Method and apparatus for diagenetic intensity analysis of oil and gas reservoir rocks

Abstract

The application provides a method and device for diagenetic strength analysis of oil and gas reservoir rocks, which comprises the following steps: determining the common boundary of each adjacent mineral particle in a mineral binary image obtained based on a preset common boundary extraction algorithm, to obtain a particle boundary extraction result image; determining the maximum circumscribed rectangle of each mineral particle, and calculating the total equivalent particle diameter of all mineral particles; performing image processing based on a preset image processing algorithm, to obtain a mineral morphological gradient image and a mineral watershed line image; performing logical AND operation, to obtain a logical operation result image; performing color space transformation, to obtain a mineral particle contact image; calculating the total point contact length and the total line contact length according to the mineral particle contact image; and obtaining a diagenetic strength coefficient according to the total equivalent particle diameter of all mineral particles, the total point contact length and the total line contact length. The result is accurate because the mineral automatic analysis image is used for automatic quantitative calculation.

Description

TECHNICAL FIELD

[0001] The present application relates to a method and device for analyzing diagenetic strength of oil and gas reservoir rock. BACKGROUND

[0002] This section is intended to provide background information to facilitate a better understanding of embodiments of the present application described in the claims. The description herein does not constitute admission of prior art.

[0003] In the evaluation system of the oil and gas reservoir layer, diagenesis is an extremely important index. Diagenesis has a great influence on the preservation of primary porosity and the development of secondary porosity in the reservoir. Analyzing and judging the strength of diagenesis is the key of reservoir evaluation. Diagenesis is defined as the physical, chemical and biological actions that occur under low temperature and low pressure conditions before the sediment is buried and metamorphosed. The diagenetic strength of rock is one of the important contents of reservoir evaluation, and is an important index affecting unconventional oil and gas exploration and development. Accurate judgment of the diagenetic strength of target stratum helps to accurately and efficiently evaluate the target oil and gas reservoir and design the development plan.

[0004] Since the 20th century, the related research based on diagenesis has made great progress, which to some extent promotes the exploration and development of unconventional oil and gas. In recent years, thanks to the innovation and enrichment of analysis methods, the research and analysis of diagenetic strength analysis has entered a high-speed development stage again. The diagenetic analysis methods in the prior art mainly include petrological methods and non-petrological methods. Among them, the petrological method mainly uses related imaging instruments to scan and image the rock, and then manually analyzes the obtained mineral distribution image to judge the diagenesis of oil and gas reservoir. Commonly used equipment and instruments include scanning electron microscope, specific microscope and other scanning instruments. The non-petrological method mainly uses related analysis techniques to qualitatively or quantitatively analyze the target. Common analysis techniques include fluid analysis method, capillary pressure method and other experimental test techniques. In the above diagenetic analysis methods, the diagenetic strength is determined by using scanning electron microscope to directly image and then observing the obtained mineral distribution image by related experts, which is the most effective method at present.

[0005] With the improvement of equipment and the development of technology, the scanning electron microscope imaging technology based on backscattering imaging and energy spectrum (EDS) technology is also more and more mature, and many companies including Thermo Fisher Scientific Company and Zeiss Company have developed mineral automatic analysis equipment software for realizing mineral petrology detection. These equipment software includes scanning electron microscope mineral quantitative evaluation software (Quantitative Evaluation of Minerals by Scanning Electron Microscopy, QEMSCAN) and advanced mineral identification and characterization system software (Automatic Mineral Identification and Characterization System, AMICS). These mineral automatic analysis software can give the data of composition, content, particle size and the like of minerals in a dense reservoir sample, and the image of mineral distribution, thereby providing a data basis for studying diagenesis. Among them, the mineral distribution image using QEMSCAN scanning imaging has the advantage of finely depicting the structural components (content, particle size and the like) of sandstone, and in actual operation analysis, can provide new ideas and methods for evaluating the strength of diagenesis.

[0006] In the prior art, the mineral automatic analysis software including QEMSCAN does not contain a method module for diagenesis strength analysis, and cannot automatically realize diagenesis strength analysis. At present, the judgment of diagenesis based on automatic mineral analysis mainly still relies on the observation of relevant technical experts in the field, and according to long-term experience, to predict the size of diagenesis strength. SUMMARY

[0007] The inventors of the present application find that in the prior art, the observation of the imaging image obtained by the mineral automatic analysis software by the technical experts does not fully exert the technical advantages of the related equipment and the mineral automatic analysis software. Moreover, due to the large variety of minerals, the complex distribution, the irregular outline shape and the different size of various mineral particles, in the actual diagenesis intensity analysis process, the technical experts in the field only judge the diagenesis by visual observation, and the diagenesis intensity analysis has many interferences, is time-consuming and low in efficiency, and the diagenesis intensity analysis data obtained only by experience lacks reliability, repeatability and comparability. The inventors of the present application further find that at present, there is a lack of a method and technology for automatically quantifying diagenesis by using the data and images of the mineral automatic analysis software, because the mineral boundaries are difficult to identify and extract due to the complex variety of minerals, the different and random distribution. In order to effectively measure and quantify the contact properties of the complex particles in the mineral distribution map obtained by the mineral automatic analysis software, the inventors of the present application propose a method for scientifically quantifying and calculating the diagenesis intensity, which takes the contact points or line contacts between the irregular particles in the map as the standard for judging the strength, and realizes the quantitative analysis and calculation of the diagenesis intensity. Based on this, the embodiments of the present application provide a method and device for analyzing the diagenesis intensity of the rock of the oil and gas reservoir.

[0008] As a first aspect of the embodiments of the present application, the embodiments of the present application provide a method for analyzing the diagenesis intensity of the rock of the oil and gas reservoir, comprising:

[0009] performing image processing on the obtained mineral distribution image of the rock to be analyzed to obtain a mineral binary image;

[0010] determining the common boundary of each adjacent mineral particle in the mineral binary image based on a preset common boundary extraction algorithm to obtain a particle boundary extraction result image;

[0011] determining the maximum circumscribed rectangle of each mineral particle in the particle boundary extraction result image, calculating the sum of the long sides of the maximum circumscribed rectangles of all mineral particles to obtain the total equivalent particle diameter of all mineral particles;

[0012] performing image processing on the particle boundary extraction result image based on a preset image processing algorithm to obtain a mineral morphological gradient image and a mineral watershed line image;

[0013] performing logical AND operation on the mineral morphological gradient image and the mineral watershed line image to obtain a logical operation result image;

[0014] performing color space transformation on the logical operation result image to obtain a mineral particle contact image;

[0015] determine, according to the mineral particle contact graph, a contact type between each adjacent mineral particle as point contact or line contact, and calculate a total length of point contact and a total length of line contact;

[0016] obtain a diagenesis intensity coefficient according to the total equivalent particle diameter of all mineral particles, the total length of point contact and the total length of line contact.

