Quantitative characterization of porosity evolution in the Triassic Chang 8 tight sandstone reservoir during hydrocarbon accumulation in the Maling area,Ordos Basin,China

Abstract

The Maling area in the Ordos Basin is characterized by delta front deposits. The principal oil-bearing layer, Chang 8 Member (the 8th member of the Triassic Yanchang Formation), manifests low-to-ultra-low porosity and permeability due to the interplay of depositional, diagenetic, and burial processes. The reservoir exhibits pronounced heterogeneity and diagenetic alterations, which limit the efficacy of hydrocarbon exploration. Methodologies employed for this research encompass grain size imaging, physical property assays, petrographic thin sections, scanning electron microscopy, high-pressure mercury intrusion, and X-ray diffraction. These techniques facilitated a comprehensive investigation into the diagenetic attributes and compaction mechanisms of the reservoir. A porosity evolution simulation equation tailored to the Chang 8 tight sandstone reservoir was formulated based on diagenetic evolution traits and geological influences. Interdependencies and correlations among various diagenetic processes were analyzed. During diagenesis, compaction, cementation, and replacement led to a reduction in primary porosity, whereas dissolution-induced secondary pores served as high-permeability channels. A quantitative model for porosity evolution was developed alongside qualitative assessments, achieving a deviation of 3.18% when compared to gas porosity measurements. Evaluation of four representative samples revealed that compaction-induced diagenetic alteration predominates, with burial depth, formation age, and cement types playing critical roles in the evolution of reservoir porosity. This differential diagenetic evolution elucidates the underlying causes of variability in reservoir properties and heterogeneity in pore structure, providing valuable insights for future exploration strategies.

Keywords

Tight sandstone differential diagenesis porosity evolution diagenetic regime Yanchang formation

Introduction

Current research on the porosity evolution of tight sandstone reservoirs remains largely confined to qualitative and semi-quantitative stages. While prior studies have predominantly concentrated on macroscopic analyses, microscopic characteristics of tight sandstone reservoirs have been comparatively underexplored (Chen et al., 2017a; Xiao et al., 2021). Given the escalating global energy demand and the waning supplies of conventional oil, tight sandstone reservoirs are increasingly gaining prominence in hydrocarbon exploration. Nonetheless, identifying zones of relatively high permeability within these reservoirs presents a significant challenge (Huang et al., 2018b; Huang et al., 2018a; Cui et al., 2019). The tight sandstone reservoirs in the Upper Triassic Yanchang Formation of the Ordos Basin have been subject to a myriad of complex diagenetic environments post deposition. These diagenetic modifications are instrumental in the formation, preservation, and obliteration of pore structures, thereby governing the evolution of both porosity and permeability in clastic rock reservoirs. The overall process is intricate and is influenced primarily by structural configuration, sedimentological factors, and diagenetic alterations (Sun et al., 2015; Lai et al., 2016; Nie et al., 2021). Extensive research has been conducted on the diagenetic mechanisms, pore evolution, and compaction factors in the Ordos Basin. Scholars have ascertained that mechanical compaction inflicts substantial damage to reservoir quality during the early diagenetic phase, resulting in elevated porosity loss across the diagenetic continuum. Conversely, cementation in later stages causes less porosity reduction, while dissolution substantially ameliorates physical and surface porosity properties (Lu et al., 2012; Xiao et al., 2021). Current approaches to modeling porosity evolution predominantly employ either single-factor analysis or multi-factor combinations, but these models often lack comprehensive, integrative frameworks. Specifically, most models focus on isolated factors like diagenesis, cementation, or secondary dissolution, and are restricted to generalized statistical descriptions, lacking in the continuity and correspondence required for a nuanced understanding of characteristic evolution (Tang et al., 2017; Li et al., 2018; Zhang et al., 2014; Zhang et al., 2023).

The tight sandstone reservoirs in the Ordos Basin are distinguished by a complex pore structure, limited storage capacity, prevalent micro-fractures, strong diagenetic alterations, subtle structural traps, and discontinuous oil-water contact zones (Zhu et al., 2014; Ehrenberg, 1990; Taylor et al., 2010; Wei et al., 2015). Diagenesis emerges as a pivotal factor in shaping these reservoir characteristics and is modulated by multiple variables including structural and depositional factors. Over the extensive geological timeline, various diagenetic processes have synergistically acted at different evolutionary stages, culminating in the unique attributes of the contemporary tight reservoirs (Xiong et al., 2017). Understanding the controlling factors and evolutionary pathways of these reservoirs is imperative for effective hydrocarbon exploration and extraction. The diagenetic timeline is categorized into early, middle, and late stages, each with distinctive mineralogical and organic markers. For instance, reservoirs in the early diagenetic stage contain immature organic matter enriched with expansive clay minerals, whereas those reservoirs in the late diagenetic stage exhibit reduced hydrocarbon-generating capacity and elevated illite concentrations—up to 75% in mixed clay minerals (Xi et al., 2015a; Wang et al., 2018; Xiao et al., 2018; Shi et al., 2018). Research by Zhang et al. focused on identifying the principal factors governing the microscopic characteristics of tight oil in the Triassic Yanchang Formation within the Ordos Basin. The study underscored the pronounced impact of mechanical compaction, particularly noting that the timing of reservoir tightening precedes the introduction of organic acids into the reservoir. Consequently, these acids do not fully dissolve or react with soluble components, limiting the formation of secondary pores and inhibiting potential improvements in reservoir physical properties (Morad et al., 2010; Xi et al., 2015b; Xu et al., 2020).

