Automatic identification method and device for shale deposition structure
By using electrical imaging logging image processing methods, shale and mudstone sedimentary structures can be automatically identified, solving the problems of high cost and low efficiency in core observation and achieving efficient and accurate identification of shale and mudstone sedimentary structures.
Patent Information
- Application Number
- CN202411030314.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-07-30
- Publication Date
- 2026-02-03
AI Technical Summary
In existing technologies, the identification of shale and mudstone sedimentary structures mainly relies on core observation, which is costly, inefficient, and highly subjective. Conventional logging curves are difficult to identify single bedding layers, making it difficult to determine shale and mudstone sedimentary structures.
The method of electrical imaging logging image processing is used to identify bedding boundaries, determine the number of bedding elements and their corresponding relationships by acquiring core images and electrical imaging logging images, and automatically identify shale sedimentary structures, including lamellar, layered and massive structures.
It enables efficient and accurate identification of mudstone and shale sedimentary structures without the need for core sampling, reducing costs, improving identification efficiency, and minimizing subjectivity.
Smart Images
Figure CN121454625A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of shale oil exploration and development, and in particular to an automatic identification method and apparatus for mudstone and shale sedimentary structures. Background Technology
[0002] The classification of shale and mudstone facies is fundamental to the geological analysis of shale oil reservoirs, and is generally based on three criteria: mineral composition, sedimentary structure, and organic matter content. The sedimentary structure of shale and mudstone is primarily classified by the degree of development of horizontal bedding. Currently, in oil and gas exploration and production, the identification of horizontal bedding and the judgment of sedimentary structures in shale are mainly carried out by experienced geologists through core observation and thin section analysis, which suffers from drawbacks such as high cost, low efficiency, and strong subjectivity. Compared to core samples, well logging curves have advantages such as low cost and strong continuity; however, the sampling rate of conventional well logging data is insufficient to identify single bedding layers, making it very difficult to determine the sedimentary structure of shale and mudstone using conventional logging. Summary of the Invention
[0003] The purpose of this invention is to provide an automatic identification method and device for shale sedimentary structures, which can identify the sedimentary structures of rocks without the need to sample actual rock cores, but can be achieved by using electrical imaging logging.
[0004] To address the aforementioned technical problems, this invention provides a method for identifying sedimentary structures, comprising:
[0005] Acquire images of the rock core and corresponding electrical imaging logging images of the rock core, wherein the rock core is a rock sample;
[0006] The process identifies bedding boundaries in the electrical imaging logging images, where the bedding boundaries are layered structures resulting from the vertical changes in rock along the wellbore extension direction.
[0007] Determine the number of bedding planes in the image of the core;
[0008] Determine the correspondence between the number of bedding boundaries in the electrical imaging logging image and the number of bedding planes in the core image;
[0009] The actual number of bedding planes in the uncored strata is determined based on the correspondence and the bedding boundaries of the electrical imaging logging images of the uncored strata.
[0010] The sedimentary structure of the strata without core samples is determined based on the actual number of bedding planes. The sedimentary structure includes lamellar structures, layered structures, and massive structures.
[0011] On the other hand, the process of identifying bedding boundaries in the electrical imaging logging images includes:
[0012] The electrical imaging logging image is processed to grayscale so as to convert the color electrical imaging logging image into a grayscale electrical imaging logging image;
[0013] The gray electrical imaging logging image is binarized to convert it into a black and white electrical imaging logging image.
[0014] The black-and-white electrical imaging logging image is filtered;
[0015] Identify the bedding boundaries in the filtered black-and-white electrical imaging logging image.
