Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression

Caiyuan Dong; Wei Yang; Jun Li; Dejiang Li; Xueqiong Wu; Weidong Miao; Haihua Zhu; Xilin Yang

doi:10.1515/geo-2022-0572

Article Open Access

Lower limits of physical properties and classification evaluation criteria of the tight reservoir in the Ahe Formation in the Dibei Area of the Kuqa depression

Caiyuan Dong , Wei Yang , Jun Li , Dejiang Li , Xueqiong Wu , Weidong Miao , Haihua Zhu and Xilin Yang

Published/Copyright: March 15, 2024

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From the journal Open Geosciences Volume 16 Issue 1

Abstract

The Ahe Formation in the Dibei Area is a key natural gas exploration formation in the northern structural belt. Based on geological data such as formation tests and physical properties, the tight reservoirs were determined using the empirical statistics method, distribution function method, oil-bearing occurrence method, and bound fluid saturation method reasonably. The lower limit of the oil-bearing physical properties of the layer was further analyzed for the differences between the methods. The pore-throat structure of tight sandstone was characterized by high-pressure mercury intrusion data, and the classification and evaluation standard of tight sandstone in the Ahe Formation in the Dibei Area was established combining fractal theory and physical property data. The results show that the lower physical limit porosity of the tight reservoir of the Ahe Formation in the Dibei Area is 2.4%, and the lower permeability limit is 0.021 × 10⁻³ μm². According to the fractal dimension characteristics of the mercury injection curve, the different structural characteristics of four types of pores (fracture, macropore, mesopore, and small pore) can be divided with the boundary values are 3,000, 1,000, and 100 nm; the tight reservoir of the Ahe Formation in Dibei Area can be classified into four categories: (a) type I reservoir (Ф > 7%), mainly composed of fracture and macropore; (b) type II reservoir (4% < Ф < 7%), mainly made up of macropore and mesopore; (c) type III reservoir (2% < Ф < 4%), mainly formed by mesopore; and (d) type IV reservoir (Ф < 2%) dominated by small pore, followed by mesopore.

Keywords: tight reservoir; lower limit of physical property; classification evaluation criteria; the Ahe Formation; the kuqa depression

1 Introduction

Tight oil and gas is a hot spot in unconventional oil and gas fields after shale gas [1]. By 2018, tight oil geological resources and technically recoverable resources in China are 178.2 × 10⁸ t and 12.34 × 10⁸ t, respectively, while tight gas geological resources and technically recoverable resources are 21.8 × 10¹² m³ and 10.9 × 10¹² m³, respectively [2]. Compared with the huge potential of tight oil and gas, the exploration and development of tight oil and gas is still lagging behind. Thus, it is necessary to strengthen the exploration, evaluation, and development of tight oil and gas, and the determination of the lower limit of physical property of tight reservoirs is the key to evaluate the development potential of tight oil and gas reservoirs. Current methods for determining the lower limit of physical properties in tight reservoirs can be categorized into two types: (i) determine the lower limit of effective reservoir physical property according to the statistical relationship between reservoir oil bearing and reservoir physical property [3,4,5,6,7,8,9], such as testing method, oil-bearing occurrence method, oil testing method, and distribution function curve method. The main parameter of this method is the ability to produce fluid, and its data are easily affected by the completion method and oil testing technology. (ii) The lower limit of the effective reservoir physical property is determined according to the correlation between different physical property parameters of the reservoir [10,11,12,13,14,15], such as porosity–permeability intersection method, empirical statistics method, minimum pore-throat radius method, irreducible water saturation method, and drilling fluid invasion method. The determination results are affected by the representativeness and quantity of samples in the methods mentioned above; thus, the lower limit of physical properties of tight reservoirs should be determined by combining various methods.

