Method and device for determining shale pore size distribution based on image morphology intelligent analysis

Abstract

The embodiments of the present specification disclose a method and device for determining shale pore size distribution based on image morphology intelligent analysis, the method comprising: obtaining a pore structure image of a target shale reservoir, and determining the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image; based on the limiting assumption that all pore spaces are cylindrical pores or all are flat pores, calculating the limiting pore size data of the target shale reservoir under the limiting assumption of cylindrical pores and the limiting pore size data under the limiting assumption of flat pores; using the proportion of cylindrical pores and the proportion of flat pores, fusing the limiting pore size data under the limiting assumption of cylindrical pores and the limiting pore size data under the limiting assumption of flat pores to obtain the pore size distribution data of the target shale reservoir, thereby improving the accuracy and efficiency of the gas storage capacity analysis results of the shale reservoir.

Description

Technical Field

[0001] The present specification relates to the technical field of oil reservoir development, and in particular, to a method and device for determining shale pore size distribution based on image morphology intelligent analysis. Background Art

[0002] Shale is an ultra-low permeability porous medium with a permeability of less than 0.1mD. The pore structure of shale is complex and is the focus of shale reservoir research. In recent years, a large number of studies have quantitatively characterized the pore structure of shale, among which specific surface area, specific pore volume and pore size distribution are the most commonly used parameters. Compared with specific surface area and specific pore volume, the research on pore size distribution is not comprehensive and in-depth enough. Usually, the pore size distribution pattern is only qualitatively described, and the pore size distribution peak is simply used as a quantitative characterization parameter. Therefore, this technical field urgently needs a method that can more accurately determine the pore size distribution of shale. Summary of the invention

[0003] The purpose of the embodiments of this specification is to provide a method and device for determining shale pore size distribution based on image morphology intelligent analysis, which can further improve the accuracy and efficiency of determining the pore size distribution of shale reservoirs, and thereby improve the accuracy and efficiency of the gas storage capacity analysis results of shale reservoirs.

[0004] This specification provides a method and device for determining shale pore size distribution based on image morphology intelligent analysis, which is implemented in the following ways:

[0005] A method for determining shale pore size distribution based on image morphology intelligent analysis comprises: obtaining a pore structure image of a target shale reservoir, and determining the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image; based on the limiting assumption that all pore spaces are cylindrical pores or all are flat pores, calculating the limiting pore size data of the target shale reservoir under the limiting assumption of cylindrical pores and the limiting pore size data under the limiting assumption of flat pores; using the proportion of cylindrical pores and the proportion of flat pores, fusing the limiting pore size data under the limiting assumption of cylindrical pores and the limiting pore size data under the limiting assumption of flat pores to obtain the pore size distribution data of the target shale reservoir.

[0006] In some other embodiments, the fusion processing of the limiting pore size data under the cylindrical pore limiting assumption and the limiting pore size data under the flat pore limiting assumption includes:

[0007]

[0008] Among them, PSD Hybrid represents the pore size distribution data of the target shale reservoir, and They represent the proportion of cylindrical pores and flat pores calculated based on the pore structure image, respectively, and PSD cyl ., PSD slit They represent the limiting pore diameter data under the cylindrical pore limiting assumption and the limiting pore diameter data under the flat pore limiting assumption, respectively.

[0009] In other embodiments, the desorption branch test data is used to calculate the limiting pore size data of the target shale reservoir under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption.

[0010] In some other embodiments, the following pore size distribution calculation model is used to calculate the limit pore size data of the target shale reservoir under the cylindrical pore limit assumption:

[0011]

[0012] Where, ΔV i represents the pore volume corresponding to the pressure drop in the i-th step during the desorption process, ΔV j represents the pore volume corresponding to the pressure drop in the jth step during the desorption process, r i 、r i-1 They represent the pore radius corresponding to the pressure drop in the i-th step and the i-1-th step in the desorption process, r j 、r j-1 They represent the pore radius corresponding to the pressure drop in the j-th step and the j-1-th step in the desorption process, Δt i =(t i-1 -t i ), t i ,t i-1 They represent the adsorption layer thickness corresponding to the pressure drop in the i-th step and the i-1-th step in the desorption process, Δv i It represents the gas desorption amount corresponding to the pressure drop in the i-th step during the desorption process.

[0013] In some other embodiments, the following pore size distribution calculation model is used to calculate the limit pore size data of the target shale reservoir under the assumption of a flat plate pore limit:

[0014]

[0015] Where, ΔV i represents the pore volume corresponding to the pressure drop in the i-th step during the desorption process, ΔV j represents the pore volume corresponding to the pressure drop in the jth step during the desorption process, d i d i-1They represent the distances between the flat plate-shaped pores corresponding to the pressure drops in the i-th step and the i-1-th step during the desorption process, d j d j-1 They represent the distances between the flat plate-shaped pores corresponding to the pressure drops at the j-th and j-1-th steps in the desorption process, Δt i =(t i-1 -t i ), t i ,t i-1 They represent the adsorption layer thickness corresponding to the pressure drop in the i-th step and the i-1-th step in the desorption process, Δv i It represents the gas desorption amount corresponding to the pressure drop in the i-th step during the desorption process.

[0016] In some other embodiments, the obtaining of the pore structure image of the target shale reservoir includes: obtaining an initial electron microscope image collected by a scanning electron microscope from a rock sample of the target shale reservoir; and performing at least median filtering, binarization, gradient filtering, image segmentation, and background information removal processing on the initial electron microscope image in sequence to obtain the pore structure image of the target shale reservoir.

