While-drilling rock breaking efficiency optimization method based on dynamic energy consumption and dynamic rock strength

Abstract

The invention relates to the technical field of oil and gas well engineering, and provides a while-drilling rock breaking efficiency optimization method based on dynamic energy consumption and dynamic rock strength, which comprises the steps of selecting a target well section and a rock, determining the type and parameters of the rock, determining the parameters of the target well section, and calculating the dynamic energy consumption corresponding to the target rock broken by a drill column through the energy flow of the drill column. Establishing a probability distribution relation model between the dynamic strength and the dynamic energy consumption of the target rock, dividing the dynamic strength of the target rock by the dynamic energy consumption to obtain dynamic rock breaking efficiency, and calculating and optimizing drilling parameters through non-dominated sorting and congestion degree by taking the drilling speed and the dynamic rock breaking efficiency as indexes. The invention provides a calculation method for optimizing drilling while drilling parameters, provides important guidance and support for parameter optimization of drilling engineering, tool optimization or optimization, speed increase, cost reduction and benefit increase, and has a wide application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas well engineering, and in particular, to a method for optimizing the rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength. Background Art

[0002] At present, most of the drilling operations on domestic drilling sites rely on experience and lack scientific theoretical basis, resulting in low mechanical drilling speed and accelerating the damage of tools and bits, which affects the improvement of drilling speed and efficiency. Based on the dynamic energy consumption theory, the method of adjusting well parameters in real time according to the change of the dynamic energy consumption curve has great value for large-scale popularization and application in the optimization of on-site drilling speed. However, although the traditional method of optimizing well parameters based on the completed well data can solve the problems existing in the real-time optimization of parameters by the specific energy method, the following problems will occur when using the data of the well being drilled for real-time optimization:

[0003] First of all, the real-time optimization of drilling parameters by using the specific energy method mainly qualitatively adjusts the parameters according to the change of the specific energy curve of the machine, and cannot quantitatively and accurately propose the optimal parameter combination, so the drilling speed cannot be optimized to the maximum extent.

[0004] Secondly, the method of realizing the real-time optimization of drilling parameters based on the dynamic energy consumption theory does not consider the drilling cost and cannot realize the real-time optimization of the comprehensive drilling cost.

[0005] Furthermore, since the drilling speed model in the traditional drilling parameter optimization method is established by regression of the data from on-site tests after stopping drilling, and the rock is under dynamic load, the rock strength will change under different dynamic loads. Therefore, it is very difficult to establish the drilling speed model in real time when using the data of the well being drilled for parameter real-time optimization.

[0006] At present, there are few studies on the method for optimizing the rock-breaking efficiency while drilling considering the dynamic rock strength and dynamic energy consumption. Summary of the Invention

[0007] The purpose of the present application is to provide a method for optimizing the rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength. It measures the dynamic rock strength according to the true triaxial Hopkinson bar rock mechanics experiment, establishes the corresponding data set of the drilling unit while drilling based on the footage in the operation period, and establishes a method for optimizing the drilling parameters while drilling according to the dynamic energy consumption and dynamic rock strength, which provides important guidance and support for the optimization of drilling engineering parameters, tool optimization or selection, and improving the speed and reducing the cost, and has broad application prospects.

[0008] To solve the above technical problems, the technical solution adopted by the present application is as follows:

[0009] The embodiment of the present application provides a method for optimizing the rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength, including: S1. Select a target well section and rock, determine the rock type and rock parameters, and determine the target well section parameters; S2. According to the parameters obtained in step S1, calculate the dynamic energy consumption corresponding to the target rock broken by the drill string through the drill string energy flow; S3. According to the corresponding relationship between the dynamic energy consumption and the target rock, establish a probability distribution relationship model between the dynamic strength and the dynamic energy consumption of the target rock; S4. Divide the dynamic strength of the target rock by the dynamic energy consumption to obtain the dynamic rock-breaking efficiency; S5. Using the drilling speed and the dynamic rock-breaking efficiency as indicators, optimize the drilling parameters through non-dominated sorting and crowding degree calculation.

[0010] In some embodiments of the present application, in the above step S1, the rock parameters include dynamic uniaxial compressive strength of rock, dynamic tensile strength of rock, and dynamic shear strength of rock, and the drilling parameters include weight on bit, top drive torque, motor torque, top drive speed, motor speed, drilling speed, bit diameter, and motor type.

