Glutenite stratum gravel contact form well logging evaluation method based on imaging well logging diagram

Abstract

The invention discloses an imaging logging diagram-based glutenite stratum gravel contact form logging evaluation method, which comprises the following steps of: S1, acquiring an imaging logging image of a target well, and extracting and segmenting gravels from the imaging logging image to obtain a segmented binary diagram; s2, the gravel content of the target well is determined according to the segmented binary image, and when the gravel content is smaller than a gravel content threshold value, evaluation is ended; otherwise, entering the step S3; s3, dividing dispersed gravels and contact gravels according to the segmented binary image, and obtaining a binary image of the contact gravels; and S4, gravel contact form parameters are calculated according to the division result of the dispersed gravel and the contact gravel, and the gravel contact form of the target well is evaluated according to the calculation result of the gravel contact form parameters. According to the method, the continuous glutenite stratum gravel contact form of the target well can be accurately obtained according to the imaging logging image.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas resource development, and in particular to a logging evaluation method for gravel contact forms in sandstone strata based on imaging logging diagrams. Background Art

[0002] Conglomerate reservoirs are widely distributed in my country and are rich in oil and gas resources, holding significant research value. Due to their deep burial depth and strong heterogeneity, the inclusion of gravel contact form parameters in studying conglomerate formations can better characterize their physical properties. Due to the complexity of gravel distribution in conglomerate formations, gravel contact form significantly influences physical properties such as permeability, rock-electrical relationships, and acoustic response. Even formations with the same gravel content can exhibit varying physical properties due to different gravel contact forms.

[0003] In previous studies, contact form parameters for sandstone were typically extracted by taking cylindrical thin-section images under a microscope. However, this method cannot provide continuous contact form information for a single well. Therefore, a new contact form parameter evaluation method is urgently needed. Summary of the Invention

[0004] In view of the above problems, the present invention aims to provide a logging evaluation method for gravel contact forms in sandstone formations based on imaging logging diagrams.

[0005] The technical solutions of the present invention are as follows:

[0006] A method for evaluating gravel contact patterns in a sandstone formation based on imaging logging diagrams comprises the following steps:

[0007] S1: Acquire an imaging logging image of a target well, and extract and segment gravel from the imaging logging image to obtain a segmentation binary image;

[0008] S2: determining the gravel content of the target well according to the segmented binary image. When the gravel content is less than the gravel content threshold, the evaluation ends; otherwise, the process proceeds to step S3;

[0009] S3: dividing the scattered gravels and the contact gravels according to the segmentation binary image, and obtaining a contact gravel binary image;

[0010] S4: Calculating gravel contact form parameters according to the division results of the dispersed gravels and the contact gravels, and evaluating the gravel contact form of the target well according to the calculation results of the gravel contact form parameters.

[0011] Preferably, in step S1, extracting and segmenting gravel from the imaging logging image specifically includes the following sub-steps:

[0012] S11: extracting depth information and grayscale information of the imaging logging image, and performing grayscale value inversion to obtain an inverted grayscale image;

[0013] S12: setting a grayscale threshold to simplify the inverted grayscale image into a binary image containing only gravel and background;

[0014] S13: Processing the binary image using a watershed algorithm to obtain a segmented binary image.

[0015] Preferably, step S12 further includes a step of performing image denoising on the binary image; during image denoising, holes that do not conform to the actual gravel size are filled, and independent areas that do not conform to the actual gravel size are eliminated.

[0016] Preferably, in step S2, the gravel content is the ratio of the number of gravel pixels to the number of background pixels in the segmented binary image; and the gravel content threshold is 3%.

[0017] Preferably, in step S3, when dividing the scattered gravels into contact gravels, the gravels are expanded by 2 circles of pixels. If there is a gravel that intersects with the expanded part, the gravel is divided into a contact gravel; otherwise, it is divided into a scattered gravel.

[0018] Preferably, in step S4, the gravel contact form parameters include:

[0019] The contact gravel ratio is the ratio of the contact gravel area to the total gravel area;

[0020] Area weighting, where the area weighting is the ratio of the total area of ​​gravel within the envelope to the area enclosed by the envelope;

[0021] BGR, wherein BGR is the ratio of the number of gravel contacts to the total number of contact gravels;

[0022] Contact length, which is the actual length of contact between two adjacent gravels.

