Coiled tubing drilling, grinding and dredging process parameter selection method and device and computer equipment

Abstract

The invention provides a coiled tubing drilling, grinding and dredging process parameter selection method and device and computer equipment, and belongs to the field of oil and gas field development. Comprising the following steps: S1, collecting related data; s2, determining a target needing to be optimized; s3, determining process parameters needing to be optimized; s4, establishing an optimization model; s5, designing an experimental scheme to verify and optimize process parameters; s6, carrying out an experiment, recording experiment data and carrying out analysis; and S7, determining an optimal process parameter combination according to an analysis result. The drilling and grinding time can be shortened, equipment abrasion is reduced, and the production efficiency of an oil-gas well is improved; the risks of blockage of a drilling and grinding tool and damage to a sleeve are avoided; standardization and normalization of drilling and grinding operation are achieved, and controllability and predictability of the operation are improved.

Description

Technical Field

[0001] The invention relates to the field of oil and gas field development, and in particular to a method, a device and a computer device for selecting process parameters for continuous oil pipe drilling, grinding and dredging. Background Art

[0002] In the development of horizontal well oil fields, production casing is an important channel for transmitting crude oil and natural gas. However, due to long-term operation and the accumulation of impurities such as oil scale and sand in the oil layer, the inner wall of the casing is prone to blockage, resulting in obstruction of oil flow and affecting production efficiency. In order to solve the problem of oil pipe blockage and improve the production capacity of oil wells, people have proposed the use of continuous tubing efficient drilling and grinding dredging technology. There are many parameter choices in the continuous tubing efficient drilling and grinding dredging technology, such as drill bit type, drill bit size, drilling pressure, drilling speed, feed rate, etc. These parameters need to be selected for different blockages. If the parameters are not selected properly, the drilling and grinding dredging will fail at the least, and engineering accidents will occur at the worst. Therefore, how to select these parameters in a targeted manner has also become a major problem of this technology. For this reason, a method for selecting parameters for continuous tubing drilling and grinding dredging process is invented to solve the problem of improper parameter selection.

[0003] Chinese invention patent CN110185415A provides an oil pipeline dredging system. Although this invention patent can be used to dredge blocked parts of oil pipelines and ensure the oil flow rate in the oil pipelines, it does not realize digital processing modeling. When dealing with more complex working conditions, new solutions must be re-introduced. Summary of the invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention provides a method, device and computer equipment for selecting process parameters for continuous oil tubing drilling, grinding and dredging. The purpose of the present invention is to shorten the drilling and grinding time, reduce equipment wear, and improve the production efficiency of oil and gas wells; avoid the risk of clogging of drilling and grinding tools and damage to casing, and further ensure the smooth progress of the operation; help to achieve standardization and normalization of drilling and grinding operations, and improve the controllability and predictability of operations.

[0005] In order to solve the above technical problems, the present invention provides a method for selecting process parameters of coiled tubing drilling, grinding and dredging, comprising the following steps: S1. Collect relevant data on special operations of horizontal well coiled tubing and historical data on drilling, grinding and dredging process parameters; S2. Determine the goals that need to be optimized based on actual conditions and needs; S3. Determine the process parameters that need to be optimized based on the existing technology; S4. Establish a process parameter optimization model based on the collected data and the determined optimization goals; S5. Design an experimental plan to verify and optimize the process parameters based on the process parameter optimization model; S6. Conduct experiments according to the designed experimental plan, record experimental data and analyze them; S7. Evaluate the impact of different process parameter combinations on the optimization target through analysis of experimental data and determine the best process parameter combination; Furthermore, in step S1, the relevant data of the special operation of the horizontal well coiled tubing includes tubing size, well depth, blockage type and location.

[0006] Furthermore, in step S2, the optimization objectives include increasing the drilling speed, reducing wear, and lowering the operating cost.

[0007] Further, in step S3, the process parameters include drill type, drill size, drilling speed, rotation speed, feed speed, injection liquid and abrasive concentration.

[0008] Furthermore, in step S4, the method used to establish the optimization model includes statistical methods, experimental design or numerical simulation.

[0009] Furthermore, after step S7 is executed, the optimal combination of process parameters is applied to the actual operation to perform drilling, grinding and unblocking operations, and the effect is monitored; and the effectiveness and feasibility of the solution are verified by monitoring and evaluating the effect of the actual operation.

[0010] Furthermore, in step S7, the method for analyzing the optimization result includes parameter optimization based on genetic algorithm and parameter optimization based on particle swarm algorithm.

