Drilling rockburst tendency prediction method based on mechanical specific energy and acoustic emission cooperative response

Abstract

The application discloses a drilling rock burst tendency prediction method based on mechanical specific energy and acoustic emission cooperative response, and belongs to the technical field of rock mechanics and drilling engineering. The application synchronously collects digital drilling parameters and acoustic emission parameters through digital drilling and acoustic emission monitoring test; calculates mechanical specific energy MSE according to the digital drilling parameters, calculates AF and RA according to the acoustic emission parameters, and draws MSE variation curve with per-turn footage and AF-RA characteristic diagram; identifies the rock toughness-brittleness transition critical point according to the slope transition point of the MSE curve, and synchronously maps the rock toughness-brittleness transition critical point to the AF-RA characteristic diagram to determine MSEc and AF CA / RA CA at the transition point; calculates rock burst tendency index EA based on the two, and evaluates rock burst tendency according to the EA value. The application simultaneously captures energy evolution and crack expansion characteristics through the cooperative response of mechanical specific energy and acoustic emission parameters, realizes dynamic prediction of rock burst tendency, and is suitable for real-time rock burst risk warning of deep drilling and underground engineering.

Description

Technical Field

[0001] This invention belongs to the field of rock mechanics drilling engineering technology, specifically relating to a drilling rockburst tendency prediction method based on the synergistic response of mechanical specific energy and acoustic emission. Background Technology

[0002] With the continuous development and utilization of underground space and energy resources, the burial depth of underground engineering projects is constantly increasing. The high ground stress experienced by deep rock masses and the dynamic disturbances caused by excavation are significantly aggravated, making rockburst hazards increasingly frequent and severe. Rockbursts not only cause severe instability of the surrounding rock and damage to support structures, but also pose a direct threat to construction workers and machinery, and may even trigger a chain of secondary disasters such as collapses and gas outbursts. Therefore, accurately assessing and predicting the rockburst tendency of rock materials has become a core issue in the safety control of deep resource development and underground geotechnical engineering.

[0003] Currently, rockburst tendency assessment methods can be mainly divided into two categories: one is laboratory test methods based on rock mechanical parameters, such as obtaining rock strength indicators (e.g., uniaxial compressive strength, tensile strength) and energy indicators (e.g., elastic strain energy index, energy release rate) through uniaxial compression tests and triaxial compression tests, and determining rockburst tendency accordingly; the other is real-time early warning methods based on field monitoring, such as microseismic monitoring, acoustic emission monitoring, and borehole photography.

[0004] However, existing methods have the following shortcomings:

[0005] (1) Most existing evaluation indicators are based on the static stress-strain relationship of rocks and mainly rely on the results of indoor tests or the static mechanical parameters of rocks for judgment. However, deep rock masses are in a dynamic fracture state during drilling or excavation, and their failure mechanism is fundamentally different from that of quasi-static loading under indoor test conditions, resulting in a large deviation between the rockburst tendency evaluation results based on static parameters and the actual engineering situation.

[0006] (2) Existing methods usually focus on characterization of a single mechanical feature or a single monitoring method. For example, acoustic emission parameters alone can reflect the characteristics of crack propagation inside rocks, but it is difficult to characterize the energy evolution law during rock fracturing at the same time; drilling parameters alone (such as torque, thrust, and rotational speed) can reflect drilling energy consumption, but it is difficult to capture the microscopic damage evolution process inside rocks. The lack of information from a single parameter leads to insufficient prediction accuracy.

[0007] (3) Most existing methods are post-hoc analyses or offline evaluations, making it difficult to achieve real-time, dynamic predictions during drilling or excavation. For deep drilling projects, rockburst risk requires early warning before the rock mass ahead is damaged, but the real-time performance of existing methods is insufficient to meet engineering requirements.

[0008] During drilling and rock breaking, Mechanical Specific Energy (MSE) can be used to characterize the energy consumed per unit volume of rock during breaking. Its variation is closely related to the rock's strength, brittleness, and fragmentation mechanism. Acoustic emission signals, on the other hand, can reflect the initiation, propagation, and penetration of microcracks within the rock mass in real time, serving as a sensitive indicator of rock damage evolution. As the rock transitions from ductile to brittle failure during drilling, its fragmentation mechanism and energy dissipation characteristics change significantly. Correspondingly, MSE and acoustic emission parameters exhibit a clear synergistic response. Therefore, utilizing the synergistic response of MSE and acoustic emission parameters to identify the ductile-brittle transition behavior of rock during drilling and dynamically predicting rockburst tendency is of great significance for improving the accuracy and real-time performance of rockburst risk identification in deep drilling. Summary of the Invention

[0009] The purpose of this invention is to provide a drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission, so as to solve the problems in the existing technology that rockburst tendency prediction relies on static mechanical parameters, single monitoring means are difficult to simultaneously characterize energy evolution and crack propagation laws, and lack real-time dynamic prediction capabilities.