[0017] In some other embodiments, a watershed algorithm is used to perform image segmentation processing on the electron microscope image obtained after gradient filtering processing.

[0018] In some other embodiments, determining the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image includes: calculating the roundness of each image unit in the pore structure image, marking the image units with a roundness less than 0.8 as flat, and marking the image units with a roundness greater than or equal to 0.8 as cylindrical; calculating the proportion of image units marked as cylindrical to the total image units in the pore structure image as the proportion of cylindrical pores; and calculating the proportion of image units marked as flat to the total image units in the pore structure image as the proportion of flat pores.

[0019] On the other hand, the embodiments of the present specification also provide a shale pore size distribution determination device based on image morphology intelligent analysis, including: a pore proportion determination module, used to obtain a pore structure image of a target shale reservoir, and determine the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image; a limit pore size data calculation module, used to calculate the limit pore size data of the target shale reservoir under the limit assumption of cylindrical pores and the limit pore size data under the limit assumption of flat pores based on the limit assumption that all pore spaces are cylindrical pores or all are flat pores; a fusion processing module, used to use the proportion of cylindrical pores and the proportion of flat pores to fuse the limit pore size data under the limit assumption of cylindrical pores and the limit pore size data under the limit assumption of flat pores to obtain the pore size distribution data of the target shale reservoir.

[0020] On the other hand, an embodiment of the present specification further provides a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the steps of the method described in any one or more of the above embodiments.

[0021] The method and device for determining the pore size distribution of shale based on image morphology intelligent analysis provided by one or more embodiments of this specification analyzes the different pore shapes of the actual shale reservoir and assumes that the shale reservoir is composed of cylindrical pores and flat pores. On the basis of reducing the complexity of data processing, the accuracy of the analysis results of the adsorption capacity of the shale reservoir can be further improved, thereby improving the accuracy and efficiency of the gas storage capacity analysis of the shale reservoir. Under the assumption that the pore shape of shale is composed of two types of cylindrical pores and flat pores, the embodiment of this specification further provides a method for calculating the pore size distribution of shale. Specifically, under the limit assumption that all pore spaces are cylindrical or flat, the test data corresponding to the desorption branch of the adsorption / desorption test is used to determine the limit pore size data of the shale; and the proportion of cylindrical pores and flat pores of the shale is determined based on the shale image; then, the proportion of cylindrical pores and flat pores of the shale and the limit pore size data determined under the limit assumption are integrated to obtain the calculation result of the pore size distribution of the shale. By using the pore size distribution calculation method given in the implementation mode of this specification, the pore size distribution calculation can be made more consistent with the fluid characteristics of pores of different shapes in the target work area shale. At the same time, the pore size distribution calculation results are also made more consistent with the actual proportion of pores of different shapes in the target work area shale, thereby making the pore size distribution calculation results more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0023] Figure 1 A schematic diagram of a flow chart of an embodiment of a method for determining shale pore size distribution provided in this specification;

[0024] Figure 2 Schematic diagram of the initial electron microscope image provided for this specification;

[0025] Figure 3 A schematic diagram of a cropped electron microscope image provided for this specification;

[0026] Figure 4 This is a schematic diagram of an electron microscope image after median filtering provided in this specification;

[0027] Figure 5 A schematic diagram of an electron microscope image after binarization provided in this specification;

[0028] Figure 6 A schematic diagram of an electron microscope image after gradient filtering provided in this specification;

[0029] Figure 7 This is a schematic diagram of an electron microscope image after image segmentation processing provided in this manual;

[0030] Figure 8 A schematic diagram of an electron microscope image after the background information provided in this specification has been removed;

[0031] Fig. 9 A schematic diagram of the flat plate pore extraction results provided in this manual;

[0032] Fig.10 Schematic diagram of cylindrical pore extraction results provided for this specification;

[0033] Fig.11 Schematic diagram of the microstructure of cylindrical pores and flat pores provided for this specification;

[0034] Fig.12 A schematic diagram of the nitrogen adsorption-desorption isotherm provided for this specification;

[0035] Fig.13 A schematic diagram of pore size distribution data under the limiting assumptions provided in this specification;

[0036] Fig.14A schematic diagram for comparing the pore size distribution results provided in this specification;

[0037] Fig.15 A schematic diagram for comparing the pore size distribution results provided in this specification;

[0038] Fig.16 A schematic diagram of the module structure of a shale pore size distribution determination device provided in this specification. DETAILED DESCRIPTION

[0039] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of the specification, not all of the embodiments. Based on one or more embodiments of the specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the embodiments of this specification.

[0040] In the embodiments of this specification, pore structure refers to the type, size, distribution and interconnectedness of pores and throats in rocks. The pore system of rocks usually consists of two parts: pores and throats. Pores are the expanded part of the system, and throats are the small parts connecting pores. Pores are the basic storage space for fluids in rocks, and throats are important channels for controlling the seepage of fluids in rocks. When fluids flow in the complex pore system in nature, they must go through a series of alternating pores and throats.

[0041] Shale refers to a type of fine-grained sedimentary rock composed of debris, clay, organic matter, etc. with a particle size of less than 0.0039 mm, with lamellae or flaky stratification and easy to break. Common shale types include black shale, carbonaceous shale, siliceous shale, iron shale, calcareous shale, etc. Among them, calcareous shale and siliceous shale are easy to fracturing and are the main types of gas-bearing shales. When a certain amount of sandy components are mixed into shale, sandy shale can be formed. Organic-rich black shale is the main rock type for the formation of shale oil and gas, mainly including black shale and carbonaceous shale. Shale is an ultra-low permeability porous medium with a permeability of less than 0.1mD, and the pore structure is complex, making it difficult to accurately determine the pore size distribution of shale.

