Controls on Microscopic Distribution and Flow Characteristics of Remaining Oil in Tight Sandstone Reservoirs: Chang 7 Reservoirs, Yanchang Formation, Ordos Basin

Abstract

The Chang 7 shale oil reservoirs of the Yanchang Formation in the Heishui Area of the Ordos Basin display typical tight sandstone characteristics, marked by complex microscopic pore structures and limited flow capacity, which severely constrain efficient development. Using a suite of laboratory techniques—including nuclear magnetic resonance, mercury intrusion porosimetry, oil–water relative permeability, spontaneous imbibition experiments, scanning electron microscopy, and thin section analysis—this study systematically characterizes representative tight sandstone samples and examines the microscopic distribution of remaining oil, flow behavior, and their controlling factors. Results indicate that residual oil is mainly stored in nanoscale micropores, whereas movable fluids are predominantly concentrated in medium to large pores. The bimodal or trimodal T₂ spectra reflect the presence of multiscale pore–fracture systems. Spontaneous imbibition and relative permeability experiments reveal low displacement efficiency (average 41.07%), with flow behavior controlled by capillary forces and imbibition rates exhibiting a three-stage pattern. The primary factors influencing movable fluid distribution include mineral composition (quartz, feldspar, lithic fragments), pore–throat structure (pore size, sorting, displacement pressure), physical properties (porosity, permeability), and heterogeneity (fractal dimension). High quartz and illite contents enhance effective flow pathways, whereas lithic fragments and swelling clay minerals significantly impede fluid migration. Overall, this study clarifies the coupled “lithology–pore–flow” control mechanism, providing a theoretical foundation and practical guidance for the fine characterization and efficient development of tight oil reservoirs. The findings can directly guide the optimization of hydraulic fracturing and enhanced oil recovery strategies by identifying high-mobility zones and key mineralogical constraints, enabling targeted stimulation and improved recovery in the Chang 7 and analogous tight reservoirs.

1. Introduction

In the Chang 7 shale reservoirs of the Yanchang Formation in the Ordos Basin, the key challenge is that nanoscale pore structures directly control reservoir capacity and development performance [1], while the complexity of the pore system and the unclear distribution of residual oil remain major obstacles to efficient development [2].

Previous studies on residual oil have primarily focused on microscopic experiments, development geology, and reservoir engineering [2]. Microscopic approaches investigate the distribution, quantity, and properties of residual oil at the pore scale, as well as the mechanisms of its formation. Shi et al. (2012) applied high-pressure mercury intrusion and nuclear magnetic resonance (NMR) to demonstrate that movable fluids are stored in both pores and large throats, and are governed by pore and throat radius rather than spatial position [3]. Yu et al. (2014) reported that movable fluid saturation in tight oil reservoirs is strongly correlated with total mercury saturation from constant-rate mercury intrusion, enabling its estimation through that parameter [4]. Sun et al. (2014) investigated residual oil distribution in polymer-flooded sandstone reservoirs of the Sartu Formation using frozen thin sections and laser scanning confocal microscopy, and further analyzed the influence of pore structure with micro-CT and scanning electron microscopy (SEM) [5]. Xia et al. (2021) combined laser confocal microscopy, core fluorescence analysis, and core displacement experiments to examine the micro-distribution and genesis of residual oil [6]. Li et al. (2023) explored residual oil distribution across different pore–throat radii using core flooding and T₂ spectra [7]. Wang et al. (2023) employed thin section analysis, SEM, X-ray diffraction (XRD), high-pressure mercury injection, low-temperature nitrogen adsorption, and NMR to evaluate pore structure and its influence on fluid flow in the Chang 7 tight sandstone reservoirs [8]. Wen et al. (2024) utilized field-emission SEM, NMR, and CT to examine the relationships between organic geochemistry, physical properties, micropore structure, and flowing fluid composition [9]. Most recently, Wang et al. (2025) constructed a three-dimensional pore model of tight sandstone using CT scanning to characterize waterflood residual oil and investigated the effects of displacement methods and wettability on its distribution [10].

Most previous studies have examined pore structure, reservoir characteristics, or residual oil distribution separately using techniques such as thin section analysis, scanning electron microscopy (SEM), nanoscale NMR imaging, and X-ray CT. However, few have integrated flow behavior with pore structure, reservoir properties, and movable fluids under conditions relevant to the current development stage. It is noteworthy that the core samples in this study were obtained from the Chang 7 reservoirs in the Heshui area, which are currently under waterflood development. Production data and pressure monitoring indicate that the reservoir pressure has fallen below the bubble-point pressure, resulting in the depletion of the original dissolved gas and a dominant oil–water two-phase flow regime in situ. Consequently, this study focuses specifically on the microscopic fluid flow mechanisms under such two-phase conditions.

Accordingly, using the Chang 7 tight sandstone reservoirs in the Heshui area as an example, thin section analysis (TSA), SEM, nuclear magnetic resonance (NMR), high-pressure mercury intrusion porosimetry (HPMI), and spontaneous imbibition experiments (SIE) were comprehensively applied to characterize the reservoir properties of different sandstone samples and to analyze the microscopic occurrence of movable fluids and their controlling factors. These findings provide a theoretical foundation and practical reference for understanding the characteristics of Chang 7 tight sandstones, the distribution of residual oil, and the flow dynamics during the current waterflooding stage in this region.