[0010] A drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission, characterized by the following steps: Step 1: Conduct digital drilling tests and acoustic emission monitoring tests on the sample, and simultaneously collect digital drilling parameters and acoustic emission parameters during the drilling process; Step 2: Calculate the mechanical energy specificity (MSE) based on the collected digital drilling parameters, and simultaneously calculate the acoustic emission characteristic parameters AF and RA based on the collected acoustic emission parameters. Plot the MSE variation curve with the advance per revolution and the AF-RA characteristic diagram. Step 3: Based on the slope transition point of the MSE curve with drilling depth per revolution, identify the critical point of the ductile-brittle transition of the rock during drilling, and synchronously map this critical point onto the AF-RA feature map to determine the mechanical specific energy MSEc and the ratio of acoustic emission characteristic parameters AF at the ductile-brittle transition point. CA / RA CA ; Step 4: Based on the MSEc and AF CA / RA CA The rockburst tendency index EA is calculated, and the rockburst tendency is evaluated and predicted based on the EA value.

[0011] Preferably, in step 1, the digital drilling parameters include: drill bit advance per revolution h, drill bit inner diameter r1, drill bit outer diameter r2, and confining pressure. s 3. Drill bit inclination angle α, friction coefficient μdf and contact stress σ df .

[0012] Preferably, the acoustic emission parameters include: rise time RT, amplitude A, and duration DUR.

[0013] Preferably, the formula for calculating the mechanical specific energy (MSE) in step 2 is based on the rock toughness-brittle transition criterion: (1) MSE is the mechanical specific energy. h It is the depth of the drill bit per revolution. h c It is the advance per revolution at the ductile-brittle transition point, σ3 is the confining pressure, and K is the diameter. mb b mb b mb '、K md b md b md '、b mb b mb ' is a constant related to the rock mechanical properties and drill bit geometry.

[0014] Preferably, , , , , , , G0 is the surface energy. b It is a constant. b It represents the yield strength, r1 represents the inner diameter of the drill bit, and r2 represents the outer diameter of the drill bit. 1 is the uniaxial compressive strength. It is the drill bit inclination angle. It is a constant, μ df It is the coefficient of friction, σ df It is contact stress.

[0015] Preferably, the method for identifying the slope transition point in step 3 is as follows: Plot MSE-h -1 Image, when h < h c At that time, the rock underwent ductile failure, with h -1 Add MSE-h -1 The slope of the image increases only slightly, h > h c At that time, the rock underwent brittle fracture, and with h -1 Add MSE-h -1The slope of the image increases significantly, and the rock toughness-brittle transition point during drilling is identified by the slope transition point of the image.

[0016] Preferably, the formula for calculating the acoustic emission parameter AF in step 2 is: (2) (3) Where RT represents the rise time, A represents the amplitude, and DUR represents the duration. The ringing count is the number of oscillations of the acoustic emission signal that exceed a preset threshold.

[0017] Preferably, step 3, which involves synchronously mapping the transformation critical point to the AF-RA feature map, specifically involves dividing the AF-RA feature map into a ductile failure stage and a brittle failure stage, using the ductile-brittle transition critical point as the boundary, calculating the proportion of tensile cracks and shear cracks in each stage, and extracting the AF at the transition point. CA / RA CA value.

[0018] Preferably, the formula for calculating the rockburst tendency index EA in step 4 is: (4) MSE c AF is the mechanical specific energy at the ductile-brittle transition point. CA / RA CA This represents the ratio of acoustic emission characteristic parameters at the ductile-brittle transition point.

[0019] Preferably, in step 4, the rockburst tendency level is classified according to the EA value, including: no rockburst tendency, light rockburst tendency, medium rockburst tendency and strong rockburst tendency; Specific criteria include:

[0020] Among them, EA1, EA2, and EA3 are the grading and calibration thresholds determined based on the experiment.