[0042] The pore size distribution refers to the percentage of pores of various levels existing in the reservoir calculated by number or volume. The pore size distribution of the reservoir can be determined by static volumetric method (nitrogen adsorption method) and mercury injection method. For example, the pore size distribution of the rock can be characterized by the tested partial pressure and the adsorption amount of the corresponding pores at various levels. Specifically, the pore size distribution of the rock can be intuitively displayed by using a curve formed by the volume of each pore size and the corresponding partial pressure.

[0043] The pore size distribution determines the occurrence state and proportion of gas. For shales in the oil window, shale gas exists in three occurrence states in the pore space: free gas in microcracks and intergranular pores, adsorbed gas mainly adsorbed on the surface of kerogen and clay minerals, and dissolved gas in kerogen and asphalt. A large number of experiments have shown that adsorbed gas in organic-rich shales is mainly related to micropores, while free gas depends more on mesopores and macropores. The pore size distribution determines the proportion of micropores, mesopores and macropores; thus further determining the proportion of various gas occurrences. In addition, the pore size distribution also controls the adsorption mechanism of gas in shale gas. The adsorption mechanism of shale gas in the micropore range is mainly the "pore filling" mechanism, while the adsorption in the mesopore range is mainly a combination of multilayer adsorption and capillary condensation. The pore size distribution also determines the adsorption capacity of shale reservoirs. The pore space in the micropore range has a significant contribution to the adsorption capacity. Therefore, a pore size distribution dominated by micropores is more conducive to adsorption. In addition, the pore size distribution is the key to fluid flow in nanopore space. Previous researchers pointed out that pores from tens of nanometers to 100-200 nanometers are more effective fluid flow paths. When the shale pore size is reduced to below 100nm, the apparent permeability deviates significantly from the Darcy permeability. This is the result of the conversion of the flow model from the traditional Darcy flow to the diffusion migration. From this perspective, pore size distribution is an important parameter reflecting the permeability of reservoir fluids in nanopore space. Therefore, the study of pore size distribution is crucial for the evaluation of natural gas production capacity and economic feasibility.

[0044] Figure 1 The figure shows a schematic diagram of the implementation process of a method for determining shale pore size distribution based on image morphology intelligent analysis provided by an embodiment of the present specification. Figure 1 As shown, the pore size distribution of shale can be determined using the following steps S101 to S103.

[0045] S101: Acquire a pore structure image of a target shale reservoir, and determine a proportion of cylindrical pores and a proportion of flat pores based on the pore structure image.

[0046] S1011: Extract the pore structure image of the rock sample.

[0047] For example, the pore structure image of the rock sample can be obtained by using field emission scanning electron microscopy (FE-SEM), focused ion beam scanning electron microscopy (FIB-SEM), CT scanning, etc. Taking field emission scanning electron microscopy (FE-SEM) as an example, the scanning electron microscope can be used to observe the scanning electron microscope image of the rock sample and qualitatively describe the pore structure characteristics of the sample. 3 to 5 typical scanning electron microscope images that can reflect the pore structure characteristics of the rock sample are selected as the initial electron microscope images. Figure 2 is a schematic diagram of an initial electron microscope image. Afterwards, the initial electron microscope image can be further processed in sequence using the following steps a to f:

[0048] a. Cropping the initial SEM image: Crop the text information at the bottom of the SEM image to reduce information interference. Figure 3 Express Figure 2 Electron microscope image obtained after cropping.

[0049] b. Median filtering: Use the median filtering algorithm or median filtering processing module to perform median filtering on the electron microscope image obtained after trimming. Figure 4 Express Figure 3 Electron microscope image obtained after median filtering.

[0050] c. Binarization: reset the grayscale value of the pixel whose grayscale value is less than or equal to the specified threshold to 0, and mark the corresponding pixel as a pore or throat; reset the grayscale value of the pixel whose grayscale value is greater than the specified threshold to 255, and mark the corresponding pixel as the background. The above method can be used to divide the scanning electron microscope image into a pore area and a background area (matrix), thereby highlighting the outline of the pore. For example, [20, 150] can be used as a value interval, 2 can be used as a step size, and a grayscale value can be selected from the above value interval as a specified threshold, and the electron microscope image obtained after median filtering can be binarized to obtain a binary electron microscope image; the binary electron microscope images corresponding to each specified threshold can be compared, and the binary electron microscope image that best reflects the pore structure distribution can be selected to further improve the accuracy of the final processing result. Figure 5 Express Figure 4 Electron microscope image obtained after binarization processing.

[0051] d. Gradient filtering: Use a gradient filtering algorithm or a gradient filtering processing module to perform gradient filtering on the electron microscope image. Figure 6 Express Figure 5 Electron microscope image obtained after gradient filtering.

[0052] e. Image segmentation: The image segmentation algorithm can be used to segment the electron microscope image after gradient filtering, and the pore space can be divided into many independent image units; and each image unit can be marked (for example, adjacent image units, image units and background images can be marked with different colors respectively) to distinguish the segmented image units from each other and from the background image, so as to facilitate subsequent processing. Figure 7 Express Figure 6 Electron microscope image obtained after image segmentation.

