Compact sandstone multi-scale pore network modeling method and system

Abstract

The invention discloses a dense sandstone multi-scale pore network modeling method and system, and relates to the technical field of digital rock physics, and the method comprises the steps: constructing a low-resolution three-dimensional digital rock core based on a CT three-dimensional gray image of a rock core sample of dense sandstone; constructing a coarse-scale pore network model based on the low-resolution three-dimensional digital core and a maximum sphere algorithm; respectively carrying out a high-pressure mercury injection experiment and a nuclear magnetic resonance experiment on the rock core sample to obtain throat radius probability distribution and T2 value probability distribution; based on structure parameters and distribution characteristic information of macropores and throats, throat radius probability distribution and T2 value probability distribution, determining multi-scale pore throat size distribution and coordination number distribution of the rock core sample through fitting; and fusing the multi-scale pore throat size distribution and coordination number distribution of the core sample based on a random network algorithm to generate a multi-scale pore network model of the core sample. According to the method, the technical problem that the multi-scale pore space of the real compact sandstone reservoir cannot be represented in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital rock physics, and in particular to a method and system for modeling a multi-scale pore network of dense sandstone. Background Art

[0002] Compared with conventional reservoirs, tight sandstone reservoirs have high clay content, strong heterogeneity, and complex pore structure, which form complex seepage characteristics of rocks and affect the performance evaluation of reservoirs. Therefore, it is necessary to quantitatively and systematically study the significance of pore structures of different scales in tight sandstone for oil and gas migration and exploration and development. However, the low porosity and low permeability characteristics of tight sandstone lead to complex seepage mechanisms of samples. Darcy's law summarized by macroscopic experiments is not applicable, and it is difficult to study its permeability based on conventional physical experimental tests. With the development of multi-scale physical imaging testing technology and computer technology, digital rock physics technology combining three-dimensional digital cores and numerical simulation algorithms has become an important research method in rock physics. Accurately establishing digital cores of tight sandstone and using numerical simulation methods to simulate seepage characteristics have important theoretical significance and application value.

[0003] At present, the main methods for constructing digital cores are physical imaging and numerical reconstruction. The numerical reconstruction method is simple and flexible, but the accuracy of modeling is low; the physical imaging method has high precision and can more accurately reflect the rock microstructure, but it is easily restricted by the scanning resolution and sample size. The larger the scanning sample size, the lower the resolution. Tight sandstone reservoirs are highly heterogeneous, and the scanning sample size needs to be large enough to ensure that the modeling is representative. This results in a single-scale three-dimensional digital core established based on the sample CT scanning grayscale image. It can only identify some of the larger intergranular pores and dissolution pores in the sample, and cannot fully characterize the pore system inside the core.

[0004] Combined with SEM, nano-CT and other higher-resolution imaging methods, the microstructure of the selected area of ​​the sample can be obtained, and a multi-scale digital core that integrates pore structure data of different scales in the sample can be established through image registration and other means. Cui Kaili et al. (Cui Likai, Sun Jianmeng, Yan Weichao et al. Construction of multi-scale and multi-component digital core based on multi-resolution image fusion [J]. Journal of Jilin University (Earth Science Edition), 2017, 47(06): 1904-1912.) Based on multi-resolution CT imaging of sandstone, the matching of core images of different resolutions was achieved through image registration, and the fused images were segmented for pores and minerals to construct a multi-scale and multi-component digital core model. However, compared with sandstone, the pore size distribution range of dense sandstone is wider, and some nano-scale pores cannot be identified by CT. The established multi-scale digital core of dense sandstone cannot reflect the distribution of pores and throats in the entire core. In addition, the digital core not only contains the microscopic pore structure of the rock, but also contains complex mineral components and particle distribution, which makes the model establishment and calculation process more complicated. Jiang Liming et al. (Jiang Liming, Liu Ningjing, Sun Jianmeng et al. Using CT images and mercury injection nuclear magnetic resonance to construct high-precision three-dimensional digital cores [J]. Well Logging Technology, 2016, 40(4): 404-407.) attempted to combine scanning imaging technology with physical experimental methods to construct multi-scale digital cores by combining CT scanning with mercury injection nuclear magnetic resonance data. The core idea is to superimpose digital cores at two resolutions without simplifying the model. Due to the limited computer operating capacity, the multi-scale digital core only expands the resolution to 1.27μm, which is not suitable for dense reservoirs with a wide range of pore distribution. And the specific implementation process of establishing a small-scale pore network model through mercury injection and nuclear magnetic resonance data is not mentioned. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for multi-scale pore network modeling of dense sandstone in order to solve at least one of the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a method for modeling a multi-scale pore network of a dense sandstone, comprising: constructing a low-resolution three-dimensional digital core based on a CT three-dimensional grayscale image of a core sample of dense sandstone; constructing a coarse-scale pore network model based on the low-resolution three-dimensional digital core and a maximum sphere algorithm; the coarse-scale pore network model includes structural parameters and distribution characteristic information of macropores and throats of the core sample; performing high-pressure mercury injection experiments and nuclear magnetic resonance experiments on the core sample to obtain a throat radius probability distribution and a T2 value probability distribution; based on the structural parameters and distribution characteristic information of the macropores and throats, the throat radius probability distribution, and the T2 value probability distribution, determining the multi-scale pore throat size distribution and coordination number distribution of the core sample by fitting; and generating a multi-scale pore network model of the core sample by fusing the multi-scale pore throat size distribution and coordination number distribution of the core sample based on a random network algorithm.

