 10^{- 3} \cdot A h ϕ S_{w} R_{sb} / B_{w}

where R_sb is the CO₂ solubility of brine (m³·m⁻³), p is the reservoir pressure (MPa), S_w is the water saturation, B_w is the formation water volume factor (m³·m⁻³), and M_solution is the solubility trapping capacity in formation water (t).

During the CO₂ flooding process, the solubility of the CO₂ in the crude oil changes dynamically because of the constant change in the remaining hydrocarbon components in the reservoir, owing to the impact of the CO₂ on crude oil extraction under the same equilibrium pressure. The crude oil in the Chang 6 formation in the Huaziping Oilfield is characterized as a typical light oil with a high content of light, transition, and intermediate components. These characteristics result in a more significant extraction effect of CO₂ (Fig. 3), leading to a large variation in the CO₂ solubility in the crude oil. Therefore, to accurately calculate the CO₂ storage capacity via solubility trapping in crude oil, it is essential to determine the CO₂ solubility at different flooding stages. The CO₂ and well fluids were divided into seven component groups by conducting a chromatographic analysis of well fluids from well H146-4 in the target region and considering the properties, content, molecular weight of each component, and their interaction rules with CO₂ (Table 1). After the CO₂ injection, an equation of state for the crude oil was established by fitting the characteristic parameters of the crude oil (i.e., volume factor, density, viscosity, and bubble point pressure) to the pressure–volume–temperature (PVT) experimental results. The error between the minimum miscible pressure obtained using this equation and the experimental capillary results was 4.07%.

Based on the crude oil equation of state, the molar ratio of dissolved CO₂ in the formation’s crude oil under any equilibrium conditions during CO₂ flooding can be obtained. Therefore, the CO₂ storage capacity due to solubility trapping in crude oil can be calculated as follows:

(8)

M_{solo} = 4.4 \times 10^{- 5} \cdot \frac{A h ϕ S_{o} ρ_{o} x_{{CO}_{2}}}{\sum M_{i} z_{i}}

where M_solo is the solubility trapping in crude oil (t), S_o is the oil saturation, ρ_o is the reservoir oil density (kg·m⁻³),

x_{{C O}_{2}}

is the molar ratio of dissolved CO₂ in crude oil, M_i is the molar mass of component i (g), and z_i is the molar content of component i.

2.4. Mineral trapping

Mineral trapping refers to the reaction of CO₂ with water after CO₂ is injected into the formation to produce carbonic acid, which results in the dissolution of rock minerals and the generation of cations such as Ca²⁺ and Mg²⁺. Subsequently, these cations react with CO₂ to form new carbonate minerals [86], [87], [88], [89], [90], [91]. Unlike other storage mechanisms, mineral trapping is relatively slow, typically occurring on a timescale ranging from 100 to 10 000 years [92]. The progress of mineralization is mainly influenced by factors such as the formation temperature, pressure, rock mineral composition, and formation water salinity. Because CO₂ is trapped in solids through mineralization, mineral trapping is believed to be the safest form of storage.

According to a rock mineral composition analysis, the dominant rock minerals in the Chang 6 formation of the Huaziping Oilfield are quartz, feldspar, and clay minerals. The mass percentages of quartz, potassium feldspar, oligoclase, and clay minerals are 23.6%, 18.2%, 30.1%, and 28.1%, respectively, and the mass percentage of chlorite within the clay minerals is 95.1%. Based on the reservoir’s rock mineral composition and the minerals’ reactivity with CO₂, and for ease of calculation and analysis, the mineral types in the target area that were mineralized with CO₂ were simplified to potassium feldspar, oligoclase, and chlorite. These minerals react with CO₂ as shown below:

(9) $\begin{array}{l} \text { Potassium feldspar: }\\ 2 \mathrm{KAlSi}_{3} \mathrm{O}_{8}+2 \mathrm{CO}_{2}+11 \mathrm{H}_{2} \mathrm{O}=\mathrm{Al}_{2} \mathrm{Si}_{2} \mathrm{O}_{5}(\mathrm{OH})_{4}+4 \mathrm{H}_{4} \mathrm{SiO}_{4}+2 \mathrm{HCO}_{3}^{-}+2 \mathrm{~K}^{+} \end{array}$

