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Pore Structure Characterization of the Tight Reservoir: Systematic Integration of Mercury Injection and Nuclear Magnetic Resonance Liang Wang,*,† Ning Zhao,† Liqiang Sima,*,† Fan Meng,† and Yuhao Guo† †

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State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, People’s Republic of China ABSTRACT: Pore structure is the most important factor affecting reservoir quality and petrophysical property of tight reservoir. The effective characterization of pore structures, including pore radius distribution (PRD), throat radius distribution (TRD), pore-throat radius distribution (PTRD), relevant pore structure parameters, etc., is of great importance for the oil exploration and exploitation. Taking the tight sandy conglomerate reservoir as research target of tight reservoir, this paper characterizes the pore structures by a combination of experiments on parallel core samples. These experiments include highpressure mercury injection (HPMI), constant-rate mercury injection (CRMI), nuclear magnetic resonance (NMR), as well as microscopic analysis of casting thin sections and scanning electron microscopy (SEM). This paper systematically analyzes the advantages and shortcomings of these commonly used experimental techniques. And then, novel methods are proposed to characterize the pore structure (especially the full-range PRD, TRD, and PTRD) by utilizing the advantages of these techniques. In addition, an advanced pore classification scheme is proposed to reclassify the pore types. Finally, the controls of the pore structure on the flow characteristics are investigated, which in turn further demonstrates the correctness and importance of the proposed novel methods for characterizing pore structures. In summary, this study proposes novel methods to characterize the pore structure by integration of HPMI, CRMI, and NMR and provides insights into the pore structure characteristics of the tight sandy conglomerate reservoir.

1. INTRODUCTION Exploration and development of tight reservoir have recently become the focus of attention with the worldwide growing demand for oil and gas and the continuous advances of relevant technologies in horizontal drilling and fracturing.1−8 As a key type of tight reservoir with huge oil and gas reserves, the sandy conglomerate reservoirs have been found all over the world, such as sandy conglomerates reservoirs in the Alberta and British Columbia Deep Basin of Western Canada,9 Southern Transylvanian Basin of Romania,10 NeogeneQuaternary Granada Basin of Spain,11 and Campos Basin of Brazil.12 Specifically, in China, the tight sandy conglomerate reservoirs are also widely distributed in clastic reservoirs of Mesozoic and Cenozoic continental oil-bearing Basins. At present, large numbers of tight sandy conglomerate reservoirs have been discovered in the northwestern and eastern margin of Junggar Basin,13−18 the Jiyang depression of Bohai Bay basin,19 the Hailar Basin,20 and the northern part of the Songliao Basin.21 However, only the oil exploration in the tight sandy conglomerate reservoir of Baikouquan Formation in Mahu Depression of Junggar Basin has made significant breakthroughs so far.14−17 The tight sandy conglomerate reservoir has special characteristics (e.g., the various pore types, complex porethroat radius and distribution, and discrepancy of pore parameters), which can be summarized as the heterogeneity in pore structure.17,18 The heterogeneous pore structure will greatly affect the characterization of reservoir quality and petrophysical property, and ultimately increases the risks of oil exploration and exploitation. Thus, good knowledge of the pore structure is necessary for the oil exploration and exploitation in the tight sandy conglomerate reservoir. © XXXX American Chemical Society

Commonly, the pore structures, defined as the pore types, porosity, pore radius distribution (PRD), throat radius distribution (TRD), pore-throat radius distribution (PTRD), etc., can be characterized by three kinds of techniques: image analysis,1,22−25 intrusive techniques,2,26,27 and nonintrusive techniques.23,28−31 The image analysis including optical microscope of casting thin sections (CTS) and SEM enable researchers to observe the pore types qualitatively and directly but has drawbacks in the prediction of pore and throat radius and their radius distribution.22,25 The intrusive technique mainly refers to the traditional HPMI, which can obtain the pore-throat radius and PTRD by the Washburn equation.26,27 Typically, the HPMI technique has advantages in detecting the nanoscale pore and throat because of its high pressure. However, the HPMI is usually affected by the pore-blocking effect in tight reservoirs,4,32−35 which leads to the limitations in detecting the large pores. Additionally, the traditional HPMI cannot distinguish between the pore and throat. To overcome the limits of HPMI in the detection of the pore and throat, the CRMI is proposed by Yuan and Swanson.36 Unlike conventional mercury injection, CRMI injects mercury into rock pores at an extremely low constant rate (typically 0.00005− 0.0001 mL/min).37 During the injection, the periodically rising and falling of pressures aids in distinguishing the pores and throats in the pore system.38 Thus, the capillary pressure curves of pore and throat are obtained. And then, the PRD, TRD, together with the pore to throat radius ratio distribution, Received: April 18, 2018 Revised: June 8, 2018 Published: June 19, 2018 A

DOI: 10.1021/acs.energyfuels.8b01369 Energy Fuels XXXX, XXX, XXX−XXX

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Figure 1. Location of the Mahu Depression and the stratigraphic division of the Baikouquan Formation sequence (modified by Tan et al.18).

can be summarized that all the experimental methods mentioned above have advantages but also limitations in characterizing the pore structures. Thus, to better characterize the pore structures of the tight sandy conglomerate reservoir, a combination of these techniques should be considered, and advanced characterizing methods should be proposed. As to the tight sandy conglomerate reservoir, especially for the reservoir of Baikouquan Formation in Mahu Depression of Junggar Basin, studies including sedimentary,15,17 geochemical characteristics of the reservoir, oil accumulation conditions,14 and lithofacies division,18 have been carried out. But, the pore structure of tight sandy conglomerate reservoir and its controls on the flow characteristics have not been studied yet. Thus, taking the tight sandy conglomerate reservoir of Baikouquan Formation as a research target, this paper aims to investigate the pore structures and its controls on the flow characteristics comprehensively by a combination of CTS, SEM, NMR, HPMI, and CRMI techniques. Specifically, the major objectives of this work are to (1) characterize pore types, pore geometry, and surface porosity (the ratio of pore area to the CTS area) by the observations of CTS and SEM images; (2) propose advanced methods to characterize PSD, TRD, and PRD by the integration of HPMI, CRMI, and NMR; (3) establish an effective pore classification scheme for the tight

