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Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin

  • Quanpei Zhang EMAIL logo , Hongpeng Qi , Yong Huo , Yong Li , Tao Li , Duoduo Zhang , Kun Lin , Chen Yang , Jian Tong , Hui Zhao , Busen Suo , Yalan Xue and Caiping Yi
Published/Copyright: September 28, 2023
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Open Geosciences
From the journal Open Geosciences Volume 15 Issue 1

Abstract

Quantitative studies of the pore-throat structure (PTS) characteristics of tight sandstone reservoirs and their effects on fluid mobility were proposed to accurately evaluate reservoir quality and predict sweet spots for tight oil exploration. This study conducted high-pressure mercury injection (HPMI) and nuclear magnetic resonance (NMR) experiments on 14 tight sandstone samples from the Chang 7 member of the Yanchang Formation in the Jiyuan area of the Ordos Basin. The HPMI was combined with the piecewise fitting method to transform the NMR movable fluid transverse relaxation time (T 2) spectrum and quantitatively characterize the PTS characteristics and the full pore-throat size distribution (PSD). Then, movable fluid effective porosity (MFEP) was proposed to quantitatively evaluate the fluid mobility of tight sandstone reservoirs and systematically elucidate its main controlling factors. The results showed that the PTS could be divided into four types (I, II, III, and IV), which showed gradual decreases in average pore-throat radius (R a), continuous increases in the total fractal dimension (D t), and successive deterioration of reservoir fluid mobility and percolation capacity. Moreover, the full PSD (0.001–10 μm) showed unimodal and multi-fractal characteristics. According to the Swanson parameter (r apex), the reservoir space types can be divided into small and large pore-throat and the corresponding fractal dimension has a relationship where D 1 < D 2. Large pore-throat had higher permeability contribution and pore-throat heterogeneity but a lower development degree and MFEP than small pore-throat, which had a relatively uniform and regular PSD and represented the primary location of movable fluids. Moreover, the development degree and heterogeneity of small pore throat controlled the flowability of reservoir fluids. MFEP can overcome the constraints of tiny throats and clay minerals on movable fluid, quantify the movable fluid content occupying the effective reservoir space, and accurately evaluate the reservoir fluid mobility. The combination and development of various pore-throat sizes and types in tight sandstone reservoirs results in different PTS characteristics, whereas differences in the mineral composition and content of reservoirs aggravate PTS heterogeneity, which is the main factor controlling the fluid mobility.

Keywords: pore-throat structure; movable fluid; Jiyuan area; fluid mobility; movable fluid effective porosity

1 Introduction

The successful development of tight oil production processes has increased global crude oil production, and China has shifted its exploration targets toward more subtle tight reservoir areas and formations [1–4]. The Yanchang Formation of the Ordos Basin hosts a large number of tight oil resources with excellent development potential [5,6]. However, tight sandstone reservoirs with tight oil are characterized by strong diagenetic transformations, dense rocks, and complex pore-throat structures (PTSs), and they also present widespread problems, such as difficulty in quantitatively characterizing the PTS and unidentified factors that control fluid mobility [7,8,9]. Therefore, the PTS characteristics and fluid mobility of tight sandstone reservoirs represent the critical factors for further study.

Many methods and technologies have been proposed to characterize the PTS of tight sandstone reservoirs [10,11], such as casting thin sections, scanning electron microscopy (SEM) [12–15], high-pressure mercury injection (HPMI) [16], low-temperature nitrogen adsorption (LTNA) [17], rate-controlled mercury injection (RCMI) [18], and nuclear magnetic resonance (NMR) [19,20]. However, the applicability of the abovementioned imaging technologies and measurement methods presents certain advantages and disadvantages [21,22], including an inability to comprehensively assess the full pore-throat size distribution (PSD) of reservoirs. Therefore, multi-method synergistic characterization technology has been widely applied, and it includes the integration of NMR and HPMI [9,23,24]; HPMI and RCMI [25]; RCMI and NMR [26]; LTNA and HPMI [27]; LTNA and NMR [25,28]; and LTNA, HPMI, and NMR [4,29,30]. Among these, the construction of NMR capillary curves based on HPMI data is a common method for studying reservoir PTS. However, NMR detects the pore size, while HPMI technology measures the pore-throat size [31,32], and these parameters cannot be directly converted. Therefore, the transformation of the NMR transverse relaxation time (T 2) spectrum based on the PSD obtained from HPMI must be thoroughly studied.

A number of current scholars have suggested that the reservoir fluid mobility is a superior parameter for characterizing the PSD compared with petrophysical properties because it can effectively guide reservoir productivity predictions and development plan formulation [3,4,9,33,34,35,36]. Previous researchers used movable fluid saturation (MFS) and movable fluid porosity (MFP) to evaluate the mobility of reservoir fluids; however, these parameters exclude the influence of fluid in tiny pore throats that are difficult to separate because of strong capillary force binding [3,37,38,39,40]. However, the pore-throat space above the T 2cutoff value still contains a certain amount of bound fluid formed by the viscosity of hydrophilic particles and the control of adjacent narrow throats, suggesting that MFS and MFP cannot accurately evaluate the reservoir fluid mobility [28,41,42,43]. In addition, the main factors affecting fluid mobility in tight sandstone reservoirs have not been clearly identified. Therefore, new effective parameters must be identified to accurately evaluate the reservoir fluid mobility.

This study selected 14 tight sandstone samples from the Chang 7 member of the Yanchang Formation in the Jiyuan area of Ordos Basin and performed casting thin sections, SEM, and X-ray diffraction (XRD) to determine the reservoir space types and mineral composition and content. Based on the NMR T 2 spectrum of saturated and bound water, we constructed the NMR movable fluid T 2 spectrum and then transformed it using the PSD obtained by HPMI to study the full PSD characteristics. On this basis, we used the fractal theory to quantitatively evaluate the full PSD heterogeneity. Furthermore, we proposed movable fluid effective porosity (MFEP) as a new parameter for the quantitative evaluation of the reservoir fluid mobility and systematically elucidated the main factors affecting the fluid mobility to accurately evaluate reservoir quality and predict favorable sweet spots for oil exploration.

