A rock strength grading method based on axial torsional coupling dynamics model of drill string

Abstract

The present application relates to the field of formation perception in downhole drilling process, and discloses a rock strength grading method based on a drill string axial torsion coupling dynamics model, comprising: introducing local damping of the drill string, establishing a drill string axial-torsion coupling three-degree-of-freedom dynamics model considering the borehole inclination and the drill bit-rock interaction; in the stage of rod retraction and rod reinsertion, taking the minimum error between the calculated orifice drilling speed and the rotation speed of the drill string dynamics model as the optimization target, and using the grey wolf optimization algorithm to identify the local damping coefficients of the axial and torsion dimensions of the drill string; in the stage of drill bit rock breaking drilling, identifying the friction coefficient of the drill bit-rock interaction and the inherent specific energy of the rock through the dynamics model with the identified local damping coefficients; establishing the correlation rules of the friction coefficient, the inherent specific energy of the rock and the rock strength, and realizing the rock strength grading. The method has good rock strength grading ability and good engineering adaptability, and can provide decision support for drill bit selection, drilling speed control and trajectory control.

Description

Technical Field

[0001] This invention relates to the field of formation sensing during underground coal mine drilling, and particularly to a rock strength classification method based on a drill string axial torsion coupling dynamic model. Background Technology

[0002] During directional drilling, the lithology of the formation changes frequently, especially in complex areas such as fractured soft coal seams and interbedded rock zones. The stability and efficiency of the drilling process are directly affected by the formation strength. Traditional formation strength classification methods mostly rely on manual logging, well logging, or experience-based judgment. The data collected cannot reflect changes in rock strength well, and they suffer from problems such as strong delay, low classification accuracy, and difficulty in achieving real-time feedback.

[0003] In recent years, some studies have begun to focus on the application of drill string mechanical response in rock strength classification, attempting to classify rock strength through the dynamic characteristics of parameters such as drilling speed and rotation speed. However, these methods are often based on simplified one-dimensional axial models, neglecting the axial-torsional coupled vibration characteristics of the drill string under inclined drilling conditions, as well as the local damping effect during the slag removal process. This leads to ambiguity in the strength classification of complex formations, especially in areas of fractured and soft coal seams where the interaction between the drill string and the formation is complex, making it difficult for traditional methods to accurately model the formation.

[0004] Therefore, it is necessary to provide a rock strength classification method based on the drill string axial-torsional coupled dynamic model, which fully considers the coupling characteristics of drill string axial and torsional vibrations. Based on the measurable drilling parameters at the borehole, a staged parameter identification and strength classification mechanism is introduced to realize real-time classification of different rock strengths and provide intelligent support for drilling condition adjustment. Summary of the Invention

[0005] The purpose of this invention is to solve the technical problem of difficulty in real-time grading of rock strength during underground drilling in coal mines.

[0006] This invention provides a rock strength classification method based on a drill string axial torsion coupling dynamic model.

[0007] Specifically, the method includes the following steps: S1: Introduce local damping in the drill string and establish a three-degree-of-freedom dynamic model of the drill string with axial-torsional coupling, considering the borehole inclination angle and the interaction between the drill bit and the rock.

[0008] S2: During the retraction and reverse drilling stage, when the drill bit is not in contact with the rock, the required power head input torque and drilling pressure are calculated using the borehole measurement parameters. The borehole drilling speed and rotation speed are calculated using the axial-torsional coupled three-degree-of-freedom dynamic model of the drill string. Combining the actual borehole drilling speed and rotation speed with the borehole drilling speed and rotation speed calculated by the model, the minimum error between the two is used as the optimization objective. The local damping coefficients of the axial and torsional dimensions of the drill string are identified using the Grey Wolf optimization algorithm.

[0009] S3: During the rock-breaking drilling stage, the power head input torque and drilling pressure required by the drill string dynamic model are calculated using the borehole measurement parameters. The borehole drilling speed and rotation speed are calculated at this time using the axial-torsional coupled three-degree-of-freedom dynamic model of the drill string after the local damping coefficient is determined. Combining the actual borehole drilling speed, rotation speed and model calculation results, the friction coefficient of the drill bit-rock interaction and the inherent specific energy of the rock are identified.

[0010] S4: Construct a rock strength characterization quantity based on the identified friction coefficient and inherent specific energy of the rock. Establish the correlation rules between friction coefficient, rock intrinsic specific energy, and rock strength, and based on... To achieve the classification of rock strength.

[0011] A storage medium storing instructions and data for implementing a rock strength grading method based on a drill string axial torsion coupling dynamic model.

[0012] A rock strength grading device based on a drill string axial torsion coupling dynamic model includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a rock strength grading method based on a drill string axial torsion coupling dynamic model.

[0013] The beneficial effects provided by this invention are: (1) By establishing a three-degree-of-freedom dynamic model of drill string axial-torsional coupling that considers borehole inclination angle, drill bit-rock interaction and local damping, this invention can accurately characterize the dynamic response of drill string system under inclined borehole conditions and provide a model basis for rock strength classification.