can be automatically calculated during the experiment with the help of specialized computer software.26,36−41 However, due to the limited maximum injection pressure of CRMI (∼6.22 MPa), the TRD with radius lower than 0.12 μm cannot be comprehensively detected.39,40 In addition, the PRD exaggerates the pore radius calculated as an equivalent spherical radius.32−34 The nonintrusive techniques, which include smallangle and ultrasmall-angle neutron scattering (SANS and USANS), nano/microcomputed tomography (CT), and NMR, can reflect the interconnected and isolated pores. However, SANS and USANS also have a limitation in the detecting radius scopes and can only detect the pores with pore diameter smaller than 200 nm.30,31 The CT enables the pore network to be analyzed in a three-dimensional image, but its pervasive application is limited because of its high cost, limited resolution scales, and extremely small sample size.23,28,29 The NMR, a fast and nondestructive method, overcomes the limitations of CT, SANS, and USANS in their detecting scopes and can better reveal pores with pore radius ranging from nanoscale to millimeter-scale.42−47 However, like HPMI, the NMR still cannot separate the throat and pores. On the basis of the data obtained by the above experimental methods, the fractal and multifractal geometries have been increasingly applied in various fields of geoscience and serve as an important approach to pore structure characterization.48−50 It B


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Energy & Fuels Table 1. Petrophysical Properties of Core Plug and Their Corresponding Surface Porosity of Chips IntarG (%) core no.

Øhe (%)

Khe (mD)

Inter-G (%)

IntarG-D

IntarG-F

Intra-M

InterC (%)

M139-4 M139-9 FN10-9 M139-3 M154-6 X90-1 FN10-23 X723-4 FN10-7 M13-3 M152-3 M152-15 X90-3 X92-1 M137-2 X94-2 FN10-4 M16-2 M133-2 M134-1 M134-6 M139-6 M15-5 M152-6 average

12.63 12.21 9.63 16.41 10.61 7.90 9.82 8.38 9.02 10.58 11.45 8.06 8.31 6.56 10.14 7.03 10.15 8.63 8.19 10.02 6.77 9.87 7.92 10.91 9.63

0.351 0.079 0.020 60.557 0.164 0.113 4.688 0.746 0.215 0.098 0.208 0.030 0.067 0.107 0.108 0.125 0.311 0.089 0.262 0.139 0.040 0.217 0.015 0.045 0.330

3.00 2.00 − 8.00 1.00 0.50 4.00 2.00 0.50 0.10 5.10 0.20 1.00 0.00 0.20 0.10 0.00 0.00 1.00 0.80 0.00 − 0.20 0.50 1.37

1.00 0.50 − 3.00 1.50 1.00 0.50 2.00 1.50 0.00 0.80 0.50 1.00 1.00 1.00 0.50 1.50 0.00 1.00 2.00 0.00 − 0.30 0.80 0.97

1.50 0.50 − 0.00 1.00 0.00 0.00 0.50 0.00 0.20 0.50 0.00 0.50 0.00 0.50 0.00 0.00 0.10 0.50 0.00 0.20 − 0.10 0.60 0.30

0.60 0.50 − 0.00 0.00 0.00 3.00 0.00 0.00 0.00 0.00 1.00 0.00 0.10 2.20 0.00 0.00 0.00 0.00 0.00 0.00 − 0.00 0.50 0.36

0.50 0.00 − 1.00 0.50 0.50 0.00 1.00 1.00 0.00 1.50 0.50 0.50 0.00 0.00 0.10 0.00 0.00 0.00 1.00 0.00 − 0.00 1.30 0.43

lithology sandstone

fine conglomerate

middle conglomerate

reservoirs of sandy conglomerates are mainly developed in the medium and bottom of Baikouquan Formation (T1b2 and T1b1).16

sandy conglomerate reservoir; and (4) reveal the controls of pore structures on the flow characteristics.

2. GEOLOGICAL SETTING Geographically, the Junggar Basin is located in northwestern China with an area of approximately 134000 km2 (Figure 1),14 and the Mahu Depression is located on the northeastern border of the Junggar basin (Figure 1). The Mahu Depression in this study spans approximately 50 km from east to west and 100 km from north to south, with an area of approximately 5000 km2. Structurally, the Junggar Basin comprises six firstorder tectonic units (i.e., Luliang Uplift, Wulungu Depression, West Uplift, East Uplift, Central Depression, and North Tianshan thrust Belt).18 The Mahu Depression belongs to the Central Depression, bordering the Wuxia fault zone to the east, and lying adjacent to the Zhongguai uplift to the south (Figure 1).13 The Mahu Depression is characterized by a monocline that gently dips to the southeast, and includes some lowamplitude anticlines and noselike structures. The drilling in the Mahu Depression reveals that the vertical sequence of strata in this area primarily comprises Carboniferous volcanic rocks at the bottom that are sequentially overlain by the Permian Formations, the Triassic Formations, the Jurassic layers, the Cretaceous layers, and the Pale-Neogene Formation (Figure 1).14 The Baikouquan Formation of lower Triassic is nonconformity contacting with the underlying Wuerhe Formation of Permian. In addition, the Baikouquan Formation can be further divided into three members, and from bottom to top these are referred to as Bai 1 Member (T1b1), Bai 2 Member (T1b2), and Bai 3 Member (T1b3).14 The Baikouquan Formation is a shallow fan delta depositional system with sandy conglomerate well developed.15,17 The oil-bearing

3. SAMPLES AND EXPERIMENTAL MEASUREMENTS 3.1. Samples. The special characteristic of the sandy conglomerate reservoir in Baikouquan Formation is its wide range of granule sizes. In accordance with the Chinese National Standard (GB/T) 17412.2−1998, the sandy conglomerate of Baikouquan Formation can be further divided into four kinds: thin-layer sandstone, fine conglomerate, middle conglomerate, and coarse conglomerate.51 The oil-bearing reservoirs are usually found in the thin-layer sandstone, fine conglomerate, and middle conglomerate. To ensure the representativeness of the cores samples, 24 core samples with different granule sizes and flow characteristics were chosen for conducting experiments (Table 1). 3.2. Experimental Measurements. To ensure the comparability of experimental results among different measurements, each sample was cut into chips and plugs and followed a specially designed experimental process. The chips of core samples were taken by CTS and SEM analysis. Core plugs were used for petrophysical parameters, NMR, HPMI, and CRMI analysis. The process of the specially designed experiments on core plugs experienced the following steps: (1) before the experiments, the two sides of the all samples were polished and then cleaned to remove the remnants of the reservoir fluids or drilling mud inside the core samples. (2) Petrophysical parameters in terms of helium porosity (Øhe) and Klinkenbergcorrected permeability (Khe) were determined after samples were dried at a temperature of 110 °C until the weight of the core samples were not to change. (3) Six core samples representing sandstone, fine conglomerate, and middle conglomerate were selected and fully saturated under a confining pressure of 30 MPa for 48 h. (4) After the full saturation of the core samples, the T2 spectra and NMR porosities were obtained. And then, all movable water inside the pores were fully centrifuged, in which state the T2 spectra and NMR porosities were obtained. (5) After the NMR measurements, each of the six core C