[0053] For example, the watershed algorithm can be used to segment the electron microscope image after gradient filtering. The watershed algorithm refers to an image segmentation algorithm that divides pixels that are spatially adjacent and have similar grayscale values ​​into one area. Specifically, the watershed algorithm can regard the grayscale space of the image as a geographical structure, and the grayscale value of each pixel represents the height. Among them, the line connected by pixels with larger grayscale values ​​can be regarded as a ridge, that is, a watershed; the binary threshold is regarded as a horizontal plane, and accordingly, the area lower than the horizontal plane will be flooded, and the water will first begin to fill each isolated valley (i.e., the local minimum). When the water level rises to a certain height, the water will overflow the current valley. A dam can be built on the watershed to avoid the water from the two valleys from converging, so that the image is divided into two pixel sets, one is the flooded valley pixel set, and the other is the watershed line pixel set. As the water level gets higher and higher, more and higher dams will be set until the grayscale value is the maximum value, and all areas meet on the watershed line. These dams partition the pixels of the entire image. Finally, the lines formed by these dams partition the entire image and achieve image segmentation.

[0054] The processing process of the watershed algorithm may include: classifying all pixels in the electron microscope image after gradient filtering according to the gray value, and setting a geodesic distance threshold (i.e., binarization threshold); finding the pixel with the smallest gray value (marked as the lowest gray value point by default), and letting the threshold increase from the minimum value, with these points as the starting point; in the process of the horizontal plane growing, it will encounter the surrounding neighboring pixels, and measure the geodesic distance from these pixels to the starting point (the lowest gray value point). If it is less than the set threshold, these pixels will be submerged, otherwise a dam will be set on these pixels, and so on, to complete the classification of adjacent domain pixels, thereby realizing the segmentation of the image.

[0055] The watershed algorithm can be used to extract pore structure features from scanning electron microscope images with higher precision and clarity, further improving the accuracy of pore structure feature extraction. Of course, other types of image segmentation algorithms can also be used, such as threshold-based segmentation algorithms or other types of region-based segmentation algorithms.

[0056] f. Background removal: Eliminate background information and separate the foreground and background of the electron microscope image obtained after image segmentation processing. Figure 8 Express Figure 7 Electron microscope image obtained after background removal.

[0057] Accordingly, in some embodiments, an initial electron microscope image collected by a scanning electron microscope for a rock sample of the target shale reservoir can be obtained, and the initial electron microscope image is subjected to at least median filtering, binarization, gradient filtering, image segmentation, and background information removal in sequence to obtain a pore structure image of the target shale reservoir. By extracting an electron microscope image of a rock sample of the target shale reservoir and sequentially performing the above-mentioned processing on the initially extracted electron microscope image, an observation image that can more accurately and intuitively characterize the pore structure characteristics of the rock can be extracted, thereby further improving the accuracy of determining the proportion of each type of pores, and further improving the accuracy of determining the pore size distribution of shale.

[0058] S1012: Quantitatively characterize the pore morphology of rock samples based on pore structure images.

[0059] Calculate the geometric parameters of each image unit in the pore structure image, such as area, equivalent disk radius, roundness, etc. Among them, the area represents the approximate area obtained by weighting each image unit with the neighborhood structure; the equivalent disk radius represents the radius of the corresponding equivalent disk when the image unit is equivalent to a disk with the same area; the roundness represents the ratio of the circumference of the equivalent disk of the image unit to the circumference of the corresponding image unit. Then, each image unit can be marked according to the geometric parameters of the image unit, and each image unit can be marked as cylindrical or flat. Calculate the proportion of image units marked as cylindrical to the total image units in the pore structure image as the proportion of cylindrical pores; and calculate the proportion of image units marked as flat to the total image units in the pore structure image as the proportion of flat pores.

[0060] Preferably, in some embodiments, each image unit can be marked using the roundness of the image unit. For example, the roundness of each image unit in the pore structure image can be calculated, and image units with a roundness less than 0.8 can be marked as flat, and image units with a roundness greater than or equal to 0.8 can be marked as cylindrical; the proportion of image units marked as cylindrical to the total image units in the pore structure image is calculated as the proportion of cylindrical pores; and the proportion of image units marked as flat to the total image units in the pore structure image is calculated as the proportion of flat pores. Using the solution provided in this embodiment, the proportion of each type of pore can be determined more accurately, thereby making the final rock pore size distribution result more accurate. Fig. 9 Indicates from Figure 8 Schematic diagram of the flat plate-shaped pores extracted from Fig.10 Indicates from Figure 8 Schematic diagram of the cylindrical pores extracted from the image.

[0061] S102: Based on the limiting assumption that all pore spaces are cylindrical pores or all are flat pores, calculate and obtain limiting pore size data of the target shale reservoir under the limiting assumption of cylindrical pores and limiting pore size data under the limiting assumption of flat pores.

[0062] The rock samples can be subjected to adsorption tests using methods such as low-temperature nitrogen adsorption tests to obtain adsorption / desorption test data. Afterwards, the pore spaces of the rock samples can be assumed to be cylindrical or flat, respectively, to characterize the pore size distribution of the rock samples.

[0063] For example, the rock sample can be pretreated first. For example, the following steps can be performed to pretreat the rock sample: 1) crush the sample: before analysis, crush the rock sample to about 200 mesh; 2) degassing and dehumidification: degas at 110°C for 8 hours under the vacuum of a turbomolecular pump. When the pressure and weight of the rock sample in the sample rack are stable, the degassing process is completed. After that, the rock sample can be subjected to an adsorption / desorption experiment: nitrogen with a purity greater than 99.999% is used as the adsorbent, and physical adsorption is performed at liquid nitrogen temperature (-195.85°C / 77.3K); the equilibrium time is set to 90s, the sample weight range is 2 to 5g, and the adsorbed gas volume when the relative pressure increases / decreases is automatically recorded by the weight method. Of course, the above-mentioned methods for pretreatment of rock samples and adsorption experiments are only preferred examples. Those skilled in the art can also adopt other similar processing methods, which are not limited in this specification.