[0017] As a second aspect of the embodiments of the present application, the embodiments of the present application further provide a device for diagenesis intensity analysis of oil and gas reservoir rock, comprising:

[0018] a first image processing module, configured to perform image processing on the obtained mineral distribution image of the rock to be analyzed to obtain a mineral binary image; determine a common boundary of each adjacent mineral particle in the mineral binary image based on a preset common boundary extraction algorithm to obtain a particle boundary extraction result image;

[0019] a first calculation module, configured to determine a maximum circumscribed rectangle of each mineral particle in the particle boundary extraction result image, calculate a sum of long sides of the maximum circumscribed rectangles of all mineral particles to obtain a total equivalent particle diameter of all mineral particles;

[0020] a second image processing module, configured to perform image processing on the particle boundary extraction result image based on a preset image processing algorithm to obtain a mineral morphological gradient image and a mineral watershed line image; perform logical AND operation on the mineral morphological gradient image and the mineral watershed line image to obtain a logical operation result image; and perform color space conversion on the logical operation result image to obtain a mineral particle contact graph;

[0021] a second calculation module, configured to determine, according to the mineral particle contact graph, a contact type between each adjacent mineral particle as point contact or line contact, and calculate a total length of point contact and a total length of line contact;

[0022] a diagenesis determination module, configured to obtain a diagenesis intensity coefficient according to the total equivalent particle diameter of all mineral particles, the total length of point contact and the total length of line contact.

[0023] As a third aspect of the embodiments of the present application, the embodiments of the present application further provide a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for diagenesis intensity analysis of oil and gas reservoir rock as described above when executing the computer program.

[0024] As a fourth aspect of the embodiments of the present application, the embodiments of the present application further provide a computer readable storage medium, which stores a computer program for implementing the method for diagenesis intensity analysis of oil and gas reservoir rock as described above.

[0025] The embodiment of the present application brings the following beneficial effects:

[0026] The method for analyzing diagenetic strength of oil and gas reservoir rock provided by the embodiment of the present application extracts the contact points or contact lines between irregular mineral particles in the mineral distribution image through image processing on the obtained mineral distribution image of the rock to be analyzed, realizes extraction of the common boundary of the contact between the mineral particles based on a preset common boundary extraction algorithm, determines the contact type of the adjacent mineral particles, obtains the parameter representing the diagenetic strength of the rock, and realizes calculation and quantization of the diagenetic strength coefficient through automatic mineral particle identification, extraction of the contact boundary between the mineral particles, and evaluation of the contact relationship between the mineral particles, so that the method can be applied to the clastic rock samples represented by medium sandstone, fine sandstone, mudstone, shale and the like, and realize quantitative evaluation of the diagenetic strength of the oil and gas reservoir rock. Compared with the method for analyzing the diagenetic strength based on artificial experience in the prior art, the method realizes in-depth microscale image processing, automatically and quantitatively calculates based on the automatic mineral analysis image, obtains quantitative data of the diagenetic strength, and the result is more accurate.

[0027] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by the structure particularly pointed out in the description, claims and drawings.

[0028] In order to make the above-mentioned objects, characteristics and advantages of the present application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without any creative labor.

[0030] Figure 1 The flowchart of the method for analyzing diagenetic strength of oil and gas reservoir rock provided by the embodiment of the present application is shown in the figure.

[0031] Figure 2 The flowchart of the method for determining the mineral binary image provided by the embodiment of the present application is shown in the figure.

[0032] Figure 3 The eight-neighborhood diagram of the pixel provided by the embodiment of the present application is shown in the figure.

[0033] Figure 4 The diagram of the contact between the mineral particles provided by the embodiment of the present application is shown in the figure.

[0034] Figure 5 A mineral distribution image schematic diagram provided for an embodiment of the present application;

[0035] Figure 6 A specific implementation flow schematic diagram of a method for diagenetic strength analysis of oil and gas reservoir rocks provided for an embodiment of the present application;

[0036] Figures 7A-7C A binaryzation diagram of three characteristic minerals of potash feldspar, quartz and albite provided for an embodiment of the present application;

[0037] Figures 8A-8C A binaryzation diagram of three characteristic minerals of potash feldspar, quartz and albite provided for an embodiment of the present application after hole filling;

[0038] Figures 9A-9C A binaryzation diagram of three characteristic minerals of potash feldspar, quartz and albite provided for an embodiment of the present application after filtering;

[0039] Figures 10A-10C A mineral diagram of three characteristic minerals of potash feldspar, quartz and albite provided for an embodiment of the present application;

[0040] Figures 11A-11C A binaryzation diagram of three characteristic minerals of potash feldspar, quartz and albite provided for an embodiment of the present application;

[0041] Figure 12 A mineral synthesis result diagram of three characteristic minerals of potash feldspar, quartz and albite provided for an embodiment of the present application;

[0042] Figure 13 A mineral binaryzation diagram provided for an embodiment of the present application;

[0043] Figure 14 A mineral morphological gradient diagram provided for an embodiment of the present application;

[0044] Figure 15 A mineral watershed line diagram provided for an embodiment of the present application;

[0045] Figure 16 A mineral particle contact diagram provided for an embodiment of the present application;

[0046] Figure 17 A structure schematic diagram of a device for diagenetic strength analysis of oil and gas reservoir rocks provided for an embodiment of the present application;

[0047] Figure 18 A system composition structure schematic diagram of a computer device provided for an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0049] In order to facilitate the understanding of the embodiments, the specific implementation modes of the present application will be described in detail below through several specific embodiments:

[0050] Embodiment 1

[0051] The embodiments of the present application provide a method for analyzing diagenetic strength of oil and gas reservoir rock, referring to the method shown in the figure, the method comprises the following steps: Figure 1 The method comprises the following steps:

[0052] S101: performing image processing on the obtained mineral distribution image of the rock to be analyzed to obtain a mineral binary image;

[0053] S102: determining the common boundary of each adjacent mineral particle in the mineral binary image based on a preset common boundary extraction algorithm to obtain a particle boundary extraction result image;

[0054] S103: determining the maximum circumscribed rectangle of each mineral particle in the particle boundary extraction result image, calculating the sum of the long sides of the maximum circumscribed rectangles of all mineral particles to obtain the total equivalent particle diameter of all mineral particles;

[0055] S104: performing image processing on the particle boundary extraction result image based on a preset image processing algorithm to obtain a mineral morphological gradient image and a mineral watershed line image;

[0056] S105: performing logical AND operation on the mineral morphological gradient image and the mineral watershed line image to obtain a logical operation result image;

[0057] S106: performing color space transformation on the logical operation result image to obtain a mineral particle contact image;

[0058] S107: determining the contact type between each adjacent mineral particle as point contact or line contact according to the mineral particle contact image, and calculating the total length of point contact and the total length of line contact;

[0059] S108: obtaining a diagenetic strength coefficient according to the total equivalent particle diameter of all mineral particles, the total length of point contact and the total length of line contact.

