Porosity calculation method based on natural gamma ray spectrum and three porosity curves

Abstract

The present application relates to the porosity calculation method based on natural gamma spectrum and three porosity curves, belongs to the unconventional shale gas exploration and development technical field. The specific steps are as follows: step 1: using data statistics 'one' regression method, through the cross analysis of the porosity data of core analysis and acoustic time difference, density, neutron, uranium logging curves in the research area, the good correlation curve is selected as the modeling parameter; step 2: through the comparative analysis of the porosity modeling method of multiple combination modes; step 3: considering the differences of different structural belt reservoir burial depth and the differences of main evaluation parameters such as physical property, brittleness and gas content, the research area is further optimized and partitioned; step 4: using core data to scale logging curve, the porosity interpretation model based on natural gamma spectrum and three porosity curves is finally established. The method can effectively improve the porosity precision, and has important guiding significance for efficient development of shale gas.

Description

Technical Field

[0001] The invention relates to a porosity calculation method based on natural gamma ray spectrum and three-porosity curves, and belongs to the technical field of unconventional shale gas exploration and development. Background Art

[0002] In recent years, with the continuous development of China's basic shale gas geological theory and the continuous advancement of the main exploration and development technologies, the Silurian Longmaxi Formation in the southern Sichuan Basin, shallower than 3,500 meters, has successfully achieved large-scale and efficient shale gas development. This set of organic-rich shale is thick, of the highest quality, with the highest degree of exploration and the best implementation effect. It is currently China's most important shale gas exploration and development formation.

[0003] Shale is primarily composed of clastic particles, matrix, cement, organic matter, and pores. The first four make up the shale skeleton, which can be divided into inorganic and organic components. Inorganic components include minerals such as quartz, clay, feldspar, calcite, dolomite, pyrite, and barite, while organic components primarily consist of kerogen and residual organic matter. Removing the skeleton only leaves the pores and the free water within them. Shale gas exists in free and adsorbed states within these pores, or in dissolved form in water. Because shale reservoirs differ from conventional reservoirs in that the mineral content of shale gas rocks varies greatly, single-porosity models are difficult to accurately capture for the rock skeleton.

[0004] Quantitative evaluation of shale gas reservoirs is crucial, with porosity being one of the most important parameters. Therefore, accurately calculating porosity is crucial for efficient shale gas development. Currently, reservoir porosity is primarily calculated using three porosity curves: the acoustic transit time curve, the density curve, and the neutron curve. This method is simple to use and easy to interpret, but suffers from a drawback in poor matching accuracy with core data for shale gas reservoirs. However, the uranium curve in the natural gamma ray spectrum accurately reflects the extent of organic pore development, improving interpretation accuracy and enhancing reservoir interpretation. Summary of the Invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the present application provides a porosity calculation method based on natural gamma ray spectrum and three-porosity curves. It uses the acoustic time difference and density curve, considers the development of organic pores in shale reservoirs, and combines the uranium curve in the natural gamma ray spectrum to establish a porosity interpretation model. By comparing with the core, the interpretation accuracy is improved.

[0006] To achieve the above objectives, the technical solution of this application is a porosity calculation method based on natural gamma ray spectrum and three-porosity curves, and the specific steps are as follows:

[0007] Step 1: Using the statistical "monad" regression method, the porosity data of the core analysis in the study area were analyzed with the acoustic time difference, density, neutron, and uranium logging curves, and the correlation coefficient R was used. 2 As the judgment standard, select several correlation coefficients R 2 Good curves are used as modeling parameters.

[0008] Step 2: Based on step 1, compare the porosity modeling methods using various combinations and use the correlation coefficient R 2 The largest combination method is used to calculate porosity, among which the various combinations include "binary" regression method, "ternary" regression method... and so on to multiple regression method.

[0009] Step 3: Based on step 2, the study area is further optimized and zoned considering the different burial depths of reservoirs in different structural belts and the differences in the main evaluation parameters of physical properties, brittleness, and gas content.

[0010] Step 4: Use core data to calibrate the well logging curves, and finally establish a porosity interpretation model based on natural gamma ray spectrum and three-porosity curves.

[0011] Furthermore, the parameter considered in the "univariate" regression method in step 1 is a certain parameter.