[0014] (2) This invention combines the actual borehole drilling speed and rotation speed with the borehole drilling speed and rotation speed calculated by the model, and takes the minimum error between the two as the optimization target. It uses the Grey Wolf optimization algorithm to identify the local damping coefficient, the friction coefficient of the drill bit-rock interaction and the inherent specific energy of the rock in stages, which can improve the consistency between the parameter identification results and the actual working conditions.

[0015] (3) The method proposed in this invention has been verified by multiple sets of actual drilling data. It has shown good adaptability in different types of rocks and lithological transition zones. It can provide important support for intelligent drilling control, drill bit selection and trajectory planning in coal mines. It has significant engineering application value and promotion potential. Attached Figure Description

[0016] Figure 1 This is a simplified flowchart of the method of the present invention; Figure 2 The rotational speed and drilling speed results output by the drill string axial torsion coupling model when identifying axial local damping and torsional local damping in this embodiment of the invention; Figure 3 The output drilling speed and output rotation speed results of the drill string axial torsion coupling model for different rocks in the embodiments of the present invention; Figure 4 This refers to the iterative results of the gray wolf optimization algorithm in this embodiment of the invention; Figure 5 This is a diagram showing the rock strength grading results based on association rules in an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware device operation according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0018] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0019] Example 1: Please refer to Figure 1 The present invention provides a rock strength grading method based on a drill string axial torsion coupled dynamic model, comprising: S1: Introduce local damping of the drill string and establish a three-degree-of-freedom dynamic model of the drill string axial-torsional coupling considering the borehole inclination angle and the drill bit-rock interaction; As a specific implementation method, step S1 introduces local damping of the drill string and establishes a three-degree-of-freedom dynamic model of the drill string with axial-torsional coupling, considering the borehole inclination angle and the interaction between the drill bit and the rock.

[0020] Specifically, the drill string system, which can extend for hundreds of meters, is simplified into three lumped-mass units: the power head unit, the intermediate drill pipe unit, and the drill bit unit. Each unit has axial and torsional degrees of freedom. The model considers the local damping (characterized by the damping coefficient) generated by the contact between the drill string and the borehole wall / drilling fluid, the influence of gravity components due to the borehole inclination angle, and the reaction forces and torques generated by the interaction between the drill bit and the rock. This model provides a physical basis for subsequent parameter identification.

[0021] S2: During the retraction and reverse drilling stage, when the drill bit is not in contact with the rock, the required power head input torque and drilling pressure for the drill string dynamics model are calculated using the borehole measurement parameters. The borehole drilling speed and rotation speed are calculated using the drill string axial-torsional coupled three-degree-of-freedom dynamics model. Combining the actual borehole drilling speed and rotation speed with the model-calculated borehole drilling speed and rotation speed, the minimum error between the two is used as the optimization objective. The local damping coefficients of the drill string axial and torsional dimensions are identified using the Grey Wolf optimization algorithm. In one specific implementation, step S2 is performed during the reverse drilling stage after the drill bit is withdrawn. At this time, the drill bit has been completely lifted off the bottom of the hole and is not in contact with the rock, therefore the drill bit-rock force is zero. Using parameters such as feed pressure, pull-out pressure, main pump pressure, and return oil pressure obtained from orifice measurements, the input torque of the power head is calculated. and drilling pressure These calculated values ​​are used as input and substituted into the dynamic model of step S1 to obtain the borehole drilling speed and rotational speed through model calculation. Then, with the minimum error between the model calculation value and the actual borehole measurement value as the optimization objective, the local damping coefficients in the axial and torsional dimensions of the drill string are identified using the GreyWolf Optimizer (GWO) algorithm. During this stage, due to the absence of drill bit-rock interaction interference, the damping characteristics of the drill string itself can be calibrated independently.

[0022] S3: During the rock-breaking drilling stage, the power head input torque and drilling pressure required by the drill string dynamic model are calculated using the borehole measurement parameters. The borehole drilling speed and rotation speed are calculated at this time using the drill string axial-torsional coupling three-degree-of-freedom dynamic model after the local damping coefficient is determined. Combining the actual borehole drilling speed, rotation speed and model calculation results, the friction coefficient of the drill bit-rock interaction and the inherent specific energy of the rock are identified. As a specific implementation method, step S3 is performed during the normal rock-breaking drilling stage. At this time, the drill bit is in contact with the rock, and the drill bit-rock interaction force is non-zero. Similarly, calculations are performed using borehole measurement parameters. and This value is then input into the dynamic model of step S1. It's important to note that the previously identified local damping coefficient in this stage is fixed at the optimal value from step S2 and remains unchanged. Then, again with the optimization objective of minimizing the error between the model's calculated borehole drilling speed and rotational speed and the actual measured values, the Grey Wolf optimization algorithm is used to identify the key parameter in the drill bit-rock interaction model: the friction coefficient. (Dimensionless) and the inherent specific energy of rocks (Unit: Pa or J / m³).

[0023] S4: Construct a rock strength characterization quantity based on the identified friction coefficient and inherent specific energy of the rock. Establish the correlation rules between friction coefficient, rock intrinsic specific energy, and rock strength, and based on... To achieve the classification of rock strength.

