Methods, apparatus and computer equipment for obtaining texture parameters of shale oil reservoirs

Abstract

This disclosure reveals a method, apparatus, and computer equipment for obtaining texture parameters of shale oil reservoirs, belonging to the field of unconventional oil and gas development technology. The method includes: determining the textured areas by acquiring fluorescence scanning images of thin sections of shale core samples; then collecting density values ​​at various points within the textured areas of the shale core sample thin sections in nanometer units to generate corresponding black-and-white scanning images; generating individual textures by boundary extraction from the black-and-white scanning images; and calculating the texture parameters corresponding to each texture. Quantitative analysis of these texture parameters using computer equipment solves the problem of inaccurate texture parameter acquisition through manual microscopic observation, thereby improving the accuracy of texture parameter acquisition and ultimately increasing the efficiency of shale oil and gas development.

Description

Technical Field

[0001] This disclosure relates to the field of unconventional oil and gas development technology, and in particular to a method, apparatus and computer equipment for obtaining texture parameters of shale oil reservoirs. Background Technology

[0002] In the process of developing unconventional oil and gas, it is necessary to assess the oil reserves in the rock formations. Shale oil is an important component of unconventional oil and gas.

[0003] In related technologies, in order to identify the texture of shale oil reservoirs, it is necessary to sample the shale of the target layer and observe the texture with a minimum precision of micrometers by scanning with an electron microscope.

[0004] However, the solutions in the relevant technologies only scan the target shale layer sample with an electron microscope at the micrometer level to obtain the micrometer-level texture, but cannot obtain the texture at a lower level. Furthermore, the texture parameters obtained by manual observation have poor accuracy, which reduces the efficiency of unconventional oil and gas development. Summary of the Invention

[0005] This disclosure provides a method, apparatus, and computer equipment for obtaining texture parameters of shale oil reservoirs. The technical solution is as follows:

[0006] On the one hand, a method for obtaining texture parameters of shale oil reservoirs is provided, the method comprising:

[0007] Obtain fluorescence scan images; the fluorescence scan images are images with fluorescence display taken by scanning thin sections of shale core samples with an electron microscope;

[0008] The texture region is obtained from the fluorescence scan image; the texture region is the area that shows fluorescence in the fluorescence scan image.

[0009] Data is collected at each sampling point in the textured area of ​​the shale core sample thin section to obtain the density value at each sampling point; the sampling point is located in nanometers.

[0010] A black and white scan image is generated based on the density values ​​at each collection point; the black and white scan image contains both black and white dots.

[0011] Boundary candidate points are obtained from the black and white scan image; the boundary candidate point is a white point that has at least one black point within a first threshold range;

[0012] Connect the boundary candidate points that are adjacent to other boundary candidate points to determine each texture;

[0013] Based on the boundary candidate points on each texture, calculate the texture parameters corresponding to each texture in the shale core sample thin section.

[0014] In one possible implementation, connecting the boundary candidate points that have adjacent other boundary candidate points to determine each texture includes:

[0015] In response to the presence of other boundary candidate points around the boundary candidate point, and the distance between the boundary candidate point and at least one of the other boundary candidate points being less than or equal to a first threshold, the boundary candidate point is connected to the other boundary candidate point with the smallest distance.

[0016] Boundary points are determined from the candidate boundary points according to specified conditions;

[0017] Each of the adjacent boundary points is connected to generate a texture; each texture includes a horizontal extension length and a vertical depth.

[0018] In one possible implementation, determining the boundary point from the candidate boundary points according to specified conditions includes:

[0019] In response to the fact that the length of the line generated by the connection is greater than or equal to the specified length, the candidate boundary points on the line are determined as boundary points;

[0020] or,

[0021] In response to the number of boundary candidate points connecting the generated lines being greater than or equal to a specified value, each of the boundary candidate points on the line is determined as a boundary point.

[0022] In one possible implementation, calculating the texture parameters corresponding to each texture in the shale core sample section based on the boundary candidate points on each texture includes:

[0023] In response to the texture parameter including the number of texture lines, the number of each generated texture is determined as the number of texture lines;

[0024] In response to the texture parameter including texture line density, the texture line density is determined by dividing the number of texture lines by the slice length of the shale core sample slice;

[0025] In response to the texture parameter including the texture length, the lengths between each adjacent boundary point connected along the horizontal direction are accumulated to generate the texture length;

[0026] In response to the texture parameters including texture aperture, the average value is obtained by accumulating the depths between each adjacent boundary point connected in the vertical direction to generate the texture aperture.

[0027] In one possible implementation, calculating the texture parameters corresponding to each texture in the shale core sample section based on the boundary candidate points on each texture includes:

[0028] In response to the texture parameters including texture area, the area of ​​each quadrilateral is determined by using the length between each adjacent boundary point connected in the horizontal direction as the length of each quadrilateral and the depth between each adjacent boundary point connected in the vertical direction as the width of each quadrilateral.

[0029] The area of ​​each quadrilateral is summed to determine the texture area.

[0030] In one possible implementation, calculating the texture parameters corresponding to each texture in the shale core sample section based on the boundary candidate points on each texture includes:

[0031] In response to the texture parameters including texture surface density, the texture area is divided by the area of ​​the shale core sample section to generate the texture surface density.