[0060] In the embodiment of the present application, the mineral distribution image described in step S101 can be obtained by detecting the rock to be analyzed based on a preset mineral petrology detection method. The preset mineral petrology detection method can be a method realized by using a mineral automatic analysis software in the prior art. By using the mineral automatic analysis software in the prior art, the distribution image of the mineral at the micron scale is obtained. The mineral automatic analysis software can be QEMSCAN software, and the mineral distribution image is an element mineral component distribution map output by the QEMSCAN software. Of course, other rock mineral component distribution maps or rock mineral thin section images obtained by image scanning acquisition devices in the prior art can also be used, and the software used can be, for example, AMICS software, Image J image processing software, PerGeos digital rock analysis software, Zeiss Zen Blue image processing software, or other software applicable to rock mineral image analysis in the prior art. For the specific implementation mode and specific acquisition process of the mineral automatic analysis software for obtaining the mineral distribution image, those skilled in the art can refer to the detailed description in the prior art, as long as the mineral distribution image with good clarity of contact relationship between mineral particles can be scanned when scanning the rock thin section. In the embodiment of the present application, this can not be specifically limited.

[0061] The method for analyzing diagenetic strength of rock in oil and gas reservoir provided in the embodiment of the present application extracts the contact points or contact lines between irregular mineral particles in the mineral distribution image by image processing on the obtained mineral distribution image of the rock to be analyzed, realizes extraction of the common boundary of the contact between mineral particles based on a preset common boundary extraction algorithm, determines the contact type of adjacent mineral particles, obtains the parameter representing the diagenetic strength of the rock, and realizes calculation and quantization of the diagenetic strength coefficient through automatic mineral particle recognition, extraction of the contact boundary between mineral particles, and evaluation of the contact relationship between mineral particles. The method can be applied to clastic rock samples represented by medium sandstone, fine sandstone, mudstone, shale and the like, and realizes quantitative evaluation of the diagenetic strength of the rock in the oil and gas reservoir. Compared with the diagenetic strength analysis method based on artificial experience in the prior art, the method realizes in-depth microscale image processing, automatically and quantitatively calculates the diagenetic strength based on the mineral automatic analysis image, obtains quantitative data of the diagenetic strength, has high automation degree, and the result is more accurate.

[0062] In one embodiment, referring to Figure 2 The image processing on the obtained mineral distribution image of the rock to be analyzed in step S101 can obtain a mineral binary image, and the specific implementation process can include the following steps:

[0063] S201: Extracting a preset type of characteristic mineral in the mineral distribution image;

[0064] S202: binarize the mineral distribution image to obtain a binarized image corresponding to each preset type of characteristic mineral;

[0065] S203: for the binarized image of each preset type of characteristic mineral, perform hole filling in the connected region, and backfill the mineral color to obtain a processed mineral image of each preset type of characteristic mineral;

[0066] S204: perform background perspective on the processed mineral image of each preset type of characteristic mineral to obtain a background perspective image of each preset type of characteristic mineral;

[0067] S205: merge the background perspective images of various preset types of characteristic minerals to obtain a mineral synthesis result image;

[0068] S206: binarize the mineral synthesis result image to obtain a mineral binarized image.

[0069] In the embodiment of the present application, the mineral distribution image includes a plurality of types of characteristic minerals, and the preset type of characteristic mineral in the mineral distribution image can be determined based on the mineral rock characteristics of the rock to be analyzed, such as quartz, sodium feldspar, and potassium feldspar, to complete the identification of the mineral type. The preset type of characteristic mineral in the mineral distribution image in step S201 can be extracted, and the specific implementation process can include:

[0070] According to the preset color space transformation algorithm, the mineral distribution image is processed to determine the preset type of characteristic mineral contained in the rock to be analyzed.

[0071] In the embodiment of the present application, the preset color space transformation algorithm can be realized by using the existing technology, for example, the HSV color space transformation algorithm. The mineral distribution image output from the mineral automatic analysis software is processed by the HSV color space transformation algorithm, and the mineral distribution image of various different color-identified mineral types is obtained. The preset type of characteristic mineral contained in the rock to be analyzed can be determined by color division. Of course, the color space transformation algorithm used in the embodiment of the present application can also be the color space transformation algorithm described in the prior art, as long as it can realize the differentiation of different types of characteristic minerals and extract the mineral types in the mineral distribution image. In this regard, the embodiment of the present application can not be specifically limited.

[0072] In the embodiment of the present application, the binarization processing of the mineral distribution image in step S202 can be performed separately for each type of characteristic mineral to obtain a binarized image corresponding to each preset type of characteristic mineral.

[0073] In the embodiment of the present application, the implementation mode of the hole filling in the connected region for the binarization image of each preset type of characteristic mineral in step S203 can adopt the mode in the prior art. The mode of determining the connected region in the image can adopt the connected region extraction method commonly used in the field of image processing, such as Two-pass and Seed-Filling, etc. The hole filling can be realized by using the Flood Fill function in the Open CV software library, and the pixel matrix of the hole is obtained by the function. In the pixel matrix, the hole value can be marked as 0, and the non-hole value can be marked as a specified value, for example, 1, so that the elements with 0 in the pixel matrix can be filled. Of course, in the embodiment of the present application, the mode of determining the connected region can also adopt other connected region marking methods in the prior art, and the implementation mode of the hole filling can also adopt other hole filling modes described in the prior art. In the embodiment of the present application, the determination method of the connected region and the implementation method of the hole filling can not be limited.

[0074] In the embodiment of the present application, the mode of backfilling the mineral color for the binarization image of each preset type of characteristic mineral after the hole filling can also be the mode described in the prior art. For example, the RGB parameter value of each preset type of characteristic mineral is recorded in advance when the mineral particles are extracted, and then the RGB value in the binarization image of each preset type of characteristic mineral is reset to the RGB value of the preset type of characteristic mineral recorded in advance, so that the mineral image of the preset type of characteristic mineral after the backfilling of the mineral color is obtained.

[0075] In the embodiment of the present application, in order to optimize the mineral image of each preset type of characteristic mineral and facilitate subsequent image processing, the binarization image of each preset type of characteristic mineral after the hole filling can also be filtered before the backfilling of the mineral color in step S203. Specifically, first, the maximum circumscribed rectangle of each mineral particle contour in the binarization image of the preset type of characteristic mineral is obtained by using a preset image contour processing method, the long side of the maximum circumscribed rectangle is taken as the diameter of the corresponding mineral particle, and the diameter of the obtained mineral particle is compared with the preset threshold. If the diameter is less than the preset threshold, all pixels of the mineral particle are set to 0 or not displayed, that is, the filtering of the mineral particle is realized. The preset image contour processing method can be realized by using the cv2.boundingRect() function in the Open CV software library, and the maximum circumscribed rectangle of the mineral particle contour is calculated by the cv2.boundingRect() function.