Pore evolution serves as a cornerstone in the analysis of reservoir properties. Quantitative modeling has emerged as a particularly effective approach in this context, contributing significantly to our understanding of pore evolution dynamics (Zhang et al., 2019; Gao et al., 2021; Huang et al., 2023). Various factors—including diagenesis, paleogeothermal conditions, burial history, and depositional environments—influence porosity changes throughout the diagenetic process. Notable contributions include the illite growth model in sandstones, which employs the Arrhenius equation to quantitatively simulate illite nucleation and growth (Robert and Linda, 2010). Likewise, Tang et al. developed statistical models that consider current porosity as a constraint and differentiate between processes of porosity increase and decrease. These models correlate porosity changes from initial burial to the present with variables such as burial depth and geological time (Su et al., 2016; Xi et al., 2019; Yang and Guo, 2020). The porosity evolution simulation model was developed through a combination of action simulation methods and effect simulation methods. For quantitative calculation of sandstone porosity evolution across diagenetic stages, this study employed the unconsolidated sandstone porosity model formula as established by Weyl, Beard, Schere, and Zhang. The Trask sorting coefficient (So) is obtained through the analysis of cumulative grain-size distribution curves. This unconsolidated sandstone porosity model was applied to physical and pore-casted thin section analysis data obtained from microscopic pore structure experimentation (Beard and Weyl, 1973; Olivarius et al., 2015).

For a nuanced understanding of the intricate oil-water distribution patterns in tight sandstone reservoirs, it is essential to investigate the types of diagenesis, accumulation sequences, and controlling factors. Scholarly consensus generally aligns with one of three viewpoints concerning the relationship between reservoir tightening and hydrocarbon accumulation: “tightening prior to accumulation,” “accumulation prior to tightening,” and “tightening while accumulating” (Liu et al., 2021; Liu et al., 2020; Chen et al., 2017b; Wang et al., 2020; Cheng et al., 2016). In the present study, mineralogical and pore-type characterizations were conducted using pore-casted thin sections and scanning electron microscopy. Grain-size imaging techniques were deployed to ascertain the structural characteristics and sorting properties of clastic particles. Methodologies employed for this research encompass grain size imaging, physical property assays, petrographic thin sections, scanning electron microscopy, high-pressure mercury intrusion, and X-ray diffraction. These techniques facilitated a comprehensive investigation into the diagenetic attributes and compaction mechanisms of the reservoir. Data sets including paleotemperature, vitrinite reflectance, maximum pyrolysis temperature, and clay mineral composition were analyzed to scrutinize diagenetic processes, pore evolution, and regions of relative high permeability. Based on these analyses, a porosity evolution simulation equation, tailored for the Chang 8 tight sandstone reservoirs in the Maling area, was formulated. This model also included an evaluation of the interdependence and correlation among various diagenetic processes (Yuan et al., 2015). This comprehensive study aims to elucidate the geological factors influencing hydrocarbon potential in the study area. The findings will serve as a foundational resource for subsequent hydrocarbon production initiatives and tight reservoir research (Ren et al., 2016).

Geological settings

The Ordos Basin is a sedimentary basin underlain by Archean and Early Proterozoic crystalline basement rocks, exhibiting a pronounced binary stratigraphic architecture. Situated on the upper North China Craton, the Ordos Basin is the second largest sedimentary basin in China, encompassing an area of approximately 3.8 × 10⁵km². It is among the earliest terrestrial basins in China with proven oil reserves and exhibits substantial extraction potential. Hydrocarbon-bearing strata are ubiquitous, albeit with low reservoir permeability. The basin retains Early Paleozoic to Mesozoic strata, punctuated by several unconformities resulting from multiple tectonic uplift events. Notably, the Late Caledonian Orogeny persisted for over 100 million years, leading to the deposition of Late Carboniferous strata atop the Ordovician layers. During the Late Paleozoic, the Ordos block, situated west of the North China Basin, accrued a sequence of marine-terrestrial transitional facies. In the Mesozoic, the Ordos Basin became isolated, accumulating fluvial-deltaic-lacustrine sediments. Subsequent uplift events in the Cenozoic, particularly affecting the eastern basin, resulted in erosion of the Jurassic-Cretaceous strata and ultimately shaped the current sedimentary architecture of the Ordos Basin. Previous research has delineated the basin into six sub-structural units: Yimeng uplift, Weibei uplift, Jinxi flexural fold belt, Shanbei slope, Tianhuan syncline, and the western margin thrust fault structure (Qiao et al., 2020).

