A real-time drilling parameter optimization method and system based on extreme value optimization

Abstract

The present invention discloses a real-time drilling parameter optimization method and system based on extreme value optimization, including obtaining logging engineering parameters under real-time working conditions, obtaining a rock breaking strength baseline under the optimal drilling state of the target well, constructing a rock breaking energy model under a composite working condition taking into account the output loss of the screw, calculating the virtual rock breaking strength, and determining the rock breaking efficiency and abnormal working conditions downhole in real time, and assisting in finding the unstable drilling parameter threshold under the composite drilling condition. Taking the rock breaking strength baseline as the adjustment target for minimizing the virtual rock breaking strength, comprehensively considering the above-mentioned unstable drilling parameter thresholds, a multivariable extreme value optimization algorithm is used to adjust the parameter combination, and finally obtain the recommended drilling parameter combination under the minimum virtual rock breaking strength. The present invention realizes the recommendation and optimization of the best drilling parameters based on the real-time virtual rock breaking strength calculation and optimization under the composite drilling condition, which can effectively avoid the blind spots of empirical drilling data and realize the optimization of drilling parameters throughout the process.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas drilling, and in particular to a real-time drilling parameter optimization method and system based on extreme value optimization. Background Art

[0002] With the deepening of oil and gas exploration and development, the quality of resources has deteriorated, and unconventional and ultra-deep oil and gas resources have become the main battlefield for major oil and gas discoveries in my country. Affected by multiple factors such as complex drilling environment and engineering technology level, the difficulty of exploration and development has continued to increase, and composite drilling speed-up technology has been widely used. However, drilling parameter optimization is a systematic project, and there are many problems such as effective recognition of process and equipment limitations and strong human dependence on immediate response measures. Especially in deep and difficult-to-drill formations, formation heterogeneity and hidden downhole conditions increase the difficulty of drilling parameter regulation. If the drilling parameters are improperly applied, it is not only easy to cause inefficient rock breaking and increase the drilling cycle, but also easy to cause abnormal vibration downhole, accelerate drill bit damage and increase the risk of drill string failure. Therefore, drilling parameter optimization is an important technical means to achieve safe and efficient drilling in complex drilling environments.

[0003] The traditional drilling parameter optimization method based on regional drilling speed model, on the one hand, mostly relies on the regression statistical analysis of regional drilling engineering data, the model prediction accuracy is low, and it is mostly limited to the optimization of pre-drilling schemes, ignoring the correlation between the output characteristics of the downhole screw and the drilling parameters under the composite drilling process. The optimization of drilling parameters in the actual drilling process mostly relies on experience control, without quantitative control basis under the influence of geological engineering integration factors such as working conditions and formation lithology changes, and it is impossible to judge whether it is the optimal solution. On the other hand, with the improvement of mechanical drilling speed as the optimization goal, the abnormal influence of abnormal drilling conditions such as drill bit stick-slip, vortex, and mud packing on drilling efficiency during actual drilling is ignored, which easily leads to the application of drilling parameters exceeding the rock breaking instability threshold, resulting in early damage to the drill bit, damage to downhole tools and other negative effects. In addition, the current real-time optimization of drilling parameters is in its infancy. Due to the nonlinear correlation of massive monitoring data in the real-time drilling process, the optimization algorithm of traditional drilling parameters has the problem of overfitting, and the model is prone to fall into the problem of local optimal solution, which affects the effect of drilling speed increase and efficiency improvement. Therefore, in summary, there is an urgent need for a real-time drilling parameter optimization method and system that can overcome the above-mentioned defects and is simple and practical. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time drilling parameter optimization method and system based on extreme value optimization.

[0005] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0006] The present invention comprises the following steps:

[0007] S1: Obtain the logging engineering parameters under real-time working conditions; the logging engineering parameters are specifically drilling pressure, rotary table speed, torque, drilling fluid displacement, and drilling fluid density.