2 Regional overview

The Ordos Basin is located on the western edge of the North China Craton Basin (Figure 1a) [44], with six first-order structural units and a regional area of 25 × 104 km2 [45,46] (Figure 1b). The Yanchang Formation in the Jiyuan area experienced a multi-stage process of fluctuating lake levels during the sedimentary period [38,47] and has ten oil-bearing members and an overall stratigraphic thickness of approximately 1,300 m (Figure 1c) [1]. Among the ten members, the Chang 7 member is a delta-lacustrine sedimentary system, with a sedimentary strata thickness of 100–120 m and an average thickness of approximately 110.9 m [48]. During the early sedimentary period of the Chang 7 member, the lake area was the widest and the water depth was greater. The sedimentary facies in the study area were mainly semi-deep and deep lacustrine subfacies and deposited a large area of dark mudstone and black oil shale, which represented the high-quality source rocks of the Upper Triassic Yanchang Formation and had an average thickness of 82.1 m. During the late stage of the Chang 7 member, the lake basin began to contract and the underwater distributary channel sand bodies mainly developed in the delta front, thus forming large-scale and contiguous thick sand bodies with an average thickness of 28.8 m. After undergoing a long period of sedimentary evolution, tectonic movement, and diagenetic transformation, the Chang 7 reservoir in the Jiyuan area formed the present tight sandstone reservoir [4]. The Chang 7 strata have a simple structure without fault and trap development and present a structural feature of slow decline from northeast to southwest, with multiple near east–west low-amplitude nose-shaped uplifts developed locally [49]. The reservoir rock types are mostly light-gray and gray feldspar and lithic feldspar sandstone, which have a high content of interstitial material, fine sedimentary particle size, moderate sorting, and low compositional and structural maturity.

Figure 1 (a) Ordos Basin is located in midwestern China (Orange area), (b) regional tectonic map of the basin and study area (red rectangle), and (c) sedimentary evolution of Yanchang Formation and the sampling point (red star).
Figure 1

(a) Ordos Basin is located in midwestern China (Orange area), (b) regional tectonic map of the basin and study area (red rectangle), and (c) sedimentary evolution of Yanchang Formation and the sampling point (red star).

3 Methodology

We selected 14 tight sandstone samples from different oil testing sections with different petrophysical properties of the Chang 7 reservoir and drilled cylindrical plugs with a diameter of 2.5 cm along the formation direction. Next, we numbered the sample plugs individually and placed them into alcohol and benzene for the extraction experiments. After oil washing and drying, we tested the wettability of the aforementioned samples by the contact angle measurement method and conducted petrophysical property analyses and other experiments.

3.1 NMR

The NMR experiments used a MARAN-2 analyzer and centrifuge manufactured by Oxford Instruments to measure the T 2 spectrum of the samples in saturated water and bound water states. After drying and weighing, we vacuumed the sample plugs, saturated them using a 50 g/L CaCl2 solution (simulated formation water), and measured the liquid porosity. To ensure that the fluid in the sample reached the bound state under a high-speed centrifugal force, we set the centrifugal speed to 9,000 r/min (406 psi). The same parameters were used for the two NMR measurements, including the Carr-Purcell-Meiboom-Gill (CPMG) pulse series, a resonance frequency of 2.38 MHz, an echo interval of 0.2 ms, a waiting time of 6 s, a scanning number of 128, and a receiver gain of 80%.

3.2 HPMI

After drying the apposition sample plugs, we cut them to a length of approximately 2.5 cm and then performed HPMI experiments using an Autopore 9250 II mercury porosimeter. During the experiments, the non-wetting phase of mercury was continuously pressurized under vacuum conditions, which allowed mercury to enter the reservoir space with a smaller pore-throat radius and recorded corresponding mercury saturation at stable pressures. Then, we quantitatively detected the PSD characteristics of the sample based on the Washburn equation. The parameters of the HPMI experiments included the following: mercury injection pressure range of 0–200.3 MPa and pore size measurement range of 0.0037–100 μm.

3.3 Fractal theory

Fractal theory can reveal the geometric structural characteristics of porous media, and the corresponding fractal dimension represents a quantitative characterization of self-similarity and heterogeneity within irregular objects [50,51]. At present, mathematical models for obtaining fractal dimension mainly include the box-counting method, two-dimensional capillary model, three-dimensional capillary model, spherical model, and wettability model [52,53,54]. Among them, the three-dimensional capillary model is more widely used [29,55], and it assumes that the interconnected pore throats in the sample are capillary bundles on a three-dimensional scale that meets the condition that r max is much higher than r min [56]. The three-dimensional capillary model suggests that if the PSD of a reservoir exhibits fractal characteristics, then the relationship between the cumulative pore volume V(<r) and pore-throat radius r can be described as follows [57]:

(1) lg V ( < r ) = ( 3 − D ) lg r + C ,

(2) D = 3 − K ,

where D represents the fractal dimension and C and K are the ordinate intercept and slope of the fitting line, respectively. When the sample exhibited multi-fractal characteristics, the total fractal dimension (D t) of the whole reservoir space was obtained by the weighted average porosity of each pore-throat space [39,52], and its formula can be expressed as follows:

(3) D t = ∑ i ( D i ∙ ∅ i ) ∅ .

3.4 Transformation of the movable fluid T 2 spectrum

Based on the principles of the NMR experiment, the distribution characteristics of fluids can be reflected by measuring the spin signal and T 2 value of hydrogen nuclei in the pores of rock samples [24,41,58,59]. The NMR T 2 spectrum of saturated brine and bound water with the same T 2 value sequence reflects the fluid distribution of the overall reservoir space and unconnected pore space, respectively [35,60,61]. Therefore, the movable fluid T 2 spectrum reflecting the fluid distribution of the interconnected pore-throat space can be obtained by subtracting the NMR signal before and after centrifugation (Figure 2a). On the basis of previous studies, this study proposes converting the movable fluid T 2 spectrum into a full PSD curve using HPMI data. Based on theoretical derivation, the method for converting the T 2 spectrum is as follows [62]:

(4) r = ( T 2 · ρ 2 · F s ) 1 / n = C T 2 1 / n ,

where ρ 2 is the surface relaxation rate, μm/ms; F s is the pore shape factor, dimensionless; and C and n are undetermined parameters.

Figure 2 The NMR movable fluid T 2 spectrum (a) and its conversion with the HPMI pore-throat data (b) of typical sample A-2.
Figure 2

The NMR movable fluid T 2 spectrum (a) and its conversion with the HPMI pore-throat data (b) of typical sample A-2.