The focus of this study, the Maling area, straddles the Yi-Shaan Slope Belt and the Tianhuan Depression Belt. The primary target reservoir is the Chang 8 member of the Triassic Yanchang Formation (Figure 1). Located in eastern Gansu Province, the Maling Oilfield constitutes the primary production site of the Changqing Oilfield. Structurally, it is positioned on the Yishan Slope and spans the transitional zone of the Tianhuan Depression. The area exhibits a formation dip angle of less than 1.0°. Geographically, the study area extends from Qiaochuan area in the north to Heqi area in the south, with east-west boundaries delineated by Shangliyuan and Huan County. It covers a north-south length of approximately 55.8 km and an east-west width of around 45.1 km. The Chang 8 reservoir of the Upper Triassic Yanchang Formation is governed by the southwestern braided river delta system and displays stable shore-shallow lake sedimentation. In the context of a gently west-dipping monocline, localized differential compaction has led to the development of a series of low-amplitude, nose-like uplifts from east to west, indicating good stratigraphic continuity.

Figure 1.

Geographical location of the Maling area in the Ordos Basin.

Experimental and samples

High-pressure mercury intrusion

As tight reservoirs garnered increasing research attention, the limitations of conventional mercury intrusion techniques for nanopore identification became evident. To address this gap, high-pressure mercury intrusion technology was developed. This method distinguished itself by operating at significantly higher pressures, typically between 200 and 400 MPa. The underlying principle of this technology was rooted in the non-wetting interaction between mercury and most solid substrates. By applying external pressure, mercury was forced into the pore spaces. The magnitude of this pressure was inversely correlated with the size of the pores that the mercury could access. Measurements of the volume of mercury intruded at varying pressures were used to calculate pore size distribution and pore volume, based on the Washburn equation. Mercury was chosen as the experimental medium due to its specific properties: it is a non-wetting phase fluid with high chemical stability and interfacial tension. These attributes rendered it suitable for generating accurate experimental results. The experimental process was relatively expedient, typically requiring only 1–2 hour for tight reservoir samples. Core samples needed to conform to specifications, with a diameter of 2.5 cm and a thickness of 2 cm. The experiment involved several steps. Initially, if the rock sample contained oil, it was washed, dried, and vacuumized. Subsequently, the sample's mass and porosity were measured. The sample was then placed in a container, and the system was activated. Measurement data were recorded and saved, and any waste liquid generated during the experiment was properly disposed of.

Constant-rate mercury intrusion

In contrast to high-pressure mercury intrusion, constant-rate mercury intrusion operated at an exceedingly low, constant rate, typically 5 × 10⁻⁵mL/min. This low rate ensured a quasi-static mercury intrusion process in which interfacial tension and contact angles within rock pores remained constant. As mercury entered the rock sample, its preferential flow into larger pores triggered a sequence of pressure changes that were indicative of the sample's capillary pressure dynamics. Specifically, each morphological change at the mercury's point of entry into a pore altered the shape of the meniscus, causing a subsequent change in system capillary pressure. Through the detection of pressure fluctuations during the mercury intrusion process, constant-rate mercury intrusion differentiated between the internal pores and throats of the rock. The resultant data provided separate capillary pressure curves for pores and throats. These curves, in turn, allowed for the calculation of key microscopic parameters, such as pore radius, throat radius, and the ratio between pore and throat radii. The experiment employed the ASPE-730 constant-rate mercury intrusion device. The intrusion rate was maintained at 5 × 10⁻⁵mL/min, with a contact angle of 140°. The maximum mercury intrusion pressure was 6.22 MPa, corresponding to a minimum throat radius of 0.12 μm. The methodology adhered to the SY/T 5346-2005 test standard. The requisite sample size for the experiment was a standard core with a diameter of 2.5 cm and a length ranging from 1 to 3 cm.

Pore-casted thin sections and SEM

The pore-casted thin section was a specialized rock slice produced by injecting colored liquid adhesive into the pore space of the rock under vacuum pressure and subsequently grinding it after the adhesive solidified. In this study, thin section samples with a thickness of 0.03 mm and an area not less than 15 mm × 15 mm were observed using the Leica DMRXHC and Linkam THMSG600 optical microscopes housed in the State Key Laboratory of Continental Dynamics at Northwest University. These pore-casted thin sections enabled direct observation of surface porosity, pores, throats, pore-throat coordination numbers, and clastic components in both the cores and the thin sections. When integrated with dynamic field data, the impact on the recovery factor could be effectively evaluated. SEM analysis was conducted using the FEI Quanta 650FEG device, also located in the State Key Laboratory of Continental Dynamics at Northwest University. The electron beam scanned the surface of the sample, capturing data on 117 core samples. This technique allowed for the analysis of the occurrence forms of pores, throats, and clay minerals. In conjunction with reservoir sensitivity evaluation experiments, it helped identify minerals sensitive to oil recovery. Environmental SEM permitted direct analysis of samples in the presence of oil or water, offering a more accurate reflection of the microscopic characteristics of minerals and rocks. This data could be used to analyze the mineralogical characteristics at different stages of oil and gas field development, thereby providing a reliable foundation for formulating development adjustment plans. The data pertaining to the experimental samples in the study area are displayed in Table 1.