[0032] In one possible implementation, generating a black-and-white scan image based on the density values ​​at each acquisition point includes:

[0033] If the density value at the sampling point is greater than or equal to a specified threshold, the sampling point is determined to be a black point.

[0034] In response to a density value at the sampling point being less than a specified threshold, the sampling point is determined to be a white point;

[0035] In response to the completion of color marking at each acquisition point, the black and white scan image is generated.

[0036] On the one hand, a device for obtaining texture parameters of shale oil reservoirs is provided, the device comprising:

[0037] The first image acquisition module is used to acquire fluorescence scan images; the fluorescence scan images are images with fluorescence display taken by scanning thin sections of shale core samples with an electron microscope;

[0038] The texture region acquisition module is used to acquire the texture region based on the fluorescence scan image; the texture region is the region with fluorescence display in the fluorescence scan image.

[0039] The density value acquisition module is used to collect data from each collection point in the textured area of ​​the shale core sample thin section and obtain the density value at each collection point; the collection point is the location where the data is collected in nanometers.

[0040] The second image generation module is used to generate a black and white scan image based on the density values ​​at each collection point; the black and white scan image contains black dots and white dots.

[0041] A candidate point acquisition module is used to acquire boundary candidate points from the black and white scan image; the boundary candidate point is a white point that has at least one black point within a first threshold range;

[0042] A texture determination module is used to connect the boundary candidate points that have adjacent other boundary candidate points to determine each texture;

[0043] The parameter calculation module is used to calculate the texture parameters corresponding to each texture in the shale core sample thin section based on the boundary candidate points on each texture.

[0044] In one possible implementation, the texture determination module includes:

[0045] The candidate point connection submodule is used to connect the boundary candidate point to the other boundary candidate point with the smallest distance in response to the existence of other boundary candidate points around the boundary candidate point and the distance between the boundary candidate point and at least one of the other boundary candidate points being less than or equal to a first threshold.

[0046] The boundary point determination submodule is used to determine boundary points from the candidate boundary points according to specified conditions;

[0047] The texture generation submodule is used to connect adjacent boundary points to generate the textures; each texture includes a horizontal extension length and a vertical depth.

[0048] In one possible implementation, the boundary point determination submodule includes:

[0049] The first boundary point determination unit is used to determine each of the candidate boundary points on the line as boundary points in response to the connection line length being greater than or equal to a specified length.

[0050] or,

[0051] The second boundary point determination unit is used to determine each of the boundary candidate points on the line as a boundary point in response to the number of boundary candidate points connecting the generated line being greater than or equal to a specified value.

[0052] In one possible implementation, the parameter calculation module includes:

[0053] The number of textures determination submodule is used to determine the number of each generated texture as the number of textures in response to the texture parameter including the number of textures;

[0054] The line density determination submodule is used to determine the texture line density by dividing the number of texture lines by the slice length of the shale core sample slice in response to the texture parameters including texture line density.

[0055] The length determination submodule is used to, in response to the texture parameters including the texture length, accumulate the lengths between each adjacent boundary point connected along the horizontal direction to generate the texture length;

[0056] The aperture determination submodule is used to generate the texture aperture by accumulating the depths between each adjacent boundary point connected along the vertical direction and taking the average value in response to the texture parameters including texture aperture.

[0057] In one possible implementation, the parameter calculation module includes:

[0058] The sub-area determination submodule is used to determine the area of ​​each quadrilateral in response to the texture parameters including the texture area, by using the length between each adjacent boundary point connected in the horizontal direction as the length of each quadrilateral, and the depth between each adjacent boundary point connected in the vertical direction as the width of each quadrilateral.

[0059] The area determination submodule is used to sum the areas of each quadrilateral to determine the texture area.

[0060] In one possible implementation, the parameter calculation module includes:

[0061] The surface density determination submodule is used to generate the surface density by dividing the texture area by the area of ​​the shale core sample section in response to the texture parameter including the texture surface density.

[0062] In one possible implementation, the second image generation module includes:

[0063] The black dot determination submodule is used to determine the sampling point as a black dot in response to the density value at the sampling point being greater than or equal to a specified threshold.

[0064] The white point determination submodule is used to determine the sampling point as a white point in response to the density value at the sampling point being less than a specified threshold.

[0065] The second image generation submodule is used to generate the black and white scan image in response to the completion of the color marking of each collection point.

[0066] On the other hand, a computer device is provided, the computer device including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the above-mentioned method for obtaining shale oil reservoir texture parameters.

[0067] In another aspect, a computer-readable storage medium is provided, wherein at least one instruction, at least one program, code set, or instruction set is stored in the storage medium, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the above-mentioned method for obtaining shale oil reservoir texture parameters.

[0068] According to one aspect of this application, a computer program product is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for obtaining shale oil reservoir texture parameters provided in various alternative implementations of the above aspect.

