Impact of clay minerals on oil displacement efficiency in tight oil reservoirs

Shanyi Zhang; Xueying Li; Jinyu Lan; Cong Lin; DeJian Zhang; Hongliang Duan

doi:10.25259/AJC_337_2025

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Original Article

:19;

3372025

doi:

10.25259/AJC_337_2025

Impact of clay minerals on oil displacement efficiency in tight oil reservoirs

Shanyi Zhang^a,b,*, Xueying Li^a, Jinyu Lan^b, Cong Lin^a, DeJian Zhang^b,, Hongliang Duan^b

aState Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Heilongjiang, Daqing, China

bChina Petroleum Daqing Oilfield Company Limited, Heilongjiang, Daqing City, China

*Corresponding author: E-mail address: zsy0537@sina.com (S. Zhang)

Received: 2025-04-03, Accepted: 2025-08-07, Epub ahead of print: 2025-11-20, Published: 2026-01-05

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This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

Abstract

Although water and gas drives are still the most commonly used methods for most reservoirs, the high clay content adversely affects production efficiency. This study investigates three development methods (water, CO₂, and N₂) in high-clay reservoirs. It aims to identify factors affecting productivity and explain the underlying mechanisms. The mineral composition of natural cores was analyzed using X-ray fluorescence (XRF) and X-ray diffraction (XRD) techniques. We studied water, CO₂, and N₂ drives in tight oil reservoirs through both core experiments and molecular simulations. The simulations focused on quartz and clay minerals (kaolinite and montmorillonite). Due to the limitation of reservoir fracturing pressure, the recovery rates of the natural cores were found to be as follows: water drive > CO₂ drive > nitrogen drive. Molecular simulations showed that all three replacement media performed best on quartz surfaces, with water drives being most effective on kaolinite surfaces and CO₂ drives being most effective on montmorillonite surfaces. The negatively charged surface of kaolinite, affected by the adsorption capacity of minerals, leads to weak CO₂ adsorption, resulting in poor recovery. Increasing the pressure to 35 MPa increased the CO₂ drive efficiency to 96.3%, confirming that the pressure increase contributes to the formation of a mixed phase. In kaolinite reservoirs, nitrogen drive outperformed CO₂ thanks to its non-polar properties and gas expansion effect, resulting in better penetration into micropores and less interference from clay minerals. Finally, the water drive performed poorly on both clay surfaces due to hydration and swelling of the clay caused by low salinity. The higher ion exchange capacity of montmorillonite, meanwhile, exacerbated pore plugging.

Keywords

Adsorption

Clay minerals

CO2-EOR

Mineral composition

Molecular modeling

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1. Introduction

Petroleum, as one of the world’s primary energy sources, has always seen efficient extraction as a core research focus in the oil and gas industry. Although researchers have developed advanced methods for creating low-permeability reservoirs [1], most of these methods are still in the exploratory stage [2]. At present, the most common recovery methods for the majority of reservoirs are water and gas displacement [3,4], demonstrating remarkable effectiveness, particularly in low-permeability and tight/shale reservoir development [5]. Regarding the efficiency variations among different displacement media. But with the expanding development of unconventional hydrocarbon resources, reservoirs with high clay content have been increasingly encountered. Clay minerals, as critical structural components in clastic reservoirs [6], exert decisive influences on reservoir properties, fluid migration, and development potential through their physicochemical characteristics. Research indicates that clay content, mineral types, and occurrence states not only govern original permeability and pore structures [7] but also induce reservoir damage through dynamic processes like hydration swelling, particle migration, and chemical adsorption, significantly constraining oil recovery enhancement [8]. For instance, montmorillonite hydration can trigger micro-fracture closure [9], illite’s flaky structure tends to cause pore-throat blockage [10], while kaolinite’s loosely attached particles frequently induce reservoir particle migration [11]. During chemical flooding processes, variations in the injected fluid’s ionic composition and pH levels [12] may exacerbate clay mineral structural instability, initiating wettability alteration [13] or mineral dissolution-precipitation reactions [14], thereby creating multiscale damage effects. In CO₂ flooding applications, clay minerals exhibit unique adsorption behaviors due to their high specific surface areas and active surface sites [15]. The adsorption/desorption and chemical reactions between injected CO₂ and clay minerals profoundly influence key aspects of displacement efficiency. For example, Mohamed Mahmoud’s team revealed through static adsorption and core flooding experiments that hot CO₂ injection simultaneously enhances oil recovery and promotes carbon sequestration, with mineral composition and temperature significantly regulating CO₂ adsorption/desorption behaviors [16]. Guangsheng Cao et al. conducted focused analyses on the adsorption characteristics of supercritical CO₂ in tight oil reservoirs, revealing that the differential adsorption of CO₂, Water, and hydrocarbons by various clay minerals modifies oil-water interfacial tension and governs fluid migration pathways [17]. Ahmad Kadoura’s molecular dynamics (MD) simulations revealed stronger adsorption affinity of CO₂ on Na-montmorillonite surfaces with density profiles closer to clay layers compared to weakly interacting CH₄, providing molecular-scale explanations for CO₂’s displacement advantages [18]. Additionally, Ahmed Hamza confirmed that clay minerals in sandstones significantly enhance CO₂ adsorption capacity, a characteristic beneficial for natural gas recovery in depleted gas reservoirs [19]. Regarding water flooding technology, Shan Jiang’s core flooding experiments [20] showed that reservoirs with high montmorillonite content achieved significant recovery improvement under low-salinity water flooding, whereas kaolinite-dominated reservoirs exhibited the poorest performance, attributable to clay minerals’ ion exchange mechanisms regulating rock wettability and interfacial tension. Kartic C et al. noted that when injection water salinity falls below critical levels, released clay particles cause permeability reduction, indicating that water flooding effectiveness depends on the compatibility between clay types and salinity levels [21].

