Quantitative characterization method and system for pore space of shale oil reservoir

Abstract

The invention relates to the technical field of geology research in oil exploration and development, and provides a shale oil reservoir pore space quantitative characterization method and system, and the method comprises the steps: carrying out the statistics of micron-order equivalent pore structure parameters of a shale oil reservoir; carrying out statistics on nanoscale organic pore structure parameters of the shale oil reservoir; performing standardization treatment on the micron-scale equivalent pore structure parameters and the nano-scale organic pore structure parameters to obtain theoretical volumes of standardized pore structures in different intervals under different resolutions of the reservoir sample; respectively calculating the volume fraction of the theoretical volume in the standardized total pores and the volume fraction of the theoretical volume in the reservoir sample; and establishing a full-scale pore distribution curve according to the volume fractions, and representing the pore volume proportions of different apertures of the shale oil reservoir. According to the method, CT scanning and FIB-SEM are organically combined in the shale oil reservoir space quantitative characterization process, and visual quantitative characterization of a shale sample from micron-scale bedding seams to nano-scale apertures is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological research in petroleum exploration and development, and in particular to a method and system for quantitatively characterizing pore space in a shale oil reservoir. Background Art

[0002] CT scanning has been initially used for rock structure analysis since the 1990s, but due to technical limitations, it is mostly used to analyze conventional sandstone and conglomerate with larger particles. However, the application of CT scanning for dense sandstone and shale with finer particles is very limited due to problems such as excessive noise and insufficient scanning accuracy. With the development of microscopic imaging technology, the emergence of micro-nano CT and scanning electron microscope imaging technology has solved the problem of two-dimensional and three-dimensional visualization quantitative analysis of the micro-nanoscale pore structure of dense reservoirs. However, scanning electron microscopes are limited by experimental conditions and are only confined to the analysis of organic matter and pore development characteristics at the nanoscale, and lack three-dimensional imaging information. Therefore, after the commercialization of focused ion beam scanning electron microscopes (FIB-SEM) in the 1990s, the characterization of reservoir microstructure in the field of petroleum geology has entered a new era. At this time, it can not only be used for two-dimensional image observation, but also for large-scale three-dimensional cutting imaging scanning. The analysis dimension and process are consistent with CT, and the imaging is clearer and the magnification is higher. The three-dimensional visualization quantitative characterization of organic and inorganic pores that cannot be performed at the micron scale can be achieved. However, like the two-dimensional imaging analysis of scanning electron microscopes, it is impossible to quantitatively characterize relatively macroscopic micron-level pores, bedding fractures and high-openness fractures. Summary of the invention

[0003] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a method and system for quantitative characterization of the pore space of a shale oil reservoir.

[0004] To achieve the above object, the present invention provides a method for quantitatively characterizing the pore space of a shale oil reservoir, comprising:

[0005] Obtain reservoir samples of shale oil reservoirs, analyze micron-scale fracture structures in the reservoir samples by micron CT scanning, and calculate micron-scale equivalent pore structure parameters of the shale oil reservoir based on the micron-scale fracture structures;

[0006] Nano-CT and FIB-SEM techniques are used to analyze and calculate the nano-scale pore structure in reservoir samples, and to calculate the nano-scale organic pore structure parameters of shale oil reservoirs;

[0007] Standardizing the micron-level equivalent pore structure parameters and the nanometer-level organic pore structure parameters to obtain the theoretical volumes of the standardized pore structures in different intervals at different resolutions of the reservoir sample;

[0008] Calculate the volume fraction of the theoretical volume to the standardized total pore volume and the volume fraction to the reservoir sample respectively;

[0009] The full-scale pore distribution curve is established according to each volume fraction to characterize the volume proportion of pores with different pore sizes in the shale oil reservoir.

[0010] According to one aspect of the present invention, a reservoir sample of a shale oil reservoir is obtained, a micron-scale fracture structure in the reservoir sample is analyzed by micron CT scanning, and micron-scale equivalent pore structure parameters of the shale oil reservoir are statistically calculated based on the micron-scale fracture structure, including:

[0011] obtaining a reservoir sample of a shale oil reservoir and drilling a plug sample on the reservoir sample;

[0012] Micron CT scanning was used to analyze the micron-scale crack structure in each plunger sample. The scanning results were then processed using Avizo software to count the crack opening and line density, and then the density of cracks with different openings in the plunger samples was calculated. When the length remained the same, the crack openings at different scales were averaged to calculate the theoretical volume of the crack: V i =a i b i h,

[0013] Using the equivalent spherical volume algorithm, fractures of different volumes are converted into spheres of equal volume. The radius of the sphere is used to represent the pore distribution range, and the average radius is used as the range of pore volume statistics:

[0014] From the various formulas, we can derive:

[0015] Among them, V i is the crack volume; a i is the crack opening; b i is the crack length; h is the crack height; V f is the total volume of the crack; r i is the equivalent pore radius of the fracture; is the average equivalent radius of the fracture; R is the standard plunger radius of the shale oil reservoir; l is the fracture line density.