Table 1.

Statistical summary of experimental samples for Chang 8 reservoir in the Maling area.

Sample	Well	Depth (m)	Porosity (%)	Permeability (-)	Rock density (g/cm³)	Pore volumn (cm³)	Surface porosity (%)	Physical properties	SEM	Thin section	High-pressure mercury intrusion	Constant-rate mercury intrusion
1	MM31	2429.5	10.02	0.31	2.21	0.34	3.29	√	√	√	√	√
2	MM30	2651.4	9.42	0.38	2.44	0.35	3.31	√	√	√	√	√
3	MM23	2427.9	5.44	0.04	2.33	0.43	2.42	√	√	√	√	√
4	MM21	2473.55	6.11	0.08	2.36	0.4	2.03	√	√	√	√	√
5	L91	2387.1	6.15	0.07	2.29	0.48	6.21	√	√	√	√	√
6	L275–21	2410.3	9.19	0.23	2.35	0.46	2.37	√	√	√	√	√
7	H56	2474.72	7.19	0.26	2.3	0.17	0	√	√	√	√	√
8	Y23	2521.85	11.18	0.6	2.24	0.52	5.91	√	√	√	√	√

Results

Petrological characteristics

The Chang 8 reservoir in the Maling area is primarily characterized by delta front underwater distributary channels, oriented in a southwest-northeast direction. A dataset comprising physical properties, image-based grain size attributes, pore-casted thin sections, and SEM data was assembled from 182 core samples for microscopic examination (Table 1). The prevalent sandstone types are gray fine-grained and very fine-grained arkose, along with lithic arkose (Figure 2). The matrix content is minimal, largely constituting clean sandstone. Grain size is predominantly medium to fine, with occasional instances of very fine sandstone. Quartz content averages 31.51%, feldspar averages 27.83%, and lithic fragments average 25.91%. The most abundant lithologies are lithic feldspar and feldspathic lithic sandstones, with lithic sandstone being less frequent and exhibiting low compositional maturity (Cheng et al., 2016).

Figure 2.

Sandstone classification schema for the Chang 8 reservoir in the Maling area.

The rock framework grains in the study area exhibit complex types and compositions, primarily consisting of quartz, feldspar, and various lithic fragments. The reservoir is generally characterized by a low quartz content and high feldspar and lithic contents. The quartz-to-feldspar ratio is approximately 1, indicating a balanced presence of these minerals. Lithic fragments mainly comprise metamorphic rock fragments (e.g., extrusive rock, phyllite, quartzite) and volcanic detritus, with sedimentary detritus being less common. Across sublayers of the Chang 8 reservoir, the type and content of rock framework grains show a good degree of inheritance (Figure 3).

Figure 3.

Mineralogical composition distribution in the Chang 8 reservoir.

The diagenetic stage of the study area predominantly falls within the mid-to-late phases of middle-diagenetic stage A, with a few target layers progressing into the early phase of middle-diagenetic stage B. Based on observations from 81 pore-casted thin sections and 117 SEM samples, the interstitial content of the Chang 8 reservoir exhibits considerable variability, averaging around 14%. The interstitial material primarily comprises cement, with minimal impurities. The dominant cement types include carbonates, clay minerals, and silica (Cheng et al., 2016). Among the clay minerals, illite is most abundant, averaging 4.1%, followed by chlorite and kaolinite at average contents of 1.8% and 0.8%, respectively. Ankerite is present in minor quantities, averaging 0.3%, while the average siliceous content stands at 1.7% (Table 2).

Table 2.

Statistical summary of interstitial material contents in the Chang 8 reservoir of the Maling area.

type	Interstitial materials (%)										Total
type	Kaolinite	Illite	Chlorite Films	Siderite	Ankerite	Barite	Goethite	Siliceous	Feldspathic	Pyrite	Total
Mean	0.8	4.1	1.8	4.8	0.3	0.0017	0.3	1.7	0.1	0	14
Minimum	0	0	0	0	0	0	0	0	0	0	3
Maximum	9	46	15	60	10	0.3	11	7	3	0.2	60

In the Chang 8 reservoir of the Maling area, the relative abundance of calcite and ferrocalcite in the interstitial material is notably high. This suggests robust early carbonate cementation in the sandstone, adversely affecting its physical properties. Authigenic kaolinite is typically a secondary mineral formed through the dissolution of aluminosilicate minerals in acidic conditions. Its presence indicates that dissolution pores in the Chang 8 reservoir are relatively well-developed. The relatively high content of chlorite in the Chang 8 reservoir enhances the support provided by the framework grains to the pores, thereby preserving pore space. However, this also leads to a corresponding constriction of the pore throats, impacting the reservoir's physical characteristics. Elevated siliceous content, primarily in the form of secondary enlarged quartz and authigenic quartz, has the potential to fill pore spaces, thereby reducing porosity.