[0007] Furthermore, based on the CT three-dimensional grayscale image of the core sample of dense sandstone, a low-resolution three-dimensional digital core is constructed, including: performing a CT scan on the core sample, and establishing a three-dimensional grayscale image by utilizing the different X-ray absorption coefficients of different components in the rock; intercepting a regional image of a preset window size in the three-dimensional grayscale image, and performing image filtering, grayscale correction and threshold segmentation respectively, to construct the low-resolution three-dimensional digital core.

[0008] Furthermore, based on the structural parameters and distribution characteristic information of the macropores and throats, the throat radius probability distribution, and the T2 value probability distribution, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined by fitting, including: determining the throat radius data distribution and T2 value data distribution of the core sample based on the throat radius probability distribution and the T2 value probability distribution; taking the structural parameters and distribution characteristic information of the macropores and throats as a reference, and determining the multi-scale pore throat size distribution and coordination number distribution of the core sample based on the throat radius data distribution and the T2 value data distribution fitting.

[0009] Furthermore, based on the throat radius probability distribution and the T2 value probability distribution, the throat radius data distribution and T2 value data distribution of the core sample are determined, including: performing least squares fitting on the throat radius probability distribution and the T2 value probability distribution, respectively, to obtain a throat radius probability distribution curve and a T2 value probability distribution curve; and generating random data whose probability distribution conforms to the throat radius probability distribution curve and the T2 value probability distribution curve, respectively, to obtain the throat radius data distribution and T2 value data distribution of the core sample.

[0010] Furthermore, the multi-scale pore throat size distribution includes: pore throat radius, pore throat length, pore throat volume and pore shape factor.

[0011] Furthermore, with reference to the structural parameters and distribution characteristic information of the macropores and throats, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined based on the throat radius data distribution and the T2 value data distribution fitting, including: fitting the throat radius data distribution and the T2 value data distribution based on the least squares method to determine the pore throat radius, and determining the connection throat radius based on the high-pressure mercury injection experiment; fitting the pore throat length and pore throat radius based on the least squares method, and determining the pore throat length; simulating the pore shape factor and coordination number distribution based on the log-normal distribution; and determining the pore throat volume based on a single pore volume calculation formula; wherein the single pore volume calculation formula includes: Where V represents the volume of a single pore, r represents the pore radius, l represents the pore length, F represents the shape factor of the pore, and k is the empirical coefficient.

[0012] In a second aspect, an embodiment of the present invention further provides a multi-scale pore network modeling system for dense sandstone, comprising: a first construction module, a second construction module, an experimental module, a determination module and a modeling module; wherein the first construction module is used to construct a low-resolution three-dimensional digital core based on a CT three-dimensional grayscale image of a core sample of dense sandstone; the second construction module is used to construct a coarse-scale pore network model based on the low-resolution three-dimensional digital core and a maximum sphere algorithm; the coarse-scale pore network model includes structural parameters and distribution characteristic information of macropores and throats of the core sample; the experimental module is used to perform high-pressure mercury injection experiments and nuclear magnetic resonance experiments on the core sample, respectively, to obtain a throat radius probability distribution and a T2 value probability distribution; the determination module is used to determine the multi-scale pore throat size distribution and coordination number distribution of the core sample by fitting based on the structural parameters and distribution characteristic information of the macropores and throats, the throat radius probability distribution, and the T2 value probability distribution; the modeling module is used to fuse the multi-scale pore throat size distribution and coordination number distribution of the core sample based on a random network algorithm to generate a multi-scale pore network model of the core sample.