[0016] According to one aspect of the present invention, nano-CT and FIB-SEM techniques are used to analyze and calculate the nano-scale pore structure in the reservoir sample, and the nano-scale organic pore structure parameters of the shale oil reservoir are statistically analyzed, including:

[0017] Nano-CT was used to analyze the organic matter proportion of the plunger samples;

[0018] Based on the nano-CT analysis, a typical field of view was selected and local cutting was performed using the FIB-SEM imaging function;

[0019] Avizo software was used to analyze the data generated by FIB-SEM, and the nanoscale organic pore structure parameters of shale oil reservoirs were statistically obtained.

[0020] According to one aspect of the present invention, the micron-scale equivalent pore structure parameters and the nano-scale organic pore structure parameters are standardized, including:

[0021] Establishing a unified coordinate system for the micron-scale equivalent pore structure parameters and the nanoscale organic pore structure parameters;

[0022] Establish a standardized coefficient to restore the actual distribution of different pore structures in the standard core;

[0023] The standardized coefficients are established as:

[0024]

[0025] Where: P k is the normalization coefficient corresponding to different scales; V is the total volume of the standard core CT 3D reconstructed image; V k is the total volume of 3D reconstructed images corresponding to different scales.

[0026] According to one aspect of the present invention, the theoretical volume of the standardized pore structure of the reservoir sample at different intervals at different resolutions is obtained, and the volume fraction of the theoretical volume to the standardized total pore volume is calculated, including:

[0027] The theoretical values ​​of pore volume sizes in different intervals at different resolutions of standard plug samples are obtained:

[0028] Where V m is the total pore volume with an equivalent pore size mean of m; is the normalized pore volume with an equivalent pore size mean of m;

[0029] Calculate the total pore volume in the corresponding interval at different scales and standardize the porosity, then calculate the percentage of the total pore volume in different intervals to the standardized total pore volume:

[0030]

[0031] is the normalized total pore volume;

[0032] is the normalized porosity;

[0033] is the pore volume fraction with the normalized equivalent pore diameter mean m.

[0034] According to one aspect of the present invention, obtaining the theoretical volume of the standardized pore structure of different intervals of the reservoir sample at different resolutions, and calculating the volume fraction of the theoretical volume to the reservoir sample, comprises:

[0035] The volume fractions of organic pores at different pore sizes obtained by FIB-SEM were counted, and the total volume of organic pores in the standard core was calculated by combining the volume fraction of organic matter obtained by nano-CT.

[0036] The total volume of organic pores in the corresponding intervals is obtained by multiplying the volume fraction of organic pores in different pore size intervals calculated by FIB-SEM and the total volume of organic pores after software processing:

[0037]

[0038] Where V Pom is the total organic pore volume of the standard core; E om is the volume fraction of organic matter; is the organic porosity;

[0039] Calculate the ratio of the total volume of organic pores to the volume of the standard plunger:

[0040]

[0041] In the formula, Q n is the pore volume fraction with the mean nanoscale equivalent pore size n; is the pore volume fraction with the normalized equivalent pore size mean n.

[0042] To achieve the above object, the present invention also provides a shale oil reservoir pore space quantitative characterization system, comprising:

[0043] A micron-level equivalent pore structure parameter statistics module is used to obtain reservoir samples of shale oil reservoirs, analyze the micron-level fracture structure in the reservoir samples using micron CT scanning, and calculate the micron-level equivalent pore structure parameters of the shale oil reservoir based on the micron-level fracture structure;

[0044] The module for statistical parameters of nano-scale organic pore structure uses nano-CT and FIB-SEM technology to analyze and calculate the nano-scale pore structure in reservoir samples and statistically analyze the nano-scale organic pore structure parameters of shale oil reservoirs.

[0045] A standardization processing module is used to standardize the micron-level equivalent pore structure parameters and the nanometer-level organic pore structure parameters to obtain the theoretical volume of the standardized pore structure in different intervals at different resolutions of the reservoir sample;

[0046] The volume fraction calculation module calculates the volume fraction of the theoretical volume to the standardized total pore volume and the volume fraction to the reservoir sample;

[0047] The porosity characterization module establishes a full-scale pore distribution curve based on each volume fraction to characterize the volume proportion of pores with different pore sizes in the shale oil reservoir.

[0048] To achieve the above objectives, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method for quantitative characterization of pore space in shale oil reservoirs as described above.

[0049] To achieve the above objectives, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for quantitatively characterizing the pore space of a shale oil reservoir as described above is implemented.

[0050] According to the solution of the present invention, the present invention organically combines CT scanning with FIB-SEM in the process of quantitative characterization of the storage space of shale oil reservoirs, thereby realizing the visualized quantitative characterization of shale samples from micron-level bedding fractures to nanometer-level pores and fractures.