[0011] In some embodiments of the present application, the above drilling parameters are obtained through on-site logging data; the rock parameters are measured through a true triaxial split Hopkinson pressure bar rock mechanics experiment, and the triaxial pressure is set to the formation pressure environment of the target well section; the triaxial pressure expression of the true triaxial split Hopkinson pressure bar rock mechanics experiment is as follows:

[0012]

[0013] In the formula, σ x (t), σ y (t), σ z (t) are the stresses of the specimen along the X, Y, and Z directions respectively, in MPa; E b is the Young's modulus of the bar, in MPa; A b is the cross-sectional area of the bar, in mm2; A s is the cross-sectional area of the specimen, in mm2; ε in (t), ε re (t), ε tr (t) are the strains of the bar caused by the incident wave, reflected wave, and transmitted wave respectively, dimensionless; ε y1 (t) and ε y2 (t) are the strains of the specimen along the Y direction, dimensionless; ε z1 (t) and ε z2 (t) are the strains of the specimen along the Z direction, dimensionless.

[0014] In some embodiments of the present application, in the above step S2, the dynamic energy consumption calculation formula is as follows:

[0015] For the drill string without a motor: E n = E a + Enr ; Drill string E with a motor m = E a + E mr ;

[0016] Wherein,

[0017]

[0018] In the formula, E n is the dynamic energy consumption of the drill string without a motor, MPa; E m is the dynamic energy consumption of the drill string with a motor, MPa; E a is the axial dynamic load breaking energy, MPa; E nr is the radial dynamic load breaking energy, MPa; WOB surf is the weight on the bit, kN; D is the diameter, mm; TQ surf is the top drive torque, kN·m; TQ mm is the motor torque kN·m; RPM surf is the top drive speed, r / min; RPM mm is the drill string speed, r / min; ROP is the drilling rate m / h.

[0019] In some embodiments of the present application, in the above step S3, the specific steps for establishing the probability distribution relationship model are: S31. Based on the dynamic rock strength and the corresponding dynamic energy consumption of the rock sample measured by the split Hopkinson pressure bar rock mechanics experiment, determine the Gaussian probability distribution of the dynamic strength corresponding to different dynamic energy consumptions, and establish a probability model describing the relationship between the dynamic strength and the dynamic energy consumption of the target rock; S32. According to the dynamic energy consumption, use the probability model of the relationship between the dynamic strength and the dynamic energy consumption of the target rock, and inversely calculate the dynamic rock strength by the Bayesian inversion method; S33. Calculate the probability value of the dynamic strength of the rock when the drill bit encounters the formation, and the calculation formula is as follows:

[0020]

[0021] In the formula, p(m∣d) is the posterior probability density of the dynamic energy consumption, dimensionless; p(m) is the prior probability density of the dynamic energy consumption, dimensionless; p(d) is the normalization constant, dimensionless; p(d∣m) is the Gaussian likelihood function, representing the probability that the data is the dynamic strength when the parameter is the dynamic energy consumption, dimensionless; d represents the dynamic strength; m represents the dynamic energy consumption; C d is the covariance matrix of the dynamic strength error, dimensionless; d(m) represents the forward response, dimensionless; The dynamic strength with the maximum probability is used as the dynamic strength of the rock when the drill bit encounters the formation.

[0022] In some embodiments of the present application, in the above step S4, the expression form of the dynamic rock breaking efficiency is as follows:

[0023]

[0024] Wherein, K d is the dynamic rock-breaking efficiency, dimensionless; σ d is the dynamic rock strength, MPa; σ x (t) is the stress of the specimen along the X direction in the true triaxial Hopkinson bar rock mechanics experiment, MPa; E is the dynamic energy consumption, MPa.

[0025] In some embodiments of the present application, in the above step S5, the specific steps for optimizing the drilling parameters are as follows: S51. Determine the well section ΔL and the drilling parameter cluster DPD=(DPD1, DPD2,..., DPD n ), and the expression of ΔL is as follows:

[0026]

[0027] Wherein, ΔL is the well section determined according to the drilling speed data, m; is the footage in one hour, m;

[0028] S52. Calculate the dynamic energy consumption of this well section according to the drilling parameter cluster, calculate the dynamic rock-breaking efficiency after determining the dynamic rock strength of this well section according to the probability distribution model of the dynamic energy consumption and the dynamic strength; S53. For the given drilling parameter cluster DPD, the mathematical definitions of maximizing the drilling speed and the dynamic rock-breaking efficiency are as follows:

[0029]

[0030] Among them, f ROP (DPD), is the objective function, representing multiple objective values for each drilling parameter set, and Maximizef(DPD) represents the drilling parameter set under the maximized objective function;

[0031] S54. Determine the dominance relationship of the drilling parameter cluster. If DPD r dominates DPD k , then there is the following expression:

[0032]

[0033] Wherein, {ROP, K d} represents two objective functions of ROP and K d , represents for any ROP and K d objective function; f i (DPD k ) is DPD kThe i-th objective function value, DPD k is the k-th drilling parameter set in the drilling parameter cluster; f j (DPD r ) is the j-th objective function value of DPD r , and DPD r is any r-th drilling parameter set in the drilling parameter cluster; ∧ is a logical operator representing the "AND" operation, indicating that both sides need to be satisfied simultaneously;

[0034] S55. Use non-dominated sorting to divide the priorities of the drilling parameter clusters, and divide the drilling parameter clusters into multiple levels according to the dominance relationship from large to small according to the objective function values. The drilling parameter set corresponding to the maximum objective function value is located in the first level, and the first level contains multiple non-dominated drilling parameter sets. S56. Through the combination of non-dominated sorting and crowding degree, preferentially select the drilling parameter sets with large objective function values and diversity. Sort all the drilling parameter sets in the first level from large to small according to the objective values, calculate the crowding degree of each drilling parameter set in the first level, and retain the drilling parameter sets with high crowding degrees. The calculation of the crowding degree is as follows:

[0035]

[0036] In the formula, D is the crowding degree, dimensionless; f j (DPD i+1 ) represents the objective function value of the (i + 1)-th drilling parameter set, and f j (DPD i-1 ) represents the objective function value of the (i - 1)-th drilling parameter set; represents the maximum value of the objective function, represents the minimum value of the objective function;

[0037] S57. Use the drilling parameter sets with higher crowding degrees in the first level obtained in step S56, respectively use the drilling speed and dynamic rock-breaking efficiency as evaluation indicators, and select the maximum value of the sorting result as the optimal drilling parameter set. The sorting methods for the drilling speed and dynamic rock-breaking efficiency are as follows:

[0038] S i (DPD) = R i (ROP) + R i (K d )

[0039] In the formula, S i (DPD) represents the result obtained by assigning scores after sorting the calculation of the drilling speed and dynamic rock-breaking efficiency of the i-th drilling parameter set, dimensionless; R i (ROP) represents the sorting result of the drilling speed of the i-th drilling parameter set, and scores are given as 1, 2, 3... in ascending order of the drilling speed, dimensionless; R i (K d)It represents the sorting result of the dynamic rock-breaking efficiency of the $i$-th drilling parameter set. Scoring is carried out in the order of increasing drilling speed as 1, 2, 3... It is dimensionless. S58. Lock the $S$ in step S57 i (DPD) The drilling parameter set corresponding to the maximum scoring result of each drilling parameter set is used as the optimal drilling parameter for this well section. Repeat steps S51 to S58 to achieve the optimization of the rock-breaking efficiency while drilling.

[0040] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0041] 1. Comprehensively considering dynamic energy consumption, dynamic rock strength, and various drilling parameters, and optimizing drilling parameters in real time, it can significantly improve the rock-breaking efficiency and reduce the drilling time. Optimization is carried out with drilling speed and dynamic rock-breaking efficiency as indicators, making the rock-breaking process more efficient.

[0042] 2. The improvement of the rock-breaking efficiency means the shortening of the drilling time, thereby reducing costs such as labor and equipment wear, and improving the economic benefits of drilling operations.

[0043] 3. The drilling parameters are obtained from on-site logging data, and the rock parameters are obtained through experiments simulating the actual formation pressure environment, ensuring the authenticity and accuracy of the parameters, and providing a reliable basis for subsequent precise calculations and analyses.

[0044] 4. When determining the dynamic rock strength, the probability distribution relationship model and Bayesian inversion method are used, fully considering the uncertainty of the dynamic rock strength, and improving the reliability of the data and the adaptability of the method.

[0045] 5. Optimize the drilling parameters through non-dominated sorting and crowding degree calculation, taking into account multiple objectives such as drilling speed and dynamic rock-breaking efficiency, and considering the diversity of parameters, avoiding falling into local optimal solutions, and making the optimization result more practical.

