Method for determining development degree of shale formation based on electric imaging data

Abstract

The invention discloses a method for determining the development degree of a shale formation based on electric imaging data, and the method comprises the steps: 1, obtaining the electric imaging data of a shale well, and processing the electric imaging data to obtain dynamic imaging apparent conductivity data corresponding to each depth point; 2, abnormal data points are eliminated; 3, dividing the dynamic imaging apparent conductivity data of each depth point into data segments, and calculating a single-point arithmetic mean value of each data segment; 4, calculating a total average value of all depth points in any depth range; 5, setting an apparent conductivity fluctuation ratio, and determining the apparent conductivity fluctuation ratio according to the relationship between the single-point arithmetic mean value and the overall mean value of the two adjacent depth points; 6, calculating an apparent conductivity difference degree; 7, calculating a lamina development index; and 8, determining the lamina development degree. The method also has a better recognition effect on fine shale strata and strata with relatively poor definition. And the method is simple to operate, good in recognition effect, high in recognition speed, high in response degree, high in replicability, and good in universality and popularization value.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas field development, and in particular to a method for determining the degree of shale lamination development based on electrical imaging data. Background Art

[0002] As an important unconventional oil and gas resource, shale oil and gas have been increasingly valued by countries around the world in recent years. Shale oil and gas are mainly present in the pores or cracks of shale reservoirs in an adsorbed or free state. Affected by factors such as tectonic background, sedimentary environment, provenance conditions and diagenesis, the properties of shale reservoirs vary greatly and show strong heterogeneity. The heterogeneity of shale includes not only heterogeneity in lithology, thickness, organic matter abundance, structural structure, mineral composition, reservoir properties and reservoir space structure, but also strong heterogeneity in microscopic components, biomarker compounds, laminations and their combinations. Laminations are the most distinctive sedimentary structures of shale. They are widely developed in shale formations with bedding structures. The lamination types are complex and highly heterogeneous. Their development characteristics control the formation and distribution of shale sweet spots to a certain extent. Therefore, a detailed evaluation of the degree of lamination development is an important part of shale oil and gas reservoir evaluation.

[0003] At present, geological research on shale lamination mainly uses a large number of thin sections or scanning electron microscopes to qualitatively describe the heterogeneous characteristics of shale lamination, such as the degree of development and rhythm. However, this method not only consumes a lot of time, but also has expensive testing and analysis costs, which is not conducive to vertically continuous and efficient quantitative research on shale heterogeneity in the entire well section.

[0004] In addition, due to the advantage of high resolution of electrical imaging logging, research on evaluating laminations through electrical imaging logging images has also emerged in recent years. The relevant literature is as follows:

[0005] 1. The patent document with publication number CN111951347A discloses a method for extracting sandy laminae parameters of shale oil and gas reservoirs. This method constructs a laminae development index model by comprehensively considering factors such as the number of laminae, laminae extension length, and laminae development width to discriminate the degree of sandy laminae development. Although this method is relatively reasonable for extracting laminae information, it still has the following disadvantages:

[0006] First, this method requires filling the blank bands of the electrical imaging image, and there is controversy over whether the filled part can represent the original geological information. Second, this method directly uses the contour boundary extraction method to identify the laminae contour, but the image noise and non-lamina boundaries will cause large errors in information extraction, and the calculation method is complicated. Finally, this method can only identify the sandy laminae that are more obvious on the imaging map, and the application effect is poor for shale laminae with smaller changes.

[0007] 2. Patent document with publication number CN113808190A discloses a method for quantitatively extracting shale lamination information based on electrical imaging logging images. This method can extract information including lamination thickness, lamination dip, lamination tendency and lamination strike. It mainly identifies lamination information by performing edge detection on imaging logging images. It has a good recognition effect on laminations with clear edges and wider sandy strips, but has a poor recognition effect on texture-level shale laminations and laminations with poor edge clarity.