[0076] In the embodiment of the present application, due to the irregularity of the mineral particles in the rock to be analyzed and the complexity of the mineral types, the mineral distribution images obtained based on the above-mentioned preset mineral petrology detection method have more or less defects, therefore, the mineral distribution images are subjected to the hole filling in the connected region, the small particle filtering and the final backfilling of the mineral color in the above-mentioned step S203, so as to realize the optimization processing of the mineral distribution images.

[0077] In the embodiment of the present application, the background perspective of the mineral map of each preset type of characteristic mineral after processing in the above-mentioned step S204 can be realized in the manner of the prior art. Specifically, the RGB value corresponding to the background color in the mineral map of each preset type of characteristic mineral obtained in the step S203 can be changed to the RGB value of the transparent color, so as to realize the background perspective and obtain the background perspective view of each preset type of characteristic mineral.

[0078] In the embodiment of the present application, the background perspective views of various preset types of characteristic minerals are combined and superimposed to restore on the same map in the above-mentioned step S205, so as to obtain the mineral synthesis result map.

[0079] In one embodiment, the preset common boundary extraction algorithm in the above-mentioned step S102 can be the eight-neighbor common boundary extraction algorithm; in the above-mentioned step S102, based on the preset common boundary extraction algorithm, the common boundary of each adjacent mineral particle in the mineral binary image is determined, and the particle boundary extraction result map is obtained, and the specific implementation process can include the following steps:

[0080] determining whether any pixel in the mineral binary image is adjacent to the eight surrounding pixels;

[0081] if yes, determining whether the eight pixels contain the label color pixels of two or more than two particles;

[0082] if yes, determining that the pixel is the common pixel between the two adjacent mineral particles;

[0083] counting the set of the common pixels between each two adjacent mineral particles, obtaining the common boundary of the mineral particles, and obtaining the particle boundary extraction result map.

[0084] In one specific embodiment, referring to Figure 3As shown, the process of performing the above-mentioned eight-neighbor common boundary extraction algorithm can be as follows: For any pixel (X, Y) in the mineral binarized image, assuming that the pixel is adjacent to its eight surrounding pixels, that is, the pixel is in contact with the pixels in its eight neighboring regions, then for the pixel (X, Y), determine whether its eight neighboring pixels (X-1, Y-1), (X-1, Y), (X-1, Y+1), (X, Y+1), (X+1, Y+1), (X+1, Y), (X+1, Y-1), and (X, Y-1) contain two or more label color pixels of different particles. If so, then the pixel (X, Y) is the common pixel in contact between two mineral particles. Perform the above-mentioned eight-neighbor pixel discrimination step on any pixel in the mineral binarized image, and the set of all common pixels obtained is the common boundary in contact between mineral particles. Specifically, determining whether the target pixel (X, Y) contains two or more label color pixels among its eight neighboring pixels is achieved by detecting the RGB values ​​of the target pixel (X, Y) at its eight neighboring pixels (X-1, Y-1), (X-1, Y), (X-1, Y+1), (X, Y+1), (X+1, Y+1), (X+1, Y), (X+1, Y-1), and (X, Y-1). If the RGB values ​​of these eight pixels include two or more RGB values, then the target pixel (X, Y) is determined to be on the common boundary between the two mineral particles.

[0085] In this embodiment of the invention, the above-described eight-neighbor common boundary extraction algorithm is only one specific common boundary extraction algorithm. Those skilled in the art can also extract the common boundaries of adjacent mineral particles in the mineral binarization map based on neighborhood algorithms described in the prior art, such as common boundary extraction algorithms based on four-neighbor or sixteen-neighbor areas. The specific implementation process can be referred to the detailed description in the above-described eight-neighbor common boundary extraction algorithm and related prior art, and will not be repeated here.

[0086] In one embodiment, the specific implementation process of determining the maximum bounding rectangle of each mineral particle in the particle boundary extraction result image in step S103 above, calculating the sum of the long sides of the maximum bounding rectangles of all mineral particles, and obtaining the equivalent total particle diameter of all mineral particles may include:

[0087] First, the maximum bounding rectangle of each mineral particle contour in the particle boundary extraction result image is obtained by using a preset image contour processing method, and the long side of the maximum bounding rectangle is taken as the diameter of the corresponding mineral particle.

[0088] Next, the sum of the diameters of all mineral particles is calculated to obtain the equivalent total diameter of the mineral particle.

[0089] The aforementioned preset image contour processing method can also be implemented using the cv2.boundingRect() function from the OpenCV software library used in step S203. The cv2.boundingRect() function calculates the maximum bounding rectangle of each mineral particle contour in the particle boundary extraction result image.

[0090] In one embodiment, the preset image processing algorithm described in step S104 may include a watershed algorithm and morphological operation functions; the image processing of the particle boundary extraction result map based on the preset image processing algorithm described in step S104 above to obtain a mineral morphological gradient map and a mineral watershed line map, specifically implemented through the following steps:

[0091] Based on the morphological operation function, the particle boundary extraction result image is processed to obtain the mineral morphological gradient map;

[0092] The mineral watershed line map is obtained by performing image processing on the particle boundary extraction result map based on the watershed algorithm.

[0093] The morphological operation functions described in this embodiment of the invention can be implemented using the morphological operation function cv2.morphologyEx() in the OpenCV software library, and the watershed algorithm can be implemented using the watershed algorithm function cv2.watershed() in the OpenCV software library. For the specific implementation process of obtaining mineral watershed line maps and mineral morphological gradient maps using the watershed algorithm and morphological operation functions, please refer to the detailed descriptions in the prior art; in this embodiment of the invention, no specific limitations are imposed.

[0094] In this embodiment of the invention, a logical AND operation is performed on the mineral morphological gradient map and the mineral watershed line map in step S105 to obtain a logical operation result map. From this logical operation result map, it can be seen that the common boundary of the contact between mineral particles is significantly different from other non-common boundary parts in the image. This difference allows for the rapid extraction and preservation of the common boundary of particle contact. To obtain a more optimized common boundary between mineral particles, the inventors of this invention further perform a color space transformation on the logical operation result map in step S106 to obtain a mineral particle contact map, thereby obtaining an image that retains only the contact between mineral particles, facilitating the extraction of contact points or contact lines between adjacent mineral particles in the rock to be analyzed.