Tight sandstone reservoir classification evaluation is also a significant part of the tight oil and gas exploration and development, and its accuracy is related to the selection of exploration targets and the evaluation of oil and gas resources [16]. The petrophysical facies method was primarily used to establish favorable petrophysical facies identification standards, clarify its plane distribution, and predict favorable reservoir distribution and tight reservoir’s “dessert” areas [17,18,19,20]. However, this method is difficult to provide quantitative criteria for reservoir classification evaluation. With the progress of experimental testing technology, some scholars have introduced parameters such as percentage of movable fluid, starting pressure gradient, clay mineral content, crude oil viscosity, and shape factor to establish reservoir classification evaluation criteria through clustering analysis, multiple classification coefficient methods, etc. [21,22,23,24]. Moreover, reservoir evaluation can be revealed by pore-throat structure classification. The pore-throat structure parameters can be obtained by high-pressure mercury injection and constant rate mercury injection, etc. [25,26,27]. This classification method has achieved good evaluation results on reservoir classification of tight oil and gas.

The extent of the exploration and development of tight oil and gas is low in the Dibei Area of the Kuqa Depression; the evaluation criteria of reservoir classification are need to be used in the estimation of tight oil and gas. Several methods, like the empirical statistical method, distribution function method, oil-bearing occurrence method, and method of bound water saturation, are used to determine lower limits of physical properties of the tight reservoir in this article. A new method, mercury intrusion fractal theory, is introduced for pore size classification in this research. Based on the classification scheme, the percentage of pore volume in different sizes is counted, and the reservoirs are classified in order to provide guidance for the exploration of tight reservoirs in the Dibei Area of the Kuqa Depression.

2 Geological setting

The Kuqa Depression is located in the northern part of the Tarim Basin, with a width of 30–120 km from north to south, with a total length of 450 km from east to west and with a total area of 2.8 × 10⁴ km². According to the characteristics of structural deformation and formation age, the Kuqa Depression can be divided into six secondary structural units from the west to the east, which are Wushi Sag, Baicheng Sag, Kelasu Thrust Belt, northern Structural Belt, Qiulitage Thrust Belt, and Yangxia Sag [28] (Figure 1). Among them, the northern structural belt can be further divided into the northern monoclinic belt and the Yiqikelik Structural Belt. The previous exploration practice confirms that the field has superior hydrocarbon geological conditions and great exploration potential [29]. The exploration degree is low, and the main producing oil layer is the Lower Jurassic Ahe Formation [15]. The results of the fourth resource evaluation of China National Petroleum Corporation (CNPC) show that the natural gas resources in the northern Structural Belt are 2.59 × 10¹² m^3, and the oil resources are 1.28 × 10⁸ t, of which the tight gas and oil geological reserves are 223 × 10⁸ m³ and 476 × 10⁴ t. However, at present, only high-yield oil and gas reservoirs are found around Well YN2 and Well DB5 in the Dibei Area.

Figure 1

Structural location of the Kuqa Depression.

The sedimentary facies of the Lower Jurassic Ahe Formation in the Dibei Area is braided river delta plain subfaces [30]; the main lithology is lithic sandstone, followed by feldspar lithic sandstone and little feldspathic litharenite. The reservoir has obvious compact characteristics, showing that the porosity ranges from 0.30 to 11.31%, with an average of 4.5%, while the permeability is in the range from 0.007 to 71.4 × 10⁻³ μm², with a mean value of 1.0 × 10⁻³ μm². Casting sheet image analysis shows that the main pore type is intergranular dissolved pore and microfracture, with an average pore size of 1.76 μm and a plane porosity of 2.5%.

3 Experiments and methodologies

The physical property data, formation test conclusion, and irreducible water saturation data of this study are provided by the Exploration and Development Research Institute of the Tarim Oilfield Branch of PetroChina. Thirty-nine tight sandstone samples of the Ahe Formation were collected in the Dibei Area. Table 1 shows the basic information of Ahe samples in the Dibei Area. Mercury intrusiong capillary porosimetry (MICP) experiments were carried out to study the pore-throat size and distribution characteristics of tight sandstone.