[0008] 3. The patent document with publication number CN110866912A discloses a method for processing shale lamination heterogeneity data based on imaging logging image texture. The method adopts the Tamura texture feature algorithm and uses a computer to automatically extract the roughness, contrast and orientation of the imaging logging image. Based on the extraction parameter results of a large number of images, the hierarchical analysis method is used to quantify each characteristic parameter, analyze its weight, and construct a quantitative characterization model for shale lamination heterogeneity. However, this method extracts the Tamura texture parameters by calculating the entire image matrix, which reflects the complexity of the entire image and can reflect more obvious sandy bands, but has a poor response to the development of fine shale laminae, and even draws the opposite conclusion.

[0009] In summary, it is necessary to develop a new technology with good recognition effect, fast recognition speed and high response degree for fine shale laminae and laminae with poor clarity. Summary of the invention

[0010] The purpose of the present invention is to provide a method for determining the development degree of shale lamination based on electrical imaging data. The method combines the core lamination description of the study area with the logging response law, obtains dynamic imaging apparent conductivity data by processing the electrical imaging data, obtains volatility and difference according to the dynamic imaging data, and determines the lamination development index according to the volatility and difference, thereby finally determining the development degree of shale lamination, and has a better recognition effect even for fine shale laminations and laminations with poor clarity. In addition, the method of the present invention is simple, easy to operate, has good recognition effect, fast recognition speed, high response degree, strong reproducibility, and the calculation results meet the needs of the oil field site, especially in unconventional fields such as shale gas and shale oil, and has good universality and promotion value.

[0011] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0012] A method for determining the degree of shale lamination development based on electrical imaging data, characterized by comprising the following steps:

[0013] Step 1: Obtain electrical imaging data at each depth point of the shale well through well logging, filter, equalize, and scale the electrical imaging data in sequence, and obtain dynamic imaging apparent conductivity data corresponding to each depth point;

[0014] Step 2: Analyze the dynamic imaging apparent conductivity data of each depth point and exclude abnormal data points;

[0015] Step 3: Divide the dynamic imaging apparent conductivity data of each depth point into data segments, and calculate the single-point arithmetic mean of the dynamic imaging apparent conductivity data in each data segment;

[0016] Step 4: Take any depth range in the shale well and calculate the overall average value of all depth points in the depth range based on the single-point arithmetic average value of each depth point in the depth range;

[0017] Step 5: Set the apparent conductivity fluctuation rate, and compare the single-point arithmetic mean of two adjacent depth points with the overall mean value in turn. If the overall mean value is between the two single-point arithmetic mean values, it is determined that the shale well has a stripe layer, and the apparent conductivity fluctuation rate is increased by 1, otherwise the apparent conductivity fluctuation rate does not change;

[0018] Step 6: Calculate the apparent conductivity difference based on the single-point arithmetic mean and the overall mean within the depth range;

[0019] Step 7: Calculate the laminar development index based on the apparent conductivity fluctuation rate and the apparent conductivity difference;

[0020] Step 8: Determine the degree of lamina development based on the lamina development index.

[0021] The calculation method of apparent conductivity difference in step 6 is:

[0022]

[0023] Where, σ is the apparent conductivity difference, dimensionless;

[0024] N is the number of depth points within the depth range;

[0025] R xi It is the single-point arithmetic mean at any depth point;

[0026] Rxx is the overall average of all depth points within the depth range.

[0027] The calculation method of the laminae development index in step 7 is:

[0028]

[0029] Where, τ is the laminae development index, dimensionless;

[0030] σ is the apparent conductivity difference, dimensionless;

[0031] γ is the apparent conductivity fluctuation rate, dimensionless.

[0032] In step 8, the standard for determining the degree of lamination development according to the lamination development index is:

[0033] If the laminae development index is ≥20, the laminae development is judged to be strong;

[0034] If the laminae development index is less than 10, the laminae development is judged to be weak;

[0035] If the lamination development index is 10≤<20, the lamination development level is judged to be medium.

