Quantitative lithofacies identification method for carbonate rock slice

Abstract

The invention discloses a carbonate rock slice quantitative lithofacies identification method, and relates to the technical field of petrology analysis and image processing.The method is based on a microscope and an image processing technology, integrates rock specimen observation, slice preparation and rock structure analysis, performs layered labeling on slice scanning images through vector mapping software, and obtains a slice sample; the pixel proportion, namely the area proportion, of the rock structure is counted through ImageJ software, quantitative analysis of the rock structure is completed, and high-precision quantitative analysis of mineral components, structure types and pore parameters can be achieved. Through automatic quantitative analysis, the problems that traditional identification depends on subjective experience and is low in efficiency are solved, the precision and the standardization level are remarkably improved, and the method is suitable for oil and gas geological survey, reservoir evaluation and carbonate rock formation cause research.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of petrologic analysis and image processing, and particularly to a carbonate rock thin section quantitative lithofacies identification method. BACKGROUND

[0002] In the study of sedimentology and petrology, the microstructure analysis of carbonate rock thin sections is a core means to reveal the rock genesis (such as sedimentary environment, diagenetic evolution, mineral metasomatism process). For a long time, this field mainly relies on manual observation under the microscope and semi-quantitative description, and its technical limitations are significant.

[0003] The traditional method needs to manually identify mineral types (such as calcite, dolomite, gypsum), structural characteristics (grains, micritic matrix, dissolution pores) and diagenesis (dissolution, cementation, dolomitization) through a polarizing microscope, and infer the sedimentary environment and diagenetic stage based on experience. For example, the "more, less, trace" description of grain content lacks quantitative standards, and different researchers may have significant differences in the genetic interpretation of the same thin section. Studies have shown that the error of manually estimated mineral content is generally more than 15%, and the accuracy of the recognition of dissolution pore stages is less than 60%.

[0004] The diagenetic evolution of carbonate rocks often experiences multiple stages of dissolution, cementation and metasomatism superimposition, such as syn-sedimentary to penecontemporaneous dissolution, supergene weathering crust dissolution and burial period hydrothermal dissolution. Manual observation is limited by optical resolution (usually ≤2 μm) and human eye recognition ability, and it is difficult to distinguish the genetic types of micron-scale dissolution pores (such as intragranular dissolution pores, mold pores and intercrystalline pores), and the judgment of the properties of the dissolution fluid (such as organic fluid and inorganic fluid) depends on indirect evidence, resulting in uncertainty in genetic interpretation. In the study of a certain oilfield reservoir, the proportion of pores with a diameter less than 5 μm missed by manual judgment is as high as 40%.

[0005] The judgment of sedimentary environment (such as tidal flat, shoal, reef flat) needs to comprehensively analyze the grain types (such as intraclast, oolite, bioclast), sedimentary structures (such as stromatolite, bird's eye structure) and cement characteristics. The traditional method needs to manually switch hundreds of fields and record data point by point, and it takes 4-8 hours to complete the analysis of a thin section, and the data is discrete, which makes it difficult to meet the rapid analysis needs of large-scale samples.

[0006] The traditional method relies on hand-drawing sketches and filling out paper forms, and the data is difficult to achieve standardized storage and cross-platform sharing. In the traditional shale gas project, the format differences of thin section identification reports by different researchers will increase the time consumption of data integration, and some key parameters (such as diagenetic sequence, mineral metasomatism relationship) may be missing, which restricts the verification and correction of sedimentology theory.

[0007] With the breakthrough and application of automatic image processing technology, with the development of digital petrography and computer vision technology, relying on the automatic analysis method of ImageJ, deep learning model and other tools has gradually become a replacement scheme. SUMMARY

[0008] In order to overcome the defects in the prior art, the present application provides a carbonate rock thin section quantitative petrographic identification method, which realizes quantitative analysis of rock fabric.