Table 1

Sample information for MICP in the Ahe Formation from the Dibei Area

Sample ID	Well	Depth (m)	Porosity (%)	Permeability (×10⁻³ μm²)
T1	YN5	4765.50	7.4	4.40
T2		4766.19	6.00	1.12
T3		4769.28	5.47	0.42
T4		4769.42	6.70	1.15
T5		4769.80	7.90	10.2
T6		4771.62	2.50	0.03
T7		4774.50	5.40	0.55
T8		4774.84	4.20	1.31
T9		4775.12	7.20	1.27
T10		4775.69	8.40	2.49
T11		4777.20	6.10	0.30
T12		4779.13	8.80	21.5
T13		4782.35	6.9	31.4
T14		4783.27	6.70	8.75
T15		4842.64	3.90	0.16
T16		4844.06	7.00	3.09
T17		4849.22	8.90	/
T18		4890.14	5.00	1.26
T19		5013.42	1.60	0.05
T20	YN2C	4729.67	3.70	0.09
T21		4729.95	5.60	0.32
T22		4730.35	9.00	29.40
T23		4730.80	2.70	/
T24		4731.02	3.90	0.39
T25		4731.30	3.50	1.15
T26		4731.50	4.90	0.30
T27		4731.85	7.10	0.96
T28		4732.77	4.00	0.16
T29		4733.86	6.00	0.83
T30		4735.86	2.60	0.05
T31		4738.88	3.80	0.21
T32		4751.66	6.70	0.44
T33		4755.08	8.10	3.87
T34		4755.28	10.10	6.95
T35		4759.23	2.70	0.06
T36	YN2	4702.30	4.40	0.09
T37		4787.50	4.50	0.21
T38		4840.20	3.90	74.60
T39		4841.50	1.60	0.08

MICP analysis of all tight sandstone samples was carried out in accordance with the GBT21650.1-2008 standard. The samples should be pretreated before analysis. The samples were first crushed into 3–6 mm particles and then dried in a vacuum oven at 150°C for 1 h. Tight sandstone samples were analyzed by an Autopore IV 9500 instrument at a temperature of 16℃, a humidity of 50%, and a standard atmospheric pressure. When the pressure range is 0.0007–245 MPa and the pore radius is 0.003–1,000 μm, the mercury entry/withdrawal curve of each sample is obtained.

4 Results and discussion

4.1 The lower limit of physical properties of a tight reservoir

According to the difference in the description content of the lower limit of reservoir physical property parameters, Dai et al. [31] divided them into the industrial and the reservoir lower limits. The industrial lower limit is the lower limit of the pore-throat radius or the physical properties of the rocks in which oil and gas can percolate from pores and form effective oil-gas flows under the existing industrial technology conditions. It is also the physical property limit of effective tight reservoirs. With the application of technologies such as volume fracturing, segmental fracturing, well pattern intensification, and horizontal wells, oil and gas in reservoirs with lower physical properties can be extracted. Reducing the lower limit of physical properties provides a favorable scientific basis for providing high oilfield production and estimating the resource potential of tight reservoirs [32–34]. The latter is the lower limit of physical properties of reservoirs with storage capacity, which is the lower limit of physical properties for judging whether tight reservoirs can contain oil. Therefore, the evaluation of tight reservoirs needs to determine whether the reservoir can produce oil and gas and whether the reservoir contains oil and gas.

4.1.1 Empirical statistical method

The empirical statistical method is a cumulative frequency statistical method based on core analysis of porosity and permeability data and is bounded by the loss of the cumulative seepage capacity in the low pore and permeability section, accounting for about 5% of the total cumulative accumulation. The lower limit value of physical properties obtained from the above method commonly has statistical characteristics, which has become a method commonly used in major oil and gas fields in the world. Considering the low physical properties of tight reservoirs in the Dibei Area, it is determined that the cumulative frequency loss of no more than 20% of the total cumulative can be used as the basis for determining the lower limit of reservoir physical properties. When the lower limit of porosity is 2.5%, the cumulative frequency loss reaches 20%. Therefore, the lower limit of porosity of tight sandstone reservoirs of the Ahe Formation in the Dibei Area is 2.5% by empirical statistical method (Figure 2).