[0024] As a specific implementation method, step S4, based on the friction coefficient μ and the rock's inherent specific energy ε identified in step S3, first performs dimensionless processing on the rock's inherent specific energy ε to obtain the dimensionless inherent specific energy ε. ,Right now Through statistical analysis of a large amount of laboratory and field data, a system was established. , The correlation rule between rock uniaxial compressive strength (UCS), i.e., the linear relationship. ,in These are the fitting constant coefficients. According to... The numerical range is used to classify different rock strength grades (such as soft rock, medium-hard rock, hard rock, and extremely hard rock) to achieve real-time classification of rock strength.

[0025] For example, the method described in this embodiment was used in directional drilling in a coal mine. The axial local damping coefficient was identified during the rod retraction and tilting stage. During normal drilling into sandstone layers, the following can be identified: , Take the reference intrinsic energy as Using a pre-fitted formula Calculated Based on the grading standard (H>35 is extremely hard rock), the current rock was determined to be white sandstone, which is consistent with the core sampling results.

[0026] Example 2: It should be noted that in step S1 of the present invention, the reaction force at the drill bit end... and reaction torque The results were obtained from the drill bit-rock interaction model, which considers both cutting and frictional effects. The expression is as follows:

[0027]

[0028] In the formula, and These are the reaction force and reaction torque generated by the interaction between the drill bit and the rock; and These are the forces and torques involved in the friction process, respectively. and These are the forces and torques acting during the cutting process; It is the rock's inherent specific energy, which is related to the rock's strength; It is the drill bit radius. It is a constant characterizing the tilt of the blade. It is the rock contact strength. It is the length at the wear surface of the drill bit. It is the coefficient of friction. These are parameters describing the direction and spatial distribution of the friction contact surface of the drill bit blades. It is the sum of the cutting depths of each blade in one revolution of the drill bit. Under steady-state conditions, the cutting depth satisfies .

[0029] As a specific implementation method, the reaction force at the drill bit end in step S1 and reaction torque The expression is derived from a drill bit-rock interaction model that simultaneously considers cutting and frictional effects:

[0030] The parameters are explained in detail below: : The reaction force on the drill bit, in N.

[0031] : The reaction torque experienced by the drill bit, in N·m.

[0032] Specific energy of rock, expressed in Pa or J / m³. Characterizes the energy required to break a unit volume of rock and is positively correlated with rock strength. Typical value range: soft coal. medium-hard sandstone Hard granite .

[0033] Drill bit radius, in meters (m). For example, a Φ98mm drill bit. Φ120mm drill bit .

[0034] The blade inclination constant is dimensionless and determined by the drill bit geometry, typically between 0.6 and 0.9. For example, 0.8 can be used for PDC drill bits.

[0035] The total depth of cut by each blade in one revolution of the drill bit, expressed in meters (m). Under stable drilling conditions, this satisfies... ,in The axial velocity of the drill bit is (m / s). ω represents the angular velocity of the drill bit rotation (rad / s).

[0036] Rock contact strength, measured in Pa, can be approximated as the compressive strength of the rock. For example, mudstone. ,sandstone .

[0037] Length of the worn surface of the drill bit, in meters. (When the drill bit is new) As wear increases, It can reach 0.001 to 0.005 m.

[0038] Friction coefficient, dimensionless, typically between 0.2 and 0.6.

[0039] : A dimensionless parameter describing the direction and spatial distribution of the friction contact surface of the drill bit blades, typically ranging from 0.4 to 0.6.

[0040] For example, a drilling operation using a Φ120mm PDC drill bit ( , , ), can be identified , The measured cutting depth Known rock contact strength Length of drill bit wear surface Then the calculation yields:

[0041] It should be noted that in step S1, the drill string system is simplified into an axial and torsional three-degree-of-freedom mass-spring-damping system consisting of a power head unit, an intermediate drill pipe unit, and a drill bit unit, and an axial-torsional coupled three-degree-of-freedom dynamic model of the drill string considering the hole inclination angle and local damping is established.

[0042] As a specific implementation method, step S1 simplifies the drill string system into an axial and torsional three-degree-of-freedom mass-spring-damped system consisting of a power head unit, an intermediate drill pipe unit, and a drill bit unit. This simplification method captures the low-frequency dominant modes of drill string vibration while avoiding excessive computational burden on the finite element model.

[0043] Specifically: Power Head Unit (Unit 1): Represents the top drive or power head and its lower section, with equivalent mass. and equivalent moment of inertia .

[0044] Intermediate drill pipe unit (unit 2): Represents the main part of the drill string, composed of multiple drill pipes combined into an equivalent mass block, possessing... and .

[0045] Drill bit unit (unit 3): Represents the drill bit and bottom hole assembly (BHA), which has and .

[0046] Adjacent units are connected by massless springs (stiffness coefficient) ) and internal dampers (damping coefficient) The connection is as follows. Each unit is also subject to local damping due to its interaction with the surrounding medium (drilling fluid, borehole wall). The model also considers the borehole inclination angle. The resulting gravitational component, for example, the gravitational component experienced by element 1 is .

[0047] For example, at the angle of inclination In the drilling process, the simplified model parameters of the drill string are as follows: , ; , ; , Axial stiffness , Torsional stiffness , Such a simplified model can more accurately simulate the axial and torsional coupled vibrations of the drill string downhole.