[0013] Furthermore, the determination module is also used to: determine the throat radius data distribution and T2 value data distribution of the core sample based on the throat radius probability distribution and the T2 value probability distribution; and determine the multi-scale pore throat size distribution and coordination number distribution of the core sample based on the throat radius data distribution and the T2 value data distribution fitting, with reference to the structural parameters and distribution characteristic information of the macropores and throats.

[0014] The present invention provides a method and system for modeling a multi-scale pore network of a dense sandstone. The multi-scale pore network model is established by combining scanning imaging technology and physical experimental means. The model is not limited by the imaging size and can simultaneously describe the characteristics of macropores and micropores in the sample, which greatly improves the connectivity of the dense sandstone pore network model. It is of great significance for the numerical simulation of rock physical properties such as permeability, and alleviates the technical problem of the prior art that it cannot characterize the multi-scale pore space of the real dense sandstone reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flowchart of a multi-scale pore network modeling method for dense sandstone provided in an embodiment of the present invention;

[0017] Figure 2 A flowchart of another method for multi-scale pore network modeling of dense sandstone provided by an embodiment of the present invention;

[0018] Figure 3 A CT three-dimensional grayscale image of a core sample of a dense sandstone provided in an embodiment of the present invention;

[0019] Figure 4 A two-dimensional SEM image of a core sample of a dense sandstone provided in an embodiment of the present invention;

[0020] Figure 5 A data distribution and probability distribution diagram of throat radius in a high-pressure mercury injection experiment result provided by an embodiment of the present invention;

[0021] Figure 6 T2 value data and probability distribution diagram generated by a T2 spectrum under saturated water state in a nuclear magnetic resonance experiment provided by an embodiment of the present invention;

[0022] Figure 7 A flow chart of converting a T2 spectrum of a nuclear magnetic resonance experiment into a pore size distribution provided in an embodiment of the present invention;

[0023] Figure 8 A schematic diagram of a least squares fitting curve between the pore radius in a coarse-scale pore network model provided in an embodiment of the present invention and the T2 value data in a saturated water state in a nuclear magnetic resonance experiment;

[0024] Fig. 9A fitting curve between pore length and radius in a pore network model of a storage scale provided in an embodiment of the present invention;

[0025] Fig.10 A schematic diagram of pore shape factor distribution in a coarse-scale pore network model provided in an embodiment of the present invention;

[0026] Fig.11 A schematic diagram of pore shape factor distribution generated by fitting in a coarse-scale pore network model provided in an embodiment of the present invention;

[0027] Fig.12 A schematic diagram of pore and throat analysis in a maximum sphere algorithm provided in an embodiment of the present invention;

[0028] Fig.13 A schematic diagram of a coarse-scale pore network model including only a portion of intergranular pores and dissolution pores provided in an embodiment of the present invention;

[0029] Fig.14 A schematic diagram of a multi-scale pore network model provided by an embodiment of the present invention;

[0030] Fig.15 A schematic diagram of a dense sandstone multi-scale pore network modeling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1

[0033] Figure 1 FIG. 1 is a flow chart of a multi-scale pore network modeling method for dense sandstone provided according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0034] Step S102: construct a low-resolution three-dimensional digital core based on the CT three-dimensional grayscale image of the core sample of the dense sandstone.

[0035] Step S104, constructing a coarse-scale pore network model based on low-resolution three-dimensional digital core and maximum sphere algorithm; the coarse-scale pore network model includes structural parameters and distribution characteristic information of macropores and throats of the core sample.

[0036] Step S106, performing high pressure mercury injection experiment and nuclear magnetic resonance experiment on the core sample respectively to obtain the probability distribution of throat radius and the probability distribution of T2 value.

[0037] Step S108, based on the structural parameters and distribution characteristic information of macropores and throats, the probability distribution of throat radius, and the probability distribution of T2 values, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined by fitting.

[0038] Step S110, generating a multi-scale pore network model of the core sample by fusing the multi-scale pore throat size distribution and coordination number distribution of the core sample based on a random network algorithm.