[0036] The method also includes drawing a lamina development degree display diagram. The method for drawing the lamina development degree display diagram is: first drawing a dynamic imaging diagram based on the dynamic imaging apparent conductivity data, drawing an average apparent conductivity curve based on the arithmetic mean of each single point, drawing a difference curve based on the apparent conductivity difference, drawing a volatility curve based on the apparent conductivity volatility, and drawing a lamina development index curve based on the lamina development index, and then combining the dynamic imaging diagram, the average apparent conductivity curve, the difference curve, the volatility curve and the lamina development index curve together to form a lamina development degree display diagram.

[0037] In step 1, the Schlumberger FMI logging tool is used to obtain electrical imaging data at various depth points in the shale well.

[0038] The dynamic imaging apparent conductivity data of each depth point in step 1 includes 192 data points.

[0039] The abnormal data points in step 2 include data points caused by wellbore collapse and high-angle fractures.

[0040] The data segments divided at each depth point in step 3 are consistent in the vertical direction of the shale well.

[0041] Any depth range in step 4 includes at least 100 depth points.

[0042] The advantages of adopting the present invention are:

[0043] 1. The present invention combines the core lamination description of the study area with the logging response law, obtains dynamic imaging apparent conductivity data by processing the electrical imaging data, obtains the volatility and difference based on the dynamic imaging data, determines the lamination development index based on the volatility and difference, and finally determines the development degree of the shale lamination, which has a better recognition effect even for fine shale laminations and laminations with poor clarity. In addition, the present invention uses dynamic imaging apparent conductivity data after filtering, equalization, and calibration, which is significantly different from other existing technologies that use imaging logging raw data or static data. Since the dynamic data is data after dynamic enhanced calibration of the resistivity of the local layer segment, the present invention has a better response to the lamination of low-resistance shale.

[0044] In summary, the method of the present invention is simple, easy to operate, has good recognition effect, fast recognition speed, high response degree, and strong replicability. The calculation results meet the needs of oil field sites, especially in unconventional fields such as shale gas and shale oil, and have good universality and promotion value.

[0045] 2. The entire determination method of the present invention is based on the analysis of the data itself rather than the analysis of the image to determine the degree of laminae development, and has higher reliability.

[0046] 3. The present invention can select any interval of 192 data points of imaging logging for calculation, avoiding interference from factors such as wellbore collapse, high-angle fractures, and image anomalies.

[0047] 4. The present invention adopts apparent conductivity fluctuation rate and apparent conductivity difference to jointly describe the degree of lamination development, which can effectively avoid the influence of imaging noise and massive dense sand bodies or calcareous interlayers, thereby facilitating improving the accuracy of the results.

[0048] 5. The present invention provides a standard for determining the degree of lamination development based on the lamination development index, and provides a lamination development degree display diagram, which is conducive to improving the speed and accuracy of determining the degree of shale lamination development, and is better than other existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flowchart of Example 1;

[0050] Figure 2 Schematic diagram of calculating apparent conductivity fluctuation rate in Example 1;

[0051] Figure 3 This is a diagram showing the degree of lamination development in Example 2;

[0052] Figure 4 This is the dynamic imaging diagram drawn in Example 3. DETAILED DESCRIPTION

[0053] Example 1

[0054] This embodiment provides a method for determining the degree of shale lamination development based on electrical imaging data, such as Figure 1 As shown, it includes the following steps:

[0055] Step 1: Obtain electrical imaging data at each depth point of the shale well through well logging. After obtaining the electrical imaging data, filter, equalize, and scale the data in sequence. After the processing is completed, the dynamic imaging apparent conductivity data corresponding to each depth point is obtained. The resolution of the dynamic imaging apparent conductivity data corresponding to each depth point is at least 5 mm, and the dynamic imaging apparent conductivity data of each depth point includes multiple apparent conductivity data points.

