A method for determining shale content of a tight sand

Abstract

The application discloses a method for determining argillaceous content of tight sandstone, and relates to the technical field of exploration and research of tight rock oil and gas reservoirs.The computer image recognition method is used to recognize rock thin section identification photos, the area percentage of the clastic particles with a diameter greater than a set threshold in the thin section identification photos is obtained, and then the area percentage of the argillaceous particles in the thin section identification photos is calculated; according to the area percentage of the argillaceous particles and the corresponding polarizing microscope magnification, the argillaceous content volume percentage change relation curve under different polarizing microscope objective magnifications is established in the form of scattered points in the rectangular coordinate system; the nonlinear least square method is used to fit all the scattered points, the curve equation is solved, and the argillaceous content of the sample is calculated by using the equation.The application avoids the error caused by the single thin section identification human visual estimation method for calculating the argillaceous content, the analysis result is more in line with the real content of the sample, and therefore the accuracy of the image method for determining the argillaceous content is greatly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tight sandstone oil and gas reservoir exploration, and more particularly to a method for determining the argillaceous content of tight sandstone. BACKGROUND

[0002] The central Sichuan Basin has multiple-scale gas-bearing channel sandstones, and the reservoirs are mainly tight sandstones with a favorable area of 10,000 km2 and a resource scale of over 100 billion cubic meters, which has great potential for exploration and development and is one of the important fields for increasing reserves and production in the southwestern oil and gas fields.

[0003] Argillaceous refers to clastic material with a particle diameter less than 0.01 mm, and its content is the volume percentage of fine silt, clay and water contained in clay in the volume of rock. By studying the argillaceous content of tight sandstone, it is helpful to further characterize the stratigraphic sequence of the oil and gas reservoir, accurately distinguish the sedimentary environment, and effectively solve the bottleneck problem of well logging interpretation of argillaceous content correction, which is of great significance to sedimentary facies division, reservoir evaluation, reservoir division, stratigraphic correlation and calculation of formation water resistivity.

[0004] Currently, the argillaceous content of tight sandstone is generally determined by natural gamma ray, natural gamma ray spectroscopy, spontaneous potential, density and neutron logging. These methods are only suitable for the case where the sandstone skeleton is pure and the clay mineral composition is stable. Once the rock composition is complex (clay mineral content > 15% in miscellaneous sandstone, and rock fragment content > 10% in rock fragment sandstone), it is difficult to accurately calculate the argillaceous content. Therefore, it is urgent to establish an experimental method for determining the argillaceous content to correct the logging interpretation results. Currently, the particle size analysis method is generally used to estimate the argillaceous content in geological laboratories. Specifically, the core is ground and sieved through multiple specifications, and the proportion of rock particles of various particle sizes in the whole rock particles is carefully measured by a laser particle size analyzer.

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

[0006] In order to overcome the defects and deficiencies existing in the prior art, the present application provides a method for determining the argillaceous content of tight sandstone. The present application aims to solve the problem of determining the argillaceous content of tight sandstone. The present application uses computer image recognition method to recognize the single polarized thin section identification photos of rock thin sections, obtains the area percentage of the clastic particles with a diameter greater than the set threshold in the thin section identification photos, and then calculates the area percentage of the argillaceous particles in all thin section identification photos. According to the area percentage of the argillaceous particles and the corresponding polarized microscope objective magnification data, the argillaceous content volume percentage change relationship curve under different polarized microscope objective magnifications is established by fitting all the scatter points in the rectangular coordinate system in the form of scatter points. The present application avoids the errors caused by the single estimation of the argillaceous content by the thin section identification method, and the analysis result is more consistent with the true content of the sample, thereby greatly improving the accuracy of the image method for determining the argillaceous content.

[0007] In order to solve the problems existing in the prior art, the present application is realized by the following technical scheme.