[0023] Preferably, the area weighting is obtained by the following sub-steps:

[0024] S41: Each gravel on the contact gravel binary image is expanded by a circle of pixels to divide each contact area, and obtain the boundary point set S of each contact area. k , S k ={(x i ,y i )|i∈index of connected region k};

[0025] S42: Set the detection radius a and calculate the envelope point set C k , There exists an empty circle with radius r ≤ 1 / a that passes through the two points and contains no other points};

[0026] S43: Calculate the area weighting by the following formula:

[0027] J k =B k / A k (1)

[0028]

[0029] Where: J k is area weighted; B k is the total area of ​​gravel in contact area k; A k is the area enclosed by the envelope; m is the envelope point set C k Contains the total number of coordinates; and They are respectively the envelope point set C k The i-th and i+1-th horizontal coordinates; and They are respectively the envelope point set C k The i-th and i+1-th vertical coordinates; and are the non-existent envelope point sets C k The m+1th horizontal and vertical coordinates; and They are respectively the envelope point set C k The first horizontal and vertical coordinates.

[0030] Preferably, the BGR is obtained by the following sub-steps:

[0031] S44: Determine the contact area according to the envelope point set Ck in step S42, and mark each contact area separately;

[0032] S45: performing contact determination on the gravels in each contact area, expanding a single gravel by 2 circles of pixels, and considering the gravels intersecting with the expanded part as being in contact with each other, and recording the number of gravels in contact with the gravel;

[0033] S46: summing the recorded numbers and dividing by 2 to obtain the gravel contact number of the contact area;

[0034] S47: Calculate the ratio of the number of gravel contacts to the total number of contact gravels to obtain the BGR of the contact area;

[0035] S48: Repeat steps S45-S47 to obtain the BGR of each contact area, and then calculate the average value to obtain the BGR of the entire contact area.

[0036] Preferably, in step S4, when evaluating the gravel contact form of the target well:

[0037] If the contact gravel ratio is greater than the contact gravel ratio threshold, the target layer is considered to be a high contact gravel content section; otherwise, the target layer is considered to be a low contact gravel content section;

[0038] If the area weight is greater than the area weight threshold or the BGR is greater than the BGR threshold, the target interval is considered to be a tight contact glutenite formation; otherwise, the target interval is considered to be a loose contact glutenite formation;

[0039] If the contact length is greater than the contact length threshold, the gravels are considered to be in line contact; otherwise, the gravels are considered to be in point contact.

[0040] Preferably, the contact gravel ratio threshold is 60%, the area weighted threshold is 0.7, the BGR threshold is 0.65, and the contact length threshold is 2.5.

[0041] The beneficial effects of the present invention are:

[0042] The present invention can accurately obtain the gravel contact form of the continuous sandy conglomerate formation in the target well based on the imaging logging images; the gravel contact form parameters can systematically reveal the spatial configuration relationship between gravels, which is of great significance for in-depth understanding of the physical properties of sandy conglomerate and the exploration of sandy conglomerate reservoir resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 Schematic diagram of the flow of the logging evaluation method for gravel contact forms in sandstone formations based on imaging logging diagrams of the present invention;

[0045] Figure 2 is an original imaging log in a specific embodiment;

[0046] Figure 3 for Figure 2 Strata binary map;

[0047] Figure 4 for Figure 3 Binary image after denoising;

[0048] Figure 5 for Figure 4 The distance transformation map of

[0049] Figure 6 for Figure 5 Schematic diagram of the result of setting a mask to filter out significant minimum areas;

[0050] Figure 7 for Figure 6 Segmented binary image after segmentation;

[0051] Figure 8 Schematic diagram of the contact gravel identification process; (a) is the original binary image, and (b) is the expanded image;

[0052] Figure 9 A segmented binary image of a specific embodiment;

[0053] Figure 10 for Figure 9 The contact gravel binary map obtained after dividing the dispersed gravel and contact gravel;

[0054] Figure 11 for Figure 9 Schematic diagram of the division results of dispersed gravel and contact gravel;

[0055] Figure 12 for Figure 10 Schematic diagram of contact area division results;