Physical properties

A total of 182 core samples were classified based on these categories, facilitating a comprehensive analysis of the reservoir's physical property distribution. The data indicate that the porosity of the Chang 8 reservoir ranges from 5.3% to 17.3%, with a mean value of 10.1%. The distribution manifests a primary peak at 45%, suggesting a fairly uniform distribution of pores. Specifically, the porosity is chiefly concentrated in the 7.5–12.5% range, which constitutes 89.4% of the total sample set. According to the classification criteria set by the Chinese national industry standard SY/T6285-2011, the target layer predominantly comprises low- to ultra-low porosity reservoirs. Medium to ultra-low porosity categories are minimally developed and sporadically distributed (Figure 4). The permeability distribution falls within a range of 0.06 × 10⁻³μm² to 6.5 × 10⁻³μm², with an average value of 0.8 × 10⁻³μm². The distribution exhibits a normal distribution with a single dominant peak at 62.6%, indicating a relatively uniform distribution of permeability. The permeability is predominantly concentrated within the 0.1 × 10⁻³μm² to 2.5 × 10⁻³μm² range, accounting for 92.8% of the total sample set. According to the criteria for permeability classification set forth by the Chinese national industry standard, the target layer in the study area predominantly falls within the category of ultra-low to extremely low permeability reservoirs. Low-permeability reservoirs are essentially undeveloped in this region.

Figure 4.

Distribution histograms of (a) porosity and (b) permeability for Chang 8 reservoir samples in the Maling area.

Figure 5 reveals an exponential positive correlation between porosity and permeability in the Chang 8 reservoir, characterized by a correlation coefficient (R²) of 0.6891. As the reservoir's physical properties improve, the relationship between porosity and permeability tends toward linearity, albeit with diminished correlation strength. This suggests that while porosity does influence permeability variations, its impact is limited in scope. In the context of ultra-low-permeability sandstone reservoirs, the intricate nature of microscopic pore structures complicates the correlation between porosity and permeability. These correlations are modulated not only by rock lithology, grain sorting, and roundness but are also strongly influenced by subsequent diagenetic alterations. Specifically, diagenesis exerts the most significant transformational effects on ultra-low-permeability reservoirs, resulting in the development of additional secondary micropores and thereby broadening the range of porosity variability (Figure 5).

Figure 5.

Correlation between porosity and permeability for the samples from the Chang 8 reservoir in the Maling area.

Quantitative study on porosity evolution

Establishment of porosity evolution model

In this study, the porosity evolution simulation model was developed through a combination of action simulation methods and effect simulation methods. Additionally, the model accounts for the relative independence between different diagenetic processes, thereby creating a porosity evolution model tailored for the Chang 8 Member reservoir in the Maling area. The diagenetic evolution sequence experienced by this reservoir encompasses several stages: initial geometric accumulation, gravitational compaction, early cementation, secondary dissolution, as well as middle-to-late cementation and replacement. The demarcation between early and middle-to-late cementation stages hinges on both cement type and cementation phase, with secondary dissolution pores serving as a key boundary. Microscopic analysis of pore-casted thin sections and SEM reveals that compaction is the primary driver of porosity and permeability reduction during the early diagenetic stage, leading to the obliteration of primary pores. In contrast, cementation and dissolution are the dominant factors modulating reservoir physical properties during the middle and late diagenetic stages. Secondary pores mainly originate from the dissolution of feldspar and lithic fragments, with a minor contribution from carbonate rock dissolution.

For quantitative calculation of sandstone porosity evolution across diagenetic stages, this study employed the unconsolidated sandstone porosity model formula as established by Weyl, Beard, Schere, and Zhang (Beard and Weyl, 1973; Olivarius et al., 2015). The specific calculation process is delineated in Table 3. The Trask sorting coefficient (So) is obtained through the analysis of cumulative grain-size distribution curves. This unconsolidated sandstone porosity model was applied to physical and pore-casted thin section analysis data obtained from microscopic pore structure experimentation.

Table 3.

Quantitative calculation method for porosity evolution of the Chang 8 reservoir in the Maling area.

