Drilling parameter optimization method and device and storage medium

Abstract

The invention discloses a drilling parameter optimization method and device and a storage medium, and relates to the technical field of parameter optimization, and the key points of the technical scheme are that the method comprises the following steps: establishing a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference value is the difference value between the energy required for removing unit volume of rock and the pressure resistance degree of the rock lateral line; constraint conditions of the drilling parameters are determined, and a multi-objective optimization model is constructed according to the constraint conditions, the drilling specific energy difference model and the mechanical drilling speed model; and according to the multi-objective optimization model, well drilling parameters are optimized in real time, the optimal well drilling parameters of well drilling operation are determined, and the well drilling parameters of the well drilling operation are adjusted according to the optimal well drilling parameters. Through the optimization method, the multi-target optimization model can be formed by combining the mechanical drilling speed model and the drilling specific energy difference model, so that drilling parameters such as drilling pressure and rotating speed are realized under constraint conditions, optimization of the drilling parameters is realized, the drilling efficiency is improved, and the drilling cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter optimization, and in particular to a drilling parameter optimization method, device and storage medium. Background Art

[0002] This section is intended to provide a background or context to the embodiments recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] During the drilling process, real-time optimization of drilling parameters can effectively solve the problem of low drilling speed caused by improper parameter application and severe drill bit wear, and is an important means to achieve low-cost, cost-effective drilling. In the prior art, the main method to achieve real-time optimization of drilling parameters is to adjust the drilling parameters in real time according to the changes in the specific energy curve to achieve the optimization of drilling speed. Most of the dynamic optimization methods of drilling parameters studied in China are based on the mechanical specific energy theory. On the basis of the Teale mechanical specific energy model, the torque is expressed as a function of the drilling pressure, and the specific energy model under composite drilling conditions is obtained. The specific energy baseline is used for comparison with the specific energy curve in the actual drilling process. By changing the drilling parameters, the closer the specific energy is to the specific energy baseline, the more the optimized drilling parameters meet the needs.

[0004] However, the drilling process is complex, multivariable, and the drilling load is nonlinear, deformable, and uncertain, so the drilling parameters are affected by many factors. Using a single mechanical specific energy model to optimize the operating parameters lacks the constraints of other operating parameters and has great limitations. In addition, as the well depth increases, the hardness of the bottom layer to be drilled will also change, and the energy required to remove a unit volume of rock is also changing. If the parameters are not optimized properly, it will not only affect the efficient drilling rate, but also easily cause damage to the drilling equipment, affecting the operating cost. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes a drilling parameter optimization method, device and storage medium, which can combine the mechanical penetration rate model and the drilling specific energy difference model to form a multi-objective optimization model, so as to realize drilling parameters such as drilling pressure and rotation speed under constraints, thereby realizing the optimization of drilling parameters, improving drilling efficiency and reducing drilling costs.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention includes four aspects.

[0007] In a first aspect, a drilling parameter optimization method is provided, comprising the following steps:

[0008] Establishing a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference is the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock side line;

[0009] Determine the constraints of drilling parameters, and build a multi-objective optimization model based on the constraints, drilling specific energy difference model and mechanical drilling speed model;

[0010] The drilling parameters are optimized in real time according to the multi-objective optimization model, the optimal drilling parameters of the drilling operation are determined, and the drilling parameters of the drilling operation are adjusted with the optimal drilling parameters.

[0011] In some embodiments, the drilling specific energy difference model is formulated as follows:

[0012]

[0013] Where w is the drilling pressure, A b is the drill bit area, N is the rotation speed, T is the torque, R is the drilling speed, λ is the drill bit hydraulic factor, ρ is the drilling fluid density, Q is the drilling fluid displacement, C is the drill bit nozzle flow coefficient, A O is the nozzle outlet cross-sectional area, UCS is the uniaxial compressive strength, P e is the pressure difference, sinθ is the internal friction angle.

[0014] In some embodiments, the multi-objective optimization model is constructed according to the constraint conditions, the drilling specific energy difference model and the mechanical drilling speed model, including:

[0015] A multi-objective optimization model is constructed based on the drilling specific energy difference model and the mechanical drilling speed model;

[0016] The multi-objective optimization model is optimized by finding the drilling parameters under the optimal working conditions using the constraints. When the drilling specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, and the multi-objective optimization model of the optimal working conditions is obtained.