Previous studies collectively demonstrate that reservoir clay minerals significantly influence displacement processes, particularly in enhanced oil recovery (EOR) techniques such as CO₂ flooding and water flooding. Clay minerals, including kaolinite and montmorillonite, play pivotal roles in hydrocarbon reservoir development due to their unique adsorption properties and mineralogical compositions. Research indicates that clay mineral types and content directly affect fluid-crude oil interactions and displacement efficiency. Through adsorption of organic substances in oil phases, clay minerals can modify reservoir wettability, thereby influencing fluid permeability and hydrocarbon distribution during displacement. In addition, the swelling, dissolution, or filling of clays during water and CO₂ displacement may lead to changes in the pore structure, which in turn affects the effectiveness of the drive. The response of clay minerals can vary greatly depending on the different drive media (e.g., CO₂, nitrogen, water). Studying these mineral-mediated effects not only helps to improve recovery but also provides a theoretical basis for optimizing reservoir development strategies.

2. Materials and Methods

This paper is divided into two parts. The first part involves the physical simulation of oil repulsion experiments in different repellent media. The second part involves the molecular simulation of different repellent media at different repellent walls, based on the high clay mineral content of the reservoir.

2.1. Materials and reagents

The experimental materials used for the experiments included: Formation natural core (provided by Daqing No. 5 Oil Production Plant; the lithology is dense sandstone supported by granular material, with linear contact between the grains); formation crude oil (provided by Daqing No. 5 Oil Production Plant; minimum miscible pressure of crude oil: 33 MPa); formation water (provided by Daqing No. 5 Oil Production Plant); distilled water; glass slides; aviation kerosene; CO₂ (purity 99.999%); N₂ (purity 99.999%).

The experimental equipment used included: TD-3500 X-ray diffractometer; Rigaku ZSX Primus III+ X-ray fluorescence (XRF) spectrometer (Rigaku Corporation, Japan); Incubator; Vacuum pump; Core cutter; Slim tube model; Hand pump; High-temperature and high-pressure physical property tester; ISCO pump; Gas flow meter; Intermediate container; Gas cylinder; Precision pressure gauge; Oil and gas separator; Oil volume measuring tube; Electronic balance; Gas volume measuring tube; Electronic stopwatch; Vernier caliper; Core holder; Back pressure regulator.

2.2. Experiments on petrographic analysis of natural rock cores

To accurately analyze the types and contents of minerals in rock samples, XRF and X-ray diffraction (XRD) were employed. XRF quantifies elemental mass fractions by measuring the fluorescent characteristics of elements in rocks, while XRD identifies mineral species and their relative abundances through analysis of crystalline diffraction patterns. The combined application of these methods provided comprehensive data support for characterizing reservoir rock mineralogy, facilitating enhanced understanding of compositional features and reservoir performance. The test temperature is 26.5°C, the scanning mode is continuous scanning, the driving mode is biaxial driving, the X-ray tube voltage is 30 KV, and the tube current is 20 mA. The scanning parameters have been shown in Table 1. The table shows the other scanning parameters.

1.

The samples were crushed and ground into homogeneous powders with particle sizes below 75 μm;
2.

Core samples underwent continuous drying at 105°C for 24 h to eliminate moisture interference;
3.

Prepared rock powders were analyzed using XRF and XRD analyzers;
4.

The X-ray beam was directed onto the sample to excite characteristic XRF. The XRF detector captures and records fluorescence signals to generate energy spectra, while XRD produces diffraction patterns based on mineral-specific diffraction angles and intensities;
5.

Fluorescence signal intensities were compared against standard calibration curves to calculate elemental concentrations and mineral contents.

Table 1. Scanning parameters.

Start angle	End angle	Step angle	Sampling time (s)	Scanning speed (min)
10	74	0.02	0.2	6

Flowchart of experimental procedure for relative permeability curve determination.

2.3. Unsteady-state method of driving replacement experiments under each driving medium

The effect of clay minerals on oil replacement efficiency is crucial in the oil and gas recovery process, especially in tight and low-permeability reservoirs. The presence of clay can significantly alter the replacement effect, primarily through the adsorption of clay minerals onto the replacement medium. Poor adsorption results in an ineffective diffusion process, which affects the recovery results. To study the effect of clay minerals on recovery under different replacement media (e.g., carbon dioxide, water, and nitrogen), we conducted core replacement experiments to evaluate oil and gas recovery in clay-rich core samples. These experiments provided the basic data for subsequent studies. The physical parameters of the cores have been shown in the Table 2.

Table 2. Physical parameters of the core.