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Energy & Fuels plugs was cut into two parts to conduct the HPMI and CRMI measurements, respectively. 3.2.1. CTS and SEM Analysis. The CTS, which was impregnated with blue-dye resins, were observed by the LEICA DMRXHC polarized light microscope produced by the Germany LEICA Company. SEM observation was analyzed by the FEI Quanta 400 FEG SEM manufactured by the Czechic FEI Company. These two measurements can provide the information on pore types, pore geometry, and surface porosity characteristics. 3.2.2. NMR Experiments. The NMR measurements were carried out with the 2 MHz Lim-MRI-D2 spectrometer produced by Beijing Limecho Technology Co., Ltd. The Carr−Purcell−Meiboom−Gill echo trains were used in the measurement, and then Butler-ReedsDawson algorithm was adopted for the T2 spectrum.44 To enhance the signal-to-noise ratio of the NMR signals, the numbers of scan and echoes were set as 64 and 4096, respectively. Hinai et al.43 and Xiao et al.32 reported that when the echo spacing time is over 0.2 ms, NMR cannot detect the fast relaxation and is insufficient to reflect the nanopore and throats. Thus, to obtain the fast relaxation components and maximum recovery of the polarized NMR longitudinal relaxation time signal, the echo time and waiting time were chosen as 0.1 ms and 6 s, respectively. 3.2.3. HPMI Measurements. The HPMI was performed on a PoreMaster-9500 mercury porosimeter following the Chinese Oil and Gas Industry Standard (SY/T) 5346−2012. Maximum intrusion pressure can reach as high as 200 MPa, which corresponds to a porethroat radius of about 3.6 nm.41 After reaching the highest injection pressure, the pressure will slowly decrease with the mercury extrusion. 3.2.4. CRMI Measurements. The CRMI apparatus used in this research was the ASPE-730 Automated Pore System Examination. The contact angle is 140°, and the interfacial tension, 485 dyn/cm. The CRMI experiment was conducted by the Chinese Oil and Gas Industry Standard (SY/T) 5346−2005. The maximum intrusion pressure is 6.2 MPa, which corresponds to the minimum value of throat radius (0.12 μm).39,40

which indicates that the permeability is not only influenced by porosity but also pore network characteristics.25,52 4.2. Pore Types, Pore Geometry, and Surface Porosity Observed from CTS and SEM Images. CTS and SEM images show that four different kinds of pore types, with pore size ranging from nanoscale to microscale, are developed in the sandy conglomerate reservoir. The four pore types dominate the pore systems including residual intergranular pore (InterG) (Figure 3a), intercrystalline pores (InterC) (Figure 3, panels b and c), intragranular dissolved pores (IntraG) (Figure 3, panels d−g), and microfractures (MicroF) (Figure 3h).

4. RESULTS 4.1. Petrophysical Parameters of Core Samples. Table 1 shows that the Øhe ranges from 6.56% to 16.41% with an average of 9.63%, whereas Khe was in the range of 0.015− 60.557mD with a logarithmic average value of 0.330mD. In accordance with the comprehensive classification results of PetroChina,39,51 the sandy conglomerate reservoir of Baikouquan Formation can be classified into the tight reservoir, since the average porosity and permeability are lower than the standers of 10% and 1mD, respectively. The large discrepancy of permeability versus porosity and their poor correlation (R2 < 0.4) implies heterogeneity in pore structures (Figure 2). For the same porosity value, an order of magnitude variation in permeability is observed (Figure 2),

Figure 3. Typical pore types found in the core samples of the tight sandy conglomerate reservoir. (a) Inter-G highlighted with blue-dye resin, M152-3; (b) InterC among the kaolinite crystals, M152-6; (c) InterC among the kaolinite crystals, M152-6; (d) IntraG-D formed by the dissolution of partial debris, M139-3; (e) IntraG-F formed by the dissolution of partial feldspar, M154-6; (f) IntraG-F formed by the dissolution of partial feldspar, M154-6; (g) IntraG-M formed by the dissolution of partial matrix M139-7; and (h) MicroF along the edge of conglomerate or through the conglomerate, FN11-3.

Inter-G is the primary intergranular pore that remains after the compaction and cementation process. This kind of pore, commonly found in sandstone and fine conglomerate, have features of triangular or polygonal shape and relatively larger pore radius and pore connectivity than other pore types (Figure 3a). The pore radius of Inter-G mainly ranges from 10 to 80 μm. InterC, which destructs the primary intergranular

Figure 2. Plots of Øhe vs Khe for all core samples. D


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Figure 4. NMR T2 spectra of core samples; (a) T2 spectra of core samples in the fully saturated state and (b) T2 spectra of core samples in the centrifuged state

Figure 5. NMR parameters derived by T2 spectrum. (a) The T2cutoff determination method by NMR experiment of sample X723−4. (b) Cross plots of ØNMR vs Øhe.