2. Geological Overview

The Ordos Basin, spanning 370,000 km² across Shaanxi, Gansu, Ningxia, Inner Mongolia, and Shanxi, is China’s second largest superimposed basin. It is bounded by the Yinshan–Daqingshan ranges to the north, Qinling to the south, Helan–Liupan to the west, and Lüliang–Taihang to the east [11,12]. During the Late Triassic, the basin evolved into a large inland depression lake basin dominated by clastic deposition of the Yanchang Formation (Chang 10–Chang 1). The study area is located in the Heshui region (Figure 1), where the Chang 7 Member was deposited in a freshwater lacustrine environment characterized by fan-delta, braided river, and delta-lacustrine systems [13,14,15,16,17,18,19]. The Chang 7 Member mainly comprises fine- to very fine-grained sandstones interbedded with thick, widespread source rocks, forming low-pressure continental oil reservoirs via in situ hydrocarbon generation [20,21,22,23,24,25]. Subdivided into Chang 7₁, Chang 7₂, and Chang 7₃, the reservoirs exhibit abundant micro- and nanoscale intercrystallite pores in clay minerals, high clay content, poor pore–throat connectivity, and dominance of small pore radii. These characteristics impart strong heterogeneity, lithological tightness, and low-pressure coefficients. Despite these limitations, in situ hydrocarbon accumulation has resulted in high oil saturation and favorable oil properties, rendering the Chang 7 a resource-rich and strategically significant shale oil play [26,27,28,29].

3. Experimental and Research Methods

3.1. Sample Collection and Basic Information

Five tight sandstone samples were collected from different depths and sublayers of the Chang 7 Member in the Heshui area, southern Ordos Basin, using five representative wells. Cylindrical cores were prepared to capture variations in movable fluid, mineralogy, and spatial distribution. Based on cores treated with washing oil and re-saturation, the differences in fluid distribution and mobility revealed by NMR and centrifuge experiments directly reflect the controlling effect of the rock’s pore structure itself, rather than the influence of the original hydrocarbon composition. This approach eliminates interference from hydrocarbon loss during the coring process.

An integrated experimental program combining physical and geological techniques was employed to characterize the reservoirs. Methods included thin section analysis (TSA), scanning electron microscopy (SEM: TESCAN MAIA3 Field Emission Scanning Electron Microscope), nuclear magnetic resonance (NMR: Nuclear Magnetic Resonance Spectrometer), high-pressure mercury intrusion porosimeter (HPMI: AutoPore IV 9505, Pore Analyzer), and spontaneous imbibition experiments (SIE). TSA and SEM provided insights into mineral composition and pore morphology, while NMR and HPMI quantified pore size distribution and connectivity. SIE assessed capillary-driven fluid migration.

These complementary methods allow a comprehensive understanding of the microscopic structure and fluid transport properties of Chang 7 tight sandstones. The results clarify the influence of mineral heterogeneity, nanoscale pores, and pore–throat systems on reservoir quality and flow capacity, advancing knowledge of the Chang 7 Member’s reservoir characteristics and offering new insights into the mechanisms controlling fluid movement in tight sandstones [13,21,22,30,31].

3.2. Experimental Methods

High-pressure mercury intrusion (HPMI) was conducted on ten core samples from various depths and sublayers of five wells to investigate pore structure. Scanning electron microscopy (SEM) and thin section analysis (TSA) were employed to characterize reservoir microfractures and evaluate pore connectivity. To assess oil–water interactions and fluid mobility, relative permeability tests, nuclear magnetic resonance (NMR), and spontaneous imbibition experiments (SIE) were performed on three representative samples.

These complementary analyses provided a comprehensive characterization of pore systems, microfractures, and fluid occurrence in the Chang 7 tight sandstones. By integrating pore-scale observations with fluid flow measurements, the study elucidates the microscopic distribution of remaining oil and identifies the key factors controlling flow behavior in tight sandstone reservoirs.

3.2.1. Mineral Composition and Reservoir Properties

Rock sample preparation involved solvent cleaning, trimming, dust-free drying, and vacuum coating. Trimmed surfaces were required to be fresh, flat, and perpendicular to the bedding plane. Scanning electron microscopy (SEM) and thin section analysis (TSA) were subsequently employed to obtain high-resolution observations of the microscopic structure and pore characteristics of the tight sandstone reservoirs.

These methods enabled evaluation of porosity, permeability, and storage capacity, as well as characterization of the microfracture network, assessment of fracture development and connectivity, and analysis of the influence of mineral assemblages on reservoir quality and hydraulic fracturing performance. The combined results provide a robust basis for examining the microscopic distribution of remaining oil.

3.2.2. High-Pressure Mercury Intrusion (HPMI)

Complex pore–throat structures in tight sandstones consist of irregular capillary networks with similar characteristics [32]. During mercury intrusion, a non-wetting phase, capillary forces resist penetration into the pores. By adjusting the injection pressure to overcome these forces, different mercury volumes correspond to specific pressures, with each volume representing the connected pore–throat space of a given size. At capillary pressure equilibrium, recording the injection pressure and mercury volume allows construction of a pressure–saturation curve, which characterizes pore–throat architecture. Using established relationships between capillary pressure and pore–throat radius, a pore–throat radius distribution curve can be derived, providing a quantitative evaluation of reservoir quality. High-pressure mercury intrusion (HPMI) can measure pore–throat sizes from 0.001 to 96 μm, making it a robust tool for assessing the microstructural properties of tight sandstone reservoirs [32].

$$P_c={\displaystyle \frac{2\mathsf{\sigma }\cos \theta }{r}}$$

(1)

where $P_{c }$ is the capillary pressure (MPa), $\theta$ is the contact angle (°), $\mathsf{\sigma }$ is the surface tension (mN/m), r is the pore–throat radius (μm).