(10) $\begin{array}{l} \text { Oligoclase: }\\ \mathrm{CaNa}_{4} \mathrm{Al}_{6} \mathrm{Si}_{14} \mathrm{O}_{40}+34 \mathrm{H}_{2} \mathrm{O}+6 \mathrm{H}^{+}=4 \mathrm{Na}^{+}+\mathrm{Ca}^{2+}+6 \mathrm{Al}(\mathrm{OH})_{3}+14 \mathrm{H}_{4} \mathrm{SiO}_{4} \end{array}$

(11) $\begin{array}{l} \text { Chlorite: }\\ 2 \mathrm{Mg}_{2.5} \mathrm{Fe}_{2.5} \mathrm{Al}_{2} \mathrm{Si}_{3} \mathrm{O}_{10}(\mathrm{OH})_{8}+20 \mathrm{H}^{+}=5 \mathrm{Mg}^{2+}+5 \mathrm{Fe}^{2+}+4 \mathrm{Al}(\mathrm{OH})_{3}+6 \mathrm{H}_{4} \mathrm{SiO}_{4} \end{array}$

The mineralization rate is a fundamental parameter for evaluating the CO₂ storage capacity via mineral trapping. The kinetic rate equation proposed by Lasaga et al. [93] was used to calculate the mineralization rate, as follows:

(12)

r = k \cdot A_{r} \cdot m {|1 - Ω^{θ}|}^{η}

(13)

k = k_{25} e^{(\frac{- E_{a}}{R} (\frac{1}{T} - \frac{1}{298.15}))}

where r is the mineral dissolution rate (mol·s⁻¹), k is the dissolution rate constant (mol·m⁻²·s⁻¹), m is the current mass of dissolved minerals (g), Ω is the saturation ratio of minerals, θ and η are the index factor (default is 1), k₂₅ is the dissolution rate constant at 25 °C (mol·m⁻²·s⁻¹), E_a is the activation energy (J·mol⁻¹), R is the Boltzmann constant (J·K⁻¹), T is the temperature (K), and A_r is the mineral surface area (m²·g⁻¹). The dissolution rate constant k of the minerals was calculated using the Arrhenius equation (Eq. (13)).

Blum and Stillings [94] and Xu et al. [95] provided dissolution rate constants for CO₂ and various rock minerals at 25 °C through geochemical simulation. The dissolution rate constants k₂₅ for potassium feldspar, oligoclase, and chlorite are 8.71 × 10⁻¹¹, 2.14 × 10⁻¹⁰, and 7.76 × 10⁻¹² mol·m⁻²·s⁻¹, respectively. The mineral surface areas A_r of potassium feldspar and oligoclase have a value of 0.01 m²·g⁻¹, and that of chlorite has a value of 0.12 m²·g⁻¹. Considering that the activation energy for the same rock mineral was constant within a certain temperature range, the dissolution rate constants for potassium feldspar, oligoclase, and chlorite were taken as values corresponding to 25 °C under the target reservoir temperature.

Therefore, the CO₂ storage capacity via mineral trapping can be calculated as follows:

(14)

M_{mineral} = \sum_{j = 1}^{3} 4.4 \times 10^{- 2} \int_{0}^{t} r d t

where j is the mineral types in the target area that were mineralized with CO₂, t is the duration time of the mineralization reaction.