[0051] The present invention forms a systematic and comprehensive evaluation technology for shale from micron-level bedding fractures to nano-level organic pores, which is a new method for quantitative analysis of pore structure parameters of shale oil reservoirs in three-dimensional visualization. It not only visualizes the development characteristics of shale reservoir space, deepens the understanding of quantitative characterization methods of shale digital cores, but also strengthens the organic combination of different scale analysis technologies and comprehensive data comparison, providing a new idea for quantitative characterization of shale oil reservoir space by digital cores, which is of great significance for guiding the development of shale oil reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart schematically showing a method for quantitatively characterizing pore space in a shale oil reservoir according to one embodiment of the present invention;

[0053] Figure 2 It is the histogram of micron-scale bedding fracture pore distribution of standard plunger shale;

[0054] Figure 3 The pore distribution histogram of micron-nanoscale inorganic pores and cracks in 2mm plunger shale;

[0055] Figure 4 is the pore distribution histogram of FIB-SEM organic pores;

[0056] Figure 5 This is a full-scale pore distribution curve of shale oil reservoirs. DETAILED DESCRIPTION

[0057] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0058] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0059] Figure 1 A flowchart schematically shows a method for quantitatively characterizing pore space in a shale oil reservoir according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the quantitative characterization method of pore space in a shale oil reservoir includes:

[0060] Obtain reservoir samples of shale oil reservoirs, analyze micron-scale fracture structures in the reservoir samples by micron CT scanning, and calculate micron-scale equivalent pore structure parameters of the shale oil reservoir based on the micron-scale fracture structures;

[0061] Nano-CT and FIB-SEM techniques are used to analyze and calculate the nano-scale pore structure in reservoir samples, and to calculate the nano-scale organic pore structure parameters of shale oil reservoirs;

[0062] Standardizing the micron-level equivalent pore structure parameters and the nanometer-level organic pore structure parameters to obtain the theoretical volumes of the standardized pore structures in different intervals at different resolutions of the reservoir sample;

[0063] Calculate the volume fraction of the theoretical volume to the standardized total pore volume and the volume fraction to the reservoir sample respectively;

[0064] The full-scale pore distribution curve is established according to each volume fraction to characterize the volume proportion of pores with different pore sizes in the shale oil reservoir.

[0065] In this embodiment, the principle of CT scanning imaging is to use the physical properties of X-rays for imaging. X-rays need to pass through the object to be detected and are received by the detector after transmission. The density distribution information inside the object is obtained based on the intensity of the X-rays. CT scanning is to scan the object to be detected from different directions by rotating the X-ray source and detector, or rotating the workbench, and then reconstruct the obtained data using a computer algorithm to finally generate a three-dimensional image. This image can extract the structure of components of different densities, and at the same time, various operations related to the basic physical properties of rocks can be performed as needed. The degree of X-ray absorption is proportional to the density. Metal minerals are much higher than other components, and air is the lowest, so the components are obviously different in imaging. The most critical part of the CT scanning process is the control of the scanning parameters to ensure that the imaging effect is within a controllable range, mainly involving the following parameters:

[0066] 1. Gray value: It is related to the current, voltage, SOD (distance between sample and radiation source), SDD (distance between detector and radiation source), exposure time and the number of image merging. It is the key parameter of image threshold segmentation. At the same time, the rendering of the 3D model of CT image depends on the range of values ​​selected during threshold segmentation. After adjusting the sample position according to the detector image, only the voltage, current and exposure time need to be adjusted. The larger these values ​​are, the larger the gray value is.

[0067] 2. Penetration: It is a key parameter that affects the imaging effect. It is controlled by voltage. The higher the voltage, the higher the penetration. Penetration is equal to the ratio of the gray value of the sample area to the gray value of the non-sample area during scanning and detection. A conventional sandstone plunger sample (diameter 25mm) can have a good imaging effect if the penetration reaches 30%-50%. If the voltage value reaches the maximum and still cannot meet the penetration requirement, scan at the maximum voltage.

[0068] 3. Resolution: The resolution of CT equipment is mainly divided into spatial resolution and density resolution. Spatial resolution refers to the smallest detail that can be distinguished in the image; density resolution is the basis for CT scanning to distinguish different components of rocks, which indicates the minimum density difference that can be displayed in the image. The density resolution of CT is 0.5%, that is, two tissues can be identified when the density is ≥ 0.5%. When the density difference is < 0.5%, it cannot be distinguished due to noise interference, so how to improve the signal-to-noise ratio is the key to improving resolution;

[0069] 4. Power: The selection of CT scanning power needs to refer to the value of resolution. In principle, 2-3 times of the scanning resolution value is the best. Too high or too low will affect the imaging quality. The focused ion beam scanning electron microscope (FIB-SEM) dual beam system refers to a system that has both focused ion beam (FIB) and scanning electron microscope (SEM) functions. By combining the corresponding gas deposition device, nanomanipulator, various detectors and controllable sample stage and other accessories, it becomes an analytical instrument integrating micro-area imaging, processing, analysis and manipulation.