When calculating movable fluid saturation in tight sandstone reservoirs using high-pressure mercury intrusion (HPMI), it is essential to account for fluid mobility in nanoscale pores and ensure accurate representation of movable fluids during mercury withdrawal. During the drainage process, the pressure is gradually reduced, allowing mercury to exit the pores, and the drained volume is recorded to construct a drainage curve that reflects the movable fluid capacity. Movable fluid saturation can then be calculated from the recorded volume and corresponding pore–throat characteristics, providing a quantitative measure of reservoir flow potential. The calculation is expressed as:

$$S_{mf}={\displaystyle \frac{V_{mf}^{corrected}}{V_p}}\times 100\%$$

(2)

$$V_{mf}^{corrected}=k\times V_{mf}^{MICP}$$

(3)

where $S_{mf}$ is movable fluid saturation, $V_{mf}^{corrected}$ is corrected movable fluid volume, $V_{p }$ is maximum mercury injection volume, $V_{mf}^{MICP}$ is measured mercury withdrawal volume, $k$ is dense sandstone correction factor.

3.2.3. Seepage Test

(1)

Oil–water phase penetration experiment

Relative permeability curves are typically determined using steady-state and unsteady-state methods. The steady-state method is suitable for a wide saturation range because capillary forces reach equilibrium during the experiment, and relative permeability is calculated based on Darcy’s law, yielding highly accurate results. In porous media with multiphase flow, each fluid phase remains stable and flows through designated channels independently of the others [14]. When the fluid phases are in equilibrium, the permeability of each phase remains constant, and the flow process conforms to Darcy’s law:

$$K={\displaystyle \frac{\mu ·L·Q}{A·∆P}}$$

(4)

where K is the fluid permeability, $\mathsf{\mu }$ is the fluid viscosity, L is the core length, A is the cross-sectional area of the core, Q is the volumetric flow rate through the core per unit time, and $∆\mathrm{P}$ is the pressure difference across the ends of the core.

When oil and water are simultaneously injected into a core at a constant flow rate, a pressure difference develops across the core ends. Once the flow rate stabilizes, the oil and water saturations reach equilibrium. According to Darcy’s law, the relative permeability of each phase can then be calculated at the corresponding saturation for different oil-to-water flow ratios.

(2)

Suction test

The laboratory core imbibition and oil displacement experiment proceeds as follows: First, cores with a diameter of 2.5 cm are washed, dried, and their original gas permeability and porosity measured. The cores are then evacuated and saturated under vacuum. Using simulated formation water and an ISCO-100DX constant-pressure, constant-flow pump, more than 10 pore volumes (PV) are displaced through the core to determine water-phase permeability, and the core weight is recorded. The core is subsequently displaced until irreducible water saturation is reached, with produced water volume recorded, followed by aging at 50 °C for 10 days. Oil-phase permeability and irreducible water saturation are then measured, and core weight recorded.

Finally, cores at irreducible water saturation are placed into an imbibition apparatus containing either the oil-displacing agent solution or simulated formation water. The cores are allowed to imbibe and displace oil for 100 h [33] until results stabilize, with cumulative produced oil volume recorded over time. Upon completion, imbibition efficiency is calculated using the following formula:

$$\eta ={\displaystyle \frac{V}{V_0}}\times 100\%$$

(5)

where $\eta$ is the imbibition efficiency (%), V is the cumulative volume of oil produced during core imbibition (mL), and $V_0$ is the initial oil volume in the core (mL).

3.2.4. Centrifugal-Nuclear Magnetic Resonance Experiment

Centrifuge experiments are widely used to simulate formation displacement pressures and to quantitatively distinguish between movable and bound fluids in reservoirs. They are a crucial method for assessing reservoir flow capacity and producible potential. These experiments determine the fraction of effectively recoverable fluids, evaluate bound fluid content, and analyze immobile fluids residing in micropores. By comparing fluid distributions before and after centrifugation, the storage characteristics of fluids across pores of different sizes can be elucidated. The centrifugal force generated during high-speed rotation overcomes capillary forces, displacing movable fluids from larger pores, while the remaining fluids remain immobile, constrained by strong capillary forces:

$$P_c=∆\rho ·{\omega }^2·r·h$$

(6)

where $P_{c }$ is the capillary pressure (MPa), $∆\rho$ is the fluid density difference (g/cm³), $\omega$ is the centrifugal angular velocity (rad/s), $r$ is the centrifugal radius (cm), and h is the height of the fluid column (cm).

When combined with nuclear magnetic resonance (NMR), pre-centrifugation NMR measurements provide the original fluid T₂ spectrum distribution, while post-centrifugation measurements enable comparison of changes in the T₂ spectrum after fluid displacement, allowing quantitative determination of movable and bound fluids [9].

Beyond directly providing physical property data, NMR experiments can indirectly reveal spatial characteristics of tight sandstone reservoirs, including pore sizes, throat dimensions, and fracture widths, thereby enabling comprehensive characterization of reservoir storage performance. In the Chang 7 tight sandstone samples, which are dominated by nanoscale pores, the transverse relaxation time (T2) distribution obtained from NMR can be converted to pore size (R), yielding the corresponding pore–throat radius distribution. The calculation principle is as follows:

$$R=T_2{\rho }_2{F}_S$$

(7)

where ${\rho }_2$ is the surface relaxation rate, and $F_S$ is a constant representing the shape factor.

From the above equation, the relaxation time is proportional to the pore–throat radius. Based on established pore–throat size classifications and this relationship, pore size ranges can be assigned as follows: relaxation times less than 1 ms correspond to nanoscale pores, 1–10 ms to micropores, 10–100 ms to mesopores, and greater than 100 ms to macropores.