[0095] In one embodiment, step S107 above, which involves determining whether the contact type between adjacent mineral particles is point contact or line contact based on the mineral particle contact diagram, and calculating the total length of point contact and the total length of line contact, may specifically include:

[0096] In the mineral particle contact diagram, the contact type of the adjacent mineral particles is determined to be either point contact or line contact based on the sum of the radii of the adjacent mineral particles and the center distance between the adjacent mineral particles.

[0097] The lengths of the common boundary between adjacent mineral particles with point contact and line contact types are counted separately to obtain the total length of point contact and the total length of line contact.

[0098] In one specific embodiment, in the mineral particle contact diagram described above, the contact type of the adjacent mineral particles is determined to be either point contact or line contact based on the sum of the radii of the adjacent mineral particles and the center distance between the adjacent mineral particles. The specific implementation process may include:

[0099] The contact distance between the adjacent mineral particles is obtained by calculating the difference between the sum of the radii of the adjacent mineral particles and the center distance between the adjacent mineral particles.

[0100] Determine whether the contact distance between adjacent mineral particles exceeds a preset proportional threshold; wherein the preset proportional threshold is obtained by multiplying the sum of the radii of the adjacent mineral particles by a preset proportional coefficient;

[0101] If so, then the contact type of the adjacent mineral particles is determined to be line contact;

[0102] If not, then the contact type of the adjacent mineral particles is determined to be point contact.

[0103] In one specific embodiment, refer to Figure 4 As shown, for any two adjacent mineral particles, assuming their diameters are R and r, where both R and r > 0, and the center distance between them is C, and the contact distance is d, then C = (R + r) - d. A pre-set proportional threshold E is obtained by multiplying the sum of the radii of the adjacent mineral particles by a preset proportional coefficient. If 0 ≤ d ≤ E, the contact between the two mineral particles is defined as point contact; if E ≤ d ≤ (R + r) / 2, the contact between the two mineral particles is defined as line contact. Assuming the preset proportional coefficient is d ≤ 1 / 10, then E = (R + r) / 10. Therefore, if 0 ≤ d ≤ (R + r) / 10, the contact between the two mineral particles is point contact; and if (R + r) / 10 ≤ d ≤ (R + r) / 2, the contact between the two mineral particles is line contact.

[0104] After determining whether the contact type between adjacent mineral particles is point contact or line contact, based on the common boundary of each adjacent mineral particle determined in step S102 above, the common pixel length of adjacent mineral particles with point contact and the pixel length of the common line segment of adjacent mineral particles with line contact are calculated respectively, thus obtaining the total length of point contact and the total length of line contact. By extracting the contact between mineral particles, including point contact and line contact, the total length of point contact and the total length of line contact between mineral particles of the same type and between mineral particles of different types can be determined.

[0105] In one embodiment, the specific implementation process of obtaining the diagenetic intensity coefficient based on the equivalent total particle diameter, total point contact length, and total line contact length of all mineral particles in step S108 above can be as follows:

[0106] Substituting the three parameters—equivalent total particle diameter, total point contact length, and total line contact length—of all obtained mineral particles into Formula 1 below, the diagenetic intensity coefficient can be obtained:

[0107]

[0108] In one specific embodiment, the diagenetic intensity of the rock to be analyzed can be determined based on the above-mentioned diagenetic intensity coefficient by comparing the diagenetic intensity coefficient with multiple preset thresholds that characterize the magnitude of diagenetic intensity, determining the threshold range to which the diagenetic intensity coefficient belongs, and thus obtaining the diagenetic intensity.

[0109] Specifically, an idealized mineral grain distribution image can be used as a reference template. Combined with the actual grain distribution, the threshold range of the diagenetic intensity coefficient corresponding to the magnitude of diagenetic activity can be pre-determined experimentally. For example, if the diagenetic intensity coefficient is pre-determined to be between 0 and 0.5, the diagenetic activity intensity is determined to be weak; between 0.5 and 0.8, it is determined to be medium; and greater than 0.8, it is determined to be strong. After obtaining the corresponding diagenetic intensity coefficient of the rock to be analyzed, this coefficient is compared with the endpoints of the three threshold ranges mentioned above to determine the threshold range to which the diagenetic intensity coefficient belongs, thus determining whether the diagenetic activity intensity of the rock to be analyzed is weak, medium, or strong.

[0110] The following specific embodiment will be used to describe in detail the implementation process of the method for analyzing the diagenetic intensity of oil and gas reservoir rocks provided by the present invention, so as to verify the beneficial effects of the method for analyzing the diagenetic intensity of oil and gas reservoir rocks provided by the present invention:

[0111] Assuming the rock to be analyzed is a tight sandstone sample from an oil and gas reservoir, the mineral distribution image of the tight sandstone sample obtained using QEMSCAN software is as follows: Figure 5 As shown, refer to Figure 6 As shown, the detailed process for calculating the diagenetic intensity coefficient in this invention is as follows:

[0112] Based on the detailed description of step 101 above, the color space transformation of the mineral distribution image is first performed to extract the three characteristic minerals of potassium feldspar, quartz and sodium feldspar in the mineral distribution image.

[0113] Next, the mineral distribution image is binarized to obtain binarized images corresponding to the three characteristic minerals, as shown in the reference image. Figures 7A-7C As shown;

[0114] Then, the binarized images of the three characteristic minerals are subjected to hole filling within connected regions to obtain a hole-filled binarized image, which is then referred to... Figures 8A-8C As shown;

[0115] Then, the binarized images of the three characteristic minerals after filling the pores are filtered by microparticles to obtain the filtered binarized images, which are then referenced. Figures 9A-9C As shown;

[0116] Then, the filtered binarized images of the three characteristic minerals are processed and backfilled with mineral colors to obtain the processed mineral images of the three characteristic minerals, as shown in the reference. Figures 10A-10C As shown;

[0117] Next, background perspective is applied to the mineral maps of the three characteristic minerals after processing to obtain a background perspective view, which is then used as a reference. Figures 11A-11C As shown;

[0118] Then, the background perspective views of the three characteristic minerals are merged to obtain the mineral composite image, which can be referenced. Figure 18 As shown;

[0119] The resulting mineral synthesis image is then binarized to obtain the binarized mineral image, which is then referred to... Figure 13 As shown.

[0120] Next, based on the above step S102, the common boundary of each adjacent mineral particle in the mineral binarization map is determined, and the particle boundary extraction result map is obtained.

[0121] Next, based on the above step S103, the equivalent total particle diameter of all mineral particles is obtained;

[0122] Next, based on the above step S104, image processing is performed on the particle boundary extraction result image to obtain a mineral morphological gradient map and a mineral watershed line map, referring to... Figure 14 and Figure 15 As shown;

[0123] Next, based on the above step S105, a logical AND operation is performed on the mineral morphology gradient map and the mineral watershed line map to obtain the logical operation result map.