[0064] Then, the pore space of the rock sample can be subjected to an extreme assumption, assuming that the pore space of the rock sample is all cylindrical pores or all flat pores, and the pore radius under the two assumptions can be calculated. After that, the calculated pore radius can be used to determine the limiting pore size data of the target shale reservoir under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption.

[0065] In some embodiments, desorption branch test data from the adsorption / desorption experimental results can also be extracted, and the desorption branch test data can be used to calculate the limiting pore size data of the target shale reservoir under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption.

[0066] After extensive analysis of the adsorption / desorption experimental results, the applicant discovered that the adsorption / desorption process of flat plate pores is different from that of cylindrical pores. First, for flat plate pores, capillary condensation will not occur until the relative pressure reaches 1 during the adsorption process, and there is no capillary condensation during the adsorption process; while cylindrical pores always have capillary condensation. Secondly, during the desorption process, the front edge of the half-moon-shaped capillary condensation liquid formed during the capillary condensation process is semi-cylindrical in flat plate pores and hemispherical in cylindrical pores. Finally, during the desorption process, if Fig.11 As shown in the figure, for a flat pore, when capillary condensation exists, the pore radius (r) is the sum of the adsorption layer thickness (t) and the radius of the capillary condensation front (r k ); For cylindrical pores, in the region where capillary condensation exists, the pore radius r can be expressed as the thickness of the adsorption layer t and the meniscus Kelvin radius r at any pressure. k sum. Fig.11 Schematic diagram showing the microstructure of cylindrical pores and plate-shaped pores during the adsorption / desorption process.

[0067] From the above analysis, it can be seen that capillary condensation does not occur during the adsorption process of flat pores, and there is a large difference between the leading edges of flat pores and cylindrical pores during capillary condensation, which leads to large differences in the calculation methods of the pore radius of the two. Therefore, if the test data corresponding to the adsorption branch is considered to calculate the pore size distribution, it will affect the accuracy of the final calculation result. This embodiment can further reduce the impact of the difference between flat pores and cylindrical pores in the adsorption process on the accuracy of the calculation results by only using the desorption branch test data for pore size distribution calculation, thereby further ensuring the accuracy of the final calculation result.

[0068] Assuming that the pore space of the rock sample consists only of cylindrical pores, the pore size distribution of the rock sample can be calculated using the following steps.

[0069] like Fig.11 As shown in the figure above, for cylindrical pores, in the region where capillary condensation exists, the pore radius r can be expressed as the adsorption layer thickness t and the meniscus Kelvin radius r at any pressure. k The adsorption layer thickness t can be calculated by formula (2), and the Kelvin radius can be calculated by formula (3).

[0070] r=t+r k (1)

[0071]

[0072]

[0073] Where r is the pore radius, t is the adsorption layer thickness (adsorption molecular layer thickness) corresponding to the relative pressure P0 / P, r k is the Kelvin radius of the meniscus, P is the actual vapor pressure, P0 is the saturated vapor pressure, x is the relative pressure, T represents the boiling temperature of nitrogen, and the boiling temperature of nitrogen is T = 77.3K; v m represents the molar volume of the adsorbed liquid, v m =34.65ml / mol; γ represents the surface tension of nitrogen at its boiling point, γ = 8.85×10 -3 J / m 2; is the angle between the half-moon front edge formed during capillary condensation and the tangent line of the contact surface, preferably, R represents the universal gas constant, R = 8.314 × 10 -23 J / mol / K.

[0074] Let V(r) be the pore volume distribution function, and calculate the pore volume of each pressure drop during the desorption process to obtain the pore size distribution. i , r i-1 ) range, the pore volume ΔV i It can be expressed as:

[0075]

[0076] Among them, r i 、r i-1 represent the pore radius corresponding to the i-th step and i-1-th step in the desorption process, r i =r k,i +t i .

[0077] Let v(r) be the total desorption within a specific pressure range, which consists of two parts: decondensation and adsorption layer thinning. Then the experimentally obtained gas desorption amount Δv i for:

[0078]

[0079] Therefore, when the relative pressure decreases from 1 to x i When , the evaporation amount of the condensed liquid is equal to:

[0080]

[0081] Therefore, the released pore volume can be expressed as:

[0082]

[0083] Since the evaporation amount of the condensed liquid is equal to the released pore volume, that is, equation (6) = equation (7). Therefore, considering two adjacent steps of pressure reduction, equation (8) can be obtained:

[0084]

[0085] By solving the above equations, we get the following results:

[0086]

[0087] set up Applying the integral mean value theorem to the first part of the right side of the above equation yields:

[0088]

[0089] Assume Δt i =(t i-1 -t i ), applying the integral mean value theorem to the second part of the right side of equation (9) yields:

[0090]

[0091] Substituting equations (10) and (11) obtained by the median integral theorem into equation (9), combined with equation (8), the pore size distribution calculation model can be obtained after sorting, as shown in equation (12):

[0092]

[0093] The above pore size distribution calculation model (12) can be used to calculate the pore size distribution of an ideal cylindrical pore space. It is also possible to define To simplify formula (12), “≡” is the identity sign.