[0021] Among them, EA1=37, EA2=1037, and EA3=1537 are the grading and calibration thresholds determined by the experiment.

[0022] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention uses the coordinated response of mechanical specific energy and acoustic emission parameters to simultaneously monitor macroscopic drilling energy consumption and microscopic crack propagation signals during the same drilling process, overcoming the defect of missing information from a single parameter and significantly improving the accuracy of rockburst tendency prediction.

[0023] (2) The present invention uses the critical point of rock toughness-brittle transition as the key identification basis. This transition point is inherently consistent with the transition of rock from stable fracture to unstable fracture, which can capture the precursor characteristics of rockburst earlier and achieve early warning.

[0024] (3) The present invention uses the ratio of mechanical specific energy (MSE) to acoustic emission characteristic parameter (AF) CA / RA CA The integrated rockburst tendency index EA reflects both the energy evolution and crack propagation behavior during rock fracturing, thus overcoming the shortcomings of traditional energy indicators in reflecting the dynamic process of microcrack evolution.

[0025] (4) The present invention is based on the real-time acquisition parameters during the drilling process for calculation and analysis, without the need for complicated post-processing. It is applicable to the real-time identification and dynamic early warning of rockburst risk during deep drilling and underground engineering construction, and has good engineering application prospects. Attached Figure Description

[0026] Figure 1 The MSE of shale under different confining pressures in Example 1 varies with h -1 The change curve; Figure 2 The MSE of marble under different confining pressures varies with h in Example 2. -1 The change curve; Figure 3 The MSE of sandstone under different confining pressures in Example 3 varies with h. -1 The change curve; Figure 4 The MSE of granite in Example 4 varies with h under different confining pressures. -1 The change curve; Figure 5 This is a diagram showing the proportion of tensile cracks to shear cracks in shale during the ductile and brittle failure stages in Example 1. Figure 6 This is a diagram showing the proportion of tensile cracks to shear cracks in the marble during the ductile and brittle failure stages in Example 2. Figure 7 This is a diagram showing the proportion of tensile cracks and shear cracks in sandstone during the ductile and brittle failure stages in Example 3. Figure 8 This is a diagram showing the proportion of tensile cracks to shear cracks in the granite during the ductile and brittle failure stages in Example 4. Figure 9 The rockburst tendency index EA and the rockburst tendency index A are calculated using the method of this invention. EF The fitting relationship between them is shown in the figure; Figure 10The rockburst tendency index EA and the rockburst tendency index W are determined based on the method of this invention. ET Correlation diagram in rockburst prediction results; Figure 11 The rockburst tendency index EA and the rockburst tendency index W are determined based on the method of this invention. P ET Correlation diagram in rockburst prediction results; Figure 12 The rockburst tendency index EA and rockburst tendency index A are determined based on the method of this invention. CF Correlation diagram in rockburst prediction results; Figure 13 The rockburst tendency index EA and rockburst tendency index A' are determined based on the method of this invention. CF Correlation diagram in rockburst prediction results; Figure 14 This is a graph showing the correlation between the rockburst tendency index EA and the rockburst tendency index PES, determined based on the method of this invention, in the rockburst prediction results. Figure 15 This is a graph showing the correlation between the rockburst tendency index EA and the rockburst tendency index BIM determined by the method of this invention in the rockburst prediction results. Figure 16 The rockburst tendency index EA and rockburst tendency index A are determined based on the method of this invention. EF Correlation diagram in rockburst prediction results. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0028] This invention provides a method for predicting drilling rockburst tendency based on the synergistic response of mechanical specific energy and acoustic emission. This method simultaneously acquires digital drilling parameters and acoustic emission parameters during the drilling process, calculates the mechanical specific energy (MSE) and acoustic emission characteristic parameters AF and RA, identifies the critical point of the ductile-brittle transition in rock, and determines the rock's ductility based on the MSEc and AF at the transition point. CA / RA CA Construct a rockburst tendency index (EA) to achieve dynamic prediction of rockburst tendency.

[0029] Step 1: Synchronously acquire digital drilling parameters and acoustic emission parameters Digital drilling and acoustic emission monitoring tests were conducted on the samples. Digital drilling parameters and acoustic emission parameters were collected simultaneously during the drilling process. The digital drilling parameters included: depth per revolution (h), drill bit inner diameter (r1), drill bit outer diameter (r2), and confining pressure (σ3). The acoustic emission parameters included: ring count and acoustic emission energy.