[0011] Furthermore, the parameter optimization based on genetic algorithm includes the following steps: S701, define a fitness function to evaluate the quality of each parameter combination; S702, initializing a set of random parameter combinations as a population; S703, generating a new parameter combination by performing selection, crossover and mutation operations on the population using a genetic algorithm; S704, evaluating the quality of the new parameter combination according to the fitness function, and selecting the parameter combination with the highest fitness as the next generation population; S705, repeating the selection, crossover and mutation operations until the set number of iterations is reached or the optimization target is met, and finally obtaining the parameter combination with the highest fitness.

[0012] Furthermore, the parameter optimization based on the particle swarm algorithm includes the following steps: H701. Define a fitness function to evaluate the quality of each parameter combination; H702, initialize a group of particles, each particle represents a set of parameter combinations; H703, evaluate the quality of each particle by calculating its fitness; H704, each particle updates its own speed and position according to its own historical optimal and global optimal parameter combination, obtains a new parameter combination, and uses the new parameter combination as a new particle; H705. Execute steps H703 and H704 for the new particles, and repeat the operation of updating the particle's speed and position until the set number of iterations is reached or the optimization goal is met, and finally the parameter combination with the highest fitness is obtained.

[0013] The present invention also provides a coiled tubing drilling, grinding and unblocking process parameter selection device, which adopts the above-mentioned coiled tubing drilling, grinding and unblocking process parameter selection method, and the device includes a data collection module, an optimization target determination module, an optimization parameter determination module, an optimization model establishment module, an experimental scheme design module, an experimental scheme execution module and a parameter combination determination module; The data collection module is used to collect relevant data of special operations of horizontal well coiled tubing and historical data of drilling, grinding and dredging process parameters; The optimization target determination module is used to determine the target to be optimized according to actual conditions and requirements; The optimization parameter determination module is used to determine the process parameters that need to be optimized according to the prior art; The optimization model building module is used to build a process parameter optimization model based on the collected data and the determined optimization target; The experimental scheme design module is used to design an experimental scheme to verify and optimize the process parameters according to the process parameter optimization model; The experimental plan execution module is used to perform experiments according to the designed experimental plan, record experimental data and analyze them; The parameter combination determination module is used to evaluate the impact of different process parameter combinations on the optimization target through analysis of experimental data, and to determine the best process parameter combination.

[0014] Furthermore, the device also includes an optimization scheme implementation module and an optimization effect evaluation module: The implementation optimization program module is used to apply the best process parameter combination to the actual operation, perform drilling, grinding and unblocking operations, and monitor the effect; The optimization effect evaluation module is used to verify the effectiveness and feasibility of the solution by monitoring and evaluating the effect of actual operations.

[0015] The present invention also provides a computer device, which includes one or more processors and one or more memories, wherein at least one instruction is stored in the one or more memories, and the at least one instruction is loaded and executed by the one or more processors to implement the operations performed by the above-mentioned method for selecting process parameters for continuous oil tubing drilling, grinding and dredging.

[0016] The present invention provides a method, device and computer equipment for selecting process parameters of coiled tubing drilling, grinding and unblocking, which have at least the following beneficial effects: 1. The present invention optimizes drilling and grinding parameters, such as drilling pressure, drilling speed, displacement, etc., to achieve efficient drilling and grinding of downhole blockages such as oil scale and gravel, thereby shortening the drilling and grinding time, reducing equipment wear, and improving the production efficiency of oil and gas wells; in order to reduce the problems of mud cementation and precipitation during the drilling and grinding process, optimizing the drilling and grinding parameters can avoid the risk of blockage of the drilling and grinding tools and damage to the casing, and further ensure the smooth progress of the operation; 2. The present invention helps to achieve standardization and regularization of drilling and grinding operations and improve the controllability and predictability of operations by summarizing, analyzing and studying the controllable variable parameters of coiled tubing.

[0017] 3. The present invention uses innovative algorithms such as genetic algorithms and particle swarm algorithms to more effectively search and optimize parameter space, find a better combination of process parameters, improve the dredging effect and operating efficiency of coiled tubing, and provide an innovative solution for optimizing the efficient drilling and grinding dredging process of coiled tubing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a method for selecting process parameters for drilling, grinding and unblocking of coiled tubing according to an embodiment of the present invention; Figure 2 This is a functional module block diagram of a coiled tubing drilling, grinding and dredging process parameter selection device of the present invention; Figure 3 The present invention is a schematic structural block diagram of a computer device. DETAILED DESCRIPTION

[0019] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0022] In order to better understand the purpose, structure and function of the present invention, the following is a detailed description of a method, device and computer equipment for selecting process parameters for drilling, grinding and unblocking of coiled tubing in conjunction with the accompanying drawings.