Figure 2

Distribution histogram of porosity loss curve of tight reservoir in Ahe Formation of the Dibei Area.

4.1.2 Distribution function method

The distribution function is one of the most important mathematical characteristics of geological entities. The overall distribution rule of research variables, such as distribution curves and characteristic functions obtained from statistical analysis, is a method commonly used in geology, especially petroleum geology research. Based on this general cognition, Yang et al. [35] proposed a method to obtain the lower limit of reservoir physical properties using the distribution function curve, that is, the distribution function curve method. The distribution function curve method is used to obtain the lower limit of reservoir physical properties. When the original data are a large sample (N > 30), the frequency method is commonly used to form the distribution function density curve. The specific method is to plot the physical property frequency distribution curves of the effective reservoir and the non-effective reservoir in the same coordinate system, and the value corresponding to the intersection point of the two curves is the lower limit value of the physical property of the effective reservoir.

Through the statistical analysis of the logging interpretation physical property data and the comprehensive geological interpretation results of oil and gas of the Ahe tight reservoir in the research region, the lower limit of the porosity of the effective reservoir was obtained based on the distribution function curve method. The value is 2.4% (Figure 3).

Figure 3

Histogram of the porosity frequency distribution of effective and ineffective reservoirs in tight sandstone reservoirs of the Ahe Formation in the Dibei Area.

4.1.3 Oil-bearing occurrence method

According to core data, physical property analysis data, and logging data, the tight sandstone reservoir with oil traces or above can be considered as an oil-bearing reservoir. Therefore, the porosity–permeability intersection diagram of different oil-bearing occurrences can be used to determine the lower limit of oil-bearing porosity and permeability of tight sandstone. The porosity–permeability crossplot of tight reservoirs of different oil-bearing grades in the Ahe Formation in the Dibei Area was drawn separately (Figure 4). It shows a clear boundary between the fluorescent and gas-bearing core samples and the core samples without display. Therefore, it can be considered that the lower limit of the oil-bearing porosity of the Ahe tight reservoir is 1.91%, and the lower limit of the permeability is 0.027 × 10⁻³ μm².

Figure 4

Porosity–permeability crossplot of reservoirs of different oil-bearing grades in the Ahe formation of the Dibei Area.

4.1.4 Method of bound fluid saturation

Studies have shown that the reservoir space in the reservoir with bound water saturation greater than 80% is mainly micropores, which have poor ability for fluid storage and seepage with fluid production generally less than 1 t/day. Therefore, the bound water saturation is the porosity value corresponding to 80%, which is taken as the lower porosity limit of the effective reservoir in the study region [36]. This method has a certain degree of objectivity, while the empirical statistical method is to determine the lower limit of reservoir physical properties from the two aspects of reservoir capacity and percolation capacity, and the determination of the loss ratio has a certain degree of subjectivity. The function equation between porosity, permeability, and bound water saturation is used to calculate the corresponding porosity and permeability when the bound water saturation is 80%, which are taken as the lower limits of porosity and permeability. According to the functional relationship between the porosity, permeability, and bound water saturation of tight sandstone reservoirs in the Ahe Formation, the corresponding lower limits of porosity and permeability are calculated as 2.60% and 0.021 × 10⁻³ μm², respectively (Figure 5).

Figure 5

The relationship between the porosity, permeability, and bound water saturation of tight sandstone in the Ahe Formation of the Dibei Area.

4.2 Classification evaluation criteria for tight reservoir

4.2.1 Classification of tight reservoir

The microscopic pore-throat structure of tight sandstone is the most essential factor determining its seepage capacity, and it is also the basis for establishing reservoir evaluation criteria. Although macroscopic physical parameters such as porosity and permeability are closely related to the oil-bearing capability of the sand body, the cause of porosity and permeability is determined by the pore structure, and the microparameters such as pore radius and sorting coefficient representing the pore-throat structure of the reservoir have good correlation with the oil-bearing capability of the sand body. The porosity, permeability, etc., which can directly characterize the reservoir quality, have a great correlation with the indicators such as pore (throat) structure parameters, displacement pressure, and sorting coefficient obtained directly from MICP data. Therefore, the classification and evaluation standard of the tight reservoir in the Dibei Area is established, owing to the combination of microscopic parameters (displacement pressure and pore-throat radius) and macro reservoir parameters (porosity and permeability).