The amplitude percentage of the movable fluid T 2 spectrum was reverse-accumulated and normalized into the T 2 cumulative frequency distribution curve and plotted on the same coordinate axis as the capillary pressure curve of HPMI (Figure 2b). The mercury injection saturation range of HPMI was considered the analysis scope, and the mercury injection data were interpolated by the linear interpolation method to obtain T 2(i) and r(i) values corresponding to the same cumulative frequency S(i) [58,63]. Equation (4) was used for the linear fitting of T 2(i) and r(i) curves, and then, the undetermined parameters C and n were obtained to determine the full PSD characteristics of each sample.

4 Results

4.1 Petrophysical properties, mineralogy, and pore and throat types

The wetting contact angle of the tight sandstone samples was between 20° and 40° at 70°C, which indicates that the reservoir has hydrophilic characteristics. The porosity values were scattered between 6.58 and 12.02%, averaging 9.01%, while the permeability values were distributed between 0.052 and 0.442 ×10−3 μm2, averaging 0.212 ×10−3 μm2 (Table 1). According to the geological evaluation method of tight reservoirs in China and the reservoir classification standard given by Changqing Oilfield in Ordos Basin, reservoirs with gas porosity lower than 10%, overburden permeability lower than 0.1 ×10−3 μm2, or air permeability lower than 1.0 ×10−3 μm2 were defined as tight sandstone reservoirs [64]. Therefore, the Chang 7 reservoir in the Jiyuan area belongs to a typical tight sandstone reservoir.

Table 1

Petrophysical property and mineral content of each sample

Sample ID Deep (m) Porosity (%) Permeability (×10‒3 μm2) Casting thin sections (%) Clay mineral content from XRD (%)
Quartz Feldspar Lithic fragment Carbonate mineral Siliceous cement Clay mineral Total content Illite Chlorite Kaolinite I/S
A-1 2300.47 12.02 0.358 25.0 30.0 30.0 5.0 1.0 9.0 6.27 22.86 39.46 25.00 12.68
A-2 2299.94 11.42 0.442 24.5 31.0 34.0 4.0 0.5 6.0 4.18 35.57 29.29 17.64 17.50
A-3 2520.55 10.30 0.206 33.0 34.0 19.0 2.0 1.0 11.0 9.11 35.29 32.93 24.07 7.71
A-4 2274.14 8.61 0.226 30.0 28.5 28.5 3.0 2.0 8.0 7.97 46.12 39.64 13.04 1.20
A-5 2174.44 10.41 0.302 27.0 33.0 24.0 6.0 1.0 9.0 5.98 49.68 24.39 20.00 5.93
A-6 2183.55 8.41 0.198 29.0 38.5 18.5 1.0 1.0 12.0 10.90 60.07 11.01 27.74 1.18
A-7 2198.65 9.15 0.264 35.0 28.0 28.0 1.0 3.0 5.0 6.53 38.53 45.29 10.29 5.89
A-8 2496.85 10.25 0.334 28.0 26.0 39.5 0.5 1.0 5.0 5.57 47.76 28.45 20.34 3.45
A-9 2453.95 7.49 0.126 29.0 31.5 22.0 1.5 1.0 15.0 16.05 64.42 18.79 12.02 4.77
A-10 2814.30 9.17 0.212 35.0 36.0 5.5 6.5 3.0 14.0 11.70 59.20 12.45 25.95 2.40
A-11 2511.65 7.47 0.115 33.0 35.0 11.5 3.5 2.0 15.0 13.80 58.60 31.60 8.70 1.10
A-12 2299.94 7.90 0.052 36.0 37.0 5.5 3.0 0.5 18.0 18.20 69.90 15.40 11.00 3.70
A-13 2297.90 6.58 0.054 32.5 39.0 4.0 2.5 2.0 20.0 19.10 65.59 21.86 11.41 1.14
A-14 2301.64 7.02 0.082 36.0 35.0 3.0 5.0 2.0 19.0 19.5.0 56.47 23.54 13.24 6.75

The Chang 7 tight sandstone samples had high feldspar and quartz contents, with average volume percentages of 33.04 and 30.93%, respectively, and an average lithic fragment content of 19.50%. SEM and XRD analyses indicated a relatively high content of interstitial materials in the Chang 7 tight sandstone samples, with average volume percentages of 16.54%, and the interstitial materials included authigenic clay minerals, carbonate cements, and siliceous cements, with average volume percentages of 11.86, 3.18, and 1.50%, respectively. Among the clay minerals, the illite content was highest (Figure 3a), followed by chlorite (Figure 3b), kaolinite (Figure 3c), and illite/smectite (I/S) mixed layer, with corresponding average relative contents of 50.72, 26.72, 17.17, and 5.39%, respectively. The contents of ferriferous calcite (Figure 3d) and dolomite were higher in the carbonate cement minerals, with average volume percentages of 1.47 and 1.24%, respectively.

Figure 3 Mineral composition, pore, and throat types in Jiyuan region: (a) Hairy illite attaches to the pore surface and forms intercrystalline pores by filling and cutting the original pores, A-6, 2183.55 m, SEM; (b) the liner chlorite filling the pores, A-7, 2198.65 m, SEM; (c) kaolinite filling pores to form intercrystalline pores, A-1, 2300.47 m, SEM; (d) kaolinite filled besides the feldspar-dissolved pores, and ferriferous calcite filled the residual intergranular pores, A-5, 2174.44 m, cast thin section; (e) development of feldspar-dissolved pores and residual intergranular pores, A-10, 2814.30 m, cast thin section; (f) development of feldspar-dissolved pores and kaolinite intercrystalline pores, A-4, 2274.14 m, cast thin section; (g) chlorite film attached between residual intergranular pores and particles, A-2, 2299.94 m, cast thin section; (h) microfractures developed between mineral particles, A-5, 2174.44 m, cast thin section; and (i) development of the bent sheet-shaped throats and tubular throats, A-13, 2297.90 m, Chang 8, cast thin section.
Figure 3

Mineral composition, pore, and throat types in Jiyuan region: (a) Hairy illite attaches to the pore surface and forms intercrystalline pores by filling and cutting the original pores, A-6, 2183.55 m, SEM; (b) the liner chlorite filling the pores, A-7, 2198.65 m, SEM; (c) kaolinite filling pores to form intercrystalline pores, A-1, 2300.47 m, SEM; (d) kaolinite filled besides the feldspar-dissolved pores, and ferriferous calcite filled the residual intergranular pores, A-5, 2174.44 m, cast thin section; (e) development of feldspar-dissolved pores and residual intergranular pores, A-10, 2814.30 m, cast thin section; (f) development of feldspar-dissolved pores and kaolinite intercrystalline pores, A-4, 2274.14 m, cast thin section; (g) chlorite film attached between residual intergranular pores and particles, A-2, 2299.94 m, cast thin section; (h) microfractures developed between mineral particles, A-5, 2174.44 m, cast thin section; and (i) development of the bent sheet-shaped throats and tubular throats, A-13, 2297.90 m, Chang 8, cast thin section.