[0094] Assuming that the pore space of the rock sample is only flat pores, the pore size distribution of the rock sample is calculated using the following steps.

[0095] like Fig.11 As shown in the figure below, when the gas-liquid equilibrium occurs at a diameter of d (d = 2 × (t + r k )) in a flat-plate pore, the Kelvin radius can be obtained by equation (13), while t and r can still be obtained by equations (2) and (1):

[0096]

[0097] It can be seen that under the same relative pressure, the Kelvin radius of cylindrical pores is twice that of flat pores. In other words, the capillary effect of cylindrical pores is stronger than that of flat pores. Therefore, by distinguishing between cylindrical pores and flat pores to determine the pore size distribution, the pore size distribution determination result can be made more accurate.

[0098] For an ideal flat pore space, the distance between the flat pores can be defined as d, instead of the pore radius r. Referring to the pore size distribution calculation method under the above cylindrical pore assumption, the pore size distribution calculation model (14) can be obtained by the same logic:

[0099]

[0100] in, d i =2(r k,i +t i ); dj =2(r k,j +t j ), the meanings of the other symbols are the same as those in formula (12). Of course, we can also define To simplify formula (14).

[0101] Then, the desorption branch in the nitrogen adsorption isotherm of the rock sample can be selected for pore size distribution calculation. Fig.12 The left figure shows the schematic diagram of the nitrogen adsorption-desorption isotherm curve of the rock sample. Fig.12 The right figure shows a schematic diagram of the desorption branch extracted from the nitrogen adsorption-desorption isotherm, where Ads represents the adsorption branch and Des represents the desorption branch.

[0102] Assuming that all desorption branches originate from cylindrical pores, the pore size distribution under this limiting state is obtained by using the above formula (12): Fig.13 As shown in the left figure, Fig.13 The left figure is a schematic diagram of the pore size distribution under the limit assumption that all pores are cylindrical. Assuming that the desorption branches all originate from flat pores, with the help of the above formula (14), the pore size distribution under this limit state is obtained as follows: Fig.13 As shown in the right figure, Fig.13 The right figure shows the pore size distribution diagram under the limiting assumption that all pores are flat. Fig.13 The horizontal axis is the pore size, and the vertical axis is the differential of the pore volume with respect to the pore radius.

[0103] S103: using the proportion of the cylindrical pores and the proportion of the flat pores, the limiting pore size data under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption are fused to obtain the pore size distribution data of the target shale reservoir.

[0104] The fusion model that can be further established is shown in the following formula (15):

[0105]

[0106] Among them, PSD Hybrid represents the pore size distribution data of the target shale reservoir, and They represent the proportion of cylindrical pores and flat pores calculated based on the pore structure image, respectively, and PSD cyl ., PSD slit They represent the limiting pore size data under the cylindrical pore limiting assumption and the limiting pore size data under the flat pore limiting assumption. Fig.14 As shown, Fig.14A schematic diagram showing the comparison of the pore size distribution (mixed model) obtained after the fusion process, the pore size distribution under the limiting assumption that all pores are flat and the pore size distribution under the limiting assumption that all pores are cylindrical. Fig.14 The horizontal axis is the pore size, and the vertical axis is the differential of the pore volume with respect to the pore radius.

[0107] The pore shapes of actual shale reservoirs are diverse, and there are certain differences in the gas flow characteristics of shale pores with different pore shapes. As described in the above embodiment, capillary condensation will only occur when the relative pressure of flat pores reaches 1 during the adsorption process, and there is no capillary condensation during the adsorption process. The pore radius calculation method of shale pores with different pore shapes during the desorption process is also different, which makes the pore volume calculation method also have certain differences. If the diversity of the pore shapes of actual shale reservoirs is not taken into account, the final analysis results of the adsorption capacity of shale reservoirs will be inaccurate, which will affect the accuracy of the gas storage capacity analysis of shale reservoirs. At the same time, the pore shapes of actual shale reservoirs are too complex. If all types of pores are considered for analysis, the analysis process will be too complicated.

[0108] Through a large number of research and analysis, it is determined that cylindrical pores and flat pores are the most common and most fluid-flowing pore shapes in shale matrix. For example, through microscopic observation of pores, it is found that the relationship between pore type and pore geometry is as follows: organic pores and dissolution pores are mainly cylindrical, while clay pores are mainly flat; microcracks, whether developed in organic matter or in minerals, are flat. And more complex pore shapes can also be achieved by using a combination of these two shapes during the processing process. Accordingly, in the embodiment of this specification, by analyzing the different pore shapes of actual shale reservoirs, the shale reservoir is assumed to be composed of cylindrical pores and flat pores, which can more realistically reflect the geometric composition of the pore space on the basis of reducing the complexity of data processing, and is suitable for the analysis of shale reservoirs including a variety of different pore space forms, further improving the accuracy of the shale reservoir adsorption capacity analysis results, and thus improving the accuracy and efficiency of the shale reservoir gas storage capacity analysis.

[0109] Under the assumption that the pore shape of shale is composed of two types of cylindrical pores and flat pores, the embodiments of this specification further provide a method for calculating the pore size distribution of shale. Specifically, under the limiting assumption that all pore spaces are cylindrical or flat, the test data corresponding to the desorption branch of the adsorption / desorption test is used to determine the limiting pore size data of shale; and the proportion of cylindrical pores and flat pores in shale is determined based on the shale image; then, the proportion of cylindrical pores and flat pores in shale and the limiting pore size data determined under the limiting assumption are combined to obtain the calculation result of the pore size distribution of shale. Using the pore size distribution calculation method given in the implementation mode of this specification, the pore size distribution calculation can be made more consistent with the fluid characteristics of pores of different shapes in the target work area shale, and at the same time, the pore size distribution calculation results are made more consistent with the actual proportion of pores of different shapes in the target work area shale, thereby making the pore size distribution calculation results more accurate.