[0056] Preferably, this solution uses Schlumberger FMI logging instrument to obtain electrical imaging data at each depth point of the shale well, and the dynamic imaging apparent conductivity data of each depth point includes 192 data points. Of course, the above setting is preferred for this embodiment, and other logging instruments can be selected as needed in actual application, and other numbers of data points can be set.

[0057] Step 2: Analyze the dynamic imaging apparent conductivity data at each depth point and exclude abnormal data points.

[0058] In this step, the abnormal data points are excluded by analyzing the dynamic imaging apparent conductivity data. The specific method is to first draw a dynamic imaging map based on the dynamic imaging apparent conductivity data, and then find the abnormal data points based on the dynamic imaging map. Specifically, the abnormal data points include data points caused by wellbore collapse and high-angle fractures. After excluding the abnormal data points, only real and effective data points are retained, which is conducive to improving the accuracy of determining the degree of laminar development.

[0059] Step 3: Divide the dynamic imaging apparent conductivity data of each depth point into data segments, and calculate the single-point arithmetic mean R of the dynamic imaging apparent conductivity data in each data segment. xi .

[0060] The data segments in this step are mainly divided manually according to the actual situation, and each depth point corresponds to a data segment. Accordingly, after the calculation is completed, each depth point corresponds to a single-point arithmetic mean R xi In addition, since abnormal data points have been excluded in step 2, the divided data segments only include real and valid data points.

[0061] It should be noted that the data segments divided by each depth point in this step are consistent in the longitudinal direction of the shale well. For example, if the first depth point divides the 50th to 120th data points into one data segment, then the subsequent depth points also divide the corresponding 50th to 120th data points into data segments.

[0062] Step 4: Take any depth range in the shale well and calculate the arithmetic mean R of each depth point in the depth range. xi Calculate the overall average Rxx of all depth points within the depth range.

[0063] It should be noted that in this step, any depth range is taken in the shale well, and the any depth range includes at least 100 depth points. However, in order to further improve the accuracy, it is preferred to take a depth range including 200 depth points in the shale well.

[0064] Step 5: Set the apparent conductivity fluctuation rate γ and calculate the arithmetic mean R of two adjacent depth points. xiCompare with the overall mean Rxx. If the overall mean Rxx is between the two single point arithmetic means R xi(j) and R xi(j+1) If the value of j is between 0 and 1 (j is the number of depth points), the shale well is judged to have a striped layer, and the apparent conductivity fluctuation rate is increased by 1; otherwise, the apparent conductivity fluctuation rate does not change.

[0065] It should be noted that this step requires the arithmetic mean R of all adjacent single points within the depth range to be calculated in sequence. xi Compare with the overall mean Rxx, for example Figure 2 As shown, the arithmetic mean R of the first and second single points xi After comparing with the overall mean Rxx, it is also necessary to calculate the arithmetic mean of the second and third single points R xi Compare with the overall mean Rxx, and so on, until the arithmetic mean of all adjacent single points R xi The comparison with the overall average Rxx is completed in sequence. In addition, each time the comparison is made, if it is determined that the shale well has a striped layer, 1 is added to the previous apparent conductivity fluctuation rate γ until the final apparent conductivity fluctuation rate γ is obtained.

[0066] Step 6: Based on the arithmetic mean R of the single points within the depth range xi The apparent conductivity difference σ is calculated using the overall average Rxx.

[0067] The calculation method of apparent conductivity difference σ in this step is:

[0068]

[0069] Where, σ is the apparent conductivity difference, dimensionless;

[0070] N is the number of depth points within the depth range;

[0071] R xi It is the single-point arithmetic mean at any depth point;

[0072] Rxx is the overall average of all depth points within the depth range.

[0073] It should be noted that in step 6, the overall mean Rxx is located between the two single-point arithmetic means R xi(j) and R xi(j+1) When the value of σ is between σ and γ (j is the number of depth points), it can only be determined that the shale well has a stripe layer, but the development degree of the stripe layer needs to be analyzed in combination with the difference degree. Therefore, the development degree of the stripe layer can be finally determined by combining the apparent conductivity difference degree σ and the apparent conductivity fluctuation rate γ in this step.

