Method for determining shale content of tight sandstone

Abstract

The invention discloses a method for determining the shale content of tight sandstone, and relates to the technical field of exploration and research of tight rock oil and gas reservoirs. The method comprises the following steps: identifying a rock slice identification photo by using a computer image identification method, obtaining the area percentage of debris particles with the diameter greater than a set threshold value in the slice identification photo, and then calculating the area percentage of argillaceous particles in the slice identification photo; according to the area percentage of the argillaceous particles and the corresponding times of the polarizing microscope, displaying the argillaceous particles in a rectangular coordinate system in a scatter mode; fitting all scatter points by using a nonlinear least square method, and establishing a shale content volume percentage change relation curve under different objective lens multiples of the polarizing microscope; and solving the curvilinear equation, and calculating the shale content of the sample by using the curvilinear equation. According to the method, the error caused by the fact that the shale content is calculated by an eye estimation method singly through a slice identifier is avoided, and the analysis result is more consistent with the real content of the sample, so that the accuracy of the shale content determination by an image method is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of tight rock oil and gas reservoir exploration and research, and more specifically to a method for determining the shale content of tight sandstone. Background Art

[0002] The Sichuan Basin has multiple large-scale gas-bearing channel sands in the central Sichuan region. The reservoirs are mainly tight sandstones, with a favorable area of ​​10,000 km2 and a resource scale of over one trillion cubic meters. It has great potential for exploration and development and is one of the important areas for increasing reserves and production in the southwestern oil and gas fields.

[0003] Mud refers to clastic material with a particle diameter of less than 0.01 mm, and its content is the percentage of the volume of fine silt, clay and water contained in clay in the rock. Research on the mud content of tight sandstone is conducive to further characterizing the stratigraphic sequence of oil and gas reservoirs, accurately distinguishing the sedimentary environment, and effectively solving the bottleneck problem of mud content correction in well logging interpretation. It is of great significance to sedimentary phase division, reservoir evaluation, reservoir division, stratigraphic correlation and formation water resistivity calculation.

[0004] At present, the mud content of tight sandstone logging interpretation is generally obtained by natural gamma, natural gamma spectrum, natural potential, density and neutron. These methods are only applicable to the case where the sandstone skeleton is pure and the clay mineral composition is stable. Once the rock composition is more complex (clay mineral content>15% of mixed sandstone, rock debris content>10% of lithic sandstone), it is difficult to accurately calculate the mud content. It is urgent to establish an experimental method for determining the mud content to correct the logging interpretation results. At present, geological laboratories generally use particle size analysis to estimate. The specific method is to fully grind the core, screen it through multiple specifications, and use a laser particle size analyzer to carefully measure the proportion of rock particles of various particle sizes in the entire rock particles.

[0005] However, the laser method, screening method, and light transmission method in the existing industry standard "Grain Size Analysis Method of Clastic Rocks: SY / T 5434-2018" are only applicable to clastic rock samples with a large amount of gravel (gravel content>75%) and a medium or lower cementation degree. They are not applicable to dense sandstones with a high degree of cementation and almost no gravel in the central Sichuan Basin. Therefore, the particle size analysis can only be performed through the artificial rock thin section identification method to roughly estimate the mud content. Therefore, it is urgent to establish a faster and more accurate analysis method to solve the problem of determining the mud content of dense sandstone. Summary of the invention

[0006] In order to overcome the defects and shortcomings of the above-mentioned prior art, the present invention provides a method for determining the mud content of dense sandstone. The present invention aims to solve the difficult problem of determining the mud content of dense sandstone. The present invention uses a computer image recognition method to identify the single polarized thin-section identification photo of the rock slice, obtain the area percentage of the debris particles with a diameter greater than the set threshold in the thin-section identification photo, and then calculate the area percentage of the mud particles in all thin-section identification photos; according to the area percentage of the mud particles and its corresponding polarizing microscope magnification data, it is displayed in the rectangular coordinate system in the form of scattered points; the nonlinear least squares method is used to fit all scattered points to establish a curve of the change relationship of the volume percentage of the mud content under different polarizing microscope objective magnifications; the equation of the curve is solved, and the equation is used to calculate the mud content percentage when the polarizing microscope objective magnification is 0, which is used as the mud content of the sample in turn. The present invention avoids the error caused by the single thin-section identification method for calculating the mud content by visual estimation, and the analysis result is more consistent with the actual content of the sample, thereby greatly improving the accuracy of the image method mud content determination.