[0069] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0070] In the scheme shown in this embodiment, the region where texture exists is determined by obtaining a fluorescence scan image of a thin section of shale core sample. Then, density values ​​of each point in the texture region of the shale core sample are collected in nanometers to generate a corresponding black and white scan image. By extracting the boundaries of the black and white scan image, each texture is generated, and the texture parameters corresponding to each texture are calculated. Quantitative analysis of each texture parameter using computer equipment can solve the problem that texture parameters cannot be accurately obtained through manual microscopic observation, thereby improving the accuracy of texture parameter acquisition and thus improving the efficiency of shale oil and gas development.

[0071] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0072] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0073] Figure 1 This is a schematic diagram illustrating a system for obtaining texture parameters of a shale oil reservoir according to an exemplary embodiment;

[0074] Figure 2This is a flowchart illustrating a method for obtaining texture parameters of a shale oil reservoir according to an exemplary embodiment;

[0075] Figure 3 This is a flowchart illustrating a method for obtaining texture parameters of a shale oil reservoir according to an exemplary embodiment;

[0076] Figure 4 yes Figure 3 The illustrated embodiment is a schematic diagram of a method for calculating texture length;

[0077] Figure 5 yes Figure 3 The illustrated embodiment is a schematic diagram of a method for calculating texture aperture;

[0078] Figure 6 yes Figure 3 The illustrated embodiment is a schematic diagram of a method for calculating texture area;

[0079] Figure 7 yes Figure 3 The illustrated embodiment is a schematic diagram of a scan image display;

[0080] Figure 8 This is a block diagram illustrating an apparatus for acquiring texture parameters of a shale oil reservoir according to an exemplary embodiment;

[0081] Figure 9 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0082] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0083] It should be understood that "several" in this article refers to one or more, and "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0084] Figure 1This is a schematic diagram of a system for acquiring texture parameters of a shale oil reservoir according to an exemplary embodiment. The system includes an electron microscope 110, a scanning device 120, and a computer device 130.

[0085] The computer device 130 is equipped with software for analyzing and extracting texture boundaries.

[0086] Both the electron microscope 110 and the scanning device 120 can be connected to the computer device 130 via wired or wireless network to transmit the acquired data to the computer device 130. The computer device 130 then processes the data.

[0087] Optionally, the wired or wireless networks described above use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to any combination of Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile wireless networks, private networks, or Virtual Private Networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0088] Figure 2 This is a flowchart illustrating a method for obtaining texture parameters of shale oil reservoirs according to an exemplary embodiment. This method for obtaining shale oil reservoir texture parameters can be applied in a computer device. It acquires black-and-white scan images by collecting density values ​​from thin sections of shale core samples. Texture boundaries are extracted from the black-and-white scan images, and texture parameters corresponding to each texture are calculated, thereby achieving quantitative acquisition of texture parameters via computer equipment. Figure 2 As shown, the method for obtaining the texture parameters of this shale oil reservoir may include the following steps:

[0089] In step 201, a fluorescence scan image is obtained; the fluorescence scan image is an image with fluorescence display taken after scanning a thin section of a shale core sample with an electron microscope.

[0090] In this embodiment of the disclosure, an electron microscope is used to photograph thin sections of shale core samples. Since the thin sections of shale core samples have the characteristic of fluorescing due to the presence of oil and gas within the texture, the resulting images are fluorescent scan images. The electron microscope uploads the captured fluorescent scan images to a computer device, which then obtains the fluorescent scan images.

[0091] In one possible implementation, the fluorescent display portion in the fluorescence scan corresponds to the area where the texture exists.

[0092] An electron microscope is an instrument used to scan thin sections of shale core samples at the micrometer level. The acquired fluorescence scan image can be a scanned image at the micrometer level.

[0093] In step 202, the texture region is obtained based on the fluorescence scan image; the texture region is the region with fluorescence display in the fluorescence scan image.

[0094] In this embodiment of the present disclosure, a computer device can determine the texture region in the fluorescence scan image based on the fluorescence display portion in the acquired fluorescence scan image.

[0095] Among them, the textured areas are the regions corresponding to each texture in the thin section of the shale core sample.

[0096] In step 203, data is collected from each sampling point in the textured area of ​​the shale core sample thin section to obtain the density value at each sampling point; each sampling point is located in nanometers.

[0097] In this embodiment of the disclosure, the scanning device connected to the computer device is a device for collecting density values ​​of shale core sample slices. The computer device controls the scanning device to collect data on the texture areas corresponding to the shale core sample slices by acquiring the texture areas. The density value data of each texture area is collected in nanometers and uploaded to the computer device.

[0098] In one possible implementation, the textured area is automatically captured by a computer device through computer image recognition of a fluorescence scan.

[0099] In step 204, a black and white scan image is generated based on the density values ​​at each collection point; the black and white scan image contains both black and white dots.

[0100] In this embodiment of the disclosure, the computer device determines the color of each acquisition point based on the density value corresponding to each acquisition point in nanometers, then generates a black and white scan image corresponding to each texture area, and finally generates a black and white scan image corresponding to the fluorescence scan image.

[0101] Among them, the black and white scan image can be a black and white scatter plot, which contains black dots and white dots.

[0102] In step 205, boundary candidate points are obtained from the black and white scan image; a boundary candidate point is a white point that has at least one black point within a first threshold range.