[0030] Step 2: Calculate the mechanical energy specificity (MSE) based on the collected digital drilling parameters, and simultaneously calculate the acoustic emission characteristic parameters AF and RA based on the collected acoustic emission parameters. Plot the MSE variation curve with the advance per revolution and the AF-RA characteristic diagram. Specifically, the formula for calculating the mechanical specific energy (MSE) is based on the rock toughness-brittle transition criterion: (1) Where MSE is mechanical specific energy, h is the depth of the drill bit per revolution, and h c It is the advance per revolution at the ductile-brittle transition point, σ3 is the confining pressure, and K is the diameter. mb b md b md '、b mb b mb ' is a constant related to the rock mechanical properties and drill bit geometry.

[0031] Furthermore, , , , , , , G0 is the surface energy. b It is a constant. b It is the yield strength, and r1 and r2 are the inner and outer diameters of the drill bit, respectively. 1 is the uniaxial compressive strength. It is the drill bit inclination angle. It is a constant. df It is the coefficient of friction. df It is contact stress.

[0032] The formula for calculating the acoustic emission parameter AF is as follows: (2) (3) Where RT represents the rise time, A represents the amplitude, and DUR represents the duration.

[0033] Step 3: Identify the critical point of the tough-brittle transition and co-map it. Based on the slope transition point of the MSE curve with drilling depth per revolution, the critical point of the ductile-brittle transition of the rock during drilling is identified, and this critical point is synchronously mapped onto the AF-RA characteristic map to determine the mechanical specific energy MSEc and the ratio of acoustic emission characteristic parameters AF at the ductile-brittle transition point. CA / RA CA ; The method for identifying the slope transition point in step 3 is as follows: Plot MSE-h -1 Image, when h < h c At that time, the rock underwent ductile failure, with h -1 Add MSE-h -1 The slope of the image increases only slightly, h > h c At that time, the rock underwent brittle fracture, and with h -1 Add MSE-h -1 The slope of the image increases significantly, and the rock toughness-brittle transition point during drilling is identified by the slope transition point of the image.

[0034] The process of synchronously mapping the transformation critical point to the AF-RA feature map specifically involves dividing the AF-RA feature map into a ductile failure stage and a brittle failure stage, using the ductile-brittle transition critical point as the boundary. The proportions of tensile cracks and shear cracks in each stage are then statistically analyzed, and the AF at the transition point is extracted. CA / RA CA value.

[0035] Step 4: Calculate the rockburst tendency index EA and evaluate the prediction. Based on the aforementioned MSEc and AF CA / RA CA The rockburst tendency index EA is calculated, and the rockburst tendency is evaluated and predicted based on the EA value.

[0036] The formula for calculating the rockburst tendency index EA is as follows: (4) MSE c AF is the mechanical specific energy at the ductile-brittle transition point. CA / RA CA This represents the ratio of acoustic emission characteristic parameters at the ductile-brittle transition point.

[0037] Rockburst tendency is classified into four levels based on EA value: no rockburst tendency, slight rockburst tendency, moderate rockburst tendency, and strong rockburst tendency. Specific criteria include:

[0038] Among them, EA1, EA2, and EA3 are the grading and calibration thresholds determined based on the experiment.

[0039] Example 1 Prediction of Shale Rockburst Tendency under Different Confining Pressures In this embodiment, shale was selected as the test sample, and the drilling rockburst tendency was predicted according to the above-described method and steps.

[0040] Experimental preparation Shale samples were taken from a deep underground engineering project, with core dimensions of 50mm × 50mm × 50mm. The samples were processed into standard cubes, with the end face flatness meeting the test requirements. Three sets of different confining pressure conditions were set up: confining pressure σ3 = 10MPa, 20MPa, 30MPa, and 40MPa, to simulate the in-situ stress environment under different burial depths.

[0041] Step 1: Synchronously collect parameters Digital drilling tests and acoustic emission monitoring tests were conducted on shale samples.

[0042] Table 1 is a summary table of basic parameters.

[0043] Step 2: Calculate MSE, AF, and RA The mechanical specific energy (MSE) is calculated according to formula (1). The MSE varies with h under different confining pressures. -1 The change curve is as follows Figure 1 As shown.

[0044] The calculated MSE changes with the advance per revolution as follows: when h -1 When h is small (corresponding to a large h), MSE is low; as h increases... -1 As h increases (corresponding to a decrease in h), MSE first rises and then tends to stabilize, indicating a turning point.