[0008] The present application provides a method for determining the argillaceous content of tight sandstone, which comprises the following steps:

[0009] S1, selecting a tight sandstone sample, and preparing the sample into a cubic sample;

[0010] S2, taking multiple faces of the cubic sample, and preparing rock thin sections of multiple faces of the cubic sample body;

[0011] S3, obtaining single polarized thin section identification photos of the rock thin sections of multiple faces in the S2 step by a polarized microscope; obtaining multiple single polarized thin section identification photos of each rock thin section under different polarized microscope objective magnifications;

[0012] S4, using a computer image recognition method to obtain the area percentage of the clastic particles with a diameter greater than the set threshold in all thin section identification photos in the S3 step;

[0013] S5, calculating the area percentage of the argillaceous particles in all thin section identification photos in the S3 step according to the area percentage of the clastic particles obtained in the S4 step;

[0014] S6, according to the area percentage of the argillaceous particles calculated in the S5 step and the corresponding polarized microscope objective magnification data, the argillaceous content volume percentage change relationship curve under different polarized microscope objective magnifications is established by fitting all the scatter points in the rectangular coordinate system in the form of scatter points;

[0015] S7, using a nonlinear least squares method to fit all the scatter points in the rectangular coordinate system in the S6 step, and establishing the argillaceous content volume percentage change relationship curve under different polarized microscope objective magnifications.

[0016] S8, according to the curve equation of the volume percentage of argillaceous content established in the S7 step, the volume percentage of argillaceous content at the objective magnification of the polarizing microscope is calculated as the argillaceous content of the sample.

[0017] Further, in the S1 step, the size of the cubic sample is 5cm*5cm*5cm.

[0018] Further, in the S2 step, multiple faces of the cubic sample are taken, specifically, two faces of the cubic sample, i.e., the horizontal face and the vertical face.

[0019] Further, the thickness of the rock slice should meet the technical requirements of the Rock Sectioning Method: SY / T5913-2004, and the identification method should meet the technical requirements of the Rock Slice Identification: SY / T5368-2016.

[0020] Further, in the S3 step, the rock slice of each face is obtained under the single-polarization slice identification photograph of the polarizing microscope objective magnification of 1x, 2.5x, 5x, 10x and 20x.

[0021] Further, in the S3 step, the polarized slice identification photograph of the rock slice under different polarizing microscope objective magnifications should meet the technical requirements of the 6.4 image method in the Detrital Rock Grain Size Analysis: SY / T5434-2018.

[0022] Further, the S4 step is specifically: through a computer image recognition method, the boundary data of the detrital particles with a diameter greater than a set threshold value is obtained, the area of the detrital particles is calculated according to the boundary data, and the area percentage W of the sum of the areas of all detrital particles with a diameter greater than the set threshold value in the rock slice is calculated. s1 .

[0023] Further, in the S4 step, the set threshold value is 0.0156mm.

[0024] Further, in the S5 step, the argillaceous particle area percentage is the area percentage of the detrital particles with a diameter less than the set threshold value, which is W s2 =1-W s1 .

[0025] Further, in the S6 step, the argillaceous area percentage and the corresponding polarizing microscope objective magnification of the rock slice identification photograph of multiple faces are projected in a rectangular coordinate system, and the conversion from the area percentage to the volume percentage is realized through curve fitting.

[0026] Further, in the step S7, a nonlinear least square method is used to fit the relationship curve, that is, the fitted relationship curve meets the change rule of all the scattered points, and the curve equation is used as the shale content volume percentage calculation equation.

[0027] Compared with the prior art, the beneficial technical effects brought by the present application are shown in

[0028] 1. The present application avoids the error caused by the single thin slice identification manual visual estimation method for calculating the shale content, and the analysis result is more in line with the real content of the sample, thereby greatly improving the accuracy of the image method shale content determination.

[0029] 2. The present application uses the computer image recognition method to obtain accurate data of the particle area, which is more accurate and reliable than the manual visual estimation method for extracting image particles and obtaining area data.

[0030] 3. The present application introduces the statistical fractal theory, according to the self-similarity of the particle distribution image in the rock thin slice, uses the statistical sense of random fractal method, realizes the conversion from the two-dimensional surface particle percentage statistics to the three-dimensional volume particle percentage statistics through curve fitting, and avoids the inaccuracy problem of the two-dimensional area percentage representing the three-dimensional volume percentage.