[0023] Example 1 like Figure 1 As shown, the present invention provides a method for selecting process parameters for drilling, grinding and unblocking of coiled tubing, comprising the following steps: S1. Collect relevant data on special operations of horizontal well coiled tubing and historical data on drilling, grinding and dredging process parameters; S2. Determine the goals that need to be optimized based on actual conditions and needs; S3. Determine the process parameters that need to be optimized based on the existing technology; S4. Establish a process parameter optimization model based on the collected data and the determined optimization goals; S5. Design an experimental plan to verify and optimize the process parameters based on the process parameter optimization model; S6. Conduct experiments according to the designed experimental plan, record experimental data and analyze them; S7. By analyzing the experimental data, evaluate the impact of different process parameter combinations on the optimization target and determine the best process parameter combination. Example 2 like Figure 1 As shown, the present invention provides a method for selecting process parameters for drilling, grinding and unblocking of coiled tubing, comprising the following steps: S1. Collect relevant data on special operations of horizontal well coiled tubing and historical data on drilling, grinding and dredging process parameters; S2. Determine the goals that need to be optimized based on actual conditions and needs; S3. Determine the process parameters that need to be optimized based on the existing technology; S4. Establish a process parameter optimization model based on the collected data and the determined optimization goals; S5. Design an experimental plan to verify and optimize the process parameters based on the process parameter optimization model; S6. Conduct experiments according to the designed experimental plan, record experimental data and analyze them; S7. By analyzing the experimental data, evaluate the impact of different process parameter combinations on the optimization target and determine the best process parameter combination.

[0024] The difference between this embodiment and embodiment 1 is that: In step S1, the relevant data of the special operation of the horizontal well coiled tubing include tubing size, well depth, blockage type and location, etc.; in step S2, the optimization objectives include increasing drilling speed, reducing wear, reducing operating costs, etc.; In step S3, the process parameters include drill bit type, drill bit size, drilling speed, rotation speed, feed speed, injection liquid and abrasive concentration, etc.; in step S4, the method used to establish the optimization model includes statistical methods, experimental design or numerical simulation methods, etc.; After step S7 is completed, the optimal process parameter combination can be applied to actual operations to perform drilling, grinding and unblocking operations, and the results can be monitored; and the effectiveness and feasibility of the solution can be verified by monitoring and evaluating the results of the actual operations.

[0025] Example 3 In this embodiment, a specific example is used to explain the parameter optimization based on the genetic algorithm. It is assumed that the parameter to be optimized is the drill bit diameter ( ) and feed rate ( ), the optimization goal is to minimize the operation time ( ) and maximize the dredging effect ( ), the following genetic algorithm is used to search for the best parameter combination: 1. Initialize the population: randomly generate a set of parameter combinations, such as and , The value range is , The value range is , assuming that there are 5 individuals in the initial population, their parameter combinations are as follows: Instance 1: , ; Individual 2: , ; Individual 3: , ; Individual 4: , Individual 5: , .

[0026] 2. Calculate fitness: For each individual, use the formula to calculate the operation time and dredging effect: Working time formula: ,in is the well depth in m; Dredging effect formula: ,in is the adjustment parameter; Assume the well is deep , adjustment parameters , the operation time and dredging effect of each individual are calculated as follows: Instance 1: , ; Individual 2: , ; Individual 3: , ; Individual 4: , Individual 5: , .

[0027] 3. Perform selection operation: According to the value of the fitness function, select individuals with higher fitness as the next generation of population. Assume that the selection operation selects the first three individuals with the highest fitness: Instance 1: , ; Individual 2: , Individual 5: , .

[0028] 4. Perform crossover operation: Select two parent individuals from the three individuals with the highest fitness, and use the single-point crossover operator to synthesize their genomes into new offspring individuals; suppose that individual 1 and individual 2 are selected for crossover operation, and the crossover point is The value of Parent individual 1: , ; Parent individual 2: , ; Offspring individual 1: , ; Offspring individual 2: , .