[0070] The FIB-SEM dual beam system is a device that installs the FIB system and the traditional scanning electron microscope system at a certain angle on the same device. The three-dimensional reconstruction analysis uses the electron beam to observe in real time and uses the ion beam to cut or micro-process the sample area. While completing the cutting and processing of the sample area, the software controls the FIB layer-by-layer cutting and SEM imaging alternately, and finally performs three-dimensional reconstruction through the software. The FIB three-dimensional reconstruction technology is effectively combined with EDS to characterize the structural morphology and composition of the material. When SEM is used for image acquisition, the working distance is generally selected to be 4 to 8 mm, the acceleration voltage is 1 to 2 kV, and the beam current is 50 pA to 1 nA. When FIB is cutting, the voltage is 30 kV and the beam current is selected to be 700 pA.

[0071] Further, according to an embodiment of the present invention, a reservoir sample of a shale oil reservoir is obtained, and a micron-scale fracture structure in the reservoir sample is analyzed by micron CT scanning, and micron-scale equivalent pore structure parameters of the shale oil reservoir are calculated based on the micron-scale fracture structure, including:

[0072] Obtaining reservoir samples of shale oil reservoirs, and drilling plug samples with diameters of 25 mm, 5 mm, and 2 mm respectively on the reservoir samples;

[0073] Micron CT scanning was used to analyze the micron-scale fracture structure in each plunger sample. The scanning results were then processed using Avizo software to count the opening and linear density of the fractures (bedding fractures). The density of fractures with different openings in the standard core (a 25 mm diameter rock plunger sample, a local sample drilled along the vertical bedding plane of the reservoir rock) was then calculated. When the length (the maximum scanning accuracy of the 25 mm plunger sample is 15 μm, and the CT data is reconstructed in three dimensions with a reconstruction height of 15 μm × 1500 = 22.5 mm, to ensure that the longitudinal heterogeneity of the bedding fracture is taken into account as much as possible when calculating the fracture linear density) was kept consistent, the average value of the fracture opening at different scales was taken to calculate the theoretical volume of the fracture: V i =a i b i h,

[0074] Using the equivalent spherical volume algorithm, fractures of different volumes are converted into spheres of equal volume. The radius of the sphere is used to represent the pore distribution range, and the average radius is used as the range of pore volume statistics:

[0075] From the various formulas, we can derive:

[0076] Among them, V i is the crack volume; a i is the crack opening; b i is the crack length; h is the crack height; V f is the total volume of the crack; r i is the equivalent pore radius of the fracture; is the average equivalent radius of the fracture; R is the standard plunger radius of the shale oil reservoir (25 mm); l is the fracture line density. The distribution histogram of the micron-scale bedding fracture pore size of the standard plunger shale oil reservoir is shown in Figure 2 shown.

[0077] Further, according to an embodiment of the present invention, nano-CT and FIB-SEM techniques are used to analyze and calculate the nano-scale pore structure in the reservoir sample, and the nano-scale organic pore structure parameters of the shale oil reservoir are statistically analyzed, including:

[0078] Nano-CT was used to analyze the organic matter proportion of the plunger samples;

[0079] Based on the nano-CT analysis, a typical field of view was selected and local cutting was performed using the FIB-SEM imaging function;

[0080] Avizo software was used to analyze the data generated by FIB-SEM, and the nanoscale organic pore structure parameters of shale oil reservoirs were statistically obtained.

[0081] In this embodiment, the FIB-SEM data is the same as the above-mentioned micron CT scanning data, and both use the equivalent sphere radius to represent the structural parameters of the organic pores, and the details are not repeated here. The pore size distribution histogram of the micron-nanoscale inorganic pores in the shale oil reservoir with a 2 mm plunger is shown in FIG. Figure 3 The FIB-SEM organic pore distribution histogram is shown in Figure 4 shown.

[0082] Further, according to an embodiment of the present invention, the micron-level equivalent pore structure parameters and the nano-level organic pore structure parameters are standardized, including:

[0083] Establishing a unified coordinate system for the micron-scale equivalent pore structure parameters and the nanoscale organic pore structure parameters;

[0084] Establish a standardized coefficient to restore the actual distribution of different pore structures in the standard core in the same coordinate system;

[0085] The standardized coefficients are established as:

[0086]

[0087] Where: Pk is the normalization coefficient corresponding to different scales (two analysis scales, CT scanning is aimed at the three-dimensional characteristics of pore development at the micron to submicron level (here 15 microns to 0.8 microns), and pore structure analysis at multiple scales can be performed, while SEM is aimed at the characterization of pore structure at the submicron to nanometer level (below 0.8 microns), but a single experiment can only quantitatively characterize one scale. In order to solve the problem that the shale pore types and structural parameters at different scales cannot be equivalently compared, multiple analysis scales are set for multiple experiments to complete the hierarchical characterization of different types of pore systems in shale reservoirs.); V is the total volume of the standard core CT three-dimensional reconstructed image (after scanning, the image is preprocessed in the software, part of the air noise area is cut off, and only the calculation results of the main part of the CT scanned rock are retained (including pores and rock skeletons), which are inconsistent with the actual volume of the 25 mm diameter and 50 mm high plunger sample); Vk is the total volume of the three-dimensional reconstructed image corresponding to different scales.