[0016] On the other hand, filtering the black-and-white electrical imaging logging image includes:
[0017] Determine the Gaussian kernel of the spatial Gaussian, the expression of which is: ;
[0018] Determine the Gaussian kernel of the scale Gaussian, the expression of which is: ;
[0019] The Gaussian kernel obtained by merging the spatial high-speed Gaussian kernel and the scale Gaussian kernel is as follows: ;
[0020] The black-and-white electrical imaging logging image is filtered according to the merged Gaussian kernel, and the filtered pixel value is... ;
[0021] Where k is the x-coordinate of the center point of the filter window, l is the y-coordinate of the center point of the filter window, i is the x-coordinate of a pixel in the black and white electrical imaging logging image, j is the y-coordinate of a pixel in the black and white electrical imaging logging image, and σ d It is the spatial domain weight, σ r These are the range weights, where d(i,j,k,l) is the spatial Gaussian kernel for the pixel with x-coordinate i and y-coordinate j, and r(i,j,k,l) is the scale Gaussian kernel for the pixel with x-coordinate i and y-coordinate j. Let f(i,j) be the combined Gaussian kernel for the pixels with x-coordinate i and y-coordinate j, f(i,j) be the original pixel value of the pixel with x-coordinate i and y-coordinate j, f(k,l) be the original pixel value of the center point of the filter window with x-coordinate k and y-coordinate l, and g(i,j) be the filtered pixel value of the pixel with x-coordinate i and y-coordinate j.
[0022] On the other hand, delineating the bedding boundaries in the electrical imaging logging image includes:
[0023] The edge function is used to perform lateral edge detection on the bedding boundaries in the electrical imaging logging image;
[0024] The detected bedding boundaries are marked as sine lines using the Hough transform equation;
[0025] If more than a preset number of pixels fall on the same sine line, then the sine line marking is considered successful.
[0026] On the other hand, determining the sedimentary structure of the uncored strata based on the actual number of bedding planes includes:
[0027] Determine the sine line corresponding to the bedding boundary of the current rock;
[0028] Determine the number of sine lines within the preset length;
[0029] Determine the number of bedding planes in the core image corresponding to the number of sinusoidal lines within a preset length;
[0030] If the number of layers is greater than the first threshold, it is determined to be a lamellar structure;
[0031] If the number of stratifications is greater than the second threshold and less than the first threshold, it is determined to be a layered structure, where the first threshold is greater than the second threshold.
[0032] If the number of stratifications is less than the second threshold, it is determined to be a blocky structure.
[0033] On the other hand, determining the number of sine lines within a preset length includes:
[0034] Determine the number of sine lines within the preset length;
[0035] Determine whether a sine wave falls on the boundary of the depth range containing the preset length;
[0036] If the sine line falls on the upper boundary of the depth interval, then the sine line will not be counted in the number of lines.
[0037] If the sine line falls on the lower boundary of the depth range, then the sine line is counted in the number of lines.
[0038] On the other hand, determining the correspondence between the number of bedding boundaries in the electrical imaging logging image and the number of bedding planes in the core image includes:
[0039] The number of bedding boundaries in the electrical imaging logging images per unit length is used as the abscissa, and the number of bedding planes in the core images per unit length is used as the ordinate for fitting, to obtain the fitted relationship.
[0040] The relation is the correspondence.
[0041] On the other hand, after obtaining the fitted relation, it also includes:
[0042] Determine the correlation between the horizontal and vertical axes of the fitted expression;
[0043] If the correlation is greater than the third threshold, then the relationship is determined to be valid;
[0044] If the correlation is less than the third threshold, the relation is determined to be invalid.
[0045] On the other hand, after determining that the relation is invalid, the following steps are also included:
[0046] The number of bedding boundaries in the image of the core was re-determined;
[0047] And / or reprocess to identify bedding boundaries in the electrical imaging logging images.
[0048] To address the aforementioned technical problems, the present invention also provides a depositional structure identification device, comprising:
[0049] Memory, used to store computer programs;
[0050] A processor is used to implement the steps of the above-described depositional structure identification method when executing the computer program.