4. Results and Discussion

4.1. Characteristics of Dense Sandstone Reservoir Development

4.1.1. Rock Type and Mineral Composition

The study area is located near the depositional center of the lake basin. Following sediment entry into the lake, deposition was dominated by mid- to deep-lake muds, accompanied by a sharp decline in hydrodynamic energy. The reservoir rocks are predominantly fine- to very fine-grained sandstones with relatively small grain sizes. In the Chang 7 Member section of the He River area, the reservoir is mainly composed of fine sandstone, siltstone, muddy siltstone, and silty mudstone.

Figure 2 presents a triangular diagram of thin section mineral identification for the study area. The triangular diagram employs a ternary component normalization classification method. Three key end-member components (e.g., quartz, feldspar, and rock fragments) are selected. After normalizing the content of each sample to a percentage, the data points are plotted onto a triangular coordinate diagram. Lithological naming and classification are performed according to the classical zoning scheme, and the geological genesis is analyzed through point cluster distribution. Analysis indicates that the Chang 7 Member reservoir is dominated by lithic fragments, followed by feldspathic sandstone and lithic fragment-rich sandstone.

As shown in Figure 3, statistical analysis of clay minerals in the study area indicates that illite is the most abundant, ranging from 19.47% to 67.83%, with an average of 49.75%. Chlorite follows, with relative abundances between 0.00% and 40.90%, averaging 22.86%. Illite–smectite mixed layers range from 2.62% to 76.54%, with a mean of 22.28%, while kaolinite exhibits the lowest abundance, ranging from 0% to 12.54% and averaging 5.11%.

4.1.2. Pore Development Types and Structures

Pore types control the size and spatial distribution of pore space, thereby influencing reservoir storage capacity and permeability. Based on thin section statistical analysis (Figure 4), the main pore types in the study area are intergranular pores, interstitial dissolution pores, and feldspar dissolution pores, accounting for 35.00%, 30.00%, and 27.50% of the total, respectively. Carbonate dissolution pores and intragranular pores are less developed, representing 5.00% and 2.50%, respectively.

During organic matter maturation in the source rocks, organic acids were generated and migrated into the sand bodies, leading to the dissolution of minerals such as feldspar. Consequently, intergranular pores, interstitial dissolution pores, and feldspar dissolution pores are well developed in the He River area.

As shown in Figure 5, the total pore surface porosity is 5.47%, with interstitial dissolution pores accounting for 58.50%, intergranular pores for 22.67%, feldspar dissolution pores for 11.70%, and carbonate dissolution and intercrystalline pore being less developed at 6.58% and 0.55%, respectively. Figure 6 shows that pore diameters range from 3.05 μm to 83.54 μm, with an average of 22.27 μm. Specifically, interstitial dissolution pores range from 3.05 μm to 29.18 μm (mean 8.39 μm), intergranular pores from 3.23 μm to 83.54 μm (mean 23.77 μm), feldspar dissolution pores from 3.05 μm to 29.18 μm (mean 8.39 μm), carbonate dissolution pores average 53.96 μm, and intragranular pores average 6.12 μm.

Figure 7 and Figure 8 present scanning electron microscope (SEM) and thin section photomicrographs of the Chang 7 Member reservoir. Comprehensive analysis indicates that the reservoir rocks are primarily composed of very fine-grained sandstone, fine sandstone, silt–very fine sandstone, and silty mudstone, with grain sizes predominantly ranging from 50 to 300 μm, most commonly between 50 and 150 μm. The grains are tightly compacted; quartz exhibits conchoidal fractures, and feldspar grains show significant dissolution. Cementation is mainly chlorite, indicating strong binding within the reservoir. Pore types are dominated by clay mineral intergranular pores, with diameters mostly less than 2 μm, representing primarily microporosity. Overall, the reservoir displays low porosity, poor permeability, and pronounced heterogeneity.

Figure 9 presents the results of mercury intrusion experiments for ten tight sandstone reservoir samples. In the He River area, the maximum mercury saturation of the Chang 7 Member reservoirs ranges from 70.78% to 96.60%, with an average of 90.28%. Displacement pressures vary from 1.36 to 13.77 MPa, averaging 6.19 MPa, while mercury withdrawal efficiency is generally below 40%, with a mean of 26.05%. The average pore–throat radius ranges from 0.014 to 0.185 μm. Significant differences are observed among the samples in the mercury intrusion curves: from sample T30-1 to T32-2, the curves shift from a “concave–flat” shape to a “convex–steep” shape, reflecting the progressive deterioration of reservoir physical properties.

Figure 10 and Figure 11 present the pore–throat size frequency curves and permeability contribution curves, respectively. Significant differences are observed among the samples: from N72-2 to N72-1, the curves shift leftward, the pore size distribution range gradually broadens, and the peak frequency moves toward smaller sizes. These trends indicate a progressive deterioration of reservoir physical properties, with micropores predominating. Correspondingly, the pore radii contributing most to permeability gradually decrease.

Figure 12 presents pore volumes obtained from high-pressure mercury intrusion experiments. Based on the intrusion data, the reservoirs are classified into three types (I, II, and III), showing distinct differences in pore volumes. Type I reservoirs have pore volumes ranging from 0.934 to 0.935 cm³, with an average of 0.804 cm³. Type II reservoirs exhibit pore volumes between 0.351 and 0.762 cm³, averaging 0.482 cm³. Type III reservoirs display pore volumes from 0.132 to 0.395 cm³, with an average of 0.313 cm³.