[0045] The acoustic emission parameters AF and RA are calculated according to formulas (2) and (3).

[0046] Step 3: Identify the toughness-brittle transition point and co-map it. Plot MSE-h -1 Images, such as Figure 1 As shown. The critical point of the ductile-brittle transition is identified by the slope transition point of the image. Figure 1 It can be seen that in h -1 Less than 6.43 mm -1 Within the range, MSE varies with h -1 The slope of the increase is relatively large; at h -1 Greater than 6.43 mm -1 Within this range, the slope increases significantly. The slope inflection point occurs at h. -1= 6.43 mm -1 Location, i.e., h c =0.156 mm / r. This determines the critical point for the ductile-brittle transition.

[0047] On the MSE curve, the mechanical specific energy MSE at the transition point c =13.12 MPa.

[0048] The transition point was synchronously mapped onto the AF-RA feature map. Acoustic emission events before the transition point (ductile stage, h < 0.156 mm / r) and after the transition point (brittle stage, h > 0.156 mm / r) were labeled separately. The proportions of tensile cracks and shear cracks in shale during the ductile and brittle failure stages were shown. Figure 5 As shown.

[0049] pass Figure 5 Statistics show that: In the ductile stage, tensile cracks accounted for approximately 86.5%, and shear cracks accounted for approximately 13.5%. In the brittle stage, tensile cracks account for approximately 98.4%, while shear cracks account for approximately 1.6%.

[0050] AF at the turning point CA / RA CA =6.12.

[0051] Step 4: Calculate the EA index and evaluate the forecast. Calculate the rockburst tendency index EA according to formula (4): (Unit: MPa) Based on the calibrated grading thresholds (in this embodiment, EA1=37, EA2=1037, EA3=1537), EA = 80.27 is between EA2 and EA3, and is judged to be a slight rockburst tendency.

[0052] Result Validation To verify the accuracy of the prediction results, the EA prediction results of this embodiment are compared with the existing rockburst tendency index A. EF Compare, such as Figure 9 As shown.

[0053] Following the above procedure, the same analysis was performed on confining pressures of 5 MPa and 15 MPa. The results are summarized below:

[0054] To further verify the reliability of the method of the present invention, EA was compared with several existing rockburst tendency indices (W). ET W P ET A CF A'CF PES, BIM, A EF Correlation analysis was performed, and the results are as follows: Figures 10 to 16 As shown in the figure. The results show that the EA index proposed in this invention has a good correlation with each index and can simultaneously reflect the energy evolution and crack propagation laws.

[0055] Comparison of different confining pressure conditions Following the above procedure, the same analysis was performed on confining pressures of 20 MPa, 30 MPa, and 40 MPa. The results show that: Confining pressure 20MPa: Transition point h c =0.128mm / r, MSEc=18.92MJ / m³, AF CA / RA CA =2.43, EA = 45.91, judged as a slight rockburst tendency; Confining pressure 30MPa: Transition point h c =0.121mm / r, MSEC=31.64MJ / m³, AF CA / RA CA = 2.31, EA = 73.08, judged as a slight rockburst tendency.

[0056] Confining pressure 40MPa: Transformation point h c =0.114mm / r, MSEc=42.6MJ / m³, AF CA / RA CA =12.35, EA =526.25, judged as a slight rockburst tendency.

[0057] The above results show that as the confining pressure increases (i.e., the burial depth increases), the rockburst tendency of shale is significantly enhanced, which is consistent with actual engineering experience and further verifies the reliability of the method of the present invention.

[0058] Example 2 Prediction of rockburst tendency in marble under different confining pressure conditions In this embodiment, marble was selected as the test sample, and the drilling rockburst tendency was predicted according to the same test procedure as in Example 1.

[0059] (1) Experimental preparation Marble samples were taken from a mining project, with core dimensions of 50mm × 50mm × 50mm. The samples were machined into standard cubes, with the end faces meeting the experimental requirements for flatness. Four different confining pressure conditions were set up: confining pressure σ3 = 10MPa, 20MPa, 30MPa, and 40MPa.

[0060] (2) Step 1: Synchronously collect parameters The experimental parameters were set the same as in Example 1. Digital drilling parameters and acoustic emission parameters were collected simultaneously.