3. Geological model of the area of interest

3.1. Geological overview

The Xingzichuan Oilfield is located in the central part of the northern Shaanxi slope of the Ordos Basin. The structure of this region is a monocline with a gradual dip of approximately 0.6° from east to west and an altitude of 1100–1600 m. The Huaziping Oilfield is a lithological reservoir located in the western part of the Xingzichuan Oilfield, and its primary oil-bearing bed is the Yanchang Formation (Chang 6 formation). The rock type of the Chang 6 formation is medium to fine sandstone, with a formation depth ranging from 1200 to 1450 m, reservoir temperature of 46 °C, initial reservoir pressure of 8.9 MPa, minimum miscibility pressure of 14.3 MPa, bubble point pressure of 5.3 MPa, effective thickness of 14.1 m, average permeability of 9.4 × 10⁻⁴ μm², average porosity of 9.6%, average oil saturation of 42%, reservoir oil density of 0.79 g·cm⁻³, and reservoir oil viscosity of 3.4 mPa·s. The reservoir water type is CaCl₂, with a salinity of 32.14–79.39 g·L⁻¹.

Above the reservoir, there is a caprock called the Chang 4+5 formations, which consists of feldspathic debris sandstone and rock fragment feldspathic sandstone with a thickness of 70–100 m, mudstone content percentage of 50%–75%, permeability lower than 1.0 × 10⁻⁴ μm², and porosity of 1.8%–7.9%. According to a preliminary evaluation, the radii of the pores and throats in the caprock are 100–150 and 1.0–2.5 μm, respectively. The caprock exhibits medium to small pores and fine throat characteristics, with a moveable fluid saturation of 9.6% and a breakthrough pressure of 9.9 MPa. Therefore, the caprock has a better sealing capacity, making it the desired area for CO₂ storage. Fig. 4 shows the well location map of the Huaziping oil area in the Xingzichuan Oilfield.

3.2. Geological model

As the study area is a monocline structure with no fault development and minimal structural variability, a structural model was built based on hierarchical data. By combining the controlling effect of sedimentary facies on the reservoir’s physical properties, a three-dimensional (3D) geological model of the area of interest was developed using multi-level facies-controlled modeling with Petrel software. The grid size of the model plan was 20 m × 20 m, with a vertical thickness of 1 m. Upon comparing the well data from wells H47-17 and H40-18 with the geological model, the actual sandstone thickness obtained through well logging was found to be more than 90% consistent with the sandstone thickness predicted by the geological model (Fig. 5).

To reduce the error resulting from the limited detection scale of well-logging methods [96], which cannot accurately represent the actual percolation characteristics of low-permeability tight reservoirs, and to further enhance the precision of the geological model, the model was calibrated using the percolation characteristic parameters from well testing.

To minimize the duration of the test and reduce the impact on well production, the best well testing plan involving one injection well and two monitoring wells was determined to be water injection-pressure drawdown and CO₂ injection-pressure drawdown. To ensure the reliability of data acquisition, both the injection and monitoring wells were equipped with direct-reading network transmission pressure gauges to monitor changes in the reservoir pressure. Before injection, the monitoring wells were shut down for 60 days. During injection, the injection well continuously injected water and CO₂ at rates of 7 and 8 t·d⁻¹, and the injection lasted for 120 and 60 days, respectively. After injection, pressure monitoring was continued for 30 and 90 days for water and CO₂, respectively. The monitoring wells were shut down throughout the testing. To minimize interference from offset wells, H146-2, which is located at the edge of the target area, was chosen as the injection well, whereas H146-3 and H146-7 were monitoring wells. Offset wells were injected and produced during the testing.

Well-testing simulations and interpretations were conducted using a homogeneous reservoir and a finite-conductivity fracture vertical well model. The three-zone composite model for CO₂ flooding includes the CO₂ zone, CO₂–crude oil transition zone, and CO₂ undisturbed zone. According to the interpretation results (Table 2), the permeability around well H146-2 was 4.60 × 10⁻⁴ μm² during water injection and CO₂ injection, and the fracture half-length and conductivity were 109.16 m and 1551.67 mD·m (1 mD = 10⁻³ μm²), respectively. For wells H146-3 and H146-7, the permeability, fracture half-length, and fracture conductivity were 5.60 × 10⁻⁴ μm², 78.72 m, and 1508.52 mD·m; and 3.10 × 10⁻⁴ μm², 112.26 m, and 1334.75 mD·m, respectively.