Core number	Gas permeability (×10^-3μm²)	Porosity (%)
1#	1.36	9.12
2#	1.02	8.80
3#	1.12	9.58
4#	1.15	8.58
5#	1.26	9.32
6#	1.02	9.52
7#	1.21	8.92
8#	1.21	9.49
9#	1.25	9.52
10#	1.12	9.01
11#	1.03	8.87
12#	1.09	8.95

Unsteady-state displacement was adopted to simulate reservoir conditions. In actual reservoirs, displacement processes are inherently unsteady-state, with fluid flow and interactions evolving dynamically over time. This approach enables realistic capture of fluid migration in porous media and accurate reflection of subsurface fluid dynamics. The unsteady-state method assumes: Negligible capillary pressure and gravitational forces, immiscible, incompressible fluids, uniform oil-water saturation across core cross-sections

Cores were initially saturated with formation crude oil, followed by displacement using secondary fluids (Water, CO₂, N₂). During displacement, oil-water saturation distribution in porous media became a function of distance and time, defining the unsteady-state process. Constant pressure differential or flow rate conditions were maintained during experiments. Fluid production rates and pressure differentials across cores were recorded at the outlet, with two-phase relative permeability calculated using Eqs. (1-5) Relative permeability vs. water saturation curves were subsequently plotted [22].

(1)

K_{r w} = K_{r o} \cdot \frac{μ_{w}}{μ_{0}} \cdot \frac{1 - f_{0} (S_{w})}{f_{0} (S_{w})}

(2)

K_{r o} = f_{0} (S_{w}) \frac{d [1 / \bar{V} (t)]}{d {1 / [I \cdot \bar{V} (t)]}}

(3)

I = \frac{Q (t)}{Q_{0}} = \frac{Δ p_{0}}{Δ p (t)}

(4)

S_{w e} = S_{w s} + \bar{V_{0}} (t) - \bar{V} (t) \cdot f_{0} (S_{w})

(5)

f_{0} (S_{w}) = \frac{d \bar{V_{0}} (t)}{d \bar{V} (t)}

where: f₀(S_w) is the fraction of pore volume occupied by oil (dimensionless); V₀(t) is the cumulative oil recovery, dimensionless; V(t) is the cumulative liquid recovery, dimensionless; K_ro is the relative permeability of the oil phase, dimensionless; K_rw is the relative permeability of the water phase, dimensionless; I is the value of the relative injection capacity, (also known as the ratio of the flow capacity); Q₀ is the oil flow rate of the exit face of the rock samples at the initial moment, cm³/s; Q(t) is the Time-dependent value of the liquid flow rate of the exit face of the rock samples, cm³/s; △p₀ is the initial drive differential pressure, MPa; △p(t) is the Time-dependent pressure difference during constant-pressure displacement, MPa; S_we is the Irreducible water saturation, expressed in decimal; S_ws is the value of water saturation of rock samples outlet face, expressed in decimal.

1.

The core was vacuumed and subsequently saturated with formation water. It was then placed in a core holder, flooded with formation water until fully saturated, and weighed after removal;
2.

The system was heated to 85°C (simulating reservoir temperature). With a confining pressure maintained at 5 MPa above the injection pressure, formation water was continuously injected under constant pressure. Effluent volumes were measured at timed intervals to calculate water-phase effective permeability;
3.

Formation crude oil was injected under constant pressure to displace water (oil flooding). The process continued until no further water production was observed, with oil production rates at the outlet monitored per unit time;
4.

Valves at both ends of the core holder were closed to enable thorough oil-core interaction. The system was aged for 12 h to restore native wettability characteristics;
5.

Unsteady-state displacement was conducted under constant pressure with continuous monitoring of cumulative gas production, cumulative oil production, and cumulative liquid production until oil production stabilized, enabling calculation of water-phase effective permeability at residual oil saturation.

The physical parameters of the cores used have been shown below:

2.4. MD under different mineral walls oil drive studies

Given the complex lithological mineral composition and high clay content of the reservoir, MD simulations were conducted to gain mechanistic insights into the interaction effects between crude oil and various injection media (e.g., Water, CO₂, N₂) within reservoir formations. This study investigates displacement processes among injection media, crude oil, and minerals at the molecular scale, specifically examining the behavioral patterns of different displacing mediums across mineral surfaces and their oil displacement efficiencies. Through MD simulations, the interaction mechanisms between displacing media, crude oil, and minerals are elucidated, enabling identification of critical factors governing displacement effectiveness. These findings provide a theoretical foundation for optimizing displacing medium selection and EOR strategies.

2.4.1. Modeling

The oil displacement simulation system comprises three primary structural components: the displacing phase, oil phase, and wall surfaces. Molecular simulation models were constructed based on the spatial configurations illustrated in Figure 2.

Using Materials Studio, corresponding computational models were developed, including: oil phase models, displacement phase models with different media, and mineral wall surface models with varying compositions.

Test results showed that the main components of the oil samples are all light components. The crude oil model consists primarily of octane (C₈H₁₈), with trace amounts of decane (C₁₀H₂₂) and dodecane (C₁₂H₂₆). Figure 2 shows the unit cell structure, and Figure 3 shows the molecular model of the oil phase.

The unit models for each displacement phase have been illustrated in Figure 4, where water molecules were modeled using the flexible SPC/E potential. CO₂ molecules adopted the EPM2 force field [23]. N₂ molecules utilized parameters from the TraPPE database [24].

The mineral wall surface unit cell configurations have been shown in Figure 5. The clay minerals primarily comprised kaolinite and montmorillonite, while quartz, montmorillonite, and kaolinite crystal structures were sourced from the American Mineralogist Crystal Structure Database (AMCSD) [25].

(a) Kaolinite crystal cell, (b) Quartz crystal cell, (c) Montmorillonite crystal cell.