[0007] In order to solve the above problems existing in the prior art, the present invention is implemented through the following technical solutions.

[0008] The present invention provides a method for determining the shale content of dense sandstone, the method comprising the following steps: S1. Select a dense sandstone sample and make the sample into a cube sample; S2, taking multiple faces of the cube sample and making rock thin sections of multiple faces of the cube sample respectively; S3. Obtain single polarized thin-section identification photos of the rock thin sections on multiple sides in step S2 through a polarizing microscope; obtain multiple single polarized thin-section identification photos of the rock thin section on each side under different polarizing microscope objective magnifications; S4, using a computer image recognition method, obtaining the area percentage of debris particles having a diameter greater than a set threshold in all thin section identification photos in step S3; S5, calculating the area percentage of muddy particles in all thin section identification photos in step S3 according to the area percentage of the debris particles obtained in step S4; S6. Display the area percentage of the mud particles and the corresponding polarizing microscope objective magnification data calculated in step S5 in a rectangular coordinate system in the form of scattered points; S7, using the nonlinear least square method to fit all the scattered points in the rectangular coordinate system of step S6, and establishing a curve of the change of the volume percentage of the mud content under different polarizing microscope objective magnifications; S8. Solve the curve equation according to the volume percentage change relationship curve of the mud content established in step S7, and use the curve equation to calculate the volume percentage of the mud content when the objective lens magnification of the polarizing microscope is 0, and use this as the mud content of the sample.

[0009] Furthermore, in step S1, the size of the cube sample is 5cm*5cm*5cm.

[0010] Furthermore, in step S2, multiple faces of the cube sample are taken, specifically, two faces, the horizontal face and the vertical face of the cube are taken.

[0011] Furthermore, the thickness of the rock thin section should meet the technical requirements of "Rock Thin Section Preparation Method: SY / T5913-2004", and the identification method should meet the technical requirements of "Rock Thin Section Identification: SY / T5368-2016".

[0012] Furthermore, in the step S3, single polarized thin film identification photos of the rock thin section on each surface are obtained under a polarizing microscope with objective lens magnifications of 1x, 2.5x, 5x, 10x, and 20x.

[0013] Furthermore, in step S3, the polarized thin section identification photos of rock thin sections obtained under different polarizing microscope objective magnifications should meet the technical requirements of 6.4 imaging method in "Grain Size Analysis of Clastic Rocks: SY / T5435-2018".

[0014] Further, step S4 is specifically as follows: by using a computer image recognition method, the boundary data of the debris particles with a diameter greater than a set threshold are obtained, the area of ​​the debris particles is calculated according to the boundary data, and the area percentage W of the sum of the areas of all the debris particles with a diameter greater than the set threshold in the rock slice is calculated. s1 .

[0015] Furthermore, in step S4, the threshold is set to 0.0156 mm.

[0016] Furthermore, in step S5, the area percentage of muddy particles is the area percentage of debris particles whose diameter is smaller than the set threshold, and its area percentage is W s2 =1-W s1 .

[0017] Furthermore, in step S6, the mud area percentages and their corresponding polarizing microscope objective magnifications in the rock thin section identification photos of multiple faces are projected into a rectangular coordinate system, and the conversion from area percentage to volume percentage is achieved by curve fitting.

[0018] Furthermore, in step S7, a nonlinear least square method is used to fit the relationship curve, that is, the fitted relationship curve satisfies the variation law of all scattered points, and the fitted curve equation is used as the calculation equation for the volume percentage of the mud content.

[0019] Compared with the prior art, the beneficial technical effects brought by the present invention are as follows: 1. The present invention avoids the error caused by calculating the mud content by visual estimation through thin section identification alone. The analysis result is more consistent with the actual content of the sample, thereby greatly improving the accuracy of mud content determination by the image method.

[0020] 2. The present invention adopts computer image recognition method to obtain accurate data of particle area, which is more accurate and reliable than extracting image particles and obtaining area data by manual visual estimation.