[0103] In this embodiment of the disclosure, the computer device obtains boundary candidate points from each acquisition point in the acquired black and white scan image. The boundary candidate points are white points in the black and white scan image that have at least one black point within a first threshold range.

[0104] Among them, boundary candidate points are used to initially extract the boundary points corresponding to each texture. When there is at least one black point among the nearest sampling points in each direction around a sampling point that has been determined to be a white point, the white point can be determined as a boundary candidate point.

[0105] In one possible implementation, each texture boundary point exists among the boundary candidate points.

[0106] In step 206, the boundary candidate points that are adjacent to other boundary candidate points are connected to determine each texture.

[0107] In this embodiment of the disclosure, the computer device connects adjacent candidate boundary points, wherein the candidate boundary points to be connected are the closest candidate boundary points to each other, and the computer device analyzes the generated lines to determine the textures in each line.

[0108] In one possible implementation, in response to the presence of lines that do not conform to the texture characteristics among the generated lines, the lines that do not conform to the texture characteristics are filtered out, and the lines that conform to the texture characteristics are determined as the corresponding textures.

[0109] In step 207, the texture parameters corresponding to each texture in the shale core sample thin section are calculated based on the boundary candidate points on each texture.

[0110] In this embodiment of the disclosure, the computer device calculates the texture parameters corresponding to each texture of the shale core sample thin section based on the boundary candidate points on each texture, that is, each boundary point.

[0111] The texture parameters include at least one of the following: texture length, number of texture lines, texture aperture, texture area, texture linear density, and texture surface density.

[0112] In summary, the scheme presented in this disclosure determines the texture region by obtaining a fluorescence scan image of a thin section of shale core sample. Then, density values ​​are collected at nanometer levels for each point within the texture region of the shale core sample, generating a corresponding black-and-white scan image. Boundary extraction is performed on the black-and-white scan image to generate individual textures, and texture parameters are calculated for each texture. Quantitative analysis of these texture parameters using computer equipment solves the problem of inaccurate texture parameter acquisition through manual microscopic observation, thereby improving the accuracy of texture parameter acquisition and ultimately increasing the efficiency of shale oil and gas development.

[0113] Figure 3 This is a flowchart illustrating a method for obtaining texture parameters of shale oil reservoirs according to another exemplary embodiment. This method for obtaining shale oil reservoir texture parameters can be applied in a computer device. It acquires black-and-white scan images by collecting density values ​​from thin sections of shale core samples. Texture boundaries are extracted from the black-and-white scan images, and texture parameters corresponding to each texture are calculated, thereby achieving quantitative acquisition of texture parameters via computer equipment. Figure 3 As shown, the method for obtaining the texture parameters of this shale oil reservoir may include the following steps:

[0114] In step 301, a fluorescence scan image is obtained.

[0115] In this embodiment of the disclosure, after scanning and photographing a thin section of a shale core sample with an electron microscope, the obtained fluorescence scan image is uploaded to a computer device, and the computer device obtains the corresponding fluorescence scan image.

[0116] The fluorescence scan image is a fluorescently displayed image captured by scanning a thin section of a shale core sample using an electron microscope. This fluorescence scan image can be obtained by performing microscopic fluorescence scanning of shale core sample thin sections with a precision of micrometers.

[0117] In one possible implementation, shale core sample thin sections are obtained by cutting a core sample from the target layer and selecting a long strip of specified length, width, and thickness as a shale core sample thin section for observation and scanning under an electron microscope.

[0118] For example, observers can cut a core sample into sections 50 mm long, 20 mm wide, and 0.03 mm thick to obtain a thin section as a shale core sample section. This shale core sample section is made perpendicular to the rock strata and can represent the texture of the rock strata observed.

[0119] In step 302, the region where the texture exists is obtained based on the fluorescence scan map.

[0120] In this embodiment of the disclosure, the computer device determines the texture area of ​​the shale core sample section based on the fluorescence display area in the acquired fluorescence scan image.

[0121] The textured areas are those that exhibit fluorescence in the fluorescence scan image.

[0122] In one possible implementation, the computer device uses automatic image recognition technology to determine the fluorescent display area in the fluorescence scan as the texture presence area, or the observer manually selects each texture presence area by observing the fluorescent display area in the fluorescence scan.

[0123] In step 303, data is collected from each sampling point in the texture area of ​​the shale core sample thin section to obtain the density value at each sampling point.

[0124] In this embodiment of the disclosure, the computer device sends the obtained texture area to the scanning device. The scanning device scans the corresponding texture area of ​​the placed shale core sample thin section according to the received texture area. The computer device obtains the rock layer density value corresponding to each collection point in each texture area in nanometers.

[0125] Each sampling point corresponds to a density value indicating the density of the rock strata at that point. Each sampling point is a location on a thin section of a shale core sample collected in nanometers.

[0126] In one possible implementation, density values ​​in micrometers are collected in non-textured regions on thin sections of shale core samples. If a collection point with a density value less than a specified threshold is detected in a non-textured region, the region containing that collection point is added to the textured region, and density values ​​in nanometers are collected.

[0127] In step 304, a black and white scan image is generated based on the density values ​​at each collection point.