[0048] It should be noted that in step S1, the drill string axial-torsional coupled three-degree-of-freedom dynamic model is expressed as follows:

[0049] In the formula, and For the rotational speed and axial velocity of a three-degree-of-freedom element, and These represent the angular displacement and axial displacement of each element. and These are the angular acceleration and acceleration of each unit, respectively; and These are the equivalent moment of inertia and equivalent mass of each element; These are the local damping coefficients for the three torsional degrees of freedom; These are the local damping coefficients for the three axial degrees of freedom; This is the torsional stiffness coefficient; This is the axial stiffness coefficient; Input torque to the power head, For drilling pressure, This represents the load torque on the drill bit; The angle of the opening. It is the acceleration due to gravity; During drilling, the input torque and drilling pressure of the power head are calculated from the parameters of feed pressure, pull-out pressure, main pump pressure, and return oil pressure, and the expression is:

[0050] in, This refers to the displacement of the rotary motor; Main pump pressure; This refers to the return oil pressure; For pressure loss in the hydraulic system; To provide pressure; For pulling pressure; To provide the piston area of ​​the oil cylinder; The piston rod area; Based on the structural parameters of the drill string system, the moment of inertia of each of the above units... ,quality Axial damping coefficient and torsional damping coefficient Axial stiffness coefficient and torsional stiffness coefficient The calculation formula is as follows:

[0051] in, It is the length of each unit. and These are the outer diameter and inner diameter of each unit. It is the density of each unit. and These are the damping coefficients in the axial and torsional dimensions, respectively. and These are the elastic modulus and shear modulus of the drill string, respectively.

[0052] As a specific implementation method, the drill string axial-torsional coupled three-degree-of-freedom dynamic model described in step S1 is expressed as the following six differential equations: Equation of torsional motion:

[0053] Axial motion equation:

[0054] The meanings of the symbols in the equation are explained below: Angular displacement, in rad; Angular velocity, unit: rad / s; Angular acceleration, unit: rad / s².

[0055] Axial displacement, in meters (m); Axial velocity, in m / s; Axial acceleration, in m / s².

[0056] Equivalent moment of inertia, in kg·m²; Equivalent mass, unit: kg.

[0057] Torsional local damping coefficient, in N·m·s / rad; : Axial local damping coefficient, unit N·s / m.

[0058] Torsional internal damping coefficient, in N·m·s / rad; : Axial internal damping coefficient, unit N·s / m.

[0059] Torsional stiffness coefficient, in N·m / rad; : Axial stiffness coefficient, unit N / m.

[0060] : Input torque of the power head, in N·m; Drilling pressure, in N.

[0061] : Drill bit load torque, unit N·m; : Drill bit reaction force, unit N.

[0062] : Angle of inclination of the opening, in degrees or rad; : Gravitational acceleration, taken as 9.81 m / s².

[0063] Calculation of input torque and drilling pressure:

[0064] Rotary motor displacement, measured in m³ / rev. For example... .

[0065] Main pump pressure, unit: Pa; Return oil pressure, in Pa; Hydraulic system pressure loss, unit: Pa. For example... , , .

[0066] Feed pressure, in Pa; Pull-out pressure, unit: Pa.

[0067] : Piston area of ​​the hydraulic cylinder, in m²; Piston rod area, in m². For example... , .

[0068] Formulas for calculating model parameters:

[0069] Unit length, in meters (m). For example, if a single drill pipe is 9.6 m long, take... .

[0070] Unit outer diameter, in meters; : Inner diameter of unit, in meters (m). For example, if the drill pipe has an outer diameter of 73 mm and an inner diameter of 60 mm, then... , .

[0071] Unit density, unit kg / m³. (For steel drill pipes) .

[0072] : Axial damping ratio, dimensionless; : Torsional damping ratio, dimensionless. These are the corresponding parameters of the local damping coefficients to be identified in step S2.

[0073] : Elastic modulus, steel ; Shear modulus, steel .

[0074] For example, calculate the length of a segment , outer diameter , inner diameter The drill pipe unit. Then:

[0075] It should be noted that in step S2, the identification of local damping is achieved by constructing a parameter identification process with the optimization objective of minimizing the error between the model output borehole drilling speed and borehole rotation speed and the actual borehole drilling speed and rotation speed, and the local damping coefficients of the drill string axial and torsional dimensions are obtained by solving the Grey Wolf optimization algorithm.

[0076] As a specific implementation method, the identification of local damping in step S2 is achieved by constructing a parameter identification process with the optimization objective of minimizing the error between the model output borehole drilling speed and borehole rotation speed and the actual borehole drilling speed and rotation speed, and obtaining the local damping coefficients of the drill string axial and torsional dimensions by solving the Grey Wolf optimization algorithm.

[0077] The specific implementation steps are as follows: Constructing the objective function: Define the root mean square error (RMSE) as the objective function.

[0078] in The parameter vector to be identified, The number of sampling points. This is a weighting factor (usually taken as 1, or adjusted according to the dimensional differences between drilling speed and rotation speed).

[0079] Grey Wolf Optimization Algorithm (GWO) Settings: Initialize population size (e.g., 20 wolves), maximum number of iterations (e.g., 100), search range (e.g., ... Between 50 and 3000 N·s / m, (50–500 N·m·s / rad).

[0080] The position of each wolf represents a set of damping coefficients to be identified.