[0031] 4. The present application is especially suitable for the tight sandstone granularity analysis, overcomes the bottleneck of the high cementation degree and small particle size of the tight sandstone, and the analysis difficulty of the conventional granularity analyzer and the thin slice identification method, and can effectively improve the accuracy of the tight sandstone shale content determination, and provides technical support for the tight sandstone oil and gas reservoir logging interpretation and reservoir evaluation.

[0032] 5. The present application can calculate the volume percentage of the shale in the rock under different magnifications, and the established relationship can be converted into the shale content under different formation thicknesses and areas, which has very important significance for the research on the geological characteristics of the oil and gas reservoir area. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 It is a flow chart of the tight sandstone shale content determination method of the present application;

[0034] Figure 2 It is a thin slice identification diagram of the tight sandstone of the present application;

[0035] Figure 3 It is a computer image recognition result diagram of the thin slice identification diagram of the tight sandstone of the present application;

[0036] Figure 4 It is a least square fitting curve schematic diagram. DETAILED DESCRIPTION

[0037] The technical solutions of the present application will be clearly and completely described below with specific embodiments. 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 the present application.

[0038] Embodiment 1

[0039] As a preferred embodiment of the present application, referring to the drawings attached to the specification, Figure 1 the present embodiment discloses a method for determining the argillaceous content of tight sandstone, which comprises the following steps:

[0040] S1, selecting a tight sandstone sample and making the sample into a cubic sample;

[0041] S2, taking multiple faces of the cubic sample and making rock slices of the multiple faces of the cubic sample;

[0042] S3, obtaining single-polarization slice identification photos of the rock slices of the multiple faces in the S2 step by a polarizing microscope; and obtaining multiple single-polarization slice identification photos of each rock slice under different polarizing microscope objective magnifications;

[0043] S4, obtaining the area percentage of the clastic particles with a diameter greater than a set threshold in all the slice identification photos in the S3 step by using a computer image recognition method;

[0044] S5, calculating the area percentage of the argillaceous particles in all the slice identification photos in the S3 step according to the area percentage of the clastic particles obtained in the S4 step;

[0045] S6, displaying the area percentage of the argillaceous particles calculated in the S5 step and the corresponding polarizing microscope objective magnification data in a rectangular coordinate system in the form of scattered points;

[0046] S7, fitting all the scattered points in the rectangular coordinate system in the S6 step by using a nonlinear least squares method to establish a curve showing the change relationship of the argillaceous content volume percentage under different polarizing microscope objective magnifications;

[0047] S8, solving the curve equation of the curve showing the change relationship of the argillaceous content volume percentage established in the S7 step, and calculating the argillaceous content volume percentage when the polarizing microscope objective magnification is 0 by using the curve equation, which is taken as the argillaceous content of the sample.

[0048] Embodiment 2

[0049] As another preferred embodiment of the present application, referring to the drawings attached to the specification, Figure 1 , Figure 2 and Figure 3As shown, the embodiment discloses a method for determining the argillaceous content of tight sandstone, which comprises the following steps:

[0050] S1, select a tight sandstone sample, and make the sample into a cubic sample; the size of the cubic sample is 5cm*5cm*5cm.

[0051] The embodiment establishes the volume percentage suitable for the sample with the size of 5cm*5cm*5cm, and takes the argillaceous content volume percentage as the reference, so that the argillaceous content volume percentage can be calculated according to the different sizes of the sample.

[0052] S2, take two faces of the cubic sample, i.e., the horizontal face and the vertical face, and make rock slices of the two faces of the cubic sample. The thickness of the rock slices should meet the technical requirements of the Method for Preparing Rock Slices: SY / T5913-2004, and the identification method should meet the technical requirements of the Rock Slice Identification: SY / T5368-2016.

[0053] As shown in Figure 2 S3, obtain single-polarization slice identification photos of the rock slices of the multiple faces in the S2 step by using a polarizing microscope; obtain multiple single-polarization slice identification photos of the rock slices of each face under different polarizing microscope objective magnifications. The polarized slice identification photos of the rock slices under different polarizing microscope objective magnifications should meet the technical requirements of the 6.4 image method in the Method for Grain Size Analysis of Clastic Rocks: SY / T5434-2018.