[0128] In this embodiment of the present disclosure, the computer device converts the fluorescence scan image into a black and white scan image and displays it on the computer device based on the density values ​​of each collection point corresponding to each texture existence area obtained from the scanning device.

[0129] The black and white scan image contains both black and white dots.

[0130] In one possible implementation, a sampling point is determined to be a black point in response to a density value at a sampling point being greater than or equal to a specified threshold; a sampling point is determined to be a white point in response to a density value at a sampling point being less than a specified threshold; and a black and white scan image is generated in response to the completion of color marking for each sampling point.

[0131] The specified threshold is used to indicate the density critical threshold corresponding to whether the rock strata at each sampling point location are textured or non-textured.

[0132] In step 305, candidate boundary points are obtained from the black-and-white scan image.

[0133] In this embodiment of the disclosure, the computer device selects boundary candidate points from various acquisition points in the black and white scan image, wherein the boundary candidate point is a white point that has at least one of the black points within a first threshold range.

[0134] In step 306, in response to the presence of other boundary candidate points around a boundary candidate point and the distance between the boundary candidate point and at least one other boundary candidate point being less than or equal to a first threshold, the boundary candidate point is connected to the other boundary candidate point with the smallest distance.

[0135] In this embodiment of the disclosure, the computer device connects the boundary candidate points that have other boundary candidate points around them and whose distance to at least one other boundary candidate point is less than or equal to a first threshold with the other boundary candidate point with the smallest distance, thereby generating various connecting lines.

[0136] The first threshold is used to indicate the maximum distance between candidate boundary points that can be connected into a line.

[0137] In step 307, boundary points are determined from the candidate boundary points according to specified conditions.

[0138] In this embodiment of the disclosure, the computer device determines the boundary points used to constitute each texture from the boundary candidate points according to specified conditions.

[0139] In one possible implementation, the specified condition is that the length of the line generated by the connection is greater than or equal to a specified length, and in response to the length of the line generated by the connection being greater than or equal to the specified length, each candidate boundary point on the line is determined as a boundary point.

[0140] In another possible implementation, the specified condition is that the number of boundary candidate points on the line generated by connecting is greater than or equal to a specified number. In response to the number of boundary candidate points on the line being greater than or equal to a specified value, each boundary candidate point on the line is determined as a boundary point.

[0141] For example, to determine whether a line of a certain length is a texture, the length of the line can be determined by detecting its length or the number of boundary points that make up the line. Short lines formed by connecting points with shorter lengths or isolated boundary candidate points are excluded.

[0142] In step 308, adjacent boundary points are connected to generate various textures.

[0143] In this embodiment of the disclosure, the computer device connects the determined adjacent boundary points to generate various textures, each texture including a horizontal extension length and a vertical depth.

[0144] Each texture can be a texture region, including the top edge, bottom edge, and depth.

[0145] In step 309, the texture parameters corresponding to each texture in the shale core sample thin section are calculated based on the boundary candidate points on each texture.

[0146] In this embodiment of the disclosure, the computer device calculates texture parameters corresponding to each texture in the shale core sample thin section, including at least one of texture length, texture number, texture aperture, texture area, texture linear density, and texture surface density, based on the boundary points on each texture.

[0147] In one possible implementation, the computer device determines the number of individual textures generated as the number of texture lines.

[0148] Each texture can be a texture region.

[0149] In one possible implementation, the lengths between adjacent boundary points connected along the horizontal direction are summed to generate the texture length.

[0150] for example, Figure 4 This is a schematic diagram illustrating the calculation of texture length according to an embodiment of this disclosure, as shown below. Figure 4 As shown, the boundary points on the generated texture 41 are labeled as a1, a2, a3, a4, and a5, respectively. The texture length of the texture 41 can be calculated by summing the lengths of the lines connecting the two boundary points, and obtaining the texture length L1 = ∑La1 + La2 + La3 + La4 + ...

[0151] In one possible implementation, the depths between adjacent boundary points connected along the vertical direction are accumulated and averaged to generate the texture aperture.

[0152] for example, Figure 5 This is a schematic diagram illustrating a method for calculating texture aperture according to an embodiment of this disclosure, as shown below. Figure 5As shown, the upper boundary points on the generated texture 51 are labeled as a1, a2, a3, a4, a5, and the lower boundary points are labeled as b1, b2, b3, b4, b5. The boundary points of the corresponding upper and lower boundaries are connected to determine the distance length of Ka1, Ka2, Ka3, Ka4, Ka5. The texture aperture K = (Ka1 + Ka2 + Ka3 + Ka4 + Ka5 + ...) / N.

[0153] In one possible implementation, the computer device determines the area of ​​each quadrilateral by using the length between adjacent boundary points connected horizontally as the length of each quadrilateral and the depth between adjacent boundary points connected vertically as the width of each quadrilateral. Then, the areas of each quadrilateral are summed to determine the texture area.

[0154] for example, Figure 6 This is a schematic diagram illustrating a method for calculating texture area according to an embodiment of this disclosure, such as... Figure 6 As shown, the texture region 61 is divided into multiple small quadrilaterals, and the area of ​​each small quadrilateral, including Sa1, Sa2, Sa3, Sa4, etc., is calculated. The texture area S = ∑Sa1 + Sa2 + Sa3 + Sa4 + ...