[0081] The optimization was achieved through iterative simulation of the social hierarchy (α, β, δ wolves) and hunting behaviors (encirclement, pursuit, attack) of gray wolves.

[0082] Simulation calculation: For each set of candidate damping coefficients, the dynamic model of S1 is used (set). In the same input The following calculation model output and .

[0083] Fitness evaluation: Calculate the objective function value; the smaller the value, the better the set of parameters.

[0084] Update and convergence: Update the wolf pack position and repeat steps 3-4 until the maximum number of iterations is reached or the change in the objective function value is less than the set threshold.

[0085] For example, during the rod retraction and reversal phase of a ZYL-6000D drilling rig, 60 seconds of drilling data (sampling rate 10Hz) were collected. The GWO algorithm (population 30, iterations 150) was used for identification. After algorithm convergence, the optimal local damping coefficient was obtained. , , Substituting the identification results into the model, the calculated RMSE between the borehole drilling speed and the actual drilling speed was 0.002 m / s, and the RMSE of the rotational speed was 0.3 rad / min, which meets the engineering requirements.

[0086] It should be noted that in step S3, the drill bit-rock interaction parameter identification is carried out by constructing a parameter identification process with the optimization objective of minimizing the error between the model output borehole drilling speed and borehole rotation speed and the actual borehole drilling speed and rotation speed, and the friction coefficient and rock intrinsic specific energy of the drill bit-rock interaction are obtained by solving the Grey Wolf optimization algorithm.

[0087] As a specific implementation method, the drill bit-rock interaction parameter identification in step S3 involves constructing a parameter identification process with the optimization objective of minimizing the error between the model-output borehole drilling speed and borehole rotation speed and the actual borehole drilling speed and rotation speed. The friction coefficient of the drill bit-rock interaction is then obtained by solving the problem using the Grey Wolf optimization algorithm. Compared with the inherent specific energy of rocks .

[0088] The specific implementation is similar to step S2, but the parameter to be identified becomes... At this point, the local damping coefficient identified in step S2 is fixed as a constant and no longer participates in the optimization. The objective function also uses the root mean square error of the borehole drilling speed and rotation speed. The search range of the GWO algorithm is set as follows: Typically, it ranges from 0.1 to 0.8. exist .

[0089] For example, when drilling into an unknown stratum, the damping coefficient identified in step S2 is fixed, and the current stage's... and Input the model and search for the optimal model using the GWO algorithm (population 20, 80 iterations). and After 45 iterations, it converged, yielding... , The error between the model's calculated output and the actual output was controlled within 5%. Subsequent core sampling verification showed that this section was fine sandstone with a standard UCS of 55 MPa. It shows a good positive correlation with this.

[0090] It should be noted that the gray wolf optimization algorithm described in steps S2 and S3 uses a population initialization, fitness evaluation and parameter update strategy to iteratively search for the optimal parameter combination.

[0091] As a specific implementation method, the gray wolf optimization algorithm described in steps S2 and S3 performs iterative search through population initialization, fitness evaluation and parameter update strategies to obtain the optimal parameter combination.

[0092] Core steps of the GWO algorithm: Population initialization: Randomly generate the positions of N gray wolves, and the position vector for each wolf. This corresponds to a set of parameters to be identified (d is the parameter dimension). Simultaneously, the parameter search boundary is defined. .

[0093] Fitness evaluation: Calculate the objective function value (e.g., RMSE) for each wolf; the smaller the value, the higher the fitness. The three wolves with the best fitness are denoted as follows: (Alpha Wolf) , .

[0094] Location update: other wolves ( )according to , , The location is updated to simulate the behavior of a gray wolf surrounding and attacking its prey. The update formula is as follows:

[0095] in, , ; The linear progression decreases from 2 to 0. It is a random vector in the range [0,1].

[0096] Iteration termination: Repeat steps 2-3 until the preset maximum number of iterations is reached or the change in global optimal fitness is less than the threshold.

[0097] For example, in phase S2, the population size N=25, dimension d=6, maximum number of iterations=200, and search boundary are set. , After initialization, the fitness rate drops rapidly in the first 10 iterations, and converges and stabilizes after about 120 iterations, taking approximately 8 seconds (based on MATLAB simulation). Compared to traditional particle swarm optimization (PSO), this algorithm has fewer control parameters and stronger global search capabilities.

[0098] It should be noted that in step S4, the inherent specific energy ε of the rock is first subjected to dimensionless processing to obtain the dimensionless inherent specific energy ε. Its expression is:

[0099] Where ε0 is the reference intrinsic specific energy; based on the friction coefficient μ and the dimensionless intrinsic specific energy ε A rock strength characterization quantity H is established, and the association rule satisfies the following relationship:

[0100] in, The constant coefficients obtained from the fitting; according to The scope of rock strength grading standards is established to achieve rock strength grading.

[0101] As a specific implementation method, the friction coefficient is established in step S4. Rock intrinsic specific energy The association rule between rock strength and rock strength satisfies the following relationship:

[0102] Methods for creating association rules: Data collection: In the laboratory, select no fewer than 10 different types of rock samples (such as coal, mudstone, shale, sandstone, limestone, granite, etc.), and take at least 3 pieces of each type of sample.