The original fracture setting in the model was calibrated using the reservoir fracture half-length and conductivity from the well-testing interpretation and model grid size. The permeability of the model was calibrated using the permeability calibration factor (the ratio of permeability from well testing to well logging). Given that the interpreted permeability from well logging for wells H146-2, H146-3, and H146-7 were 8.60 × 10⁻⁴, 1.03 × 10⁻³, and 7.20 × 10⁻⁴ μm², respectively, the average permeability correction factor for the model was set to 0.50.

4. Numerical simulation of CO₂ storage

Based on the geological model, numerical simulations of CO₂ storage with Eclipse software were conducted for two well groups, H146-6 and H146-2, consisting of a total of two injection wells and ten production wells. By matching the production and injection history data, such as the cumulative liquid production, daily oil production rate, and overall water cut for a single well and well groups, a numerical model with a high degree of conformity to the actual production scenario was established (Fig. 6, Fig. 7, Fig. 8).

Two CO₂ storage scenarios were simulated: continuous gas injection (scenario 1) and water–gas alternating injection (scenario 2). Based on previous research on CO₂ flooding and storage in reservoirs, the injection rates for CO₂ and water were set at 15 and 10 t·d⁻¹, respectively. In scenario 2, continuous gas injection was performed for the first five years, with a water–gas alternation frequency of three months in the later injection. Based on the combination factors of formation fracture pressure, actual CO₂ flooding production, and economic benefits, the constraints for both scenarios were as follows: The minimum daily oil production rates were 0.15 and 0.10 m³·d⁻¹, the gas–oil ratios were both 4000 m³·m⁻³, and the maximum injection pressures at the wellhead of injection wells were both 15 MPa. In scenario 1, the well was closed when the daily oil production rate or gas–oil ratio reached its constraints; in scenario 2, the perforation was closed.

4.1. CO₂ storage performance

Unlike in saline aquifer storage, injection and production occur simultaneously during CO₂ storage in oil reservoirs. The CO₂ storage performance not only depends on the reservoir and fluid properties but is also closely related to the production process. For a better understanding of the CO₂ storage performance, the CO₂ storage capacity is defined as the difference between the amount of CO₂ injected and the amount of CO₂ produced. The CO₂ storage rate is defined as the ratio of the CO₂ storage capacity to the cumulative injection amount. The oil exchange ratio is defined as the ratio of the CO₂ cumulative injection to the cumulative oil increment. The simulation results in Fig. 9 show that both the reservoir cumulative oil production and the CO₂ storage capacity increase with the injection of CO₂. The oil exchange ratio initially increases and then decreases, whereas the CO₂ storage rate continuously decreases until it stabilizes after the shut-in of the injection and production wells. As the injection wells close over time, the oil reservoir reaches a relatively stable state of CO₂ storage 20 years after injection.

Considering the significant differences in CO₂ flooding and storage performance at the different stages, CO₂ storage can be divided into three stages.

(1) Complete CO₂ storage stage. This stage occurs during the early stage of CO₂ injection into the reservoir, and its duration is relatively short. During this stage, the injected CO₂ rapidly diffuses and dissolves in the crude oil and formation water in the form of free gas. CO₂ is primarily stored via structural and solubility trapping, which causes the reservoir pressure to recover rapidly. The CO₂ displacement effect gradually begins to play an important role. The amount of CO₂ stored in the reservoir is equivalent to the amount of injected CO₂ at this stage, resulting in an almost 100% CO₂ storage rate.