[0061]

[0062] (3) Step 2: Calculate MSE and AF, RA The mechanical specific energy (MSE) is calculated according to formula (1). The MSE of marble under different confining pressures varies with h. c The change curve is as follows Figure 2 As shown.

[0063] Calculation table of MSE and acoustic emission parameters of marble

[0064] Taking a confining pressure of 10 MPa as an example, the variation of MSE with each revolution of advance shows a similar but different pattern to that of shale. The calculated result near the transition point is: MSEc = 11.71 MJ / m³.

[0065] The acoustic emission parameters AF and RA are calculated according to formulas (2) and (3). The calculated AF at the transition point is... CA / RA CA =1.59.

[0066] (4) Step 3: Identify the toughness-brittle transition point and co-map it Plot MSE-h -1 Image. The critical point of the ductile-brittle transition is identified by the slope transition point, and h is determined. c = 0.51mm / r, MSEc=11.71MJ / m³.

[0067] This transition point is synchronously mapped onto the AF-RA feature map. The proportions of tensile cracks and shear cracks in marble during the ductile and brittle failure stages are shown below. Figure 6 As shown.

[0068] Statistics show that: In the ductile stage, tensile cracks accounted for approximately 98.3%, while shear cracks accounted for approximately 1.7%. In the brittle stage, tensile cracks account for approximately 95.1%, and shear cracks account for approximately 4.9%.

[0069] AF at the turning point CA / RA CA =1.59.

[0070]

[0071] (5) Step 4: Calculate the EA index and evaluate the forecast. Calculate the rockburst tendency index EA according to formula (4):

[0072] Based on the grading thresholds (EA1=37, EA2=1037, EA3=1537), EA = 18.66 is between EA1 and EA2, but closer to EA1. Combined with the crack evolution characteristics (the proportion of tensile cracks in the brittle stage is relatively low), it is determined to be without rockburst tendency.

[0073] (6) Comparison of different confining pressure conditions The same analysis was performed for confining pressures of 5 MPa and 15 MPa. The results show that the EA value of marble is less than or slightly higher than EA1 under all confining pressure conditions, indicating no tendency for rockburst, which is consistent with the experience that marble has a low rockburst risk in engineering practice.

[0074] Example 3 Prediction of rockburst tendency in sandstone under different confining pressures In this embodiment, sandstone was selected as the test sample, and the drilling rockburst tendency was predicted according to the same test procedure as in Example 1.

[0075] (1) Experimental preparation The sandstone sample was taken from a tunnel project, and the core sample had a side length of 50mm×50mm×50mm. Four different confining pressure conditions were set up: confining pressure σ3 = 10MPa, 20MPa, 30MPa, and 40MPa.

[0076] (2) Step 1: Synchronously collect parameters The experimental parameters were set the same as in Example 1. Digital drilling parameters and acoustic emission parameters were collected simultaneously.

[0077] (3) Step 2: Calculate MSE and AF, RA The mechanical specific energy (MSE) is calculated according to formula (1). The MSE of sandstone under different confining pressures varies with h. -1 The change curve is as follows Figure 3 As shown.

[0078] Taking a confining pressure of 10 MPa as an example, the calculation result at the transition point is: MSEc = 16 MJ / m³.

[0079] Calculate the acoustic emission parameters AF and RA according to formulas (2) and (3). Calculate AF at the transition point. CA / RA CA =12.34.

[0080] (4) Step 3: Identify the toughness-brittle transition point and perform co-mapping Plot MSE-h -1 Image. The critical point of the ductile-brittle transition was identified by the slope transition point, and hc = 0.1385 mm / r and MSEc = 16 MJ / m³ were determined.

[0081] This transition point is synchronously mapped onto the AF-RA feature map. The proportions of tensile cracks and shear cracks in sandstone during the ductile and brittle failure stages are shown below. Figure 7 As shown.

[0082] Statistics show that: In the ductile stage, tensile cracks accounted for approximately 98.6%, while shear cracks accounted for approximately 1.4%. In the brittle stage, tensile cracks accounted for approximately 82.9%, and shear cracks accounted for approximately 17.1%.

[0083] AF at the turning point CA / RA CA =12.34.

[0084] (5) Step 4: Calculate the EA index and evaluate the forecast. Calculate the rockburst tendency index EA according to formula (4):

[0085] Based on the grading thresholds (EA1=37, EA2=1037, EA3=1537), EA = 197.51 falls between EA1 and EA2, and is therefore judged to be a slight rockburst tendency.