[0017] In some embodiments, optimizing the drilling parameters according to the multi-objective optimization model to determine the optimal drilling parameters for the drilling operation includes:

[0018] Based on the multi-objective optimization model and through intelligent algorithms, the drilling parameters are optimized under constraints to determine the optimal drilling parameters for the drilling operation.

[0019] In some embodiments, the intelligent algorithm includes an entropy weight method and a multi-objective optimization algorithm; the optimization of drilling parameters under constraints based on the multi-objective optimization model and the intelligent algorithm includes:

[0020] A multi-objective optimization problem is defined based on the multi-objective optimization model; the multi-objective optimization problem includes an objective function and decision variables; the objective function includes a mechanical rotation speed and a specific energy difference constituting the multi-objective optimization model; the decision variables include drilling pressure and rotation speed;

[0021] Solve through multi-objective optimization algorithm to obtain all feasible solutions and the objective function values ​​and decision variables corresponding to the feasible solutions;

[0022] The entropy method is used to calculate the weight of the objective function, and the comprehensive score of each feasible solution is calculated based on the weight;

[0023] According to the comprehensive scores of each feasible solution, the solution with the best performance is selected as the optimal solution output.

[0024] In some embodiments, the formula of the multi-objective optimization model is:

[0025]

[0026] In the formula, ΔS is the drilling specific energy difference, and ROP is the mechanical penetration rate;

[0027] When T is minimum, that is, the specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, that is, the multi-objective optimization model of the optimal working condition.

[0028] In some embodiments, the formula of the mechanical penetration rate model is as follows:

[0029]

[0030] Where K is the formation drillability coefficient, C P is the pressure difference influence coefficient, C H is the bottom hole purification coefficient, W is the drilling pressure, M is the threshold drilling pressure, a is the speed index, C2 is the tooth wear coefficient, and h is the drill bit wear.

[0031] In some embodiments, the constraints include drilling pressure constraints, rotation speed constraints and displacement constraints; the rotation speed of the rotation speed constraint is the rotation speed controlled by the on-site drilling equipment, and the displacement of the displacement constraint is the displacement controlled by the on-site drilling equipment.

[0032] In a second aspect, a drilling parameter optimization device is provided, comprising:

[0033] The first construction module is used to establish a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference is the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock side line;

[0034] The second construction module is used to determine the constraints of drilling parameters and construct a multi-objective optimization model based on the constraints, the drilling specific energy difference model and the mechanical drilling speed model;

[0035] The parameter optimization module is used to optimize the drilling parameters in real time according to the multi-objective optimization model, determine the optimal drilling parameters of the drilling operation, and adjust the drilling parameters of the drilling operation with the optimal drilling parameters.

[0036] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the aforementioned optimization method when executing the computer program.

[0037] In a fourth aspect, a computer-readable storage medium is provided, and when the computer program is executed by a processor, the steps of the aforementioned optimization method are implemented.

[0038] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0039] The present application provides a drilling parameter optimization method, device and storage medium. The drilling parameter optimization method defines the concept of drilling specific energy difference, and can monitor the working status of the drill bit during the drilling process, that is, the lower the specific energy difference, the higher the rock breaking efficiency of the drill bit; at the same time, a multi-objective optimization model is proposed in combination with the drilling specific energy difference model and the mechanical drilling speed model. When the multi-objective optimization model takes the minimum value, that is, the drilling specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, so that it is convenient to solve through the optimized multi-objective optimization model, so as to accurately and efficiently obtain the optimal drilling parameters; on the other hand, an intelligent algorithm is used to achieve the optimal value output of drilling pressure and rotation speed under constraints, so as to achieve the optimization of drilling parameters and achieve the purpose of optimizing rotation speed, improving drilling efficiency and reducing drilling costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Hereinafter, the present application will be described in more detail based on embodiments and with reference to the accompanying drawings;

[0041] Figure 1 An exemplary flow chart of a drilling parameter optimization method provided in Embodiment 1 of the present invention;

[0042] Figure 2 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S2 shown in;

[0043] Figure 3 In the embodiment of the present invention, Figure 1 An exemplary flow chart of step S3 shown in FIG.

