A method and apparatus for determining the multi-scale pore size distribution of shale.

Abstract

This specification provides a method and apparatus for determining the multi-scale pore size distribution of shale. Based on first and second nuclear magnetic resonance (NMR) signals, the fluid NMR signal of the shale sample to be tested is determined. Based on image data, pore structure parameters at different field-of-view sizes are determined, and when the fluctuation amplitude of the pore structure parameters meets preset conditions, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image pore size distribution characteristics based on the target observation range. Based on the fluid NMR signal and the image pore size distribution characteristics, the corresponding NMR distribution components and image pore size distribution components are determined. The image pore size distribution components are used to match the NMR distribution components to establish a mapping relationship between NMR relaxation time and physical pore size. Using this mapping relationship, the fluid NMR signal is converted into a pore size distribution characterization result of the shale sample to be tested. This significantly improves the accuracy and reliability of the shale pore size characterization results.

Description

Technical Field

[0001] This specification belongs to the field of oil and gas development technology, and in particular relates to a method and apparatus for determining the multi-scale pore size distribution of shale. Background Technology

[0002] Shale oil reservoirs are highly heterogeneous and possess complex micro- and nano-scale pore structures. Accurate characterization of their multi-scale pore size distribution is crucial for reservoir evaluation and production prediction. However, existing technologies often employ destructive methods with limited measurement scales to calibrate nuclear magnetic resonance signals, resulting in conversion relationships that are difficult to accurately characterize the true multi-scale pore size distribution.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This specification provides a method and apparatus for determining the multi-scale pore size distribution of shale, which solves the technical problem that existing shale pore size distribution characterization methods are destructive and difficult to accurately characterize the true multi-scale pore size distribution.

[0005] This specification provides a method and apparatus for determining the multi-scale pore size distribution of shale, including: Image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states, are acquired; wherein, the different physical states include saturated fluid state and dry state; Based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal, the fluid nuclear magnetic resonance signal of the shale sample to be tested is determined; Based on the image data, the pore structure parameters under different field of view sizes are determined, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, the corresponding field of view size is determined as the target observation range, so as to determine the corresponding image aperture distribution characteristics based on the target observation range. Based on the fluid NMR signal and the image aperture distribution characteristics, the corresponding NMR distribution component and image aperture distribution component are determined; The image aperture distribution component is used to match the nuclear magnetic resonance distribution component in order to establish a mapping relationship between nuclear magnetic resonance relaxation time and physical aperture. Using the mapping relationship, the fluid NMR signal is converted into a pore size distribution characterization result of the shale sample to be tested.

[0006] In one embodiment, acquiring image data of the shale sample to be tested includes: The cross-section of the shale sample to be tested was subjected to mechanical polishing and argon ion beam fine polishing in sequence to determine the corresponding flat imaging area; Conductivity enhancement processing is performed on the flat imaging area, and the flat imaging area is automatically acquired using the secondary electron imaging mode of the scanning electron microscope to determine multiple raw image data. The multiple original image data are stitched together to determine a large field-of-view stitched scanning electron microscope (SEM) image, and this large field-of-view stitched SEM image is identified as the image data of the shale sample to be tested.

[0007] In one embodiment, determining the pore structure parameters under different field-of-view sizes based on the image data includes: Based on the image data, multiple sub-viewfield images with different viewfield sizes are determined; Based on each of the sub-view images, determine the area of ​​the corresponding view region and the total area of ​​pores within the view region; Based on the area of ​​the field of view and the total area of ​​the apertures, determine the face ratio of each sub-field image at the corresponding field of view size; The pore structure parameters are determined based on the porosity corresponding to each of the stated field sizes.

[0008] In one embodiment, determining the corresponding image aperture distribution features based on the target observation range includes: Based on the target observation range, multiple discrete aperture intervals are determined; Based on each aperture interval, determine the total number of pore pixels belonging to each aperture interval within the target observation range from the image data; Based on the total number of pore pixels and the actual physical length corresponding to a single pixel, the local porosity corresponding to each pore size range is determined; The image aperture distribution features are determined based on the local face rate corresponding to each aperture interval.

[0009] In one embodiment, determining the corresponding NMR distribution component and image aperture distribution component based on the fluid NMR signal and the image aperture distribution characteristics includes: The fluid NMR signal is discretized to determine the NMR signal intensity corresponding to multiple NMR discrete intervals; The image aperture distribution features are discretized to determine the inter-interval aperture ratios corresponding to multiple discrete image intervals; The total amplitude of the NMR signal is determined by performing cumulative statistical processing on the intensity of each NMR signal, and normalization processing is performed on the intensity of each NMR signal based on the total amplitude of the NMR signal to determine the NMR distribution component corresponding to each NMR discrete interval; The total face rate is determined by performing cumulative statistical processing on the face rates of each of the said segmented intervals, and normalization processing is performed on the face rates of each of the said segmented intervals based on the total face rate to determine the image aperture distribution components corresponding to each of the said discrete image intervals.

[0010] In one embodiment, the step of matching the NMR distribution component with the image aperture distribution component to establish a mapping relationship between NMR relaxation time and physical aperture includes: Based on the discrete aperture sampling points corresponding to the image aperture distribution components, interpolation resampling processing is performed on the nuclear magnetic resonance distribution components to determine the initial nuclear magnetic resonance distribution signal corresponding to each discrete aperture sampling point. Based on the preset search range and iteration step size, candidate values ​​for the scaling factor between the nuclear magnetic resonance relaxation time and the physical aperture are determined. The candidate values ​​are used to perform scaling processing on the initial NMR distribution signal corresponding to each discrete aperture sampling point to determine the NMR signal component to be matched for each discrete aperture sampling point. Based on each of the discrete aperture sampling points, the distribution residual between the NMR signal component to be matched and the image aperture distribution component is calculated, and the target optimization function value is determined based on the distribution residual. When the target optimization function value satisfies the preset convergence condition, the corresponding candidate value is determined as the transformation coefficient. Based on the conversion coefficient, a mapping operator between the nuclear magnetic resonance relaxation time and the physical aperture is determined, and the mapping operator is defined as the mapping correspondence.

[0011] In one embodiment, converting the fluid NMR signal into a pore size distribution characterization result of the shale sample under test using the mapping correspondence includes: Based on the mapping relationship, each discrete transverse relaxation time point corresponding to the fluid NMR signal is mapped to the physical aperture axis to determine each discrete physical aperture value. Based on the conversion coefficient, the signal amplitude of the fluid NMR signal at each of the discrete transverse relaxation time points is calibrated to determine the porosity contribution component corresponding to each of the discrete physical aperture values. Based on the discrete physical pore size values ​​and the porosity contribution components corresponding to each discrete physical pore size value, the pore size distribution characterization results of the shale sample to be tested are determined.

[0012] This specification provides a device for determining the multi-scale pore size distribution of shale, including: The data acquisition module is used to acquire image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states; wherein, the different physical states include saturated fluid state and dry state; The signal determination module is used to determine the fluid nuclear magnetic resonance signal of the shale sample to be tested based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal; The feature determination module is used to determine the pore structure parameters under different field-of-view sizes based on the image data, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image aperture distribution features based on the target observation range; The component determination module is used to determine the corresponding nuclear magnetic resonance distribution component and the image aperture distribution component based on the fluid nuclear magnetic resonance signal and the image aperture distribution characteristics; The relationship determination module is used to perform matching processing on the nuclear magnetic resonance distribution component using the image aperture distribution component, so as to establish a mapping correspondence between nuclear magnetic resonance relaxation time and physical aperture; The result determination module is used to convert the fluid NMR signal into a pore size distribution characterization result of the shale sample under test by utilizing the mapping correspondence.

[0013] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements a method for determining the multi-scale pore size distribution of shale.

[0014] This specification also provides a computer-readable storage medium storing computer instructions that, when executed, implement a method for determining the multi-scale pore size distribution of shale.