(2) Dynamic CO₂ storage stage. In this stage, CO₂ is produced in various forms in the sequence of dissolved gas, dissolved-free gas, and free gas. Owing to the differences in reservoir properties controlled by production wells and their connectivity with injection wells, the production of CO₂ and the time to trigger constraints for different wells are different, resulting in CO₂ storage within this dynamic production process. During the dissolved gas production stage, a large amount of crude oil is produced, leading to a rapid increase in the cumulative oil production and oil exchange ratio. The dissolved CO₂ in the reservoir crude oil gradually escapes as the pressure decreases. However, the impact of the dissolved gas production on the CO₂ storage efficiency is not significant, and the amount of stored CO₂ continues to increase rapidly. At this stage, the CO₂ storage rate is approximately 95%. As the CO₂ injection continues, the produced CO₂ gradually shifts from dissolved gas to free gas. With the further production of crude oil, the oil exchange ratio reaches its maximum value, whereas the CO₂ storage rate rapidly decreases. For example, in scenario 1, the oil exchange ratio reaches 0.28; however, the CO₂ storage rate decreases to approximately 82%. As CO₂ channeling begins, free gas production increases, causing a decrease in the oil exchange ratio, and the CO₂ storage rate for the well groups drops to its lowest point, with the produced CO₂ existing in the form of free gas. During the free-gas stage, CO₂ channeling in the reservoir is fully established, and the injected CO₂ flows through CO₂ channeling and is produced by the production wells. A large amount of CO₂ is inefficiently circulated between the injection and production wells. During this period, the amount of stored CO₂ remained unchanged, and the wells are closed until the production well constraints are triggered. In the production process of the well groups, owing to the existence of multiple production wells, the well group performances differ when some of the wells are closed by triggering constraints, resulting in a change in the CO₂ seepage pathway. Therefore, residual oil that has not been previously disturbed by the CO₂ is mobilized, which leads to further increases in the cumulative oil production and CO₂ storage capacity of the reservoir. The oil exchange ratio gradually decreases, the CO₂ storage rate increases, and the produced CO₂ returns to the dissolved-free gas form. Therefore, during reservoir development, the dissolved free gas CO₂ production phase and free gas CO₂ production phase are generally closely connected.

(3) Stable CO₂ storage stage. In this stage, all CO₂ injection and production wells are closed. Without CO₂ geological leakage, stable CO₂ storage in a reservoir can be achieved. This stage lasts the longest, and the evolution of CO₂ storage in the reservoir mainly occurs during this stage.

Because of the different critical conditions and the influence of the production performance of well H146-4 (in scenario 1, this well is closed 11 months after CO₂ injection), the CO₂ flooding and storage for the well groups under the two scenarios differ in the corresponding time period. Table 3 shows the CO₂ flooding and storage effects in the reservoir over a period of 100 years under the two scenarios. It can be seen that, on a time scale of over 100 years for CO₂ storage, the water–gas alternation scenario has a larger impact than the continuous gas injection scenario, which means that the CO₂ displaces more reservoir crude oil with water–gas alternation. Therefore, compared with scenario 1, scenario 2 has a higher oil increment and oil exchange ratio, with its cumulative oil increment and oil exchange ratio being 1.24 and 1.25 times those of scenario 1, respectively. Because water–gas alternation has an anti-gas-channeling effect under the same gas–oil ratio and bottom-hole pressure constraints, scenario 2 requires a longer time to trigger the critical well shut-in condition. In addition, once scenario 2 reaches its gas–oil ratio constraint, only the perforation is closed, which results in a higher gas production rate. If the amount of injected CO₂ is the same, scenario 2 has a lower CO₂ storage capacity and storage rate, which are reduced by 6.78% and 24.59%, respectively, compared with those of scenario 1. This can also be demonstrated by the change in the oil exchange ratio and storage rate when free gas is produced in scenario 2.

4.2. Evolution of CO₂ storage mechanisms

According to the numerical simulation results, the CO₂ storage mechanisms and their partitioning of the reservoir CO₂ storage change over time (Fig. 10). After CO₂ is injected into the reservoir, CO₂ rapidly moves toward the near-well zone in the form of free gas, owing to the injection well pressure. It also dissolves in the formation fluid through diffusion. Therefore, during the complete CO₂ storage stage, structural and solubility trapping are the primary CO₂ storage mechanisms, with the proportions of storage capacities of 32.31% and 67.62%, respectively. Because the diffusion coefficient and solubility of CO₂ are higher in crude oil than in formation water, more CO₂ can be dissolved in the reservoir crude oil. Therefore, the CO₂ storage capacity for solubility trapping in the crude oil is higher than that in the formation water. The proportions of solubility-trapping capacity in oil and water were 39.08% and 28.54% of the total storage, respectively.