[0044] Figure 4 In the embodiment of the present invention, Figure 1 Step S3 shown in the flowchart is specifically implemented;

[0045] Figure 5 This is a multi-objective parameter optimization flow chart in the first embodiment of the present invention;

[0046] Figure 6This is a diagram of multi-objective parameter optimization results in the second embodiment of the present invention;

[0047] Figure 7 A schematic block diagram of a seismic signal denoising device provided in Embodiment 3 of the present invention;

[0048] Figure 8 A schematic block diagram of an electronic device provided in Embodiment 4 of the present invention;

[0049] Fig. 9 This is a schematic diagram of a computer-readable storage medium provided in Embodiment 5 of the present invention.

[0050] In the drawings, the same reference numerals are used for the same components and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0051] The present disclosure is further described below in conjunction with the embodiments shown in the accompanying drawings.

[0052] Embodiment 1:

[0053] The present application embodiment discloses a drilling parameter optimization method, such as Figure 1 As shown, the method includes the following steps: establishing a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference is the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock side line; determining the constraints of the drilling parameters, and constructing a multi-objective optimization model according to the constraints, the drilling specific energy difference model and the mechanical drilling speed model; optimizing the drilling parameters in real time according to the multi-objective optimization model, determining the optimal drilling parameters of the drilling operation, and adjusting the drilling parameters of the drilling operation with the optimal drilling parameters. Through this method, the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock line can be used as the drilling specific energy difference, so that the optimized drilling parameters can be used for rock breaking drilling more efficiently, and the drilling specific energy difference is combined with the multi-objective optimization model, so as to calculate an optimal value for recommendation, so as to obtain the optimized drilling parameters quickly and accurately, and achieve the purpose of optimizing the rotation speed, improving the drilling efficiency and reducing the drilling cost.

[0054] At least one embodiment of the present disclosure provides a drilling parameter optimization method, which can be implemented in the form of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, a network server, etc., so as to accurately optimize the drilling parameters, perform drilling work at the optimal rotation speed and drilling pressure, improve drilling efficiency and reduce drilling costs.

[0055] Reference below Figure 1 As shown, a drilling parameter optimization method provided by at least one embodiment of the present disclosure is described, and the optimization method includes steps S1 to S3.

[0056] S1. Establish a drilling energy difference model and a mechanical drilling speed model; the drilling energy difference is the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock side line.

[0057] In some embodiments, by constructing a drilling specific energy difference, the working status of the drill bit can be monitored during the drilling process. The lower the specific energy difference, the higher the rock breaking efficiency of the drill bit.

[0058] In some embodiments, the drilling energy specific difference model is calculated based on the drilling energy specific model and the rock confined compressive strength model under confining pressure. While collecting drilling parameters during the drilling process, drilling pressure, uniaxial compressive strength and pressure difference values ​​are also collected to facilitate the calculation of the drilling energy specific difference model.

[0059] Furthermore, the drilling specific energy difference is recorded as ΔS, which is calculated using the following formula:

[0060]

[0061] Where w is the drilling pressure, A b is the drill bit area, N is the rotation speed, T is the torque, R is the drilling speed, λ is the drill bit hydraulic factor, ρ is the drilling fluid density, Q is the drilling fluid displacement, C is the drill bit nozzle flow coefficient, A O is the nozzle outlet cross-sectional area, UCS is the uniaxial compressive strength, P e is the pressure difference, sinθ is the internal friction angle.

[0062] In some embodiments, the formula for the mechanical penetration rate model is as follows:

[0063]

[0064] Where K is the formation drillability coefficient, C P is the pressure difference influence coefficient, C H is the bottom hole purification coefficient, W is the drilling pressure, M is the threshold drilling pressure, a is the speed index, C2 is the tooth wear coefficient, and h is the drill bit wear.

[0065] S2. Determine the constraints of drilling parameters, and build a multi-objective optimization model based on the constraints, drilling specific energy difference model and mechanical drilling speed model.

[0066] In some embodiments, step S2, such as Figure 2 As shown, including:

[0067] S21. Construct a multi-objective optimization model based on the drilling specific energy difference model and the mechanical drilling speed model;

[0068] S22. Optimize the multi-objective optimization model by finding the drilling parameters under the optimal working conditions using the constraint conditions. When the drilling specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, and the multi-objective optimization model of the optimal working conditions is obtained.