Table 2. Petrophysical Parameters and NMR Parameters for All Samples core no.

diameter (cm)

length (cm)

Øhe (%)

Khe (mD)

ØNMR (%)

ØBVI (%)

ØFFI (%)

Swi (%)

T2 lm (ms)

T2cutoff (ms)

FN10-23 X723-4 M137-2 X94-2 M152-6 M15-5

2.51 2.51 2.54 2.51 2.53 2.51

3.74 4.55 4.60 4.48 4.58 4.61

9.82 8.38 10.14 7.03 10.91 7.92

4.688 0.746 0.108 0.125 0.045 0.015

10.66 8.36 9.64 7.00 10.11 7.83

4.79 4.28 5.56 3.41 6.11 5.62

5.87 4.08 4.08 3.59 4.00 2.21

44.93 51.20 57.68 48.71 60.44 71.78

1.603 2.169 1.891 1.048 1.899 0.627

0.870 1.520 2.009 0.756 1.748 0.870

pore obviously with pore radius usually lower than 0.2 μm, is often found among the kaolinite crystals (Figure 3, panels b and c). IntraG is commonly formed by the dissolution of partial debris (IntraG-D) (Figure 2d), feldspar (IntraG-F) (Figure 3e and f), and matrix (IntraG-M) (Figure 3g). The radius of IntraG varies dramatically and mainly ranges from 0.1 to 20 μm. Generally, the radius of IntraG relates to the subpore types, following the order of IntraG-M < IntraG-F < IntraG-D. These subpores show irregular geometry, and their connectedness are relatively poor. The shapes of these pores are mainly dependent on dissolution degree and their origin. MicroF with microscaled in length and nanoscaled in width, which could not be observed in cores,1,8,25,53 can be detected under microscopic observations. The MicroF usually develops along the edge of conglomerate or through the conglomerate (Figure 3h); however, the MicroF is not well-developed and seldom found in the observation of the CTS images. The surface porosities of the different kinds of pore types from the CTS images are displayed in Table 1. 4.3. Pore Structure Characterized by NMR Measurements. 4.3.1. NMR T2 Spectra Characteristics. The NMR T2 spectra of the six core samples in fully saturated, and centrifuged states are displayed in Figure 4 (panels a and b,

respectively). The T2 spectra of core samples in the fully saturated state show bimodal characteristics with two spectra peaks distributed in the ranges of 0.01−10 ms and 10−1000 ms, respectively (Figure 4a). Since long T2 value represents macropores and short T2 value represents micropores,54−57 it can be concluded that both macropores with T2 in range of 10−1000 ms and micropores with T2 in range of 0.01−10 ms are developed in the core samples. The values of incremental porosity with T2 greater than 10 ms are obviously smaller than that with T2 less than 10 ms, which implies that the micropores are better developed than the macropores. Additionally, the left peak shows a relatively wider peak distribution ranging from 0.01 to 10 ms than that of the right peak ranging from 10 to 100 ms. The comparison of the distribution ranges of the two peaks indicates that the micropores have much wider pore radius ranges than that of macropores. The T2 spectra of core samples in the centrifuged state display unimodal characteristics with spectra peaks distributed in the ranges of 0.01−10 ms (Figure 4b), which implies only the irreducible water are left in micropores after the centrifugation. 4.3.2. NMR Parameters. By accumulating the incremental porosity of T2 spectra in the fully saturated state, the cumulative porosity will reach a maximum value, which is E


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Figure 6. (a) Characteristics of HPMI curves and (b) throat radius distribution.

Table 3. Pore Structure Parameters of the Core Samples Derived from HPMI Tests core no.

Pd (MPa)

rmax (μm)

ra (μm)

P50 (MPa)

r50 (μm)

Shgmax (%)

Shgre (%)

FN10-23 X723-4 M137-2 X94-2 M152-6 M15-5 average

0.11 0.68 0.27 0.67 0.67 0.68 0.51

6.68 1.09 2.76 1.09 1.09 1.09 2.30

1.13 0.17 0.41 0.12 0.17 0.15 0.36

25.34 14.00 13.40 37.71 10.75 41.40 23.77

0.03 0.05 0.06 0.02 0.07 0.02 0.04

91.02 95.72 87.84 88.74 93.55 85.72 90.43

65.27 51.42 62.41 55.02 57.26 55.15 57.76

and the Shgmax value of X723-4 even reaches up to 95.72%. In addition, extrusion curves display high amounts of residual mercury saturation (Shgre) with an average value of 57.7%, implying larger discrepancy between pores and throats which result in the high content of mercury trapped in the pores.8,25,38 On the basis of the Washburn equation and HPMI curves, the TRDs for all core samples are calculated and shown in Figure 6b. The results show that the throat radius of core samples distributes in the range of 0.0037-10 μm. However, the TRDs show different characteristics. The core samples of M15-5, X94-2, X723-4, FN10-23, and M137-2 show bimodal characteristics with two peaks distributed in ranges of 0.00370.1 μm and 0.1-10 μm, respectively. The bimodal characteristics of the TRDs in peak amplitudes and peak distribution ranges are consistent with the characteristics of NMR spectra. Compared with the bimodal characteristics of TRDs of the majority of core samples, the core sample of M152-6 display unimodal characteristics with spectra peaks mainly distributed in the ranges of 0.02−0.2 μm. 4.4.2. Pore Structure Parameters Derived from HPMI. Quantitative pore structure parameters such as entry pressure (Pd), maximum throat radius (rmax), medium throat radius (r50), medium throat pressure (P50), average throat radius (ra), maximum mercury intrusion saturations (Shgmax), and residual mercury saturation (Shgre) are derived from HPMI measures. The Pd ranges from 0.11 to 0.676 MPa, corresponding to the rmax in range of 6.68−1.088 μm. The r50 and ra are in the ranges of 0.019−0.07 μm and 0.118−1.127 μm, respectively, implying poor pore structures in throat radius. The average Shgre of 57.76% fits well with the average Swi value of 55.79% obtained by the NMR tests (Table 3). 4.5. Pore Structure Characterized by CRMI Measurements. 4.5.1. Characteristics of CRMI Curves. Unlike HPMI, CRMI can measure the pores and throats separately. Thus, the intrusion curves of total, pore, and throat, are recorded