[0009] To achieve the above purpose, the application adopts the following technical solutions, including:

[0010] A carbonate rock thin section quantitative petrographic identification method, comprising the following contents:

[0011] Preparation of carbonate rock thin section;

[0012] Microscopic observation of thin section, analysis of rock fabric;

[0013] The thin section is scanned by microscope to obtain a thin section scanning image, the thin section scanning image is imported into a vector drawing software, the thin section scanning image is layered labeled, the boundaries of each fabric are outlined with different colors, and a vector labeled graph of the thin section is obtained.

[0014] The vector labeled graph of the thin section is imported into ImageJ, the pixel number of each fabric is counted, the area ratio of each fabric, i.e. pixel ratio, is calculated, and quantitative analysis of rock fabric is completed.

[0015] Preferably, the carbonate rock thin section is prepared according to the standard of SY / T5913-2021, the thickness of the thin section is 30μm, and the surface roughness is ≤0.1μm.

[0016] Preferably, the rock fabric is analyzed according to the specification of SY / T5368-2016, and the analysis of rock fabric includes the spatial combination relationship of all observable structural components in the rock, the mineral composition and the formation process.

[0017] Preferably, the vector drawing software adopts CorelDraw or AdobeIllustrator.

[0018] Preferably, when outlining the boundaries of each fabric with different colors, pure color blocks are selected.

[0019] Preferably, the pixel number of each fabric is counted by using color threshold segmentation method:

[0020] Select Image>Adjust>ColorThreshold, and drag the slider to separate different color areas;

[0021] Check Threshold color: Red / Green / Blue, adjust the threshold range, real-time preview segmentation effect;

[0022] Click Select to generate a selection area, and execute Analyze>Measure to record the pixel number of the current color area;

[0023] Repeat the operation to sequentially count the pixel numbers of all color areas, that is, sequentially count the pixel numbers of each fabric.

[0024] Preferably, the pixel numbers of each fabric are counted using the magic wand tool in ImageJ:

[0025] Adjust the tolerance of the magic wand tool to accurately select a single color area;

[0026] Click the target color area to generate a selection area, and execute Analyze>Measure to record the pixel number of the current color area;

[0027] Repeat the operation to sequentially count the pixel numbers of all color areas, that is, sequentially count the pixel numbers of each fabric.

[0028] Preferably, if there is anti-aliasing or gradient in the color in ImageJ, preprocessing is required, and Process>Noise>Despeckle is used for denoising, or Process>Binary>Watershed is used to separate the adhesion area.

[0029] The application also provides a computer program product comprising computer programs / instructions, which, when executed by a processor, realize the carbonate rock thin section quantitative facies identification method.

[0030] The application also provides a readable storage medium having a computer program stored thereon, which, when executed, realizes the carbonate rock thin section quantitative facies identification method

[0031] The application has the following advantages:

[0032] (1) The application provides a carbonate rock thin section quantitative facies identification method based on image processing technology, which is based on a microscope and image processing technology, integrates rock sample observation, thin section preparation and rock fabric analysis, performs layered labeling on the thin section scanning image through a vector drawing software, counts the pixel proportion of the rock fabric, that is, the area proportion, through an ImageJ software, completes quantitative analysis of the rock fabric, and can realize high-precision quantitative analysis of mineral composition, fabric type and pore parameters.

[0033] (2) Through the color threshold segmentation function of ImageJ, each rock fabric area (a pure color block that can be distinguished by naked eyes in a vector diagram) is automatically separated, and pixel-level classification is realized by combining RGB channel adjustment.

[0034] (3) The Fourier shape analysis plug-in of ImageJ can automatically calculate the roundness and sphericity of particles, and in combination with the digital characteristics of grain size grading bedding and cross-bedding, the sedimentary environment can be quickly distinguished.

[0035] (4) The present application solves the problems of low efficiency and dependence on subjective experience in traditional identification by means of automatic quantitative analysis, and significantly improves the precision and standardization level, and is suitable for oil and gas geological survey, reservoir evaluation and carbonate genesis research.