Microscopic observations and analyses showed that the pore types of the reservoir included dissolution pores, residual intergranular pores, intercrystalline pores, and microfractures, with an average total facial ratio of 2.47%. Among these, dissolution pores were the most developed (an average facial ratio of 1.21%) and primarily consisted of feldspar dissolution pores (an average facial ratio of 1.08%) (Figure 3e and f), and they were followed by residual intergranular pores (an average facial ratio of 1.01%), which mainly included intergranular pores retained after compaction and filling of the original pores by reservoir diagenesis (Figure 3d–e and g). The pore shapes were mostly triangular or irregular, and some intergranular pores developed chlorite films at the edges (Figure 3g), which can provide good compaction resistance while filling the intergranular pores. Affected by cementation and tectonic stress, the Chang 7 reservoir developed many clay mineral intercrystalline pores (an average facial ratio of 0.15%) (Figure 3a–d and f) and microfractures (Figure 3h) (an average facial ratio of 0.10%), which contributed to connecting reservoir pores and throats. The throat types were predominantly bent sheet-shaped and tubular (Figure 3i).

4.2 HPMI results

Table 2 shows the PTS parameters of each sample obtained by HPMI. Based on the capillary pressure curve morphology, threshold pressure (P t), and average pore-throat radius (R a) of 14 samples, the PTSs were divided into four types: I, II, III, and IV. Among these types, the capillary pressure curve distribution increased gradually (Figure 4a) and P t increased successively, with average values of 0.451, 0.728, 1.228, and 4.402 MPa. The corresponding PSD curves showed a unimodal distribution and gradually shifted to the left (Figure 4b), and the R a gradually decreased, with an average value of 0.31, 0.20, 0.13, and 0.03 μm. Moreover, the values of other PTS parameters were significantly different among the four types of sandstone samples, indicating that tight sandstone reservoirs have complex configuration relationships and heterogeneous of pore throats.

Table 2

Statistical table of PTS parameters obtained by HPMI and movable fluid parameters obtained by NMR

Type Sample ID HPMI NMR
P t (MPa) R max (μm) R a (μm) S p α S max (%) W e (%) Liquid porosity (%) MFS (%) MFP (%) MFEP (%) T 2cutoff (ms)
Ⅰ A-1 0.487 1.51 0.28 2.94 0.25 87.55 47.70 11.89 55.19 6.63 5.43 30.09
A-2 0.405 1.81 0.34 3.16 0.27 82.51 50.14 11.35 64.75 7.40 5.90 13.04
A-3 0.462 1.59 0.31 2.22 0.20 88.04 43.67 10.23 58.95 6.07 4.56 17.23
Average 0.451 1.64 0.31 2.77 0.24 86.04 47.17 11.16 59.63 6.70 5.29 20.12
Ⅱ A-4 0.742 0.99 0.21 3.05 0.19 84.81 47.12 8.58 56.33 4.85 4.03 1.92
A-5 0.735 1.00 0.18 3.12 0.20 79.18 52.11 10.35 56.93 5.93 4.54 1.73
A-6 0.786 0.94 0.19 3.30 0.18 77.22 54.28 8.40 48.42 4.07 3.05 13.06
A-7 0.650 1.13 0.23 2.94 0.15 79.18 43.61 9.08 51.17 4.68 3.83 7.54
Average 0.728 1.01 0.20 3.10 0.18 80.10 49.28 9.10 53.21 4.88 3.86 6.06
Ⅲ A-8 0.792 0.93 0.19 2.56 0.27 76.70 43.61 10.23 55.22 5.66 4.20 7.97
A-9 1.500 0.49 0.13 2.14 0.26 86.57 34.03 7.35 48.48 3.63 2.44 6.24
A-10 0.815 0.90 0.14 2.26 0.29 72.14 41.20 9.11 43.23 3.96 3.07 9.62
A-11 1.805 0.41 0.06 1.76 0.32 80.21 40.70 7.42 40.39 3.02 2.46 13.58
Average 1.228 0.68 0.13 2.18 0.29 78.90 39.89 8.53 46.83 4.07 3.04 9.35
Ⅳ A-12 7.389 0.10 0.02 4.92 0.12 45.29 13.56 7.72 26.31 2.08 0.64 0.53
A-13 2.911 0.25 0.03 3.92 0.16 70.40 16.91 6.44 33.46 2.20 1.30 0.55
A-14 2.907 0.16 0.03 4.88 0.13 62.00 19.19 6.89 37.26 2.62 1.02 0.69
Average 4.402 0.17 0.03 4.58 0.14 59.23 16.55 7.02 32.34 2.30 0.99 0.59
Figure 4 Capillary pressure curve characteristics (a) and PSD characteristics (b) of various samples from HPMI.
Figure 4

Capillary pressure curve characteristics (a) and PSD characteristics (b) of various samples from HPMI.