As the continuous injection and production of CO₂ proceed, CO₂ gradually migrates into deeper reservoirs, and reservoir connate fluids are produced. The pore spaces previously occupied by connate fluids are filled with the injected CO₂, and the proportion of solubility trapping capacity gradually decreases while the proportion of structural trapping capacity increases. Simultaneously, due to the effect of capillary force, some CO₂ is trapped in the micropore throats of the reservoir, causing residual trapping. Taking scenario 1 as an example, before the closure of the injection wells, the proportions of structural trapping, residual trapping, and solubility trapping capacities are 64.60%, 6.97%, and 28.38%, respectively. The proportions of the solubility-trapping capacities in crude oil and formation water are 19.53% and 8.85% of the total storage, respectively.

When the injection wells are closed upon reaching their pressure constraints, changes occur in the reservoir seepage field. The CO₂ near the injection wells continues to migrate forward, the formation water retreats and re-saturates the pore space, and fluid seepage in the porous medium enters the imbibition process. Due to the non-wetting phase, the relative permeability of the CO₂ in the imbibition process is always lower than that in the displacement process under the same saturation; a large amount of free CO₂ is trapped, resulting in a decline in the structural trapping capacity and an increase in the residual trapping capacity. Taking scenario 1 as an example, the proportions of structural trapping, residual trapping, and solubility trapping capacities are 37.36%, 33.95%, and 28.64%, respectively.

Unlike other storage mechanisms, the mineral trapping process is extremely slow, resulting in a relatively low mineral trapping capacity. However, at longer timescales, the mineral-trapping capacity increases. According to the numerical simulation results, under both scenarios, the proportions of the reservoir’s CO₂ mineral trapping capacities are 0.10% and 0.25% for 20 years, and 0.21% and 0.49% for 100 years. Based on the mineralization rate in the target area, the proportions of the CO₂ mineral trapping capacity for the two scenarios are assumed to be 1.24% and 2.07%, respectively, after 1000 years.

After stopping the injection, the evolution law of the CO₂ storage mechanism is basically consistent with relevant research results [97], [98]. However, the average reservoir permeability of SACROC, a large oilfield in the United States, is 1.94 × 10⁻² μm², its oil saturation is 78.1%, the original formation pressure is 31.2 MPa, the minimum miscible pressure of CO₂–oil is 16.4 MPa, the block is of miscible flooding, and the CO₂ solubility in oil is about 30–40 times greater than its solubility in formation water. In comparison, the average reservoir permeability of the Huaziping Oilfield is 9.4 × 10⁻⁴ μm², its oil saturation is 42%, the original formation pressure is 8.9 MPa, and the minimum miscible pressure of CO₂–oil is 14.3 MPa, which is immiscible flooding. Moreover, the CO₂ solubility in oil is only about seven times greater than its solubility in formation water. Due to the large differences between the Huaziping Oilfield reservoir’s physical and fluid properties and those of the SACROC oilfield, the proportions of structural and residual trapping in Huaziping Oilfield is higher, and the proportion of CO₂ dissolved in oil trapping is less than that in SACROC. In the SACROC oilfield, the structural trapping accounts for 18%, the proportion of residual trapping is 18%, and the proportion of CO₂ dissolved in oil trapping is 62%.

With the extension of the time scale, the storage capacity of CO₂ mineralization increases gradually. It is worth noting that, due to the low water saturation of the reservoir, and because the CO₂ migration range affected by the injection and production development process is relatively small, the timescale of CO₂ mineralization and sequestration in the reservoir is generally greater than 100 years [33], [99], which is consistent with our research. Because of the influence of the reservoir’s physical properties, mineral composition, formation temperature, and pressure, the ratio of CO₂ mineral trapping in the Huaziping Oilfield is much lower than that in the SACROC Oilfield, where the ratio of mineral trapping is less than 2.5% in 200 years, or that in the Jilin Oilfield in China, where the ratio of mineral trapping is 16% in 800 years.