[0036] (5) The method of the present application also has the advantage of strong data traceability. Since the entire analysis process is based on layered labeling, parameter statistics and plug-in calculation of digital images, all original images, labeling information and quantitative data can be completely saved, which is convenient for subsequent review, verification or comparison and sharing of achievements between different researchers, avoids the problem of traceability difficulty caused by easy omission of data recording and difficulty in unification of description in traditional manual identification, and further improves the reliability and reuse value of research results. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The flowchart of the carbonate rock grain beach fine depiction and prediction method of the present application.

[0038] Figure 2 The thin section image taken by a microscope.

[0039] Figure 3 The vector labeling diagram of the carbonate rock thin section.

[0040] Figure 4 The three-dimensional diagram of the classification of the carbonate rock grain structure.

[0041] Figure 5 The classification diagram of the carbonate rock mineral composition. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] Embodiment 1

[0044] From Figure 1As shown, this invention provides a method for quantitative lithofacies identification of carbonate rock thin sections, mainly used for quantitative analysis and description of carbonate rock thin sections. The specific process is as follows:

[0045] S1, to collect outcrop photographs and provide macroscopic descriptions of the rocks.

[0046] Take photos of carbonate rock outcrops using a camera with a resolution of ≥300dpi, covering complete characteristic areas (such as bedding, cracks, bioturbation structures), and label the scale bar and orientation.

[0047] Record the color, structure and oil-bearing characteristics of the outcrops in accordance with the SY5518-2021 specification ("Specification for Field Petroleum and Natural Gas Geological Survey"), and mark the key areas corresponding to the thin section analysis (such as fracture-dense zones and biological remnant areas).

[0048] In this embodiment, when collecting outcrops in the Wangyao Zhangxia Formation section (as shown in Table 1), a camera was used to photograph the thick nodular limestone outcrops, covering the bedding and bioclastic enrichment areas.

[0049] Table 1. Stratigraphic profile of the Zhangxia Formation at Wangyao

[0050]

[0051]

[0052] S2, visual observation and thin section preparation of rock samples.

[0053] The rock samples were visually observed, and their color, density (dense / medium / loose), structural features (bedding, ripple marks, etc.), and degree of acid dripping reaction were described.

[0054] Carbonate rock thin sections were prepared according to the SY / T5913-2021 standard (Rock Section Preparation Method), ensuring that the thickness of the thin sections was 30 μm and the surface was smooth and free of scratches.

[0055] In this embodiment, the rock block corresponding to the outcrop “Biodebris Dense Area A-01” was selected for thin section preparation. The thin section was prepared according to the SY / T5913-2021 standard: after cold mounting and fixing, it was coarsely ground to 45μm and finely ground to 30μm±1μm (measured value by micrometer); the surface roughness Ra=0.08μm was detected by laser confocal microscope, which meets the requirement of ≤0.1μm.

[0056] S3, Microscopic observation of thin sections to analyze rock texture.

[0057] The thin section is observed using a microscope, and the rock fabric is analyzed according to the specification SY / T5368-2016 (Rock Thin Section Identification). The analysis of the rock fabric includes the spatial combination relationship, mineral composition and formation process of all observable structural components (grains, micritic matrix, sparry cement, etc.) in the rock.

[0058] The thin section is observed using a microscope, and the rock fabric is analyzed according to the specification SY / T5368-2016 (Rock Thin Section Identification). The analysis of the rock fabric includes the spatial combination relationship, mineral composition and formation process of all observable structural components (grains, micritic matrix, sparry cement, etc.) in the rock. Figure 2 As shown in the image, according to manual observation, the rock fabric characteristics are as follows:

[0059] Coagulate limestone: with coagulate fabric, composed of non-laminated coagulate structure of algal origin, without obvious internal bedding, irregular in shape;

[0060] Micritic limestone: mainly composed of micritic matrix with a particle size of less than 0.01 mm, with a grain content of less than 25%, uniform structure, and a small amount of fine powder dispersed therein;

[0061] Sparry limestone: with a grain content of more than 50%, the intergranular space is filled with sparry cement, the cement has high transparency, and the boundary is clear;

[0062] Microsparry limestone: with a grain content of 25% to 50%, the micritic matrix content is higher than the sparry cement, and the sparry cement is only partially filled in the intergranular space, and the structure is relatively dense;

[0063] Crystal grain dolomite: supported by dolomite crystal grains, the crystal grains are self-formed, semi-self-formed or other-formed, and intercrystalline pores are often observed.