[0029] 5. Perform mutation operation: mutate the offspring individuals and introduce a certain degree of random changes. Suppose that the offspring individual 1 is mutated. The value of becomes 4, Offspring individual 1: , ; Repeat steps 2-5 until the set number of iterations is reached or the optimization goal is met; Finally, the individual with the highest fitness is the best parameter combination. Assume that after multiple iterations, the best individual is: Best Individual: , , , .

[0030] The genetic algorithm in this embodiment is designed based on the actual needs of the high-efficiency drilling and grinding process of coiled tubing; the drill bit diameter ( ) and feed rate ( ) are important parameters that affect the dredging effect and operation time. By optimizing the values ​​of these two parameters, the efficiency and effect of drilling and grinding dredging can be improved; The optimization goal is to minimize the operation time ( ) and maximize the dredging effect ( ), the operation time is related to the drill diameter and feed speed. By reducing the operation time, the operation efficiency can be improved; the dredging effect is calculated by the dredging effect formula. By maximizing the dredging effect, the effect of drilling and grinding dredging can be improved; The selection, crossover and mutation operations of the genetic algorithm can search the parameter space, gradually optimize the parameter combination and find the best individual; through iterative optimization, the best individual finally obtained represents the optimal parameter combination under given constraints, which can be used for the practical application of the efficient drilling, grinding and dredging process of coiled tubing. This design aims to improve the efficiency and effect of efficient drilling, grinding and dredging of coiled tubing and provide innovative solutions for process optimization.

[0031] Example 4 In this embodiment, a specific example is used to explain the parameter optimization based on the particle swarm algorithm. It is assumed that the parameter to be optimized is the drill bit diameter ( ) and feed rate ( ); The optimization goal is to minimize the operation time ( ) and maximize the dredging effect ( ), the particle swarm algorithm is used to search for the best parameter combination; 1. Initialize the particle swarm: randomly generate a group of particles, each particle represents a set of parameter combinations, such as The value range is , The value range is , assuming that there are 10 particles in the initial particle swarm, their parameter combinations are as follows: Particle 1: , ; Particle 2: , ; Particle 3: , ; ...; Particle 10: , .

[0032] 2. Calculate fitness: For each particle, use the formula to calculate the operation time and dredging effect: Working time formula: ,in is the well depth, in m; Dredging effect formula: ,in is the adjustment parameter; Assume the well is deep , adjustment parameters , the operation time and dredging effect of each particle are calculated.

[0033] 3. Update speed and position: Update the speed and position of the particle according to the particle's own historical optimal and global optimal parameter combination, obtain a new parameter combination, and use the new parameter combination as a new particle; the specific operations are as follows: For each particle, the new velocity and position are calculated based on its historical optimal parameter combination and the global optimal parameter combination, assuming that each particle has three velocity components and three position components .

[0034] The new speed is calculated using the following formula: , in is the inertia weight, and is the learning factor, and is a random number, pbest is the optimal position that the particle itself has ever experienced, gbest is the optimal position experienced by the particle swarm, t is the present moment, t+1 It is the next moment; The new position is calculated using the following formula: .

[0035] Repeat steps 2-3 for new particles until the set number of iterations is reached or the optimization goal is met; Finally, the particle with the highest fitness is obtained, which is the optimal parameter combination.

[0036] In this embodiment, the particle swarm algorithm is designed based on the simulation of biological group behavior. Each particle represents a set of parameter combinations, and the optimal solution is searched by updating the speed and position. The algorithm has global search capability and convergence, and is widely used in parameter optimization problems. By evaluating the fitness of each particle, i.e., the operation time and dredging effect, its quality can be measured; each particle gradually approaches the optimal parameter combination by updating its speed and position based on its own historical optimal and global optimal parameter combination; By iteratively updating the speed and position of particles, the particle swarm algorithm can search for the best solution in the parameter space. The particle with the highest fitness finally obtained is the optimal parameter combination. Applying the optimal parameter combination to the efficient drilling and grinding dredging process of coiled tubing can improve the operation efficiency and dredging effect.