[0069] In some embodiments, the multi-objective optimization value of the multi-objective optimization model is denoted as T, and the formula of the multi-objective optimization model is as follows:

[0070]

[0071] In the formula, ΔS is the drilling specific energy difference, and ROP is the mechanical penetration rate;

[0072] When T is minimum, that is, the specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, that is, the multi-objective optimization model of the optimal working condition.

[0073] like Figure 5 As shown, when the specific energy difference is minimized and the mechanical speed is maximized, the value of the established multi-objective optimization model is the smallest, that is, the multi-objective optimization model reaches the optimal value.

[0074] In some embodiments, the constraints in step S2 include drilling pressure constraints, speed constraints and displacement constraints; the speed of the speed constraint is the speed controlled by the on-site drilling equipment, and the displacement of the displacement constraint is the displacement controlled by the on-site drilling equipment.

[0075] Specifically, the constraints include: drilling pressure is greater than the maximum value of the threshold drilling pressure, and less than the drilling pressure influence coefficient ratio; the rotation speed is greater than or equal to the lower limit of the rotation speed controlled by the on-site drilling equipment, and less than or equal to the upper limit of the rotation speed controlled by the on-site drilling equipment; the displacement is greater than or equal to the lower limit of the displacement controlled by the on-site drilling equipment, and less than or equal to the upper limit of the displacement controlled by the on-site drilling equipment. By constraining drilling pressure, rotation speed and displacement, not only the working intensity of the on-site drilling equipment is considered, but also the drilling pressure value is constrained, and the drilling load factor is taken into account, so as to improve the working efficiency of the drilling equipment and reduce the loss of the drilling equipment.

[0076] S3. Optimize the drilling parameters in real time according to the multi-objective optimization model, determine the optimal drilling parameters for the drilling operation, and adjust the drilling parameters for the drilling operation with the optimal drilling parameters.

[0077] In some embodiments, step S3 includes: optimizing the drilling parameters under constraints based on the multi-objective optimization model and using an intelligent algorithm to determine the optimal drilling parameters for the drilling operation.

[0078] Among them, the intelligent algorithm includes the entropy weight method and the multi-objective optimization algorithm. After the drilling parameters are optimized through the multi-objective optimization algorithm, the entropy weight method is used to determine the weights of each indicator, so as to quickly obtain the optimal results for output.

[0079] Specifically, step S3, such as Figure 3 and Figure 4 As shown, including S31 to S34,

[0080] S31. Define a multi-objective optimization problem based on the multi-objective optimization model; the multi-objective optimization problem includes an objective function and decision variables; the objective function includes the mechanical rotation speed and specific energy difference that constitute the multi-objective optimization model; the decision variables include drilling pressure and rotation speed.

[0081] In some embodiments, a MyProblem class is defined as the objective function to be solved, in which decision variables, optimization objective functions, and constraints are set; wherein the constraints are the aforementioned drilling pressure, rotation speed, and displacement.

[0082] S32. Solve the problem through a multi-objective optimization algorithm to obtain all feasible solutions and the objective function values ​​and decision variables corresponding to the feasible solutions.

[0083] In some embodiments, step S32 is solved by calling NSGA-III (multi-objective optimization algorithm) in the geatpy library. During the solution process, the operation mode of the algorithm is controlled by setting parameters such as MAXGEN (number of iterations), Pm (mutation probability), XOVR (crossover probability), and NIND (population size), and finally all feasible solutions and their corresponding objective function values ​​and decision variables are solved.

[0084] S33. Calculate the weight of the objective function using the entropy method, and calculate the comprehensive score of each feasible solution based on the weight.

[0085] S34. Select the solution with the best performance as the optimal solution output according to the comprehensive scores of each feasible solution.

[0086] In some embodiments, when using the entropy method to calculate the weight of the objective function, the input variables are first standardized so that the value range of all variables is between [0,1]; then the information entropy and redundancy of each indicator are calculated, and finally the weight of each indicator is calculated based on the redundancy; and the comprehensive score of each feasible solution is calculated based on the weight, and the solution with the best performance is selected as the optimal solution output.

[0087] Furthermore, a scatter plot is used to show the performance of all feasible solutions on the two objective functions, and the solution with the best performance is marked, so as to quickly determine the optimal solution and perform drilling operations with the optimal drilling parameters.

