Rock drillability prediction method fusing penetration fracture characteristics and multi-scale parameters

Abstract

The present application relates to geotechnical engineering and image processing technical field, specifically relates to rock drillability prediction method fusing penetration broken characteristic and multi-scale parameter, in the present application, using image recognition technology, according to the broken characteristic of the penetration of the insert tooth to calculate the drillability index calibration value, and combining multi-scale parameter establishes the prediction model of drillability index, to predict rock drillability, so, under the experimental condition, rock drillability can be predicted, and the prediction result is very consistent with actual situation, and the prediction accuracy is high.

Description

Technical Field

[0001] This invention relates to the fields of geotechnical engineering and image processing technology, specifically to a method for predicting rock drillability by integrating penetration fracture characteristics and multi-scale parameters. Background Technology

[0002] In oil extraction, tunnel excavation, and pile foundation drilling, drilling operations are often required in rock. The rock drillability index characterizes the ease with which rock can be broken by a drill bit. This index is a comprehensive one, as it is related not only to the properties of the rock itself but also to the drilling methods, drill bit shapes, and other technological processes used in the construction.

[0003] Currently, the main methods for obtaining rock drillability indicators are physical testing and comprehensive evaluation. Regarding physical testing, micro-drilling is commonly used in laboratory settings to obtain rock drillability indicators. However, due to differences in testing conditions and drill bit size compared to field conditions, the drillability indicators obtained through this method do not accurately reflect the true drillability of the rock. Especially when using toothed hobbing cutters for rock breaking, the toothed cutters are sequentially driven into the rock, causing breakage between the cutters. Factors such as the shape, size, spacing, and penetration depth of the toothed cutters all affect the rock's penetration and breakage characteristics, thus influencing its drillability. As for comprehensive evaluation methods, current methods primarily use macroscopic indicators such as strength and hardness as evaluation metrics, without considering the influence of microscopic factors in the rock. Summary of the Invention

[0004] Based on this, the present invention provides a method for predicting rock drillability by integrating penetration fracture characteristics and multi-scale parameters, which solves at least one problem in the prior art.

[0005] In a first aspect, the present invention provides a method for predicting rock drillability by integrating penetration fracture characteristics and multi-scale parameters, comprising the following steps: The insert is inserted into the calibration group rock sample to cut the calibration group rock sample and obtain an image of the cut surface; The drillability index calibration value of the rock samples in the calibration group was calculated based on the fracture characteristics caused by the toothed penetration in the image. ; Based on the drillability index calibration values ​​of the rock samples in the calibration group Establish a predictive model for drillability indices using multi-scale parameters; The multi-scale parameters of the rock group to be tested are input into the prediction model, and the drillability index of the rock group to be tested is output. According to the drillability index Determine the drillability of the rock.

[0006] In this invention, image recognition technology is used to calculate the drillability index calibration value based on the fracture characteristics caused by the penetration of the insert teeth, and a predictive model for the drillability index is established by combining multi-scale parameters, thereby predicting the drillability of rocks. Thus, rock drillability can be predicted under experimental conditions, and the prediction results are highly consistent with the actual situation, demonstrating high prediction accuracy.

[0007] In some optional embodiments, the drillability index calibration value of the calibration group rock samples is calculated based on the fracturing features caused by the toothed penetration in the image. include: Identify the fractured and cracked areas caused by the penetration of the insert teeth in the image; The drillability index rating value is calculated using the following formula. : in, This indicates the drillability index calibration value; 、 They represent the weighting coefficients, where , ,and ; This indicates the depth to which the insert penetrates the rock sample of the calibration group; Indicates the vertical axis length of the fractured zone; This indicates the vertical axis length of the cracked area.

[0008] It should be noted that distinguishing between the fractured zone and the cracked zone improves the consistency between the prediction results and the actual situation, and further improves the accuracy of the prediction.

[0009] In some alternative embodiments, the number of teeth is two or more, and all teeth are located on the same straight line. Preferably, the number of teeth is two.