[0088] In this embodiment, Avizo is used to perform grayscale segmentation on CT and FIB-SEM images, an interactive threshold segmentation command is used to extract a pore-crack three-dimensional model, a label analysis command is input to perform voxel analysis, the volume of each pore structure and crack structure is calculated, and then a table file in CSV format is output. On this basis, statistics are performed according to the principle of statistical equivalent pore structure parameters at the micron level, and then the relevant data are sorted. When performing pore data statistics, the units are uniformly converted into microns, thereby establishing the above-mentioned unified coordinate system.

[0089] In this embodiment, when establishing a unified coordinate system for analysis of CT data and FIB-SEM scanning data of different scales, there are differences in the sizes of the selected samples, resulting in uneven actual sizes of image reconstruction. Therefore, in order to restore the actual distribution of different pore spaces in the standard core, it is necessary to establish the above-mentioned standardization coefficient.

[0090] Further, according to an embodiment of the present invention, the theoretical volume of the standardized pore structure in different intervals of the reservoir sample at different resolutions is obtained, and the volume fraction of the theoretical volume to the standardized total pore volume is calculated, including:

[0091] The theoretical values ​​of pore volume sizes in different intervals at different resolutions of standard plug samples are obtained:

[0092] Where V m is the total pore volume with an equivalent pore size mean of m; is the normalized pore volume with an equivalent pore size mean of m;

[0093] Calculate the total pore volume in the corresponding interval at different scales and standardize the porosity, then calculate the percentage of the total pore volume in different intervals to the standardized total pore volume:

[0094]

[0095] is the normalized total pore volume;

[0096] is the normalized porosity;

[0097] is the pore volume fraction with the normalized equivalent pore diameter mean m.

[0098] In this embodiment, in order to ensure the continuity of the data, the CT scan data processing results will inevitably repeat the statistics of some pore structure parameters. Therefore, it is necessary to correct this part of the repeated data, remove the data obtained at low resolution, and use the calculation results at high resolution as the basis to eliminate the intersection of the statistical intervals to avoid the theoretical value being higher than the actual result. For example, the pore size distribution range obtained at low resolution is in the range of 400 to 1000 μm, and the pore size distribution obtained at high resolution is 100 to 600 μm, and the intersection part is 400 to 600 μm. After removing the intersection, the pore size range counted by the low resolution is limited to 600 to 1000 μm, and so on.

[0099] Further, according to an embodiment of the present invention, the theoretical volume of the standardized pore structure in different intervals of the reservoir sample at different resolutions is obtained, and the volume fraction of the theoretical volume to the reservoir sample is calculated, including:

[0100] The volume fractions of organic pores at different pore sizes obtained by FIB-SEM were counted, and the total volume of organic pores in the standard core was calculated by combining the volume fraction of organic matter obtained by nano-CT.

[0101] The total volume of organic pores in the corresponding intervals is obtained by multiplying the volume fraction of organic pores in different pore size intervals calculated by FIB-SEM and the total volume of organic pores after software processing:

[0102]

[0103] Where V Pom is the total organic pore volume of the standard core; E om is the volume fraction of organic matter; is the organic porosity;

[0104] Calculate the ratio of the total volume of organic pores to the volume of the standard plunger:

[0105]

[0106] In the formula, Q n is the pore volume fraction with the mean nanoscale equivalent pore size n; is the pore volume fraction with the normalized equivalent pore size mean n.

[0107] Furthermore, in this embodiment, a full-scale pore distribution curve is established according to each volume fraction. The full-scale pore distribution curve of the shale oil reservoir is shown in FIG. Figure 5 As shown, it can characterize the volume proportion of pores with different pore sizes in shale oil reservoirs to achieve quantitative characterization of the pore space in shale oil reservoirs.

[0108] According to the above scheme of the present invention, the present invention organically combines CT scanning with FIB-SEM in the process of quantitative characterization of the storage space of shale oil reservoirs, thereby realizing the visualized quantitative characterization of shale samples from micron-level bedding fractures to nanometer-level pores and fractures.

[0109] The present invention forms a systematic and comprehensive evaluation technology for shale from micron-level bedding fractures to nano-level organic pores, which is a new method for quantitative analysis of pore structure parameters of shale oil reservoirs in three-dimensional visualization. It not only visualizes the development characteristics of shale reservoir space, deepens the understanding of quantitative characterization methods of shale digital cores, but also strengthens the organic combination of different scale analysis technologies and comprehensive data comparison, providing a new idea for quantitative characterization of shale oil reservoir space by digital cores, which is of great significance for guiding the development of shale oil reservoirs.

[0110] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a shale oil reservoir pore space quantitative characterization system, comprising:

[0111] A micron-level equivalent pore structure parameter statistics module is used to obtain reservoir samples of shale oil reservoirs, analyze the micron-level fracture structure in the reservoir samples using micron CT scanning, and calculate the micron-level equivalent pore structure parameters of the shale oil reservoir based on the micron-level fracture structure;

[0112] The module for statistical parameters of nano-scale organic pore structure uses nano-CT and FIB-SEM technology to analyze and calculate the nano-scale pore structure in reservoir samples and statistically analyze the nano-scale organic pore structure parameters of shale oil reservoirs.