[0021] 3. The present invention introduces statistical fractal theory. According to the self-similarity of the particle distribution image in the rock thin section, the statistical random fractal method is used to realize the conversion of the statistics of the two-dimensional rock surface particle percentage to the three-dimensional volume particle percentage through curve fitting, thus avoiding the inaccuracy problem of representing the three-dimensional volume percentage with the two-dimensional area percentage.

[0022] 4. The present invention is particularly suitable for the particle size analysis of dense sandstones. It overcomes the bottleneck of high cementation degree and fine particles of dense sandstones, which are difficult to analyze with conventional particle size analyzers and thin section identification methods. It can effectively improve the accuracy of the determination of the mud content of dense sandstones and provide technical support for well logging interpretation and reservoir evaluation of dense sandstone oil and gas reservoirs.

[0023] 5. The present invention can calculate the volume percentage of mud in rocks at different magnifications, and the established relationship can be converted into mud content at different formation thicknesses and areas, which is of great significance for the study of regional geological characteristics of oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of the method for determining the shale content of dense sandstone according to the present invention; Figure 2 This is the identification diagram of the dense sandstone thin section of the present invention; Figure 3 This is a computer image recognition result diagram of a dense sandstone thin section identification diagram of the present invention; Figure 4 This is a schematic diagram of the least squares fitting curve. DETAILED DESCRIPTION

[0025] The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example 1 As a preferred embodiment of the present invention, refer to the attached specification Figure 1As shown, this embodiment discloses a method for determining the shale content of dense sandstone, which comprises the following steps: S1. Select a dense sandstone sample and make the sample into a cube sample; S2, taking multiple faces of the cube sample and making rock thin sections of multiple faces of the cube sample respectively; S3. Obtain single polarized thin-section identification photos of the rock thin sections on multiple sides in step S2 through a polarizing microscope; obtain multiple single polarized thin-section identification photos of the rock thin section on each side under different polarizing microscope objective magnifications; S4, using a computer image recognition method, obtaining the area percentage of debris particles having a diameter greater than a set threshold in all thin section identification photos in step S3; S5, calculating the area percentage of muddy particles in all thin section identification photos in step S3 according to the area percentage of the debris particles obtained in step S4; S6. Display the area percentage of the mud particles and the corresponding polarizing microscope objective magnification data calculated in step S5 in a rectangular coordinate system in the form of scattered points; S7, using the nonlinear least square method to fit all the scattered points in the rectangular coordinate system of step S6, and establishing a curve of the change of the volume percentage of the mud content under different polarizing microscope objective magnifications; S8. Solve the curve equation according to the volume percentage change relationship curve of the mud content established in step S7, and use the curve equation to calculate the volume percentage of the mud content when the objective lens magnification of the polarizing microscope is 0, and use this as the mud content of the sample.

[0027] Example 2 As another preferred embodiment of the present invention, refer to the attached specification Figure 1 , Figure 2 and Figure 3 As shown, this embodiment discloses a method for determining the shale content of dense sandstone, which comprises the following steps: S1. Select a dense sandstone sample and make the sample into a cube sample; the size of the cube sample is 5cm*5cm*5cm.

[0028] The volume percentage established in this embodiment is applicable to samples with a size of 5 cm×5 cm×5 cm. The volume percentage of the mud content is used as a benchmark, and the volume percentage of the mud content can be calculated according to different sizes of samples.

[0029] S2. Take the horizontal and vertical surfaces of the cube and make rock thin sections on the two surfaces of the cube sample. The thickness of the rock thin sections should meet the technical requirements of "Rock Thin Section Preparation Method: SY / T5913-2004", and the identification method should meet the technical requirements of "Rock Thin Section Identification: SY / T5368-2016".

[0030] like Figure 2 As shown, S3, through a polarizing microscope, obtain single polarizing thin-section identification photos of the rock thin sections on multiple surfaces in step S2; obtain multiple single polarizing thin-section identification photos of the rock thin section on each surface under different polarizing microscope objective magnifications. The polarizing thin-section identification photos of the rock thin section obtained under different polarizing microscope objective magnifications should meet the technical requirements of 6.4 image method in "Grain Size Analysis of Clastic Rocks: SY / T5435-2018".