[0054] As shown in Figure 3 S4, obtain the area percentage of the clastic particles with a diameter greater than a set threshold in all the slice identification photos in the S3 step by using a computer image recognition method.

[0055] S5, calculate the area percentage of the argillaceous particles in all the slice identification photos in the S3 step according to the area percentage of the clastic particles obtained in the S4.

[0056] S6, display the area percentage of the argillaceous particles calculated in the S5 step and the corresponding polarizing microscope objective magnification data in the Cartesian coordinate system in the form of scattered points.

[0057] As shown in Figure 4 S7, fit all the scattered points in the Cartesian coordinate system in the S6 step by using a nonlinear least squares method, and establish a curve of the change relationship of the argillaceous content volume percentage under different polarizing microscope objective magnifications.

[0058] S8, solve the curve equation of the change relationship of the argillaceous content volume percentage established in the S7 step, calculate the argillaceous content volume percentage when the polarizing microscope objective magnification is 0 by using the curve equation, and take the argillaceous content volume percentage as the argillaceous content of the sample.

[0059] Embodiment 3

[0060] As another preferred embodiment of the present application, referring to the description attached hereto Figure 1 、 Figure 2 and Figure 3 , the method comprises the following steps:

[0061] S1, select a tight sandstone sample, and make the sample into a cubic sample; in this embodiment, the size of the cubic sample can be determined according to the size of the tight sandstone sample, for example, the size is controlled to be 5cm*5cm*5cm, or 10cm*10cm*10cm, or 15cm*15cm*15cm. According to different sizes of the sample, the volume percentage of argillaceous content is calculated.

[0062] S2, take multiple faces of the cubic sample, and make rock slices of the multiple faces of the cubic sample respectively; it can be six faces of the cubic sample, or two adjacent faces. The thickness of the rock slice should meet the technical requirements of “Rock slicing method: SY / T5913-2004”, and the identification method should meet the technical requirements of “Rock slice identification: SY / T5368-2016”.

[0063] As shown in Figure 2 , S3, obtain single-polarization slice identification photos of the rock slices of the multiple faces in the S2 step by a polarizing microscope; obtain multiple single-polarization slice identification photos of each face of the rock slice under different polarizing microscope objective magnifications; obtain single-polarization slice identification photos of each face of the rock slice under polarizing microscope objective magnifications of 1 times, 2.5 times, 5 times, 10 times, and 20 times. Obtaining polarized slice identification photos of the rock slice under different polarizing microscope objective magnifications should meet the technical requirements of 6.4 image method in “Detrital rock grain size analysis: SY / T5434-2018”.

[0064] As shown in Figure 3 , S4, obtain the area percentage of the detrital particles with a diameter greater than a set threshold value in all slice identification photos in the S3 step by using a computer image recognition method; obtain the boundary data of the detrital particles with a diameter greater than the set threshold value by the computer image recognition method, calculate the area of the detrital particles according to the boundary data, and calculate the area percentage W s1 of the sum of the areas of all detrital particles with a diameter greater than the set threshold value in the rock slice.

[0065] S5, calculate the area percentage of the argillaceous particles in all slice identification photos in the S3 step according to the area percentage of the detrital particles obtained in S4; the area percentage of the argillaceous particles is the area percentage of the detrital particles with a diameter less than the set threshold value, which is W s2 =1-W s1 .

[0066] S6, the percentage of argillaceous particles calculated according to the S5 step and the corresponding polarizing microscope objective magnification data are displayed in the form of scattered points in a rectangular coordinate system; the percentage of argillaceous area and the corresponding polarizing microscope objective magnification in the rock thin section identification photos of multiple faces are projected in a rectangular coordinate system, and the conversion of the percentage of area to the percentage of volume is realized by curve fitting.

[0067] As shown in Figure 4 S7, the nonlinear least squares method is used to fit all the scattered points in the rectangular coordinate system of the S6 step, and the relationship curve of the volume percentage of argillaceous content under different polarizing microscope objective magnifications is established; the nonlinear least squares method is used to fit the relationship curve, that is, the fitted relationship curve meets the change rule of all the scattered points, and the fitted curve equation is used as the calculation equation of the volume percentage of argillaceous content.

