A method for determining the degree of shale lamination development based on electrical imaging data

Abstract

The present invention discloses a method for determining the degree of shale lamination development based on electrical imaging data, comprising the following steps: 1: acquiring electrical imaging data from a shale well and processing it to obtain dynamic imaging apparent conductivity data corresponding to each depth point; 2: eliminating abnormal data points; 3: dividing the dynamic imaging apparent conductivity data at each depth point into data segments and calculating the single-point arithmetic mean of each data segment; 4: calculating the overall average of all depth points within any depth range; 5: setting the apparent conductivity fluctuation rate and determining the apparent conductivity fluctuation rate based on the relationship between the single-point arithmetic mean and the overall average of two adjacent depth points; 6: calculating the apparent conductivity difference; 7: calculating the lamination development index; and 8: determining the degree of lamination development. This method also has an improved recognition effect for fine shale laminations and laminations with poor clarity. It is simple to operate, has good recognition effect, fast recognition speed, high response, and strong replicability, and has good universality and promotion value.

Description

Technical Field

[0001] The present 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] Shale oil and gas, as an important unconventional oil and gas resource, have received increasing attention worldwide in recent years. Shale oil and gas are primarily present in the pores or fractures of shale reservoirs in an adsorbed or free state. Shale reservoir properties vary widely, exhibiting strong heterogeneity, influenced by factors such as tectonic setting, sedimentary environment, provenance, and diagenesis. Shale heterogeneity encompasses not only lithology, thickness, organic matter abundance, structural texture, mineral composition, reservoir properties, and reservoir space structure, but also strong heterogeneity in maceral composition, biomarker compounds, laminae, and their assemblages. Laminae, the most distinctive sedimentary structure of shale, are widely developed in shale formations as bedding structures. Laminae are complex in type and highly heterogeneous, and their developmental characteristics, to a certain extent, control the formation and distribution of shale sweet spots. Therefore, a detailed assessment of laminae development is crucial for shale reservoir evaluation.

[0003] Currently, geological research on shale laminations mainly uses large numbers of thin sections or scanning electron microscopes to qualitatively describe heterogeneous characteristics such as the development degree and rhythm of shale laminations. However, this method is not only time-consuming but also expensive to test and analyze. It is not conducive to vertically continuous and efficient quantitative research on shale heterogeneity across 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. Patent document CN111951347A discloses a method for extracting sandy laminae parameters in 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 width to determine the degree of sandy laminae development. Although this method is relatively effective in extracting laminae information, it still has the following shortcomings:

[0006] First, this method requires filling blank areas in the electrical imaging image, and there is controversy over whether the filled areas represent the original geological information. Second, this method directly uses contour boundary extraction to identify laminae, but image noise and non-lamina boundaries can cause significant errors in information extraction, and the calculation method is complex. Finally, this method can only identify sandy laminae that are more obvious in the image, and is less effective for shale laminae with smaller variations.

[0007] 2. Patent document 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. Patent document CN110866912A discloses a method for processing shale lamination heterogeneity data based on imaging logging image texture. This method uses the Tamura texture feature algorithm to automatically extract the roughness, contrast, and orientation of the imaging logging image using a computer. Based on the parameter extraction results of a large number of images, a hierarchical analysis method is used to quantify each characteristic parameter, analyze their weights, and construct a quantitative characterization model for shale lamination heterogeneity. However, this method extracts Tamura texture parameters by calculating the entire image matrix, reflecting the overall complexity of the image. It can reflect more obvious sandy bands, but has a poor response to the development of fine shale laminae, and may even reach 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 degree of shale lamination development based on electrical imaging data. This 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 the volatility and difference based on the dynamic imaging data, and determines the lamination development index based on the volatility and difference, thereby ultimately determining the degree of shale lamination development. It 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, 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.

[0011] To achieve the above object, 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 to obtain dynamic imaging apparent conductivity data corresponding to each depth point;

[0014] Step 2: Analyze the dynamic imaging apparent conductivity data at 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 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 average. If the overall average is between the two single-point arithmetic means, 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 remains unchanged.

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

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

[0020] Step 8: Determine the degree of lamination development based on the lamination 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 arithmetic mean of the single point at any depth;

[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 based on 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 moderate.

[0036] The method also includes drawing a laminae development degree display diagram. The method for drawing the laminae development degree display diagram is as follows: first, a dynamic imaging diagram is drawn based on the dynamic imaging apparent conductivity data, an average apparent conductivity curve is drawn based on the arithmetic mean of each single point, a difference curve is drawn based on the apparent conductivity difference, a volatility curve is drawn based on the apparent conductivity volatility, and a laminae development index curve is drawn based on the laminae development index; then, the dynamic imaging diagram, the average apparent conductivity curve, the difference curve, the volatility curve and the laminae development index curve are combined together to form the laminae 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 for 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, and determines the lamination development index based on the volatility and difference, thereby ultimately determining the development degree of the shale lamination. It 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 that has been filtered, equalized, and scaled. This is significantly different from other existing technologies that use imaging logging raw data or static data. Since dynamic data is data that has been dynamically enhanced and scaled for the resistivity of local layers, 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 is more reliable.

[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 cracks, and image anomalies.