The mineral unit cells were replicated to construct monolayer wall models with dimensions of 150 Å (length) × 50 Å (width), as depicted in Figure 6. The displacement phase, oil phase, and wall models were assembled into a complete simulation box (Figure 7) for MD studies.

(a) Kaolinite wall model, (b) Montmorillonite wall model, (c) Quartz wall model.

(a) Repulsion system simulation box: the displacement phase, (b) Repulsion system simulation box: the oil phase and the wall surface.

MD simulations were performed, as shown in Figure 7.

Nine distinct models were constructed by combining different displacement media and wall surfaces (Figure 8).

(a) CO2-Kaolinite, (b) Water-Kaolinite, (c) N2-Kaolinite, (d) CO2-Quartz, (e) Water-Quartz, (f) N2-Quartz, (g) CO2-Montmorillonite, (h) Water-Montmorillonite, (i) N2-Montmorillonite.

2.4.2. Model parameterization

Classical MD simulations were performed using the LAMMPS package [26]. Molecular visualization and analysis were conducted with OVITO [27].

Short-range interactions were described by the consistent valence forcefield (CVFF_AUG) force field with a cutoff radius of 1.0 nm. Long-range electrostatic interactions were calculated using the Ewald summation method with a cutoff of 1.5 nm.

Systems were relaxed at a reference temperature of 358 K (85°C) under the NVT ensemble (constant particle number, volume, and temperature) [28]. Periodic boundary conditions (PBC) were applied along the x-axis, while y- and z-directions were fixed with non-periodic boundaries. The step size is 1 fs, the force field is CVFF, and the equilibrium criteria are that the configuration of the system no longer changes on a large scale, the temperature and energy are stable, and the pressure remains in dynamic equilibrium.

NPT ensemble simulations (constant particle number, pressure, and temperature) were conducted at 358 K under pressures of 20 MPa and 35 MPa. PBC were applied along the x- and y-axes, with the z-direction fixed. Displacement phases were injected at 0.02 mL/min, using a 1 fs timestep for 50,000 steps until complete oil phase displacement.

2.4.3. Repulsion efficiency based on different mineral walls calculations

The residual oil phase on the wall surface after displacement was quantified by dynamically partitioning the wall region along the displacement direction (y-axis) into statistical grids. Each grid dynamically computed the number of oil-phase molecules within its boundaries [29]. The relative oil-phase concentration across the wall surface was subsequently calculated, enabling immediate determination of oil displacement efficiency (CC) (Eqs. 6,7).

(6)

N_{c} = \sum_{i = 1}^{n} N_{i}

(7)

C = \frac{N - N_{c}}{N}

where: N_C is the number of oil phase molecules on the wall region; i is the number of grid divisions; C is the oil repulsion efficiency.

3. Results and Discussion

In this paper, we firstly carried out reservoir physical property analysis based on XRF and XRD experiments, and analyzed the mineral composition of reservoir rock samples. Subsequently, the core replacement experiments were carried out, and the preliminary analyses of the oil driving efficiency under different replacement media were conducted. Immediately after that, molecular simulation experiments with different wall conditions and different replacement media were conducted based on the analysis of the mineral composition of the rock samples. The results of all the experiments are shown below.

3.1. Results of reservoir lithology analysis

In this study, XRF and XRD were employed to cross-validate the mineral composition analysis, ensuring the accuracy and reliability of the results. XRF primarily quantifies the major elemental concentrations in rock samples, while XRD identifies mineral species and their relative abundances by analyzing crystalline diffraction patterns. The combined application of these methods enables comprehensive characterization of the sample’s mineralogical composition.

The chemical composition of the rock sample, as determined by XRD, is summarized in the Table 3.

Table 3. Elemental content of the formation natural core.

Core number	Na₂O (%)	MgO (%)	Al₂O₃ (%)	SiO₂ (%)	K₂O (%)	CaO (%)	Fe₂O₃ (%)
1#	2.83	0.69	19.36	69.79	3.09	2.68	2.68
2#	3.03	0.66	18.38	70.54	2.07	2.27	2.34
3#	3.07	0.63	17.28	72.14	3.07	2.17	2.55
4#	2.62	1.07	16.07	70.37	3.17	2.21	2.85
5#	2.48	1.3	18.06	68.92	3.13	2.19	2.06
6#	2.65	1.11	18.07	69.61	3.11	2.15	2.11
7#	3.09	0.75	20.66	67.97	2.15	2.2	2.32
8#	3.61	1.05	13.82	73.8	3.05	2.21	2.25
9#	3.21	0.95	18.64	68.26	2.51	2.16	2.21
10#	3.11	0.85	17.26	70.24	3.54	2.51	2.01
11#	2.64	1.12	17.23	70.65	2.44	2.14	2.31
12#	3.12	1.31	18.65	67.65	2.63	2.68	2.44

The results of the lithological analysis demonstrate that the reservoir rock contains a high proportion of SiO₂, estimated at approximately 70%. This finding suggests that the reservoir is primarily composed of quartz. Quartz, an exemplar of a hard and stable mineral, has been demonstrated to enhance reservoir porosity and permeability, thus optimizing hydrocarbon storage capacity. The Al₂O₃ content in the reservoir rock has been estimated to be approximately 18%, suggesting the presence of well-developed feldspar and clay minerals. Furthermore, the presence of trace amounts of Na₂O, MgO, and CaO serves to indicate the existence of acid-sensitive minerals such as calcite and dolomite.