[0074] Step 7: Calculate the laminae development index τ based on the apparent conductivity fluctuation rate γ and the apparent conductivity difference σ.

[0075] The calculation method of the laminae development index τ in this step is:

[0076]

[0077] Where, τ is the laminae development index, dimensionless;

[0078] σ is the apparent conductivity difference, dimensionless;

[0079] γ is the apparent conductivity fluctuation rate, dimensionless.

[0080] In practical applications, after obtaining the apparent conductivity difference σ and apparent conductivity fluctuation rate γ, we can also define the shale structure type as follows:

[0081]

[0082] Step 8: Determine the degree of lamination development according to the lamination development index τ. The specific determination criteria are:

[0083] If the laminae development index is ≥20, the laminae development is judged to be strong;

[0084] If the laminae development index is less than 10, the laminae development is judged to be weak;

[0085] If the lamination development index is 10≤<20, the lamination development level is judged to be medium.

[0086] In actual application of this embodiment, once the lamination development index τ is calculated by the above method, the lamination development degree can be determined according to the above standard. In summary, this method obtains dynamic imaging apparent conductivity data by processing electrical imaging data, obtains volatility and difference according to dynamic imaging data, and determines the lamination development index according to volatility and difference, so as to finally determine the development degree of shale lamination, and has better recognition effect even for fine shale lamination and lamination with poor clarity. In addition, this method is easy to operate, has good recognition effect, fast recognition speed, high response degree, strong replicability, and the calculation results meet the needs of oil field sites, especially in unconventional fields such as shale gas and shale oil, and has good universality and promotion value.

[0087] Example 2

[0088] On the basis of Example 1, in order to more intuitively display the development degree of shale laminae, this example further provides a method for drawing a laminae development degree display diagram, which is as follows:

[0089] First, the depth is drawn in the first track on the logging professional drawing software. The second step is to draw a dynamic imaging map in the second track based on the dynamic imaging apparent conductivity data. The third step is to calculate the arithmetic mean R of each single point. xi Draw the average apparent conductivity curve (the left scale is 0, the right scale is 127), the fourth step is to draw the difference curve according to the apparent conductivity difference σ (the left scale is 10, the right scale is 50), the fifth step is to draw the volatility curve according to the apparent conductivity volatility γ (the left scale is 0, the right scale is 80), the sixth step is to draw the lamination development index curve according to the lamination development index τ (the left scale is 0, the right scale is 50), fill the lamination development index from the right to 20, marked as a strong degree of lamination development, fill the lamination development index from the right to 10, marked as a medium degree of lamination development, fill the lamination development index from the right to 0, marked as a weak degree of lamination development, and then combine the dynamic imaging map, the average apparent conductivity curve, the difference curve, the volatility curve and the lamination development index curve together to form the following Figure 3 The diagram shows the degree of lamination development.

[0090] Example 3

[0091] This example verifies the method of the present invention as follows:

[0092] Step 1: Obtain the electrical imaging data of 2433-2441 meters of a shale well, and perform pre-processing such as filtering, equalization, and calibration on the electrical imaging data in turn, and obtain a total of 3150 depth points and corresponding dynamic imaging apparent conductivity data, as shown in the following table.

[0093]

[0094] Step 2: Draw a dynamic imaging diagram based on the dynamic imaging apparent conductivity data in step 1, such as Figure 4 As shown, it can be seen from the dynamic imaging diagram that the data points 0-35 of the shale well section are abnormal, and points 35-192 are selected as valid data points during calculation.