[0039] Figure 2 FIG. 1 is a flow chart of another method for multi-scale pore network modeling of dense sandstone provided according to an embodiment of the present invention. Figure 2 As shown, the modeling method provided by the embodiment of the present invention combines scanning imaging technology and physical experimental means. The established multi-scale pore network model is not limited by the imaging size and can simultaneously describe the characteristics of macropores and micropores in the sample, which significantly improves the connectivity of the dense sandstone pore network model.

[0040] Specifically, step S102 also includes the following steps:

[0041] Step S1021, perform CT scanning on the core sample, and establish a three-dimensional grayscale image by using the different X-ray absorption coefficients of different components in the rock; wherein the three-dimensional grayscale image is as follows: Figure 3 shown.

[0042] Step S1022, intercepting an area image of a preset window size in the three-dimensional grayscale image, performing image filtering, grayscale correction and threshold segmentation respectively, and constructing a low-resolution three-dimensional digital core. Optionally, intercepting an area of ​​600×600×600 pixels in the three-dimensional grayscale image of the core sample.

[0043] Figure 4 2 is a two-dimensional SEM image of a core sample of dense sandstone provided in an embodiment of the present invention. In the embodiment of the present invention, due to the low resolution of CT scanning, only the larger intergranular pores and dissolution pores in the core sample can be identified, and the Figure 4The micropores in the clay minerals shown in Figure 1. Based on the above low-resolution three-dimensional digital core, only a coarse-scale pore network model can be established using the maximum sphere algorithm (Dong H. Micro-CT imaging and pore network extraction [D]. London, Imperial College, 2007). The connectivity between pores in the coarse-scale pore network model is extremely poor, and it is impossible to form a seepage channel that runs through the core. The model only contains information related to the structural parameters and distribution characteristics of larger pores and throats.

[0044] Specifically, step S108 also includes the following steps:

[0045] Step S1081, determining the throat radius data distribution and the T2 value data distribution of the core sample based on the throat radius probability distribution and the T2 value probability distribution;

[0046] Step S1082, with reference to the structural parameters and distribution characteristics of macropores and throats, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined based on the throat radius data distribution and the T2 value data distribution fitting.

[0047] Specifically, step S1081 also includes:

[0048] The probability distribution of throat radius and the probability distribution of T2 value were fitted by least squares respectively to obtain the probability distribution curve of throat radius and the probability distribution curve of T2 value. Specifically, the probability distribution data of T2 value under saturated water state in nuclear magnetic resonance data and the probability distribution data of throat radius in high pressure mercury injection experiment results were sorted and statistically analyzed, and the above data were imported into Matlab, and the "cftool" command was used to perform least squares fitting to generate the corresponding probability distribution curve and obtain the corresponding linear equation.

[0049] Then, random data whose probability distribution conforms to the throat radius probability distribution curve and the T2 value probability distribution curve are generated respectively, and the throat radius data distribution and T2 value data distribution of the core sample are obtained. Specifically, random data whose probability distribution conforms to the linear equation can be generated by the self-simulated Matlab algorithm, that is, the throat radius data distribution and T2 value data distribution, such as Figure 5 and Figure 6 As shown, the probability distribution of generated random data is consistent with the original probability distribution. Figure 5 Figure (a) shows the distribution of throat radius data in the high-pressure mercury injection experiment. Figure 5 Figure (b) shows the probability distribution of throat radius in the high-pressure mercury injection experiment results; Figure 6 Figure (a) is the T2 value data distribution diagram generated by the T2 spectrum under saturated water state in the nuclear magnetic resonance experiment. Figure 6Figure (b) is a probability distribution diagram of T2 values ​​generated by T2 spectrum in saturated water state in NMR experiment.

[0050] Specifically, the multi-scale pore throat size distribution includes: pore throat radius, pore throat length, pore throat volume and pore shape factor. Specifically, in the embodiment of the present invention, the coordination number distribution is the pore throat connectivity relationship.

[0051] Specifically, step S1082 includes the following steps:

[0052] (1) The throat radius data distribution and T2 value data distribution were fitted based on the least squares method to determine the pore throat radius, and the connection throat radius was determined based on the high-pressure mercury injection experiment.