[0051] This application provides an automatic identification method and apparatus for shale sedimentary structures, relating to the field of shale oil exploration and development. The method includes acquiring images of rock cores and corresponding electrical imaging logging images; processing and identifying bedding boundaries in the electrical imaging logging images; determining the number of bedding planes in the rock core images; determining the correspondence between the number of bedding boundaries in the electrical imaging logging images and the number of bedding planes in the rock core images; determining the actual number of bedding planes in the uncored strata based on the correspondence and the bedding boundaries in the electrical imaging logging images of uncored strata; and determining the sedimentary structures of the uncored strata based on the actual number of bedding planes, including lamellar, layered, and massive structures. After determining the correspondence between the actual rock core images and the corresponding electrical imaging logging images, the sedimentary structures of shale formations can be automatically determined based on the electrical imaging logging images. This eliminates the need for sampling actual rock cores and manual identification; instead, electrical imaging logging can be used to automatically identify the sedimentary structures of shale formations. Attached Figure Description
[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the prior art and embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0053] Figure 1 A flowchart of an automatic identification method for shale sedimentary structures provided by the present invention;
[0054] Figure 2 A schematic diagram of an image processing method provided by the present invention;
[0055] Figure 3 A schematic diagram of a sine wave marking provided by the present invention;
[0056] Figure 4 A schematic diagram of the number of horizontal bedding stripes in a core image provided by the present invention;
[0057] Figure 5 This invention provides a diagram showing the relationship between the number of sinusoidal lines in an electrical imaging marker and the number of horizontal bedding stripes in a core image.
[0058] Figure 6 This is a schematic diagram of the structure of an automatic identification device for mudstone and shale sedimentary structures provided by the present invention. Detailed Implementation
[0059] The core of this invention is to provide an automatic identification method and device for shale sedimentary structures, which can automatically identify shale sedimentary structures without the need to sample actual rock cores, but by using electrical imaging logging.
[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0061] The classification of shale and mudstone facies is fundamental to the geological analysis of shale oil reservoirs, and is generally based on three criteria: mineral composition, sedimentary structure, and organic matter content. The sedimentary structure of shale and mudstone is primarily classified by the degree of development of horizontal bedding. Currently, in oil and gas exploration and production, the identification of horizontal bedding and the judgment of sedimentary structures in shale are mainly carried out by experienced geologists through core observation and thin section analysis, which suffers from drawbacks such as high cost, low efficiency, and strong subjectivity. Compared to core samples, well logging curves have advantages such as low cost and strong continuity; however, the sampling rate of conventional well logging data is insufficient to identify single bedding layers, making it very difficult to determine the sedimentary structure of shale and mudstone using conventional logging.
[0062] Figure 1 A flowchart of an automatic identification method for shale sedimentary structures provided by the present invention is provided. The sedimentary structure identification method includes:
[0063] S11: Acquire images of the core and corresponding electrical imaging logging images of the core. The core is a rock sample.
[0064] This application requires prior acquisition of actual core images. These core images can be obtained by sampling the formation and then photographing the core. Electrical imaging logging images are obtained by logging the formation at the same location. It can be understood that the core image and the corresponding electrical imaging logging image represent the same formation.
[0065] S12: Processing and identifying bedding boundaries in electrical imaging logging images. Bedding boundaries are the layered structures caused by the vertical changes of the rock along the extension direction of the well body.
[0066] During the formation of shale and mudstone formations, due to the differentiated depositional processes of different materials, horizontal bedding is formed along a horizontal direction. These different materials have different electrical conductivity, which manifests as different colors in electrical imaging logging images, thus exhibiting distinct boundaries. The bedding boundaries in electrical imaging logging images can be identified using image recognition and other methods.
[0067] It should be noted that, due to the resolution limitations of electrical imaging logging, the bedding boundaries in electrical imaging logging images are fewer than the number of horizontal bedding lines in the actual formation.
[0068] S13: Determine the number of bedding planes in the core image;
[0069] Core samples obtained from drilling are direct samples of the formation. Judging the sedimentary structure of mudstone and shale by the number of horizontal bedding strips on the core image is the most direct and accurate method. Users can visually observe and count the number of bedding strips in the core image with the naked eye.
[0070] S14: Determine the correspondence between the number of bedding boundaries in the electrical imaging logging image and the number of bedding planes in the core image;
[0071] The core images and corresponding electrical imaging logging images in this case were obtained by processing the same formation. Therefore, the number of bedding boundaries in the electrical imaging logging images corresponds to the number of bedding planes in the core images. Once the correspondence is established, the actual number of bedding planes in the rock can be obtained from the electrical imaging logging images.
[0072] Specifically, the number of bedding boundaries in electrical imaging logging images is less than the number of bedding planes in core images.