[0008] S2: Based on the rock confined compressive strength (CCS) calculation model, the rock breaking strength baseline under the optimal drilling state of the target well is obtained, and the rock breaking strength baseline is equivalent to the theoretical minimum rock breaking energy ( VCS min ） ; The specific process of obtaining the rock confined compressive strength (CCS) is:

[0009] S21: Obtain gamma, density and sonic logging data of adjacent wells;

[0010] S22: Obtain the well history data of adjacent wells, including the lithology and stratigraphic sequence data of the actual drilled formations;

[0011] S23, obtaining the target well's predicted formation lithology, predicted formation pressure, predicted formation sequence, and wellbore trajectory design data;

[0012] S24: Based on the logging data of the adjacent wells, the rock uniaxial compressive strength (UCS) calculation model is used to remove the frequency jump points and perform smoothing preprocessing to construct a single well longitudinal uniaxial compressive strength logging response curve. The rock uniaxial compressive strength (UCS) model calculation method is to select corresponding empirical calculation formulas for sandstone, mudstone, and carbonate formations according to the lithology of the formation.

[0013] S25: According to the target well data and the well history data of the adjacent wells, the logging response curve of the uniaxial compressive strength of the rock of the adjacent wells is mapped to the corresponding well depth of the target well by using the spatial interpolation and extrapolation method; and the logging response curve of the uniaxial compressive strength of the rock of the target well is obtained;

[0014] S26: Calculating the real-time bottom hole drilling fluid equivalent circulating density (ECD) according to the real-time logging engineering data of the target well;

[0015] S27: Calculate the effective bottom hole confining pressure according to the predicted formation pressure of the target well and the real-time bottom hole drilling fluid equivalent circulating density (ECD);

[0016] S28: According to the uniaxial compressive strength and bottom confining pressure of the target well, a rock confined compressive strength (CCS) calculation model is constructed; the vertical upper confined rock compressive strength response curve of the target well is calculated through the rock confined compressive strength calculation model, and the confined rock compressive strength response curve is equivalent to the rock breaking strength baseline under the optimal drilling state of the target well. The rock confined compressive strength calculation formula is:

[0017] ;

[0018] Where, CCS is the rock confined compressive strength, MPa; UCS is the rock uniaxial compressive strength, MPa; D P is the effective confining pressure at the bottom of the well, MPa; is the rock internal friction angle, °

[0019] S3: constructing a rock crushing energy model under a composite working condition taking into account screw output loss, and calculating a virtual rock crushing strength (VCS) under a current engineering parameter state according to the rock crushing energy model;

[0020] S31: Considering the friction resistance between the rotor and the stator and the leakage between the sealing chambers of the screw drill under the actual composite drilling conditions, as well as the mechanical and hydraulic losses of the transmission shaft and the bearing joint, the actual output torque and pressure difference of the screw are determined according to the volumetric efficiency under the leakage effect and the mechanical efficiency under the torque loss;

[0021] S32: Based on the friction force analysis between the drill string and the wellbore wall at different sections of the directional well and the sliding friction analysis of the drill bit, the surface drilling pressure and torque are corrected to the drilling pressure and torque at the bottom of the wellbore drill bit;

[0022] S33: Taking the interface between the drill bit and the rock as the energy input and output interface, comprehensively considering the ground energy input and the screw energy input and the energy loss caused by the drill string transmission, friction and downhole vibration, the rock crushing energy model under the composite working condition considering the screw output loss is obtained;

[0023] The calculation model of virtual rock crushing strength is:

[0024] ;

[0025] In the formula, VCS Virtual rock crushing strength, MPa; W The work done per unit time to break the rock; V is the volume of rock broken per unit time; E is the volume of rock broken per unit time. m Mechanical efficiency of new drill bit; WOB surface measured drilling pressure, MPa; μ s Drill string friction coefficient; γ b Bottom hole inclination angle, °; A b Drill cross-sectional area, m 2 ; η Screw output efficiency; △P m Screw pressure drop, MPa; Q drilling fluid displacement, L / s; ROP mechanical drilling rate, m / h.

[0026] S34: Based on the rock crushing theory and Mohr-Coulomb rock strength failure mechanism, the rock crushing energy that needs to be overcome when the PDC drill bit cutting teeth penetrate into the formation is equivalent to the rock lateral confining compressive strength, and thus the actual rock crushing energy is equivalent to the virtual rock crushing strength.