[0015] Based on the method for determining the multi-scale pore size distribution of shale provided in this specification, image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance (NMR) signals under different physical states are acquired; wherein, the different physical states include saturated fluid state and dry state; the fluid NMR signal of the shale sample to be tested is determined according to the first and second NMR signals; pore structure parameters under different field-of-view sizes are determined according to the image data, and when the fluctuation amplitude of the pore structure parameters meets a preset condition, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image pore size distribution characteristics according to the target observation range; the corresponding NMR distribution components and image pore size distribution components are determined according to the fluid NMR signal and the image pore size distribution characteristics; the image pore size distribution components are used to match the NMR distribution components to establish a mapping relationship between NMR relaxation time and physical pore size; the fluid NMR signal is converted into a pore size distribution characterization result of the shale sample to be tested using the mapping relationship. In this way, by introducing image data to replace traditional destructive physical experiments such as nitrogen adsorption or high-pressure mercury intrusion, and combining it with nuclear magnetic resonance signals under different physical states, the pore size distribution characterization results of shale can be accurately obtained without damaging the basic structure of the shale sample. The image pore size distribution characteristics are determined by using the target observation range that meets the preset conditions, ensuring the statistical representativeness of the characterization parameters in strongly heterogeneous shale reservoirs. By establishing a mapping relationship between lateral relaxation time and physical pore size, non-destructive and high-precision quantitative characterization of multi-scale pore size distribution, including connected and disconnected pores, can be achieved using nuclear magnetic resonance signals. Attached Figure Description

[0016] To more clearly illustrate the embodiments of this specification, the accompanying drawings used in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a method for determining the multi-scale pore size distribution of shale, provided in one embodiment of this specification. Figure 2 This is a schematic diagram of the electronic device structure provided in one embodiment of this specification; Figure 3 This is a schematic diagram of the structural composition of a shale multi-scale pore size distribution determination device provided in one embodiment of this specification; Figure 4 This is a schematic diagram of a large field-of-view stitched scanning electron microscope under different scale fields of view, provided by one embodiment of this specification; Figure 5 This is a schematic diagram of the aperture distribution of a large field-of-view stitched scanning electron microscope provided in one embodiment of this specification; Figure 6 This is a schematic diagram of the fitting of nuclear magnetic resonance relaxation time T2 with aperture provided in one embodiment of this specification; Figure 7 This is a schematic diagram of the nuclear magnetic resonance pore size distribution of five shale samples provided in one embodiment of this specification. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0019] See Figure 1 As shown in the embodiments of this specification, a method for determining the multi-scale pore size distribution of shale is provided, wherein the method is specifically applied to the server side. In specific implementation, the method may include the following: S101: Acquire image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states; wherein, the different physical states include saturated fluid state and dry state; S102: Determine the fluid nuclear magnetic resonance signal of the shale sample to be tested based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal; S103: Based on the image data, determine the pore structure parameters under different field-of-view sizes, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, determine the corresponding field-of-view size as the target observation range, so as to determine the corresponding image aperture distribution characteristics based on the target observation range; S104: Determine the corresponding NMR distribution component and image aperture distribution component based on the fluid NMR signal and the image aperture distribution characteristics; S105: The image aperture distribution component is used to match the nuclear magnetic resonance distribution component to establish a mapping relationship between nuclear magnetic resonance relaxation time and physical aperture. S106: Using the mapping correspondence, the fluid nuclear magnetic resonance signal is converted into the pore size distribution characterization result of the shale sample to be tested.

[0020] The aforementioned image data refers to the digital morphological information obtained by acquiring cross-sections of the shale sample under test using high-resolution imaging equipment (such as a large field-of-view mosaic imaging system). It records the spatial distribution, geometric morphology, and contact relationship of pores and mineral matrix in the microstructure of shale in the form of a pixel matrix, and is the basic data source for extracting true physical scale features in this invention.

[0021] The aforementioned saturated fluid state refers to a physical state in which all developable and interconnected pore spaces within a shale sample are completely filled with a specific fluid (such as simulated oil, formation water, or a solvent) through physical experimental methods such as vacuum pressurization saturation. In this state, the hydrogen content of the shale sample reaches its peak, and the signal captured by the nuclear magnetic resonance (NMR) instrument not only includes the background response of the rock mineral framework (such as organic matter and clay minerals) but also reflects the characteristics of the fluid filling the pores to the greatest extent possible. This is the core benchmark state for determining the full-range pore volume and original signal distribution of the sample.

[0022] The aforementioned dry state refers to the physical state of the shale sample after it has undergone processes such as isothermal salt washing, drying, or vacuum extraction to completely remove all flowable and volatile fluids (such as free water, mobile oil, and some bound fluids) from the pores. Establishing this state aims to minimize the contribution of fluid hydrogen nuclei to the measurement, thereby enabling precise calibration of the hydrogen nucleus background signal generated by the shale matrix (especially solid organic matter such as kerogen) using nuclear magnetic resonance (NMR) technology. As a form of "differential noise floor," the signal under the dry state is crucial for subsequently extracting the pure fluid NMR signal, representing only the pore space, from the overlapping signals.

[0023] The aforementioned first nuclear magnetic resonance signal can refer to the transverse relaxation time distribution signal measured in a completely dry state of the shale sample. Since continental shale contains a certain amount of organic matter (kerogen) or clay minerals, the hydrogen nuclei present in its framework will produce background interference. This signal is used as the "noise floor baseline" for subsequent differential calculations to eliminate the influence of non-porous fluids.

[0024] The aforementioned second nuclear magnetic resonance signal can refer to the transverse relaxation time distribution signal measured in a saturated fluid state (such as saturated simulated oil or water) of the shale sample under test. This signal reflects the superposition response of the sample's mineral framework and the hydrogen nuclei of the fluid within the pores, containing the original characteristic information of the full-scale pore volume distribution of the shale, and is the core original data for calculating the pore distribution.

[0025] The aforementioned fluid NMR signal can refer to the differential signal obtained by subtracting the first NMR signal from the second NMR signal. Essentially, it represents the pure pore fluid response after eliminating interference from hydrogen nuclei in the mineral framework. This signal has a clear proportional relationship with the pore volume, ensuring that the subsequent mapping accurately reflects the physical dimensions of the pore space, rather than matrix interference.

[0026] The field of view size mentioned above can refer to the geometric side length or area of ​​a sub-image cropped from the original large field of view image data. It is the independent variable for multi-scale statistical analysis. By changing different field of view sizes, the evolution of porosity parameters with the expansion of the observation scale can be systematically observed, and it is a key parameter for determining the representative field of view (REA).

[0027] The aforementioned pore structure parameters can be physical indicators used to quantitatively describe the degree of development of the microscopic space in shale. In this specification, they are typically expressed as porosity (i.e., the proportion of pores per unit area). The sensitivity of this parameter to change is used to assess the heterogeneity of shale and serves as a core criterion for determining whether the observation range is statistically representative.

[0028] The aforementioned target observation range can refer to the minimum field of view (REA) determined when the fluctuation amplitude of pore structure parameters tends to stabilize as the field of view size increases and meets the preset convergence conditions. Information extracted within this range can represent the overall pore characteristics of the sample, effectively solving the measurement bias caused by the strong heterogeneity of continental shale.

[0029] The aforementioned image aperture distribution characteristics refer to the pore development abundance or local porosity sequence corresponding to each discrete aperture interval obtained statistically using image recognition technology within the target observation range. It transforms complex image textures into statistical distributions with real length units (nanometers / micrometers), serving as a physical "ruler" for calibrating the nuclear magnetic resonance time axis.

[0030] The aforementioned NMR distribution components can be normalized fluid NMR signals, representing the proportion of the signal contribution to the total signal amplitude at each discrete transverse relaxation time point. By eliminating the original dimensions, it transforms the signal intensity into a dimensionless probability distribution, making it mathematically comparable to image features.

[0031] The aforementioned image aperture distribution components can be normalized image aperture distribution features, representing the contribution ratio of local surface area ratio to the total surface area ratio corresponding to each discrete aperture interval. It transforms the two-dimensional area ratio into a numerical sequence reflecting spatial distribution probability, serving as an intermediate medium for establishing cross-scale mapping relationships.

[0032] The above mapping relationship is a mathematical correlation operator connecting the abstract time domain (relaxation time T_2) with the real physical spatial domain (aperture d). It determines the real physical scale location corresponding to each energy point in the NMR signal and is the core logic for realizing quantitative calibration and full-scale inversion of NMR signals.

[0033] The pore size distribution characterization results described above can be the final quantitative data output of this specification. They refer to the pore size distribution data reflecting the full-range characteristics of the shale sample under test, obtained by restoring the fluid NMR signal using a defined mapping relationship. This result has the advantages of being non-destructive, highly accurate, and simultaneously covering both connected and disconnected pore information, and can be directly used for reservoir evaluation and productivity prediction.

[0034] In some embodiments, determining the fluid NMR signal of the shale sample to be tested based on the first NMR signal and the second NMR signal may specifically include: Based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal, the amplitude of the dry state signal and the amplitude of the saturated state signal at each discrete transverse relaxation time point are determined respectively. The amplitude difference corresponding to each discrete transverse relaxation time point is determined based on the amplitude of the saturated state signal and the amplitude of the dry state signal corresponding to each discrete transverse relaxation time point. The fluid NMR signal of the shale sample under test is determined based on the amplitude difference corresponding to each discrete transverse relaxation time point.

[0035] Specifically, a pore size calibration experiment was conducted on a typical continental shale sample from the Dishahejie Formation in a basin. First, standard plunger samples were prepared from the original shale cores using wire cutting technology. The samples were approximately 2.5 cm in diameter and 5 cm in length. To eliminate interference from the original formation fluids, dichloromethane was used as the organic extraction solvent to wash the shale samples. After washing, the samples were placed in a drying oven and dried continuously at 105°C for 24 hours to ensure complete drying.