[0110] In order to further verify the effect of the solution given in the embodiments of this specification, this specification also provides the following comparison examples.

[0111] 1.1. Compared with the pore size distribution obtained by the DFT model. The DFT model is implemented by molecular simulation. The pore size distribution obtained by analyzing the nitrogen adsorption data according to the DFT model is closest to the real pore size distribution. Therefore, the pore size distribution obtained by the DFT model can be used as the real pore size distribution for comparison to verify the validity of the pore size distribution calculation results.

[0112] Fig.15 Schematic diagram showing the comparison of pore size distribution obtained by different calculation methods. Fig.15 The horizontal axis is the pore size, and the vertical axis is the differential of the pore volume with respect to the pore radius. Fig.15 In the pore space of the sample shown, the pore size distribution obtained under the cylindrical limit assumption has a deviation of 36.74% from the pore size distribution obtained by the DFT model, and the pore size distribution obtained under the flat plate limit assumption has a deviation of 89.07% from the pore size distribution obtained by the DFT model. The scheme given in the embodiment of this specification further reduces the deviation by 16.55%, and improves the accuracy of the pore size distribution interpretation. It can be explained that within the range of 5nm to 100nm, whether it is the pore size distribution (Cylindrical) obtained under the cylindrical limit assumption or the pore size distribution (Slit) obtained under the flat plate limit assumption, it will deviate from the pore size distribution of the DFT model, while the pore size distribution obtained by the scheme (Hybrid) given in the embodiment of this specification is more consistent with DFT, which shows that the reservoir pore size distribution characteristics explained by the scheme given in the embodiment of this specification are more accurate.

[0113] At the same time, in order to determine whether the scheme given in the embodiment of this specification is effective for explaining heterogeneity, this example further analyzes the nitrogen adsorption data of the sample based on the FHH fractal model. The data show that the piecewise linear fitting effect is good, and the fitting coefficient is greater than 97%. This proves that the scheme given in the embodiment of this specification is effective for explaining heterogeneity. Among them, the principle of FHH is: using the gas adsorption relative pressure and adsorption amount data to calculate the fractal dimension of shale pores, the calculation model is ln(V / V m )=C+A|ln(ln(P0 / P))|, C is a constant, A is a coefficient related to the fractal dimension D. The A value can be obtained by using the logarithmic slope of the adsorption volume and the inverse of the relative pressure, that is, the pore fractal dimension D of the shale.

[0114] At the same time, this example calculates the specific pore volume (PV) using the calculation results under the limit assumption and the fused calculation results, respectively, and compares the correlation between the specific pore volume and D2 obtained under different calculation results. The specific pore volume (PV) further calculated based on the fused calculation results is between the cylindrical limit assumption and the flat plate limit assumption. In addition, the correlation between the specific pore volume (PV) and D2 further calculated based on the fused calculation results is also higher than the correlation obtained under the limit assumption, which indicates that the pore size distribution characterized by the fused calculation results (i.e., the pore size distribution results finally obtained in this application) is more reasonable.

[0115] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For details, please refer to the description of the above-mentioned related processing related embodiments, and no further description is given here.

[0116] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] Based on the above-mentioned method for determining shale pore size distribution based on image morphology intelligent analysis, one or more embodiments of this specification also provide a device for determining shale pore size distribution based on image morphology intelligent analysis. Specifically, Fig.16 A schematic diagram of the module structure of an embodiment of a shale pore size distribution determination device based on image morphology intelligent analysis provided in the specification is shown in FIG. Fig.16As shown, the device may include: a pore proportion determination module 1601, which is used to obtain a pore structure image of the target shale reservoir, and determine the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image; a limit pore size data calculation module 1602, which is used to calculate the limit pore size data of the target shale reservoir under the limit assumption of cylindrical pores and the limit pore size data under the limit assumption of flat pores based on the limit assumption that all pore spaces are cylindrical pores or all are flat pores; a fusion processing module 1603, which is used to use the proportion of cylindrical pores and the proportion of flat pores to fuse the limit pore size data under the limit assumption of cylindrical pores and the limit pore size data under the limit assumption of flat pores to obtain the pore size distribution data of the target shale reservoir. It should be noted that the above-mentioned device may also include other implementation methods according to the description of the method embodiment. The specific implementation method can refer to the description of the relevant method embodiment, and will not be repeated here.

[0118] Based on the above-mentioned method for determining shale pore size distribution based on image morphology intelligent analysis, this specification also provides a computer-readable storage medium on which computer instructions are stored. When the instructions are executed, the steps of the method described in any one or more of the above embodiments are implemented. The storage medium may include a physical device for storing information, which is usually to digitize the information and then store it in a medium using electrical, magnetic or optical methods. The storage medium may include: devices that store information using electrical energy, such as various types of memories, such as RAM, ROM, etc.; devices that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memories, bubble memories, and U disks; devices that store information optically, such as CDs or DVDs. Of course, there are other types of readable storage media, such as quantum memories, graphene memories, and so on.