[0086] (6) Comparison of different confining pressure conditions The same analysis was performed for confining pressures of 20 MPa, 30 MPa, and 40 MPa. The results show that the EA value of sandstone under each confining pressure condition is between EA1 and EA2, and is generally judged to have a slight tendency for rockburst, which is consistent with the experience in engineering practice that sandstone has a certain risk of rockburst but is generally controllable.

[0087] Example 4 Prediction of rockburst tendency in granite under different confining pressures In this embodiment, granite was selected as the test sample, and the drilling rockburst tendency was predicted according to the same test procedure as in Example 1.

[0088] (1) Experimental preparation The granite sample was taken from a deep mining project, with a core side length of 50mm×50mm×50mm. Three different confining pressure conditions were set: confining pressure σ3=10MPa, 20MPa, 30MPa, and 40MPa.

[0089] (2) Step 1: Synchronously collect parameters The experimental parameters were set the same as in Example 1. Digital drilling parameters and acoustic emission parameters were collected simultaneously.

[0090] (3) Step 2: Calculate MSE and AF, RA The mechanical specific energy (MSE) is calculated according to formula (1). The MSE of granite under different confining pressures varies with h. -1 The change curve is as follows Figure 4 As shown.

[0091] Taking the confining pressure of 10MPa as an example, the calculation result at the transition point is: MSEc=46.8 MJ / m³.

[0092] Calculate the acoustic emission parameters AF and RA according to formulas (2) and (3). Calculate AF at the transition point. CA / RA CA =20.21.

[0093] (4) Step 3: Identify the toughness-brittle transition point and perform co-mapping Plot MSE-h -1 Images, such as Figure 4 As shown. The critical point of the ductile-brittle transition was identified by the slope transition point, and hc = 0.12 mm / r and MSEc = 46.8 MJ / m³ were determined.

[0094] This transition point is synchronously mapped onto the AF-RA feature map. The proportions of tensile cracks and shear cracks in granite during the ductile and brittle failure stages are shown. Figure 8 As shown.

[0095] Statistics show that in the ductile stage, tensile cracks accounted for approximately 76.8%, and shear cracks accounted for approximately 23.2%. In the brittle stage, tensile cracks account for approximately 97.6%, while shear cracks account for approximately 2.4%.

[0096] AF at the turning point CA / RA CA =20.21.

[0097] (5) Step 4: Calculate the EA index and evaluate the forecast. Calculate the rockburst tendency index EA according to formula (4):

[0098] Based on the grading thresholds (EA1=37, EA2=1037, EA3=1537), EA=945.85 is between EA1 and EA2, close to EA3, and is judged to be a slight rockburst tendency.

[0099] (6) Comparison of different confining pressure conditions The results were obtained under confining pressures of 20 MPa, 30 MPa, and 40 MPa. Confining pressure 20MPa: EA = 897.28, indicating a tendency for minor rockbursts; Confining pressure 30MPa: EA = 1248.83, judged as moderate rockburst tendency; Confining pressure 40MPa: EA = 1615.99, indicating a strong tendency for rockburst.

[0100] The above results indicate that granite has a significantly enhanced tendency to rockburst under high confining pressure, which is highly consistent with the experience in engineering practice that granite is prone to strong rockbursts under deep high ground stress conditions.

[0101] The experimental results of the above four embodiments show that: Shale with a confining pressure of 10 MPa has an EA value of 80.27, indicating a tendency for minor rockbursts. Under a confining pressure of 10 MPa, the marble has an EA value of 18.66, indicating no tendency for rockburst. The sandstone has an EA value of 197.51 under a confining pressure of 10 MPa, indicating a tendency for minor rockbursts. The granite was determined to have a slight rockburst tendency under a confining pressure of 10 MPa (EA = 945.85).

[0102] like Figure 9 As shown, the EA calculated by the method of the present invention is related to the rockburst tendency index A. EF The fit between them is good, with a correlation coefficient exceeding 0.92. Furthermore, as... Figures 10 to 16 As shown, EA and several existing rockburst tendency indicators (W) ET W P ET A CF A' CF PES, BIM, A EF Correlation analysis shows that the EA index proposed in this invention has the best fit consistency and can simultaneously reflect the energy evolution and crack propagation laws, overcoming the limitations of traditional single indexes.