[0103] Standard test: Uniaxial compressive strength (UCS) test is performed on each rock sample to obtain its true strength value (unit MPa).

[0104] Drilling experiment: Using a standardized miniature drilling rig and standard drill bit, each rock was drilled according to the set drilling procedure (constant drilling pressure and rotation speed), and the borehole parameters were recorded.

[0105] Parameter identification: Using the methods S1-S3 of this invention, the drilling data of each rock block is identified to obtain the corresponding parameters. and .

[0106] Multiple linear regression: with UCS as the dependent variable, and Using [variable name] as the independent variable, perform linear regression to obtain the coefficients. and the model's goodness of fit .generally The order of magnitude is Nearby, and Between 0.1 and 0.6, to balance the differences in magnitude, the independent variables can be standardized before regression, or the original units can be retained in the formula but smaller units can be used. .

[0107] For example, by regressing drilling experimental data from 25 different rocks, the following fitting formula was obtained:

[0108] Right now , , goodness of fit Then the rock strength characterization quantity The predicted UCS value (in MPa) is taken. Then, based on the actual needs of coal mine engineering, a grading standard is established: MPa: Soft rock (such as coal, mudstone); MPa: Medium-hard rocks (such as shale, medium-fine sandstone); MPa: Hard rock (such as coarse sandstone, limestone); MPa: Extremely hard rocks (such as granite and quartzite).

[0109] For example, when entering a tunnel, it can be identified in real time. , Substituting into the formula, we get It was determined to be soft rock; drilling continued to the next section, where it was identified as soft rock. , ,calculate It was determined to be medium-hard rock, consistent with subsequent core sampling results.

[0110] Example 3: As a general embodiment, this embodiment provides a rock strength classification method based on a drill string axial torsion coupled dynamic model, the main steps of which are as follows: S1: Introducing local damping of the drill string, a three-degree-of-freedom dynamic model of the drill string axial-torsional coupling considering the borehole inclination angle and the drill bit-rock interaction is established:

[0111] In this embodiment, the drilling pressure and torque at the borehole are calculated using parameters such as feed pressure, pull-out pressure, main pump pressure, and return oil pressure measured at the borehole. The drilling speed and rotational speed at the borehole are calculated using a drill string axial-torsional coupled three-degree-of-freedom dynamic model, expressed as follows:

[0112] The structural parameters of the model are determined based on the drill string size parameters, material parameters, and borehole inclination angle, and are solved using numerical methods, providing a basis for subsequent parameter identification.

[0113] In this embodiment, the calculation parameters of the drill string axial-torsional coupled dynamic model are shown in Table 1 below.

[0114] Table 1 Model Calculation Parameters

[0115] S2: To improve the calculation accuracy of the drill string axial-torsional coupled three-degree-of-freedom dynamic model, it is first necessary to identify the local damping coefficients in the torsional and axial dimensions respectively. Using actual borehole data from a coal mine, during the reverse drilling stage after rod retraction (when the drill bit is not in contact with the rock), the input torque and drilling pressure of the power head are calculated using parameters such as feed pressure, pull-out pressure, main pump pressure, and return oil pressure measured at the borehole opening. These parameters are then input into the drill string dynamic model described in step S1 to calculate the borehole drilling speed and rotational speed. Combining the actual borehole drilling speed and rotational speed with those calculated by the drill string dynamic model, and using the minimum error between the two as the optimization objective, a local damping coefficient identification problem is constructed:

[0116] in, , The actual drilling speed and rotation speed at the borehole opening during the reverse drilling stage after the rod is withdrawn. , The borehole drilling speed and rotational speed are calculated using the drill string dynamics model described in step S1. The root mean square error is calculated using the following expression:

[0117] in, and These are the weighting coefficients for drilling speed and rotation speed errors, respectively. In this embodiment, the weighting coefficients are both set to 0.5 according to expert rules.

[0118] This embodiment employs the Grey Wolf optimization algorithm to solve the above identification process. Through steps such as population initialization, fitness calculation, and position updating, the local damping coefficient is iteratively searched. The identified local damping coefficients are shown in Table 2. Substituting the identification results into the drill string axial-torsional coupled dynamic model, the rotational speed and drilling speed output by the model are calculated. An example is provided below. Figure 2 As shown, the iterative process of the gray wolf optimization algorithm is as follows: Figure 4 (a) and Figure 4 As shown in (b) of the diagram.

[0119] Table 2 Results of local damping coefficients

[0120] S3: Based on the local damping coefficient identified in S2, further identification of drill bit-rock interaction parameters is performed. During the drill bit rock-breaking drilling stage, the input torque and drilling pressure of the power head are calculated using parameters such as feed pressure, pull-out pressure, main pump pressure, and return oil pressure measured at the wellhead. These parameters are then input into the drill string dynamics model with the determined local damping coefficient to calculate the wellhead drilling speed and rotational speed output by the model. Combining the actual wellhead drilling speed and rotational speed with the model calculation results, a parameter identification problem is constructed with the minimum error between the two as the optimization objective. The friction coefficient and inherent specific energy of the rock in the drill bit-rock interaction are identified under different rock conditions.

[0121] in, , This represents the actual drilling speed and rotation speed at the borehole opening during the rock-breaking drilling stage. , Substituting the identified local damping coefficients into the borehole drilling speed and rotational speed calculated from the drill string dynamics model described in step S1; weighting coefficients and Keep in line with step S2.