[0046] 6. It provides a calculation method for optimizing the drilling parameters while drilling, providing important guidance and support for the optimization of drilling engineering parameters, tool optimization or selection, and improving the speed, reducing costs and increasing efficiency, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a schematic flow chart of a method for optimizing the rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength provided for the embodiment. Specific implementation mode

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Those not specified in the embodiments are carried out according to conventional conditions or conditions recommended by the manufacturer.

[0050] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The present invention will be described in detail below with reference to specific embodiments.

[0051] The features and performance of the present invention will be further described in detail below in conjunction with the embodiments.

[0052] Embodiment

[0053] This embodiment provides an optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength, including the following steps:

[0054] Please refer to Figure 1 , Figure 1 which shows the schematic flow chart of the optimization method of this application.

[0055] An optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength, including the following steps:

[0056] S1. Select the target well section and rock, determine the rock type and rock parameters, and determine the target well section parameters. The rock parameters include dynamic uniaxial compressive strength of rock, dynamic tensile strength of rock, and dynamic shear strength of rock. The drilling parameters include weight on bit, top drive torque, motor torque, top drive speed, motor speed, drilling speed, bit diameter, and motor type. The drilling parameters are obtained through on-site logging data, and the rock parameters are measured through a true triaxial Hopkinson bar rock mechanics experiment, and the triaxial pressure is set to the formation pressure environment of the target well section. The triaxial pressure expression of the true triaxial Hopkinson bar rock mechanics experiment is as follows:

[0057]

[0058] In the formula, σ x (t), σ y (t), σ z (t) are the stresses of the specimen along the X, Y, and Z directions respectively, in MPa; E b is the Young's modulus of the bar, in MPa; A b is the cross-sectional area of the bar, in mm2; A s is the cross-sectional area of the specimen, in mm2; ε in (t), ε re (t), ε tr(t) are the strains of the rod caused by the incident wave, reflected wave, and transmitted wave, dimensionless; ε y1 (t) and ε y2 (t) is the strain of the specimen along the Y direction, dimensionless; ε z1 (t) and ε z2 (t) is the strain of the specimen along the Z direction, dimensionless;

[0059] S2. According to the parameters obtained in step S1, calculate the dynamic energy consumption corresponding to the target rock broken by the drill string through the drill string energy flow. The calculation formula for the dynamic energy consumption is as follows:

[0060] Drill string without motor: E n = E a + E nr ; Drill string with motor E m = E a + E mr ;

[0061] Among them,

[0062]

[0063] In the formula, E n is the dynamic energy consumption of the drill string without motor, MPa; E m is the dynamic energy consumption of the drill string with motor, MPa; E a is the axial dynamic load breaking energy, MPa; E nr is the radial dynamic load breaking energy, MPa; WOB surf is the weight on the drill bit, kN; D is the diameter, mm; TQ surf is the top drive torque, kN·m; TQ mm is the motor torque kN·m; RPM surf is the top drive speed, r / min; RPM mm is the drill string speed, r / min; ROP is the drilling speed m / h;

[0064] S3. According to the corresponding relationship between the dynamic energy consumption and the target rock, establish a probability distribution relationship model between the dynamic strength of the target rock and the dynamic energy consumption. The specific steps are as follows:

[0065] S31. Based on the dynamic rock strength and the corresponding dynamic energy consumption of the rock sample measured by the split Hopkinson pressure bar rock mechanics experiment, determine the Gaussian probability distribution of the dynamic strength corresponding to different dynamic energy consumptions, and establish a probability model describing the relationship between the dynamic strength of the target rock and the dynamic energy consumption;

[0066] S32. According to the dynamic energy consumption, use the probability model of the relationship between the dynamic strength of the target rock and the dynamic energy consumption, and inversely calculate the dynamic rock strength through the Bayesian inversion method;

[0067] S33. Calculate the probability value of the dynamic strength of the formation rock encountered by the drill bit. The calculation formula is as follows:

[0068]

[0069] In the formula, p(m∣d) is the posterior probability density of dynamic energy consumption, dimensionless; p(m) is the prior probability density of dynamic energy consumption, dimensionless; p(d) is the normalization constant, dimensionless; p(d∣m) is the Gaussian likelihood function, representing the probability that the data is the dynamic strength when the parameter is the dynamic energy consumption, dimensionless; d represents the dynamic strength; m represents the dynamic energy consumption; C d is the covariance matrix of the dynamic strength error, dimensionless; d(m) represents the forward response, dimensionless; Take the dynamic strength with the maximum probability as the dynamic strength of the formation rock encountered by the drill bit.