[0010] In some alternative embodiments, the vertical axis of the crushing zone is... The vertical axis of the semi-ellipse surrounding the fractured area is the horizontal axis length of the distance between the two insert teeth, and the vertical axis length of the cracked area is... The vertical axis of the semi-ellipse surrounding the cracked area is the horizontal axis length of the distance between the two insert teeth.

[0011] It should be noted that viewing the broken and cracked areas in the image as semi-ellipses with the horizontal axis length being the distance between the two inserts simplifies the calculation process.

[0012] In some alternative embodiments, the insert teeth are the insert teeth of an insert hob. Preferably, the insert teeth are the bevel teeth of a bevel hob.

[0013] In some alternative embodiments, dye is injected along the edge of the insert before cutting the rock sample, allowing the dye to enter the fractured areas and cracks of the rock sample.

[0014] It should be noted that using dye to color the fractured areas and cracks can make them easier to distinguish and improve the image quality of the cut surface.

[0015] In some alternative embodiments, the multi-scale parameters include macroscopic mechanical property indicators, mineral composition indicators, microstructural feature indicators, and inhomogeneity indicators.

[0016] In some optional embodiments, the drillability index is calibrated based on the calibration values ​​of the calibration group rock samples. The predictive models for drillability indices based on multi-scale parameters include: Based on the drillability index calibration values ​​of the rock samples in the calibration group A weight matrix is ​​established based on macroscopic mechanical properties, mineral composition, microstructure characteristics, and inhomogeneity. and weight matrix ; Based on the macroscopic mechanical properties, mineral composition, microstructure characteristics, and heterogeneity of the rocks to be tested, a multi-factor nested matrix was constructed. Among them, multi-factor nested matrix Including a matrix of macroscopic mechanical property index factors Mineral composition index factor matrix Microstructural characteristic index factor matrix Non-uniformity index factor matrix ; multi-factor nested matrix Standardization is performed to obtain a standardized multi-factor nested matrix. ; Based on the standardized multi-factor nested matrix and weight matrix , Calculate the drillability index of the rock group to be tested. The calculation formula is: .

[0017] It should be noted that the drillability index calibration value and standardized multi-factor nested matrix Together they are used to calculate drillability index This method integrates penetration fracture characteristics and multi-scale parameters (macroscopic mechanical properties, mineral composition, microstructure, and heterogeneity), further improving prediction accuracy. Macroscopic mechanical properties of rocks include uniaxial compressive strength, triaxial compressive strength, uniaxial tensile strength, Brazilian splitting strength, elastic modulus, Poisson's ratio, and tensile-compressive strength ratio. Mineral composition indicators include the content of various minerals in the rock. Microstructure indicators include the average grain size, roundness, porosity, and connectivity of rock particles. Heterogeneity indicators include the heterogeneity coefficient, curvature coefficient, ratio of different mineral types, fractal dimension, and coefficient of variation. A factor matrix is ​​constructed. , , and At that time, some or all of the above indicators can be selected, and , , and Both can be column matrices.

[0018] In some optional embodiments, the drillability index is calibrated based on the calibration values ​​of the calibration group rock samples. A weight matrix is ​​established based on macroscopic mechanical properties, mineral composition, microstructure characteristics, and inhomogeneity. and weight matrix include: The macroscopic mechanical property index factor matrix is ​​divided into... Mineral composition index factor matrix Microstructural characteristic index factor matrix Non-uniformity index factor matrix The elements involved and the drillability index calibration values ​​of the rock samples in the calibration group. Perform data fitting and construct weight matrices based on the data fitting results. Weight matrix Weight matrix Weight matrix and weight matrix ; where, for the weight matrix Weight matrix Weight matrix and weight matrix Weight matrix The first in element Calculated by the following formula: in, , , Indicates the first Factor matrix The number of factors in Indicates the first Factor matrix Elements and drillability index calibration values Data fitting results; for the weight matrix Weight matrix No. element Calculated by the following formula: in, ; The weight matrix is ​​calculated according to the following formula. : in, This represents the zero matrix.