Porosity evolution parameter			Calculation formula	Symbol annotation
Sorting coefficient			S_d = (D₂₅/D₇₅)/2	Φ: porosity, %; D₂₅: the particle diameter corresponding to 25% on the cumulative particle content curve, mm; D₇₅- the particle diameter corresponding to 75% on the cumulative particle content curve, mm; Φ_ce: present cement content, %; Φ_ori: intergranular porosity in casting body, %; Φ_matr: surface porosity of matrix-hosted primary micropores in the casting body, %; Φ_ave: measured average porosity of sample, %; Φ_por: total surface porosity in casting body, %; Φ_diss: surface porosity of dissolution pores in casting body, %; Φ_i−c: surface porosity of authigenic intergranular pores in casting body, %; Φ_fiss: surface porosity of diagenetic fractures in casting body, %; Φ_te: measured porosity, %.
Porosity of unconsolidated sandstone (%)			Φ₁ = 20.91 + 22.90/S_d
Porosity evolution simulation	Compaction	Residual porosity after compaction (%)	Φ₂ = Φ_ce + (Φ_ori + Φ_matr)*Φ_ave/Φ_por
		Porosity loss due to compaction (%)	L₁ = Φ₁−Φ₂
		Relative porosity loss due to compaction (%)	F₁ = L₁*100%/Φ₁
	Cementation and replacement	Residual porosity after compaction, cementation and replacement (%)	Φ₃ = Φ₂−Φ_ce
		Porosity loss due to cementation and replacement (%)	L₂ = Φ₂−Φ₃
		Relative porosity loss due to cementation and replacement (%)	F₂ = L₂*100%/Φ₁
	Secondary pore development	Porosity increase due to dissolution (%)	Φ₄ = Φ_diss*Φ_ave/Φ_por
		Porosity increase due to authigenic intergranular pore development (%)	Φ₅ = Φ_i-_c*Φ_ave/Φ_por
		Porosity increase due to microfractures (%)	Φ₆ = Φ _fiss*Φ_ave/Φ_por
		Porosity increase due to diagenesis (%)	Φ₇ = Φ₄ + Φ₅ + Φ₆
		Porosity increase due to secondary pore development (%)	F₃ = Φ₇*100%/Φ₁
		Porosity of micro pores (%)	F_micr = (Φ_te−Φ_por)*100/Φ_te
	Present porosity (%)		Φ_n = Φ₃ + Φ₇
	Error (%)		E = \|Φ_ave−Φ_n\|*100/Φ_ave

Calculation result analysis

In the designated study area, 182 representative core samples were subjected to comprehensive analyses involving physical property measurements, pore-casted thin sections, scanning electron microscopy, X-ray diffraction, and image-based grain size analysis. Subsequently, a porosity evolution model was employed to quantify stage-specific porosity alterations across different diagenetic sequences (Table 4). Utilizing the unconsolidated sandstone porosity model outlined in Table 3, it was determined that the sorting coefficients for Chang 8 reservoir samples predominantly range between 1.2 and 1.6, averaging at 1.3, and exhibit a minor skewness toward finer grains. The initial porosity of the 182 analyzed samples varied from 35.4% to 40.4%, with a mean value of 38.5%, and manifested a distribution marginally skewed towards coarser grains which is similar to normal distribution. The minimal variation in sorting coefficients and initial porosity implies that sedimentary parameters such as sand body compaction thickness and water body depth exert limited influence on the Chang 8 reservoir's porosity during the depositional phase. Therefore, the primary factors contributing to reservoir heterogeneity do not lie in the structure, distribution, or sorting of the clastic particles.

Table 4.

Porosity changes due to different diagenetic evolution processes.

Porosity parameters		Porosity loss due to compaction (%)	Porosity loss due to early cementation and replacement (%)	Generated secondary porosity (%)	Porosity loss due to middle-late cementation and replacement (%)	Calculated porosity (%)	Gas porosity (%)	Relative error (%)
Chang 8 reservoir	Maximum value	32.3	17.1	10.1	26.4	11.4	12	27.8
	Mean value	25	3.2	5.5	7.8	9.5	8.3	10
	Minimum value	5.3	0.3	0	2	2.2	2.8	0.7
Samples with maximum compaction rates		32.3	2.2	2.4	4	2.2	2.8	23.1
Samples with maximum cementation rates		7.1	3.8	5.2	26.4	6.4	3.4	7.8
Samples with maximum dissolution rates		28.5	3.2	10.1	6.2	10.2	10.1	1.4
Samples with most developed intergranular pores		22.8	4.8	3.1	3.3	11.4	11.6	1.6
Mean		22.7	3.5	5.2	10	7.6	7	8.5

During the initial sedimentary compaction and consolidation phases of diagenesis in the Chang 8 reservoir, porosity losses ranged from 5.3% to 32.3%, averaging at 25%, signifying substantial alteration of physical properties. In contrast, porosity losses during early and mid-to-late diagenetic stages were 3.2% and 5.5%, respectively. The higher porosity loss in the mid-to-late stages suggests more extensive cementation compared to the early stages. The distribution of secondary dissolution pores within the Chang 8 reservoir varied considerably, with porosity increments due to dissolution ranging from 0 to 10.1%, averaging 5.5%.

According to the experimental data summarized in Table 4, the calculated mean porosity for the Chang 8 reservoir in the Maling area stands at 9.5%. The high porosity fitting coefficient (R²) of 0.9 confirms that the porosity model employed aligns well with the empirical error standards for reservoir evaluation.