[0036] Subsequently, the transverse relaxation time (T2) of the shale sample under the washed-and-dried state was measured using a nuclear magnetic resonance (NMR) analyzer, and the first NMR signal was acquired. The signal measured at this time is mainly contributed by the shale matrix (such as hydrogen nuclei in kerogen and clay minerals), and is defined as the shale matrix signal. After acquisition, each discrete transverse relaxation time point (T2) after discretization was analyzed. The corresponding NMR signal amplitude was extracted and recorded as the signal amplitude in the dry state. .

[0037] Next, the dried shale sample was placed in a saturation device, and crude oil saturation was performed at a pressure of 30 MPa for 48 hours to ensure that the crude oil fully entered the effective pore space of the shale. After saturation, the T2 relaxation spectrum of the sample under saturation was measured again using a nuclear magnetic resonance analyzer to obtain the second nuclear magnetic resonance signal. Similarly, the discrete transverse relaxation time points ( The corresponding signal value is recorded as the saturated state signal amplitude S_{saturation}(T_{2,i}).

[0038] Finally, the computational unit calculates the fluid NMR signal of the shale sample under test. Specifically, for each discrete transverse relaxation time point ( ), using the obtained saturation state signal amplitude Subtract the amplitude of the drying state signal at the same time point The NMR signal of the i-th point in the real pore is obtained through point-to-point differential operation. Its calculation logic follows the formula below:

[0039] in, This represents the i-th discrete relaxation time point; The amplitude of the NMR signal at the i-th point under saturation; The amplitude of the NMR signal at the i-th point under dry conditions; The NMR signal is the signal at the i-th point in the real pore.

[0040] By performing the above calculations on discrete points across the entire frequency band, the fluid NMR signal of the shale sample under test is finally determined.

[0041] By performing point-to-point differential processing on NMR signals from washed-oil dried and saturated fluid states, a matrix signal stripping mechanism for continental shale was constructed, effectively eliminating background interference from framework components such as kerogen and clay minerals. Compared to traditional direct measurement methods, this step can accurately extract the effective signal contributed solely by the fluid within the actual pores from the overlapping physical responses, significantly improving the purity and signal-to-noise ratio of the target data. This lays a reliable data foundation for subsequent high-precision matching of microscopic image distribution and NMR relaxation time, thereby ensuring the accuracy and objectivity of the final pore size distribution evaluation results.

[0042] In some embodiments, when the fluctuation amplitude of the pore structure parameters meets a preset condition, the corresponding field of view size is determined as the target observation range. Specifically, this may include: Based on the different field-of-view sizes and the pore structure parameters corresponding to each field-of-view size, determine the fluctuation characteristic curve of the pore structure parameters as a function of the field-of-view size; Based on the fluctuation characteristic curve, determine the parameter volatility corresponding to each of the field-of-view sizes; The volatility of each parameter is compared with a preset volatility threshold, and when the number of times the parameter volatility continuously reaches the preset volatility threshold range meets the preset number condition, the current corresponding field of view size is determined as the target observation range.

[0043] Specifically, high-resolution raw images of the shale sample to be tested are first acquired using a large field-of-view mosaic scanning electron microscope (MAPS). The computing unit uses the center point or random anchor point of the raw image as a starting point and automatically sets a series of progressively increasing field-of-view sizes. For each field-of-view size, an image segmentation algorithm is used to identify the pore pixels and calculate the corresponding pore structure parameters. In this embodiment, the preferred parameter is the porosity (i.e., the ratio of the total pore area to the total area of ​​the current field of view).

[0044] Subsequently, a fluctuation characteristic curve is constructed based on the acquired field-of-view size sequence and its corresponding porosity values. In this curve, the horizontal axis represents the field-of-view side length, and the vertical axis represents the porosity. The calculation unit uses a sliding window algorithm or a difference algorithm to calculate the slope or range of the curve at each point in real time, thereby determining the parameter fluctuation rate for each field-of-view size. This fluctuation rate reflects the degree to which the heterogeneity within the shale affects the porosity statistics as the observation scale increases.

[0045] Finally, the calculated parameter volatility is compared with the preset volatility threshold in the background. When the fluctuation amplitude of the face rate gradually decreases and remains within the threshold range within multiple consecutive field-of-view step points (i.e., the preset number of steps), it is determined that the image statistical results have entered a stable stage and have met the requirements of the representative field of view (REA). At this point, the scale growth is automatically stopped, and the field of view size at the current stable starting point is locked as the target observation range. The image information extracted based on this range will serve as the sole physical reference for subsequent calibration of the NMR signal.

[0046] By introducing an automatic representative field-of-view determination mechanism, this method effectively solves the problem of statistical bias in the microscopic characterization of terrestrial shale caused by its extremely strong heterogeneity. By dynamically monitoring the fluctuation characteristics of pore parameters with varying observation scales and using preset thresholds and frequency conditions for convergence determination, this step automatically selects the smallest observation range with statistical representativeness, thereby ensuring that the subsequently extracted image features can accurately represent the overall properties of the sample. Compared to traditional manual random point selection observations, this method significantly improves the objectivity and scientific rigor of data acquisition, providing a solid statistical foundation for achieving high-precision pore size distribution mapping across scales.

[0047] In some embodiments, the method for acquiring image data of the shale sample to be tested may further include the following: S1: The cross-section of the shale sample to be tested is subjected to mechanical polishing and argon ion beam fine polishing in sequence to determine the corresponding flat imaging area; S2: Perform conductivity enhancement processing on the flat imaging area, and automatically acquire data of the flat imaging area using the secondary electron imaging mode of the scanning electron microscope to determine multiple raw image data. S3: Perform stitching processing on the multiple original image data to determine a large field-of-view stitched scanning electron microscope image, and determine the large field-of-view stitched scanning electron microscope image as the image data of the shale sample to be tested.

[0048] Specifically, the image acquisition process for the shale sample begins with meticulous surface preparation. Specifically, shale cores collected from the strata are cut into 1cm × 1cm × 0.5cm pieces using a wire cutter and then subjected to multi-stage mechanical polishing on an automatic polishing machine (gradually increasing the sandpaper grit from 800 to 2000 grit) to initially eliminate surface cutting marks. Subsequently, the sample is transferred to an argon ion polisher (such as the Leica TIC3X), where the accelerating voltage is set to 5kV to 7kV, and the cross-section is subjected to argon ion beam fine polishing for 3 to 4 hours under vacuum conditions. This step aims to utilize the ablation effect of the ion beam to completely eliminate scratches, dragging, and pore clogging caused by mechanical polishing, thereby identifying a smooth imaging area with minimal surface undulation that can truly expose the original pore structure.

[0049] Subsequently, to suppress charge accumulation during electron microscopy scanning and improve the imaging signal-to-noise ratio, conductivity enhancement treatment was performed on the flat imaging region. Specifically, a high-purity carbon or gold film with a thickness of approximately 5 nm to 10 nm was sprayed onto the sample surface using a vacuum coating machine. After processing, automated acquisition was performed using a field emission scanning electron microscope (FE-SEM) in secondary electron imaging mode. By setting the accelerating voltage of the scanning electron microscope to 2 kV to 3 kV and the working distance to 4 mm to 6 mm, a preset stepping trajectory was set to perform a matrix grid scan of the flat imaging region. During this process, the overlap between adjacent scanning fields was set to 10% to 15% to ensure that feature points at the edges of each field of view could be accurately matched, thereby determining a series of multiple raw image data containing local microscopic information.

[0050] Finally, the computing unit invokes large field-of-view image processing software (such as MAPS) to perform stitching processing on the multiple original image data using grayscale registration and feature recognition algorithms. By performing geometric correction and brightness equalization on the overlapping areas, the contrast differences between different fields of view are eliminated, thereby determining a single large field-of-view stitched scanning electron microscope image with a coverage area at the millimeter level and a resolution maintained at the nanometer level. This stitched image is identified as the image data of the shale sample to be tested. It not only completely records the spatial morphology of the shale matrix from nanoscale micropores to micrometer-scale macropores, but also provides a statistically significant digital base for subsequent determination of the representative field of view (REA).

[0051] By employing a dual-stage combined process of mechanical polishing and argon ion fine polishing, the interference of "pseudo-clogging" and "false fragmentation" caused by traditional grinding methods on the micropores of shale was effectively overcome, ensuring the true inversion of pore edge morphology. By utilizing conductivity enhancement processing combined with automated secondary electron imaging technology, the resolution accuracy and data acquisition efficiency of nanoscale pores were significantly improved. In particular, the introduction of large field-of-view stitching technology completely solved the technical problem of the extremely limited observation range of single-field scanning electron microscopy images, which makes it difficult to characterize the strong heterogeneity of shale. This achieved an organic unity between microscopic precision and macroscopic scale, providing high-quality, full-range physical evidence for subsequent determination of statistically representative pore size distribution characterization results.