Moreover, the maximum mercury injection saturation (S max) and mercury removal efficiency (W e) of type I PTSs had average values of 86.04 and 47.17%, respectively, and the sorting coefficient (S p) and homogeneity coefficient (α) of the PSD had average values of 2.77 and 0.24, respectively. This indicates that this type of sample developed larger pore throats and the PSD was relatively concentrated and uniformly distributed, thereby corresponding to a higher reservoir space and seepage capacity. The PSD of type II PTSs showed a wide and slow single-peak characteristic, with a high S p and low α (the average values were 3.10 and 0.18, respectively). Meanwhile, the type II PTSs had larger S max and the highest W e (the average values were 80.10 and 49.28%, respectively), indicating that this type of sample had a better configuration relationship and connectivity of pore throats. Type III PTSs had a small pore-throat radius, a concentrated distribution, the lowest S p, and the highest α (the average values were 2.18 and 0.29, respectively), which corresponded to a lower S max and W e (the average values were 78.90 and 39.89%, respectively). The type IV PTSs had the highest S p and lowest α (the average values were 4.58 and 0.14, respectively). This type of sample developed more tiny pore throats and showed the lowest effective volume and configuration relationship of pore-throat space (the average values of S max and W e were 59.23 and 16.55%, respectively).

4.3 NMR results

The liquid porosity of each sample obtained by the NMR experiment was similar to the gas porosity, except that it was low overall (an average value of 8.93%) (Table 2) because of the failure to detect the bound fluid occurring in tiny pores or on clay mineral surfaces due to the short T 2 relaxation time [65]. Since the reservoir was in the original formation water state before oil and gas were charged into the reservoir, the liquid porosity obtained from NMR testing reflects the true storage capacity of the reservoir. In this study, we proposed the MFEP parameter to characterize the flowability of reservoir fluids. Specifically, the MFEP represents the volume percentage of the effective reservoir space that is greater than the T 2cutoff value in the unit volume rock sample (shaded area in Figure 2a), which is numerically equal to the sum of porosity increments greater than the T 2cutoff value in the movable fluid T 2 spectrum.

Based on the classification of PTSs by HPMI, the NMR T 2 spectrum curves of corresponding samples were analyzed in four categories (Figure 5). The T 2 spectrum curve morphology of saturated water presents a unimodal and bimodal distribution, while that of movable fluid only shows a unimodal distribution, which is consistent with the HPMI-based PSD curve morphology of the corresponding samples (Figures 4b and 5b). The T 2cutoff values of tight sandstone samples ranged from 0.53 to 30.09 ms (Table 2) and averaged 8.84 ms. The corresponding MFS distribution ranged from 26.31 to 64.75% and averaged 48.29%, and the MFEP distribution varied from 0.64 to 5.90% and averaged 3.32%, indicating that the movable fluid content and reservoir space in tight sandstone reservoirs were small and fluid flow ability through tight porous media was weak. Thus, fluid mobility was poor. Due to the fact that only simulated the single-phase flow of formation water in the pore-throat space during NMR centrifugation, the MFS and MFEP obtained by NMR experiments reflected the theoretical or maximum mobility of fluids in tight sandstone reservoirs, and the T 2cutoff value corresponded to the lower limit of the pore-throat radius of the movable fluid in the reservoir. Moreover, the corresponding fluid mobility decreased sequentially for type I to type IV PTSs, with average MFS values of 59.63, 53.21, 46.83, and 32.34% and average MFEP values of 5.29, 3.86, 3.04, and 0.99%.

Figure 5 Distribution characteristics of NMR saturated water T 2 spectrum curves (a) and movable fluid T 2 spectrum curves (b) of each sample.
Figure 5

Distribution characteristics of NMR saturated water T 2 spectrum curves (a) and movable fluid T 2 spectrum curves (b) of each sample.

4.4 Full PSD characteristics

Using the pore-throat size data obtained via HPMI, the NMR T 2 spectrum of movable fluid can be converted into the full PSD. When calculating the corresponding conversion coefficients C and n, the fitting curves between r(i) and T 2(i) for each sample do not appear as straight lines but have two obvious linear relationships with an inflection point (Figure 6a). Interestingly, we found that the pore-throat radius corresponding to this inflection point was the same as that corresponding to the Swanson parameter, which is defined as the hyperbolic peaks of mercury saturation (S Hg)/injection pressure (P c) and S Hg (Figure 6b) [66]. The corresponding pore-throat radius (r apex) was the boundary point of transition from poor to good connectivity and thus represented the minimum connectable pore-throat radius [67]. Table 3 indicates that the r apex corresponding to tight sandstone samples ranged from 0.016 to 0.404 μm and averaged 0.154 μm. Therefore, according to r apex, the types of reservoir space were divided into small and large pore-throat, and linear fitting was then performed for the two parts to obtain the corresponding conversion coefficients C and n, respectively.

Figure 6 Fitting curves of between r (i) and T 2 (i) (a) and relationship curves of S Hg/P c and S Hg (b) for typical samples A-2.
Figure 6

Fitting curves of between r (i) and T 2 (i) (a) and relationship curves of S Hg/P c and S Hg (b) for typical samples A-2.

Table 3

r apex and movable fluid parameters, permeability contribution, and fractal dimension corresponding to large and small pore-throat of each sample

Sample ID r apex (μm) Small pore-throat section Large pore-throat section D t
MFS (%) MFEP (%) K 1 (×10−3 μm2) D 1 R 2 MFS (%) MFEP (%) K 2 (×10−3 μm2) D 2 R 2
A-1 0.243 35.91 3.53 0.0264 2.1701 0.9822 19.27 1.89 0.3316 2.5185 0.9440 2.3616
A-2 0.404 36.19 3.30 0.0252 2.2227 0.9745 28.56 2.60 0.4168 2.6046 0.9373 2.3794
A-3 0.253 32.69 2.53 0.0189 2.4036 0.9903 26.26 2.03 0.1871 2.7558 0.9438 2.4605
A-4 0.272 28.40 2.03 0.0169 2.2603 0.9123 27.93 2.00 0.2088 2.6116 0.9765 2.3353
A-5 0.164 35.02 2.79 0.0264 2.1880 0.9521 21.91 1.75 0.2756 2.5408 0.9712 2.3238
A-6 0.064 27.00 1.70 0.0134 2.3576 0.9054 21.42 1.35 0.1846 2.7261 0.9620 2.5207
A-7 0.160 30.12 2.25 0.0425 2.2719 0.9182 21.05 1.57 0.2212 2.6734 0.9742 2.5371
A-8 0.136 46.60 3.54 0.0614 2.0861 0.9470 8.63 0.66 0.2730 2.5252 0.9385 2.6172
A-9 0.040 34.73 1.75 0.0238 2.4105 0.9043 13.74 0.69 0.1022 2.6256 0.9690 2.4715
A-10 0.201 38.58 2.74 0.0533 2.3560 0.9897 4.65 0.33 0.1586 2.9281 0.9421 2.6175
A-11 0.100 32.57 1.98 0.0200 2.3528 0.9917 7.82 0.48 0.0952 2.7532 0.9404 2.5304
A-12 0.016 21.09 0.51 0.0042 2.4528 0.9580 5.22 0.13 0.0473 2.9268 0.9624 2.8270
A-13 0.040 29.76 1.16 0.0203 2.4989 0.9667 3.70 0.14 0.0340 2.7642 0.9291 2.7283
A-14 0.062 34.07 0.93 0.0144 2.3540 0.9714 3.19 0.09 0.0673 2.8442 0.9170 2.7959