[0124] Next, based on the above step S106, the logic operation result diagram is transformed in color space to obtain the mineral particle contact diagram, referring to... Figure 16 As shown;

[0125] Next, based on the above step S107, the total length of point contact and the total length of line contact are calculated;

[0126] Finally, based on the above step S108, the equivalent total particle diameter, total point contact length, and total line contact length of all mineral particles are substituted into Formula 1 above to obtain the diagenetic intensity coefficient.

[0127] As can be seen from the above embodiments, in these embodiments of the present invention, by processing the acquired mineral distribution image of the rock to be analyzed, the contact points or contact lines between irregular mineral particles in the mineral distribution image are extracted. Based on a preset common boundary extraction algorithm, the common boundary of the contact between mineral particles is extracted, the contact type of adjacent mineral particles is determined, and parameters characterizing the intensity of rock diagenesis are obtained. Through automatic mineral particle identification, extraction of contact boundaries between mineral particles, and evaluation of contact relationships between mineral particles, the diagenesis intensity coefficient is calculated and quantified, enabling a quantitative evaluation of the diagenesis intensity of oil and gas reservoir rocks. Compared with the existing methods for analyzing diagenesis intensity based on human experience, this method, by using image processing to delve into the microscopic scale and performing automatic quantitative calculations based on automatically analyzed mineral images, obtains more accurate quantitative data on diagenesis intensity.

[0128] Based on the same inventive concept, this invention also provides an apparatus for analyzing the diagenetic intensity of oil and gas reservoir rocks, as described in Embodiment 2 below. Since the principle by which this apparatus solves the problem is similar to the method for analyzing the diagenetic intensity of oil and gas reservoir rocks described above, the specific implementation methods of these apparatuses can be found in the detailed description of the relevant methods described above; repeated details will not be repeated.

[0129] Example 2

[0130] This invention also provides an apparatus for analyzing the diagenetic intensity of oil and gas reservoir rocks, see [link to relevant documentation]. Figure 17 As shown, the device includes:

[0131] The first image processing module 101 is used to process the acquired mineral distribution image of the rock to be analyzed to obtain a mineral binarized image; based on a preset common boundary extraction algorithm, it determines the common boundary of each adjacent mineral particle in the mineral binarized image to obtain a particle boundary extraction result image.

[0132] The first calculation module 102 is used to determine the maximum bounding rectangle of each mineral particle in the particle boundary extraction result image, calculate the sum of the long sides of the maximum bounding rectangle of all mineral particles, and obtain the equivalent total particle diameter of all mineral particles.

[0133] The second image processing module 103 is used to process the particle boundary extraction result image based on a preset image processing algorithm to obtain a mineral morphology gradient map and a mineral watershed line map; to perform a logical AND operation on the mineral morphology gradient map and the mineral watershed line map to obtain a logical operation result map; and to perform a color space transformation on the logical operation result map to obtain a mineral particle contact map.

[0134] The second calculation module 104 is used to determine whether the contact type between adjacent mineral particles is point contact or line contact based on the mineral particle contact diagram, and to calculate the total length of point contact and the total length of line contact.

[0135] The diagenesis determination module 105 is used to obtain the diagenesis intensity coefficient based on the equivalent total particle diameter, total point contact length, and total line contact length of all the mineral particles.

[0136] In one embodiment, the diagenetic determination module 104 is further configured to, after obtaining the diagenetic intensity coefficient, compare the diagenetic intensity coefficient with a plurality of preset thresholds characterizing the magnitude of diagenetic intensity, determine the threshold range to which the diagenetic intensity coefficient belongs, and obtain the diagenetic intensity.

[0137] In one embodiment, the diagenetic determination module 104 is further configured to determine, in the mineral grain contact diagram, whether the contact type of the adjacent mineral grains is point contact or line contact based on the sum of the radii of the adjacent mineral grains and the center distance of the adjacent mineral grains.

[0138] The lengths of the common boundary between adjacent mineral particles with point contact and line contact types are counted separately to obtain the total length of point contact and the total length of line contact.

[0139] In one embodiment, the second calculation module 104 is specifically used to calculate the difference between the sum of the radii of the adjacent mineral particles and the center distance of the adjacent mineral particles, so as to obtain the contact distance of the adjacent mineral particles.

[0140] Determine whether the contact distance between adjacent mineral particles exceeds a preset proportional threshold; wherein the preset proportional threshold is obtained by multiplying the sum of the radii of the adjacent mineral particles by a preset proportional coefficient;

[0141] If so, then the contact type of the adjacent mineral particles is determined to be line contact;

[0142] If not, then the contact type of the adjacent mineral particles is determined to be point contact.

[0143] In one embodiment, the first image processing module 101 is specifically used to determine whether any pixel in the mineral binarization image is adjacent to its eight surrounding pixels.

[0144] If so, determine whether the eight pixels contain two or more label color pixels of different particles;

[0145] If so, then the pixel is determined to be a common pixel in contact between two adjacent mineral particles;

[0146] The set of common pixels in contact between every two adjacent mineral particles is counted to obtain the common boundary of each adjacent mineral particle, and the particle boundary extraction result map is obtained.

[0147] In one embodiment, the second image processing module 103 is specifically used to perform image processing on the particle boundary extraction result map based on the morphological operation function to obtain the mineral morphological gradient map.

[0148] The mineral watershed line map is obtained by performing image processing on the particle boundary extraction result map based on the watershed algorithm.

[0149] In one embodiment, the first image processing module 101 is specifically used to extract a preset type of feature mineral from the mineral distribution image;

[0150] The mineral distribution image is binarized to obtain binarized images corresponding to each preset type of feature mineral;

[0151] For the binarized image of each preset type of feature mineral, the holes in the connected regions are filled and the mineral color is backfilled to obtain the processed mineral image of each preset type of feature mineral.

[0152] Background perspective is applied to the mineral map of each preset type of feature mineral after processing to obtain a background perspective view of each preset type of feature mineral;

[0153] The background perspective views of various preset types of characteristic minerals are merged to obtain a mineral composite image.

[0154] The mineral synthesis result image is binarized to obtain the mineral binarized image.

[0155] In one embodiment, the first image processing module 101 is specifically used to perform image processing on the mineral distribution image according to a preset color space transformation algorithm to determine the preset type of characteristic minerals contained in the rock to be analyzed.

[0156] In one embodiment, the first image processing module 101 is further configured to filter mineral particles with a diameter smaller than a preset threshold in the binarized image of the preset type feature mineral after hole filling, before backfilling the mineral color.

[0157] In one embodiment, the first image processing module 101 is further configured to detect the rock to be analyzed based on a preset mineralogy detection method to obtain the mineral distribution image.