[0073] S15: Determine the actual number of bedding planes in the uncored strata based on the correspondence and the bedding boundaries of the electrical imaging logging images of the uncored strata;
[0074] Based on the above correspondence, when determining the sedimentary structure of uncored strata, it is not necessary to collect cores; only electrical imaging logging images are needed. The actual number of bedding planes in the strata can be determined based on the number of bedding boundaries in the electrical imaging logging images.
[0075] S16: Determine the sedimentary structure of the strata without core samples based on the actual number of bedding planes. The sedimentary structures include lamellar structures, layered structures, and massive structures.
[0076] The sedimentary structure of a rock can be determined by the density of its bedding. The structures, ordered from highest to lowest density, are lamellar, layered, and massive.
[0077] In summary, after establishing the correspondence, this application eliminates the need for rock sampling. It can automatically determine the sedimentary structure of shale formations based solely on the correspondence and electrical imaging logging images, making the determination process accurate, efficient, and convenient.
[0078] This application provides a method for identifying sedimentary structures in the field of shale oil exploration and development. The method includes acquiring images of rock cores and corresponding electrical imaging logging images; delineating bedding boundaries in the electrical imaging logging images; determining the number of bedding boundaries in the rock core images; determining the correspondence between the number of bedding boundaries and the number of boundaries in the electrical imaging logging images; determining the bedding boundaries of the current rock based on the correspondence and the bedding boundaries in the current electrical imaging logging images; and determining the sedimentary structures of the current rock based on the bedding boundaries, including lamellar, layered, and massive structures. After determining the correspondence between the actual rock image and the corresponding electrical imaging logging images, the sedimentary structures of the rock can be determined based on the electrical imaging logging images, eliminating the need for actual rock core sampling; instead, sedimentary structure identification can be achieved using electrical imaging logging.
[0079] Based on the above embodiments:
[0080] Figure 2 A schematic diagram of an image processing method provided by the present invention;
[0081] In some embodiments, processing to identify bedding boundaries in electrical imaging logging images includes:
[0082] Grayscale processing is performed on electrical imaging logging images to convert color electrical imaging logging images into grayscale electrical imaging logging images;
[0083] The gray electrical imaging logging image is binarized in order to convert the gray electrical imaging logging image into a black and white electrical imaging logging image.
[0084] Filtering of black-and-white electrical imaging logging images;
[0085] Identify bedding boundaries in filtered black-and-white electrical imaging logging images.
[0086] Figure 2 From left to right: color electrical imaging logging image, grayscale electrical imaging logging image, black and white electrical imaging logging image, and filtered black and white electrical imaging logging image.
[0087] Grayscale conversion and binarization were performed using the `rgb2gray` and `imbinarize` functions in Matlab, respectively. Grayscale and binarization of the image make the layer boundaries more clearly visible, thus facilitating subsequent segmentation.
[0088] In some embodiments, filtering of black-and-white electrical imaging logging images includes:
[0089] Determine the Gaussian kernel of the space Gaussian; the expression for the Gaussian kernel of the space Gaussian is: ;
[0090] Determine the Gaussian kernel for the scaled Gaussian; the expression for the scaled Gaussian kernel is: ;
[0091] Merging the spatial high-speed Gaussian kernel and the scale Gaussian kernel yields the merged Gaussian kernel, whose expression is: ;
[0092] The black and white electrical imaging logging image is filtered based on the merged Gaussian kernel, and the filtered pixel value is... ;
[0093] Where k is the x-coordinate of the center point of the filter window, l is the y-coordinate of the center point of the filter window, i is the x-coordinate of a pixel in the black and white electrical imaging logging image, j is the y-coordinate of a pixel in the black and white electrical imaging logging image, and σ d It is the spatial domain weight, σ r These are the range weights, where d(i,j,k,l) is the spatial Gaussian kernel for the pixel with x-coordinate i and y-coordinate j, and r(i,j,k,l) is the scale Gaussian kernel for the pixel with x-coordinate i and y-coordinate j. Let f(i,j) be the combined Gaussian kernel for the pixels with x-coordinate i and y-coordinate j, f(i,j) be the original pixel value of the pixel with x-coordinate i and y-coordinate j, f(k,l) be the original pixel value of the center point of the filter window with x-coordinate k and y-coordinate l, and g(i,j) be the filtered pixel value of the pixel with x-coordinate i and y-coordinate j.