[0056] Figure 13 for Figure 12 Schematic diagram of envelope drawing results;

[0057] Figure 14 for Figure 13 Schematic diagram of the results of different contact areas marked with different colors;

[0058] Figure 15 for Figure 14 Schematic diagram of contact length results for each contact area;

[0059] Figure 16 Schematic diagram of gravel contact form evaluation results of sandstone formations of various imaging logging images in a specific embodiment. DETAILED DESCRIPTION

[0060] The present invention is further described below with reference to the accompanying drawings and examples. It should be noted that, in the absence of conflict, the embodiments in this application and the technical features in the embodiments can be combined with each other. It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as those commonly understood by those of ordinary skill in the art to which this application belongs. The use of similar words such as "include" or "comprising" in the present invention means that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0061] like Figure 1 As shown, the present invention provides a logging evaluation method for gravel contact form in a sandstone formation based on an imaging logging diagram, comprising the following steps:

[0062] S1: Acquire an imaging logging image of a target well, and extract and segment gravel from the imaging logging image to obtain a segmentation binary image.

[0063] In a specific embodiment, extracting and segmenting gravel from the imaging logging image specifically includes the following sub-steps:

[0064] S11: extracting depth information and grayscale information of the imaging logging image, and performing grayscale value inversion to obtain an inverted grayscale image;

[0065] It should be noted that the grayscale value is inverted, that is, X'=255-X, where X' is the inverted grayscale value and X is the initial grayscale value; the grayscale value is inverted to make the image more intuitive to express gravel, that is, the white foreground represents gravel and the black background represents non-gravel.

[0066] S12: setting a grayscale threshold to simplify the inverted grayscale image into a binary image containing only gravel and background;

[0067] In this step, the grayscale threshold is used to identify gravel in the formation. Pixels on the imaging log with grayscale values ​​greater than this threshold are considered gravel. The grayscale threshold needs to be set based on the actual imaging log. By observing the imaging log, multiple adjustments are made to find an appropriate value. Furthermore, if the original data being processed is a static image, a higher threshold is typically set compared to a dynamic image.

[0068] In a specific embodiment, the obtained imaging logging image is as follows: Figure 2 As shown, the binary image obtained in this step is Figure 3 As shown. Figure 3 As can be seen, the image still contains noise points. Therefore, this step also includes the step of performing image denoising on the binary image; during image denoising, holes that do not conform to the actual gravel size are filled, and independent areas that do not conform to the actual gravel size are eliminated.

[0069] In the above embodiment, image denoising can ensure the accuracy of subsequent calculation results. Figure 3 In the figure, the noise point marked as a is a hole smaller than 4 pixels in a single gravel, which needs to be filled; the noise point marked as b is an independent area smaller than 4 pixels on the binary image, which needs to be eliminated. It should be noted that the 4 pixels in this embodiment are based on Figure 2The ratio of the imaging logging image is determined mainly by comparing the size of the pixel expression with the actual gravel size. Points that do not conform to the actual size are noise points. Figure 3 The result after denoising the binary image is shown as Figure 4 shown.

[0070] S13: Processing the binary image using a watershed algorithm to obtain a segmented binary image.

[0071] Step S12 determines the situation in which two or more touching gravels are identified as a single gravel only by threshold segmentation. In this step, a more accurate segmentation result can be obtained by further processing the binary image through the watershed algorithm.

[0072] The watershed algorithm is essentially a "terrain immersion" model based on gradient strength. The watershed algorithm requires a distance transform, a key preprocessing technique. Its core principle is to identify the "center region" of an object by calculating the distance from each foreground pixel to the nearest background pixel, thereby generating uniform and representative seed points. In the directly generated distance transform map, the pixel values ​​of the foreground region are inversely proportional to their distance from the background. High-value regions (although negative, they have large absolute values) form at the center of the object, while transition gradients form at the edges. Using the original distance transform directly may result in a large number of tiny local minima in the image. The watershed algorithm generates a separate watershed boundary for each minimum, leading to oversegmentation. To prevent oversegmentation, a mask is set to control the segmented area, and a height threshold is set to filter out significant minimum regions as mandatory markers. Minima in unmarked areas are eliminated, leaving only the segmentation starting points at pre-set locations, fundamentally preventing oversegmentation. The specific process of the watershed algorithm is as follows:

[0073] (1) Calculate the distance transform of the binary matrix B

[0074]

[0075] S(B)={(k,l)|B k,l =1} (5)

[0076] Where: B is the binary matrix obtained in S12; D i,j is the result of distance transformation; S(B) is the set of foreground pixel coordinates of B; k and l are the horizontal and vertical coordinates of the foreground pixels respectively; i and j are the horizontal and vertical coordinates of the background pixels respectively;

[0077] (2) Mask generation (local minimum detection)

[0078]

[0079] Where: maski,j For mask; is the minimum distance transform of the neighborhood of pixel (i, j); N(i, j) is the neighborhood of pixel (i, j); ∈ is a tolerance parameter; D2 i,j is the distance transform after mask processing; δ is a small positive offset;

[0080] (3) Watershed segmentation

[0081] L1=Watershed(D2) (8)

[0082]

[0083] Where: L1 is the initial segmentation result of the watershed algorithm; Watershed(D2) is the watershed algorithm; L2 is the retained segmentation line; SI i,j is the matrix after matrix B is segmented by watershed.

[0084] In a specific embodiment, Figure 4 The binary graph distance transformation is as follows Figure 5 As shown, setting the mask to filter out the significant minimum area results are as follows Figure 6 As shown, the segmentation binary image obtained is as follows Figure 7 shown.

[0085] S2: Determine the gravel content of the target well based on the segmented binary image. When the gravel content is less than the gravel content threshold, the evaluation ends; otherwise, proceed to step S3.

[0086] Gravel content is a key parameter in studying conglomerate formations. Gravel content affects rock porosity and permeability. In a high-energy, stable environment, higher gravel content indicates better sorting, and well-sorted conglomerates are more likely to form high-porosity reservoirs. In one specific embodiment, the gravel content is the ratio of the number of gravel pixels to the number of background pixels in the segmented binary image; the gravel content threshold is 3%.

[0087] In addition, gravel size is also an important parameter in the study of sandstone formations. This step can also determine the gravel size of the target well through the segmented binary map. Gravel size affects porosity. Large-sized gravel easily forms inter-gravel pores, but it needs to be well sorted and weakly cemented to maintain high porosity. When small gravel is mixed with sand, it is easy to form a matrix support structure, the pores are filled with fine particles, and the permeability is reduced. Gravel size can be quantified by equivalent diameter. Specifically: make a circle with the same area as the gravel, and the diameter of the circle is the equivalent diameter of the gravel. The average of the equivalent diameters of all gravels in a single depth section is used to obtain the gravel size reference value for that depth section.

[0088] S3: Divide the scattered gravels and the contact gravels according to the segmentation binary map, and obtain a contact gravel binary map.

[0089] The unique support structure of contact gravels can significantly influence the reservoir and permeability properties of a formation. Due to direct contact between the gravels, contact gravels form a highly permeable, highly porous interconnected network, making them a key target for high-quality oil and gas reservoirs or groundwater aquifers. Especially when diagenetic cementation is weak, the long-term preservation of their primary pores significantly enhances resource extraction potential.

[0090] In a binary image, two pebbles with a minimum distance of only one pixel are considered to be in contact with each other, and any pebbles that are in contact with each other are classified as contact pebbles. To automatically identify pebbles with a minimum distance of only one pixel, in a specific embodiment, when classifying scattered pebbles and contact pebbles, the pebbles are expanded by two pixels. If there is a pebbles that intersect with the expanded part, the pebbles are classified as contact pebbles; otherwise, they are classified as scattered pebbles.

[0091] In the above embodiment, if Figure 8 As shown, gravel 1 is expanded by 2 pixels. The green area and the red area are the expanded parts of gravel 1. The red area is the intersection area of ​​gravel 1 and gravel 2 after expansion. In a specific embodiment, the segmentation binary image is as follows Figure 9 As shown in the figure, the binary diagram of contact gravel after dividing the scattered gravel and contact gravel is as follows Figure 10 shown.