[0037] The particle swarm algorithm has a good effect in parameter optimization problems and can find the best solution in the combination space of multiple parameters. Through the process of fitness evaluation and parameter update, the particle swarm algorithm can gradually adjust the parameter combination and find the optimal solution. In this embodiment, the formula can be further improved as follows: Working time formula: , in It is the depth of the well. Dredging effect formula: , in is the adjustment parameter; In the improved formula, the calculation of operation time no longer uses pi Instead of using the square of the drill bit diameter D, the well depth is used directly. Divide by feed rate and drill diameter The product of, such an improvement can simplify the calculation process and reduce the complexity; The calculation formula of the dredging effect is changed to an exponential function. The exponential function gradually approaches 1 when the parameter approaches 0, and gradually approaches 0 when the parameter increases. This improvement can better reflect the relationship between the dredging effect and the operation time. By adjusting the parameters The size of the dredging effect can control the sensitivity of the operation time; The improved formula reduces the calculation complexity and improves the calculation efficiency while maintaining the calculation accuracy; the introduction of the exponential function makes the calculation of the dredging effect more flexible, and the sensitivity of the parameters can be adjusted according to specific needs. Such design improvements help to improve the effect and efficiency of parameter optimization in the efficient drilling and grinding dredging process of continuous tubing.

[0038] Example 5 like Figure 2 As shown, the present invention also provides a coiled tubing drilling, grinding and unblocking process parameter selection device 400, which adopts the above-mentioned coiled tubing drilling, grinding and unblocking process parameter selection method, and the device includes a data collection module 401, an optimization target determination module 402, an optimization parameter determination module 403, an optimization model establishment module 404, an experimental scheme design module 405, an experimental scheme execution module 406 and a parameter combination determination module 407; The data collection module 401 is used to collect relevant data of special operations of horizontal well coiled tubing and historical data of drilling, grinding and dredging process parameters; The optimization target determination module 402 is used to determine the target to be optimized according to the actual situation and requirements; The optimization parameter determination module 403 is used to determine the process parameters that need to be optimized according to the prior art; The optimization model building module 404 is used to build an optimization model based on the collected data and objectives; The design experiment scheme module 405 is used to design an experiment scheme to verify and optimize process parameters according to the optimization model; The experimental plan execution module 406 is used to perform experiments according to the designed experimental plan, record experimental data and analyze them; The parameter combination determination module 407 is used to evaluate the impact of different process parameter combinations on the optimization target by analyzing the experimental data, and determine the best process parameter combination.

[0039] The device may also include an optimization solution implementation module 408 and an optimization effect evaluation module 409; The implementation optimization solution module 408 is used to apply the best process parameter combination to the actual operation, perform the drilling and grinding operation, and monitor the effect; The optimization effect evaluation module 409 is used to verify the effectiveness and feasibility of the optimization scheme by monitoring and evaluating the effect of the actual operation.

[0040] like Figure 3 As shown, the present invention further provides a computer device, the computer device comprising one or more processors and one or more memories, the one or more memories storing at least one instruction, the at least one instruction being loaded and executed by the one or more processors to implement the operations performed by the above-mentioned method for selecting process parameters for drilling, grinding and dredging of coiled tubing; The memory 300 is directly or indirectly electrically connected to the processor 200 to achieve data transmission or interaction. For example, the two are electrically connected via a communication bus or a signal line. The coiled tubing drilling and dredging process parameter selection device 400 includes at least one software function module that can be stored in the memory 300 in the form of software or firmware; The processor 200 is used to execute the executable modules stored in the memory 300, such as the software function modules or computer programs included in the coiled tubing drilling, grinding and dredging process parameter selection device 400; The memory 300 may be, but not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable read-only memory, an electrically erasable read-only memory, etc. The memory 300 is used to store programs, and the processor 200 executes the programs after receiving the execution instruction. The method of any embodiment of the present invention may be applied to the processor 200 or implemented by the processor 200; The processor 200 may be an integrated circuit chip with signal processing capability. The processor 200 may be a general-purpose processor, including a central processing unit, a network processor, etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and may implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present invention.

[0041] The above description is only the preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. In the absence of contradiction, various embodiments may be combined with each other to form new embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A method for selecting process parameters for coiled tubing drilling, grinding and dredging. It is characterized in that The following steps are involved: S1. Collect relevant data of horizontal well coiled tubing operations and historical data of drilling, grinding and dredging process parameters; S2. Determine the goals that need to be optimized based on actual conditions and needs; S3. Determine the process parameters that need to be optimized based on the existing technology; S4. Establish a process parameter optimization model based on the collected data and the determined optimization goals; S5. Design an experimental plan to verify and optimize the process parameters based on the process parameter optimization model; S6. Conduct experiments according to the designed experimental plan, record experimental data and analyze them; S7. Evaluate the impact of different process parameter combinations on the optimization target through analysis of experimental data and determine the best process parameter combination; in, In step S7, the method for analyzing the optimization result includes parameter optimization based on genetic algorithm and parameter optimization based on particle swarm algorithm. The parameter optimization based on genetic algorithm includes the following steps: S701, define a fitness function to evaluate the quality of each parameter combination; S702, initializing a set of random parameter combinations as a population; S703, generating a new parameter combination by performing selection, crossover and mutation operations on the population using a genetic algorithm; S704, evaluating the quality of the new parameter combination according to the fitness function, and selecting the parameter combination with the highest fitness as the next generation population; S705, repeating the selection, crossover and mutation operations until the set number of iterations is reached or the optimization target is met, and finally obtaining the parameter combination with the highest fitness.