[0094] During the filtering process, the coordinates of the window and the coordinates of the pixel being filtered need to be taken into account. The window moves continuously until every pixel in the image is filtered.
[0095] Figure 3 A schematic diagram of a sine wave marking provided by the present invention;
[0096] Figure 4 A schematic diagram of the number of horizontal bedding stripes in a core image provided by the present invention;
[0097] In some embodiments, delineating bedding boundaries in electrical imaging logging images includes:
[0098] Lateral edge detection of bedding boundaries in electrical imaging logging images is performed using the edge function.
[0099] The detected bedding boundaries are marked as sine lines using the Hough transform equation;
[0100] If more than a preset number of pixels fall on the same sine line, then the sine line marking is considered successful.
[0101] Lateral edge detection is used to obtain the boundaries of layering, implemented using the `edge` function in Matlab. An improved Hough transform equation is used to mark the layering boundaries as sine lines. A sine line is considered successfully marked by the Hough transform if more than X pixels of the layering boundary fall on the same sine line. X is typically between 20 and 50, and the fitting degree of the Hough transform for marking sine lines can be adjusted by modifying the value of X. This method is used to mark all detected layering boundaries as sine lines.
[0102] Figure 3From left to right, the data includes edge detection, labeled sine lines, labeling results, and the number of sine lines.
[0103] In some embodiments, determining the sedimentary structure of the uncored strata based on the actual number of bedding strips includes:
[0104] Determine the sine curve corresponding to the bedding boundary of the current rock;
[0105] Determine the number of sine waves within the preset length;
[0106] Determine the number of bedding planes in the core image corresponding to the number of sinusoidal lines within a preset length;
[0107] If the number of bedding layers is greater than the first threshold, it is determined to be a lamellar structure;
[0108] If the number of bedding layers is greater than the second threshold and less than the first threshold, it is determined to be a layered structure, where the first threshold is greater than the second threshold.
[0109] If the number of bedding layers is less than the second threshold, it is determined to be a blocky structure.
[0110] Depositional structures are identified by statistically analyzing the development of horizontal bedding on core images. Structures with highly developed horizontal bedding are classified as lamellar structures; those with distinct bedding features and moderate horizontal bedding development are also classified as layered structures; and those without obvious bedding features are classified as massive structures. Bedding structures can be categorized into three levels by setting a first threshold and a second threshold, thus enabling structural identification.
[0111] In some embodiments, determining the number of sine lines within a preset length includes:
[0112] Determine the number of sine waves within the preset length;
[0113] Determine whether a sine wave falls on the boundary of a depth range with a preset length;
[0114] If the sine line falls on the upper boundary of the depth interval, then the sine line will not be counted.
[0115] If the sine line falls on the lower boundary of the depth interval, then the sine line is counted.
[0116] To achieve more accurate counting, the boundary values need to be standardized. If values at both the upper and lower boundaries are included in the depth range corresponding to the preset length, such as the 0-0.25m and 0.25m-0.5m depth ranges, a sine line at the 0.25m depth will be counted twice, leading to inaccurate counting. Therefore, this application uses the lower boundary to count the sine lines, excluding the upper boundary. Taking a preset length of 25cm as an example, the unit length for counting the sine lines marked by the Hough transform is 25cm, i.e., counting the number of sine lines detected within the 0.25m depth range. If a sine line falls exactly on the upper boundary of the 0.25m depth range, it is not counted; if it falls exactly on the lower boundary, it is counted. The unit length for counting horizontal bedding lines on core images is 25 cm, meaning the number of horizontal bedding lines developed on a 0.25 m core is counted. On core images, horizontal bedding appears as continuous, fine, parallel lines parallel to each other. Due to differences in material composition, they exhibit varying shades and brightness, thus allowing for identification and counting.
[0117] It should also be noted that the upper boundary can be included while the lower boundary is not, but this application does not impose any restrictions on this.
[0118] Figure 5 This invention provides a diagram showing the relationship between the number of sinusoidal lines in an electrical imaging marker and the number of horizontal bedding stripes in a core image.