[0019] In some alternative embodiments, based on the drillability index Determining rock drillability includes: According to drillability index Rock drillability is classified; among them, when When the value is ≥0.8, it is considered to have extremely high drillability; when 0.7≤ When the value is <0.8, it is considered to have strong drillability; when 0.6 ≤ When the value is <0.7, the drillability is considered relatively strong; when 0.5 ≤ A value <0.6 indicates moderate drillability, while a value ≤0.4 indicates moderate drillability. When the value is <0.5, the drillability is considered weak; when 0.3 ≤ When the value is less than 0.4, it is considered to have poor drillability. When the value is less than 0.3, it is considered to have extremely poor drillability.

[0020] In a second aspect, the present invention provides an electronic device comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores instructions that, when executed by at least one processor, implement the rock drillability prediction method that integrates penetration fracture characteristics and multi-scale parameters.

[0021] Thirdly, the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the rock drillability prediction method that integrates penetration fracture characteristics and multi-scale parameters.

[0022] Because of the adoption of the above technical solutions, the embodiments of the present invention have at least the following beneficial effects: (1) By inserting the insert into the rock sample, the rock breaking process at the construction site can be reproduced well, and the rock breaking characteristics under the action of the insert can be observed directly. Based on these breaking characteristics, the drillability can be predicted, and the prediction effect is good. (2) By fusing the intrusion fragmentation features and multi-scale parameters, the prediction results are made more accurate; (3) The rock drillability index obtained through the embodiments of the present invention is close to the rock drillability index when drilling is carried out on site using toothed hobbing cutter bit. It can reflect the rock damage characteristics and solve the problem that the conventional prediction results of rock drillability under some special circumstances have a large deviation from the actual situation. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the rock drillability prediction method that integrates penetration fracture characteristics and multi-scale parameters in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram illustrating the process of obtaining the range of the fractured zone and the cracked zone caused by the tooth in the rock based on image recognition technology in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram illustrating the composition of a multi-factor nested matrix in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the process for obtaining rock particle size distribution curves based on image recognition technology in an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram illustrating the relationship between qualitative evaluation of rock drillability and multi-scale parameter indices in an embodiment of the present invention. Detailed Implementation

[0028] The following will provide a clear and complete description of the concept and technical effects of the present invention, so as to fully explain the purpose, solution and effects of the present invention.

[0029] This invention provides a method for predicting rock drillability by integrating penetration fracture characteristics and multi-scale parameters, such as... Figure 1 As shown, it includes the following steps: S1. Insert the insert into the calibration group rock sample, cut the calibration group rock sample and obtain an image of the cut surface; S2. Calculate the drillability index calibration value of the rock samples in the calibration group based on the fracture characteristics caused by the toothed penetration in the image. ; S3. Calibrate the drillability index values ​​based on the rock samples in the calibration group. Establish a predictive model for drillability indices using multi-scale parameters; S4. Input the multi-scale parameters of the rock group to be tested into the prediction model, and output the drillability index of the rock group to be tested. According to the drillability index Determine the drillability of the rock.

[0030] In step S1, multiple rock samples are taken from a pile foundation drilling project (the project requires drilling approximately 30m into the rock strata, which can be classified into four types: extremely coarse sandstone, coarse sandstone, medium sandstone, and fine sandstone) and prepared as rock specimens. Each rock specimen is a cuboid 30cm long, 15cm wide, and 15cm high, labeled as Rock Specimen A, Rock Specimen B, Rock Specimen C, Rock Specimen D, and Rock Specimen E, collectively known as the calibration group. The crushing tool used in the field is a conical toothed cutter. Two identical conical teeth are placed vertically on the end face of the rock specimen, with both teeth aligned along the same straight line along the length of the specimen and a fixed spacing of 60mm between them. The tips of the conical teeth are brought into contact with the center of the end face of the rock specimen, and both teeth are simultaneously pressed vertically into the rock specimen to a depth of 8mm. Dye is injected into the damaged area of ​​the rock. After the dye dries, the rock sample is vertically cut using the line between the two conical teeth as a reference to obtain a vertical cross-section (cut surface) of the rock sample. An image of the cut surface is then captured using a camera. This process is as follows: Figure 2 As shown.