regarded as the NMR porosity (ØNMR). For instance, the NMR porosity of sample X723-4 in Figure 5a is 8.36%. The comparison of the ØNMR and Øhe shows that the NMR porosities are consistent with the helium porosities (Figure 5b), which demonstrates that the diffusion effects can be neglected,58,59 and the measuring parameters are reasonable.32,43,44 The corresponding T2 value of the crossing point in Figure 5a is defined as the T2cutoff value (Figure 5a). The T2cuttoff values are not a fixed constant value and vary in the ranges of 0.87−2.0 ms (Table 2), which implies the complexity of pore-throat distributions. The T2cutoff can separate the total pore volume into the moveable fluid volume (FFI) and immovable fluid volume (BVI).60,61 By accumulating the incremental porosity of T2 spectra in the centrifuged sate, the irreducible fluid porosity (ØBVI) can be obtained. Moreover, by subtracting the BVI volume from the ØNMR, the moveable fluid porosity (ØFFI) can be further obtained and shown in Table 2. Beside ØNMR, T2cutoff, ØFFI, and ØBVI, the immovable fluid saturation (Swi) and the T2 logarithmic mean value (T2lm) are also calculated. The Swi, representing the ratio of ØBVI to ØNMR, are in the range of 44.93−71.78% with an average value of 55.79%. The high values of Swi are mainly attributed to the high amount of micropores in tight sandy conglomerate reservoirs as discussed above. The T2lm range from 0.627 to 2.169 ms with an average of 1.539 ms. 4.4. Pore Structure Characterized by HPMI Measurements. 4.4.1. Characteristics of HPMI Curves and TRDs. The HPMI curves of the six core sample with different porosities and permeabilities are shown in Figure 6a. The mercury intrusion curves display that the maximum mercury intrusion saturations (Shgmax) for all core samples exceed 85%, that is, the mercury can enter most of the pore systems when the injection pressure reaches up to a high pressure of 200 MPa. The core samples with higher permeabilities display relative higher Shgmax values. For instance, the Shgmax values of FN1023 and X723-4 whose permeabilities over 0.5mD exceed 90% F


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different porosity and permeability values have similar characteristics with an average value of 125.93 μm. The pore radius ranges from 100 to 300 μm with the main peaks in ranges of 100−200 μm (Figure 7b). Unlike the PRD, the TRDs show variations among core samples with different characteristics in spectral peaks and radius ranges, which seems to relate to the variety of permeabilities of core samples (Figure 7c). The core samples of X723-4 and FN10-23 with relative higher permeability values have larger average values of throat radius, wider peak ranges, and peak shifts of TRD to the larger radius. In addition, core samples with relative larger permeabilities show smaller values of pore throat radius ratio, narrow distributions of pore throat radius ratio, and peak shifts to the smaller value of the pore throat radius ratio (Figure 7d). The distribution of pore throat radius ratio commonly ranges from 100 to 600 with average values in the range of 230.99− 413.45. The high value of the pore throat radius ratio implies the great discrepancy of the pore and throat radius.32,33 4.5.3. Pore Structure Parameters Derived from CRMI. After the calculation of the PRDs and TRDs by their corresponding intrusion curves, the more comprehensive PTRDs can be obtained by the overlapping of the PRDs and TRDs. The PTRDs derived from the CRMI curves show bimodal characteristics, that is, the PRDs (right peaks) in the range of 100−300 μm and TRDs (left peaks) in the range of 0.1−2 μm, respectively (Figure 7e). On the basis of the CRMI curves and PTRDs displayed above, quantitative pore structure parameters such as entry pressure (Pd), maximum throat radius (rmax), average throat radius (rta), average pore radius (rpa), maximum mercury intrusion saturations of pores and throat (Sp and St, respectively), and average pore throat radius ratio (ηa) are calculated and shown in Table 4. The Pd ranges from 0.032 to 0.065 MPa, corresponding to the rta in ranges of 22.82−11.23 μm. The rta ranges from 0.37 to 2.27 μm with an average value of 1.16 μm. The average value of Sp (32.12%) is much bigger than that of St (22.19%), which means that the pore body has more contribution to the porosity of the sandy conglomerate reservoirs in the detecting radius ranges of CRMI tests.

simultaneously. The CRMI curves of the core samples have similar characteristics, which is illustrated by the core sample of M137-2 (Figure 7a). The trend of the throat intrusion curve is

Figure 7. Results of HPMI measurements: (a) the CRMI curve of core sample M137-2; (b) the PRDs derived from CRMI curves; (c) the TRDs of core samples; (d) the pore to throat radius ratio of core samples; and (e) the PTRDs derived by the combination of TRDs and PRDs.

consistent with the total intrusion curve at the early stages of mercury intrusion when the injection pressures are lower than 2 MPa. With the increase of pressure, the throat intrusion increases continuously, while the increment of pore body intrusion only occurs in a narrow pressure range, indicating that pore bodies revealed by CRMI are mainly connected by a small number of relatively large throats.32,33 4.5.2. Characteristics of PRD, TRD, and Pore to Throat Distribution. On the basis of the CMRI curves, the pore radius, and throat radius, as well as the distributions of pore throat radius ratio of core samples, are calculated and displayed in Figure 7 (panels b−d). The PRD of the core samples with

5. DISCUSSION 5.1. Comparison of the HPMI and CRMI Results. Figure 8 shows the comparison of the CRMI and HPMI curves. It can be seen from Figure 8 that the mercury injection curves of CRMI and HPMI show a similar trend when the injection pressure exceeds 2 MPa, while displaying a great discrepancy when the injection pressure is lower than 2 MPa. The discrepancy between HPMI and CRMI might be caused by the following two reasons: (1) the injection velocity for CRMI is a quasi-static constant value that keeps the interfacial tension and contact angle constant. However, the high injection

Table 4. Pore Structure Parameters of the Core Samples Derived from CRMI Tests core no.

rta (μm)

rpa (μm)

rmax (μm)

ηa

Sf (%)

Sp (%)

St (%)

Pd (MPa)

FN10-23 X723-4 M137-2 X94-2 M152-6 M15-5 average

1.26 0.56 0.37 0.67 1.81 2.27 1.16

121.02 125.31 128.53 127.07 125.95 127.71 125.93

19.38 11.23 11.99 12.54 22.82 22.02 16.66

230.99 303.43 413.45 357.74 397.19 319.95 337.13

60.00 52.07 50.09 44.19 36.09 48.27 48.45

33.42 30.67 32.43 29.10 20.14 27.16 28.82

26.58 21.40 17.66 15.09 15.95 21.12 19.63

0.038 0.065 0.061 0.059 0.032 0.033 0.048

G


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Figure 8. Comparison of the CRMI and HPMI curves, taking the core sample of M137-2 as an example.