[0088] The drilling parameter optimization method provided by the embodiments of the present disclosure can construct a multi-objective optimization model based on the drilling specific energy difference model, combine the drilling specific energy difference model with the mechanical drilling speed model, and calculate an optimal value from the multi-objective recommendation results in combination with an intelligent algorithm for recommendation, thereby achieving the optimal value output of drilling pressure and rotation speed under constraints, thereby achieving the optimization of drilling parameters, improving drilling efficiency and reducing drilling costs.

[0089] Embodiment 2:

[0090] In this embodiment, the drilling parameter optimization method disclosed in the first embodiment is used to optimize the drilling parameters of a certain oil field at 3000m. Figure 6 As shown in the figure, the well section was drilled with a Φ251mm 21-type drill bit. Assume that the hydraulic purification coefficient CH = 1 and the pressure difference influence coefficient Cp = 1. The formation coefficients involved in the model are: K, M, a, C2, which are closely related to the actual working conditions of the drilling and the surrounding environment. Regression analysis shows that K = 0.00256, M = 10kN, a = 0.624, and C2 = 4. According to the given control parameter optimization interval: drilling pressure (0-1000), speed (20-100), the objective function is optimized to obtain the optimal solution of the control parameters (drilling pressure: 441KN, speed: 96r / min). The entropy weight method is used to calculate the weight of the objective function and visualize it. The scatter plot is used to show the performance of all feasible solutions on the two objective functions, and the solution with the best performance is marked.

[0091] Embodiment three:

[0092] At least one embodiment of the present disclosure further provides a drilling parameter optimization device, wherein each module in the device corresponds to the steps in the above method, and the effects achieved and the problems solved have been described above and will not be repeated here. Figure 7 As shown, including:

[0093] The first construction module 11 is used to establish a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference is the difference between the energy required to remove a unit volume of rock and the rock sideline compressive strength;

[0094] The second construction module 12 is used to determine the constraints of the drilling parameters and to construct a multi-objective optimization model based on the constraints, the drilling specific energy difference model and the mechanical drilling speed model;

[0095] The parameter optimization module 13 is used to optimize the drilling parameters in real time according to the multi-objective optimization model, determine the optimal drilling parameters of the drilling operation, and adjust the drilling parameters of the drilling operation with the optimal drilling parameters.

[0096] In some embodiments, the output end of the first building module 11 is connected to the input end of the second building module 12 , and the output end of the second building module 12 is connected to the input end of the parameter optimization module 13 .

[0097] The output end of the parameter optimization module 13 may be connected to a display device (such as a display) to present the optimization result to the staff through the screen.

[0098] In some embodiments, the device may be a part of the software, or may be implemented in combination with corresponding hardware, which will not be described in detail here.

[0099] Embodiment 4:

[0100] At least some embodiments of the present disclosure also provide an electronic device, such as Figure 8 As shown, the electronic device includes a memory 21 and a processor 22, wherein the memory 21 stores a computer program, and when the computer program is executed by the processor, the steps of the optimization method provided in any embodiment of the present disclosure are performed.

[0101] In some embodiments, the processor 22 is used to execute all or part of the steps in the optimization method of any embodiment of the present disclosure. The memory 21 is used to store various types of data, which may include instructions of any application or method in the electronic device, and data related to the application.

[0102] The processor 22 can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is used to execute the optimization method in the above-mentioned embodiment 1.

[0103] The memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0104] Embodiment five:

[0105] At least some embodiments of the present disclosure also provide a computer-readable storage medium, such as Fig. 9 As shown, a computer program 31 is stored on the readable storage medium, and when the computer program 31 is executed by a processor, the steps of the optimization method provided by any embodiment of the present disclosure are implemented.

[0106] In some embodiments, the storage medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0109] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0110] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the optimization method provided in any embodiment of the present disclosure.

[0111] In summary, the present application provides a drilling parameter optimization method, device and storage medium, which can construct a multi-objective optimization model based on the drilling specific energy difference model, combine the drilling specific energy difference model with the mechanical drilling speed model, and combine the multi-objective recommendation results with an optimal value for recommendation in combination with an intelligent algorithm, thereby achieving the optimal value output of drilling pressure and rotation speed under constraints, thereby realizing the optimization of drilling parameters, improving drilling efficiency and reducing drilling costs.

[0112] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0113] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A drilling parameter optimization method, characterized in that: The following steps are involved: Establishing a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference is the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock side line; Determine the constraints of drilling parameters, and build a multi-objective optimization model based on the constraints, drilling specific energy difference model and mechanical drilling speed model; The drilling parameters are optimized in real time according to the multi-objective optimization model, the optimal drilling parameters of the drilling operation are determined, and the drilling parameters of the drilling operation are adjusted with the optimal drilling parameters.