[0027] S4: Based on the rock breaking strength baseline and the real-time virtual rock breaking strength, define the rock breaking energy transfer efficiency (E), determine the rock breaking efficiency and abnormal downhole conditions in real time, and assist in finding the threshold of unstable drilling parameters under composite drilling conditions; compare the deviation between the virtual rock breaking strength and the rock breaking strength baseline in real time while drilling, define the rock breaking energy transfer efficiency to provide feedback on the real-time rock breaking efficiency. The rock breaking energy transfer efficiency (E) is specifically:

[0028] ;

[0029] Where, E is the rock breaking energy transfer efficiency, dimensionless; VCS min is the theoretical minimum rock breaking energy, MPa

[0030] The step S5 comprises the following steps:

[0031] S5: Taking the rock breaking strength baseline as the adjustment target of minimizing the virtual rock breaking strength, comprehensively considering the above unstable drilling parameter thresholds, and using a multivariable extreme value optimization algorithm, the recommended drilling parameter combination under the minimum virtual rock breaking strength is obtained.

[0032] S51: using the real-time acquired drilling pressure and rotation speed as input values;

[0033] S52: using different filters to obtain the gradient of drilling pressure and rotation speed, and then synthesizing and demodulating the integrated variable values ​​as the control quantity of the algorithm;

[0034] S53: Determine whether it is the best fitness and update the best fitness value;

[0035] S54: Update input parameters; repeat steps S51-S53;

[0036] S55: Seek the best combination of drilling parameters by continuously adjusting drilling pressure and drilling speed.

[0037] The system of the present invention implements a real-time drilling parameter optimization method based on extreme value optimization: through real-time collection of on-site logging data, daily drilling reports, and drilling fluid reports, noise points are screened out of logging engineering data and input into neural network calculations, and comprehensive logging second data is automatically converted into input parameters for engineering modeling calculations, a drill tool assembly is selected, and drilling fluid performance and inclination data are input. Finally, engineering calculation modeling and real-time dynamic analysis of real-time second data are implemented through an engineering calculation engine, so as to realize real-time scientific optimization decision-making of drilling parameters.

[0038] The beneficial effects of the present invention are:

[0039] The present invention is a real-time drilling parameter optimization method and system based on extreme value optimization. Compared with the prior art, the present invention can start from the geological engineering dual-factor fusion analysis method, based on the single well rock compressive strength calculation, fine geological data, and at the same time take into account the limiting factors such as the output characteristics and output loss of the drill tool assembly under the drilling condition, and obtain the virtual rock crushing strength under the composite drilling condition. And with the help of real-time acquisition and processing of logging engineering parameters during the drilling process, the key parameters after denoising are input into the neural network calculation, and the downhole drill bit rock breaking efficiency is comprehensively analyzed with the drilling data, and then the real-time global optimal solution is calculated by the multivariate extreme value optimization algorithm, so as to establish a virtual rock crushing strength fast calculation and mechanical drilling speed real-time optimization model based on machine learning methods and optimization algorithms, and finally embed the model into the parameter optimization system for on-site operation guidance, and use the minimum virtual rock crushing strength as an indicator to reduce the mechanical energy loss and negative effects caused by the stick-slip vibration of the drill bit and the buckling vibration of the drill tool, and obtain the maximum chip efficiency within the strengthening parameter range to achieve drilling speed-up. It breaks the data blind spot of conventional drilling parameter optimization, ensures the maximum rock breaking efficiency in the whole drilling process, and realizes the mechanical drilling speed limit improvement. It has strong application value and provides support for saving drilling costs and improving economic benefits in drilling projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of a real-time drilling parameter optimization method based on extreme value optimization of the present invention;

[0041] Figure 2 It is a schematic diagram of the process of establishing the rock breaking strength baseline of the present invention;

[0042] Figure 3 This is an example diagram of the method for real-time determination of rock breaking efficiency and abnormal downhole working conditions of the present invention;

[0043] Figure 4 It is a schematic diagram of the method for optimizing the minimum virtual rock crushing strength of the present invention;

[0044] Figure 5 It is a schematic diagram of the minimum virtual rock crushing strength optimization technology process of the present invention;

[0045] Figure 6 It is a schematic diagram of constructing a real-time drilling parameter optimization system based on extreme value optimization of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0047] like Figure 1 As shown: The real-time drilling parameter optimization method based on extreme value optimization of the present invention comprises the following steps:

[0048] S1: Obtain the logging engineering parameters under real-time working conditions; the logging engineering parameters are specifically drilling pressure, rotary table speed, torque, drilling fluid displacement, and drilling fluid density.