Figure 13 presents a histogram of pore–throat statistics observed under thin section microscopy. Pore diameters between 20 and 40 μm account for 94.72% of the number frequency and 68.81% of the area frequency. Diameters between 40 and 60 μm represent 4.44% of the number frequency and 16.67% of the area frequency. The largest pores, ranging from 140 to 160 μm, constitute only 0.03% of the number frequency and 2.40% of the area frequency. Throat widths range from 2.5 to 30 μm, with the most frequent width at 5 μm, corresponding to 67.57% of occurrences. The maximum throat width of 30 μm accounts for 2.7% of the area frequency.

The Chang 7 tight sandstone reservoirs in the study area exhibit substantial heterogeneity in physical properties, characteristic of ultra-low porosity and ultra-low permeability. Based on pore–throat characteristics, the reservoirs are classified into three types, as summarized in

Table 1. Statistical table of high-pressure mercury injection curve parameters for different types of interbedded tight sandstone reservoirs in the Chang 7 Member of the Triassic System in the Heshui area of the Ordos Basin.

Curve Form	Porosity (%)	Permeability (mD)	Maximum Hg Saturation (%)	Mercury Removal Efficiency (%)	Discharge Pressure (MPa)	Separation Factor	Fractal Dimension
I	9.51~6.99 (8.05)	0.016~0.005 (0.010)	96.60~94.60 (95.32)	26.40~15.35 (21.04)	2.74~1.36 (1.82)	1.98~1.50 (1.76)	3.52~3.47 (3.49)
II	7.25~3.74 (4.83)	0.010~0.003 (0.006)	94.97~85.45 (90.94)	35.13~25.73 (30.13)	5.52~4.12 (5.17)	1.67~1.43 (1.55)	3.49~3.41 (3.46)
III	4.12~1.24 (3.10)	0.002~0.001 (0.0012)	93.32~70.78 (84.36)	32.74~20.78 (25.61)	13.77~11.02 (11.94)	1.45~1.22 (1.34)	3.50~3.46 (3.48)

. Type I reservoirs display the highest maximum mercury saturation and sorting coefficient, the lowest displacement pressure, and the most favorable pore–throat structure. Type II reservoirs exhibit intermediate values of maximum mercury saturation and sorting coefficient, slightly higher displacement pressure, and moderate pore–throat quality. Type III reservoirs show the lowest maximum mercury saturation and sorting coefficient, the highest displacement pressure, and the poorest pore–throat structure.

Analysis of the mercury intrusion–extrusion curves and pore–throat data of the tight sandstone reservoirs indicates that the Chang 7 Member reservoirs in the He River area contain pores of varying sizes, with a relatively wide pore diameter distribution and a comparatively narrow throat width distribution. Movable fluids are primarily hosted in large pores and wide throats, whereas bound fluids are concentrated in small pores and narrow throats. Overall, the reservoirs exhibit low porosity and permeability, combined with pronounced heterogeneity.

4.1.3. Reservoir Properties

Based on rock physical property measurements, the porosity and permeability of the Chang 7 Member tight sandstone reservoirs in the study area are summarized in

Table 2. Measured physical properties of representative shale oil reservoir samples from the Long 7 section of the study area.

Number	Depth (m)	Permeability (10−3 μm2)	Porosity (%)	Pore–Throat Radius (μm)			Pore–Throat Distribution		Penetration Distribution
				Max	Mean	Median	Peaks (μm)	Peak Value (%)	Peaks (μm)	Peak Value (%)
L19-1	1610.67	0.010	4.430	0.133	0.030	0.024	0.025	20.637	0.100	43.614
L19-2	1635.42	0.006	3.740	0.133	0.035	0.029	0.025	20.526	0.100	35.895
N72-1	1407.20	0.001	1.240	0.053	0.014	0.009	0.006	16.567	0.040	51.401
N72-2	1424.10	0.010	7.640	0.539	0.185	0.180	0.250	21.853	0.400	43.412
T30-1	1779.29	0.016	9.507	0.538	0.134	0.114	0.100	19.471	0.400	34.079
T30-2	1791.00	0.005	7.251	0.178	0.053	0.053	0.040	25.944	0.100	40.038
T32-1	1885.90	0.003	3.904	0.133	0.039	0.029	0.025	16.141	0.100	46.512
T32-2	1910.12	0.001	4.120	0.067	0.024	0.017	0.025	18.196	0.040	51.817
T40-1	1636.80	0.005	6.990	0.268	0.083	0.083	0.063	26.405	0.160	41.190
T40-2	1667.90	0.002	3.949	0.067	0.021	0.018	0.025	17.436	0.040	51.286

. The ten experimental samples span depths from 1407.2 to 1910.12 m. Permeability ranges from (0.001 to 0.016) × 10⁻³ μm², with an average of 0.00587 × 10⁻³ μm². The permeability peak positions vary between 0.04 and 0.40 × 10⁻³ μm², with peak values ranging from 34.08% to 51.82%. Porosity ranges from 1.24% to 9.51%, with a mean of 5.28%. The maximum pore–throat radius is 0.539 μm, while the average pore–throat radius varies from 0.014 to 0.085 μm. The maximum and minimum median pore–throat radii are 0.180 μm and 0.009 μm, respectively. Pore–throat distribution peaks range from 0.006 to 0.250 μm, with peak frequencies fluctuating between 16.14% and 26.41%.

4.1.4. Reservoir Heterogeneity

The pore–throat structures of the tight sandstone reservoirs in the study area exhibit distinct fractal characteristics. High-pressure mercury intrusion data were used to calculate fractal dimensions, summarized in

Table 3. Fractal dimension of pore–throat structure and its correlation parameters in the study area.