[0052] In some embodiments, the method for determining pore structure parameters under different field-of-view sizes based on the image data may further include the following: S1: Based on the image data, determine multiple sub-viewfield images with different viewfield sizes; S2: Based on each of the sub-view images, determine the area of ​​the corresponding view region and the total area of ​​the pores within the view region; S3: Determine the face density of each sub-view image at the corresponding view size based on the area of ​​the view region and the total area of ​​the apertures; S4: Determine the pore structure parameters based on the porosity corresponding to each of the stated field sizes.

[0053] Specifically, the process of determining multiple sub-field images with different field-of-view sizes is as follows: In the acquired large-field stitched scanning electron microscope image, the computing unit first determines an initial anchor point (preferably the geometric center coordinates of the image). Using this anchor point as the center, a series of square sampling windows with side lengths increasing in an arithmetic progression are established according to a preset step interval. For example, the initial field-of-view size is set to a side length of 10 μm, the step interval is 10 μm, and the final field-of-view size is set to a side length of 500 μm. This automatically extracts and determines 50 sets of sub-field images with multi-scale features from the original image. This isocentric enlargement sampling method ensures the spatial inheritance of observation data at different scales and eliminates local heterogeneity interference caused by random location selection.

[0054] Subsequently, for each acquired sub-view image, digital image processing techniques are used to determine the corresponding spatial metrics. Specifically, firstly, the area of ​​the current view region is calculated based on the total number of pixels in the current sub-view image and the physical resolution represented by a single pixel (e.g., 2nm / pixel). Next, a preset image segmentation model (e.g., Otsu's maximum inter-class variance method or adaptive thresholding algorithm) is used to binarize the sub-view image, separating low-grayscale pixels representing pores from high-grayscale pixels representing the mineral matrix. The number of all pore pixels in the binarized image is counted using a pixel accumulation algorithm, and the total pore area within the view region is determined by combining this with the physical resolution.

[0055] Finally, the calculation unit performs a ratio calculation based on the area of ​​the field of view and the total area of ​​pores to determine the porosity of each sub-field of view at the corresponding field of view size. Each field of view size is correlated and mapped with its corresponding porosity value to generate a data sequence describing the dynamic evolution of porosity with the observation scale. This sequence is then identified as a pore structure parameter reflecting the spatial heterogeneity of the shale sample under test. This parameter not only records the porosity development level at a single scale but also provides a statistically significant quantitative criterion for subsequent determination of the representative field of view (REA) and the target observation range through the fluctuation trend of porosity with scale.

[0056] By constructing an isocentric step-by-step multi-scale sampling mechanism, this invention effectively overcomes the statistical bias caused by the extremely strong heterogeneity of shale in traditional single-point observation methods, ensuring the continuity and logic of observation data in scale evolution. Utilizing adaptive image segmentation and pixel-level area statistics techniques, the objectivity and accuracy of porosity calculation are significantly improved, eliminating subjective errors from human porosity identification. Most importantly, by acquiring the dynamic evolution sequence of porosity as the field of view changes, this invention can scientifically reveal the scale effect of shale porosity development, thus providing a data foundation for accurately defining representative observation boundaries and ensuring that the final pore size distribution results possess statistical representativeness across the entire sample.

[0057] In some embodiments, the method for determining the corresponding image aperture distribution features based on the target observation range may further include the following: S1: Based on the target observation range, determine multiple discrete aperture intervals; S2: Based on each aperture interval, determine the total number of pore pixels belonging to each aperture interval within the target observation range from the image data; S3: Determine the local porosity corresponding to each aperture range based on the total number of aperture pixels and the actual physical length of a single pixel; S4: Determine the image aperture distribution features based on the local aperture ratio corresponding to each aperture interval.

[0058] Specifically, the process of determining multiple discrete aperture intervals is as follows: The computing unit sets the full-range aperture analysis and sampling frequency based on the physical size of the determined target observation range (REA) and the image's limiting resolution. Specifically, the actual physical length of a single pixel in the image (e.g., 2 nm) is used as the lower limit of the minimum aperture, and the maximum aperture that can be accommodated within the target observation range is used as the upper limit. The full-range is divided into N continuous and non-overlapping discrete intervals (e.g., 100 intervals covering a range from 2 nm to 10,000 nm) using logarithmic or linear division. This discretization ensures that subsequent statistical processes can precisely capture the minute developmental differences from micropores to macropores.

[0059] Subsequently, image processing algorithms are used to extract morphological features from the pores within the target observation range. Specifically, the binarized target image is first processed to label connected components, identifying each individual pore object. For each pore object, the total number of pixels it contains is calculated, and the equivalent physical diameter of the pore is calculated based on a pre-defined geometric equivalent model (such as an equivalent circle diameter model).

[0060] Next, each pore is assigned to the corresponding aperture interval according to its equivalent physical diameter, and the number of pixels occupied by all pores in the same interval is accumulated to determine the total number of pore pixels belonging to each aperture interval within the target observation range.

[0061] Furthermore, a quantitative conversion between area and proportion is performed. The calculation unit multiplies the total number of pore pixels by the square of the actual physical length corresponding to a single pixel (i.e., the physical area of ​​a single pixel) to determine the actual physical area of ​​the pores corresponding to each pore size interval. Subsequently, the actual physical area of ​​the pores corresponding to each interval is divided by the total physical area of ​​the target observation range to obtain the local porosity corresponding to each pore size interval. This local porosity directly reflects the contribution of the pore space to the total shale reservoir space at a specific spatial scale.

[0062] Finally, the calculation unit uses the determined aperture intervals as the abscissa and the local porosity corresponding to each aperture interval as the ordinate to construct the corresponding probability distribution sequence, which is then determined as the image aperture distribution feature. This feature not only digitally records the spatial structural distribution pattern of shale within a representative observation range, but also provides a physical reference system with real spatial dimension for subsequent "point-to-point" matching of NMR distribution components through the physical quantity of "area proportion".

[0063] By performing fine discretization and pixel-level statistics within a representative field of view, the accuracy loss caused by traditional visual observation or coarse statistics is effectively overcome, ensuring the continuity and accuracy of the pore size distribution description. Using an equivalent geometric model, two-dimensional image pixels are transformed into one-dimensional physical pore sizes. Combined with the calculation of local porosity, a quantitative and digital characterization of shale pore structure is achieved. This extraction of distribution features based on a real physical scale not only objectively reflects the complex pore development abundance of continental shale, but more importantly, it transforms intuitive "image information" into "distribution components" that can be directly compared with physical signals, providing a crucial physical benchmark for ultimately achieving non-destructive, high-precision full-range pore size distribution inversion.

[0064] In some embodiments, the method for determining the corresponding NMR distribution component and image aperture distribution component based on the fluid NMR signal and the image aperture distribution characteristics may further include the following: S1: Discretize the fluid NMR signal to determine the NMR signal intensity corresponding to multiple NMR discrete intervals; S2: Discretize the image aperture distribution features to determine the aperture ratio of multiple discrete image intervals; S3: Perform cumulative statistical processing based on the intensity of each NMR signal to determine the total amplitude of the NMR signal, and perform normalization processing on the intensity of each NMR signal based on the total amplitude of the NMR signal to determine the NMR distribution component corresponding to each NMR discrete interval; S4: Perform cumulative statistical processing on the face rates of each of the said segmented intervals to determine the total face rate, and perform normalization processing on the face rates of each of the said segmented intervals based on the total face rate to determine the image aperture distribution components corresponding to each of the said discrete image intervals.

[0065] Specifically, the discretization process for the fluid NMR signal is as follows: the computing unit retrieves the differentially processed fluid NMR T2 spectrum data and, according to a preset sampling frequency or echo interval, determines a series of discrete sampling points on the relaxation time axis (e.g., setting 64, 128, or 256 discrete points). For each discrete transverse relaxation time point T... 2,i The corresponding resonance energy amplitude is extracted to determine the NMR signal intensity corresponding to multiple discrete NMR intervals. This process transforms the continuous physical signal into a digital discrete sequence that is easy for computers to process.

[0066] Subsequently, the image-side data is processed synchronously. Specifically, based on the discrete aperture intervals determined in the preceding steps, the image aperture distribution features are discretized and mapped. The computational unit statistically analyzes the data falling within each aperture interval [d]. j ,d j+1The sum of the local porosity contributed by all pores within the image is used to determine the inter-interval porosity corresponding to multiple discrete image intervals. Thus, both the NMR signal and image information are transformed into numerical representations based on discrete intervals of the same order of magnitude.