[0053] Figure 7 FIG. 1 is a flow chart of converting a T2 spectrum of a nuclear magnetic resonance experiment into a pore size distribution according to an embodiment of the present invention. Figure 7 As shown in the figure, first, pores with radii higher than the CT resolution are screened, and the proportion of the number of pores in the total pores measured by CT is calculated; then, data with the same proportion are screened in the generated T2 distribution, and the T2 data and the pore radius data of large pores are sorted respectively; finally, the least squares fitting is performed to obtain a linear equation, and the pore radii of different scales are obtained based on the total T2 distribution data. Among them, the least squares fitting data are the pore radius in the coarse-scale pore network model and the T2 value data in the saturated water state in the nuclear magnetic resonance experiment.

[0054] Specifically, Figure 8 1 is a schematic diagram of a least squares fitting curve between the pore radius in a coarse-scale pore network model provided by an embodiment of the present invention and the T2 value data in a saturated water state in a nuclear magnetic resonance experiment. Figure 8 As shown in Figure 3, the relevant linear equation can be obtained by least square fitting the pore radius and T2 data measured by CT within the specified range, and the connection throat radius is directly provided by the high-pressure mercury injection result.

[0055] (2) The pore throat length and pore throat radius are fitted based on the least squares method, and the pore throat length is determined. Fig. 9 is a fitting curve between pore length and radius in a pore network model of a storage scale provided according to an embodiment of the present invention. Fig. 9 As shown in the figure, it has been verified that the pore throat length and radius extracted by CT have a strong correlation, and the two show a high degree of logical correlation, and a linear equation can be obtained by least squares fitting. Therefore, the pore throat length required for modeling can be calculated by substituting the pore throat radius of different scales into the equation.

[0056] (3) The pore shape factor and coordination number distribution are simulated based on the log-normal distribution. Specifically, the pore shape factor reflects the connectivity and geometry of the pores, and it is assumed that the distribution of pore shape factors of different scales in the same sample is consistent. Fig.10 is a schematic diagram of pore shape factor distribution in a coarse-scale pore network model provided according to an embodiment of the present invention, Fig.11 FIG. 1 is a schematic diagram of the pore shape factor distribution generated by fitting in a coarse-scale pore network model provided in an embodiment of the present invention. Fig.10 As shown in Figure 2, the macropore shape factor extracted from the CT image conforms to the log-normal distribution. The corresponding parameters can be obtained using the "distribution fitter" in Matlab. The random data conforming to the log-normal distribution generated based on the known parameters is used as the pore shape factor required for modeling. The distribution diagram is shown in Figure 2. Fig.11 shown.

[0057] (4) Determine the pore throat volume based on a single pore volume calculation formula; wherein the single pore volume calculation formula includes:

[0058] V=πr 2 l

[0059] k

[0060] Where V represents the volume of a single pore, r represents the pore radius, l represents the pore length, F represents the shape factor of the pore, and k is the empirical coefficient.

[0061] Specifically, the pore shapes are complex and diverse, and the pore model cannot be simply simplified using spheres or cylinders. Fig.12 FIG. 1 is a schematic diagram of pore and throat analysis in a maximum sphere algorithm provided according to an embodiment of the present invention. Fig.12 As shown in the figure, based on the principle of the maximum ball algorithm, the pore space is filled with balls of different sizes. The multiple clusters in the figure represent pores, and the small balls connecting two adjacent pore clusters are defined as throats. Their shapes are all irregular. It has been verified that there is a relationship between the pore volume extracted from the CT image and other pore structure parameters as shown in the above single pore volume calculation formula.

[0062] (5) The coordination number reflects the network topology. The coordination number between pores in the large-scale pore network model extracted based on the sample CT image conforms to the normal distribution. Different samples have different permeabilities and slightly different mean values ​​of the coordination number. The coordination number required for modeling must conform to the normal distribution. In the embodiment of the present invention, the mean value of the coordination number between large pores and small pores ranges from 2.13 to 3.72.

[0063] (6) The number of pores required for modeling can be calculated based on the proportion of pores within the specified radius in the CT and MRI data and the number of pores measured by CT within the corresponding range. The required number of throats is obtained by multiplying the total number of pores by the coordination number. The physical size of the constructed network should be the same as the size of the initially generated binary CT image.

[0064] In an embodiment of the present invention, in step S110, a random network algorithm (Idowu NA. Pore-Scale Modeling: Stochastic Network Generation and Modeling of Rate Effects in Water Flooding [J]. Imperial College London, 2009.) is used to fuse pores and throat information of different scales to generate a multi-scale pore network model that is not limited by the CT scanning size, and compared with the porosity of the samples measured by physical experiments to verify the accuracy of the modeling.