[0155] In one possible implementation, the texture line density is determined by dividing the number of texture lines by the length of the thin section of the shale core sample.

[0156] For example, when the number of texture lines is T and the section length of the shale core sample is x, the texture linear density ρ x =T / x.

[0157] In one possible implementation, the texture area is divided by the area of ​​the shale core sample section to generate the texture surface density.

[0158] For example, when the texture area is S and the area of ​​the shale core sample section is x*y, where y is the width of the shale core sample section and the texture surface density is ρ... M =S / xy.

[0159] Taking the quantitative identification of the shale oil reservoir texture quantity in well G108-8 of a certain block as an example, Figure 7 This is a schematic diagram of a scanned image display according to an embodiment of this disclosure. For example... Figure 7As shown, the observers cut the core sample to prepare a 22 mm * 50 mm, 0.03 mm thick sample section, and then performed a fluorescence scan under a microscope, displaying fluorescence scan image 71. This revealed the basic morphology of the micron-level texture and marked the texture development locations, preparing for the next step of quantitative identification. Next, a quantitative identification device was used to quantitatively identify the number of texture lines. This device includes scanning equipment and a computer. Using the quantitative identification device, the nano-level texture was scanned, generating a black and white scan image 72. Boundary points were then picked up and connected, generating a connected scan image 73. Finally, the number of texture lines was calculated based on scan image 73. Based on the identified texture planar features, the quantitative identification device was used to quantitatively calculate texture parameters, including texture length, texture aperture, texture area, texture linear density, and texture surface density.

[0160] In summary, the scheme presented in this disclosure determines the texture region by obtaining a fluorescence scan image of a thin section of shale core sample. Then, density values ​​are collected at nanometer levels for each point within the texture region of the shale core sample, generating a corresponding black-and-white scan image. Boundary extraction is performed on the black-and-white scan image to generate individual textures, and texture parameters are calculated for each texture. Quantitative analysis of these texture parameters using computer equipment solves the problem of inaccurate texture parameter acquisition through manual microscopic observation, thereby improving the accuracy of texture parameter acquisition and ultimately increasing the efficiency of shale oil and gas development.

[0161] Figure 8 This is a block diagram illustrating an apparatus for acquiring texture parameters of a shale oil reservoir according to an exemplary embodiment, such as... Figure 8 As shown, the device for acquiring shale oil reservoir texture parameters can be implemented entirely or partially within a computer device through hardware or a combination of hardware and software, in order to execute... Figure 2 or Figure 3 The method described in the corresponding embodiment includes all or part of the steps. The device for acquiring shale oil reservoir texture parameters may include:

[0162] The first image acquisition module 810 is used to acquire fluorescence scan images; the fluorescence scan images are images with fluorescence display taken by scanning thin sections of shale core samples with an electron microscope;

[0163] The texture region acquisition module 820 is used to acquire the texture presence region based on the fluorescence scan image; the texture presence region is the region with fluorescence display in the fluorescence scan image.

[0164] The density value acquisition module 830 is used to acquire data at each acquisition point in the texture area of ​​the shale core sample thin section, and acquire the density value at each acquisition point; the acquisition point is the location where the data is acquired in nanometers.

[0165] The second image generation module 840 is used to generate a black and white scan image based on the density values ​​at each collection point; the black and white scan image contains black dots and white dots.

[0166] The candidate point acquisition module 850 is used to acquire boundary candidate points from the black and white scan image; the boundary candidate point is a white point that has at least one black point within a first threshold range;

[0167] Texture determination module 860 is used to connect the boundary candidate points that have adjacent other boundary candidate points to determine each texture;

[0168] The parameter calculation module 870 is used to calculate the texture parameters corresponding to each texture in the shale core sample thin section based on the boundary candidate points on each texture.

[0169] In one possible implementation, the texture determination module 860 includes:

[0170] The candidate point connection submodule is used to connect the boundary candidate point to the other boundary candidate point with the smallest distance in response to the existence of other boundary candidate points around the boundary candidate point and the distance between the boundary candidate point and at least one of the other boundary candidate points being less than or equal to a first threshold.

[0171] The boundary point determination submodule is used to determine boundary points from the candidate boundary points according to specified conditions;

[0172] The texture generation submodule is used to connect adjacent boundary points to generate the textures; each texture includes a horizontal extension length and a vertical depth.

[0173] In one possible implementation, the boundary point determination submodule includes:

[0174] The first boundary point determination unit is used to determine each of the candidate boundary points on the line as boundary points in response to the connection line length being greater than or equal to a specified length.

[0175] or,

[0176] The second boundary point determination unit is used to determine each of the boundary candidate points on the line as a boundary point in response to the number of boundary candidate points connecting the generated line being greater than or equal to a specified value.

[0177] In one possible implementation, the parameter calculation module 870 includes:

[0178] The number of textures determination submodule is used to determine the number of each generated texture as the number of textures in response to the texture parameter including the number of textures;

[0179] The line density determination submodule is used to determine the texture line density by dividing the number of texture lines by the slice length of the shale core sample slice in response to the texture parameters including texture line density.