2. A drilling parameter optimization method according to claim 1, characterized in that: The formula of the drilling specific energy difference model is as follows: Where w is the drilling pressure, A b is the drill bit area, N is the rotation speed, T is the torque, R is the drilling speed, λ is the drill bit hydraulic factor, ρ is the drilling fluid density, Q is the drilling fluid displacement, C is the drill bit nozzle flow coefficient, A O is the nozzle outlet cross-sectional area, UCS is the uniaxial compressive strength, P e is the pressure difference, sinθ is the internal friction angle.

3. A drilling parameter optimization method according to claim 1, characterized in that: The multi-objective optimization model is constructed according to the constraint conditions, the drilling specific energy difference model and the mechanical drilling speed model, including: A multi-objective optimization model is constructed based on the drilling specific energy difference model and the mechanical drilling speed model; The multi-objective optimization model is optimized by finding the drilling parameters under the optimal working conditions using the constraints. When the drilling specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, and the multi-objective optimization model of the optimal working conditions is obtained.

4. A drilling parameter optimization method according to claim 1, characterized in that: The step of optimizing the drilling parameters according to the multi-objective optimization model to determine the optimal drilling parameters for the drilling operation includes: Based on the multi-objective optimization model and through intelligent algorithms, the drilling parameters are optimized under constraints to determine the optimal drilling parameters for the drilling operation.

5. A drilling parameter optimization method according to claim 4, characterized in that: The intelligent algorithm includes an entropy weight method and a multi-objective optimization algorithm; the optimization of drilling parameters under constraints based on the multi-objective optimization model and the intelligent algorithm includes: A multi-objective optimization problem is defined based on the multi-objective optimization model; the multi-objective optimization problem includes an objective function and decision variables; the objective function includes a mechanical rotation speed and a specific energy difference constituting the multi-objective optimization model; the decision variables include drilling pressure and rotation speed; Solve through multi-objective optimization algorithm to obtain all feasible solutions and the objective function values ​​and decision variables corresponding to the feasible solutions; The entropy method is used to calculate the weight of the objective function, and the comprehensive score of each feasible solution is calculated based on the weight; According to the comprehensive scores of each feasible solution, the solution with the best performance is selected as the optimal solution output.

6. A drilling parameter optimization method according to claim 1, characterized in that: The formula of the multi-objective optimization model is: In the formula, ΔS is the drilling specific energy difference, and ROP is the mechanical penetration rate; When T is minimum, that is, the specific energy difference is minimized and the mechanical drilling speed is maximized, the output drilling parameters are the optimal solution, that is, the multi-objective optimization model of the optimal working condition.

7. A drilling parameter optimization method according to claim 1, characterized in that: The formula of the mechanical penetration rate model is as follows: Where K is the formation drillability coefficient, C P is the pressure difference influence coefficient, C H is the bottom hole purification coefficient, W is the drilling pressure, M is the threshold drilling pressure, a is the speed index, C2 is the tooth wear coefficient, and h is the drill bit wear.

8. A drilling parameter optimization method according to claim 1, characterized in that: The constraint conditions include drilling pressure constraint conditions, rotation speed constraint conditions and displacement constraint conditions; the rotation speed of the rotation speed constraint condition is the rotation speed controlled by the on-site drilling equipment, and the displacement of the displacement constraint condition is the displacement controlled by the on-site drilling equipment.

9. A drilling parameter optimization device, characterized in that: include: The first construction module is used to establish a drilling specific energy difference model and a mechanical drilling speed model; the drilling specific energy difference is the difference between the energy required to remove a unit volume of rock and the compressive strength of the rock side line; The second construction module is used to determine the constraints of drilling parameters and construct a multi-objective optimization model based on the constraints, the drilling specific energy difference model and the mechanical drilling speed model; The parameter optimization module is used to optimize the drilling parameters in real time according to the multi-objective optimization model, determine the optimal drilling parameters of the drilling operation, and adjust the drilling parameters of the drilling operation with the optimal drilling parameters.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the optimization method according to any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium, characterized in that: When the computer program is executed by a processor, the steps of the optimization method according to any one of claims 1 to 8 are implemented.

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