[0119] It should be noted that the embodiments of this specification are not limited to the situations that must comply with the standard data model / template or the embodiments of this specification. Certain industry standards or slightly modified implementation plans based on the implementation described in the custom method or the embodiment can also achieve the same, equivalent or similar, or predictable implementation effects after deformation of the above-mentioned embodiments. The embodiments obtained by using these modified or deformed data acquisition, storage, judgment, processing methods, etc. can still fall within the scope of the optional implementation plans of this specification.

[0120] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0121] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A method for determining shale pore size distribution based on image morphology intelligent analysis, characterized in that: include: Acquire a pore structure image of the target shale reservoir, and determine the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image; Based on the limiting assumption that all pore spaces are cylindrical pores or all are flat pores, the limiting pore size data of the target shale reservoir under the limiting assumption of cylindrical pores and the limiting pore size data under the limiting assumption of flat pores are calculated; By using the proportion of the cylindrical pores and the proportion of the flat pores, the limiting pore size data under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption are fused to obtain the pore size distribution data of the target shale reservoir; Wherein, determining the proportion of cylindrical pores and the proportion of flat pores based on the pore structure image includes: calculating the roundness of each image unit in the pore structure image, marking the image units with a roundness less than 0.8 as flat, and marking the image units with a roundness greater than or equal to 0.8 as cylindrical; calculating the proportion of image units marked as cylindrical to the total image units in the pore structure image as the proportion of cylindrical pores; and calculating the proportion of image units marked as flat to the total image units in the pore structure image as the proportion of flat pores.

2. The method according to claim 1, characterized in that The fusion processing of the limiting pore size data under the cylindrical pore limiting assumption and the limiting pore size data under the flat pore limiting assumption includes: Among them, PSD Hybrid represents the pore size distribution data of the target shale reservoir, and They represent the proportion of cylindrical pores and flat pores calculated based on the pore structure image, respectively, and PSD cyl. ,PSD slit They represent the limiting pore diameter data under the cylindrical pore limiting assumption and the limiting pore diameter data under the flat pore limiting assumption, respectively.

3. The method according to claim 1, characterized in that By using the desorption branch test data, the limiting pore size data of the target shale reservoir under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption are calculated.

4. The method according to any one of claims 1 to 3, characterized in that: The following pore size distribution calculation model is used to calculate the limit pore size data of the target shale reservoir under the cylindrical pore limit assumption: Where, ΔV i represents the pore volume corresponding to the pressure drop in the i-th step during the desorption process, ΔV j represents the pore volume corresponding to the pressure drop in the jth step during the desorption process, r i 、r i-1 They represent the pore radius corresponding to the pressure drop in the i-th step and the i-1-th step in the desorption process, r j 、r j-1 They represent the pore radius corresponding to the pressure drop in the j-th step and the j-1-th step in the desorption process, Δt i =(t i-1 -t i ), t i ,t i-1 They represent the adsorption layer thickness corresponding to the pressure drop in the i-th step and the i-1-th step in the desorption process, Δv i It represents the gas desorption amount corresponding to the pressure drop in the i-th step during the desorption process.

5. The method according to any one of claims 1 to 3, characterized in that: The following pore size distribution calculation model is used to calculate the limit pore size data of the target shale reservoir under the assumption of a flat plate pore limit: Where, ΔV i represents the pore volume corresponding to the pressure drop in the i-th step during the desorption process, ΔV j represents the pore volume corresponding to the pressure drop in the jth step during the desorption process, d i d i-1 They represent the distances between the flat plate-shaped pores corresponding to the pressure drops in the i-th step and the i-1-th step during the desorption process, d j d j-1 They represent the distances between the flat plate-shaped pores corresponding to the pressure drops at the j-th and j-1-th steps in the desorption process, Δt i =(t i-1 -t i ), t i ,t i-1 They represent the adsorption layer thickness corresponding to the pressure drop in the i-th step and the i-1-th step in the desorption process, Δv i It represents the gas desorption amount corresponding to the pressure drop in the i-th step during the desorption process.

6. The method according to claim 1, characterized in that The step of obtaining the pore structure image of the target shale reservoir comprises: Acquiring an initial electron microscope image collected by a scanning electron microscope from a rock sample of the target shale reservoir; The initial electron microscope image is subjected to at least median filtering, binarization, gradient filtering, image segmentation and background information removal in sequence to obtain a pore structure image of the target shale reservoir.

7. The method according to claim 6, characterized in that The watershed algorithm is used to perform image segmentation on the electron microscope image obtained after gradient filtering.

8. A device for determining shale pore size distribution based on image morphology intelligent analysis, characterized in that: include: A pore ratio determination module, used to obtain a pore structure image of a target shale reservoir, and determine the ratio of cylindrical pores and the ratio of flat pores based on the pore structure image; A limit pore size data calculation module is used to calculate the limit pore size data of the target shale reservoir under the limit assumption of cylindrical pores and the limit pore size data under the limit assumption of flat pores based on the limit assumption that all pore spaces are cylindrical pores or all are flat pores; A fusion processing module, for fusing the limiting pore size data under the cylindrical pore limit assumption and the limiting pore size data under the flat pore limit assumption by using the proportion of the cylindrical pores and the proportion of the flat pores, to obtain the pore size distribution data of the target shale reservoir; Among them, the pore proportion determination module is specifically used to: calculate the roundness of each image unit in the pore structure image, mark the image units with a roundness less than 0.8 as flat, and mark the image units with a roundness greater than or equal to 0.8 as cylindrical; calculate the proportion of image units marked as cylindrical to the total image units in the pore structure image, as the proportion of cylindrical pores; and calculate the proportion of image units marked as flat to the total image units in the pore structure image, as the proportion of flat pores.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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