[0180] The length determination submodule is used to, in response to the texture parameters including the texture length, accumulate the lengths between each adjacent boundary point connected along the horizontal direction to generate the texture length;

[0181] The aperture determination submodule is used to generate the texture aperture by accumulating the depths between each adjacent boundary point connected along the vertical direction and taking the average value in response to the texture parameters including texture aperture.

[0182] In one possible implementation, the parameter calculation module 870 includes:

[0183] The sub-area determination submodule is used to determine the area of ​​each quadrilateral in response to the texture parameters including the texture area, by using the length between each adjacent boundary point connected in the horizontal direction as the length of each quadrilateral, and the depth between each adjacent boundary point connected in the vertical direction as the width of each quadrilateral.

[0184] The area determination submodule is used to sum the areas of each quadrilateral to determine the texture area.

[0185] In one possible implementation, the parameter calculation module 870 includes:

[0186] The surface density determination submodule is used to generate the surface density by dividing the texture area by the area of ​​the shale core sample section in response to the texture parameter including the texture surface density.

[0187] In one possible implementation, the second image generation module 840 includes:

[0188] The black dot determination submodule is used to determine the sampling point as a black dot in response to the density value at the sampling point being greater than or equal to a specified threshold.

[0189] The white point determination submodule is used to determine the sampling point as a white point in response to the density value at the sampling point being less than a specified threshold.

[0190] The second image generation submodule is used to generate the black and white scan image in response to the completion of the color marking of each collection point.

[0191] In summary, the scheme presented in this disclosure determines the texture region by obtaining a fluorescence scan image of a thin section of shale core sample. Then, density values ​​are collected at nanometer levels for each point within the texture region of the shale core sample, generating a corresponding black-and-white scan image. Boundary extraction is performed on the black-and-white scan image to generate individual textures, and texture parameters are calculated for each texture. Quantitative analysis of these texture parameters using computer equipment solves the problem of inaccurate texture parameter acquisition through manual microscopic observation, thereby improving the accuracy of texture parameter acquisition and ultimately increasing the efficiency of shale oil and gas development.

[0192] It should be noted that the device provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules according to actual needs, that is, the content structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0193] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0194] Figure 9 This is a schematic diagram illustrating the structure of a computer device according to an exemplary embodiment. The computer device 900 includes a Central Processing Unit (CPU) 901, a system memory 904 including Random Access Memory (RAM) 902 and Read-Only Memory (ROM) 903, and a system bus 905 connecting the system memory 904 and the CPU 901. The computer device 900 also includes a basic input / output system (I / O system) 906 that facilitates information transfer between various components within the computer device, and a mass storage device 907 for storing an operating system 913, application programs 914, and other program modules 915.

[0195] The basic input / output system 906 includes a display 908 for displaying information and an input device 909 for user input, such as a mouse or keyboard. Both the display 908 and the input device 909 are connected to the central processing unit 901 via an input / output controller 910 connected to the system bus 905. The basic input / output system 906 may also include the input / output controller 910 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 910 also provides output to a display screen, printer, or other types of output devices.

[0196] The mass storage device 907 is connected to the central processing unit 901 via a mass storage controller (not shown) connected to the system bus 905. The mass storage device 907 and its associated computer device-readable media provide non-volatile storage for the computer device 900. That is, the mass storage device 907 may include computer device-readable media (not shown), such as a hard disk or a compact disc read-only memory (CD-ROM) drive.

[0197] Without loss of generality, the computer device readable medium may include computer device storage media and communication media. Computer device storage media include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer device readable instructions, data structures, program modules, or other data. Computer device storage media include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), CD-ROM, digital video disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that the computer device storage media are not limited to the above-mentioned types. The system memory 904 and mass storage device 907 described above can be collectively referred to as memory.

[0198] According to various embodiments of this disclosure, the computer device 900 can also be connected to a remote computer device on a network, such as the Internet. That is, the computer device 900 can be connected to a network 912 via a network interface unit 911 connected to the system bus 905, or it can use the network interface unit 911 to connect to other types of networks or remote computer device systems (not shown).

[0199] The memory also includes one or more programs, which are stored in the memory, and the central processing unit 901 implements these programs by executing them. Figure 2 or Figure 3 All or part of the steps of the method shown.

[0200] Those skilled in the art will recognize that the functions described in the embodiments of this disclosure in one or more of the foregoing examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer device-readable medium or transmitted as one or more instructions or code on a computer device-readable medium. A computer device-readable medium includes computer device storage media and communication media, wherein a communication medium includes any medium that facilitates the transmission of a computer device program from one location to another. A storage medium can be any available medium accessible by a general-purpose or special-purpose computer device.

[0201] This disclosure also provides a computer device storage medium for storing computer device software instructions used by the above-described testing apparatus, which includes a program designed to execute the above-described component sharing method.

[0202] According to one aspect of this disclosure, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for obtaining shale oil reservoir texture parameters provided in various alternative implementations of the above aspect.