[0064] Among them, Figure 4 is a carbonate rock particle structure classification triangle, Figure 5 is a carbonate rock mineral composition classification chart, which can be used to determine the rock fabric according to Figure 4 and Figure 5 .

[0065] S4, full scanning and image processing of the thin section are performed to obtain a vector annotation diagram of the thin section (reference standard: SY / T6103, Determination of Rock Pore Structure Characteristics by Image Analysis Method).

[0066] The thin section is scanned using a microscope, with a resolution of greater than or equal to 2400 dpi, to generate a high-definition image in JPG format, i.e., a thin section scan image.

[0067] The thin section scan image is imported into a vector drawing software (CorelDraw or Adobe Illustrator) for layered annotation of the thin section scan image, and the following operations are performed:

[0068] A new independent vector layer is created, and the rock fabric boundary is outlined according to the preset color coding. The exported annotation file is in PNG format, as shown in Figure 3 , which is a vector annotation diagram of a carbonate rock thin section.

[0069] In this embodiment, the vector drawing software CorelDraw is used for operation, and the five-layer vector layer is constructed as follows:

[0070] Background layer: import the scanning image of the thin section (resolution 5766x4652, JPG format), transparency 80%;

[0071] Layer 1: outline the boundary of the clotted limestone and fill the color;

[0072] Layer 2: outline the boundary of the micritic limestone and fill the color;

[0073] Layer 3: outline the boundary of the sparry limestone and fill the color;

[0074] Layer 4: outline the boundary of the microsparry limestone and fill the color;

[0075] Layer 5: outline the boundary of the grain dolomite and fill the color;

[0076] In this embodiment, pure color blocks are selected during coloring, without gradient color or complex color, which is convenient for color threshold segmentation.

[0077] In this embodiment, the operation result of the vector drawing software CorelDraw is: exporting the carbonate rock thin section vector annotation map in PNG format, and the vector boundary error is ≤0.05mm.

[0078] S5, ImageJ quantitative analysis is performed on the vector annotation map of the thin section to complete the quantitative analysis of rock fabric (reference standard: SY / T6103, "Determination of rock pore structure characteristics-image analysis method").

[0079] S51, import the carbonate rock thin section vector annotation map in PNG format into ImageJ and convert it into an image in RGB format (if it is an index layer, it needs to be converted, select Image > Type > RGB Color).

[0080] S52, select any of the following ways to count the pixel number of rock fabric:

[0081] Use color threshold segmentation to count the pixel number of each fabric:

[0082] (1) Select Image>Adjust>ColorThreshold, and drag the slider to separate different color areas;

[0083] (2) Check Threshold color: Red / Green / Blue, adjust the threshold range, and preview the segmentation effect in real time;

[0084] (3) Click Select to generate a selection area, and execute Analyze > Measure to record the pixel number of the current color region;

[0085] (4) Repeat the operation to sequentially count the pixel numbers of all color regions, i.e., sequentially count the pixel numbers of each texture.

[0086] Use the magic wand tool to count the pixel numbers of each texture:

[0087] (1) Adjust the tolerance of the magic wand tool to accurately select a single color region;

[0088] (2) Click the target color region to generate a selection area, and execute Analyze > Measure to record the pixel number of the current color region;

[0089] (3) Repeat the operation to sequentially count the pixel numbers of all color regions, i.e., sequentially count the pixel numbers of each texture.

[0090] If the color has anti-aliasing or gradient, preprocessing is required, using Process > Noise > Despeckle to remove noise, or using Process > Binary > Watershed to separate the adhesion region.

[0091] S53, calculate the area percentage of each texture, i.e., the pixel percentage, wherein the mineral composition area percentage = (mineral region pixel number / total pixel number) x 100%; the pore rate = (pore region pixel number / total pixel number) x 100%.