2. The method for selecting process parameters for coiled tubing drilling, grinding and dredging according to claim 1, It is characterized in that In step S1, the relevant data of the horizontal well coiled tubing special operation includes tubing size, well depth, blockage type and location.

3. The method for selecting process parameters for coiled tubing drilling, grinding and dredging according to claim 1, It is characterized in that In step S2, the optimization objectives include increasing the drilling speed, reducing wear, and lowering the operating cost.

4. The method for selecting process parameters for coiled tubing drilling, grinding and dredging according to claim 1, It is characterized in that In step S3, the process parameters include drill type, drill size, drilling speed, rotation speed, feed speed, injection liquid and abrasive concentration.

5. The method for selecting process parameters for coiled tubing drilling, grinding and dredging according to claim 1, It is characterized in that In step S4, the method used to establish the optimization model includes statistical method, experimental design or numerical simulation.

6. The method for selecting process parameters for drilling, grinding and unblocking of coiled tubing according to claim 1, It is characterized in that The parameter optimization based on particle swarm algorithm includes the following steps: H701. Define a fitness function to evaluate the quality of each parameter combination; H702, initialize a group of particles, each particle represents a set of parameter combinations; H703, evaluate the quality of each particle by calculating its fitness; H704, each particle updates its own speed and position according to its own historical optimal and global optimal parameter combination, obtains a new parameter combination, and uses the new parameter combination as a new particle; H705. Execute steps H703 and H704 for the new particles, and repeat the operation of updating the particle's speed and position until the set number of iterations is reached or the optimization goal is met, and finally the parameter combination with the highest fitness is obtained.

7. The method for selecting process parameters for coiled tubing drilling, grinding and dredging according to claim 1, It is characterized in that After step S7 is completed, the optimal combination of process parameters is applied to the actual operation to perform drilling, grinding and unblocking operations, and the results are monitored; and the effectiveness and feasibility of the plan are verified by monitoring and evaluating the results of the actual operation.

8. A coiled tubing drilling, grinding and unblocking process parameter selection device, using the coiled tubing drilling, grinding and unblocking process parameter selection method according to any one of claims 1 to 7, the device comprising a data collection module, an optimization target determination module, an optimization parameter determination module, an optimization model establishment module, an experimental scheme design module, an experimental scheme execution module and a parameter combination determination module; The data collection module is used to collect relevant data of horizontal well coiled tubing operations and historical data of drilling, grinding and dredging process parameters; The optimization target determination module is used to determine the target to be optimized according to actual conditions and requirements; The optimization parameter determination module is used to determine the process parameters that need to be optimized according to the prior art; The optimization model building module is used to build a process parameter optimization model based on the collected data and the determined optimization target; The experimental scheme design module is used to design an experimental scheme to verify and optimize the process parameters according to the process parameter optimization model; The experimental plan execution module is used to perform experiments according to the designed experimental plan, record experimental data and analyze them; The parameter combination determination module is used to evaluate the impact of different process parameter combinations on the optimization target through analysis of experimental data, and to determine the best process parameter combination.

9. The coiled tubing drilling, grinding and dredging process parameter selection device according to claim 8, It is characterized in that It also includes the modules for implementing optimization plans and evaluating optimization effects: The implementation optimization program module is used to apply the best process parameter combination to the actual operation, perform drilling, grinding and unblocking operations, and monitor the effect; The optimization effect evaluation module is used to verify the effectiveness and feasibility of the solution by monitoring and evaluating the effect of actual operations.

10. A computer device, It is characterized in that The computer device includes one or more processors and one or more memories, wherein at least one instruction is stored in the one or more memories, and the at least one instruction is loaded and executed by the one or more processors to implement the operations performed by the method for selecting process parameters for continuous tubing drilling, grinding and dredging as described in any one of claims 1 to 7.

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