[0031] In step S2, after acquiring the vertical cross-sectional images of rock samples A to E, the vertical cross-sectional images are further divided into two sub-images, using the axis of the conical teeth as the boundary. The fracture and cracking ranges caused by the conical teeth within the rock are defined as the fracture zone and cracking zone, respectively, and these zones are located between two adjacent conical teeth. The fracture and cracking zones in each sub-segmented vertical cross-sectional image are simplified into a semi-ellipse with a fixed horizontal axis length of 60 mm. Based on the following formula for calculating the rock drillability index, the drillability index calibration value of the calibration group of rocks can be obtained. : in, This indicates the drillability index calibration value; 、 They represent the weighting coefficients, where , ,and ; Indicates the depth to which the insert penetrates the rock sample; Indicates the vertical axis length of the fractured zone; This indicates the vertical axis length of the cracked area. The higher the value, the more drillable the rock. Set to 1. Set it to 0, and since the bevel tooth indentation depth is 8mm, therefore: It can be seen that the drillability index calibration value It is related to the vertical axis length of the fractured zone and the vertical axis length of the cracked zone.

[0032] In step S3, the macroscopic mechanical properties, mineral composition, microstructure characteristics, and inhomogeneity of the calibration group rock samples and the rocks to be tested (labeled as groups A to D) are obtained, such as... Figure 3 As shown in the figure. Among them, the macroscopic mechanical properties selected are uniaxial compressive strength, elastic modulus, and Brazilian splitting strength as representative indicators; the mineral composition properties selected are quartz mineral content and clay mineral content as representative indicators; the microstructure characteristics selected are average grain size as representative indicator; and the heterogeneity properties selected are the ratio of quartz mineral content to clay mineral content and the heterogeneity coefficient as representative indicators. The average grain size and heterogeneity coefficient are obtained from the rock's grain size distribution curve. The process of obtaining the rock grain size distribution curve based on image recognition technology is as follows: Figure 4 As shown in Table 1, the multi-scale parameter indices of the rocks tested in groups A to D are summarized.

[0033] Table 1. Multiscale parameters of rocks in groups A to D. Based on the indicators in Table 1, a multi-factor nested matrix is ​​constructed. : in, , , , .

[0034] The multi-scale parameter indices of each group of rocks are stored in the form of a column matrix in a multi-factor nested matrix. middle.

[0035] Drillability index calibration values ​​of rock samples in calibration group The quantitative and qualitative relationships with multi-scale parameter indices are shown in Table 2 and... Figure 5 As shown in Table 2 and Figure 5 As can be seen from the data, with the increase of compressive strength, quartz content, the ratio of quartz content to clay mineral content, and the coefficient of uniformity, the drillability index calibration value decreases. The drillability index gradually decreases as tensile strength, elastic modulus, clay mineral content, and average particle size increase. Gradually increase.

[0036] Table 2. Numerical Relationship between Drillability Index Calibration Values ​​and Multi-Scale Parameter Indicators Based on the distribution range of various multi-scale parameters of typical rocks, the factor matrix is ​​determined. , , and The upper and lower limits of each factor are shown in Table 3.

[0037] Table 3 Upper and lower limits of multi-scale parameter indices Based on the numerical relationships shown in Table 2 and the upper and lower limits of each factor shown in Table 3, a standardized processing matrix is ​​constructed. : in, , , , .

[0038] At the same time, a standardized processing matrix is ​​constructed. : in, , , , .