According to Washburn [37], the pore radius can be derived from the mercury pressure curve:

r = − 2 σ cos θ P c

where P _c is the mercury intrusion capillary pressure, MPa; r is the radius of the pore throat, μm; σ is the surface tension, N/m; θ is the mercury wetting angle, °. In shale, mercury is a non-wetting phase, and σ is generally 0.48 N/m and θ is 140°.

MICP data of sandstone samples show that there are four different types of mercury pressure curves in the study area (Figure 6), and there are three inflection points (P _c = 0.245 MPa, P _c = 0.735 MPa, and P _c = 7.35 MPa) in the capillary pressure curve of sandstone samples, which can be used to divide the connected pore fracture system in the sandstone of the Ahe Formation into fracture, macropore, mesopore, and small pore. Mercury can enter into those samples rich in fractures quickly and massively within a small capillary pressure range, which ends at the first inflection points (around 0.245 MPa, corresponding to a capillary diameter of about 3 μm). Similarly, macropore, mesopore, and small pore are also clearly distinguished by inflection points, which usually occur at 0.735 and 7.35 MPa (the corresponding pore radius is about 1,000 and 100 nm) (Figure 6). Therefore, the range of fracture is >3,000 nm, macropore is 3,000–1,000 nm, mesopore is 1,000–100 nm, and small pore is <100 nm.

Figure 6

Mercury intrusion curve of the Ahe Formation in the Dibei Area.

The rationality of the classification of sandstone pore fracture system can be verified by mercury intrusion fractal theory, which is commonly used to study the self-similarity and complexity of irregular shapes (it is usually expressed by fractal dimension). Elements with the same fractal dimension usually have certain self-similarity. Previous studies have shown that the pore structure of shale within a certain scale has self-similarity, and the pores in different scale ranges have different fractal dimensions [38,39]. For porous materials, the fractal characteristics of pore fractures can be described by mercury pressure intrusion curves, and the relationship is as follows:

S Hg = 1 − P c P c , min D − 3

Available through formula transformation:

lg ( 1 − S Hg ) = ( D − 3 ) lg P c − ( D − 3 ) lg P c , min

where r is the radius of the pore throat; P _c is the mercury inlet capillary pressure, MPa; P _c,min is the minimum capillary pressure, MPa; S _Hg is the percentage of accumulated mercury volume in the pores, %; D is the fractal dimension, a dimensionless factor. Based on the above classification method, the macropore, mesopore, and small pores have different fractal dimensions (Figure 7), which reflects the different structural characteristics of these four kinds of pores. Therefore, 100, 1,000, and 3,000 nm are the limit radius boundaries of small pores, mesopores, macropores, and fissures, respectively, so as to distinguish four different scales of pores.

$Figure 7 Mercury intake–mercury removal curves and fractal characteristics of the Ahe Formation.$

Figure 7

Mercury intake–mercury removal curves and fractal characteristics of the Ahe Formation.

In summary, the pore classification scheme established in this study can be used to classify the pore size of the Ahe sandstone reservoir in the Dibei Area, and four types by pore radius (100, 1,000, and 3,000 nm) can be divided. Based on the classification scheme, the percentage of pore volume in different sizes is calculated, and the reservoirs in the study area are divided into four levels: type Ⅰ reservoir, type Ⅱ reservoir, type Ⅲ reservoir, and type Ⅳ reservoir (Table 1).