Name	Fractal Dimension	R2
N72-1	3.366	0.7938
T32-2	3.403	0.8078
L19-1	3.409	0.8477
T40-2	3.410	0.8485
T30-2	3.463	0.8812
T32-1	3.467	0.8790
N72-2	3.470	0.9002
L19-2	3.489	0.8559
T40-1	3.499	0.8770
T30-1	3.520	0.9148

. The pore–throat fractal dimensions range from 3.366 to 3.520, with an average of 3.450. Corresponding fitting correlation coefficients vary from 0.798 to 0.915, indicating a good degree of fit for the fractal dimension estimations.

4.2. Microscale Characteristics of Mobile Fluids and Their Influencing Factors

4.2.1. Microscopic Distribution Characteristics of Mobile Fluids and Residual Oil

Prior to nuclear magnetic resonance (NMR) experiments, standardized oil-washing pretreatment was performed. High-speed centrifugation expelled mobile fluids from pores, leaving only bound fluids. Consequently, T₂ spectra measured before and after centrifugation reflect the microscopic distribution of mobile fluids versus residual fluids within pores of varying sizes.

As shown in Figure 14, sample 7N72 exhibits a bimodal T₂ distribution prior to centrifugation, with the left peak larger than the right. The corresponding pore radii range from <1 μm to <1000 μm, with volume fractions of approximately 0.07% and 0.02%, respectively. After centrifugation, only the <1 μm peak remains, with the pore volume fraction unchanged, indicating a movable fluid saturation of 29.61% and a bound fluid saturation of 70.39%.

For sample T30, the pre-centrifugation spectrum shows a trimodal distribution, with the middle peak largest, followed by the left and then the right. The pore radii correspond to <1 μm, <10 μm, and <1000 μm, with volume fractions of 0.07%, 0.14%, and 0.02%, respectively. Post-centrifugation, only the <1 μm and <10 μm peaks remain; the left peak fraction is unchanged, while the middle peak decreases to 0.08%, yielding a movable fluid saturation of 41.10% and a bound fluid saturation of 58.90%.

Sample T32 also exhibits a trimodal pre-centrifugation spectrum, with the left peak highest, followed by the right and then the middle. The corresponding pore radii are <1 μm, <100 μm, and <1000 μm, with volume fractions of approximately 0.105%, 0.015%, and 0.015%. After centrifugation, only the <1 μm and <100 μm peaks remain, with fractions reduced to 0.09% and 0.01%, corresponding to a movable fluid saturation of 24.22% and a bound fluid saturation of 75.78%.

NMR measurements indicate that sample porosity ranges from 4.12% to 6.21%, with an average of 4.99%. Movable fluid saturations vary between 24.22% and 41.10%, averaging 31.65%. The relative content of movable fluids in small pores is low, indicating that these pores contribute minimally to movable fluid storage in the studied reservoirs. Rapid initial decay rates and low plateau values in the T₂ spectra before and after centrifugation suggest that bound fluids predominantly occupy small pores, reflecting the low permeability characteristic of these tight sandstones.

Experimental results show that sample T30 exhibits the largest difference in spectral peak areas before and after centrifugation, corresponding to the highest movable fluid saturation, followed by N72, and T32 with the lowest. All samples are dominated by small pores, with large pores present in subordinate proportions. Peak area analysis indicates that T30 has relatively good pore connectivity and strong fluid mobility, N72 displays moderate connectivity and fluid mobility, and T32 exhibits poor connectivity and weak fluid mobility. Residual oil is primarily distributed in small pores ranging from 0.1 to 10 μm. The T₂ spectra display bimodal or trimodal distributions, reflecting the coexistence of nanoscale small pores and micron-scale large pores or fractures. Centrifuge experiments confirm that small pores mainly host bound fluids, contributing minimally to the movable fluid fraction.

4.2.2. Factors Affecting Movable Fluids

Movable fluid saturation in tight sandstone samples is strongly influenced by the content and composition of detrital minerals, including quartz, feldspar, and rock fragments (Figure 15). Movable fluid saturation exhibits a positive correlation with quartz content (correlation coefficient = 0.6614), as quartz, a major brittle mineral, readily forms complex fracture networks during hydraulic fracturing, enhancing flow pathways. Its high resistance to compaction also helps preserve primary porosity during diagenesis. Similarly, movable fluid saturation is positively correlated with feldspar content (correlation coefficient = 0.5851), particularly plagioclase, which is prone to dissolution, generating secondary pores (intragranular and moldic pores) that increase fluid storage capacity. In contrast, movable fluid saturation is negatively correlated with rock fragment content (correlation coefficient = −0.8295), as clay-rich fragments deform during compaction, reducing pore space and permeability. Volcanic fragments, such as tuffaceous components, may develop secondary pores through dissolution, although this process is strongly dependent on diagenetic fluid conditions.

In the He River area, high quartz content and relatively low clay content are key factors controlling fluid mobility, movable fluid volume, and overall reservoir flow capacity. Quartz enhances movable fluid saturation due to its brittleness, which promotes fracture development and helps preserve primary porosity. Feldspar, particularly plagioclase, generates secondary pores through dissolution, increasing the space available for movable fluids. Conversely, high rock fragment content reduces porosity and permeability after compaction, although some rigid fragments, such as tuffaceous material, may undergo dissolution, locally improving pore connectivity.