Figure 9. Advanced full-range TRDs by integration of CRMI and HPMI.

velocity in HPMI may make the contact angle change, creating an inconsistency. (2) Additionally, the compressibility effects between gravels at the high injection pressure of HPMI can cause the capillary pressure curves to shift. Moreover, the radius distribution derived from HPMI is obviously influenced by the pore-blocking effect;38,62 that is, the HPMI neglects the large pores. Many large pores with pore radius larger than 1 μm or even exceeding 100 μm, which are associated with the Inter-G and IntraG and observed by the TCS and SEM images (Figure 3), cannot be detected by the HPMI (Figure 6b). These large pores are usually connected by narrow throats. During the process of mercury injection, the high injection pressure is needed to break through the narrow throat, which corresponds to a small radius value when calculated by the Washburn equation.2,26,41 The CRMI can effectively overcome this problem caused by the pore-blocking effect by revealing the distribution of pore body and throat separately.36 The right peaks of PTRDs derived from the CRMI curves show the characteristics of big pores (Figure 7e). However, due to the limited injection pressure of CRMI, the left peaks of PTRDs cannot reflect the throat with radius lower than 0.12 μm. The HPMI has advantages in detection of the throat with a throat radius lower than 0.12 μm due to its high mercury injection pressure. The comparisons of the average maximum mercury intrusion saturations of CRMI and HPMI further demonstrates the insufficiency of CRMI in the radius detecting range of pore and throat since its average value of Sf (48.45%) is lower than the average value of Shgmax (90.43%). 5.2. Full-Range TRD Calculated by Integration of CRMI and HPMI. The comparison of HPMI and CRMI results demonstrates that it is inadequate to characterize the TRD by either CRMI or HPMI tests since they have advantages but also disadvantages in detecting the throat. Thus, to fully reveal the TRD of core samples, the advantages of CRMI and HPMI in detecting different scopes of throat radius should be comprehensively utilized. By integration of the radius lower than 0.12 μm calculated by HPMI and throat larger than 0.12 μm derived by CRMI, the advanced full-range TRDs are proposed and displayed in Figure 9, which show a bimodal characteristic with two peaks distributed in ranges of 0.0037−0.1 μm and 0.1−2 μm, respectively. The left peaks derived by HPMI have a relatively wider radius range than that of right peaks calculated by the CRMI, implying the complexity of tiny throat in the tight sandy conglomerate reservoir. Additionally, the incremental mercury injection saturations of the left peaks are higher than that of right

peaks, which indicates that the overall throats of core samples are dominated by the tiny throat. The comparison of T2 spectra and TRDs shows that they have similar characteristics in peak amplitude and distributions. 5.3. Full-Range PTRD Calculated by Integration of NMR and TRD. In accordance with the theory of NMR, the T2 value of the actual reservoir has a relationship with the pore-throat radius (rpt).40,42,54−56 The relationship between T2 and rpt is commonly expressed as T2 = C × rpt n

(1)

Where C = 1/(ρ2 × Fs), which can be regarded as a constant parameter for a particular reservoir; Fs is the pore shape factor, dimensionless; ρ2 is the surface relaxivity, μm/ms. rpt is the pore-throat radius, μm; n is the power exponent, dimensionless. Eq 1 shows that the NMR T2 spectrum can be transformed to PTRD if the values of C and n are calibrated and determined with the help of other experimental methods. At present, there are two major types of methods to calibrate the T2 spectrum to PTRD. (1) Zhang et al.26 and Xiao et al.32−35 use the TRD derived by CRMI to calibrate T2 peaks, that is, the T2 peaks with high magnitudes and short relaxation time correspond to the dominant throat peaks of TRD calculated by CRMI. This method will mismatch the peaks of the T2 spectra to the right peaks of TRDs in Figure 9, and overlarge the PTRD. (2) Huang et al.40 and Xiao et al.63 used the whole TRD derived from HPMI to calibrate the T2 spectrum. As mentioned above, the TRD derived from HPMI at low mercury injection pressures is influenced by the pore-blocking effect and neglects the large pores. Thus, the PTRD derived from the T2 spectrum by the calibration of the whole TRD of HPMI will be indirectly influenced by the poreblocking effect and ultimately miss the information on large pores. In this study, novel method and procedures are proposed to overcome the shortcomings of the above calibration methods. The specific method and procedures to convert the T2 spectrum into the PTRD by the calibration of full-range TRD are listed as follows: (1) the incremental mercury injection saturation of TRD in Figure 9 is multiplied by Øhe to obtain the incremental mercury injection porosity. And then, the incremental porosity is cumulated to obtain the cumulative porosity (Figure 10a). In the same way, the cumulative porosity of T2 spectrum can be obtained by the cumulation of incremental porosity (Figure 10a). (2) Figure 10a shows that the trends of the cumulative porosity of mercury and T2 spectrum match well with each other when the throat is lower H


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Figure 10. Methods for characterizing the full-range PTRD by the integration of NMR and TRD, taking the M152-6 sample as an example: (a) data selection; (b) correlation of pore-throat radius and T2 value; (c) comparison of the PTRD and TRD; and (d) the PTRDs of all core samples calculated from NMR spectra.

radius is lower than 0.12 μm (Figure 10c), which demonstrated the property of the calibration of the NMR by TRD. In the same way, the calibration equations for all core samples can be obtained, and then the PTRDs are calculated and displayed in Figure 10d. 5.4. Comparison of the CRMI and Microscopic analysis of CTS and SEM. The comparison of pore radius obtained by CRMI and microscopic analysis of SEM images shows that the pore radius they detected exhibit two conflicting points: (1) the overall pore radius derived from CRMI is larger than 100 μm with an average value of 125.93 μm (Figure 7b and Table 4). But the observation of CTS and SEM images shows that only Inter-G has the pore radius larger than 100 μm, and the pore radius for parts of the Inter-G are even smaller than 100 μm. (2) The PRD shows that the pores with pore radius in the range of 1−100 μm are seldom developed (Figure 7b), whereas these pores are commonly found in the Inter-G and IntraG (Figure 3). The comparison above indicates that the CRMI only reflect the big pores and even exaggerate the radius of small pores. This point of view can be further demonstrated by the comparison of the pore radius of tight conglomerate reservoir of Baikouquan Formation in this study and sandstone reservoirs of the Cretaceous Shahejie and Denglouku Formation,32−34 Cretaceous Quantou Formation of Songliao Basin,37 and Middle Permian Shihezi Formation of the Ordos Basin.38 The tight conglomerate reservoir and sandstone reservoirs have different sedimentary environments, diagenetic evolutions, and petrophysical parameters. They should display inconsistent pore radius and distributions. However, they have similar characteristics of PRD with pore radius in ranges of 100−300 μm. Thus, the PRD derived from CRMI characterizes the pore radius incorrectly, and it is necessary to propose a