The XRD results corroborate these findings, showing quartz constitutes approximately 30% of the rock matrix, thus establishing it as the primary mineral component. The clay mineral content approaches similar proportions to quartz (28%), indicating relatively poor reservoir stability. In certain circumstances, clay minerals have been observed to undergo a series of physical changes, including swelling, dissolution, or anglicization. These alterations have the potential to modify the reservoir structure, thereby compromising the efficiency of hydrocarbon recovery processes.

Notably, the elevated feldspar content (25%) poses dual challenges: 1) Secondary mineral precipitation induced by geological processes may cause pore blockage; 2) Feldspar weathering processes promote clay mineral formation, further reducing permeability and compromising reservoir stability. The presence of acid-sensitive minerals such as dolomite and calcite can have an adverse effect on the utilization of reservoir crude oil.

3.2. Results of oil driving under each oil driving medium

The relative permeability of the oil phase in the reservoir core declines rapidly during displacement, while the water and gas phases exhibit an initial rise followed by a plateau in their relative permeability. The maximum relative permeabilities of the aqueous and gas phases remain notably low, resulting in upward-convex relative permeability curves. This curve morphology is governed by pore structure and mineral composition. The narrow pore-throat radii in the oil-bearing formation lead to significant oil entrapment by the aqueous phase. As water or gas saturation increases, the oil phase transitions from a continuous to a discontinuous state, with dispersed oil droplets generating strong Jamin effects at pore-throat inlets. These characteristics render water flooding particularly challenging in such reservoirs.

Although CO₂ is generally considered to be more effective than water flooding, the results of the experiment show that water flooding is the most effective. The analysis of geological data and oil driving effect shows that the poor oil driving effect of carbon dioxide is related to the adsorption of clay minerals, and the subsequent use of MD further investigates the oil driving efficiency of each driving medium on different wall surfaces.

Mechanical instability: High clay content (>20%) weakens intergranular cohesion, resulting in brittle rock frameworks. Field data indicate a minimum miscibility pressure (MMP) of 33 MPa for reservoir crude oil. However, during displacement experiments, rock fracturing occurred at pressures exceeding 25 MPa, preventing miscibility and halting further oil recovery.

Uncertain CO₂-Clay Interactions: The adsorption behavior of CO₂ on clay minerals remains ambiguous, necessitating validation through molecular simulations.

Additionally, trace carbonate minerals (calcite and dolomite) exhibit mild acid sensitivity, potentially influencing CO₂ displacement efficiency through dissolution-precipitation reactions. However, their low abundance (<3%) relegates their impact to a secondary role in this study.

This comprehensive evaluation underscores the critical interplay between mineralogy, pore geometry, and fluid dynamics in dictating EOR efficacy for tight, clay-rich reservoirs.

3.3. Molecular modeling of the results of the exfoliation

Previous experimental studies suggested that the presence of clay minerals negatively impacts CO₂ displacement efficiency. However, the specific influence of different clay types on CO₂ adsorption and subsequent oil recovery remains unclear. To address this, MD simulations were conducted to investigate water flooding, CO₂ flooding, and N₂ flooding on various clay mineral surfaces, focusing on the relationship between displacing phases and mineral walls. The Materials Studio used this time is able to illustrate the microscopic interaction between the two phases in a more microscopic way.

At the initiation of displacement, the system was relaxed to equilibrium, with crude oil uniformly distributed on the mineral surface (Figure 10, using the kaolinite-CO₂ system as an example). Temperature and energy stabilized post-relaxation (Figures 11 and 12). Oil molecule counts on the surface were quantified before and after displacement to calculate displacement efficiency.

(a) Oil-gas (N2) relative permeability curves, (b) Oil-water relative permeability curve, (c). Oil-gas (CO2) relative permeability curves.

(a) Before relaxation, (b) After relaxation.

Uniform distribution of crude oil molecules after relaxation.

Temperature and energy converging to a steady state.

At 358 K (85°C) and 20 MPa (see Figure 13 and Tables 4 and 5), all three displacement media demonstrated favorable oil recovery on quartz surfaces. However, water displacement (a polar fluid) was less efficient on kaolinite and montmorillonite due to clay-fluid interactions. CO₂ underperformed on acidic kaolinite surfaces due to weak adsorption, which was attributed to limited chemical bonding with surface hydroxyl groups.

(a) CO2-Quartz system, (b) Water-Montmorillonite system, (c) Water-Quartz systems, (d) N2-Kaolinite system, (e) N2-Montmorillonite system, (f) N2-Quartz system, (g) CO2-Montmorillonite system, (h) CO2-Kaolinite system, (i) Water-Kaolinite system.

Table 4. Mineral content of the formation natural core.

Core number	Clay mineral		Quartz (%)	Feldspar (%)	Calcite (%)	Dolomite (%)
Core number	Kaolinite (%)	Montmorillonite (%)	Quartz (%)	Feldspar (%)	Calcite (%)	Dolomite (%)
1#	28.3	7.4	33.4	23.1	1.2	0
2#	25.1	6.3	29.4	22.6	2.3	4.5
3#	29.9	5.4	34.4	25.5	0	2.8
4#	26.2	4.8	32.5	22.7	2.4	0
5#	26.5	7.8	29.4	26.4	2.7	5.4
6#	29.2	6.7	37.3	22.1	2.1	1.7
7#	27.6	8.9	32.6	23.4	0	2.4
8#	29.1	4.4	32.8	25.4	2.5	2.4
9#	28.5	5.4	35.2	23.5	1.8	0
10#	28.6	5.7	32.1	24.8	2.1	1.2
11#	26.2	6.2	33.8	25.4	0	2.4
12#	29.4	5.1	29.7	26.7	1.5	1.2

Table 5. Simulation results at a displacement pressure of 20 MPa.