[0095] Step 3: Select points 35-192 as valid data points, and obtain the single-point arithmetic mean R of each depth point by arithmetic averaging. xi , as shown in the following table (due to the huge amount of data, only part of the data is shown here):

[0096] depth Rx 2433.0025 75.7969 2433.005 74.526 2433.0076 74.5677 2433.0101 74.526 2433.0127 73.4635 2433.0152 71.4271 . . . . . . . . . . . . 2440.9883 69.8542 2440.9908 63.6458 2440.9933 68.9219 2440.9959 75.6146 2440.9984 70.9844 2441.001 58.5833

[0097] Step 4: Take a depth range including 200 depth points in the shale well, and calculate the overall average value of all depth points in the depth range to be 64.46 (the first row in the table below, and the same below).

[0098] Step 5: The apparent conductivity fluctuation rate γ is calculated to be 15.75.

[0099] Step 6: The apparent conductivity difference σ is calculated to be 10.24.

[0100] Step 7: The calculated lamination development index τ is 1.65.

[0101] Step 8: According to the lamination development index τ, it is determined that the lamination development degree of the shale well section is weak.

[0102] Step 9: According to the apparent conductivity fluctuation rate γ and the apparent conductivity difference σ, the structural type of this section is determined to be blocky, as shown in the following table (the first line is the calculation results of 200 depth points):

[0103] depth RXX Difference σ Volatility γ Lamina Development Index Degree of lamination development Structure Type 2433.2337 64.46 10.24 15.75 1.65 weak Block 2433.4877 67.32 14.48 11.81 2.48 weak Block 2433.7417 67.69 18.41 17.72 6.01 weak Block 2433.9957 69.11 19.32 15.75 5.88 weak Block 2434.2497 69.40 19.82 13.78 5.41 weak Block 2434.5037 66.15 22.10 13.78 6.73 weak Sandwich 2434.7577 61.42 20.65 15.75 6.72 weak Sandwich 2435.0117 60.59 15.06 31.50 7.14 weak Laminated 2435.2657 62.46 16.94 43.31 12.42 medium Laminated 2435.5197 55.69 19.49 35.43 13.46 medium Laminated 2435.7737 59.28 23.28 29.53 16.00 medium Interlayer 2436.0277 66.92 27.31 29.53 22.03 powerful Interlayer 2436.2817 59.71 27.02 39.37 28.75 powerful Interlayer 2436.5357 53.55 26.70 49.21 35.09 powerful Interlayer 2436.7897 54.84 27.02 39.37 28.75 powerful Interlayer 2437.0437 61.18 21.41 31.50 14.44 medium Interlayer 2437.2977 62.80 17.53 21.65 6.65 weak Laminated 2437.5517 54.45 21.60 7.87 3.67 weak Sandwich 2437.8057 54.61 26.08 9.84 6.69 weak Sandwich 2438.0597 59.46 24.71 23.62 14.42 medium Interlayer 2438.3137 65.96 18.68 25.59 8.93 weak Laminated 2438.5677 66.31 21.98 15.75 7.61 weak Sandwich 2438.8217 61.23 24.76 17.72 10.86 medium Sandwich 2439.0757 64.61 25.21 19.68 12.51 medium Interlayer 2439.3297 66.90 24.07 17.72 10.26 medium Sandwich 2439.5837 67.01 18.64 15.75 5.47 weak Block 2439.8377 66.99 13.58 19.69 3.63 weak Laminated 2440.09169 69.50 9.79 29.53 2.83 weak Laminated 2440.34569 71.12 10.54 27.56 3.06 weak Laminated 2440.59969 68.79 11.34 29.53 3.80 weak Laminated 2440.85369 64.06 11.63 41.34 5.59 weak Laminated

[0104] It can be seen from the above content that the present invention can more accurately determine the development degree of shale laminations, and has a better recognition effect even for fine shale laminations and laminations with poor clarity.

[0105] The above description is only a specific implementation mode of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other alternative features that are equivalent or have similar purposes; all the disclosed features, or all the steps in the methods or processes, except for mutually exclusive features and / or steps, can be combined in any way.