[0012] Furthermore, in the step 1, R 2 Refers to the ratio of the sum of squares of the differences between the sample points and the mean line to the sum of squares of the differences between the predicted results and the sample mean. 2 It quantifies the strength of the relationship between the model's response variable and the dependent variable. The absolute value of the R value ranges from 0 to 1. Generally speaking, the closer R is to 1, the stronger the correlation between x and y. Conversely, the closer R is to 0, the weaker the correlation between x and y.

[0013] in: x represents a variable, represents the mean of x, and ∑ represents the summation symbol.

[0014] Furthermore, the parameters considered by the "binary" regression method in step 2 are any two parameter combinations, and the "ternary" regression method considers three parameter combinations at the same time.

[0015] Among them: binary regression method

[0016] x and y represent two variables, and They represent the means of x and y respectively, and ∑ represents the summation symbol; similarly, the ternary regression method can be deduced accordingly.

[0017] Furthermore, the zoning standard in step 3 is to divide the study area into anticline and syncline areas according to the different buried depths of reservoirs in different structural belts and the differences in the main evaluation parameters of physical properties, brittleness and gas content.

[0018] The present invention utilizes the above technical solution to achieve the following technical benefits: Taking into account the diverse pore types in shale reservoirs, the relatively developed organic pores, and the positive correlation between the uranium curve in the energy spectrum and the organic matter content, a new porosity calculation method is established by combining the uranium curve in the natural gamma ray spectrum with the conventional three-porosity curve. This method effectively improves porosity accuracy, better serves reservoir evaluation, and has important guiding significance for the efficient development of shale gas. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a pore type diagram of a portion of the shale reservoir in Example 1 of the present invention;

[0020] Figure 2 This is a univariate regression analysis diagram of Example 1 of the present invention;

[0021] Figure 3 This is a comparison chart of the porosity before and after adding uranium curves and the syncline area of ​​core analysis in Example 1 of the present invention;

[0022] Figure 4 This is a comparison chart of the porosity before and after adding uranium curves and the anticline area of ​​core analysis in Example 1 of the present invention. DETAILED DESCRIPTION

[0023] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. This application is further described using this as an example.

[0024] Example 1:

[0025] This example uses marine shale samples from the Longmaxi Formation in the Zigong area of ​​the Sichuan Basin as an example. Shale is primarily composed of clastic particles, matrix, cement, organic matter, and pores. Shale gas exists in pores in free and adsorbed states, or in dissolved form in water. Shale reservoir characteristics can be characterized by its pore structure. Scanning electron microscopy reveals that shale micropores include intergranular pores, intragranular pores, organic pores, and fractures.

[0026] like Figure 1 As shown, this embodiment provides a porosity calculation method based on natural gamma ray spectrum and three porosity curves, and the specific steps are as follows:

[0027] Step 1: Using the statistical "monolithic" regression method, the porosity data of the core analysis in the study area were analyzed by intersection analysis with the acoustic time difference, density, neutron, and uranium logging curves, such as Figure 2As shown in the figure, the correlation analysis between the core porosity after depth relocation and the well logging curve in the study area was carried out, and it was found that the acoustic wave time difference curve had the best correlation with the core porosity, and the correlation coefficient R 2 =0.7471; the density curve has the second highest correlation with the core porosity, with a correlation coefficient of R 2 =0.7049; the correlation between the uranium curve and the core porosity is the second, the correlation coefficient R 2 =0.6125, the correlation between the neutron curve and the core porosity is the worst, the correlation coefficient R 2 =0.4091, and finally the acoustic time difference curve, density curve and uranium curve were selected as modeling parameters.

[0028] Step 2: Based on step 1, compare the porosity modeling methods using various combinations and use the correlation coefficient R 2 The porosity is calculated by the largest combination method. There are many samples of core physical property analysis data in the study area. Wells with good matching between analysis results and logging curve responses are selected. The data statistical multivariate regression method is adopted, among which various combination methods include "binary" regression method and "ternary" regression method, and various porosity interpretation models are established. The complex correlation coefficient R between the acoustic time difference curve and the uranium curve is obtained. 2 =0.764; Multiple correlation coefficient R between uranium curve and density curve 2 =0.768; Complex correlation coefficient R between acoustic time difference curve and density curve 2 =0.798; the complex correlation coefficient R of the density curve, the acoustic time difference curve and the uranium curve 2 = 0.864. The porosity evaluation accuracy was the highest using the "ternary regression method" of uranium curve, density curve, and acoustic transit time curve.