Figure 7 shows the full PSD of all samples is mainly distributed between 0.001 and 10 μm and showed unimodal characteristics. From the type I to type IV PTSs, the full PSD curves gradually shifted to the left and the distribution range narrowed. The comparison shows that the full PSD curve had the same pore-throat main peak as the PSD curve obtained from the HPMI date but presented a wider distribution range and lower corresponding frequency distribution (Figure 8). The reason for the difference is that the principles of the two tests are different. The HPMI experiment is a staged pressurization process, the detection results are continuous data points recorded at equal time intervals with fewer measured pressure points, and the obtained PSD frequency reflects the percentage sum of pore-throat space within a certain range of pore throats. However, many data points are recorded in NMR experiments, and the full PSD frequency only represents the percentage of the pore-throat space occupied by a single pore-throat radius. Furthermore, mercury is a non-wetting phase relative to salt water, thus hindering its ability to enter petite pore throats. Therefore, the PSD range detected by HPMI was small.

Figure 7 Full PSD characteristics of 14 samples after T 2 spectrum transformation of NMR movable fluid.
Figure 7

Full PSD characteristics of 14 samples after T 2 spectrum transformation of NMR movable fluid.

Figure 8 Comparison between the full PSD obtained by NMR and the PSD obtained by HPMI of typical samples A-2 (a) and A-12 (b).
Figure 8

Comparison between the full PSD obtained by NMR and the PSD obtained by HPMI of typical samples A-2 (a) and A-12 (b).

4.5 Fractal characteristics of pore throat

The fractal characteristics of the full PSD were analyzed by applying equation (1), and the results revealed that the curves of Lg V(<r) and Lg r of each sample exhibited obvious inflection points, which were consistent with the division boundary of the pore-throat types (Figure 9). This indicated that the full PSD had multi-fractal characteristics, with each pore-throat type showing self-similarity and different complexity degrees. Therefore, the fractal dimensions of the small and large pore-throat spaces were defined as D 1 and D 2, respectively. The statistical results indicated that the D 1 values were scattered between 2.0861 and 2.4989 and averaged 2.3132, and the D 2 values were distributed between 2.5185 and 2.9281 and averaged 2.6997 (Table 3), thus showing a relationship of D 1 < D 2. Compared to the large pore throat, the small pore throat had weak structural heterogeneity, relatively uniform PSDs, and smooth surfaces. Moreover, the D t value of each sample ranged between 2.3238 and 2.8270 with an average value of 2.5364, which indicates that the overall heterogeneity of the studied PTS was relatively substantial. The D t of types I, II, III, and IV PTS gradually increased, with average values of 2.4005, 2.4292, 2.5601, and 2.7837, respectively, indicating that type III and type IV PTSs had substantial heterogeneity and complex PSDs. These characteristics were associated with higher clay mineral contents, which filled the reservoir space and facilitated the development of smaller pore throats, resulting in uneven PSDs and substantial pore-throat heterogeneity. In summary, the combination and development of different pore-throat types and sizes in tight sandstone reservoirs result in different heterogeneity degrees of PTSs.

Figure 9 Pore-throat fractal curves of typical samples A-2 (a) and A-12 (b).
Figure 9

Pore-throat fractal curves of typical samples A-2 (a) and A-12 (b).

4.6 Occurrence characteristics of movable fluid

The MFS, MFEP, and permeability contributions corresponding to large and small pore-throat of each sample were counted to quantitatively evaluate the mobility of reservoir fluid (Table 3). The average values of MFS and MFEP occupied by small pore-throat in the 14 sandstone samples were 33.05 and 2.20%, respectively, which were higher than the average values of the large pore-throat at 15.24 and 1.12%, respectively. This indicates that small pore-throat occupies a larger area of the effective reservoir space and is the primary location of movable fluids in the rock sample. Although large pore-throat serves as the main contributor to reservoir permeability (average permeability contribution values of the large and small pore-throat were 0.1859 and 0.0262 × 10−3   μm2, respectively), they show a low development degree and occupy a small proportion of the storage space (the average proportion of movable fluid in large and small pore-throat was 29.33 and 70.67%, respectively). However, the small pore-throat had a weak fluid flowability but was observed over a large amount of reservoir space. Therefore, the development degree of small pore-throat can control the seepage ability of the reservoir fluid and determine the sizes of the MFS and MFEP.

5 Discussion

5.1 Correlation between petrophysical properties and fluid mobility

MFS and MFEP were positively correlated with permeability but weakly correlated with porosity (Figure 10a and b) because porosity only characterizes the reservoir performance, while MFS and MFEP reflect the reservoir seepage capacity. The greater the reservoir permeability, the less control that small throats and clay minerals have on movable fluid that exceeds the T 2cutoff value, resulting in increased flowability of the reservoir fluids. Moreover, the correlation between MFEP and permeability corresponding to the small pore throat was better than that between MFEP and permeability corresponding to the large pore throat (Figure 10c). This demonstrates that although the presence of large pore throats help to improve permeability, they occupy a small reservoir space and have a strong heterogeneity, which has a little influence on the percolation capacity of reservoir fluids. The development of small pore throat has a dominant controlling effect on the seepage capacity of the large pore throat and the reservoir fluid mobility, as affirmed by Meng [26]. A strong correlation is found between the reservoir quality index ( RQI = K ∅ ) and MFS and MFEP (Figure 10d), indicating that the flowability of reservoir fluids is comprehensively affected by the overall quality of the reservoir.