[0039] According to the formula multi-factor nested matrix Each column in the data is standardized, where Represent the Hadamard Product, and according to the "standardized multi-factor nested matrix" Following the principle of "if an element is greater than 1, it is set to 1; if it is less than 0, its absolute value is taken," a standardized multi-factor nested matrix can be obtained. : in, , , , .

[0040] Next, construct the weight matrices respectively. Weight matrix Weight matrix Weight matrix and weight matrix Weight matrix , , , All are row matrices, and their construction process is as follows: With factor matrix The first in Each element is an independent variable. Rock drillability index calibration value As the dependent variable, for Data fitting is performed on the set of data to obtain the fitting function. ,remember Corresponding weight matrix The first in The elements are : In the formula, , The first Factor matrix The Middle Upper limit of each factor and lower limit value , , , For the first Factor matrix The number of factors in.

[0041] weight matrix Row matrix, weight matrix The Middle The elements are : , In the formula, , , For the first Factor matrix The number of factors in.

[0042] According to the formula , Based on the data in Tables 2 and 3, the calculation results of the change can be obtained, as shown in Table 4.

[0043] Table 4 Calculation Results of Changes According to the formula , Based on the data in Table 4, the weight calculation results are shown in Table 5.

[0044] Table 5 Weight Calculation Results Therefore, the following weight matrix can be obtained: According to the formula And rock drillability classification "when A value ≥0.8 is considered extremely strong, while a value ≤0.7 is considered very strong. A value <0.8 is considered strong, and 0.6 ≤ A value <0.7 is considered relatively strong, while a value ≤0.5 is considered relatively strong. A value <0.6 is assessed as moderate, and 0.4 ≤ A value <0.5 is considered weak, and a value ≤0.3 is considered weak. When <0.4, it is assessed as weak; when "A value <0.3 is assessed as extremely weak" refers to the drillability index of the rocks tested in groups A to D. The calculations were performed separately, and the results are shown in Table 6. Table 6 shows that the drillability is: medium sandstone < fine sandstone < coarse sandstone < very coarse sandstone.

[0045] Table 6 Drillability Calculation Results As shown in Table 7, a comparison with the actual drilling speed during on-site construction reveals that the average drilling speed of fine sandstone is lower than that of very coarse sandstone and coarse sandstone, but higher than that of medium sandstone. Therefore, the drillability is: medium sandstone < fine sandstone < coarse sandstone < very coarse sandstone. Conventional indoor micro-drilling tests show: medium sandstone < coarse sandstone < very coarse sandstone < fine sandstone, which does not reflect the fact that the drillability of fine sandstone is weaker than that of very coarse and coarse sandstone. The method of this invention can predict that the drillability of fine sandstone is stronger than that of medium sandstone, but weaker than that of very coarse and coarse sandstone, which is consistent with actual conditions, demonstrating the accuracy and advancement of the method.

[0046] Table 7 Comparison of Actual Drilling Rate and Test Results The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. Any embodiment that achieves the technical effects of the present invention by the same or equivalent means should fall within the protection scope of the present invention. Within the protection scope of the present invention, various modifications and variations can be made to the technical solutions and / or implementation methods.