[0119] In some embodiments, determining the correspondence between the number of bedding boundaries in an electrical imaging logging image and the number of bedding planes in a core image includes:
[0120] The number of bedding boundaries in the electrical imaging logging image per unit length is used as the abscissa, and the number of bedding planes in the core image per unit length is used as the ordinate for fitting, and the fitted relationship is obtained.
[0121] The relation is a correspondence relation.
[0122] Since both the electrical imaging logging image and the core image are from the same rock sample, the number of marked sinusoidal lines in the electrical imaging logging image should be positively correlated with the number of horizontal bedding planes in the core image. For example... Figure 5 The fitted image has the following relationship: y = 5.9336x + 13.106, where x is the number of sine lines in the electrical imaging logging image and y is the number of horizontal bedding lines in the core image. Therefore, the bedding boundaries can be determined based on the electrical imaging logging image without actually obtaining the core image.
[0123] In some embodiments, after obtaining the fitted relation, the method further includes:
[0124] Determine the correlation between the x-axis and y-axis of the fitted expression;
[0125] If the correlation is greater than the third threshold, the relation is determined to be valid;
[0126] If the correlation is less than the third threshold, the relationship is deemed invalid.
[0127] It should also be noted that after fitting, the correlation coefficient (Rfit) needs to be determined. 2 The fitted relationship should show a positive correlation. If it is not positively correlated or has a low correlation, the fitted relationship is invalid and cannot be used as a basis for judgment. If the correlation is high, the fitted relationship can be used as a basis for judgment. Therefore, a third threshold is set for R. 2 Make a judgment.
[0128] In some embodiments, after determining that the relation is invalid, the method further includes:
[0129] Redetermine the number of bedding boundaries in the core image;
[0130] And / or reprocess to identify bedding boundaries in electrical imaging logging images.
[0131] If the correlation after fitting is poor, there may be two reasons: one is that the bedding boundaries obtained from the core image are incorrect, and the other is that the bedding boundaries in the electrical imaging logging image are incorrect.
[0132] Therefore, it is possible to redetermine the bedding boundaries in the core image. It should be noted that the bedding boundaries in the core image are determined by the user, so there may be errors. In addition, the bedding boundaries in the electrical imaging logging image are obtained by fitting a sine curve, so there may be fitting errors, and the aforementioned X value needs to be readjusted to correct them.
[0133] The discrimination results during actual use are shown in Table 1.
[0134] Table 1 shows the results of automatic sedimentary structure identification.
[0135]
[0136] Figure 6 This is a schematic diagram of an automatic identification device for shale sedimentary structures provided by the present invention. The sedimentary structure identification device includes:
[0137] Memory 61 is used to store computer programs;
[0138] The processor 62 is used to implement the steps of the above-described sedimentary structure identification method when executing a computer program.
[0139] Please refer to the above embodiments for a description of the sedimentary structure identification device provided in this application, and it will not be repeated here.
[0140] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0141] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.
[0142] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. An automatic identification method for mudstone and shale sedimentary structures, characterized in that, include: Acquire images of the rock core and corresponding electrical imaging logging images of the rock core, wherein the rock core is a rock sample; The process identifies bedding boundaries in the electrical imaging logging images, where the bedding boundaries are layered structures resulting from the vertical changes in rock along the wellbore extension direction. Determine the number of bedding planes in the image of the core; Determine the correspondence between the number of bedding boundaries in the electrical imaging logging image and the number of bedding planes in the core image; The actual number of bedding planes in the uncored strata is determined based on the correspondence and the bedding boundaries of the electrical imaging logging images of the uncored strata. The sedimentary structure of the strata without core samples is determined based on the actual number of bedding planes. The sedimentary structure includes lamellar structures, layered structures, and massive structures.
2. The automatic identification method for mudstone and shale sedimentary structures as described in claim 1, characterized in that, Processing to identify bedding boundaries in the electrical imaging logging images includes: The electrical imaging logging image is processed to grayscale so as to convert the color electrical imaging logging image into a grayscale electrical imaging logging image; The gray electrical imaging logging image is binarized to convert it into a black and white electrical imaging logging image. The black-and-white electrical imaging logging image is filtered; Identify the bedding boundaries in the filtered black-and-white electrical imaging logging image.