[0067] Next, the computational unit performs normalization of the NMR signal. First, it performs cumulative statistical processing on the NMR signal intensities corresponding to all discrete intervals to calculate the total amplitude of the NMR signal across the entire range (i.e., the total area under the T2 spectrum envelope). Then, using this total amplitude as the denominator, it is divided by the NMR signal intensity corresponding to each NMR discrete interval to determine the NMR distribution component corresponding to each NMR discrete interval. This component represents the percentage contribution of a specific relaxation time to the total pore volume, eliminating the absolute numerical differences in the original signal caused by instrument gain or sample volume.

[0068] Finally, the image data is processed according to the same mathematical logic. The total face area within the target observation range is determined by summing the face area ratios of each of the aforementioned intervals. Subsequently, the total face area ratio is used to normalize the face area ratios of each interval, determining the image aperture distribution components corresponding to each discrete image interval. Through the above bidirectional normalization process, the "NMR energy distribution" and "image spatial distribution" are successfully transformed into mathematically equivalent probability density distribution functions, providing a standardized data foundation for subsequently establishing the mapping relationship between relaxation time and physical aperture.

[0069] By introducing a two-way discretization and normalization mechanism, the technical challenge of directly comparing NMR signals (electromagnetic energy dimension) and SEM images (spatial area dimension) due to their incompatible dimensions is completely resolved from a mathematical perspective. Using the "total amplitude" and "total porosity" determined through cumulative statistics as benchmarks, absolute values ​​are transformed into relative probability distribution components reflecting pore structure characteristics, effectively filtering errors caused by inconsistent sample sizes, instrument environmental noise, and heterogeneity. This processing method not only significantly improves the computational stability of subsequent "point-to-point" matching but also ensures that the final determined mapping relationship can truly and objectively reflect the essential physical scale characteristics of shale, greatly enhancing the scientific reliability of cross-scale characterization results.

[0070] In some embodiments, the matching processing of the NMR distribution component with the image aperture distribution component to establish a mapping relationship between NMR relaxation time and physical aperture may further include the following: S1: Based on the discrete aperture sampling points corresponding to the image aperture distribution components, perform interpolation resampling processing on the nuclear magnetic resonance distribution components to determine the initial nuclear magnetic resonance distribution signal corresponding to each discrete aperture sampling point; S2: Based on the preset search range and iteration step size, determine the candidate values ​​of the scaling factor between the nuclear magnetic resonance relaxation time and the physical aperture; S3: Using the candidate values, perform scale scaling processing on the initial NMR distribution signal corresponding to each discrete aperture sampling point to determine the NMR signal component to be matched for each discrete aperture sampling point; S4: Based on each of the discrete aperture sampling points, calculate the distribution residual between the NMR signal component to be matched and the image aperture distribution component, and determine the target optimization function value based on the distribution residual; S5: When the target optimization function value satisfies the preset convergence condition, the current corresponding candidate value is determined as the conversion coefficient; S6: Based on the conversion coefficient, determine the mapping operator between the nuclear magnetic resonance relaxation time and the physical aperture, and define the mapping operator as the mapping correspondence.

[0071] Specifically, the first step in performing the matching process is to align the coordinates of the heterogeneous data. The computational unit employs a cubic spline interpolation algorithm, using the discrete aperture sampling points (physical size d) in the image aperture distribution component as a reference. i Using the reference coordinates, the original NMR distribution components are interpolated and resampled. This process projects the NMR signal, originally on the relaxation time axis (ms), onto the physical aperture axis (nm), thus determining the initial NMR distribution signal at the same physical scale. This step eliminates the computational obstacle caused by the inconsistency between the NMR sampling frequency and the electron microscope pixel resolution, achieving the prerequisite of "point-to-point" residual comparison.

[0072] Subsequently, the parameter iterative optimization stage begins. Specifically, the computational unit presets the search range of the conversion coefficient C (e.g., [0.01, 5.0]) and the iteration step size (e.g., 0.001) based on the empirical range of mineral composition of the shale sample. For each candidate value within the range, a scaling process is performed: the horizontal axis of the initial NMR distribution signal is linearly scaled using the current candidate value to simulate the pore distribution morphology under different physical mapping relationships, thereby determining the NMR signal component to be matched corresponding to each discrete pore size sampling point.

[0073] Next, a refined quantitative assessment of the deviation is performed. The calculation unit utilizes the objective optimization function (preferably the root mean square error (RMSE) function or the correlation coefficient R0). 2The function calculates the distribution residual between each NMR signal component to be matched and the image aperture distribution component point by point. By accumulating the residuals across the entire frequency band, the current target optimization function value is determined. During the iteration process, the changing trend of the function value is monitored in real time. When the target optimization function value reaches the global minimum, or when the change in function value between two consecutive iterations is less than a preset convergence threshold, the optimal convergence condition is satisfied. At this point, the search stops, and the current candidate value is locked as the final conversion coefficient.

[0074] Finally, the computing unit constructs a mapping operator with clear physical meaning based on the determined transformation coefficients. In this embodiment, the operator is manifested as follows: The linear function relationship is given (where d is the aperture and T2 is the relaxation time). This operator is determined as the mapping correspondence and stored in the calculation module. Through the above iterative optimization process, this invention successfully utilizes image information with real physical scale to perform "self-calibration" on NMR signals with volumetric statistical advantages, thereby establishing a rigorous cross-scale conversion logic.

[0075] By introducing interpolation resampling and iterative optimization mechanisms, the technical challenges of traditional shale calibration methods, such as reliance on manual experience and poor model adaptability, are fundamentally solved. The "deterministic physical dimensions" of image data are used to constrain the "distribution envelope" of NMR signals. By calculating the distribution residuals and performing automated convergence judgment, the extraction process of conversion coefficients is ensured to have extremely high objectivity and uniqueness. This self-calibration mode can sensitively capture the mapping shift caused by differences in mineral composition and fluid properties among different shale samples, significantly improving the computational accuracy of the mapping operator. The final established mapping correspondence not only achieves a globally optimal fit at the mathematical level but also realistically reproduces the correspondence between the microscopic space and relaxation characteristics inside shale at the physical level, providing core algorithmic support for achieving highly reliable full-scale pore size distribution characterization.

[0076] In some embodiments, the plunger sample after being saturated with oil is re-washed with the organic solvent dichloromethane and continuously dried at 105°C for 24 hours. Subsequently, the sample is processed into a block sample by wire cutting.

[0077] The fresh cross-sections of the shale samples under test were sequentially subjected to mechanical polishing and argon ion beam fine polishing to obtain a smooth and non-destructive imaging area. Subsequently, a conductive carbon film was sprayed onto the sample surface to improve conductivity. Using scanning electron microscopy (SEM) in secondary electron imaging mode, automated and non-destructive acquisition of the sample surface pore structure was performed, obtaining large-field-of-view stitched SEM images with a single-pixel resolution of 10 nm and a total pixel size of 45000x45000 pixels.

[0078] To quantitatively characterize the porosity development features at different observation scales, a scale-dependent total porosity was introduced. Define it as having a side length of... The ratio of the total area of ​​pores within a square field of view to the total area of ​​the field of view is calculated using the following formula:

[0079] in, For the scale The rate of faces; This corresponds to the total area of ​​pores within the field of view. Let be the side length of the field of view. The total area of ​​pores within the field of view. The calculation logic is shown in the formula:

[0080] in, Let i be the area of ​​the i-th pore. This represents the number of pores within the field of view. The number of pixels occupied by the i-th aperture; This represents the actual length corresponding to a single pixel.

[0081] By continuously changing the scale of the field of vision Obtain the relationship between face ratio and scale. When the rate of change of face ratio at adjacent scales is less than 5%, it can be determined that... The field of view has stabilized, and this region is the representative pore field of view. With a defined side length of... Within a representative field of view, the pores are divided into multiple intervals according to their pore size. The face rate of the k-th type aperture The calculation is as follows:

[0082] in, It belongs to the aperture range The total number of pixels. The total face value is the sum of the face values ​​of different apertures within a representative field of view, as shown in the formula:

[0083] The obtained shale NMR T2 distribution curves and the aperture distribution results from large-field-of-view stitched scanning electron microscopy were discretized. The signal amplitude values ​​corresponding to different relaxation times in the NMR T2 spectrum and the aperture ratios corresponding to different aperture ranges in the scanning electron microscopy images were accumulated and statistically analyzed to obtain the total NMR signal amplitude and total aperture ratio.

[0084] Based on this, the signal amplitude and porosity are normalized to eliminate dimensional differences. The porosity component corresponding to the i-th relaxation time in the NMR T2 spectrum. The definition is as follows:

[0085] in, Let be the nuclear magnetic resonance signal intensity corresponding to the i-th relaxation time.

[0086] Porosity component corresponding to the i-th aperture interval in a large field-of-view mosaic scanning electron microscope The definition is as follows:

[0087] in, For a side length of The face rate of the i-th aperture interval within the representative field of view.