[0092] S4: Calculating gravel contact form parameters according to the division results of the dispersed gravels and the contact gravels, and evaluating the gravel contact form of the target well according to the calculation results of the gravel contact form parameters.

[0093] In a specific embodiment, the gravel contact form parameters include: contact gravel ratio, which is the ratio of the contact gravel area to the total gravel area; area weighting, which is the ratio of the total gravel area within the envelope to the area of ​​the area enclosed by the envelope; BGR, which is the ratio of the number of gravel contacts to the total number of gravel contacts; contact length, which is the actual length of contact between two adjacent gravels.

[0094] In the above embodiment, the contact gravel ratio is one of the core indicators of sedimentation dynamics research. Through quantitative analysis, the sedimentation mechanism can be distinguished. When the contact gravel ratio is high, it indicates that the sediment has not been transported or sorted over long distances and is mostly formed in a near-source rapid accumulation environment, such as alluvial fans, debris flows or glacial deposits. Conversely, a low content suggests that the gravel has undergone long-distance transportation and strong hydrodynamic transformation, such as in a river or coastal environment, where the gravels are filled with matrix and have better sorting properties. In a specific embodiment, Figure 9 The contact gravels in the segmented binary image are marked in red and the scattered gravels are marked in green. Figure 11 shown. Figure 11 In the figure, the contact gravel ratio can be obtained by dividing the area of ​​red contact gravel by the total area of ​​red contact gravel and green scattered gravel.

[0095] Area weighting is defined as the ratio of the total gravel area within the reference area boundary to the total reference area (i.e., the ratio of the total gravel area within the envelope to the area enclosed by the envelope). It is used to quantify the effect of gravel packing density on rock texture. The texture coefficient is a dimensionless quantitative indicator, and area weighting serves as a weighting factor for the texture coefficient, comprehensively describing the complexity of rock microtexture. Area weighting incorporates gravel shape and orientation, enabling the texture coefficient to more comprehensively describe rock microstructure.

[0096] In a specific embodiment, the area weighting is obtained by the following sub-steps:

[0097] S41: Each gravel on the contact gravel binary image is expanded by a circle of pixels to divide each contact area, and obtain the boundary point set S of each contact area. k , S k ={(x i ,y i )|i∈index of connected region k};

[0098] S42: Set the detection radius a and calculate the envelope point set C k , There exists an empty circle with radius r ≤ 1 / a that passes through the two points and contains no other points};

[0099] S43: Calculate the area weighting by the following formula:

[0100] J k =B k / A k (1)

[0101]

[0102] Where: J k is area weighted; B k is the total area of ​​gravel in contact area k; A k is the area enclosed by the envelope; m is the envelope point set C k Contains the total number of coordinates; and They are respectively the envelope point set C k The i-th and i+1-th horizontal coordinates; and They are respectively the envelope point set C k The i-th and i+1-th vertical coordinates; and are the non-existent envelope point sets C kThe m+1th horizontal and vertical coordinates; and They are respectively the envelope point set C k The first horizontal and vertical coordinates.

[0103] In the above embodiment, Figure 10 The binary image of the contact gravels is shown, where each gravel is expanded by a circle of pixels to divide each contact area. Figure 12 As shown in the figure, the detection radius a is set to make the drawn envelope as smooth as possible close to the edge of the gravel without generating sub-envelopes inside. The final calculation effect is as follows Figure 13 shown.

[0104] The Bond-to-Grain Ratio (BGR) is the ratio of the number of gravel contacts to the total number of gravels. Gravel contacts (Bonds) refer to the contact interfaces between adjacent gravels. BGR reflects the degree of connectivity between gravels, that is, the average number of contact points per gravel. BGR quantifies the development of gravel contacts during diagenesis and reveals the systematic transformation of sandstone microstructure by diagenesis. It provides a direct predictive indicator for key properties such as mechanical strength, elastic modulus, and permeability; it goes beyond the limitations of porosity to more accurately reflect the influence of microstructure on rock behavior; and it provides a fundamental parameter for comparative studies of complex sandstones.