[0113] A standardization processing module is used to standardize the micron-level equivalent pore structure parameters and the nanometer-level organic pore structure parameters to obtain the theoretical volume of the standardized pore structure in different intervals at different resolutions of the reservoir sample;

[0114] The volume fraction calculation module calculates the volume fraction of the theoretical volume to the standardized total pore volume and the volume fraction to the reservoir sample;

[0115] The porosity characterization module establishes a full-scale pore distribution curve based on each volume fraction to characterize the volume proportion of pores with different pore sizes in the shale oil reservoir.

[0116] Further, according to an embodiment of the present invention, a reservoir sample of a shale oil reservoir is obtained, and a micron-scale fracture structure in the reservoir sample is analyzed by micron CT scanning, and micron-scale equivalent pore structure parameters of the shale oil reservoir are calculated based on the micron-scale fracture structure, including:

[0117] Obtaining reservoir samples of shale oil reservoirs, and drilling plug samples with diameters of 25 mm, 5 mm, and 2 mm respectively on the reservoir samples;

[0118] Micron CT scanning was used to analyze the micron-scale fracture structure in each plunger sample. The scanning results were then processed using Avizo software to count the opening and linear density of the fractures (bedding fractures). The density of fractures with different openings in the standard core was then calculated. When the length remained the same, the average opening of the fractures at different scales was taken to calculate the theoretical volume of the fracture: V i =a i b i h,

[0119] Using the equivalent spherical volume algorithm, fractures of different volumes are converted into spheres of equal volume. The radius of the sphere is used to represent the pore distribution range, and the average radius is used as the range of pore volume statistics:

[0120] From the various formulas, we can derive:

[0121] Among them, V i is the crack volume; a i is the crack opening; b i is the crack length; h is the crack height; V f is the total volume of the crack; r i is the equivalent pore radius of the fracture; is the average equivalent radius of the fracture; R is the standard plunger radius of the shale oil reservoir; l is the fracture line density. The distribution histogram of the micron-scale bedding fracture pore size of the standard plunger shale oil reservoir is shown in Figure 2 shown.

[0122] Further, according to an embodiment of the present invention, nano-CT and FIB-SEM techniques are used to analyze and calculate the nano-scale pore structure in the reservoir sample, and the nano-scale organic pore structure parameters of the shale oil reservoir are statistically analyzed, including:

[0123] Nano-CT was used to analyze the organic matter proportion of the plunger samples;

[0124] Based on the nano-CT analysis, a typical field of view was selected and local cutting was performed using the FIB-SEM imaging function;

[0125] Avizo software was used to analyze the data generated by FIB-SEM, and the nanoscale organic pore structure parameters of shale oil reservoirs were statistically obtained.

[0126] In this embodiment, the FIB-SEM data is the same as the above-mentioned micron CT scanning data, and both use the equivalent sphere radius to represent the structural parameters of the organic pores, and the details are not repeated here. The pore size distribution histogram of the micron-nanoscale inorganic pores in the shale oil reservoir with a 2 mm plunger is shown in FIG. Figure 3 The FIB-SEM organic pore distribution histogram is shown in Figure 4 shown.

[0127] Further, according to an embodiment of the present invention, the micron-level equivalent pore structure parameters and the nano-level organic pore structure parameters are standardized, including:

[0128] Establishing a unified coordinate system for the micron-scale equivalent pore structure parameters and the nanoscale organic pore structure parameters;

[0129] Establish a standardized coefficient to restore the actual distribution of different pore structures in the standard core in the same coordinate system;

[0130] The standardized coefficients are established as:

[0131]

[0132] Where: P k is the normalization coefficient corresponding to different scales; V is the total volume of the standard core CT 3D reconstructed image; V k is the total volume of 3D reconstructed images corresponding to different scales.

[0133] In this embodiment, Avizo is used to perform grayscale segmentation on CT and FIB-SEM images, an interactive threshold segmentation command is used to extract a pore-crack three-dimensional model, a label analysis command is input to perform voxel analysis, the volume of each pore structure and crack structure is calculated, and then a table file in CSV format is output. On this basis, statistics are performed according to the principle of statistical equivalent pore structure parameters at the micron level, and then the relevant data are sorted. When performing pore data statistics, the units are uniformly converted into microns, thereby establishing the above-mentioned unified coordinate system.

[0134] In this embodiment, when establishing a unified coordinate system for analysis of CT data and FIB-SEM scanning data of different scales, there are differences in the sizes of the selected samples, resulting in uneven actual sizes of image reconstruction. Therefore, in order to restore the actual distribution of different pore spaces in the standard core, it is necessary to establish the above-mentioned standardization coefficient.