Based on mineralogical analysis, correlations between clay mineral content (illite, illite-smectite mixed layers, kaolinite, and chlorite) and movable fluid saturation were evaluated (Figure 16). Movable fluid saturation is positively correlated with illite content (correlation coefficient = 0.4854), as illite commonly occurs as hair-like or plate-like fills in pores, increasing the proportion of nanometer-scale pores and limiting pore connectivity; its brittleness also facilitates microfracture formation under stress, enhancing fluid mobility. In contrast, movable fluid saturation is negatively correlated with illite-smectite mixed layers (correlation coefficient = −0.2641), as their swelling in contact with water reduces effective porosity and permeability, and their hydrophilicity impedes oil-phase flow. Kaolinite exhibits a positive correlation (correlation coefficient = 0.2426), as stacked book-like kaolinite improves pore connectivity and facilitates fluid flow. Chlorite shows a weak negative correlation (correlation coefficient = −0.1394); although its compaction resistance preserves primary porosity and its oil-wet nature promotes oil-phase flow, it can block pore–throat by coating other mineral surfaces.

Although illite can occupy nanometer-scale pores and reduce pore connectivity, its high brittleness may induce microfractures, indirectly enhancing fluid mobility. The illite-smectite mixed layer swells upon contact with water, compressing pore space and, due to its hydrophilic nature, hindering oil-phase flow. Kaolinite improves pore connectivity, and its weakly oil-wet characteristics facilitate oil-phase movement. Chlorite, while preserving primary porosity, may excessively coat pore–throat, potentially obstructing fluid flow.

As shown in Figure 17, movable fluid saturation exhibits a positive correlation with pore–throat size parameters, including the maximum, average, and median pore–throat radii. This indicates that pore–throat size is a critical factor controlling fluid mobility in tight sandstones: larger pore–throat correspond to higher fluid mobility. The correlation coefficient between movable fluid saturation and average pore–throat radius is 0.4923, slightly higher than that for the maximum pore–throat radius (0.4875) and the median pore–throat radius (0.4583), suggesting that fluid mobility is more strongly influenced by the average pore–throat size.

As shown in Figure 18, the relationship between movable fluid saturation and pore–throat connectivity and homogeneity was evaluated. Movable fluid saturation exhibits a weak positive correlation with maximum mercury injection saturation (correlation coefficient 0.1347), indicating that higher mercury injection volumes correspond to larger effective storage spaces, providing more sites for movable fluid storage. In contrast, movable fluid saturation shows a negative correlation with displacement pressure (correlation coefficient 0.3352), as reservoirs with high displacement pressures (>5 MPa) are dominated by nanoscale pore–throat, where capillary forces strongly restrict fluid mobility. Additionally, movable fluid saturation is negatively correlated with the sorting coefficient (correlation coefficient 0.1922). The sorting coefficient reflects the uniformity of pore–throat size distribution: smaller values correspond to better sorting and more homogeneous pore–throat networks.

Overall, movable fluid saturation shows a positive correlation with reservoir physical properties (Figure 19). The correlation coefficients between movable fluid saturation and porosity and permeability are 0.4925 and 0.5339, respectively, indicating that fluid mobility is strongly controlled by reservoir quality. The slightly higher correlation with permeability compared to porosity suggests that pore size exerts a more pronounced influence on fluid mobility than overall storage capacity. Higher-quality reservoirs thus correspond to enhanced fluid mobility.

Reservoir flow capacity in the study area is variable, and microscopic heterogeneity is pronounced. As a direct indicator of pore structure complexity, the fractal dimension shows a stable negative exponential relationship with movable fluid saturation (Figure 20), with a correlation coefficient of 0.1055. Higher fractal dimensions correspond to more complex pore structures, featuring multiscale mixing from nanoscale to microscale, which increases the proportion of bound fluids and significantly reduces movable fluid content. Therefore, heterogeneity exerts a suppressive effect on fluid mobility.

4.3. Seepage Characteristics

Fluid flow in porous media, or reservoir flow, is strongly influenced by lithology, mineral composition, pore structure, reservoir properties, and heterogeneity. Reservoir flow characteristics were evaluated through steady-state two-phase (oil–water) relative permeability tests and imbibition experiments. In two-phase oil–water flow, the presence of water reduces the flow capacity of the oil phase, complicating overall fluid mobility. Oil–water relative permeability curves reflect the flow capacity of both phases in the porous medium and indicate residual oil saturation. Imbibition refers to the spontaneous uptake of a wetting fluid into the nanopores of tight sandstones, driven by capillary forces. By analyzing the effects of capillary forces and wetting fluid properties, the imbibition–diffusion behavior in nanopore–throat systems can be characterized.

As shown in Figure 21, with increasing water saturation, the oil-phase relative permeability initially decreases rapidly in an approximately linear fashion, then declines gradually in a concave manner toward zero, whereas the water-phase relative permeability increases slowly. Bound water saturation ranges from 44.37% to 7.22%, with an average of 49.77%; water saturation ranges from 58.10% to 69.30%, with an average of 63.29%; and residual oil saturation ranges from 25.17% to 30.76%, with an average of 28.18%.

As the injection ratio increases, the water cut gradually rises, accompanied by a corresponding increase in oil recovery efficiency. As shown in Figure 22, when the injection ratio exceeds 0.2, the water cut approaches 100%, and the oil recovery efficiency reaches a plateau. Experimental results indicate that within the injection ratio range of 0–0.2, the effective pore volume participating in oil displacement ranges from 3.23% to 4.78%, with an average of 3.87%. Oil recovery efficiency varies between 37.06% and 45.11%, with a mean value of 41.07%, while the ultimate recovery ranges from 37.06% to 45.11%, averaging 41.08%.

The experimental results indicate that the water phase occupies the primary flow channels, influenced by variations in sample permeability, pore connectivity, and crude oil viscosity. This leads to a rapid decline in oil relative permeability, limited water displacement within core pores, and high residual oil saturation. Dispersed oil droplets retained in small pores generate significant flow resistance, resulting in a lower oil–water relative permeability crossover point. The relative permeability curves from left to right correspond to samples T30, N72, and T32. As reservoir quality deteriorates, the curves shift rightward: relative permeability at the crossover point decreases, bound water saturation increases, effective water permeability declines, water saturation rises, and both the oil–water relative permeability and the crossover triangle area shrink. Consequently, two-phase flow capacity is low, recoverable oil space under water flooding is limited, and displacement efficiency is poor.