Table 4 shows the results of the CO₂ storage capacity and its percentage in the reservoir over 100 years for the continuous gas injection and water–gas alternating injection scenarios. Compared with scenario 1, scenario 2 has a higher percentage of CO₂ residual, solubility, and mineral trapping capacity. This is primarily due to the large amount of formation water injected into the reservoir in scenario 2. Under a similar reservoir pressure, the more CO₂ dissolves in the formation water, the higher the solubility trapping capacity. Furthermore, carbic acid is formed due to the dissolution of CO₂, resulting in a broader scope of CO₂–water–rock mineralization and a larger CO₂ mineral trapping capacity, with a higher percentage of total storage. In addition, the gas–water contact increases owing to the alternating gas–water injection, which causes an increase in fluid migration resistance. Under imbibition conditions, free CO₂ is captured more easily in the porous media of the reservoir, resulting in residual trapping. Compared with continuous gas injection, water–gas alternating injection has a higher capacity for residual trapping, solubility trapping, and mineral trapping, but a relatively lower capacity for structural trapping, which is consistent with relevant studies [98]. The percentage of the residual trapping capacity in scenario 2 is higher; however, because the total CO₂ storage capacity and storage rate in scenario 2 are lower, the CO₂ residual trapping capacity is slightly lower than that in scenario 1. Although more crude oil is produced through scenario 2, the injection wells in scenario 2 close at a later time (the injection durations for the H146-6 and H146-2 wells are extended by 38 and 72 months, respectively, compared with those in scenario 1), resulting in a slower injection of CO₂ into the formation. This prolongs the contact time of the CO₂ with the remaining oil in the less accessible areas of the reservoir. The CO₂ solubility trapping in the crude oil is higher in scenario 2 under the same reservoir pressure.

Based on the above research and considering the effects of the CO₂ storage mechanisms and their partitioning into the reservoir’s CO₂ storage capacity over time, a diagram of the evolution of the CO₂ storage mechanisms in a low-permeability tight sandstone reservoir was developed (Fig. 11). In the early stages of CO₂ injection, structural and solubility trapping dominate, typically lasting for less than ten years. As crude oil is continuously produced until the injection–production wells are closed, the reservoir development life cycle ends, the structural and solubility trapping capacities decrease, and the residual trapping capacity increases. During this stage, CO₂ storage mainly consists of structural, residual, and solubility trapping, with a timescale of approximately 10–100 years. At the end of the reservoir life cycle, the CO₂ storage in the reservoir enters a stage similar to that of saline aquifer. The CO₂ storage mechanism gradually transforms into solubility trapping from structure trapping and residual trapping, ultimately leading to mineral trapping because of the long-term contact between the CO₂ and the formation water. Therefore, the structure, residual, and solubility trapping capacities decrease, and the mineral trapping capacity increases. As CO₂ is injected and produced during the reservoir CO₂ storage process, the CO₂ storage rate during the stable storage stage is approximately 40%–70%.

The evolutionary principle of the reservoir CO₂ storage mechanism in the diagram is generally consistent with the Intergovernmental Panel on Climate Change (IPCC) study results [33], but the results in the IPCC study are mainly based on a saline aquifer sequestration scenario, and the CO₂ storage rate is 100%. In the current example, CO₂ is produced in the reservoir CO₂ storage process, and the CO₂ storage rate is less than 100%. Under the development systems, the CO₂ storage rate in the Huaziping field is 40%–70%, which is consistent with the results of relevant reports [100]. It should be noted that the solubility of CO₂ in oil is higher than that in formation water. After CO₂ injection, a large part of the CO₂ is dissolved in oil, and the contribution of the structural storage in the reservoir is much lower than that in a saline aquifer storage scenario, which is especially obvious in the early stage of CO₂ storage. Due to the low water saturation of the reservoir, the mineral storage in the reservoir takes a longer time to play a role, and the proportion of CO₂ mineral storage in the reservoir is about 18% at 10 000 years, which is lower than the results of the IPCC study (about 30% of the mineral storage). In addition, due to the low permeability, small pore throat, and complex structure of the Huaziping reservoir, the proportion of residual trapping is higher than that of a medium- and high-permeability reservoir, which accounts for 15%–25% [101].