[0070] S4. Divide the dynamic strength of the target rock by the dynamic energy consumption to obtain the dynamic rock-breaking efficiency. The expression form of the dynamic rock-breaking efficiency is as follows:

[0071]

[0072] In the formula, K d is the dynamic rock-breaking efficiency, dimensionless; σ d is the dynamic rock strength, MPa; σ x (t) is the stress of the specimen along the X direction in the true triaxial Hopkinson bar rock mechanics experiment, MPa; E is the dynamic energy consumption, MPa.

[0073] S5. Take the drilling speed and the dynamic rock-breaking efficiency as indicators, and optimize the drilling parameters through non-dominated sorting and crowding degree calculation. The specific optimization steps are as follows:

[0074] S51. Determine the well section ΔL and the drilling parameter cluster DPD=(DPD1, DPD2,..., DPD n ) according to the drilling speed data while drilling. The expression of ΔL is as follows:

[0075]

[0076] In the formula, ΔL is the well section determined according to the drilling speed data, m; is the footage in one hour, m;

[0077] S52. Calculate the dynamic energy consumption of this well section according to the drilling parameter cluster, and calculate the dynamic rock-breaking efficiency after determining the dynamic rock strength of this well section according to the probability distribution model of the dynamic energy consumption and the dynamic strength;

[0078] S53. For the given drilling parameter cluster DPD, the mathematical definitions of maximizing the drilling speed and the dynamic rock-breaking efficiency are as follows:

[0079]

[0080] Among them, f ROP (DPD), is the objective function, representing multiple objective values for each set of drilling parameters. Maximizef(DPD) represents the set of drilling parameters under the maximized objective function;

[0081] S54. Determine the dominance relationship of the drilling parameter cluster. If DPD r dominates DPD k , then there is the following expression:

[0082]

[0083] In the formula, {ROP, K d} represents ROP and K d two objective functions, represents for any ROP and K d objective function; f i (DPD k ) is the i-th objective function value of DPD k , and DPD k is the k-th set of drilling parameters in the drilling parameter cluster; f j (DPD r ) is the j-th objective function value of DPD r , and DPD r is any r-th set of drilling parameters in the drilling parameter cluster; ∧ is a logical operator representing the "AND" operation, indicating that both sides need to be satisfied simultaneously;

[0084] S55. Use non-dominated sorting to divide the priority levels of the drilling parameter cluster. Divide the drilling parameter cluster into multiple levels according to the dominance relationship from largest to smallest based on the objective function values. The set of drilling parameters corresponding to the largest objective function value is in the first level, and the first level contains multiple non-dominated sets of drilling parameters;

[0085] S56. Through the combination of non-dominated sorting and crowding degree, preferentially select the set of drilling parameters with large objective function values and diversity. Sort all the sets of drilling parameters in the first level from largest to smallest according to the objective values, calculate the crowding degree of each set of drilling parameters in the first level, and retain the sets of drilling parameters with high crowding degrees. The crowding degree calculation is as follows:

[0086]

[0087] In the formula, D is the crowding degree, dimensionless; f j (DPD i+1 ) represents the objective function value of the (i + 1)-th set of drilling parameters, fj (DPD i-1 ) represents the objective function value of the (i - 1)-th drilling parameter set; represents the maximum value of the objective function, represents the minimum value of the objective function;

[0088] S57. Using the first-level drilling parameter set with a higher crowding degree obtained in step S56, taking the drilling rate and the dynamic rock-breaking efficiency as evaluation indexes respectively, and selecting the maximum value of the sorting result as the optimal drilling parameter set; the sorting methods for the drilling rate and the dynamic rock-breaking efficiency are as follows:

[0089] S i (DPD)=R i (ROP)+R i (K d )

[0090] In the formula, S i (DPD) represents the result obtained by scoring after sorting the drilling rate and the dynamic rock-breaking efficiency of the i-th drilling parameter set, dimensionless; R i (ROP) represents the sorting result of the drilling rate of the i-th drilling parameter set, which is scored as 1, 2, 3... in ascending order of the drilling rate, dimensionless; R i (K d ) represents the sorting result of the dynamic rock-breaking efficiency of the i-th drilling parameter set, which is scored as 1, 2, 3... in ascending order of the drilling rate, dimensionless;

[0091] S58. Lock the drilling parameter set corresponding to the maximum score of each drilling parameter set of S i (DPD) in step S57 as the optimal drilling parameter for this well section, and repeat steps S51 to S58 to realize the optimization of the rock-breaking efficiency while drilling.