Claims

1. A method for determining the degree of shale lamination development based on electrical imaging data, characterized in that The following steps are involved: Step 1: Obtain electrical imaging data at each depth point of the shale well through well logging, filter, equalize, and scale the electrical imaging data in sequence, and obtain dynamic imaging apparent conductivity data corresponding to each depth point; Step 2: Analyze the dynamic imaging apparent conductivity data of each depth point and exclude abnormal data points; Step 3: Divide the dynamic imaging apparent conductivity data of each depth point into data segments, and calculate the single-point arithmetic mean of the dynamic imaging apparent conductivity data in each data segment; Step 4: Take any depth range in the shale well and calculate the overall average value of all depth points in the depth range based on the single-point arithmetic average value of each depth point in the depth range; Step 5: Set the apparent conductivity fluctuation rate, and compare the single-point arithmetic mean of two adjacent depth points with the overall mean value in turn. If the overall mean value is between the two single-point arithmetic mean values, it is determined that the shale well has a stripe layer, and the apparent conductivity fluctuation rate is increased by 1, otherwise the apparent conductivity fluctuation rate does not change; Step 6: Calculate the apparent conductivity difference based on the single-point arithmetic mean and the overall mean within the depth range; Step 7: Calculate the laminar development index based on the apparent conductivity fluctuation rate and the apparent conductivity difference; Step 8: Determine the degree of lamina development based on the lamina development index.

2. The method for determining the degree of shale lamination development based on electrical imaging data according to claim 1, characterized in that: The calculation method of apparent conductivity difference in step 6 is: Where, σ is the apparent conductivity difference, dimensionless; N is the number of depth points within the depth range; R xi It is the single-point arithmetic mean at any depth point; Rxx is the overall average of all depth points within the depth range.

3. A method for determining the degree of shale lamination development based on electrical imaging data according to claim 2, characterized in that: The calculation method of the laminae development index in step 7 is: Where, τ is the laminae development index, dimensionless; σ is the apparent conductivity difference, dimensionless; γ is the apparent conductivity fluctuation rate, dimensionless.

4. The method for determining the degree of shale lamination development based on electrical imaging data according to claim 1, characterized in that: In step 8, the standard for determining the degree of lamination development according to the lamination development index is: If the laminae development index is ≥20, the laminae development is judged to be strong; If the laminae development index is less than 10, the laminae development is judged to be weak; If the lamination development index is 10≤<20, the lamination development level is judged to be medium.

5. A method for determining the degree of shale lamination development based on electrical imaging data according to any one of claims 1 to 4, characterized in that: The method also includes drawing a lamina development degree display diagram. The method for drawing the lamina development degree display diagram is: first drawing a dynamic imaging diagram based on the dynamic imaging apparent conductivity data, drawing an average apparent conductivity curve based on the arithmetic mean of each single point, drawing a difference curve based on the apparent conductivity difference, drawing a volatility curve based on the apparent conductivity volatility, and drawing a lamina development index curve based on the lamina development index, and then combining the dynamic imaging diagram, the average apparent conductivity curve, the difference curve, the volatility curve and the lamina development index curve together to form a lamina development degree display diagram.

6. The method for determining the degree of shale lamination development based on electrical imaging data according to claim 1, characterized in that: In step 1, the Schlumberger FMI logging tool is used to obtain electrical imaging data at various depth points in the shale well.

7. The method for determining the degree of development of shale laminations based on electrical imaging data according to claim 6, characterized in that: The dynamic imaging apparent conductivity data of each depth point in step 1 includes 192 data points.

8. The method for determining the degree of shale lamination development based on electrical imaging data according to claim 1, characterized in that: The abnormal data points in step 2 include data points caused by wellbore collapse and high-angle fractures.

9. The method for determining the degree of shale lamination development based on electrical imaging data according to claim 1, characterized in that: The data segments divided at each depth point in step 3 are consistent in the vertical direction of the shale well.

10. The method for determining the degree of development of shale laminations based on electrical imaging data according to claim 1, characterized in that: Any depth range in step 4 includes at least 100 depth points.

Images