[0135] Further, according to an embodiment of the present invention, the theoretical volume of the standardized pore structure in different intervals of the reservoir sample at different resolutions is obtained, and the volume fraction of the theoretical volume to the standardized total pore volume is calculated, including:

[0136] The theoretical values ​​of pore volume sizes in different intervals at different resolutions of standard plug samples are obtained:

[0137] Where V m is the total pore volume with an equivalent pore size mean of m; is the normalized pore volume with an equivalent pore size mean of m;

[0138] Calculate the total pore volume in the corresponding interval at different scales and standardize the porosity, then calculate the percentage of the total pore volume in different intervals to the standardized total pore volume:

[0139]

[0140] is the normalized total pore volume;

[0141] is the normalized porosity;

[0142] is the pore volume fraction with the normalized equivalent pore diameter mean m.

[0143] In this embodiment, in order to ensure the continuity of the data, the CT scan data processing results will inevitably repeat the statistics of some pore structure parameters. Therefore, it is necessary to correct this part of the repeated data, remove the data obtained at low resolution, and use the calculation results at high resolution as the basis to eliminate the intersection of the statistical intervals to avoid the theoretical value being higher than the actual result. For example, the pore size distribution range obtained at low resolution is in the range of 400 to 1000 μm, and the pore size distribution obtained at high resolution is 100 to 600 μm, and the intersection part is 400 to 600 μm. After removing the intersection, the pore size range counted by the low resolution is limited to 600 to 1000 μm, and so on.

[0144] Further, according to an embodiment of the present invention, the theoretical volume of the standardized pore structure in different intervals of the reservoir sample at different resolutions is obtained, and the volume fraction of the theoretical volume to the reservoir sample is calculated, including:

[0145] The volume fractions of organic pores at different pore sizes obtained by FIB-SEM were counted, and the total volume of organic pores in the standard core was calculated by combining the volume fraction of organic matter obtained by nano-CT.

[0146] The total volume of organic pores in the corresponding intervals is obtained by multiplying the volume fraction of organic pores in different pore size intervals calculated by FIB-SEM and the total volume of organic pores after software processing:

[0147]

[0148] Where V Pom is the total organic pore volume of the standard core; E om is the volume fraction of organic matter; is the organic porosity;

[0149] Calculate the ratio of the total volume of organic pores to the volume of the standard plunger:

[0150]

[0151] In the formula, Q n is the pore volume fraction with the mean nanoscale equivalent pore size n; is the pore volume fraction with the normalized equivalent pore size mean n.

[0152] Furthermore, in this embodiment, a full-scale pore distribution curve is established according to each volume fraction. The full-scale pore distribution curve of the shale oil reservoir is shown in FIG. Figure 5 As shown, it can characterize the volume proportion of pores with different pore sizes in shale oil reservoirs to achieve quantitative characterization of the pore space in shale oil reservoirs.

[0153] According to the above scheme of the present invention, the present invention organically combines CT scanning with FIB-SEM in the process of quantitative characterization of the storage space of shale oil reservoirs, thereby realizing the visualized quantitative characterization of shale samples from micron-level bedding fractures to nanometer-level pores and fractures.

[0154] The present invention forms a systematic and comprehensive evaluation technology for shale from micron-level bedding fractures to nano-level organic pores, which is a new method for quantitative analysis of pore structure parameters of shale oil reservoirs in three-dimensional visualization. It not only visualizes the development characteristics of shale reservoir space, deepens the understanding of quantitative characterization methods of shale digital cores, but also strengthens the organic combination of different scale analysis technologies and comprehensive data comparison, providing a new idea for quantitative characterization of shale oil reservoir space by digital cores, which is of great significance for guiding the development of shale oil reservoirs.

[0155] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for quantitative characterization of the pore space of a shale oil reservoir as described above is implemented.

[0156] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for quantitative characterization of pore space in a shale oil reservoir as described above is implemented.

[0157] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0158] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0159] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0160] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0161] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0162] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0163] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0164] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A quantitative characterization method for pore space in shale oil reservoirs, characterized in that: include: Obtain reservoir samples of shale oil reservoirs, analyze micron-scale fracture structures in the reservoir samples by micron CT scanning, and calculate micron-scale equivalent pore structure parameters of the shale oil reservoir based on the micron-scale fracture structures; Nano-CT and FIB-SEM techniques are used to analyze and calculate the nano-scale pore structure in reservoir samples, and to calculate the nano-scale organic pore structure parameters of shale oil reservoirs; Standardizing the micron-level equivalent pore structure parameters and the nanometer-level organic pore structure parameters to obtain the theoretical volumes of the standardized pore structures in different intervals at different resolutions of the reservoir sample; Calculate the volume fraction of the theoretical volume to the standardized total pore volume and the volume fraction to the reservoir sample respectively; The full-scale pore distribution curve is established according to each volume fraction to characterize the volume proportion of pores with different pore sizes in the shale oil reservoir.