[0065] Specifically, step S110 also includes the following steps:

[0066] Step S1101, organize the multi-scale pore throat size distribution and coordination number distribution to create "PoreData.txt" and "throatData.txt" input files, fill in the required modeling size and number of pore throats, run the random network algorithm, and generate a multi-scale pore network model containing both macropores and micropores. By proportionally modifying the modeling size and the number of pore throats included, pore network models of different sizes can be obtained.

[0067] Step S1102, calculating the porosity of the multi-scale pore network model and comparing it with the experimental results to verify the accuracy of the modeling.

[0068] For example, the experimental porosity of a dense sandstone sample is 7.9% and the permeability is 0.044mD.

[0069] The resolution of the CT scan of the plunger sample was 6.78 μm. Fig.13 A model for extracting pore networks based on the maximum sphere algorithm was demonstrated, with a calculated model porosity of 0.2% and a permeability of 0, which further verified that the coarse-scale pore network model of dense sandstone could not form a seepage channel that penetrated the core.

[0070] Combined with the multi-scale pore network model generated by random network algorithm, such as Fig.14 As shown, the porosity identified by the fused pore network model is 7.5%, and the permeability result calculated by numerical simulation is 0.045 mD. From the experimental results, it can be seen that the application effect of the dense sandstone multi-scale fusion method proposed in the present invention is good.

[0071] From the above description, it can be seen that the embodiment of the present invention provides a method for modeling a multi-scale pore network of dense sandstone, which combines scanning imaging technology and physical experimental means to establish a multi-scale pore network model. The model is not limited by the imaging size and can simultaneously describe the characteristics of large pores and micropores in the sample, greatly improving the connectivity of the dense sandstone pore network model, which is of great significance for the numerical simulation of rock physical properties such as permeability, and alleviates the technical problem that the existing technology cannot characterize the multi-scale pore space of the real dense sandstone reservoir.

[0072] Embodiment 2

[0073] Fig.15 Schematic diagram of a multi-scale pore network modeling system for dense sandstone provided according to an embodiment of the present invention. Fig.15 As shown, the system includes: a first construction module 10, a second construction module 20, an experiment module 30, a determination module 40 and a modeling module 50.

[0074] Specifically, the first construction module 10 is used to construct a low-resolution three-dimensional digital core based on a CT three-dimensional grayscale image of a core sample of dense sandstone.

[0075] The second construction module 20 is used to construct a coarse-scale pore network model based on low-resolution three-dimensional digital core and maximum sphere algorithm; the coarse-scale pore network model includes structural parameters and distribution characteristic information of macropores and throats of core samples.

[0076] The experimental module 30 is used to perform high-pressure mercury injection experiment and nuclear magnetic resonance experiment on the core samples to obtain the probability distribution of throat radius and the probability distribution of T2 value.

[0077] The determination module 40 is used to determine the multi-scale pore throat size distribution and coordination number distribution of the core sample by fitting based on the structural parameters and distribution characteristic information of the macropores and throats, the probability distribution of the throat radius, and the probability distribution of the T2 value.

[0078] The modeling module 50 is used to generate a multi-scale pore network model of the core sample by fusing the multi-scale pore throat size distribution and coordination number distribution of the core sample based on a random network algorithm.

[0079] Specifically, the determination module 40 is also used to: determine the throat radius data distribution and T2 value data distribution of the core sample based on the throat radius probability distribution and the T2 value probability distribution; and determine the multi-scale pore throat size distribution and coordination number distribution of the core sample based on the throat radius data distribution and the T2 value data distribution fitting, with reference to the structural parameters and distribution characteristic information of the macropores and throats.

[0080] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0081] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for modeling multi-scale pore networks of dense sandstone, characterized in that: include: Based on the CT 3D grayscale image of the core sample of dense sandstone, a low-resolution 3D digital core is constructed; Based on the low-resolution three-dimensional digital core and the maximum sphere algorithm, a coarse-scale pore network model is constructed; the coarse-scale pore network model includes structural parameters and distribution characteristic information of macropores and throats of the core sample; A high-pressure mercury injection experiment and a nuclear magnetic resonance experiment are respectively performed on the core sample to obtain a probability distribution of throat radius and a probability distribution of T2 value; Based on the structural parameters and distribution characteristic information of the macropores and throats, the probability distribution of the throat radius, and the probability distribution of the T2 value, the multi-scale pore throat size distribution and the coordination number distribution of the core sample are determined by fitting; The multi-scale pore throat size distribution and coordination number distribution of the core sample are integrated based on a random network algorithm to generate a multi-scale pore network model of the core sample.