[0049] like Figure 2 As shown, S2: Based on the rock confined compressive strength (CCS) calculation model, obtain the rock breaking strength baseline under the optimal drilling state of the target well; the rock breaking strength baseline is equivalent to the theoretical minimum rock breaking energy ( VCS min ） ; The specific process of obtaining the rock confined compressive strength (CCS) is:

[0050] S21: Obtain gamma, density and sonic logging data of adjacent wells;

[0051] S22: Obtain the well history data of adjacent wells, including the lithology and stratigraphic sequence data of the actual drilled formations;

[0052] S23, obtaining the target well's predicted formation lithology, predicted formation pressure, predicted formation sequence, and wellbore trajectory design data;

[0053] S24: Based on the logging data of the adjacent wells, the rock uniaxial compressive strength (UCS) calculation model is used to remove the frequency jump points and perform smoothing preprocessing to construct a single well longitudinal uniaxial compressive strength logging response curve. The rock uniaxial compressive strength (UCS) model calculation method is to select corresponding empirical calculation formulas for sandstone, mudstone, and carbonate formations according to the lithology of the formation.

[0054] S25: According to the target well data and the well history data of the adjacent wells, the logging response curve of the uniaxial compressive strength of the rock of the adjacent wells is mapped to the corresponding well depth of the target well by using the spatial interpolation and extrapolation method; and the logging response curve of the uniaxial compressive strength of the rock of the target well is obtained;

[0055] S26: Calculating the real-time bottom hole drilling fluid equivalent circulating density (ECD) according to the real-time logging engineering data of the target well;

[0056] S27: calculating the effective bottom hole confining pressure according to the predicted formation pressure of the target well and the real-time bottom hole drilling fluid equivalent circulating density (ECD);

[0057] S28: Construct a rock confined compressive strength (CCS) calculation model based on the uniaxial compressive strength and bottom hole confining pressure of the target well. The rock confined compressive strength response curve of the target well in the vertical direction is calculated by the rock confined compressive strength calculation model, and the confined rock compressive strength response curve is equivalent to the rock breaking strength baseline under the optimal drilling state of the target well;

[0058] The calculation formula of rock confined compressive strength is:

[0059] ;

[0060] Where, CCS is the rock confined compressive strength, MPa; UCS is the rock uniaxial compressive strength, MPa; DP is the effective confining pressure at the bottom of the well, MPa; is the internal friction angle of rock, (°).

[0061] S3: constructing a rock crushing energy model under a composite working condition taking into account screw output loss, and calculating a virtual rock crushing strength (VCS) under a current engineering parameter state according to the rock crushing energy model;

[0062] S31: Considering the friction resistance between the rotor and the stator and the leakage between the sealing chambers of the screw drill under the actual composite drilling conditions, as well as the mechanical and hydraulic losses of the transmission shaft and the bearing joint, the actual output torque and pressure difference of the screw are determined according to the volumetric efficiency under the leakage effect and the mechanical efficiency under the torque loss;

[0063] S32: Based on the friction force analysis between the drill string and the wellbore wall at different sections of the directional well and the sliding friction analysis of the drill bit, the surface drilling pressure and torque are corrected to the drilling pressure and torque at the bottom of the wellbore drill bit;

[0064] S33: Taking the interface between the drill bit and the rock as the energy input and output interface, comprehensively considering the ground energy input and the screw energy input and the energy loss caused by the drill string transmission, friction and downhole vibration, the rock crushing energy model under the composite working condition considering the screw output loss is obtained;

[0065] The calculation model of virtual rock crushing strength is:

[0066] ;

[0067] In the formula, VCS Virtual rock crushing strength, MPa; W The work done per unit time to break the rock; V is the volume of rock broken per unit time; E is the volume of rock broken per unit time. mMechanical efficiency of new drill bit; WOB surface measured drilling pressure, MPa; μ s Drill string friction coefficient; γ b Bottom hole inclination angle, °; A b Drill cross-sectional area, m 2 ; η Screw output efficiency; △Pm screw pressure drop, MPa; Q drilling fluid displacement, L / s; ROP mechanical drilling rate, m / h.