[0158] Based on the same inventive concept, this invention also provides an embodiment of a computer device for implementing all or part of the above-mentioned method for analyzing the intensity of diagenesis in oil and gas reservoir rocks. This computer device specifically includes the following:

[0159] The computer device comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between related devices; the computer device can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the computer device can be implemented with reference to the embodiments of the method for implementing the above-mentioned diagenetic intensity analysis of oil and gas reservoir rocks and the embodiments of the apparatus for implementing the above-mentioned diagenetic intensity analysis of oil and gas reservoir rocks, the contents of which are incorporated herein, and repeated parts will not be described again.

[0160] Figure 18 This is a schematic diagram of the system composition structure of a computer device provided in an embodiment of the present invention. Figure 18 As shown, the computer device 180 may include a processor 1801 and a memory 1802; the memory 1802 is coupled to the processor 1801. It is worth noting that... Figure 18 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0161] In one embodiment, the function of the method for analyzing the diagenetic intensity of oil and gas reservoir rocks can be integrated into the processor 1801. The processor 1801 can be configured to perform the following controls:

[0162] Image processing is performed on the mineral distribution image of the rock to be analyzed to obtain a mineral binarized image;

[0163] Based on the preset common boundary extraction algorithm, the common boundary of each adjacent mineral particle in the mineral binarization map is determined, and the particle boundary extraction result map is obtained.

[0164] Determine the maximum bounding rectangle of each mineral particle in the particle boundary extraction result image, calculate the sum of the long sides of the maximum bounding rectangles of all mineral particles, and obtain the equivalent total particle diameter of all mineral particles.

[0165] Based on a preset image processing algorithm, the particle boundary extraction result map is processed to obtain a mineral morphological gradient map and a mineral watershed line map.

[0166] Perform a logical AND operation on the mineral morphological gradient map and the mineral watershed line map to obtain the logical operation result map;

[0167] The color space transformation of the logical operation result diagram is performed to obtain the mineral particle contact diagram;

[0168] Based on the mineral particle contact diagram, determine whether the contact type between adjacent mineral particles is point contact or line contact, and calculate the total length of point contact and the total length of line contact.

[0169] The diagenetic intensity coefficient is obtained based on the equivalent total particle diameter, total point contact length, and total line contact length of all the mineral particles.

[0170] In another embodiment, the device can be configured separately from the processor 1801. For example, the device for analyzing the intensity of diagenesis in oil and gas reservoir rocks can be configured as a chip connected to the processor 1801, and the function of the method for analyzing the intensity of diagenesis in oil and gas reservoir rocks can be realized through the control of the processor.

[0171] like Figure 18 As shown, the computer device 180 may also include: a communication module 1803, an input unit 1804, an audio processing unit 1805, a display 1806, and a power supply 1807. It is worth noting that the computer device 180 does not necessarily need to include... Figure 18 All components shown; in addition, computer device 180 may also include Figure 18 For components not shown, please refer to existing technologies.

[0172] like Figure 18 As shown, processor 1801, sometimes also referred to as controller or operation control, may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of computer device 180.

[0173] The memory 1802 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The processor 1801 may execute the program stored in the memory 1802 to perform information storage or processing, etc.

[0174] Input unit 1804 provides input to processor 1801. This input unit 1804 may be, for example, a keypad or touch input device. Power supply 1807 provides power to computer device 180. Display 1806 displays images and text. This display may be, for example, an LCD display, but is not limited thereto.

[0175] The memory 1802 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 1802 can also be some other type of device. The memory 1802 includes a buffer memory 18021 (sometimes referred to as a buffer). The memory 1802 may include an application / function storage unit 18022 for storing application programs and function programs or processes for executing operations of the computer device 180 via the processor 1801.

[0176] The memory 1802 may also include a data storage unit 18023 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the computer device. The driver storage unit 18024 of the memory 1802 may include various drivers for the computer device for communication functions and / or for performing other functions of the computer device (such as messaging applications, address book applications, etc.).

[0177] The communication module 1803 is a transmitter / receiver that transmits and receives signals via the antenna 1808. The communication module (transmitter / receiver) 1803 is coupled to the processor 1801 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0178] Based on different communication technologies, multiple communication modules 1803 can be configured in the same computer device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 1803 is also coupled to a speaker 1809 and a microphone 1810 via an audio processing unit 1805 to provide audio output via the speaker 1809 and receive audio input from the microphone 1810, thereby realizing typical telecommunications functions. The audio processing unit 1805 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processing unit 1805 is also coupled to a processor 1801, enabling on-device recording via the microphone 1810 and on-device playback of stored sound via the speaker 1809.

[0179] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium for implementing all steps of the method for analyzing the diagenetic intensity of oil and gas reservoir rocks in the above embodiments. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements all steps of the method for analyzing the diagenetic intensity of oil and gas reservoir rocks in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0180] Image processing is performed on the mineral distribution image of the rock to be analyzed to obtain a mineral binarized image;

[0181] Based on the preset common boundary extraction algorithm, the common boundary of each adjacent mineral particle in the mineral binarization map is determined, and the particle boundary extraction result map is obtained.

[0182] Determine the maximum bounding rectangle of each mineral particle in the particle boundary extraction result image, calculate the sum of the long sides of the maximum bounding rectangles of all mineral particles, and obtain the equivalent total particle diameter of all mineral particles.

[0183] Based on a preset image processing algorithm, the particle boundary extraction result map is processed to obtain a mineral morphological gradient map and a mineral watershed line map.

[0184] Perform a logical AND operation on the mineral morphological gradient map and the mineral watershed line map to obtain the logical operation result map;

[0185] The color space transformation of the logical operation result diagram is performed to obtain the mineral particle contact diagram;

[0186] Based on the mineral particle contact diagram, determine whether the contact type between adjacent mineral particles is point contact or line contact, and calculate the total length of point contact and the total length of line contact.

[0187] The diagenetic intensity coefficient is obtained based on the equivalent total particle diameter, total point contact length, and total line contact length of all the mineral particles.

[0188] While this invention provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0189] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0190] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process.Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0193] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0194] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying 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. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0195] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0196] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention is not limited to any single aspect, nor to any single embodiment, nor to any combination and / or substitution of these aspects and / or embodiments. Each aspect and / or embodiment of the present invention can be used alone, or in combination with one or more other aspects and / or other embodiments.