[0031] like Figure 3 As shown, S4, using a computer image recognition method, obtain the area percentage of debris particles with a diameter greater than a set threshold in all thin-section identification photos in step S3.

[0032] S5. Calculate the area percentage of muddy particles in all thin section identification photos in step S3 based on the area percentage of the debris particles obtained in S4.

[0033] S6. The area percentage of the mud particles and the corresponding polarizing microscope objective magnification data calculated in step S5 are displayed in a rectangular coordinate system in the form of scattered points.

[0034] like Figure 4 As shown in S7, all the scattered points in the rectangular coordinate system of step S6 are fitted using the nonlinear least square method to establish the relationship curve of the volume percentage change of mud content under different polarizing microscope objective magnifications.

[0035] S8. Solve the curve equation according to the volume percentage change relationship curve of the mud content established in step S7, and use the curve equation to calculate the volume percentage of the mud content when the objective lens magnification of the polarizing microscope is 0, and use this as the mud content of the sample.

[0036] Example 3 As another preferred embodiment of the present invention, refer to the attached specification Figure 1 , Figure 2 and Figure 3 As shown, the method comprises the following steps: S1. Select a dense sandstone sample and make it into a cube sample. In this embodiment, the size of the cube sample can be determined according to the size of the dense sandstone sample, for example, the size is controlled at 5cm*5cm*5cm, or 10cm*10cm*10cm, or 15cm*15cm*15cm. According to the different sizes of the samples, the volume percentage of the shale content is calculated.

[0037] S2. Take multiple faces of the cube sample and make rock thin sections of multiple faces of the cube sample respectively; it can be six faces of the cube sample or two adjacent faces. The thickness of the rock thin section should meet the technical requirements of "Rock Thin Section Preparation Method: SY / T5913-2004", and the identification method should meet the technical requirements of "Rock Thin Section Identification: SY / T5368-2016".

[0038] like Figure 2 As shown, S3, through a polarizing microscope, obtain single polarizing slice identification photos of the rock slices on multiple surfaces in step S2; obtain multiple single polarizing slice identification photos of the rock slice on each surface under different polarizing microscope objective magnifications; obtain single polarizing slice identification photos of the rock slice on each surface under polarizing microscope objective magnifications of 1x, 2.5x, 5x, 10x, and 20x. Obtaining polarizing slice identification photos of rock slices under different polarizing microscope objective magnifications should meet the technical requirements of 6.4 image method in "Grain Size Analysis of Clastic Rocks: SY / T5435-2018".

[0039] like Figure 3 As shown, S4, using a computer image recognition method, obtaining the area percentage of the debris particles with a diameter greater than the set threshold in all the thin section identification photos in step S3; using the computer image recognition method, obtaining the boundary data of the debris particles with a diameter greater than the set threshold, calculating the area of ​​the debris particles according to their boundary data, and counting the area percentage W of the sum of the areas of all the debris particles with a diameter greater than the set threshold in the rock thin section s1 .

[0040] S5. Calculate the area percentage of muddy particles in all thin-section identification photos in step S3 based on the area percentage of the debris particles obtained in S4. The area percentage of muddy particles is the area percentage of the debris particles whose diameter is less than the set threshold, and its area percentage is W. s2 =1-W s1 .

[0041] S6. Display the mud area percentage and its corresponding polarizing microscope objective lens magnification data calculated in step S5 in a rectangular coordinate system in the form of scattered points; project the mud area percentage and its corresponding polarizing microscope objective lens magnification in the rock thin section identification photos of multiple faces into a rectangular coordinate system, and realize the conversion of area percentage to volume percentage by curve fitting.

[0042] like Figure 4As shown, S7, use the nonlinear least squares method to fit all the scattered points in the rectangular coordinate system of step S6, and establish the relationship curve of the volume percentage change of the mud content under different polarizing microscope objective magnifications; use the nonlinear least squares method to fit the relationship curve, that is, the fitted relationship curve is to meet the change law of all the scattered points, and the fitted curve equation is used as the calculation equation of the volume percentage of the mud content.