[0068] S34: Based on the rock crushing theory and Mohr-Coulomb rock strength failure mechanism, the rock crushing energy that the PDC cutter needs to overcome when entering the formation is equivalent to the rock lateral confining compressive strength, and thus the actual rock crushing energy is equivalent to the virtual rock crushing strength.

[0069] S4: Based on the rock breaking strength baseline and the real-time virtual rock breaking strength, the rock breaking energy transfer efficiency (E) is defined, the rock breaking efficiency and downhole abnormal conditions are determined in real time, and an auxiliary search is made for the threshold of unstable drilling parameters under the composite drilling conditions; the deviation between the virtual rock breaking strength and the rock breaking strength baseline is compared in real time while drilling, and the rock breaking energy transfer efficiency is used to provide feedback on the real-time rock breaking efficiency.

[0070] Under ideal conditions, the rock-breaking energy transfer efficiency tends to 1. The more the rock-breaking energy transfer efficiency value deviates from 1, the lower the rock-breaking efficiency, indicating that the downhole drill bit is in an unstable rock-breaking state.

[0071] In the method, the rock breaking effect is specifically divided into two categories: a rock breaking energy transfer efficiency between 0.8 and 1.2 indicates efficient drilling, and a rock breaking energy transfer efficiency below 0.8 indicates inefficient drilling.

[0072] In a specific embodiment, as shown in the attached Figure 3 As shown, the real-time calculated rock breaking energy transfer efficiency is lower than 0.8, and the virtual rock crushing strength shows a spike-like area, showing a large fluctuation. At the same time, the acquired logging engineering parameters show low frequency, high amplitude, and periodic fluctuations, indicating that the drilling pressure instability threshold is exceeded at this time, and the drilling pressure must be reduced step by step. The specific adjustment range is controlled to 10% until the rock breaking energy transfer efficiency is restored to 0.8-1.2.

[0073] In a specific embodiment, the real-time calculated rock-breaking energy transfer efficiency is lower than 0.8. If the rock property does not change, the virtual rock crushing strength increases steadily with a fluctuation range of about 5%, indicating that it is lower than the minimum threshold of hydraulic energy at this time, and the drill bit hydraulic horsepower must be increased until the rock-breaking energy transfer efficiency is restored to 0.8-1.2.

[0074] In a specific embodiment, the real-time calculated rock-breaking energy transfer efficiency is lower than 0.8. If the rock properties do not change, the virtual rock crushing strength continues to increase and there is no improvement through drilling parameter adjustment, indicating that the drill bit is lower than the drill bit blunting threshold and the drill bit must be replaced.

[0075] The rock breaking energy transfer efficiency is specifically:

[0076] ;

[0077] Where, E is the rock breaking energy transfer efficiency, dimensionless; VCS min is the theoretical minimum rock breaking energy, MPa.

[0078] like Figure 4 , 5 As shown, S5: Taking the rock breaking strength baseline as the adjustment target of minimizing the virtual rock breaking strength, comprehensively considering the above unstable drilling parameter thresholds, and adopting a multivariable extreme value optimization algorithm, obtain the recommended drilling parameter combination under the minimum virtual rock breaking strength.

[0079] When the rock breaking capacity transfer efficiency is between 0.8 and 1.2, a multivariable extreme value search algorithm is considered, which regards the drilling operation as a multi-stage decision-making process (the number of stages (optimal drilling parameter combination) is unknown). In the variable extreme value search algorithm, the controllable variables for optimizing drilling are drilling pressure and rotation speed. The rock confined compressive strength is the objective function, and the combination of quantified drilling pressure and rotation speed is used to achieve the minimum virtual rock crushing strength to achieve the optimization of drilling parameters.