3. The automatic identification method for mudstone and shale sedimentary structures as described in claim 2, characterized in that, Filtering the black-and-white electrical imaging logging image includes: Determine the Gaussian kernel of the spatial Gaussian, the expression of which is: ； Determine the Gaussian kernel of the scale Gaussian, the expression of which is: ； The Gaussian kernel obtained by merging the spatial high-speed Gaussian kernel and the scale Gaussian kernel is as follows: ； The black-and-white electrical imaging logging image is filtered according to the merged Gaussian kernel, and the filtered pixel value is... ； Where k is the x-coordinate of the center point of the filter window, l is the y-coordinate of the center point of the filter window, i is the x-coordinate of a pixel in the black and white electrical imaging logging image, j is the y-coordinate of a pixel in the black and white electrical imaging logging image, and σ d It is the spatial domain weight, σ r These are the range weights, where d(i,j,k,l) is the spatial Gaussian kernel for the pixel with x-coordinate i and y-coordinate j, and r(i,j,k,l) is the scale Gaussian kernel for the pixel with x-coordinate i and y-coordinate j. Let f(i,j) be the combined Gaussian kernel for the pixels with x-coordinate i and y-coordinate j, f(i,j) be the original pixel value of the pixel with x-coordinate i and y-coordinate j, f(k,l) be the original pixel value of the center point of the filter window with x-coordinate k and y-coordinate l, and g(i,j) be the filtered pixel value of the pixel with x-coordinate i and y-coordinate j.
4. The automatic identification method for mudstone and shale sedimentary structures as described in claim 1, characterized in that, Identifying bedding boundaries in the electrical imaging logging images includes: The edge function is used to perform lateral edge detection on the bedding boundaries in the electrical imaging logging image; The detected bedding boundaries are marked as sine lines using the Hough transform equation; If more than a preset number of pixels fall on the same sine line, then the sine line marking is considered successful.
5. The automatic identification method for mudstone and shale sedimentary structures as described in claim 4, characterized in that, The sedimentary structure of the uncored strata is determined based on the actual number of bedding planes, including: Determine the sine curve corresponding to the bedding boundary of the current rock; Determine the number of sine lines within the preset length; Determine the number of bedding planes in the core image corresponding to the number of sinusoidal lines within a preset length; If the number of layers is greater than the first threshold, it is determined to be a lamellar structure; If the number of stratifications is greater than the second threshold and less than the first threshold, it is determined to be a layered structure, where the first threshold is greater than the second threshold. If the number of stratifications is less than the second threshold, it is determined to be a blocky structure.
6. The automatic identification method for mudstone and shale sedimentary structures as described in claim 5, characterized in that, Determining the number of sine lines within a preset length includes: Determine the number of sine lines within the preset length; Determine whether a sine wave falls on the boundary of the depth range containing the preset length; If the sine line falls on the upper boundary of the depth interval, then the sine line will not be counted in the number of lines. If the sine line falls on the lower boundary of the depth range, then the sine line is counted in the number of lines.
7. The automatic identification method for mudstone and shale sedimentary structures as described in any one of claims 1 to 6, characterized in that, Determining the correspondence between the number of bedding boundaries in the electrical imaging logging image and the number of bedding planes in the core image includes: The number of bedding boundaries in the electrical imaging logging images per unit length is used as the abscissa, and the number of bedding planes in the core images per unit length is used as the ordinate for fitting, to obtain the fitted relationship. The relation is the correspondence.
8. The automatic identification method for mudstone and shale sedimentary structures as described in claim 7, characterized in that, After obtaining the fitted relation, it also includes: Determine the correlation between the horizontal and vertical axes of the fitted expression; If the correlation is greater than the third threshold, then the relationship is determined to be valid; If the correlation is less than the third threshold, the relation is determined to be invalid.
9. The automatic identification method for mudstone and shale sedimentary structures as described in claim 8, characterized in that, After determining that the relation is invalid, the following steps are also included: The number of bedding planes in the image of the core was re-determined; And / or reprocess to identify bedding boundaries in the electrical imaging logging images.
10. An automatic identification device for mudstone and shale sedimentary structures, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the depositional structure identification method as described in any one of claims 1 to 9.