Claims

1. A method for predicting rock drillability by integrating penetration fracture characteristics and multi-scale parameters, characterized in that, Includes the following steps: The insert is inserted into the calibration group rock sample to cut the calibration group rock sample and obtain an image of the cut surface; The drillability index calibration value of the rock samples in the calibration group was calculated based on the fracture characteristics caused by the toothed penetration in the image. ; Based on the drillability index calibration values ​​of the rock samples in the calibration group Establish a predictive model for drillability indices using multi-scale parameters; The multi-scale parameters of the rock group to be tested are input into the prediction model, and the drillability index of the rock group to be tested is output. According to the drillability index Determine the drillability of the rock; Among them, multi-scale parameters include macroscopic mechanical property indicators, mineral composition indicators, microstructural characteristic indicators, and non-uniformity indicators; Based on the drillability index calibration values ​​of the rock samples in the calibration group The predictive models for drillability indices based on multi-scale parameters include: Based on the drillability index calibration values ​​of the rock samples in the calibration group A weight matrix is ​​established based on macroscopic mechanical properties, mineral composition, microstructure characteristics, and inhomogeneity. and weight matrix ; Based on the macroscopic mechanical properties, mineral composition, microstructure characteristics, and heterogeneity of the rocks to be tested, a multi-factor nested matrix was constructed. Among them, multi-factor nested matrix Including a matrix of macroscopic mechanical property index factors Mineral composition index factor matrix Microstructural characteristic index factor matrix Non-uniformity index factor matrix ; multi-factor nested matrix Standardization is performed to obtain a standardized multi-factor nested matrix. ; Based on the standardized multi-factor nested matrix and weight matrix , Calculate the drillability index of the rock group to be tested. The calculation formula is: .

2. The method according to claim 1, characterized in that, The drillability index calibration value of the rock samples in the calibration group was calculated based on the fracture characteristics caused by the toothed penetration in the image. include: Identify the fractured and cracked areas caused by the penetration of the insert teeth in the image; The drillability index rating value is calculated using the following formula. : in, This indicates the drillability index calibration value; 、 They represent the weighting coefficients, where , ,and ; This indicates the depth to which the insert penetrates the rock sample of the calibration group; Indicates the vertical axis length of the fractured zone; This indicates the vertical axis length of the cracked area.

3. The method according to claim 2, characterized in that, The number of teeth is two or more, and all teeth are located on the same straight line.

4. The method according to claim 3, characterized in that, Vertical axis length of the fracture zone The vertical axis of the semi-ellipse surrounding the fractured area is the horizontal axis length of the distance between the two insert teeth, and the vertical axis length of the cracked area is... The vertical axis of the semi-ellipse surrounding the cracked area is the horizontal axis length of the distance between the two insert teeth.

5. The method according to claim 1, characterized in that, Based on the drillability index calibration values ​​of the rock samples in the calibration group A weight matrix is ​​established based on macroscopic mechanical properties, mineral composition, microstructure characteristics, and inhomogeneity. and weight matrix include: The macroscopic mechanical property index factor matrix is ​​divided into... Mineral composition index factor matrix Microstructural characteristic index factor matrix Non-uniformity index factor matrix The elements involved and the drillability index calibration values ​​of the rock samples in the calibration group. Perform data fitting and construct weight matrices based on the data fitting results. Weight matrix Weight matrix Weight matrix and weight matrix ; where, for the weight matrix Weight matrix Weight matrix and weight matrix Weight matrix The first in element Calculated by the following formula: in, , , Indicates the first Factor matrix The number of factors in Indicates the first Factor matrix Elements and drillability index calibration values Data fitting results; for the weight matrix Weight matrix No. element Calculated by the following formula: in, ; The weight matrix is ​​calculated according to the following formula. : in, This represents the zero matrix.

6. The method according to claim 1, characterized in that, According to drillability index Determining rock drillability includes: According to drillability index Rock drillability is classified; among them, when When the value is ≥0.8, it is considered to have extremely high drillability; when 0.7≤ When the value is <0.8, it is considered to have strong drillability; when 0.6 ≤ When the value is <0.7, the drillability is considered relatively strong; when 0.5 ≤ A value <0.6 indicates moderate drillability, while a value ≤0.4 indicates moderate drillability. When the value is <0.5, the drillability is considered weak; when 0.3 ≤ When the value is less than 0.4, it is considered to have poor drillability. When the value is less than 0.3, it is considered to have extremely poor drillability.

7. An electronic device, characterized in that, include: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores instructions that, when executed by at least one processor, implement the rock drillability prediction method that integrates penetration fracture characteristics and multi-scale parameters as described in any of claims 1-6.

8. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed by a processor, implement the rock drillability prediction method that integrates penetration fracture characteristics and multi-scale parameters as described in any of claims 1-6.

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