[0088] Based on the nuclear magnetic resonance relaxation mechanism, the transverse relaxation time T2 and the porosity specific surface area (the ratio of surface area S to volume V) satisfy the following formula:

[0089] Under the assumption that the pores have a regular geometric shape, a pore shape factor is introduced. This can be further transformed into the following:

[0090] Thus, the conversion relationship between aperture d and transverse relaxation time T2 is obtained, as shown in the formula:

[0091] in, Pore ​​shape factor; It represents the surface transverse relaxation strength.

[0092] Finally, obtain and Subsequently, internal interpolation was performed on the T2 distribution data of nuclear magnetic resonance imaging to match it with the image aperture range on the aperture scale. Based on the matched data, the conversion coefficient C was determined using the least squares method, establishing the final quantitative calibration relationship, as shown in the formula:

[0093] Using this mapping operator, this embodiment successfully achieved physical scale calibration of three-dimensional nuclear magnet signals by utilizing the high spatial resolution features of two-dimensional images, producing high-precision full-scale pore size distribution results for shale.

[0094] In some embodiments, the method of converting the fluid nuclear magnetic resonance signal into a pore size distribution characterization result of the shale sample under test using the mapping correspondence may further include the following: S1: Based on the mapping relationship, map each discrete transverse relaxation time point corresponding to the fluid nuclear magnetic resonance signal to the physical aperture axis to determine each discrete physical aperture value; S2: Based on the conversion coefficient, the signal amplitude of the fluid nuclear magnetic resonance signal at each of the discrete transverse relaxation time points is calibrated to determine the porosity contribution component corresponding to each of the discrete physical aperture values. S3: Determine the pore size distribution characterization result of the shale sample to be tested based on each of the discrete physical pore size values ​​and the porosity contribution component corresponding to each of the discrete physical pore size values.

[0095] Specifically, the process of performing coordinate axis transformation using the determined mapping correspondence is as follows: the computing unit retrieves the mapping operator (e.g., a linear mapping function) determined in the aforementioned steps. ), and the discrete transverse relaxation time points corresponding to the fluid NMR signals. Substitute these values ​​into the operator. Through this mapping process, the discrete sampling points originally in the time domain are projected onto the physical aperture axis in the physical space domain, thereby determining a series of discrete physical aperture values ​​d that correspond one-to-one with the NMR sampling points. i (Units are nanometers or micrometers). This process gives NMR signals a clear geometric dimension, transforming them from abstract relaxation characteristics into intuitive pore-scale features.

[0096] Subsequently, quantitative calibration of the amplitude is performed. Based on the conversion coefficient C determined through iterative optimization, the computational unit performs dimensional transformation and scaling on the original signal amplitude of the fluid NMR signal at each discrete transverse relaxation time point. Specifically, the conversion coefficient is used to convert the signal amplitude, reflecting the intensity of hydrogen nucleus resonance energy, into a physical quantity reflecting the pore space filling capacity. This quantity is then normalized using the total porosity benchmark of the sample to determine the porosity contribution component corresponding to each discrete physical pore size value. This component quantifies the contribution of pores within a specific diameter range to the total shale reservoir space, achieving a physical-logical alignment from "signal intensity" to "pore volume ratio."

[0097] Finally, the data integration and characterization results are generated. The computational unit uses the determined discrete physical pore size values ​​as the abscissa and the corresponding porosity contribution components as the ordinate, constructing a distribution curve reflecting the frequency of pore size development in a double logarithmic or semi-logarithmic coordinate system. By performing cubic spline interpolation or Gaussian smoothing, local fluctuations caused by experimental noise are eliminated, thereby determining the pore size distribution characterization results of the tested shale sample. This result not only demonstrates the continuous development characteristics of shale from micropores (<2nm), mesopores (2-50nm) to macropores (>50nm), but also intuitively reflects the connectivity and reservoir potential of pores at different scales through the curve envelope area, providing final data support for accurate reservoir evaluation.

[0098] By coupling the mapping correspondence with the transformation coefficients, a precise cross-dimensional inversion of NMR signals from the time domain to the spatial domain was successfully achieved, completely solving the technical bottleneck of fuzzy physical scales and reliance on manual experience calibration in traditional NMR characterization. Utilizing dual processing, not only was the transformed pore size distribution result ensured to have a rigorous mathematical logic and physical basis, but adaptive coefficient adjustment also eliminated biases caused by differences in shale mineral composition. The final full-scale pore size distribution characterization result not only fully covers the complex pore spectrum of shale but also possesses advantages such as non-destructiveness, high signal-to-noise ratio, and strong statistical representativeness, providing a highly reliable quantitative basis for evaluating reservoir space and predicting permeability of continental shale oil and gas.

[0099] As can be seen from the above, the embodiment of this specification provides a method for determining the multi-scale pore size distribution of shale, which acquires image data of a shale sample to be tested, as well as first and second nuclear magnetic resonance (NMR) signals under different physical states; wherein, the different physical states include a saturated fluid state and a dry state; based on the first and second NMR signals, the fluid NMR signal of the shale sample to be tested is determined; based on the image data, pore structure parameters under different field-of-view sizes are determined, and when the fluctuation amplitude of the pore structure parameters meets a preset condition, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image pore size distribution characteristics based on the target observation range; based on the fluid NMR signal and the image pore size distribution characteristics, the corresponding NMR distribution component and image pore size distribution component are determined; the image pore size distribution component is used to perform matching processing on the NMR distribution component to establish a mapping correspondence between NMR relaxation time and physical pore size; using the mapping correspondence, the fluid NMR signal is converted into a pore size distribution characterization result of the shale sample to be tested. In this way, by introducing image data to replace traditional destructive physical experiments such as nitrogen adsorption or high-pressure mercury intrusion, and combining it with nuclear magnetic resonance signals under different physical states, the pore size distribution characterization results of shale can be accurately obtained without damaging the basic structure of the shale sample. The image pore size distribution characteristics are determined by using the target observation range that meets the preset conditions, ensuring the statistical representativeness of the characterization parameters in strongly heterogeneous shale reservoirs. By establishing a mapping relationship between lateral relaxation time and physical pore size, non-destructive and high-precision quantitative characterization of multi-scale pore size distribution, including connected and disconnected pores, can be achieved using nuclear magnetic resonance signals.

[0100] See Figure 2 As shown in the embodiments of this specification, a specific electronic device is also provided, wherein the electronic device includes a network communication port 201, a processor 202 and a memory 203, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0101] Specifically, the network communication port 201 can be used to acquire image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states; wherein, the different physical states include saturated fluid state and dry state.

[0102] The processor 202 can be specifically configured to: determine the fluid NMR signal of the shale sample under test based on the first NMR signal and the second NMR signal; determine the pore structure parameters under different field-of-view sizes based on the image data, and when the fluctuation amplitude of the pore structure parameters meets a preset condition, determine the corresponding field-of-view size as the target observation range, so as to determine the corresponding image aperture distribution characteristics based on the target observation range; determine the corresponding NMR distribution component and image aperture distribution component based on the fluid NMR signal and the image aperture distribution characteristics; perform matching processing on the NMR distribution component using the image aperture distribution component to establish a mapping relationship between NMR relaxation time and physical aperture; and use the mapping relationship to convert the fluid NMR signal into the pore size distribution characterization result of the shale sample under test.

[0103] The memory 203 can be used to store the corresponding instruction program.

[0104] Based on the above method, the relevant structural performance of electronic equipment can be effectively utilized to improve the data processing speed of electronic equipment and efficiently realize a method for determining the multi-scale pore size distribution of shale.

[0105] In this embodiment, the network communication port 201 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0106] In this embodiment, the processor 202 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0107] In this embodiment, the memory 203 may include a hierarchy. In a digital system, anything that can store binary data can be a memory. In an integrated circuit, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc. In a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0108] This specification also provides a computer-readable storage medium based on the above-described method for determining the multi-scale pore size distribution of shale, which acquires image data of a shale sample to be tested, as well as first and second nuclear magnetic resonance (NMR) signals under different physical states; wherein, the different physical states include a saturated fluid state and a dry state; based on the first and second NMR signals, the fluid NMR signal of the shale sample to be tested is determined; based on the image data, pore structure parameters under different field-of-view sizes are determined, and when the fluctuation amplitude of the pore structure parameters meets a preset condition, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image pore size distribution characteristics based on the target observation range; based on the fluid NMR signal and the image pore size distribution characteristics, the corresponding NMR distribution component and image pore size distribution component are determined; the image pore size distribution component is used to perform matching processing on the NMR distribution component to establish a mapping correspondence between NMR relaxation time and physical pore size; using the mapping correspondence, the fluid NMR signal is converted into a pore size distribution characterization result of the shale sample to be tested.

[0109] In this embodiment, the storage medium includes, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions. The network communication unit can be an interface configured according to standards specified in the communication protocol for network connection communication.