Type I reservoir is dominated by fractures and macropores, which account for 94.8% (Table 2). The radius distribution of the main pore-throat is more than 3 μm (Figure 8), the skewness is large, and the sorting is good. The average porosity and permeability are 8.2% and 14.97 × 10⁻³ μm², respectively, and the maximum pore-throat radius is more than 4 μm, and the average pore-throat radius is from 1 to 1.3 μm. The displacement pressure is about 0.1 MPa, and the maximum mercury injection saturation is 70–80% (Figure 9). Type II reservoir mainly develops mesopore and macropore (Table 1), and the radius of the main pore throat is from 1 to 3 μm (Figure 8), and the platform area of the capillary pressure curve is wide and slow, the skewness is slightly large, and the sorting is good. The porosity and permeability of this type of reservoir are in the range from 6 to 8.7% and from 0.9 × 10⁻³ to 4.4 × 10⁻³ μm², respectively. The average pore-throat radius ranges from 0.5 to 0.7 μm, the displacement pressure is 0.26–0.43 MPa, and the maximum mercury injection saturation is from 50 to 70% (Figure 9). Type III reservoir is dominated by mesopore, and the small pores are also developed (Table 2). The distribution range of the main pore-throat radius ranges from 0.1 to 1 μm (Figure 8). The capillary pressure curve platform area is wide and gentle, with fine skewness and good sorting. The porosity is distributed from 2.5 to 6.1%, the permeability is in the range from 0.02 × 10⁻³ to 1.15 × 10⁻³ μm². The maximum pore-throat radius of this type of reservoir ranges from 0.3 to 0.8 μm, the average pore-throat radius is from 0.1 to 0.3 μm, and the displacement pressure is 1–2 MPa, and the maximum mercury saturation is in the range from 36.5 to 58.6% (Figure 9). Type IV reservoir is dominated by small pores and mesopores (Table 2). The distribution range of the main pore-throat radius is less than 1 μm (Figure 8). The capillary pressure curve platform area is narrow and finely skewed. The porosity of this type of reservoir is from 1.6 to 2.7% with permeability lower than 0.05 × 10⁻³ μm², maximum pore-throat radius less than 0.1 μm, average pore-throat radius below 0.5 μm, displacement pressure more than 2 MPa, and maximum mercury saturation below 30% (Figure 9).

Table 2

Pore development characteristics of tight sandstone in the Dibei Area

Type	Pore characteristics	Percentage
Type	Pore characteristics	Small pore	Mesopore	Macropore	Fracture
Ⅰ	Mainly fracture and macropore	0.1	5.1	41.1	53.7
Ⅱ	Mainly macropore and mesopore	0.1	50.1	49.8	0
Ⅲ	Mainly medium pore, a small number of small pore	5.1	94.9	0	0
Ⅳ	Mainly small pore with some mesopore	58.8	41.2	0	0

Figure 8

Distribution characteristics of pore diameter of mercury injection in four types of tight reservoir samples of the Ahe Formation in the Dibei Area.

Figure 9

Characteristics of main mercury intrusion parameters of four types of sandstone samples in the Ahe Formation in the Dibei Area.

4.2.2 Establishment of classification evaluation standards for tight reservoir

Figure 10 shows that the maximum connected pore-throat radius, average pore-throat radius, and sorting coefficient generally increase with the increase of porosity. In addition, all the above parameters show obvious segmentation, and the maximum connected pore-throat radius, the average pore-throat radius, and the porosity show obvious “tripartition” characteristics, that is, when the porosity is more than 7%, the pore-throat radius increases with the increase of porosity. While the porosity is between 4 and 7%, the pore-throat radius tends to be stable. And when the porosity is between 2 and 4%, the pore-throat radius increases obviously. When the porosity is less than 2%, the pore-throat radius becomes stable again. The sorting coefficient and porosity have similar segmental characteristics. According to the segmentation of the correlation between the above parameters and porosity, the reservoirs can be divided into four categories: invalid porosity (Ф < 2%), inefficient porosity (2% < Ф < 4%), medium porosity (4% < Ф < 7%), and high porosity (7% < Ф).