[0043] S8. Solve the curve equation according to the volume percentage change relationship curve of the mud content established in step S7, and use the curve equation to calculate the volume percentage of the mud content when the objective lens magnification of the polarizing microscope is 0, and use this as the mud content of the sample.

Claims

1. A method for determining the shale content of dense sandstone, characterized in that: The method comprises the following steps: S1. Select a dense sandstone sample and make the sample into a cube sample; S2, taking multiple faces of the cube sample and making rock thin sections of multiple faces of the cube sample respectively; S3. Obtain single polarized thin-section identification photos of the rock thin sections on multiple sides in step S2 through a polarizing microscope; obtain multiple single polarized thin-section identification photos of the rock thin section on each side under different polarizing microscope objective magnifications; S4, using a computer image recognition method, obtaining the area percentage of debris particles having a diameter greater than a set threshold in all thin section identification photos in step S3; S5, calculating the area percentage of muddy particles in all thin section identification photos in step S3 according to the area percentage of the debris particles obtained in step S4; S6. Display the area percentage of the mud particles and the corresponding polarizing microscope objective magnification data calculated in step S5 in a rectangular coordinate system in the form of scattered points; S7, using the nonlinear least square method to fit all the scattered points in the rectangular coordinate system of step S6, and establishing a curve of the change of the volume percentage of the mud content under different polarizing microscope objective magnifications; S8. Solve the curve equation according to the volume percentage change relationship curve of the mud content established in step S7, and use the curve equation to calculate the volume percentage of the mud content when the objective lens magnification of the polarizing microscope is 0, and use this as the mud content of the sample.

2. A method for determining the shale content of dense sandstone according to claim 1, characterized in that: In step S6, the mud area percentages and their corresponding polarizing microscope objective magnifications in the rock thin section identification photos of multiple faces are projected into a rectangular coordinate system, and the conversion from area percentage to volume percentage is achieved by curve fitting.

3. A method for determining the shale content of dense sandstone according to claim 1 or 2, characterized in that: In step S7, the nonlinear least square method is used to fit the relationship curve, that is, the fitted relationship curve satisfies the variation law of all scattered points, and the fitted curve equation is used as the calculation equation for the volume percentage of the mud content.

4. A method for determining the shale content of dense sandstone according to claim 1, characterized in that: Step S4 is as follows: by using a computer image recognition method, the boundary data of the debris particles with a diameter greater than a set threshold are obtained, the area of ​​the debris particles is calculated based on the boundary data, and the area percentage W of the sum of the areas of all the debris particles with a diameter greater than the set threshold in the rock slice is calculated. s1 .

5. A method for determining the shale content of dense sandstone according to claim 1 or 4, characterized in that: In step S4, the threshold is set to 0.0156 mm.

6. A method for determining the shale content of dense sandstone according to claim 4, characterized in that: In step S5, the area percentage of muddy particles is the area percentage of debris particles whose diameter is smaller than the set threshold, and its area percentage is W. s2 =1-W s1 .

7. A method for determining the shale content of dense sandstone according to claim 1, 2, 4 or 6, characterized in that: In step S1, the size of the cube sample is 5cm*5cm*5cm.

8. A method for determining the shale content of dense sandstone according to claim 1, 2, 4 or 6, characterized in that: In step S2, multiple faces of the cube sample are taken, specifically, two faces, the horizontal face and the vertical face of the cube are taken.

9. A method for determining the shale content of dense sandstone according to claim 1, 2, 4 or 6, characterized in that: The thickness of the rock thin section should meet the technical requirements of "Rock Section Preparation Method: SY / T5913-2004", and the identification method should meet the technical requirements of "Rock Thin Section Identification: SY / T5368-2016".

10. A method for determining the shale content of dense sandstone according to claim 1, 2, 4 or 6, characterized in that: In step S3, single polarized thin-section identification photographs of the rock thin section on each surface are obtained under polarized microscope objective lens magnifications of 1x, 2.5x, 5x, 10x, and 20x.

11. A method for determining the shale content of dense sandstone according to claim 1, 2, 4 or 6, characterized in that: In step S3, the polarized thin section identification photos of rock thin sections obtained under different polarizing microscope objective magnifications should meet the technical requirements of 6.4 imaging method in "Grain Size Analysis of Clastic Rocks: SY / T5435-2018".

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