5. Discussion

This study proposes a method for evaluating the evolution of CO₂ storage mechanisms. A mathematical model considering structural, residual, solubility, and mineral trapping for estimating CO₂ storage capacity was established through gas–water relative permeability, the CO₂ phase, and water–rock simulation experiments. Well test results were used to calibrate the established geological models. Based on these models, a numerical simulation of CO₂ flooding and storage was performed to determine the variation in CO₂ storage capacity corresponding to different CO₂ storage mechanisms in a low-permeability tight sandstone reservoir.

The evolution of the CO₂ storage mechanism is a complex and gradual process over a long timescale. Related studies have typically been conducted using numerical simulations. In the present study, the time range of the evolution of the reservoir’s CO₂ storage mechanisms was extended from 100 to 10 000 years, which significantly expanded the evolution process of the CO₂ storage mechanisms and provided technical support for understanding the CO₂ storage evolutionary mechanisms, including structural trapping, residual trapping, solubility trapping (in crude oil and formation water), and mineral trapping, over a large timescale. The variations in the estimated CO₂ storage capacities corresponding to the different storage mechanisms obtained in this study are consistent with related research results [68], [97]. However, owing to differences in reservoir physical properties, fluid properties, rock or mineral compositions, and development strategies, each storage mechanism dominates the partitioning at a different time period.

Because the evolutionary principles and controlling factors of CO₂ storage mechanisms differ, in order to further improve the evolutionary principles of reservoir CO₂ storage mechanisms, different storage evolutionary mechanisms need to be identified in terms of their complexities in microscopic effects and transformation mechanisms. Simultaneously, the evolution of the CO₂ storage mechanism should be continuously verified and improved using the long-term CO₂ storage dynamic monitoring results of low-permeability tight sandstone reservoirs.

6. Conclusions

Based on a mathematical model for the evaluation of CO₂ storage capacity and a geological model calibrated from well test results, the evolution of the CO₂ storage mechanism for a low-permeability tight sandstone reservoir across a large-term timescale was established through numerical simulation. According to the results, CO₂ storage has the significant stage characteristics of complete storage, dynamic storage, and stable storage throughout the entire reservoir life cycle. The CO₂ storage capacity and storage rate under the continuous gas injection scenario are 6.34 × 10⁴ t and 61%, and those under the water–gas alternating scenario are 4.62 × 10⁴ t and 46%, respectively. The proportions of storage capacity for scenarios 1 and 2 for structural or stratigraphic, residual, solubility, and mineral trapping are 33.36%, 33.96%, 32.43%, and 0.25%; and 15.09%, 38.65%, 45.77%, and 0.49%, respectively.

The evolution of the CO₂ storage mechanism showed an overall trend: stratigraphic and residual trapping first increased and then decreased, whereas solubility trapping gradually decreased, and mineral trapping continuously increased. Based on this result, an evolution diagram of the CO₂ storage mechanism of a low-permeability tight sandstone reservoir across a large-term timescale was established, which provides technical support for determining CO₂ storage evolutionary mechanisms, including structural trapping, residual trapping, solubility trapping (in crude oil and formation water), and mineral trapping over a large timescale.

CRediT authorship contribution statement

Xiangzeng Wang: Writing – review & editing, Methodology, Conceptualization. Hong Yang: Writing – review & editing, Writing – original draft, Investigation, Formal analysis. Yongjie Huang: Writing – review & editing, Validation, Methodology. Quansheng Liang: Validation, Supervision, Formal analysis. Jing Liu: Validation, Software, Resources. Dongqing Ye: Writing – review & editing, Software.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Key Research and Development Program of China (2022YFE0206700).

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