Residual oil saturations for N72, T30, and T32 are 30.76%, 28.60%, and 25.17%, respectively. Reducing residual oil saturation is therefore critical for improving recovery and requires targeted measures based on reservoir characteristics and fluid properties.

As shown in Figure 23, the scatter plot of imbibition time versus per-minute proportion for the three tight sandstone samples illustrates changes in imbibition rate. The slope of the curve reflects the imbibition rate: the steeper the slope, the faster the fluid uptake. During the initial imbibition stage (0–200 min), the slope is steep, indicating rapid fluid uptake dominated by capillary forces. In the intermediate stage (200–12,000 min), imbibition gradually slows under the combined effects of capillary and gravitational forces. In the late stage (>12,000 min), imbibition approaches equilibrium, and the rate nears zero. The presence of two distinct slope changes indicates multiscale pores and demonstrates fluid displacement via imbibition.

As illustrated in Figure 24, the scatter plot of imbibition time versus cumulative proportion depicts the fraction of the total fluid volume imbibed at each time point. The final cumulative value represents the total imbibition volume, and the plateau of the curve indicates imbibition equilibrium, suggesting that the process is effectively complete. When imbibition time is less than 300 min, efficiency is relatively high; between 300 and 12,000 min, efficiency gradually decreases; and beyond 12,000 min, efficiency approaches zero. Among the samples, N72 exhibits the highest imbibition efficiency, followed by T30, with T32 displaying the lowest. During the initial stage, imbibition is predominantly driven by capillary forces, whereas in the intermediate stage, fluid movement is governed by both capillary and gravitational forces. The cumulative proportion curves remain relatively smooth, without abrupt increases, indicating the presence of well-connected pores or fractures.

The results of the relative permeability and imbibition experiments indicate a progressive deterioration in reservoir quality for samples T30, N72, and T32. As a result, both the oil–water relative permeability and the area of the crossover triangle decrease, the crossover interval narrows, and oil recovery efficiency and effective pore volume are low, reflecting limited two-phase flow capacity. Imbibition efficiency declines in the order of N72, T30, and T32, highlighting pronounced heterogeneity in pore connectivity and the dependence of imbibition efficiency on reservoir properties. During imbibition, tight sandstone samples exhibit rapid fluid uptake driven predominantly by capillary forces in the initial stage, followed by the combined influence of capillary and gravitational forces in the intermediate stage, and finally approach equilibrium in the late stage.

As shown in Figure 25 and Figure 26, the Chang 7 tight sandstone reservoirs generally display mixed-wettability characteristics, with water-wet pores accounting for 41.1% and oil-wet pores for 58.9%. The correlation coefficients between spontaneous imbibition efficiency and the wettability index and between water imbibition-oil displacement efficiency and the wettability index are 0.3843 and 0.494, respectively, indicating a strong linear relationship between imbibition efficiency, imbibition rate, and wettability.

5. Conclusions

Based on experiments conducted with cleaned and re-saturated cores under uniform fluid conditions, this study employs integrated core analysis and microstructural characterization to systematically elucidate the microscopic distribution, flow behavior, and governing mechanisms of residual oil in the Chang 7 tight sandstone reservoir within the Heshui region of the Ordos Basin:

(1)

The Chang 7 tight sandstone reservoir is predominantly composed of fine- to very fine-grained feldspathic lithic sandstone, with illite as the dominant clay mineral. Pore systems are characterized by intergranular dissolution pores, intragranular dissolution pores, and feldspar dissolution pores, with pore sizes mainly in the micrometer to nanometer range. The reservoir overall exhibits low porosity, low permeability, and pronounced heterogeneity.

(2)

Nuclear magnetic resonance (NMR) and centrifugation experiments reveal that movable fluids are mainly stored in pores and microfractures larger than 10 μm, whereas residual oil is largely retained in nanopores smaller than 1 μm. The bimodal or trimodal T₂ spectra reflect the complex, multiscale pore–fracture network within the reservoir.

(3)

Oil–water relative permeability and imbibition tests show an average displacement efficiency of only 41.07%, marked by high irreducible water saturation and poor waterflood performance. The imbibition process follows a three-stage “fast–slow–equilibrium” pattern, with flow behavior strongly controlled by pore–throat structure and capillary forces.

(4)

The distribution of movable fluids is governed collectively by lithology, mineral composition, pore–throat geometry, physical properties, and heterogeneity. High quartz and illite contents promote effective flow pathways, whereas abundant rock fragments and expandable clay minerals such as illite–smectite mixed layers significantly hinder fluid mobility.

These insights offer direct engineering guidance for the efficient development of the Chang 7 and analogous tight sandstone reservoirs. Building on the “lithology–pore–flow” coupling mechanism, a sweet-spot prediction model should be established using quartz content and pore-size distribution as key indicators to optimize horizontal well placement and target selection. Fracturing designs must be tailored to enhance connectivity in pores >10 μm, while also advancing imbibition-based EOR agents to mobilize oil trapped in nanopores. A synergistic “imbibition–displacement” recovery strategy is recommended, with soak times and production schedules optimized according to microscale flow dynamics. Through integrated geology engineering management, a full-cycle optimization—from sweet-spot identification to effective reservoir drainage—can be achieved, ultimately improving well performance and overall recovery.