[0197] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing the intensity of diagenesis in oil and gas reservoir rocks, characterized in that, include: Image processing is performed on the mineral distribution image of the rock to be analyzed to obtain a mineral binarized image; Based on the preset common boundary extraction algorithm, the common boundary of each adjacent mineral particle in the mineral binarization map is determined, and the particle boundary extraction result map is obtained. Determine the maximum bounding rectangle of each mineral particle in the particle boundary extraction result image, calculate the sum of the long sides of the maximum bounding rectangles of all mineral particles, and obtain the equivalent total particle diameter of all mineral particles. Based on a preset image processing algorithm, the particle boundary extraction result map is processed to obtain a mineral morphological gradient map and a mineral watershed line map. Perform a logical AND operation on the mineral morphological gradient map and the mineral watershed line map to obtain the logical operation result map; The color space transformation of the logical operation result diagram is performed to obtain the mineral particle contact diagram; Based on the mineral particle contact diagram, determine whether the contact type between adjacent mineral particles is point contact or line contact, and calculate the total length of point contact and the total length of line contact. The diagenetic intensity coefficient is obtained based on the equivalent total particle diameter, total point contact length, and total line contact length of all the mineral particles. The step of determining whether the contact type between adjacent mineral particles is point contact or line contact based on the mineral particle contact diagram, and calculating the total length of point contact and the total length of line contact, includes: In the mineral particle contact diagram, the difference between the sum of the radii of the adjacent mineral particles and the center distance of the adjacent mineral particles is calculated to obtain the contact distance of the adjacent mineral particles; Determine whether the contact distance between adjacent mineral particles exceeds a preset proportional threshold; wherein the preset proportional threshold is obtained by multiplying the sum of the radii of the adjacent mineral particles by a preset proportional coefficient; If so, then the contact type of the adjacent mineral particles is determined to be line contact; If not, then the contact type of the adjacent mineral particles is determined to be point contact; The lengths of the common boundary between adjacent mineral particles with point contact and line contact types are counted separately to obtain the total length of point contact and the total length of line contact.

2. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in claim 1, characterized in that, After obtaining the diagenetic intensity coefficient, the process also includes: The diagenetic intensity coefficient is compared with multiple preset thresholds that characterize the magnitude of diagenetic intensity to determine the threshold range to which the diagenetic intensity coefficient belongs, thereby obtaining the diagenetic intensity.

3. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in claim 1, characterized in that, The preset common boundary extraction algorithm is an eight-neighborhood common boundary extraction algorithm; The preset common boundary extraction algorithm determines the common boundary of each adjacent mineral particle in the mineral binarization map, and obtains a particle boundary extraction result map, including: Determine whether any pixel in the mineral binarized image is adjacent to its eight surrounding pixels; If so, determine whether the eight pixels contain two or more label color pixels of different particles; If so, then the pixel is determined to be a common pixel in contact between two adjacent mineral particles; The set of common pixels in contact between every two adjacent mineral particles is counted to obtain the common boundary of each adjacent mineral particle, and the particle boundary extraction result map is obtained.

4. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in claim 1, characterized in that, The preset image processing algorithm includes the watershed algorithm and morphological operation functions; The image processing algorithm based on the preset image processing algorithm is used to process the particle boundary extraction result map to obtain a mineral morphological gradient map and a mineral watershed line map, including: Based on the morphological operation function, the particle boundary extraction result image is processed to obtain the mineral morphological gradient map; The mineral watershed line map is obtained by performing image processing on the particle boundary extraction result map based on the watershed algorithm.

5. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in any one of claims 1-4, characterized in that, The process of image processing the acquired mineral distribution image of the rock to be analyzed to obtain a mineral binarized image specifically includes: Extract the predefined type of feature minerals from the mineral distribution image; The mineral distribution image is binarized to obtain binarized images corresponding to each preset type of feature mineral; For the binarized image of each preset type of feature mineral, the holes in the connected regions are filled and the mineral color is backfilled to obtain the processed mineral image of each preset type of feature mineral. Background perspective is applied to the mineral map of each preset type of feature mineral after processing to obtain a background perspective view of each preset type of feature mineral; The background perspective views of various preset types of characteristic minerals are merged to obtain a mineral composite image. The mineral synthesis result image is binarized to obtain the mineral binarized image.

6. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in claim 5, characterized in that, The extraction of preset type feature minerals from the mineral distribution image specifically includes: The mineral distribution image is processed according to a preset color space transformation algorithm to determine the preset type of characteristic minerals contained in the rock to be analyzed.

7. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in claim 5, characterized in that, Before backfilling with mineral colorant, the following is also included: Mineral particles with diameters smaller than a preset threshold are filtered out in the binarized image of the preset type of feature minerals after the holes have been filled.

8. The method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in claim 5, characterized in that, Also includes: The mineral distribution image was obtained using the following method: The rock to be analyzed is tested using a pre-defined mineralogy and petrology detection method to obtain a mineral distribution image.

9. An apparatus for analyzing the intensity of diagenesis in oil and gas reservoir rocks, characterized in that, include: The first image processing module is used to process the acquired mineral distribution image of the rock to be analyzed to obtain a mineral binarized image; based on a preset common boundary extraction algorithm, it determines the common boundary of each adjacent mineral particle in the mineral binarized image to obtain a particle boundary extraction result image. The first calculation module is used to determine the maximum bounding rectangle of each mineral particle in the particle boundary extraction result image, calculate the sum of the long sides of the maximum bounding rectangle of all mineral particles, and obtain the equivalent total particle diameter of all mineral particles. The second image processing module is used to process the particle boundary extraction result image based on a preset image processing algorithm to obtain a mineral morphology gradient map and a mineral watershed line map; to perform a logical AND operation on the mineral morphology gradient map and the mineral watershed line map to obtain a logical operation result map; and to perform a color space transformation on the logical operation result map to obtain a mineral particle contact map. The second calculation module is used to determine whether the contact type between adjacent mineral particles is point contact or line contact based on the mineral particle contact diagram, and to calculate the total length of point contact and the total length of line contact; the step of determining whether the contact type between adjacent mineral particles is point contact or line contact based on the mineral particle contact diagram, and calculating the total length of point contact and the total length of line contact, includes: In the mineral particle contact diagram, the difference between the sum of the radii of the adjacent mineral particles and the center distance of the adjacent mineral particles is calculated to obtain the contact distance of the adjacent mineral particles; Determine whether the contact distance between adjacent mineral particles exceeds a preset proportional threshold; wherein the preset proportional threshold is obtained by multiplying the sum of the radii of the adjacent mineral particles by a preset proportional coefficient; If so, then the contact type of the adjacent mineral particles is determined to be line contact; If not, then the contact type of the adjacent mineral particles is determined to be point contact; The lengths of the common boundary between adjacent mineral particles with point contact and line contact types are counted separately to obtain the total length of point contact and the total length of line contact. The diagenesis determination module is used to obtain the diagenesis intensity coefficient based on the equivalent total particle diameter, total point contact length, and total line contact length of all the mineral particles.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs a method for analyzing the diagenetic intensity of oil and gas reservoir rocks as described in any one of claims 1-8.

Images