[0092] The embodiments described above are some embodiments of the present invention, rather than all embodiments. The detailed description of the embodiments of the present invention is not intended to limit the scope of the present invention claimed, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

Claims

1. An optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength, characterized in that, It includes the following steps: S1. Select the target well section and rock, determine the rock type and rock parameters, and determine the target well section parameters; S2. According to the parameters obtained in step S1, calculate the dynamic energy consumption corresponding to the drill string breaking the target rock through the drill string energy flow; S3. According to the corresponding relationship between the dynamic energy consumption and the target rock, establish a probability distribution relationship model between the dynamic strength and the dynamic energy consumption of the target rock; S4. Divide the dynamic strength of the target rock by the dynamic energy consumption to obtain the dynamic rock breaking efficiency; S5. Taking the drilling speed and the dynamic rock breaking efficiency as indicators, optimize the drilling parameters through non-dominated sorting and crowding degree calculation.

2. The method for optimizing the rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength according to claim 1, wherein In step S1, the rock parameters include dynamic uniaxial compressive strength of rock, dynamic tensile strength of rock, and dynamic shear strength of rock; The drilling parameters include weight on bit, top drive torque, motor torque, top drive speed, motor speed, drilling speed, bit diameter, and motor type.

3. An optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength according to claim 2, characterized in that, The drilling parameters are obtained through on-site logging data; the rock parameters are measured through true triaxial Hopkinson bar rock mechanics experiments, and the triaxial pressure is set to the formation pressure environment of the target well section; The triaxial pressure expression of the true triaxial Hopkinson bar rock mechanics experiment is as follows: where, σ x (t), σ y (t), σ z (t) are the stresses of the specimen along the X, Y, and Z directions respectively, in MPa; E b is the Young's modulus of the rod, in MPa; A b is the cross-sectional area of the rod, in mm2; A s is the cross-sectional area of the specimen, in mm2; ε in (t), ε re (t), ε tr (t) are the strains of the rod caused by the incident wave, reflected wave, and transmitted wave, dimensionless; ε y1 (t) and ε y2 (t) is the strain of the specimen along the Y direction, dimensionless; ε z1 ε(t) and ε z2 ε(t) is the strain of the specimen along the Z direction, dimensionless.

4. The optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength according to claim 1, characterized in that, In step S2, the dynamic energy consumption calculation formula is as follows: Drill string without a motor: E n = E a + E nr ; Drill string with a motor E m = E a + E mr ; Among them, where E n is the dynamic energy consumption of the drill string without the motor, MPa; E m is the dynamic energy consumption of the drill string with the motor, MPa; E a is the axial dynamic load breaking energy, MPa; E nr is the radial dynamic load breaking energy, MPa; WOB surf is the weight on the bit, kN; D is the diameter, mm; TQ surf is the top drive torque, kN·m; TQ mm is the motor torque kN·m; RPM surf is the top drive speed, r / min; RPM mm is the drill string speed, r / min; ROP is the drilling rate m / h.

5. The optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength according to claim 1, wherein In step S3, the specific steps for establishing the probability distribution relationship model are as follows: S31. Based on the dynamic rock strength measured by the split Hopkinson bar rock mechanics experiment and the corresponding dynamic energy consumption of the rock sample, determine the Gaussian probability distribution of the dynamic strength corresponding to different dynamic energy consumptions, and establish a probability model describing the relationship between the dynamic strength and the dynamic energy consumption of the target rock; S32. According to the dynamic energy consumption, use the probability model of the relationship between the dynamic strength and the dynamic energy consumption of the target rock to invert the dynamic strength of the rock through Bayesian inversion method; S33. Calculate the probability value of the dynamic strength of the formation rock encountered by the bit, and the calculation formula is as follows: where \(p(m|d)\) is the posterior probability density of dynamic energy consumption, dimensionless; \(p(m)\) is the prior probability density of dynamic energy consumption, dimensionless; \(p(d)\) is the normalization constant, dimensionless; \(p(d|m)\) is the Gaussian likelihood function, representing the probability that the data is the dynamic intensity when the parameter is the dynamic energy consumption, dimensionless; \(d\) represents the dynamic intensity; \(m\) represents the dynamic energy consumption; \(C\) d is the covariance matrix of the dynamic intensity error, dimensionless; \(d(m)\) represents the forward response, dimensionless; Take the dynamic strength with the highest probability as the dynamic strength of the formation rock encountered by the bit.