[0110] In this embodiment, the specific functions and effects implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and will not be repeated here.

[0111] See Figure 3 At the software level, this specification also provides a device for determining the multi-scale pore size distribution of shale, which may specifically include the following structural modules: The data acquisition module 301 is used to acquire image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states; wherein, the different physical states include saturated fluid state and dry state; The signal determination module 302 is used to determine the fluid nuclear magnetic resonance signal of the shale sample to be tested based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal; The feature determination module 303 is used to determine the pore structure parameters under different field of view sizes based on the image data, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, the corresponding field of view size is determined as the target observation range, so as to determine the corresponding image aperture distribution features based on the target observation range; The component determination module 304 is used to determine the corresponding nuclear magnetic resonance distribution component and the image aperture distribution component based on the fluid nuclear magnetic resonance signal and the image aperture distribution characteristics; The relationship determination module 305 is used to perform matching processing on the nuclear magnetic resonance distribution component using the image aperture distribution component, so as to establish a mapping correspondence between nuclear magnetic resonance relaxation time and physical aperture. The result determination module 306 is used to convert the fluid nuclear magnetic resonance signal into the pore size distribution characterization result of the shale sample to be tested by utilizing the mapping correspondence.

[0112] In some embodiments, the data acquisition module 301, in its specific implementation, sequentially performs mechanical polishing and argon ion beam fine polishing on the cross-section of the shale sample to be tested to determine the corresponding flat imaging area; performs conductivity enhancement processing on the flat imaging area, and automatically acquires data from the flat imaging area using the secondary electron imaging mode of a scanning electron microscope to determine multiple raw image data; performs stitching processing on the multiple raw image data to determine a large-field-of-view stitched scanning electron microscope image, and determines the large-field-of-view stitched scanning electron microscope image as the image data of the shale sample to be tested.

[0113] In some embodiments, the pore structure parameters in the feature determination module 303 described above are specifically implemented by determining multiple sub-viewfield images with different viewfield sizes based on the image data; determining the corresponding viewfield area and the total pore area within the viewfield area based on each sub-viewfield image; determining the porosity of each sub-viewfield image at the corresponding viewfield size based on the viewfield area and the total pore area; and determining the pore structure parameters based on the porosity corresponding to each viewfield size.

[0114] In some embodiments, the image aperture distribution feature in the feature determination module 303 described above is specifically implemented by determining multiple discrete aperture intervals based on the target observation range; determining the total number of pore pixels belonging to each aperture interval within the target observation range from the image data based on each aperture interval; determining the local porosity corresponding to each aperture interval based on the total number of pore pixels and the actual physical length corresponding to a single pixel; and determining the image aperture distribution feature based on the local porosity corresponding to each aperture interval.

[0115] In some embodiments, the component determination module 304 specifically performs discretization processing on the fluid NMR signal to determine the NMR signal intensity corresponding to multiple NMR discrete intervals; discretizes the image aperture distribution features to determine the inter-interval porosity corresponding to multiple image discrete intervals; performs cumulative statistical processing based on each NMR signal intensity to determine the total NMR signal amplitude, and performs normalization processing on each NMR signal intensity based on the total NMR signal amplitude to determine the NMR distribution component corresponding to each NMR discrete interval; performs cumulative statistical processing based on each inter-interval porosity to determine the total porosity, and performs normalization processing on each inter-interval porosity based on the total porosity to determine the image aperture distribution component corresponding to each image discrete interval.

[0116] In some embodiments, the relationship determination module 305, in specific implementation, performs interpolation resampling processing on the NMR distribution component based on each discrete aperture sampling point corresponding to the image aperture distribution component to determine the initial NMR distribution signal corresponding to each discrete aperture sampling point; determines candidate values ​​for the scaling factor between the NMR relaxation time and the physical aperture based on a preset search interval and iteration step size; performs scaling processing on the initial NMR distribution signal corresponding to each discrete aperture sampling point using the candidate values ​​to determine the NMR signal component to be matched corresponding to each discrete aperture sampling point; calculates the distribution residual between the NMR signal component to be matched and the image aperture distribution component based on each discrete aperture sampling point, and determines the target optimization function value based on the distribution residual; when the target optimization function value satisfies a preset convergence condition, determines the currently corresponding candidate value as a conversion coefficient; determines the mapping operator between the NMR relaxation time and the physical aperture based on the conversion coefficient, and determines the mapping operator as the mapping correspondence.

[0117] In some embodiments, the result determination module 306, in specific implementation, maps each discrete transverse relaxation time point corresponding to the fluid NMR signal to the physical pore size axis according to the mapping correspondence, and determines each discrete physical pore size value; according to the conversion coefficient, calibrates the signal amplitude of the fluid NMR signal at each discrete transverse relaxation time point, and determines the porosity contribution component corresponding to each discrete physical pore size value; and determines the pore size distribution characterization result of the shale sample to be tested according to each discrete physical pore size value and the porosity contribution component corresponding to each discrete physical pore size value.

[0118] It should be noted that the units, devices, or modules described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described by dividing them into various modules according to their functions. Of course, in implementing this specification, the functions of each module can be implemented in the same software and / or hardware, or modules that implement the same function can be implemented by a combination of sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection between the devices or units shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0119] As can be seen from the above, based on the shale multi-scale pore size distribution determination device provided in the embodiments of this specification, image data of the shale sample to be tested, and first and second nuclear magnetic resonance signals under different physical states are acquired; wherein, the different physical states include saturated fluid state and dry state; the fluid nuclear magnetic resonance signal of the shale sample to be tested is determined according to the first and second nuclear magnetic resonance signals; the pore structure parameters under different field-of-view sizes are determined according to the image data, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image pore size distribution characteristics according to the target observation range; the corresponding nuclear magnetic resonance distribution component and image pore size distribution component are determined according to the fluid nuclear magnetic resonance signal and the image pore size distribution characteristics; the image pore size distribution component is used to perform matching processing on the nuclear magnetic resonance distribution component to establish a mapping correspondence between nuclear magnetic resonance relaxation time and physical pore size; the fluid nuclear magnetic resonance signal is converted into the pore size distribution characterization result of the shale sample to be tested using the mapping correspondence.

[0120] In a specific scenario example, the method and apparatus for determining the multi-scale pore size distribution of shale provided in this specification can be applied, solving the problem that existing shale pore size distribution characterization methods are destructive and difficult to accurately characterize the true multi-scale pore size distribution. The specific implementation process may include the following:

[0121] In some embodiments, see Figure 4 As shown, a representative field of view (REA) analysis was conducted on a shale sample from a basin. First, a high-resolution original topographic image of the sample cross-section was obtained using large field-of-view (LOR) stitching scanning electron microscopy. Then, using a pre-defined anchor point in the LLR image as the geometric center, a series of square sub-REA images with side lengths of 75 μm, 150 μm, 225 μm, 300 μm, 375 μm, and 450 μm were successively cropped using an isocentric step-enlargement method. These sub-REA images were marked as C in the image. 75,75 C 150,150 C 225,225 C 300,300 C 375,375 and C 450,450 By performing pore structure parameter statistics on this set of sub-field images with scale evolution characteristics, the computing unit can dynamically monitor the fluctuation trend of key indicators such as face ratio as the observed area expands. Thus, when the preset convergence conditions are met, the representative observation boundary of the sample can be accurately locked, providing a data foundation for subsequent cross-scale accurate calibration of NMR-EM based on statistical representativeness.

[0122] In some embodiments, see Figure 5 As shown, the pore size distribution characteristics of shale samples obtained using a large-field-of-view stitched scanning electron microscope (SEM) are illustrated. The horizontal axis represents the physical pore size in nanometers; the vertical axis represents the local porosity corresponding to each discrete pore size interval. As can be seen from the figure, by performing refined pixel statistics on the pores within the target observation range, this specification can obtain full-range distribution curves covering micropores, mesopores, and macropores. With the increase in the observation field size, the distribution curves exhibit significant overlap and convergence characteristics across the entire range. This indicates that after reaching a representative field of view, the extracted image pore size distribution characteristics can effectively eliminate the influence of local heterogeneity in shale, thus realistically and stably characterizing the overall pore structure of the sample. This result provides a high-precision spatial dimensional benchmark for subsequent physical scale calibration of nuclear magnetic resonance signals.

[0123] In some embodiments, see Figure 6 The diagram illustrates the matching and calibration process and results between nuclear magnetic resonance signals and image data. Figure 6 As shown in the middle left figure, by modifying the NMR distribution components Aperture distribution components of images obtained by large field-of-view stitched scanning electron microscopes Internal interpolation and alignment are performed to achieve point-to-point matching between the two components under the same porosity component ratio. Based on this matching result, such as... Figure 6 As shown in the middle right figure, a linear mapping relationship between the physical aperture and the nuclear magnetic resonance relaxation time T2 was established using the least squares method. The fitted conversion coefficient is 664.07, meaning the mapping operator satisfies... The coefficient of determination R of this mapping relationship 2 The result reached 0.9905, verifying that the calibration method of the present invention can convert NMR signals into aperture distribution data with real physical meaning with extremely high precision.