[0047] 4. The present invention uses 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 helping to improve 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 This is a flow chart 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. 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 processing, obtain dynamic imaging apparent conductivity data corresponding to each depth point. 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 for each depth point includes multiple apparent conductivity data points.

[0056] Preferably, this solution uses a Schlumberger FMI logging tool to acquire electrical imaging data at various depth points in the shale well. The dynamic imaging apparent conductivity data for each depth point includes 192 data points. Of course, the above configuration is preferred for this embodiment. In actual applications, other logging tools and a different number of data points may be selected as needed.

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

[0058] In this step, anomalous data points are eliminated by analyzing the dynamic imaging apparent conductivity data. The specific method is to first create a dynamic imaging map based on the dynamic imaging apparent conductivity data, and then identify anomalous data points based on the dynamic imaging map. Specifically, anomalous data points include those caused by wellbore collapse and high-angle fractures. After eliminating anomalous data points, only valid data points are retained, which helps improve 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 a data segment, then the subsequent depth points will 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 within 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 value 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 1 and 2 (j is the number of depth points), the shale well is determined to have a striped layer, and the apparent conductivity fluctuation rate is increased by 1; otherwise, the apparent conductivity fluctuation rate remains unchanged.

[0065] It should be noted that this step requires the arithmetic mean value 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 value 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 based on 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 arithmetic mean of the single point at any depth;

[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 at the arithmetic mean of the two single points R xi(j) and R xi(j+1) When the value of σ is between σ and σ (where j is the number of depth points), the shale well can only be determined to have a single striated layer. However, the extent of the striated layer needs to be analyzed in combination with the apparent conductivity difference σ and the apparent conductivity fluctuation rate γ in this step to ultimately determine the extent of the striated layer.

[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 lamination development degree based on 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 moderate.

[0086] In practical applications of this embodiment, once the lamination development index τ is calculated using the above method, the degree of lamination development can be determined according to the above standards. In summary, this method obtains dynamic imaging apparent conductivity data by processing electrical imaging data, obtains volatility and difference based on the dynamic imaging data, and determines the lamination development index based on the volatility and difference, thereby ultimately determining the degree of development of shale laminations. This method has a better recognition effect even for fine shale laminations and laminations with poor clarity. In addition, this method is easy to operate, has good recognition effect, fast recognition speed, high response, 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 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, draw the depth in the first track on the logging professional drawing software. Second, draw the dynamic imaging map in the second track based on the dynamic imaging apparent conductivity data. Third, 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 lamina development index curve according to the lamina development index τ (the left scale is 0, the right scale is 50), fill the lamina development index from the right to 20, marked as a strong lamina development degree, fill the lamina development index from the right to 10, marked as a medium lamina development degree, fill the lamina development index from the right to 0, marked as a weak lamina development degree, and then combine the dynamic imaging map, the average apparent conductivity curve, the difference curve, the volatility curve and the lamina 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 electrical imaging data from 2433 to 2441 meters in a shale well. Perform preprocessing on the electrical imaging data, including filtering, equalization, and scaling. A total of 3150 depth points and corresponding dynamic imaging apparent conductivity data are obtained, 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 in the figure, 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 calculate the arithmetic mean of each depth point R 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 Rxi 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 applies below).

[0098] Step 5: Calculate the apparent conductivity fluctuation rate γ 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: Determine the lamination development degree of the shale well section as weak based on the lamination development index τ.

[0102] Step 9: Based on 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 row 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 interlayered 2436.0277 66.92 27.31 29.53 22.03 powerful interlayered 2436.2817 59.71 27.02 39.37 28.75 powerful interlayered 2436.5357 53.55 26.70 49.21 35.09 powerful interlayered 2436.7897 54.84 27.02 39.37 28.75 powerful interlayered 2437.0437 61.18 21.41 31.50 14.44 medium interlayered 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 interlayered 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 interlayered 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] From the above content, it can be seen 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 embodiment of the present invention. Any feature disclosed in this specification, unless otherwise stated, can be replaced by other equivalent or alternative features with similar purposes; all disclosed features, or all 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 to obtain dynamic imaging apparent conductivity data corresponding to each depth point; Step 2: Analyze the dynamic imaging apparent conductivity data at 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 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 average. If the overall average is between the two single-point arithmetic means, 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 remains unchanged. Step 6: Calculate the apparent conductivity difference based on the arithmetic mean of the single points and the overall mean within the depth range; Step 7: Calculate the laminae development index based on the apparent conductivity fluctuation rate and apparent conductivity difference; Step 8: Determine the degree of lamination development based on the lamination 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. The 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 based on 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 moderate.

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 laminae development degree display diagram. The method for drawing the laminae development degree display diagram is as follows: first, a dynamic imaging diagram is drawn based on the dynamic imaging apparent conductivity data, an average apparent conductivity curve is drawn based on the arithmetic mean of each single point, a difference curve is drawn based on the apparent conductivity difference, a volatility curve is drawn based on the apparent conductivity volatility, and a laminae development index curve is drawn based on the laminae development index; then, the dynamic imaging diagram, the average apparent conductivity curve, the difference curve, the volatility curve and the laminae development index curve are combined together to form the laminae 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 shale lamination development based on electrical imaging data according to claim 6, characterized in that: The dynamic imaging apparent conductivity data for 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 shale lamination development 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