[0068] S8, the volume percentage of argillaceous content at the polarizing microscope objective magnification of 0 is calculated according to the curve equation of the volume percentage of argillaceous content established in the S7 step, and the volume percentage of argillaceous content at the polarizing microscope objective magnification of 0 is used as the argillaceous content of the sample.

Claims

1. A method of determining shale content of a tight sand, characterized by, The method comprises the following steps: S1, selecting a dense sandstone sample, and making the sample into a cubic sample; S2, taking multiple faces of the cubic sample, and making rock slices of the multiple faces of the cubic sample; S3, obtaining single-polarization slice identification photos of the rock slices of the multiple faces in the S2 step through a polarizing microscope; each rock slice obtains multiple single-polarization slice identification photos under different polarizing microscope objective magnifications; S4, obtaining the area percentage of the detrital particles with a diameter greater than a set threshold in all slice identification photos in the S3 step by using a computer image recognition method; S5, calculating the area percentage of the argillaceous particles in all slice identification photos in the S3 step according to the area percentage of the detrital particles obtained in the S4 step; S6, displaying the area percentage of the argillaceous particles calculated in the S5 step and the corresponding polarizing microscope objective magnification data in a rectangular coordinate system in the form of scattered points; S7, fitting all scattered points in the rectangular coordinate system in the S6 step by using a nonlinear least squares method, and establishing a curve of the change relationship of the argillaceous content volume percentage under different polarizing microscope objective magnifications; S8, solving the curve equation of the change relationship of the argillaceous content volume percentage established in the S7 step, and calculating the argillaceous content volume percentage when the polarizing microscope objective magnification is 0 by using the curve equation, which is taken as the argillaceous content of the sample.

2. A method of determining shale content of a tight sand as claimed in claim 1, wherein: In the S6 step, the area percentage of the argillaceous particles and the corresponding polarizing microscope objective magnification in the rock slice identification photos of the multiple faces are projected in a rectangular coordinate system, and the conversion from the area percentage to the volume percentage is realized by curve fitting.

3. A method of determining shale content of a tight sand as claimed in claim 1 or 2, wherein: In the S7 step, the nonlinear least squares method is used to fit the relationship curve, that is, the fitted relationship curve meets the change law of all scattered points, and the curve equation is used as the calculation equation of the argillaceous content volume percentage.

4. The method of determining shale content of a tight sand of claim 1, wherein: The S4 step specifically refers to: obtaining the boundary data of the clastic particles with a diameter greater than a set threshold value through a computer image recognition method, calculating the area of the clastic particles according to the boundary data, and counting the area percentage W of the sum of the areas of all the clastic particles with a diameter greater than the set threshold value in the rock thin section s1 .

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

6. A method of determining shale content of a tight sand as claimed in claim 4, wherein: In step S5, the percentage of argillaceous particles is the percentage of the area of the detrital particles having a diameter less than a set threshold value, W s2 = 1 - W s1 .

7. A method of determining shale content of a tight sand as claimed in claim 1, 2, 4 or 6, wherein: In the S1 step, the size of the cubic sample is 5 cm*5 cm*5 cm.

8. A method of determining shale content of a tight sand as claimed in claim 1, 2, 4 or 6, wherein: In the S2 step, the multiple faces of the cubic sample refer to the horizontal face and the vertical face.

9. A method of determining shale content of a tight sand as claimed in claim 1, 2, 4 or 6, wherein: The thickness of the rock slice should meet the technical requirements of “Rock Sectioning Method: SY / T5913-2004”, and the identification method should meet the technical requirements of “Rock Slice Identification: SY / T5368-2016”.

10. A method of determining shale content of a tight sand as claimed in claim 1, 2, 4 or 6, wherein: In the S3 step, each rock slice obtains single-polarization slice identification photos under polarizing microscope objective magnifications of 1 times, 2.5 times, 5 times, 10 times and 20 times.

11. A method of determining shale content of a tight sand as claimed in claim 1, 2, 4 or 6, wherein: In the S3 step, the polarized slice identification photos of the rock slice under different polarizing microscope objective magnifications should meet the technical requirements of the 6.4 image method in “Detrital Rock Grain Size Analysis: SY / T5434-2018”.

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