Displacement phase	Wall material	Displacement efficiency
Water	Quartz	99.1%
	Kaolinite	98.6%
	Montmorillonite	97.1%
CO₂	Quartz	99.5%
	Kaolinite	95.1%
	Montmorillonite	99.3%
N₂	Quartz	99.0%
	Kaolinite	98.0%
	Montmorillonite	99.0%

At 35 MPa (Figure 14, Table 6), displacement efficiency improved across all systems. Elevated pressure facilitated CO₂-oil miscibility, particularly enhancing CO₂ performance on kaolinite surfaces.

(a) N2-Kaolinite system, (b). N2-Quartz system, (c) N2-Montmorillonite system, (d) CO2-Quartz system, (e) Water-Quartz systems, (f) Water-Montmorillonite system, (g) Water-Kaolinite system, (h) CO2-Montmorillonite system, (i) CO2-Kaolinite system.

Table 6. Simulation results at a displacement pressure of 35 MPa.

Displacement phase	Wall material	Displacement efficiency
Water	Quartz	100.0%
	Kaolinite	99.0%
	Montmorillonite	97.5%
CO₂	Quartz	100.0%
	Kaolinite	96.3%
	Montmorillonite	100.0%
N₂	Quartz	100.0%
	Kaolinite	98.5%
	Montmorillonite	99.5%

Kaolinite, the dominant clay mineral in the reservoir, is an acidic mineral with polar characteristics. Its surface carries negative charges under acidic/neutral conditions, primarily from hydroxyl (–OH) groups and oxygen atoms. Weakly polar CO₂ molecules (near-zero dipole moment) exhibit minimal electrostatic interaction with kaolinite, resulting in poor adsorption. CO₂ adsorption typically relies on surface hydroxyl or reactive sites, yet kaolinite’s low surface activity and weak chemisorption capacity further limit CO₂ retention.

CO₂ displacement primarily enhances oil recovery by dissolving into crude oil to reduce viscosity and interfacial tension. However, weak adsorption on kaolinite restricts CO₂-rock contact, leading to uneven CO₂ distribution and suboptimal wettability alteration at oil-rock interfaces. Consequently, oil detachment from kaolinite surfaces remains inefficient, compounded by kaolinite’s prevalence in the reservoir.

In contrast, N₂, a non-polar gas, relies on gas expansion and physical displacement mechanisms, showing less dependence on mineral surfaces. Its weak interaction with kaolinite enables better penetration into micro-pores. Experimental results indicated higher N₂ recovery than CO₂ on kaolinite surfaces, as N₂ stabilizes gas-liquid interfaces and reduces oil viscosity through expansion (Figure 15).

Repulsion schematic for each repellent medium on a wall of highly clay-bearing minerals.

Water flooding efficiency suffers significantly on both kaolinite and montmorillonite due to clay swelling and particle migration during injection. Montmorillonite, with higher ion-exchange capacity and swelling potential, demonstrates the poorest performance, as hydration-induced pore blockage drastically impedes fluid flow.

4. Conclusions

The reservoir is predominantly composed of quartz, which provides favorable porosity and permeability. However, the abundance of clay minerals compared to quartz compromises the stability of the reservoir.

The permeability of the oil phase decreases rapidly due to the pore structure and mineral composition of the reservoir, while the permeability of the water and gas phases first increases and then stabilizes. The maximum relative permeability of the water and gas phases remains low, resulting in a convex curve. In terms of recovery rate, the order is: water drive > CO₂ > N₂.

Lithological analyses attribute the poor CO₂ performance to the high clay content, which reduces mechanical strength and causes the rock to fracture at driving pressures of more than 25 MPa. This results in an inability to reach the miscibility conditions of 85°C and 33 MPa, thus preventing effective miscibility. Clay mineral-induced instability is a key factor that limits CO₂ efficiency in this reservoir.

Kaolinite is the main mineral component of the reservoir. The poor adsorption capacity of kaolinite for CO₂ hinders its uniform distribution, which is another key factor limiting the effectiveness of the CO₂-oil drive process.

N₂ outperforms CO₂ in kaolinite-rich reservoirs due to its non-polar nature and gas expansion mechanism, which reduces oil viscosity and enhances pore-scale mobility with minimal adsorption. In contrast, water flooding is severely hindered in clay-rich reservoirs (e.g., montmorillonite-dominated systems) by clay swelling and pore blockage, exacerbated by montmorillonite’s high swelling capacity and ion-exchange capability.

This paper compares mining methods for high clay-content mineral reservoirs based on their physical conditions, analyzing the factors affecting mining effectiveness and providing ideas and research directions for subsequent mining of similar reservoirs.

Due to the limitations of the experimental research conditions, we were unable to characterize the mineral content and types within each pore, nor the effect of the corresponding mineral species content on crude oil mobilization within each microscopic pore. This should be explored further in subsequent research.