Figure 10 Correlation between MFS and MFEP with porosity (a) and permeability (b), and the correlation between MFEP corresponding to large and small pore-throat with their corresponding permeability (c), and the correlation between MFS and MFEP with RQI (d).
Figure 10

Correlation between MFS and MFEP with porosity (a) and permeability (b), and the correlation between MFEP corresponding to large and small pore-throat with their corresponding permeability (c), and the correlation between MFS and MFEP with RQI (d).

5.2 Effects of the PTS on reservoir petrophysical properties and fluid mobility

5.2.1 Correlation between the PTS parameters and reservoir petrophysical properties

The representative parameter characterizing the PSD of tight sandstone samples is R a, which is positively correlated with reservoir petrophysical properties (Figure 11a), indicating that larger R a values present higher corresponding reservoir performance and percolation capacity. A certain correlation is observed between W e and petrophysical properties (Figure 11b) because mercury is a non-wetting phase against air. During mercury withdrawal, the capillary force becomes a driving force and smaller throats empty mercury first, which destroys the connectivity of the whole pore-throat space and leads certain larger pores and throats to become isolated and no longer discharge mercury. There are many small pore throats in tight sandstone reservoirs, and although they can effectively withdraw more mercury, fluid stagnation areas are formed in the pore throats, which leads to lower corresponding MFS and MFEP values. The S max profile reflects the development degree of the connectable pore throats within the reservoir. The higher the S max, the larger the connectable pore-throat space of the reservoir. However, the final volume of mercury entering the pore throat was controlled by the reservoir space size and less affected by the reservoir seepage capacity. Therefore, S max only had a weak correlation with reservoir porosity (Figure 11c). The correlation between D t and reservoir petrophysical properties was better (Figure 11d), indicating that the larger the D t, the stronger the complexity of PTS and the worse the reservoir petrophysical properties.

Figure 11 Correlation between porosity and permeability with R a (a), W e (b), S max (c), and D t (d).
Figure 11

Correlation between porosity and permeability with R a (a), W e (b), S max (c), and D t (d).

5.2.2 Correlation between PTS parameters and movable fluid parameters

Figure 12a shows a clear positive correlation between R a and MFS and MFEP. This finding indicates that larger pore throats have a weaker binding effect on movable fluids, which is more conducive to the seepage of reservoir fluids. Simultaneously, the correlation between r apex and MFS and MFEP was weaker than that for R a (Figure 12b), indicating that the larger r apex value, the larger the connectable effective pore-throat space, which corresponds to higher reservoir fluid mobility. However, S max and W e presented a certain correlation with MFS and MFEP (Figure 12c and d), indicating that a larger reservoir space may not result in good reservoir fluid mobility and W e cannot effectively reflect the pore-throat connectivity of tight sandstone reservoirs, as discussed in Section 5.2.1.

Figure 12 Correlation between MFS and MFEP with R a (a), r apex (b), S max (c), W e (d), S p (e), α (f), D t (g), D 1 (h) and D 2 (i).
Figure 12

Correlation between MFS and MFEP with R a (a), r apex (b), S max (c), W e (d), S p (e), α (f), D t (g), D 1 (h) and D 2 (i).

Almost no correlation was observed for S p and α with MFS and MFEP (Figure 12e and f), which is primarily because tight sandstone reservoirs contain tiny pores and throats. When the development degree of large pore throats increases (such as with the occurrence of microfractures), the R a of the reservoir increases, which is conducive to improving the reservoir quality, thus leading to higher fluid mobility. However, the appearance of large pore throats also leads to poor pore-throat sorting and an uneven PSD. Therefore, the increase in S p and decrease in α do not necessarily lead to a decreased reservoir fluid mobility. This indicates that the S p and α of pore throats are no longer suitable for evaluating the complexity of PSDs in the tight sandstone reservoirs, which was also discovered by Su [68] and Zhang [69]. The correlations between D t and MFS and MFEP were better than those observed for S p and α (Figure 12g), indicating that the more substantial the heterogeneity of PTSs, the more unfavorable the reservoir fluid flow. Figure 12h and i shows that D 1 was only negatively correlated with MFEP. This negative correlation is due to small pore throat occupying a higher amount of reservoir space compared with large pore throat and is the primary location of movable fluids. Thus, their development degree and heterogeneity controlled the flowability of reservoir fluids.

The aforementioned analysis reveals that the mobility and occurrence characteristics of reservoir fluids are influenced by the heterogeneity of PTSs and reservoir quality, and the complexity of PTS also causes the difference of reservoir petrophysical properties. Therefore, the heterogeneity of PTSs is the main factor controlling fluid mobility in tight sandstone reservoirs. However, the main controlling factors affecting the heterogeneity of PTS in tight sandstone reservoirs have not been clarified. In addition, the correlation between the PTS parameters and MFEP was higher than that between the parameters and MFS, indicating that the MFEP parameter can more accurately reflect the mobility of reservoir fluids.

5.2.3 Effects of different PTS types on reservoir petrophysical properties and fluid mobility

Various pore-throat sizes and types in tight sandstones result in different PTSs. Four types of PTSs (type I to type IV) were observed in the tight sandstone reservoirs, corresponding to different reservoir petrophysical properties and fluid mobility. Figure 13 shows the pore types and a schematic diagram of the fluid seepage capacity corresponding to different PTSs. The type I PTS developed residual intergranular pores with a high average facial ratio and low cement content, and these pores were weakly affected by reservoir cementation. The R a was large and pore-throat connectivity was good, which facilitated the formation of effective seepage channels with high reservoir space and seepage capacity; thus, this type corresponded to the best fluid mobility (Figure 13a and b). The type II PTS had a high dissolution intensity, with more feldspar-dissolved pores and microfractures, and the corresponding PSD range was relatively wide. Meanwhile, this type had the highest W e and a higher MFEP than type I, indicating that feldspar-dissolved pores and microfractures were conducive to improving the fluid mobility of the reservoir (Figure 13c and d). The type III PTS showed a decreased development degree of residual intergranular pores, the reservoir particles were closely arranged, and more cement was developed to fill them compared with the previous types; thus, more bound fluids were formed, which prevented the formation of effective seepage channels (Figure 13e and f). Type IV PTS presents intercrystalline pores as the main pore type and has the smallest R a and highest clay mineral content, which cut and fill the reservoir space and increase pore-throat complexity. This leads to the formation of complex seepage channels and large amounts of bound fluids, thus corresponding to the worst fluid mobility (Figure 13g and h). In summary, from type I to type IV PTS, the R a decreased gradually, the D t and cement content increased gradually, and the corresponding reservoir fluid mobility and seepage ability deteriorated successively.