[0080] In the algorithm, since the measurement of drilling parameters and the commands provided to the control system on the drilling rig are performed at regular intervals, discrete time representation is used. It is assumed that the relevant measurements are performed at time intervals of Dt seconds, and the top drive and automatic drilling rig can receive updated set points for the target bit pressure and speed every Dt seconds. The current time step is represented by t, and the command for the iterative time step is represented by the symbol t+Dt.

[0081] S51: using the real-time acquired drilling pressure and rotation speed as input values;

[0082] S52: using different filters to obtain the gradient of drilling pressure and rotation speed, and then synthesizing and demodulating the integrated variable values ​​as the control quantity of the algorithm;

[0083] S53: Determine whether it is the best fitness and update the best fitness value;

[0084] S54: Update input parameters; repeat steps S51-S53;

[0085] S55: Seek the best combination of drilling parameters by continuously adjusting drilling pressure and drilling speed.

[0086] like Figure 6 As shown, the present invention implements a system for a real-time drilling parameter optimization method based on extreme value optimization: by collecting field logging data, daily drilling reports, and drilling fluid reports in real time, filtering out noise points from logging engineering data and inputting them into neural network calculations, automatically converting comprehensive logging second data into input parameters for engineering modeling calculations, selecting a drill tool assembly, and inputting drilling fluid performance and inclination data, and finally implementing engineering calculation modeling and real-time dynamic analysis of real-time second data through an engineering calculation engine, thereby achieving real-time scientific optimization decisions for drilling parameters.

[0087] The technical solution of the present invention is not limited to the above-mentioned specific embodiments. All technical variations made according to the technical solution of the present invention fall within the protection scope of the present invention.

Claims

1. A real-time drilling parameter optimization method based on extreme value optimization, characterized in that: The following steps are involved: S1: Obtain logging engineering parameters under real-time working conditions; S2: Based on the rock confined compressive strength calculation model, the rock breaking strength baseline under the optimal drilling state of the target well is obtained, and the rock breaking strength baseline is equivalent to the theoretical minimum rock breaking energy; the specific process of obtaining the rock confined compressive strength is as follows: S21: Obtain gamma, density and sonic logging data of adjacent wells; S22: Obtain the well history data of adjacent wells, including the lithology and stratigraphic sequence data of the actual drilled formations; S23, obtaining the target well's predicted formation lithology, predicted formation pressure, predicted formation sequence, and wellbore trajectory design data; S24: Based on the logging data of adjacent wells, the single well longitudinal uniaxial compressive strength logging response curve is constructed by removing the frequency jump points and performing smoothing preprocessing through the rock uniaxial compressive strength calculation model; S25: Based on the target well data and the well history data of the adjacent wells, the logging response curve of the uniaxial compressive strength of the rock of the adjacent wells is mapped to the corresponding well depth of the target well by using the spatial interpolation and extrapolation method; the logging response curve of the uniaxial compressive strength of the rock of the target well is obtained; S26: Calculate the real-time bottom hole drilling fluid equivalent circulating density according to the real-time logging engineering data of the target well; S27: Calculate the effective confining pressure at the bottom of the well according to the predicted formation pressure of the target well and the real-time equivalent circulating density of the drilling fluid at the bottom of the well; S28: construct a rock confined compressive strength calculation model based on the uniaxial compressive strength and bottom hole confining pressure of the target well; calculate the vertical upper confined rock compressive strength response curve of the target well through the rock confined compressive strength calculation model, and equate the confined rock compressive strength response curve to the rock breaking strength baseline under the optimal drilling state of the target well; The calculation formula of rock confined compressive strength is: Where, CCS is the rock confined compressive strength, MPa; UCS is the rock uniaxial compressive strength, MPa; D P is the effective confining pressure at the bottom of the well, MPa; φ is the internal friction angle of the rock; S3: constructing a rock crushing energy model under a composite working condition taking into account screw output loss, and calculating the virtual rock crushing strength under the current engineering parameter state according to the rock crushing energy model; The specific process is: S31: Considering the friction resistance between the rotor and the stator and the leakage between the sealing chambers of the screw drill under the actual composite drilling conditions, as well as the mechanical and hydraulic losses of the transmission shaft and the bearing joint, the actual output torque and pressure difference of the screw are determined according to the volumetric efficiency under the leakage effect and the mechanical efficiency under the torque loss; S32: Based on the friction force analysis between the drill string and the wellbore wall at different sections of the directional well and the sliding friction analysis of the drill bit, the surface drilling pressure and torque are corrected to the drilling pressure and torque at the bottom of the wellbore drill bit; S33: Taking the interface between the drill bit and the rock as the energy input and output interface, comprehensively considering the ground energy input and the screw energy input and the energy loss caused by the drill string transmission, friction and downhole vibration, the rock crushing energy model under the composite working condition considering the screw output loss is obtained; The calculation model of virtual rock crushing strength is: Where, VCS virtual rock crushing strength, MPa; W is the work done per unit time to break the rock; V is the volume of rock broken per unit time; E is the m Mechanical efficiency of new drill bit; WOB ground measurement drilling pressure, MPa; μ s Drill string friction coefficient; γ b Bottom hole inclination angle, °; A b Drill cross-sectional area, m 2 ; η screw output efficiency; △P m Screw pressure drop, MPa; Q drilling fluid displacement, L / s; ROP mechanical drilling speed, m / h; S34: Based on the rock crushing theory and the Mohr-Coulomb rock strength failure mechanism, the rock crushing energy that the PDC drill bit cutting teeth need to overcome when entering the formation is equivalent to the rock confined compressive strength, so the actual rock crushing energy is equivalent to the virtual rock crushing strength; S4: Based on the rock breaking strength baseline and the real-time virtual rock crushing strength, define the rock breaking energy transfer efficiency, determine the rock breaking efficiency and downhole abnormal conditions in real time, and assist in finding the unstable drilling parameter threshold under the composite drilling conditions; S5: Taking the rock breaking strength baseline as the adjustment target of minimizing the virtual rock breaking strength, comprehensively considering the above unstable drilling parameter thresholds, and using a multivariable extreme value optimization algorithm, the recommended drilling parameter combination under the minimum virtual rock breaking strength is obtained.