Figure 10

Correlation diagram between mercury injection parameters and porosity in the Ahe Formation.

It suggests that the connected pore-throat radius, average pore-throat radius, and sorting coefficient generally increase with the increase of permeability, and the tight sandstone reservoir in the study area shows obvious segmentation of the above parameters (Figure 11). The maximum pore-throat radius, average pore-throat radius, and permeability showed obvious “dichotomous” characteristics, that is, when the permeability is less than 0.1 × 10⁻³ μm², the maximum pore-throat radius increases significantly with the increase of permeability. When the permeability is from 0.1 × 10⁻³ to 1 × 10⁻³ μm², the maximum pore-throat radius rises to stabilize. The maximum pore-throat radius increases obviously, when the permeability is over 1 × 10⁻³ μm². The sorting coefficient, average pore-throat radius, and permeability also show similar trends. According to the segmentation of the correlation between the above parameters and permeability, the permeability of tight sandstone reservoirs in the study area can be divided into three categories: ineffective permeability (K < 0.1 × 10⁻³ μm²), medium permeability (0.1 × 10⁻³ to 1 × 10⁻³ μm²), and high permeability (K > 1 × 10⁻³ μm²). In conclusion, according to the fractal theory of MICP and the relationship between physical properties and micro-pore (throat) structure parameters, the tight reservoirs can be divided into types Ⅰ, Ⅱ, Ⅲ, and Ⅳ (Table 3, Figure 12).

Figure 11

Correlation diagram between mercury injection parameters and permeability in the Ahe Formation.

Table 3

Pore characteristics of tight sandstone in the Dibei Area

	Ⅰ	Ⅱ	Ⅲ	Ⅳ
Porosity (%)	>7	4–7	2–4	<2
Permeability (10⁻³ μm²)	>1.0	0.1–1.0	<0.1
Displacement pressure	<0.2	0.2–1	1–2	>2
Maximum pore-throat radius (μm)	>3.0	1–3	0.1–1	<0.1
Average pore-throat radius (μm)	>1.0	0.5–1	<0.5
Maximum mercury injection saturation (%)	>60	30–60	<30

Figure 12

The Ahe tight sandstone grading evaluation standard.

5 Conclusions

The lower limit porosity of the tight reservoir of the Ahe Formation in the Dibei Area is 2.4%, and the lower limit permeability of the tight reservoir is 0.021 × 10⁻³ μm², by using the distribution function method, empirical statistics method, bound fluid saturation method, and other methods.
According to the fractal dimension characteristics of the mercury injection curve, the different structural characteristics of the four kinds of pores (fissure, macropore, mesopore, and small pore) are revealed. Among them, 3,000, 1,000, and 100 nm are the limit radius boundaries of the above four types of pores.
The tight reservoirs of the Ahe Formation in the Dibei Area can be divided into four types based on fractal theory and physical property data: (a) type I reservoir (Ф > 7%), mainly composed of fracture and macropore; (b) type II reservoir (4% < Ф < 7%), mainly made up of macropore and mesopore; (c) type III reservoir (2% < Ф < 4%), mainly formed by mesopore; (d) type IV reservoir (Ф < 2%) dominated by small pore, followed by mesopore.

Acknowledgments

This work was supported by Joint Fund Project of National Natural Science Foundation (Nos U22B6002 and 42202176) and CNPC Scientific Research and Technology Development Project (No. 2021DJ0605).

Author contributions: C.D. contributed to the design of the study and wrote the manuscript and was the principal author of the manuscript. W.Y. contributed to the discussion of the results and manuscript refinement. J.L., D.L., X.W., W.M., H.Z., and X.Y. were responsible for the geological survey.
Conflict of interest: Authors state no conflict of interest.

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Received: 2023-04-12

Revised: 2023-10-11

Accepted: 2023-10-22

Published Online: 2024-03-15

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0572

Keywords for this article

tight reservoir; lower limit of physical property; classification evaluation criteria; the Ahe Formation; the kuqa depression

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