2. The method according to claim 1, characterized in that: Based on the CT 3D grayscale image of the core sample of dense sandstone, a low-resolution 3D digital core is constructed, including: Performing CT scanning on the core sample, and establishing a three-dimensional grayscale image by utilizing the differences in X-ray absorption coefficients of different components in the rock; A regional image of a preset window size is intercepted from the three-dimensional grayscale image, and image filtering, grayscale correction and threshold segmentation are performed respectively to construct the low-resolution three-dimensional digital core.

3. The method according to claim 1, characterized in that: Based on the structural parameters and distribution characteristic information of the macropores and throats, the probability distribution of the throat radius, and the probability distribution of the T2 value, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined by fitting, including: Based on the throat radius probability distribution and the T2 value probability distribution, determine the throat radius data distribution and the T2 value data distribution of the core sample; With reference to the structural parameters and distribution characteristic information of the macropores and throats, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined based on the throat radius data distribution and the T2 value data distribution fitting.

4. The method according to claim 3, characterized in that: Based on the throat radius probability distribution and the T2 value probability distribution, determining the throat radius data distribution and the T2 value data distribution of the core sample, including: Performing least square fitting on the throat radius probability distribution and the T2 value probability distribution respectively to obtain a throat radius probability distribution curve and a T2 value probability distribution curve; Random data whose probability distribution conforms to the throat radius probability distribution curve and the T2 value probability distribution curve are generated respectively, and the throat radius data distribution and the T2 value data distribution of the core sample are obtained.

5. The method according to claim 3, characterized in that: The multi-scale pore throat size distribution includes: pore throat radius, pore throat length, pore throat volume and pore shape factor.

6. The method according to claim 5, characterized in that: Taking the structural parameters and distribution characteristic information of the macropores and throats as reference, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined based on the throat radius data distribution and the T2 value data distribution fitting, including: Fitting the throat radius data distribution and the T2 value data distribution based on the least squares method to determine the pore throat radius, and determining the connection throat radius based on the high-pressure mercury injection experiment; The pore throat length and pore throat radius are fitted based on the least square method, and the pore throat length is determined; The pore shape factor and coordination number distribution were simulated based on the log-normal distribution; The pore throat volume is determined based on a single pore volume calculation formula; wherein the single pore volume calculation formula includes: Where V represents the volume of a single pore, r represents the pore radius, l represents the pore length, F represents the shape factor of the pore, and k is the empirical coefficient.

7. A dense sandstone multi-scale pore network modeling system, characterized in that: include: A first building module, a second building module, an experimental module, a determination module and a modeling module; wherein, The first construction module is used to construct a low-resolution three-dimensional digital core based on a CT three-dimensional grayscale image of a core sample of dense sandstone; The second construction module is used to construct a coarse-scale pore network model based on the low-resolution three-dimensional digital core and the maximum sphere algorithm; the coarse-scale pore network model includes structural parameters and distribution characteristic information of macropores and throats of the core sample; The experimental module is used to perform high-pressure mercury injection experiment and nuclear magnetic resonance experiment on the core sample to obtain the probability distribution of throat radius and the probability distribution of T2 value; The determination module is used to determine the multi-scale pore throat size distribution and coordination number distribution of the core sample by fitting based on the structural parameters and distribution characteristic information of the macropores and throats, the throat radius probability distribution, and the T2 value probability distribution; The modeling module is used to fuse the multi-scale pore throat size distribution and coordination number distribution of the core sample based on a random network algorithm to generate a multi-scale pore network model of the core sample.

8. The system according to claim 7, characterized in that: The determining module is further used for: Based on the throat radius probability distribution and the T2 value probability distribution, determine the throat radius data distribution and the T2 value data distribution of the core sample; With reference to the structural parameters and distribution characteristic information of the macropores and throats, the multi-scale pore throat size distribution and coordination number distribution of the core sample are determined based on the throat radius data distribution and the T2 value data distribution fitting.

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