Acknowledgment

Supported by the National Natural Science Foundation of China (U20A201009; 41972157)

CRediT authorship contribution statement

Shanyi Zhang: Idea, Formulation of overarching research goals; Xueying Li: Management activities;Conducting a research and investigation process; Jinyu Lan: Management and coordination responsibility for the research; Cong Lin: Oversight and leadership responsibility for the research activity planning and execution; DeJian Zhang: Verify the feasibility and repeatability of the experiment; Hongliang Duan: Run numerical simulation experiments.

Declaration of competing interest

No potential conflict of interest was reported by the author(s).

Declaration of generative AI and AI-assisted technologies in the writing process

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

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1. Introduction

2. Materials and Methods

2.1. Materials and reagents
2.2. Experiments on petrographic analysis of natural rock cores
2.3. Unsteady-state method of driving replacement experiments under each driving medium
2.4. MD under different mineral walls oil drive studies

3. Results and Discussion

3.1. Results of reservoir lithology analysis
3.2. Results of oil driving under each oil driving medium
3.3. Molecular modeling of the results of the exfoliation

4. Conclusions

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[1] Tavakkoli O., Kamyab H., Junin R., Ashokkumar V., Shariati A., Mohamed A.M.. SDS–aluminum oxide nanofluid for enhanced oil recovery: IFT, adsorption, and oil displacement efficiency. ACS Omega. 2022;7:14022-14030. https://doi.org/10.1021/acsomega.2c00567
[Google Scholar]

[2] Tavakkoli O., Kamyab H., Shariati M., Mustafa Mohamed A., Junin R.. Effect of nanoparticles on the performance of polymer/surfactant flooding for enhanced oil recovery: A review. Fuel. 2022;312:122867. https://doi.org/10.1016/j.fuel.2021.122867
[Google Scholar]

[3] Yusuf M., Ridha S., Kamyab H.. Recent progress in NP-based enhanced oil recovery: Insights from molecular studies. Journal of Molecular Liquids. 2024;396:124104. https://doi.org/10.1016/j.molliq.2024.124104
[Google Scholar]

[4] Rezaei N., Zendehboudi S., Chatzis I., Lohi A.. Combined benefits of capillary barrier and injection pressure control to improve fluid recovery at breakthrough upon gas injection: An experimental study. Fuel. 2018;211:638-648. https://doi.org/10.1016/j.fuel.2017.09.048
[Google Scholar]

[5] Yu W., Lashgari H.R., Wu K., Sepehrnoori K.. CO₂ injection for enhanced oil recovery in Bakken tight oil reservoirs. Fuel. 2015;159:354-363. https://doi.org/10.1016/j.fuel.2015.06.092
[Google Scholar]

[6] Shang L.. Experimental study on improving oil displacement efficiency by air foam flooding in tight oil layer. IOP Conference Series: Earth and Environmental Science. 2021;651:032062. https://doi.org/10.1088/1755-1315/651/3/032062
[Google Scholar]

[7] Yu H., Fu W., Zhang Y., Lu X., Cheng S., Xie Q., Qu X., Yang W., Lu J.. Experimental study on EOR performance of CO₂-based flooding methods on tight oil. Fuel. 2021;290:119988. https://doi.org/10.1016/j.fuel.2020.119988
[Google Scholar]

[8] Wei-yi P.A.N., Dong-jiang L., Zeng-min L.U.N., Cheng-yuan L.V., Shu-xia Z.H.A.O., Hai-tao W.A.N.G., Qing-min Z.H.A.O.. Experimental study of effective displacement characteristics of different displacing methods in tight oil reservoir[J] CT Theory and Applications. 2016;25:647-652. https://dx.doi.org/10.15953/j.1004-4140.2016.25.06.04
[Google Scholar]

[9] Dong-Jiang L., Zeng-Min L, cheng-Yuan L.V., et al. Study of displacement characteristics of CO₂ in tight sandstone reservoir by nuclear magnetic resonance. CT Theory and Applications. 2016;25:141-147. https://doi.org/10.15953/j.1004-4140.2016.25.02.03
[Google Scholar]

[10] Zhang K., Nojabaei B., Ahmadi K., Johns R.T.. Effect of gas/oil capillary pressure on minimum miscibility pressure for tight reservoirs. SPE Journal. 2020;25:820-831. https://doi.org/10.2118/199354-pa
[Google Scholar]

[11] Zhang, K., Nojabaei, B., Ahmadi, K., Johns, R.T., 2018. Minimum miscibility pressure calculation for oil shale and tight reservoirs with large gas–oil capillary pressure. In: Proceedings of the URTeC 2901892 Presented at the Unconventional Resources Technology Conference, Houston, TX, USA, 23–25 July 2018. https://doi.org/10.15530/urtec-2018-2901892

[12] Yang S., Tian X.. A new approach to calculate CO₂ displacement recovery considering near miscibility in tight oil reservoirs. Journal of Petroleum Exploration and Production Technology. 2024;14:1953-1962. https://doi.org/10.1007/s13202-024-01792-w
[Google Scholar]

[13] Sohrabi M., Danesh A., Tehrani D.H., Jamiolahmady M.. Microscopic mechanisms of oil recovery by near-miscible gas injection. Transport in Porous Media. 2008;72:351-367. https://doi.org/10.1007/s11242-007-9154-z
[Google Scholar]