6. The optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength according to claim 1, characterized in that In step S4, the expression form of the dynamic rock breaking efficiency is as follows: where K d is the dynamic rock-breaking efficiency, dimensionless; σ d is the dynamic rock strength, MPa; σ x (t) is the stress along the X direction of the specimen in the true triaxial Hopkinson bar rock mechanics experiment, MPa; E is the dynamic energy consumption, MPa.

7. An optimization method for rock-breaking efficiency while drilling based on dynamic energy consumption and dynamic rock strength according to claim 1, characterized in that In step S5, the specific steps for optimizing the drilling parameters are as follows: S51. Determine the well section ΔL and the drilling parameter cluster DPD = (DPD1, DPD2,..., DPD n ) according to the drilling rate data while drilling. The expression of ΔL is as follows: Where ΔL is the well section determined according to the drilling rate data, in m; is the footage in one hour, in m; S52. Calculate the dynamic energy consumption of this well section according to the drilling parameter cluster, and calculate the dynamic rock breaking efficiency after determining the dynamic rock strength of this well section according to the probability distribution model of the dynamic energy consumption and the dynamic strength; S53. For the given drilling parameter cluster DPD, the mathematical definitions of maximizing the drilling speed and the dynamic rock breaking efficiency are as follows: where f ROP (DPD), is the objective function, representing multiple objective values for each set of drilling parameters, and Maximizef(DPD) represents the set of drilling parameters under the maximized objective function; S54. Determine the dominance relationship of the drilling parameter cluster. If DPD r dominates DPD k , then there is the following expression: where, {ROP,K d} represents ROP and K d two objective functions, represents for any ROP and K d objective function; f i (DPD k ) is the i-th objective function value of DPD k , DPD k is the k-th drilling parameter set in the drilling parameter cluster; f j (DPD r ) is the j-th objective function value of DPD r , DPD r any r-th drilling parameter set in the drilling parameter cluster; ∧ is a logical operator, representing the "AND" operation, indicating that both sides need to be satisfied simultaneously; S55. Use non-dominated sorting to divide the priority levels of the drilling parameter clusters, divide the drilling parameter clusters into multiple levels according to the dominance relationship from large to small according to the objective function values, and the drilling parameter set corresponding to the largest objective function value is located in the first level, and the first level contains multiple non-dominated drilling parameter sets; S56. Through the combination of non-dominated sorting and crowding degree, preferentially select the drilling parameter sets with large objective function values and diversity. Sort all the drilling parameter sets in the first level from large to small according to the objective values, calculate the crowding degree of each drilling parameter set in the first level, and retain the drilling parameter sets with high crowding degree. The crowding degree calculation is as follows: where D is the congestion degree, dimensionless; f j (DPD i+1 ) represents the objective function value of the (i + 1)-th drilling parameter set, and f j (DPD i-1 ) represents the objective function value of the (i - 1)-th drilling parameter set; represents the maximum value of the objective function, represents the minimum value of the objective function; S57. Using the first-level drilling parameter set with high congestion obtained in the step S56, taking the drilling speed and the dynamic rock-breaking efficiency as evaluation indicators respectively, and selecting the maximum value of the sorting result as the optimal drilling parameter set; the sorting methods for the drilling speed and the dynamic rock-breaking efficiency are as follows: S i (DPD) = R i (ROP) + R i (K d ) Where S i (DPD) represents the result obtained by scoring the calculated drilling rate and dynamic rock-breaking efficiency of the i-th drilling parameter set after sorting, dimensionless; R i (ROP) represents the ranking result of the drilling rate of the $i$-th drilling parameter set, and scores are given as 1, 2, 3... in ascending order of the drilling rate, dimensionless; R i (K d ) represents the ranking result of the dynamic rock-breaking efficiency of the $i$-th drilling parameter set, and scores are given as 1, 2, 3... in ascending order of the drilling rate, dimensionless; S58. Lock S in step S57 i (DPD) The drilling parameter set corresponding to the maximum score result of each drilling parameter set is used as the optimal drilling parameter for this well section. Repeat steps S51 to S58 to optimize the rock-breaking efficiency while drilling.

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