[0029] Step 3: On the basis of step 2, considering the different buried depths of reservoirs in different structural belts and the differences in the main evaluation parameters of physical properties, brittleness and gas content, the zoning modeling of the study area is further optimized. The main structure of the study area is located in the Zigong low-fold structure of the southwestern Sichuan low-fold structural belt. The structure is relatively flat, with a structural pattern of alternating high northeast and low southwest uplifts. There are multiple northeast-trending anticlines in the area, extending in a steep and narrow strip-like shape, with developed axial faults and a wide and gentle syncline in the middle. Therefore, the study area is divided into two areas on the plane according to the anticline area and the syncline area.

[0030] Step 4: If Figure 3 、 4 As shown in the figure, the core data were used to calibrate the well logging curves, and finally a porosity interpretation model combining uranium curves, density and acoustic time difference was established for the zoning.

[0031] Syncline area: PORE = 0.095*AC+0.049*U-8.624*DEN+18.428;

[0032] Anticline area: PORE = 0.081*AC+0.185*U-14.11*DEN+35.201;

[0033] Where, AC is the acoustic wave time difference, us / m; U is the uranium curve, %; DEN is the density, g / cm3.

[0034] In addition, a comparative analysis of the syncline region was conducted: other conditions remained unchanged, and only the three-porosity curve (black solid line) before the addition of the uranium curve was considered to obtain the porosity interpretation model:

[0035] PORE=0.1146*AC-10.16*DEN+21.465;

[0036] Where, AC is the acoustic wave time difference, us / m; DEN is the density, g / cm3.

[0037] That is Figure 3 、 4 As shown in the figure, the porosity calculated before and after adding the uranium curve is compared with the core porosity analysis. It can be seen that the porosity calculated after adding the uranium curve (red solid line) is in good agreement with the core analysis porosity (blue rod-shaped data points). The relative error in the syncline area is reduced from the original 5.7% to 3.1%, and the relative error in the anticline area is reduced from the original 6.3% to 2.6%. The accuracy is significantly improved. The relative error refers to the ratio of the absolute value of the difference between the porosity and the core porosity to the core porosity.

[0038] The above description is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A porosity calculation method based on natural gamma ray spectrum and three-porosity curves, characterized in that: The specific steps are as follows: Step 1: Using the statistical "monolithic" regression method, the porosity data of the core analysis in the study area were analyzed with the acoustic time difference, density, neutron, and uranium logging curves, and the correlation coefficient R was used. 2 As the judgment standard, select several correlation coefficients R 2 Good curves are used as modeling parameters; Step 2: Based on step 1, compare the porosity modeling methods using various combinations and use the correlation coefficient R 2 The largest combination method is used for porosity calculation; Step 3: Based on step 2, the study area is further optimized and zoned considering the different reservoir burial depths in different structural belts and the differences in the main evaluation parameters of physical properties, brittleness, and gas content; Step 4: Use core data to calibrate the well logging curves, and finally establish a porosity interpretation model based on natural gamma ray spectrum and three-porosity curves.

2. The porosity calculation method based on natural gamma ray spectrum and three-porosity curve according to claim 1, characterized in that: The parameter considered in the "univariate" regression method in step 1 is a certain parameter.

3. The porosity calculation method based on natural gamma ray spectrum and three-porosity curve according to claim 1, characterized in that: The various combinations in step 2 include "binary" regression method, "ternary" regression method... and so on to multiple regression method.

4. The porosity calculation method based on natural gamma ray spectrum and three-porosity curve according to claim 3, characterized in that: In step 2, the parameters considered by the "binary" regression method are any two parameter combinations, and the "ternary" regression method considers three parameter combinations at the same time.

5. The porosity calculation method based on natural gamma ray spectrum and three-porosity curve according to claim 1, characterized in that: The zoning standard in step 3 is to divide the study area into anticline and syncline areas based on the different buried depths of reservoirs in different structural belts and the differences in the main evaluation parameters of physical properties, brittleness, and gas content.

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