[0103] In summary, the drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission provided by this invention can accurately identify the ductile-brittle transition point of rock during drilling, and construct a rockburst tendency index based on the coordinated characteristic parameters at the transition point, thereby realizing dynamic and accurate prediction of rockburst tendency. It is applicable to rockburst risk identification and early warning in deep drilling and underground engineering construction.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting drilling rockburst tendency based on the coordinated response of mechanical specific energy and acoustic emission, characterized in that, Includes the following steps: Step 1: Conduct digital drilling tests and acoustic emission monitoring tests on the sample, and simultaneously collect digital drilling parameters and acoustic emission parameters during the drilling process; Step 2: Calculate the mechanical energy specificity (MSE) based on the collected digital drilling parameters, and simultaneously calculate the acoustic emission characteristic parameters AF and RA based on the collected acoustic emission parameters. Plot the MSE variation curve with the advance per revolution and the AF-RA characteristic diagram. Step 3: Based on the slope transition point of the MSE curve with drilling depth per revolution, identify the critical point of the ductile-brittle transition of the rock during drilling, and synchronously map the critical point of the transition onto the AF-RA feature map to determine the mechanical specific energy MSEc and the ratio of acoustic emission characteristic parameters AF at the ductile-brittle transition point. CA / RA CA ; Step 4: Based on the MSEc and AF CA / RA CA The rockburst tendency index EA is calculated, and the rockburst tendency is evaluated and predicted based on the EA value.

2. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 1, characterized in that, In step 1, the digital drilling parameters include: drill bit advance per revolution h, drill bit inner diameter r1, drill bit outer diameter r2, confining pressure σ3, drill bit inclination angle α, and friction coefficient μ. df and contact stress σ df .

3. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 1, characterized in that, The acoustic emission parameters include: rise time RT, amplitude A, and duration DUR.

4. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 1, characterized in that, The formula for calculating the mechanical specific energy (MSE) in step 2 is based on the rock ductility-brittle transition criterion: （1） Where MSE represents mechanical specific energy. h This indicates the depth of the drill bit per revolution. h c σ3 represents the advance per revolution at the ductile-brittle transition point, and K represents the confining pressure. md b md b md ' represents the constant corresponding to toughness, K mb b mb '、b mb This represents the constant corresponding to brittleness.

5. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission according to claim 4, characterized in that, ， ， ， ， ， ， G0 is the surface energy. b It is a constant. b It represents the yield strength, r1 represents the inner diameter of the drill bit, and r2 represents the outer diameter of the drill bit. 1 is the uniaxial compressive strength. It is the drill bit inclination angle. It is a constant, μ df It is the coefficient of friction, σ df It is contact stress.

6. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 3, characterized in that, The method for identifying the slope transition point in step 3 is as follows: Plot MSE-h -1 Image, when h < h c At that time, the rock undergoes ductile failure, and with h -1 Add MSE-h -1 The slope of the image increases by a smaller amount, h > h c At that time, the rock undergoes brittle fracture, and with h -1 Add MSE-h -1 The slope of the image increases more significantly, and the transition points between the ductility and brittleness of the rock during drilling are identified by the slope transition points in the image.

7. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 1, characterized in that, The formula for calculating the acoustic emission parameter AF in step 2 is as follows: （2） （3） Where RT represents the rise time, A represents the amplitude, and DUR represents the duration. The ringing count is the number of oscillations of the acoustic emission signal that exceed a preset threshold.

8. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 5, characterized in that, In step 3, the transition critical point is synchronously mapped onto the AF-RA feature map. Specifically, the AF-RA feature map is divided into ductile failure stage and brittle failure stage, with the ductile-brittle transition critical point as the boundary. The proportion of tensile cracks and shear cracks in the two stages is counted, and the AF at the transition point is extracted. CA / RA CA value.

9. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 1, characterized in that, The formula for calculating the rockburst tendency index EA in step 4 is as follows: （4） MSE c AF is the mechanical specific energy at the ductile-brittle transition point. CA / RA CA This represents the ratio of acoustic emission characteristic parameters at the ductile-brittle transition point.

10. The drilling rockburst tendency prediction method based on the coordinated response of mechanical specific energy and acoustic emission as described in claim 7, characterized in that, In step 4, rockburst tendency is classified according to EA value, including: no rockburst tendency, light rockburst tendency, medium rockburst tendency and strong rockburst tendency; Specific criteria include: Among them, EA1, EA2, and EA3 are the grading and calibration thresholds determined based on the experiment.

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