[0105] In a specific embodiment, the BGR is obtained by the following sub-steps:

[0106] S44: Determine the contact area (i.e. Figure 13 ) and mark each contact area individually (e.g. Figure 14 shown);

[0107] S45: performing contact determination on the gravels in each contact area, expanding a single gravel by 2 circles of pixels, and considering the gravels intersecting with the expanded part as being in contact with each other, and recording the number of gravels in contact with the gravel;

[0108] S46: summing the recorded numbers and dividing by 2 to obtain the gravel contact number of the contact area;

[0109] S47: Calculate the ratio of the number of gravel contacts to the total number of contact gravels to obtain the BGR of the contact area;

[0110] S48: Repeat steps S45-S47 to obtain the BGR of each contact area, and then calculate the average value to obtain the BGR of the entire contact area.

[0111] Contact length can reveal the evolution mechanism of elastic properties of sandstone during compaction and cementation by quantifying the physical characteristics of contact between gravels. It is especially important for its regulation of acoustic wave velocity in the early cementation stage.

[0112] In one specific embodiment, the contact length is obtained by the following sub-step: dilating two contacting gravels and taking the number of pixels of their intersection after dilation as the contact length. The specific implementation process is as follows: traverse all gravels in the formation, dilate a single gravel by two pixels, search for gravels that intersect with its expanded portion, and record the contact relationships of the corresponding gravels. For example, if gravel 1, after dilution, contacts gravel 2 and gravel 3, the sequence is recorded as 1, 2, 3, which is the contact relationship of gravel 1. The contact length is then calculated based on each contact relationship. For example, if the contact relationship is the sequence 1, 2, 3, after dilating gravel 1, gravel 2 and gravel 3 are expanded separately to obtain the number of pixels of intersection between gravel 1 and gravel 2 and the number of pixels of intersection between gravel 1 and gravel 3. The number of intersection pixels is the contact length of the corresponding contacting gravels. The average of all contact lengths in a single depth segment is taken as the reference value of the contact length for that depth segment.

[0113] In a specific embodiment, Figure 10 The contact length of each contact area in the contact gravel binary map is shown as Figure 15 As shown, Figure 15 The red line in the figure is the contact length of the corresponding contact area.

[0114] When evaluating gravel contact patterns in target wells, the larger the BGR value, the more contact between gravels; the closer the area weighting is to 1, the smaller the gaps between gravels. Combining the BGR and area weighting values ​​provides a quantitative standard for determining whether contact gravels are loose or tight. In one specific embodiment, if the contact gravel ratio is greater than the contact gravel ratio threshold, the target interval is considered to have a high contact gravel content; otherwise, the target interval is considered to have a low contact gravel content.

[0115] If the area weight is greater than the area weight threshold or the BGR is greater than the BGR threshold, the target interval is considered to be a tight contact glutenite formation; otherwise, the target interval is considered to be a loose contact glutenite formation;

[0116] If the contact length is greater than the contact length threshold, the gravels are considered to be in line contact; otherwise, the gravels are considered to be in point contact.

[0117] In a specific embodiment, the contact gravel ratio threshold is 60%, the area weighted threshold is 0.7, the BGR threshold is 0.65, and the contact length threshold is 2.5.

[0118] In a specific embodiment, based on the threshold value of the above embodiment, a target well is taken as an example, and the gravel contact form of the gravel formation in the gravel formation is evaluated by the logging evaluation method of the present invention based on the imaging logging diagram. The results are as follows: Figure 16 As shown. Figure 16 It can be seen that the present invention can continuously evaluate the gravel contact form of the sandstone formation of the target well through imaging logging images.

[0119] In summary, the present invention can accurately obtain the continuous gravel contact pattern of the sandstone formation in the target well based on the imaging logging image. Compared with the existing technology, the present invention has significant progress.

[0120] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A logging evaluation method for gravel contact forms in sandstone formations based on imaging logging diagrams, characterized in that: The following steps are involved: S1: Acquire an imaging logging image of a target well, and extract and segment gravel from the imaging logging image to obtain a segmentation binary image; S2: determining the gravel content of the target well according to the segmented binary image. When the gravel content is less than the gravel content threshold, the evaluation ends; otherwise, the process proceeds to step S3; S3: dividing the scattered gravels and the contact gravels according to the segmentation binary image, and obtaining a contact gravel binary image; S4: Calculating gravel contact form parameters according to the division results of the dispersed gravels and the contact gravels, and evaluating the gravel contact form of the target well according to the calculation results of the gravel contact form parameters.

2. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 1, characterized in that: In step S1, extracting and segmenting gravel from the imaging logging image specifically includes the following sub-steps: S11: extracting depth information and grayscale information of the imaging logging image, and performing grayscale value inversion to obtain an inverted grayscale image; S12: setting a grayscale threshold to simplify the inverted grayscale image into a binary image containing only gravel and background; S13: Processing the binary image using a watershed algorithm to obtain a segmented binary image.

3. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 2, characterized in that: Step S12 also includes a step of performing image denoising on the binary image; during image denoising, holes that do not conform to the actual gravel size are filled, and independent areas that do not conform to the actual gravel size are eliminated.

4. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 1, characterized in that: In step S2, the gravel content is the ratio of the number of gravel pixels to the number of background pixels in the segmented binary image; and the gravel content threshold is 3%.

5. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 1, characterized in that: In step S3, when dividing scattered gravels into contact gravels, the gravel is expanded by 2 circles of pixels. If there is a gravel that intersects with its expanded part, the gravel is classified as a contact gravel; otherwise, it is classified as a scattered gravel.

6. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to any one of claims 1 to 5, characterized in that: In step S4, the gravel contact form parameters include: The contact gravel ratio is the ratio of the contact gravel area to the total gravel area; Area weighting, where the area weighting is the ratio of the total area of ​​gravel within the envelope to the area enclosed by the envelope; BGR, wherein BGR is the ratio of the number of gravel contacts to the total number of contact gravels; Contact length, which is the actual length of contact between two adjacent gravels.

7. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 6, characterized in that: The area weighting is obtained by the following sub-steps: S41: Each gravel on the contact gravel binary image is expanded by a circle of pixels to divide each contact area, and obtain the boundary point set S of each contact area. k , S k ={(x i ,y i )|i∈index of connected region k}; S42: Set the detection radius a and calculate the envelope point set C k , There exists an empty circle with radius r ≤ 1 / a that passes through the two points and contains no other points}; S43: Calculate the area weighting by the following formula: J k =B k / A k (1) Where: J k is area weighted; B k is the total area of ​​gravel in contact area k; A k is the area enclosed by the envelope; m is the envelope point set C k Contains the total number of coordinates; and They are respectively the envelope point set C k The i-th and i+1-th horizontal coordinates; and They are respectively the envelope point set C k The i-th and i+1-th vertical coordinates; and are the non-existent envelope point sets C k The m+1th horizontal and vertical coordinates; and They are respectively the envelope point set C k The first horizontal and vertical coordinates.

8. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 7, characterized in that: The BGR is obtained by the following sub-steps: S44: Envelope point set C according to step S42 k Identify the contact areas and mark each contact area individually; S45: performing contact determination on the gravels in each contact area, expanding a single gravel by 2 circles of pixels, and considering the gravels intersecting with the expanded part as being in contact with each other, and recording the number of gravels in contact with the gravel; S46: summing the recorded numbers and dividing by 2 to obtain the gravel contact number of the contact area; S47: Calculate the ratio of the number of gravel contacts to the total number of contact gravels to obtain the BGR of the contact area; S48: Repeat steps S45-S47 to obtain the BGR of each contact area, and then calculate the average value to obtain the BGR of the entire contact area.

9. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 6, wherein: In step S4, when evaluating the gravel contact form of the target well: If the contact gravel ratio is greater than the contact gravel ratio threshold, the target layer is considered to be a high contact gravel content section; otherwise, the target layer is considered to be a low contact gravel content section; If the area weight is greater than the area weight threshold or the BGR is greater than the BGR threshold, the target interval is considered to be a tight contact glutenite formation; otherwise, the target interval is considered to be a loose contact glutenite formation; If the contact length is greater than the contact length threshold, the gravels are considered to be in line contact; otherwise, the gravels are considered to be in point contact.

10. The method for evaluating gravel contact patterns in sandstone formations based on imaging logging diagrams according to claim 9, characterized in that: The contact gravel ratio threshold is 60%, the area weighted threshold is 0.7, the BGR threshold is 0.65, and the contact length threshold is 2.5.

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