than 0.12 μm, whereas an obvious discrepancy exists between the cumulative porosity of mercury and T2 spectrum when the throat is larger than 0.12 μm. It can be explained as follows: when the micropores with pore radius lower than 0.12 μm, the pore and throat have similar characteristics, and it is hard to distinguish the pore from the throat. The micropores with pore radius lower than 0.12 μm can be regarded as throat in the HPMI measurements.4,32−34,40 Since the T2 spectra also detect the pore and throat information, the cumulative porosity of mercury with radius lower than 0.12 μm and T2 spectrum reflect the whole micropore and throat information, which lead to similar trends of the cumulative porosity of mercury and T2 spectrum. When the throat is larger than 0.12 μm, the cumulative porosity of mercury obtained by the CRMI measurement only reflects the throat information, but the cumulative porosity of the T2 spectrum reflect the combined information on pores and throats, which results in the trend discrepancy between these two kinds of cumulative porosity. The analysis above further demonstrates that only the TRD in Figure 9 with radius lower than 0.12 μm can be used to calibrate the T2 spectrum since they reflect the same pore and throat information. The TRD in Figure 9 with throat larger than 0.12 μm should be excluded from the calibration. (3) When the throat radius is lower than 0.12 μm, for a certain cumulative porosity of mercury and T2 spectrum (Ø(i)), the corresponding rpt(i) and T2(i) can be obtained. Taking the logarithm of rpt(i) and T2(i) and fitting the data by the linear least-square method, the C and n can be determined. In Figure 10b, the C and n for the core sample of M152-6 are 0.036 and 1.489, respectively. After the parameters C and n are determined, the NMR T2 spectrum can be transformed to PTRD by 1. The PTRD of core sample M152-6 matches well with the TRD when the I


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the above two schemes are extremely disproportionate (e.g., the macropores in IUPAC classification scheme and nanopores in Loucks classification scheme occupy extremely large proportions), while the other pores only account for small percentages; (2) the schemes cannot be associated with the pore types observed by the CTS and SEM images; and (3) the schemes do not take into account the relationships between the classified pores and flow characteristics. To better reveal proportionate pores with different porethroat radius range and taking into consideration the microscopic results of CTS and SEM, the pore systems are classified into three kinds by PTRD: pore type I (PI) (porethroat radius more than 1 μm), pore type II (PII) (pore-throat radius in the range of 0.1−1 μm), and pore type III (PIII) (pore-throat radius less than 0.1 μm). The relative proportions of different pore types are calculated by the PTRDs and shown in Table 5. The PI accounts for 16.82% of the pore system for

new method to derive the PRD of the tight conglomerate reservoir. 5.5. Advance PRD Calculated by Integration of PTRD and TRD. The shortcomings of the PRD derived from CRMI in only reflecting the big pores and even exaggerating the radius of small pores mentioned above can be overcome by the integration of PTRD and TRD. The advanced PRD can be obtained by the following two steps: (1) the remaining region between the PTRD and TRD in Figure 10c reflects the pore bodies; thus, the cumulative PRD can be determined by the subtracting TRD from PTRD; (2) the PRD can be further obtained by resampling the cumulative PRD (Figure 11). In

Table 5. Pore Classification and Pore Structure Parameters Derived from PTRDs, TRDs, and PRDs

Figure 11. Advanced PRD of core samples by integration of PTRD and TRD.

Figure 11, secondary peaks of PRD in the range of 0.12−0.3 μm occur in all core samples, which are likely related to the membrane irreducible-water retained in larger pores associated with clay surfaces.60,64 Compared with Figure 7b, the newly obtained PRD shows a wide range of pore radius with pore radius ranging from 0.12 μm to 1 mm. The newly obtained PRD overcomes the shortcoming of that derived by CRMI. It reflects not only the big pores (Inter-G) with pore radii larger than 100 μm but also relatively small pores (IntraG and parts of Inter-G) with radii in the range of 0.1−100 μm. The PRD shows consistency with the observation results of the CTS and SEM images, that is, the pore radius observed in the images can be effectively reflected by the newly determined PRD without conflicting points. However, it should be noted that the InterC with pore radius mainly smaller than 0.12 μm are not included in the PRD since the pore and throat have similar characteristics, and it is hard to distinguish the pore from the throat at the nanoscale. 5.6. Controls of Pore Structures on the Flow Characteristics. 5.6.1. Pore Classification Scheme and Its Controls on Flow Characteristics. Currently, two schemes are commonly used to classify the pore systems, which are proposed by Union of Applied Chemistry (IUPAC)65 and Loucks et al.,22 respectively. The IUPAC classification scheme [i.e., micropores (less than 2 nm), mesopores (2−50 nm), and macropores (greater than 50 nm)] is suitable to classify the pores of gas shale because of the dominant nanoscale pores in gas shale. The Loucks et al.22 extended Choquette and Pray’s pore classification scheme and proposed a new pore classification scheme for mudrock. In this scheme, the pores are classified as nanopores (1 nm to 1.0 μm), micropores (1.0−62.5 μm), mesopores (62.5 μm to 4.0 mm), etc. However, these two schemes seem not suitable for the tight sandy conglomerate reservoir, the reasons of which are as follows: (1) the pores with different radius range divided by

core no.