Porosity evolution model based on geological responses

Figure 6 delineates the characteristics of various diagenetic stages of the Chang 8 reservoir in the Maling area, which are influenced by burial and thermal maturation processes. Well M serves as a case study for analyzing diagenesis-related pore evolution (Figure 6(a)). Based on the criteria for diagenetic stage classification, the paleotemperature during the mid-to-late stage A of the middle diagenesis ranged between 90–110°C. During this period, organic matter reached maturity, initiating thermal degradation to produce hydrocarbons and generate substantial quantities of organic acids, thereby facilitating the formation of secondary dissolution pores. As the reservoir transitioned into the early phase of stage B of middle diagenesis, the temperature continued to rise. Organic matter underwent further cracking, significantly contributing to hydrocarbon accumulation. Concurrently, the organic acid concentration decreased, leading to the inhibition of secondary dissolution. Diagenetic sequence analysis indicates that the principal phase of pore-enhancing dissolution occurred within the temperature range of 70–90°C and geologically dates between 129–151.5 Ma (Figure 6(c)). The development of dissolution is the basis for the high permeability of the Chang 8 reservoir.

Figure 6.

Porosity evolution framework for Well M in the Chang 8 reservoir: (a) general map of burial history of Well M containing paleogeo temperature and reservoir history; (b) distribution of porosity section of Well M; (c) general porosity evolution of Chang 8 Member of Well M; (d) Diagenetic evolution characteristics of Chang 8 Member in Maling oilfield.

The Chang 8 reservoir porosity evolution model incorporates functional relationships among various parameters including sand body distribution, burial depth, overburden pressure, and dissolution. Analysis of Well M reveals that early compaction accounted for a 25% loss in porosity, while early and mid-to-late cementation and replacement caused additional losses of 3.2% and 7.8%, respectively. During compaction and early cementation stages, porosity losses were 3.1% and 2.9%, respectively, but were offset by a secondary dissolution-induced porosity increase of 5.5%. The primary dissolution stage saw a porosity decrease of 1.5% and 1.2% due to compaction and cementation, respectively, countered by a 3.8% increase from dissolution, resulting in a net porosity increment of approximately 25.3%. During the key hydrocarbon accumulation and chemical compaction phases, the porosity losses from compaction and cementation were 3.6% and 6.7%, respectively, while secondary dissolution contributed to a 0.7% increase in porosity (Li et al., 2018; Yang and Guo, 2020).

The critical porosity threshold during the main hydrocarbon charging phase was identified as 10.18%. When coupled with the correlation equation for porosities exceeding 10.0%, the fitting coefficient (R²) for porosity and permeability was determined to be 0.8053, indicating a strong correlation.

Discussion

The complex diagenetic phenomena affecting the Chang 8 reservoir in the Maling area encompass mechanical compaction, pressure solution, clay mineral cementation, and carbonate cementation. The research focuses on core samples that exemplify extreme diagenetic effects—particularly high levels of mechanical compaction, cementation, and dissolution—and those displaying optimal intergranular pore development. A comprehensive comparative analysis was conducted on these key samples to trace the pathways of porosity evolution within the Chang 8 reservoir, supported by thin-section petrography, SEM, and grain size imaging (Figures 7 and 8; Table 4).

Figure 7.

Microscopic characteristics of porosity evolution in representative samples from the Chang 8 reservoir in the Maling area. (a) Residual intergranular pore; (b) intergranular pore and feldspar dissolution pores; (c) pore with illite filling and Intergranular pore; (d) chlorite and quartz overgrowth; (e) pore with kaolinite filling; (f) micropore and pore with illite/smectite mixed layer.

Figure 8.

Temporal porosity evolution curve for the Chang 8 reservoir in the Maling area.

Porosity evolution characteristics of samples with maximum compaction rates

For samples with maximum compaction rates from the Chang 8 reservoir, corresponding to Point A in Figures 7(a), 7(e), 8, and 9, the porosity loss due to early mechanical compaction was substantial, accounting for 38.3%. This resulted in a significant reduction of fluid storage capacity and negligible development of intergranular pores. Granular contacts were primarily linear to concave-convex, with plastic grains such as mica exhibiting marked deformation. During the early and medium-to-late stages of cementation-replacement, the porosity loss was 2.2%. Specifically, early-stage cementation resulted in a 2.2% reduction in porosity, while the medium-to-late-stage cementation contributed to a 4% loss. The overall impact of cementation was relatively minor. Secondary porosity was also low, registering at 2.4% (Table 4). The minimum porosity observed in the experimental samples was 2.2%, reinforcing the notion that mechanical compaction is the predominant factor influencing porosity degradation in the Chang 8 reservoir in the study area (Figure 7(a), 7(e), 8).

Figure 9.

Comparative analysis of porosity loss across diverse samples from the Chang 8 reservoir in the Maling area. (The blue data points represent the amount of porosity loss due to cementation and compaction of the samples. The pink data points represent the region where porosity loss data points are distributed.)