[0122] Applying the optimization algorithm to the parameter identification process in the second stage allows for the acquisition of friction coefficients and intrinsic rock energy under different rock conditions. Substituting the identified parameters into the drill string axial-torsional coupled dynamic model for simulation yields the output drilling speed and rotational speed under different lithological conditions. Examples include... Figure 3 As shown in Table 3, the results of the drill bit-rock interaction parameter identification for the three types of rocks are presented, and the corresponding optimization algorithm iteration process is shown in [the table]. Figure 4 (c) Figure 4 (d) and Figure 4 (e) The above results are used to demonstrate the parameter identification process and application of the method of the present invention under different rock conditions.

[0123] Table 3 Identification results of rock action parameters for three types of rock drill bits

[0124] S4: After completing the friction coefficient Compared with the inherent specific energy of rocks After identifying the interaction parameters between the two drill bits and the rock, a friction coefficient is established to achieve rock strength grading. Rock intrinsic specific energy The correlation rules between friction coefficient μ and rock strength. Since the friction coefficient μ is a dimensionless parameter, while the rock's inherent specific energy ε has physical dimensions, to avoid direct combination of physical quantities with different dimensions, this invention first performs dimensionless processing on the rock's inherent specific energy ε to obtain the dimensionless inherent specific energy ε. :

[0125] Wherein, ε0 is the reference intrinsic specific energy. As a specific implementation, since the intrinsic specific energy ε of the rock in this embodiment is expressed in MPa, the average intrinsic specific energy of the three rock samples is taken as the reference intrinsic specific energy: =6.9512MPa After dimensionless processing, the friction coefficient μ and the dimensionless intrinsic energy ε All are dimensionless parameters. Based on μ and ε A rock strength characterization quantity H was constructed. Through statistical analysis and regression fitting of multiple sets of actual drilling sample data, the following rock strength correlation was established:

[0126] in, , , The values ​​represent weighting coefficients. Based on actual geological data, the uniaxial compressive strength ranges for three lithologies—white sandstone, coal gangue, and coal-rock—can be obtained, as shown in Table 4.

[0127] Table 4 Uniaxial compressive strength range

[0128] To obtain a stable parameter mapping relationship, this embodiment uses the average uniaxial compressive strength of each lithology as a representative value, and maps it to the identification parameters of the corresponding lithology. , Together, they form three groups of samples, which are then solved multiple times through regression:

[0129] In this context, subscripts 1, 2, and 3 correspond to the identification parameters in white sandstone, coal gangue, and coal-rock respectively. These parameters are obtained after multiple calculations. It is -9.6972. It is 22.7814. It is 26.3931.

[0130] In obtaining rock strength characterization Subsequently, a rock strength grading criterion was established based on its value range, and a corresponding grading table was generated, as shown in Table 5. This grading criterion table classifies rocks into five levels according to strength: extremely soft rock, relatively soft rock, medium-hard rock, relatively hard rock, and extremely hard rock, and lists the corresponding index ranges and example rock types for each level. A schematic diagram of the rock strength grading results can be drawn based on the grading table, as shown below. Figure 5 As shown.

[0131] Table 5 Rock strength classification criteria based on rock strength characterization parameter H

[0132] To illustrate the application process of the method of the present invention, measured data under different drilling conditions are selected as examples, and the friction coefficient, rock intrinsic specific energy, and rock strength characterization parameters are obtained according to the steps. The strength level was determined based on a preset grading criterion. Table 6 shows the strength grading results.

[0133] Table 6 Rock strength grading results from multiple datasets

[0134] Example 5: Please see Figure 3 , Figure 3 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: a rock strength grading device 401 based on a drill string axial torsion coupling dynamic model, a processor 402, and a storage medium 403.

[0135] A rock strength grading device 401 based on a drill string axial torsion coupling dynamic model: The rock strength grading device 401 based on a drill string axial torsion coupling dynamic model implements the rock strength grading method based on the drill string axial torsion coupling dynamic model.

[0136] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the rock strength classification method based on the drill string axial torsion coupling dynamic model.

[0137] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the rock strength classification method based on the drill string axial torsion coupling dynamic model.

[0138] 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, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A rock strength classification method based on a drill string axial torsion coupled dynamic model, characterized in that: Includes the following steps: S1: Introduce local damping of the drill string and establish a three-degree-of-freedom dynamic model of the drill string axial-torsional coupling considering the borehole inclination angle and the drill bit-rock interaction; S2: During the reverse drilling stage after rod retraction, the drill bit is not in contact with the rock. The power head input torque and drilling pressure required for the drill string dynamics model are calculated using the borehole measurement parameters. The borehole drilling speed and rotation speed are calculated through the drill string axial-torsional coupled three-degree-of-freedom dynamics model. By combining the actual borehole drilling speed and rotation speed with the borehole drilling speed and rotation speed calculated by the model, and taking the minimum error between the two as the optimization objective, the local damping coefficients of the drill string axial and torsional dimensions are identified using the Grey Wolf optimization algorithm. S3: During the rock-breaking drilling stage, the power head input torque and drilling pressure required by the drill string dynamic model are calculated using the borehole measurement parameters. The borehole drilling speed and rotation speed are calculated at this time using the drill string axial-torsional coupling three-degree-of-freedom dynamic model after the local damping coefficient is determined. Combining the actual borehole drilling speed, rotation speed and model calculation results, the friction coefficient of the drill bit-rock interaction and the inherent specific energy of the rock are identified. S4: Construct a rock strength characterization quantity based on the identified friction coefficient and inherent specific energy of the rock. Establish the correlation rules between friction coefficient, rock intrinsic specific energy, and rock strength, and based on... To achieve the classification of rock strength.

2. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 1, characterized in that, In step S1, the reaction force at the drill bit end and reaction torque The results were obtained from the drill bit-rock interaction model, which considers both cutting and frictional effects. The expression is as follows: In the formula, and These are the reaction force and reaction torque generated by the interaction between the drill bit and the rock; and These are the forces and torques involved in the friction process, respectively. and These are the forces and torques acting during the cutting process; It is the rock's inherent specific energy, which is related to the rock's strength; It is the drill bit radius. It is a constant characterizing the tilt of the blade. It is the rock contact strength. It is the length at the wear surface of the drill bit. It is the coefficient of friction. These are parameters describing the direction and spatial distribution of the friction contact surface of the drill bit blades. It is the sum of the cutting depths of each blade in one revolution of the drill bit. Under steady-state conditions, the cutting depth satisfies .

3. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 1, characterized in that: In step S1, the drill string system is simplified into an axial and torsional three-degree-of-freedom mass-spring-damping system consisting of a power head unit, an intermediate drill pipe unit, and a drill bit unit. An axial-torsional coupled three-degree-of-freedom dynamic model of the drill string considering the hole inclination angle and local damping is established.

4. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 1, characterized in that, In step S1, the axial-torsional coupled three-degree-of-freedom dynamic model of the drill string is expressed as follows: In the formula, and For the rotational speed and axial velocity of a three-degree-of-freedom element, and These represent the angular displacement and axial displacement of each element. and These are the angular acceleration and acceleration of each unit, respectively. and These are the equivalent moment of inertia and equivalent mass of each element; These are the local damping coefficients for the three torsional degrees of freedom; These are the local damping coefficients for the three axial degrees of freedom; This is the torsional stiffness coefficient; This is the axial stiffness coefficient; Input torque to the power head, For drilling pressure, This represents the load torque on the drill bit; The angle of the opening. It is the acceleration due to gravity; During drilling, the input torque and drilling pressure of the power head are calculated from the parameters of feed pressure, pull-out pressure, main pump pressure, and return oil pressure, and the expression is: in, This refers to the displacement of the rotary motor; Main pump pressure; This refers to the return oil pressure; For pressure loss in the hydraulic system; To provide pressure; For pulling pressure; To provide the piston area of ​​the oil cylinder; The piston rod area; Based on the structural parameters of the drill string system, the moment of inertia of each of the above units... ,quality Axial damping coefficient and torsional damping coefficient Axial stiffness coefficient and torsional stiffness coefficient The calculation formula is as follows: in, It is the length of each unit. and These are the outer diameter and inner diameter of each unit. It is the density of each unit. and These are the damping coefficients in the axial and torsional dimensions, respectively. and These are the elastic modulus and shear modulus of the drill string, respectively.

5. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 1, characterized in that, In step S2, the identification of local damping is achieved by constructing a parameter identification process with the optimization objective of minimizing the error between the model output borehole drilling speed and borehole rotation speed and the actual borehole drilling speed and rotation speed, and then obtaining the local damping coefficients in the axial and torsional dimensions of the drill string through the Grey Wolf optimization algorithm.

6. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 1, characterized in that, In step S3, the drill bit-rock interaction parameter identification is carried out by constructing a parameter identification process with the optimization objective of minimizing the error between the model output borehole drilling speed and borehole rotation speed and the actual borehole drilling speed and rotation speed, and the friction coefficient and rock intrinsic specific energy of the drill bit-rock interaction are obtained by solving the Grey Wolf optimization algorithm.

7. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 6, characterized in that, The gray wolf optimization algorithm described in steps S2 and S3 uses a population initialization, fitness evaluation, and parameter update strategy to iteratively search for the optimal parameter combination.

8. The rock strength classification method based on the drill string axial torsion coupling dynamic model as described in claim 1, characterized in that, In step S4, the inherent specific energy ε of the rock is first subjected to dimensionless processing to obtain the dimensionless inherent specific energy ε. Its expression is: Where ε0 is the reference intrinsic specific energy; based on the friction coefficient μ and the dimensionless intrinsic specific energy ε A rock strength characterization quantity H is established, and the association rule satisfies the following relationship: in, The constant coefficients obtained from the fitting; according to The scope of rock strength grading standards is established to achieve rock strength grading.

9. A storage medium, characterized in that: The storage medium stores instructions and data for implementing the rock strength classification method based on the drill string axial torsion coupling dynamic model as described in any one of claims 1 to 8.

10. A rock strength grading device based on a drill string axial torsion coupling dynamic model, characterized in that: include: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement the rock strength classification method based on the drill string axial torsion coupling dynamic model as described in any one of claims 1 to 8.

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