PI (f)

PII (%)

PIII (%)

rt (μm)

rp(μm)

η

FN10-23 X723-4 M137-2 X94-2 M152-6 M15-5 average

21.43 21.70 17.75 13.44 13.85 12.79 16.82

23.42 22.18 24.01 17.17 22.16 19.44 21.40

55.15 56.12 58.25 69.39 64.00 67.77 61.78

0.043 0.041 0.024 0.020 0.036 0.022 0.031

3.06 3.28 3.82 2.81 4.25 3.80 3.50

71.95 80.31 156.49 138.55 118.58 172.88 123.13

all core samples, which comprises the Inter-G and MicroF. Similar results are found in the Bashijiqike tight gas sandstone of Tarim Basin25 and Yanchang tight oil reservoirs of the Ordos Basin.41 The PII with an average percent value of 21.40% is mainly dominated by the IntraG. The classification results of PI to PII is consistent with the microscopic results of CTS since the porosity ratio of PI to PII (79%) matches well with the surface porosity ratio of Inter-G to IntraG (84%) (Table 1 and Table 5, respectively). The PIII accounts for a large proportion of the pore systems with an average percent value of 61.78% and corresponds to the InterC, which can be easily observed by the SEM. Nelson66 also regards that pores with a radius smaller than 0.1 μm are associated with InterC between clay mineral aggregates. Figure 12 shows the correlations of classified pore types to the ØNMR and Khe. The PI and PII show a positive contribution to the flow characteristics since PI and PII are positively correlated to the ØNMR and Khe. However, the PIII displays negative control on the flow characteristics with negative correlation to the ØNMR and Khe. The PIII is mainly dominated by InterC between clay mineral aggregates, the existence of which will promote the compaction and clay-cementation during the diagenesis process and eventually damage the pore system and seepage capacity.17 5.6.2. Pore Structure Parameters and Their Effects on Flow Characteristics. On the basis of the full-range TRD and advanced PRD, the parameters of logarithmic mean pore radius (rp), logarithmic mean throat radius (rt), and average pore throat radius ratio (η), which quantitatively reflect the pore structure characteristics, are calculated and displayed in Table 5. The rp, rt, and η are obviously smaller than those calculated from CRMI since they only reflect the big pores and large throat due to its limited mercury injection pressure. J


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Figure 12. Relationships between flow characteristics and classified pore types; (a), (b), and (c) are the cross plots of ØNMR vs PI, PII, and PIII, separately; (d), (e), and (f) are the cross plots of Khe vs PI, PII, and PIII, separately.

Figure 13. Relationships between flow characteristics and classified pore types: (a) and (d) are the cross plots of Øhe vs rp and rpa, respectively; (b) and (e) represent the cross plots of Khe vs rt and rta, respectively; and (c) and (f) displays the cross plots of Khe vs η and ηa, respectively.

6. CONCLUSIONS This paper investigates the pore systems, proposes novel methods to characterize the pore structure, establishes advanced pore classification scheme, and reveals the controls of pore structures on the flow characteristics. The following conclusions have been reached. (1) Four pore types, including Inter-G, InterC, IntraG (IntraG-D, IntraG-F, and IntraG-M), and MicroF, dominate the pore systems of the tight sandy conglomerate reservoir. The pore radius of these pore types follows the order of InterC < IntraG < Inter-G and MicroF. These pore types show irregular geometry and different surface porosity, which results in the complexity of pore structures and discrepancy of petrophysical parameters. (2) The HPMI can effectively characterize the throat with a radius of less than 0.12 μm at high mercury injection pressures. However, at low mercury injection pressures, the HPMI is influenced by the

Figure 13 shows the correlations of the pore structure parameters to the Øhe and Khe. The rp and rt display positive effects on the porosity and permeability of tight sandy conglomerate reservoirs (i.e., the bigger the rp and rt, the higher the Øhe and Khe (Figure 13, panels a and b). But, the rpa and rta derived from the CRMI do not show correlations to Øhe and Khe (Figure 13, panels d and e) and can not reflect the relationships between pore structures and flow characteristics since they merely reflect parts of the pore structures, which can be further demonstrated by the comparison between η and ηa. Compared to the ηa, the η, which represents the connectivity of pore systems,25 displays a better relevance to the Khe with a square of the correlation coefficient of 0.81 (Figure 13, panels c and f). K


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CSTC2017SHMSA120001), and Chongqing Land Bureau Science and Technology Planning Project (Grant CQGT-KJ2017026).

pore-blocking and neglects the large pores. The CMRI has advantages in detecting the pore and throat separately but cannot detect the pore and throat with radius lower than 0.12 μm due to its limited mercury injection pressure. Thus, the full-range TRD can be obtained by the integration of HPMI and CRMI. (3) When pore and throat radius lower than 0.12 μm, the NMR and HPMI reflect the same information. Thus, the TRD with radius lower than 0.12 μm is suitable to calibrate the T2 spectrum, which is further demonstrated by the consistent trend of cumulative porosity between mercury and T2 spectrum. And then, novel calibration method and procedure to calculate the full-range PTRD by T2 spectrum have been proposed. (4) The PRD derived by the CRMI has conflicts with the pore radius observed by the SEM and CTS images, since it only reflects the big pores and exaggerates the radius of small pores. The advanced PRDs can be obtained by subtracting TRDs from PTRDs and resampling the cumulative PRDs, which overcome the shortcomings of PRDs derived by CRMI. The advanced PRDs show a wide range of pore radius, which are consistent with the observation results of the CTS and SEM images. (5) An advanced pore classification scheme is proposed to reclassify the pore systems, which classifies the pore system into three kinds by PTRDs: PI (pore-throat radius more than 1 μm), PII (pore-throat radius in the range of 0.1−1 μm), and PIII (pore-throat radius less than 0.1 μm). The PI and PII, which mainly corresponds to the Inter-G and IntraG, respectively, account for a small proportion of the pore systems. The classification results of PI to PII is consistent with the microscopic results of casting thin section. Both PI and PII show positive contributions to the flow characteristics. In contrast, the PIII mainly associated with InterC accounts for a large proportion of the pore system and displays negative control on the flow characteristics. (6) On the basis of the fullrange of TRD and advanced PRD, the newly calculated pore structure parameters, in terms of logarithmic mean pore radius (rp), logarithmic mean throat radius (rt), and average pore throat radius ratio (η), shows more reasonable values than that derived by CRMI. The rp and rt display positive effects on the flow characteristics, whereas the η has negative effects on the flow characteristics.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. Tel: +86-183-821-06629. *E-mail: [email protected]. ORCID

Liang Wang: 0000-0002-3958-8986 Liqiang Sima: 0000-0003-0309-8667 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research was supported jointly by the National Science and Technology Major Project of China (Grant 2016ZX05052), National Natural Science Foundation of China (Grant 41504108), Fund Project of Sichuan Provincial Education Department (Grant 15ZB0057), Fund Project of China P ost docto ral Science F oundat ion (Grant 2015M582568), Chongqing Basic and Frontier Research Projects (Grant CSTC2015JCYJBX0120), Chongqing City Social Undertakings and Livelihood Protection Science and Technology Innovation Special Project (Grant L


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