[14] Zhang K., Gu Y.. Two different technical criteria for determining the minimum miscibility pressures (MMPs) from the slim-tube and coreflood tests. Fuel. 2015;161:146-156. https://doi.org/10.1016/j.fuel.2015.08.039
[Google Scholar]

[15] Liu J., Pi Y., Liu L., Gu X., Li Z., Dai Z.. Effect of water saturation on CO₂ minimum miscibility pressure and oil displacement performance. Journal of Dispersion Science and Technology. 2024;45:1793-1803. https://doi.org/10.1080/01932691.2023.2232013
[Google Scholar]

[16] Mahmoud M., Hussein I., Carchini G., Shawabkeh R., Eliebid M., Al-Marri M.J.. Effect of rock mineralogy on Hot-CO₂ injection for enhanced gas recovery. Journal of Natural Gas Science and Engineering. 2019;72:103030. https://doi.org/10.1016/j.jngse.2019.103030
[Google Scholar]

[17] Cao G., Bai Y., Nan X., An H., Wang L., Du T., Li D.. Effect of supercritical carbon dioxide adsorption on oil displacement in tight reservoir. Case Studies in Thermal Engineering. 2021;24:100880. https://doi.org/10.1016/j.csite.2021.100880
[Google Scholar]

[18] Kadoura A., Narayanan Nair A.K., Sun S.. Molecular dynamics simulations of carbon dioxide, methane, and their mixture in montmorillonite clay hydrates. The Journal of Physical Chemistry C. 2016;120:12517-12529. https://doi.org/10.1021/acs.jpcc.6b02748
[Google Scholar]

[19] Hamza A., Hussein I.A., Al-Marri M.J., Mahmoud M., Shawabkeh R.. Impact of clays on CO₂ adsorption and enhanced gas recovery in sandstone reservoirs. International Journal of Greenhouse Gas Control. 2021;106:103286. https://doi.org/10.1016/j.ijggc.2021.103286
[Google Scholar]

[20] Jiang S., Liang P., Han Y.. Effect of clay mineral composition on low-salinity water flooding. Energies. 2018;11:3317. https://doi.org/10.3390/en11123317
[Google Scholar]

[21] Khilar K.C., Fogler H.S.. Water sensitivity of sandstones. Society of Petroleum Engineers Journal. 1983;23:55-64. https://doi.org/10.2118/10103-pa
[Google Scholar]

[22] Xu D.R., Li X.F.. Technological aspects of oxidation processes of special interest to the petroleum engineering. Oxidation Communications. 2017;40:825-838.
[Google Scholar]

[23] Jiao L., Wang Z., Li J., Zhao P., Wan R.. Stability and dissociation studies of CO₂ hydrate under different systems using molecular dynamic simulations. Journal of Molecular Liquids. 2021;338:116788. https://doi.org/10.1016/j.molliq.2021.116788
[Google Scholar]

[24] Potoff J.J., Siepmann J.I.. Vapor–liquid equilibria of mixtures containing alkanes, carbon dioxide, and nitrogen. AIChE Journal. 2001;47:1676-1682. https://doi.org/10.1002/aic.690470719
[Google Scholar]

[25] Downs R.T., Hall-Wallace M.. The American mineralogist crystal structure database. American Mineralogist. 2003;88:247-250. https://api.semanticscholar.org/CorpusID:39137936
[Google Scholar]

[26] Thompson A.P., Aktulga H.M., Berger R., Bolintineanu D.S., Brown W.M., Crozier P.S., in ‘t Veld P.J., Kohlmeyer A., Moore S.G., Nguyen T.D., Shan R., Stevens M.J., Tranchida J., Trott C., Plimpton S.J.. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Computer Physics Communications. 2022;271:108171. https://doi.org/10.1016/j.cpc.2021.108171
[Google Scholar]

[27] Stukowski A.. Visualization and analysis of atomistic simulation data with OVITO–the open visualization tool. Modelling and Simulation in Materials Science and Engineering. 2010;18:015012. https://doi.org/10.1088/0965-0393/18/1/015012
[Google Scholar]

[28] Evans D.J., Holian B.L.. The nose–hoover thermostat. The Journal of Chemical Physics. 1985;83:4069-4074. https://doi.org/10.1063/1.449071
[Google Scholar]

[29] Liu Y., Cao G., Cheng Q., Bai Y., Zhang N., Zhai S.. Montmorillonite swelling properties with various surfactants based on molecular simulation. Journal of Dispersion Science and Technology. 2024;45:1613-1622. https://doi.org/10.1080/01932691.2023.2225587
[Google Scholar]

Impact of clay minerals on oil displacement efficiency in tight oil reservoirs

Abstract

Keywords

Adsorption

Clay minerals

CO2-EOR

Mineral composition

Molecular modeling

1. Introduction

2. Materials and Methods

2.1. Materials and reagents

2.2. Experiments on petrographic analysis of natural rock cores

2.3. Unsteady-state method of driving replacement experiments under each driving medium

2.4. MD under different mineral walls oil drive studies

2.4.1. Modeling

2.4.2. Model parameterization

2.4.3. Repulsion efficiency based on different mineral walls calculations

3. Results and Discussion

3.1. Results of reservoir lithology analysis

3.2. Results of oil driving under each oil driving medium

3.3. Molecular modeling of the results of the exfoliation

4. Conclusions

Acknowledgment

CRediT authorship contribution statement

Declaration of competing interest

Declaration of generative AI and AI-assisted technologies in the writing process

References

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