[0092] In this embodiment, the ImageJ processing result is shown in Table 2 below, and there is no pore in the slice of this embodiment:

[0093] Table 2 ImageJ processing result

[0094]

[0095] In Table 2, Area is the pixel number of the mineral region; Area / % is the mineral composition area percentage, i.e., the percentage of the mineral region in the total area, i.e., the percentage of the pixel number of the mineral region in the total pixel number; Mean is the average value of the pixel gray scale in the region (0-255); Min is the minimum gray scale value in the region (0-255); Max is the maximum gray scale value in the region (0-255).

[0096] S54, output the statistical result in CSV format, and generate a visual chart (histogram, triangle chart).

[0097] Example 2

[0098] Besides the above method, the embodiments of the present application can also be a computer program product, which includes computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the decision-making behavior decision-making method according to various embodiments of the present application described in Embodiment 1 above.

[0099] The computer program product can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0100] Embodiment 3

[0101] The embodiments of the present application can also be a computer readable storage medium, which stores computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the decision-making behavior decision-making method according to various embodiments of the present application described in Embodiment 1 above.

[0102] The computer readable storage medium can take any combination of one or more of the following readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0103] The above is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for quantitative lithofacies identification of carbonate rock thin sections, characterized in that, Includes the following: Preparation of thin sections of carbonate rocks; Microscopic observation of thin sections to analyze rock texture; The thin section was scanned in its entirety using a microscope to obtain a scanned image of the thin section. The scanned image of the thin section was then imported into vector drawing software, and the scanned image of the thin section was annotated in layers. Different colors were used to delineate the boundaries of each component to obtain a vector annotated image of the thin section. Import the vector-annotated image of the thin section into ImageJ, count the number of pixels for each texture, calculate the area ratio (i.e., pixel ratio) of each texture, and complete the quantitative analysis of rock texture.

2. The method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, characterized in that, Carbonate rock thin sections were prepared according to the SY / T5913-2021 standard. The thickness of the thin sections was 30 μm and the surface roughness was ≤0.1 μm.

3. The method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, characterized in that, According to the SY / T5368-2016 standard, the analysis of rock texture includes: the spatial combination relationship of all observable structural components in the rock, mineral composition and formation process.

4. The method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, characterized in that, Vector drawing software includes CorelDraw or Adobe Illustrator.

5. The method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, characterized in that, When using different colors to outline the boundaries of each component, choose a solid color block.

6. The method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, characterized in that, The number of pixels in each component is counted using color thresholding. Select Image > Adjust > ColorThreshold, and drag the slider to separate the different color areas; Check the Threshold color: Red / Green / Blue to adjust the threshold range and preview the segmentation effect in real time; Click Select to generate a selection area, then execute Analyze>Measure to record the number of pixels in the current color area; Repeat the operation to count the number of pixels in all color regions, that is, count the number of pixels in each component in turn.

7. The method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, characterized in that, Use the Magic Wand tool in ImageJ to count the number of pixels in each component: Use the magic wand tool to adjust the tolerance to precisely select a single color area; Click on the target color area to generate a selection, and execute Analyze>Measure to record the number of pixels in the current color area; Repeat the operation to count the number of pixels in all color regions, that is, count the number of pixels in each component in turn.

8. A method for quantitative lithofacies identification of carbonate rock thin sections according to claim 1, 6, or 7, characterized in that, If the colors in ImageJ have anti-aliasing or gradients, preprocessing is required. Use Process > Noise > Despeckle to remove noise, or use Process > Binary > Watershed to separate the stuck areas.

9. A computer program product, characterized in that, It includes a computer program / instruction that, when executed by a processor, implements the quantitative petrographic identification method for thin sections of carbonate rocks as described in any one of claims 1 to 8.

10. A readable storage medium, characterized in that, It contains a computer program that, when executed, implements the quantitative petrographic identification method for carbonate rock thin sections as described in any one of claims 1 to 8.

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