2. The real-time drilling parameter optimization method based on extreme value optimization according to claim 1, characterized in that: The logging engineering parameters in step S1 are specifically drilling pressure, rotary table speed, torque, drilling fluid displacement, and drilling fluid density.

3. The real-time drilling parameter optimization method based on extreme value optimization according to claim 1, characterized in that: The rock uniaxial compressive strength model calculation method in step S24 is to select corresponding empirical calculation formulas for sandstone, mudstone and carbonate rock formations according to the lithology of the formation.

4. The real-time drilling parameter optimization method based on extreme value optimization according to claim 1, characterized in that: The step S4 compares the deviation between the virtual rock crushing strength and the rock breaking strength baseline in real time while drilling, and defines the rock breaking energy transfer efficiency to provide feedback on the real-time rock breaking efficiency.

5. The real-time drilling parameter optimization method based on extreme value optimization according to claim 4 is characterized in that: The rock breaking energy transfer efficiency is specifically: Where, E is the rock breaking energy transfer efficiency, dimensionless; VCS min is the theoretical minimum rock breaking energy, MPa.

6. The real-time drilling parameter optimization method based on extreme value optimization according to claim 1, characterized in that: The step S5 comprises the following steps: S51: using the real-time acquired drilling pressure and rotation speed as input values; S52: using different filters to obtain the gradient of drilling pressure and rotation speed, and then synthesizing and demodulating the integrated variable values ​​as the control quantity of the algorithm; S53: Determine whether it is the best fitness and update the best fitness value; S54: Update input parameters; repeat steps S51-S53; S55: Seek the best combination of drilling parameters by continuously adjusting drilling pressure and drilling speed.

7. A system for executing the real-time drilling parameter optimization method based on extreme value optimization according to any one of claims 1 to 5, characterized in that: Through real-time collection of on-site logging data, daily drilling reports, and drilling fluid reports, the logging engineering data is filtered out of noise and input into the neural network calculation, the comprehensive logging second data is automatically converted into input parameters for engineering modeling calculations, the drill tool combination is selected, and the drilling fluid performance and inclination data are input. Finally, the engineering calculation modeling and real-time dynamic analysis of the real-time second data are realized through the engineering calculation engine, realizing real-time scientific optimization decision-making of drilling parameters.

Images