[0124] In some embodiments, see Figure 7 The figure shows the NMR pore size distribution results of five typical continental shale samples (samples 1 to 5) determined using the method described in this specification. The left ordinate of the figure represents the NMR signal intensity (in au), the right ordinate represents the cumulative porosity component (in %), and the horizontal axis represents the physical pore size (in nm) obtained using the aforementioned mapping relationship. The solid curves in the figure represent the pore size distribution intensity of each sample, reflecting the contribution frequency of different pore size ranges to the reservoir space; the dashed curves represent the cumulative porosity component of each sample, reflecting the accumulation process of pore volume with increasing scale. Figure 7 As can be seen, the pore size distribution envelopes of different samples exhibit obvious bimodal or multimodal characteristics, with the pore size characterization spanning the entire range from 10-nanometer micropores to 100-micrometer macropores. By comparing the distribution characteristics of each sample, the differences in reservoir capacity and seepage potential of shale at different stratigraphic levels or lithologies can be quantitatively evaluated, demonstrating that the mapping operator established in this invention possesses extremely high universality and accuracy, and can provide core physical parameters for the refined evaluation of shale oil and gas reservoirs.

[0125] While this specification provides the steps of operation for the methods described in the embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps listed in the embodiments is merely one possible order of execution among many steps and does not represent the only possible order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even a distributed data processing environment). The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, product, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, product, or apparatus. Without further limitations, the presence of other identical or equivalent elements in a process, method, product, or apparatus that includes said elements is not excluded. The terms "first," "second," etc., are used to denote names and do not indicate any particular order.

[0126] Those skilled in the art will also know that, besides implementing the controller using purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller function as logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices within it used to implement various functions can also be considered structures within that hardware component. Alternatively, the devices used to implement various functions can be considered as both software modules implementing the method and structures within a hardware component.

[0127] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions of this specification can essentially be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments of this specification.

[0128] Although this specification has been described by way of examples, those skilled in the art will recognize that many variations and modifications are possible without departing from the spirit of this specification, and it is intended that the appended claims cover such variations and modifications without departing from the spirit of this specification.

Claims

1. A method for determining a shale multi-scale pore size distribution, the method comprising: include: Image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states, are acquired; wherein, the different physical states include saturated fluid state and dry state; Based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal, the fluid nuclear magnetic resonance signal of the shale sample to be tested is determined; Based on the image data, the pore structure parameters under different field of view sizes are determined, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, the corresponding field of view size is determined as the target observation range, so as to determine the corresponding image aperture distribution characteristics based on the target observation range. Based on the fluid NMR signal and the image aperture distribution characteristics, the corresponding NMR distribution component and image aperture distribution component are determined; The image aperture distribution component is used to match the nuclear magnetic resonance distribution component in order to establish a mapping relationship between nuclear magnetic resonance relaxation time and physical aperture. Using the mapping relationship, the fluid NMR signal is converted into a pore size distribution characterization result of the shale sample to be tested.

2. The method of claim 1, wherein, The acquisition of image data of the shale sample to be tested includes: The cross-section of the shale sample to be tested was subjected to mechanical polishing and argon ion beam fine polishing in sequence to determine the corresponding flat imaging area; Conductivity enhancement processing is performed on the flat imaging area, and the flat imaging area is automatically acquired using the secondary electron imaging mode of the scanning electron microscope to determine multiple raw image data. The multiple original image data are stitched together to determine a large field-of-view stitched scanning electron microscope (SEM) image, and this large field-of-view stitched SEM image is identified as the image data of the shale sample to be tested.

3. The method of claim 2, wherein, The step of determining the pore structure parameters under different field-of-view sizes based on the image data includes: Based on the image data, multiple sub-viewfield images with different viewfield sizes are determined; Based on each of the sub-view images, determine the area of ​​the corresponding view region and the total area of ​​pores within the view region; Based on the area of ​​the field of view and the total area of ​​the apertures, determine the face ratio of each sub-field image at the corresponding field of view size; The pore structure parameters are determined based on the porosity corresponding to each of the stated field sizes.

4. The method of claim 3, wherein, The step of determining the corresponding image aperture distribution features based on the target observation range includes: Based on the target observation range, multiple discrete aperture intervals are determined; Based on each aperture interval, determine the total number of pore pixels belonging to each aperture interval within the target observation range from the image data; Based on the total number of pore pixels and the actual physical length corresponding to a single pixel, the local porosity corresponding to each pore size range is determined; The image aperture distribution features are determined based on the local face rate corresponding to each aperture interval.

5. The method of claim 4, wherein, The step of determining the corresponding NMR distribution component and image aperture distribution component based on the fluid NMR signal and the image aperture distribution characteristics includes: The fluid NMR signal is discretized to determine the NMR signal intensity corresponding to multiple NMR discrete intervals; The image aperture distribution features are discretized to determine the inter-interval aperture ratios corresponding to multiple discrete image intervals; The total amplitude of the NMR signal is determined by performing cumulative statistical processing on the intensity of each NMR signal, and normalization processing is performed on the intensity of each NMR signal based on the total amplitude of the NMR signal to determine the NMR distribution component corresponding to each NMR discrete interval; The total face rate is determined by performing cumulative statistical processing on the face rates of each of the said segmented intervals, and normalization processing is performed on the face rates of each of the said segmented intervals based on the total face rate to determine the image aperture distribution components corresponding to each of the said discrete image intervals.

6. The method of claim 5, wherein, The step of matching the NMR distribution components with the image aperture distribution components to establish a mapping relationship between NMR relaxation time and physical aperture includes: Based on the discrete aperture sampling points corresponding to the image aperture distribution components, interpolation resampling processing is performed on the nuclear magnetic resonance distribution components to determine the initial nuclear magnetic resonance distribution signal corresponding to each discrete aperture sampling point. Based on the preset search range and iteration step size, candidate values ​​for the scaling factor between the nuclear magnetic resonance relaxation time and the physical aperture are determined. The candidate values ​​are used to perform scaling processing on the initial NMR distribution signal corresponding to each discrete aperture sampling point to determine the NMR signal component to be matched for each discrete aperture sampling point. Based on each of the discrete aperture sampling points, the distribution residual between the NMR signal component to be matched and the image aperture distribution component is calculated, and the target optimization function value is determined based on the distribution residual. When the target optimization function value satisfies the preset convergence condition, the corresponding candidate value is determined as the transformation coefficient. Based on the conversion coefficient, a mapping operator between the nuclear magnetic resonance relaxation time and the physical aperture is determined, and the mapping operator is defined as the mapping correspondence.

7. The method of claim 6, wherein, The process of converting the fluid NMR signal into a pore size distribution characterization result of the shale sample under test using the mapping correspondence includes: Based on the mapping relationship, each discrete transverse relaxation time point corresponding to the fluid NMR signal is mapped to the physical aperture axis to determine each discrete physical aperture value. Based on the conversion coefficient, the signal amplitude of the fluid NMR signal at each of the discrete transverse relaxation time points is calibrated to determine the porosity contribution component corresponding to each of the discrete physical aperture values. Based on the discrete physical pore size values ​​and the porosity contribution components corresponding to each discrete physical pore size value, the pore size distribution characterization results of the shale sample to be tested are determined.

8. A shale multi-scale pore size distribution determination apparatus, characterized by, include: The data acquisition module is used to acquire image data of the shale sample to be tested, as well as first and second nuclear magnetic resonance signals under different physical states; wherein, the different physical states include saturated fluid state and dry state; The signal determination module is used to determine the fluid nuclear magnetic resonance signal of the shale sample to be tested based on the first nuclear magnetic resonance signal and the second nuclear magnetic resonance signal; The feature determination module is used to determine the pore structure parameters under different field-of-view sizes based on the image data, and when the fluctuation amplitude of the pore structure parameters meets the preset conditions, the corresponding field-of-view size is determined as the target observation range, so as to determine the corresponding image aperture distribution features based on the target observation range; The component determination module is used to determine the corresponding nuclear magnetic resonance distribution component and the image aperture distribution component based on the fluid nuclear magnetic resonance signal and the image aperture distribution characteristics; The relationship determination module is used to perform matching processing on the nuclear magnetic resonance distribution component using the image aperture distribution component, so as to establish a mapping correspondence between nuclear magnetic resonance relaxation time and physical aperture; The result determination module is used to convert the fluid NMR signal into a pore size distribution characterization result of the shale sample under test by utilizing the mapping correspondence.

9. An electronic device, comprising: It includes a processor and a memory for storing processor-executable instructions, wherein the processor, when executing the instructions, implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

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