Figure 13 Pore types and fluid mobility characteristics corresponding to type I PTS (a and b), type II PTS (c and d), type III PTS (e and f), and type IV PTS (g and h).
Figure 13

Pore types and fluid mobility characteristics corresponding to type I PTS (a and b), type II PTS (c and d), type III PTS (e and f), and type IV PTS (g and h).

5.3 Effects of reservoir mineral composition and content on PTS heterogeneity and fluid mobility

5.3.1 Correlation between mineral content and fractal dimension

Figure 14a shows that the correlation between the quartz contents with D 2 and D t is better than that with D 1, indicating that quartz content has a significant influence on the heterogeneity of large pore throat and overall PTS, which is mainly due to the fact that more quartz content promotes the development of residual intergranular pores. Therefore, a higher quartz content can increase the reservoir space and show greater susceptibility to being filled by clay minerals, resulting in more complex PSDs. The feldspar content was only positively correlated with D 1 (Figure 14b), indicating that the complexity of the small pore throat was greatly influenced by the feldspar-dissolved pores. Zhang et al. [4] also found that the complexity of small pore throats, such as nanopores, is related to the development degree of feldspar dissolution pores. The higher the feldspar content of the reservoir, the more favorable the development of dissolved pores and kaolinite intercrystalline pores and the greater the heterogeneity of small pore-throat. A strong positive correlation was observed between D 1 and D t and the clay mineral content (Figure 14c), and a specific positive correlation was only observed between D 1 and illite (Figure 14d), which had the highest relative content, indicating that the occurrence form and filling degree of clay minerals have an essential impact on the complexity of small pore throat and the overall PTS. Therefore, higher clay mineral content and irregular PSDs will lead to higher PTS heterogeneity.

Figure 14 Correlation between fractal dimensions with quartz volume percentage content (a), feldspar volume percentage content (b), total clay mineral content (c), and illite relative content (d).
Figure 14

Correlation between fractal dimensions with quartz volume percentage content (a), feldspar volume percentage content (b), total clay mineral content (c), and illite relative content (d).

5.3.2 Correlation between clay mineral composition and content and fluid mobility

The mass fraction and type of clay minerals significantly affected the connectivity of pore throats, resulting in reservoir fluids presenting a bound state. MFS and MFEP were only negatively correlated with the relative content of illite (Figure 15a) because illite has a strong adsorption on brine [26] and usually appears as curved or filamentous vertical clastic particles that fill the primary pores and throats, thus increasing the roughness of the pore throats and hindering fluid flow and bound fluid formation. Although chlorite and kaolinite fill the pore throats to form more micropores and the reservoir fluid cannot easily flow and is bound by capillary force, their relative content is small and has a weak impact on the fluid mobility. Moreover, the total clay mineral content has a better negative correlation with MFS and MFEP (Figure 15b), indicating that the mutual transformation and interaction between different clay minerals jointly constrain the mobility of reservoir fluids.

Figure 15 Correlation between MFS and MFEP with illite relative content (a), and total clay mineral content (b).
Figure 15

Correlation between MFS and MFEP with illite relative content (a), and total clay mineral content (b).

Based on the aforementioned analysis, the mineral composition and content of the reservoir were the internal factors that affect the microscopic characteristics (PTS parameters) of the reservoir, thereby affecting the macroscopic performance (fluid mobility and petrophysical characteristics) of the tight sandstone reservoirs, and PTS heterogeneity was the main factor affecting the fluid mobility and petrophysical properties of the reservoir. In addition, fluid mobility was comprehensively affected by the reservoir petrophysical properties and total clay mineral content.

6 Conclusion

  1. The pore types of the Chang 7 reservoirs of the Jiyuan area include dissolution pores, residual intergranular pores, intercrystalline pores, and microfractures. Based on the capillary pressure curve morphology, P t, and R a, PTSs can be divided into four types (type I to type IV), among which R a gradually decreases, D t continuously increases, and MFS and MFEP successively deteriorate.

  2. The movable fluid T 2 spectrum was constructed based on the T 2 spectrum obtained before and after NMR centrifugation, and it could represent the spatial distribution of the interconnected pore throats. Combining the PSD data of HPMI and the piecewise fitting method to transform the T 2 spectrum of the movable fluid, the full PSD in the range of 0.001–10 μm can be characterized accurately, and it showed multi-fractal characteristics.

  3. MFEP overcomes the constraints of tiny throats and clay minerals on movable fluid and can quantitatively characterize movable fluid occurring in the effective reservoir space of rock samples. It can be used as a key parameter for evaluating the reservoir fluid mobility.

  4. Small pore-throat had a relatively uniform and regular PSD and was the primary location of movable fluids, and their development degrees and heterogeneities controlled the flowability of reservoir fluids. Moreover, the combination and development of various pore-throat sizes and types resulted in different PTS characteristics in tight sandstone reservoirs, whereas differences in mineral compositions and contents aggravated PTS heterogeneity, which was the main factor controlling the fluid mobility.

Acknowledgement

The author thank China Shaanxi Yanchang Petroleum (Group) Company for providing the samples and related materials.

  1. Author contributions: Quanpei Zhang contributed to the conceptualization and project administration of the manuscript. Hongpeng Qi and Yong Huo contributed to the methodology and supervision of the manuscript. Yong Li and Tao Li contributed importantly to writing, review, and editing. Duoduo Zhang and Kun Lin helped perform data curation. Chen Yang, Jian Tong, and Hui Zhao contributed to the investigation and resources. Busen Suo, Yalan Xue, and Caiping Yi were responsible for data management and algorithms. All authors have read and agreed to the published version of the manuscript.

  2. Conflict of interest: All authors declare that there is no conflict of interest.

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Received: 2023-05-12
Revised: 2023-07-17
Accepted: 2023-08-17
Published Online: 2023-09-28

© 2023 the author(s), published by De Gruyter

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

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https://doi.org/10.1515/geo-2022-0534
Keywords for this article
pore-throat structure; movable fluid; Jiyuan area; fluid mobility; movable fluid effective porosity
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
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  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
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  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
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  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
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  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
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