Porosity evolution characteristics of samples with maximum cementation rates

Samples with the maximum cementation rates, corresponding to Point B in Figures 7(b), 7(f), 8, and 9, exhibited minimal porosity loss from mechanical compaction due to rapid burial, accounting for only 7.1%. The clastic grains were mainly in point-to-point and point-to-line contacts. Early-stage cementation-replacement led to a 3.8% loss in porosity; however, due to the low content of cementing materials, the impact of early-stage cementation was relatively weak. By contrast, the medium-to-late stages of cementation-replacement had a more pronounced effect, significantly reducing porosity by 26.4%. The influence of dissolution on porosity was comparatively minor, contributing to a 5.2% increase in porosity (Table 4). The terminal porosity of these samples was low, registering at 6.4%, which indicates that cementation exerts a significant impact on the porosity of the Chang 8 reservoir in the study area.

Porosity evolution characteristics of samples with maximum dissolution rates

Samples with maximum dissolution rates, corresponding to Point C in Figures 7(c), 8, and 9, exhibit mechanical compaction strength that is second only to those with maximum compaction rates. SEM and petrographic thin-section analyses reveal severe degradation of intergranular pores. The grains are primarily in linear to concave-convex contacts. Mineral infilling comprises iron calcite and other carbonates, as well as siliceous material, leading to a 28.5% loss in porosity due to compaction. Early-stage cementation accounted for a 3.2% reduction in porosity, while middle-to-late-stage cementation replacement contributed to a 6.2% loss. Secondary porosity, predominantly manifested as feldspar dissolution pores, registered at 10.1% (Table 4). The terminal porosity of these samples was 10.2%, signifying that dissolution plays a pivotal role in forming a dominant high-permeability zone within the Chang 8 reservoir in the study area.

Porosity evolution characteristics of samples with developed intergranular pores

Samples with well-developed intergranular pores correspond to Point D in Figures 7(d), 8, and 9. Early chlorite film-type cementation in these samples attenuates the mechanical compaction strength, thereby effectively preserving primary intergranular pores. The grains predominantly exhibit point-line contact. Concurrently, grain margins and intragranular feldspar dissolution are evident, while a minor quantity of iron calcite infills intergranular pores, resulting in some porosity loss. The porosity loss due to mechanical compaction, early-stage cementation, and mid-to-late-stage cementation were 22.8%, 4.8%, and 3.3%, respectively. Secondary porosity arising primarily from dissolution was measured at 3.1% (Table 4). The terminal porosity of this sample set was 11.4%, the highest among all experimental samples.

According to the error calculation formula presented in Table 3, the calculated porosity for the samples with maximum compaction rates was 2.2%, with a relative error of 23.1%. For the samples with maximum cementation rates, the calculated porosity was 6.4%, with a relative error of 7.8%. The calculated porosity for the samples with maximum dissolution rates was 11.4%, with a minimal relative error of 1.6%. Overall, compaction was the dominant transformative process, accounting for an average porosity loss of 22.7%. Influence from cement contents and fluid properties led to a 10% loss in porosity due to mid-to-late-stage cementation-replacement, while secondary porosity generated was 5.2% (Table 4). The comprehensive analysis indicates that mechanical compaction exerts a stronger control on porosity alteration in the Chang 8 reservoir in the study area compared to cementation. Only a subset of samples fell within the domain of strong cementation (Figure 9). Variability in cement contents serves as a key parameter in porosity evolution, alongside diagenetic mineral composition and fluid properties that modulate the rock's resistance to compaction and influence microscale pore characteristics.

Conclusions

This comprehensive study aims to elucidate the geological factors influencing hydrocarbon potential in the study area. The findings will serve as a foundational resource for subsequent hydrocarbon production initiatives and tight reservoir research.

The Chang 8 reservoir in the Maling area has undergone distinct diagenetic stages: early geometric accumulation stage, gravity compaction stage, early cementation stage, secondary dissolution stage, and middle-to-late cementation-replacement stages. The demarcation between the early and middle-to-late stages of cementation is primarily based on cement type and cementation phase and is further delineated by the onset of extensive secondary dissolution pores. Variability in this diagenetic evolution is the fundamental reason for differences in reservoir physical properties and microscale pore structures.

Computational results reveal that the initial porosity of the Chang 8 tight sandstone reservoir in the Maling area stands at 38.5%. Average porosity losses attributable to compaction and early-stage cementation-replacement are 25% and 3.2%, respectively. The average secondary porosity is 5.5%, while the average porosity loss during the middle-to-late stages of cementation-replacement is 7.8%, leading to a final average porosity of 9.5%.

Through the scrutiny of four archetypal sample types and differential analysis of physical properties across various diagenetic stages and diagenetic facies belts, it is evident that the porosity of the Chang 8 reservoir in the study area is predominantly influenced by compaction, which led to an average porosity loss of 22.7%. Influenced by cement contents and fluid properties, the porosity loss in the middle-to-late stages of cementation-replacement is 10%, and the secondary porosity generated is 5.2%. Mechanical compaction exerts a more potent control on porosity alteration in the Chang 8 reservoir than does cementation. Variability in cement contents emerges as a key determinant of porosity evolution, alongside diagenetic mineral composition and fluid properties, which modulate the rock's resistance to compaction and affect microscale pore characteristics.

Footnotes

Data availability

The authors are not allowed to share the data.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Zhen Wang

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