[0203] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0204] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for obtaining texture parameters of shale oil reservoirs, characterized in that, The method includes: Obtain fluorescence scan images; the fluorescence scan images are images with fluorescence display taken by scanning thin sections of shale core samples with an electron microscope; The texture region is obtained from the fluorescence scan image; the texture region is the area that shows fluorescence in the fluorescence scan image. Data is collected at each sampling point in the textured area of ​​the shale core sample thin section to obtain the density value at each sampling point; the sampling point is located in nanometers. A black and white scan image is generated based on the density values ​​at each collection point; the black and white scan image contains both black and white dots. Boundary candidate points are obtained from the black and white scan image; the boundary candidate point is a white point that has at least one black point within a first threshold range; Connect the boundary candidate points that are adjacent to other boundary candidate points to determine each texture; Based on the boundary candidate points on each texture, calculate the texture parameters corresponding to each texture in the shale core sample thin section; The process of generating a black-and-white scan image based on the density values ​​at each collection point includes: determining the collection point as a black point in response to a density value at the collection point being greater than or equal to a specified threshold; determining the collection point as a white point in response to a density value at the collection point being less than a specified threshold, wherein the specified threshold is used to indicate the density threshold corresponding to whether the rock strata at each collection point location is textured or non-textured; and generating the black-and-white scan image in response to the completion of color marking at each collection point.

2. The method according to claim 1, characterized in that, The step of connecting the boundary candidate points that have adjacent other boundary candidate points to determine each texture includes: In response to the presence of other boundary candidate points around the boundary candidate point, and the distance between the boundary candidate point and at least one of the other boundary candidate points being less than or equal to a first threshold, the boundary candidate point is connected to the other boundary candidate point with the smallest distance. Boundary points are determined from the candidate boundary points according to specified conditions; Each of the adjacent boundary points is connected to generate a texture; each texture includes a horizontal extension length and a vertical depth.

3. The method according to claim 2, characterized in that, The step of determining the boundary point from the candidate boundary points according to specified conditions includes: In response to the fact that the length of the line generated by the connection is greater than or equal to the specified length, the candidate boundary points on the line are determined as boundary points; or, In response to the number of boundary candidate points connecting the generated lines being greater than or equal to a specified value, each of the boundary candidate points on the line is determined as a boundary point.

4. The method according to claim 2, characterized in that, The step of calculating the texture parameters corresponding to each texture in the shale core sample thin section based on the boundary candidate points on each texture includes: In response to the texture parameter including the number of texture lines, the number of each generated texture is determined as the number of texture lines; In response to the texture parameter including texture line density, the texture line density is determined by dividing the number of texture lines by the slice length of the shale core sample slice; In response to the texture parameter including the texture length, the lengths between each adjacent boundary point connected along the horizontal direction are accumulated to generate the texture length; In response to the texture parameters including texture aperture, the average value is obtained by accumulating the depths between each adjacent boundary point connected in the vertical direction to generate the texture aperture.

5. The method according to claim 2, characterized in that, The step of calculating the texture parameters corresponding to each texture in the shale core sample thin section based on the boundary candidate points on each texture includes: In response to the texture parameters including texture area, the area of ​​each quadrilateral is determined by using the length between each adjacent boundary point connected in the horizontal direction as the length of each quadrilateral and the depth between each adjacent boundary point connected in the vertical direction as the width of each quadrilateral. The area of ​​each quadrilateral is summed to determine the texture area.

6. The method according to claim 5, characterized in that, The step of calculating the texture parameters corresponding to each texture in the shale core sample thin section based on the boundary candidate points on each texture includes: In response to the texture parameters including texture surface density, the texture area is divided by the area of ​​the shale core sample section to generate the texture surface density.

7. A device for acquiring texture parameters of shale oil reservoirs, characterized in that, The device includes: The first image acquisition module is used to acquire fluorescence scan images; the fluorescence scan images are images with fluorescence display taken by scanning thin sections of shale core samples with an electron microscope; The texture region acquisition module is used to acquire the texture region based on the fluorescence scan image; the texture region is the region with fluorescence display in the fluorescence scan image. The density value acquisition module is used to collect data from each collection point in the textured area of ​​the shale core sample thin section and obtain the density value at each collection point; the collection point is the location where the data is collected in nanometers. The second image generation module is used to generate a black and white scan image based on the density values ​​at each collection point; the black and white scan image contains black dots and white dots. A candidate point acquisition module is used to acquire boundary candidate points from the black and white scan image; the boundary candidate point is a white point that has at least one black point within a first threshold range; A texture determination module is used to connect the boundary candidate points that have adjacent other boundary candidate points to determine each texture; The parameter calculation module is used to calculate the texture parameters corresponding to each texture in the shale core sample thin section based on the boundary candidate points on each texture. The second image generation module is configured to: determine the collection point as a black point in response to a density value at the collection point being greater than or equal to a specified threshold; determine the collection point as a white point in response to a density value at the collection point being less than a specified threshold, wherein the specified threshold is used to indicate the density threshold corresponding to whether the rock strata at each collection point location is textured or non-textured; and generate the black and white scan image in response to the completion of color marking at each collection point.

8. A computer device, characterized in that, The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method for obtaining shale oil reservoir texture parameters as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one piece of program code, which is loaded and executed by a processor to implement the method for obtaining shale oil reservoir texture parameters as described in any one of claims 1 to 6.

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