2. The method for quantitative characterization of pore space in shale oil reservoirs according to claim 1, characterized in that: Obtain reservoir samples of shale oil reservoirs, use micron CT scanning to analyze the micron-level fracture structure in the reservoir samples, and calculate the micron-level equivalent pore structure parameters of the shale oil reservoir based on the micron-level fracture structure, including: obtaining a reservoir sample of a shale oil reservoir and drilling a plug sample on the reservoir sample; Micron CT scanning was used to analyze the micron-scale crack structure in each plunger sample. The scanning results were then processed using Avizo software to count the crack opening and line density, and then the density of cracks with different openings in the plunger samples was calculated. When the length remained the same, the crack openings at different scales were averaged to calculate the theoretical volume of the crack: V i =a i b i h, Using the equivalent spherical volume algorithm, fractures of different volumes are converted into spheres of equal volume. The radius of the sphere is used to represent the pore distribution range, and the average radius is used as the range of pore volume statistics: From the various formulas, we can derive: Among them, V i is the crack volume; a i is the crack opening; b i is the crack length; h is the crack height; V f is the total volume of the crack; r i is the equivalent pore radius of the fracture; is the average equivalent radius of the fracture; R is the standard plunger radius of the shale oil reservoir; l is the fracture line density.

3. The method for quantitative characterization of pore space in shale oil reservoirs according to claim 2, characterized in that: Nano-CT and FIB-SEM technology are used to analyze and calculate the nano-scale pore structure in reservoir samples, and the nano-scale organic pore structure parameters of shale oil reservoirs are statistically analyzed, including: Nano-CT was used to analyze the organic matter proportion of the plunger samples; Based on the nano-CT analysis, a typical field of view was selected and local cutting was performed using the FIB-SEM imaging function; Avizo software was used to analyze the data generated by FIB-SEM, and the nanoscale organic pore structure parameters of shale oil reservoirs were statistically obtained.

4. The method for quantitative characterization of pore space in shale oil reservoirs according to claim 3, characterized in that: The micron-scale equivalent pore structure parameters and the nano-scale organic pore structure parameters are standardized, including: Establishing a unified coordinate system for the micron-scale equivalent pore structure parameters and the nanoscale organic pore structure parameters; Establish a standardized coefficient to restore the actual distribution of different pore structures in the standard core; The standardized coefficients are established as: Where: P k is the normalization coefficient corresponding to different scales; V is the total volume of the standard core CT 3D reconstructed image; V k is the total volume of 3D reconstructed images corresponding to different scales.

5. The method for quantitative characterization of pore space in shale oil reservoirs according to claim 4, characterized in that: Obtain the theoretical volume of the standardized pore structure in different intervals of the reservoir sample at different resolutions, and calculate the volume fraction of the theoretical volume to the standardized total pore volume, including: The theoretical values ​​of pore volume sizes in different intervals at different resolutions of standard plug samples are obtained: Where V m is the total pore volume with an equivalent pore size mean of m; is the normalized pore volume with an equivalent pore size mean of m; Calculate the total pore volume in the corresponding interval at different scales and standardize the porosity, then calculate the percentage of the total pore volume in different intervals to the standardized total pore volume: is the normalized total pore volume; is the normalized porosity; is the pore volume fraction with the normalized equivalent pore diameter mean m.

6. The method for quantitative characterization of pore space in shale oil reservoirs according to claim 5, characterized in that: Obtain the theoretical volume of the standardized pore structure in different intervals of the reservoir sample at different resolutions, and calculate the volume fraction of the theoretical volume to the reservoir sample, including: The volume fractions of organic pores at different pore sizes obtained by FIB-SEM were counted, and the total volume of organic pores in the standard core was calculated by combining the volume fraction of organic matter obtained by nano-CT. The total volume of organic pores in the corresponding intervals is obtained by multiplying the volume fraction of organic pores in different pore size intervals calculated by FIB-SEM and the total volume of organic pores after software processing: Where V Pom is the total organic pore volume of the standard core; E om is the volume fraction of organic matter; is the organic porosity; Calculate the ratio of the total volume of organic pores to the volume of the standard plunger: In the formula, Q n is the pore volume fraction with the mean nanoscale equivalent pore size n; is the pore volume fraction with the normalized equivalent pore size mean n.

7. Shale oil reservoir pore space quantitative characterization system, characterized by: include: The micron-level equivalent pore structure parameter statistics module obtains reservoir samples of shale oil reservoirs, uses micron CT scanning to analyze the micron-level fracture structure in the reservoir samples, and calculates the micron-level equivalent pore structure parameters of the shale oil reservoir based on the micron-level fracture structure; The module for statistical parameters of nano-scale organic pore structure uses nano-CT and FIB-SEM technology to analyze and calculate the nano-scale pore structure in reservoir samples and statistically analyze the nano-scale organic pore structure parameters of shale oil reservoirs. A standardization processing module is used to standardize the micron-level equivalent pore structure parameters and the nanometer-level organic pore structure parameters to obtain the theoretical volume of the standardized pore structure in different intervals at different resolutions of the reservoir sample; The volume fraction calculation module calculates the volume fraction of the theoretical volume to the standardized total pore volume and the volume fraction to the reservoir sample; The porosity characterization module establishes a full-scale pore distribution curve based on each volume fraction to characterize the volume proportion of pores with different pore sizes in the shale oil reservoir.

8. An electronic device, characterized in that The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the method for quantitative characterization of pore space of a shale oil reservoir as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for quantitatively characterizing the pore space of a shale oil reservoir according to any one of claims 1 to 6 is implemented.

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