Dual-objective optimization shaft bottom cooling parameter determination method and device and storage medium

Abstract

The embodiment of the invention provides a dual-objective optimization shaft bottom cooling parameter determination method and device and a storage medium, and belongs to the field of shaft temperature control. The method comprises the following steps: acquiring working condition data of a to-be-processed drilling well; determining a to-be-optimized decision variable for the to-be-processed drilling well, wherein the to-be-optimized decision variable is one or more adjustable engineering parameters used for controlling well bottom cooling measures; establishing a dual-objective optimization function by taking the maximization of the well bottom cooling efficiency and the minimization of the economic cost of cooling measure implementation as objectives; setting an allowable value range for the to-be-optimized decision variable according to the wellhead environment temperature parameter and the engineering safety range; adopting a multi-objective optimization algorithm to solve the dual-objective optimization function in an allowable value range to obtain a Pareto leading-edge solution set; and according to engineering requirements, an optimal solution is selected from the Pareto leading-edge solution set to serve as an optimal shaft bottom cooling scheme, and shaft bottom cooling operation for the to-be-processed well is guided according to the parameters of the decision variables corresponding to the optimal shaft bottom cooling scheme.

Description

Technical Field

[0001] This application relates to the field of wellbore temperature control, specifically to a method, apparatus, and storage medium for determining bottom hole cooling parameters with dual-objective optimization. Background Technology

[0002] In deep and ultra-deep well drilling, high temperatures at the bottom of the well are a significant factor restricting drilling safety and efficiency. High temperatures can lead to thermal failure of drill strings and deterioration of drilling fluid properties, thereby increasing the risk of drilling accidents and reducing drilling efficiency. However, existing bottom-hole cooling methods, on the one hand, adopt conservative estimation methods to ensure cooling effectiveness, resulting in excessively high actual cooling costs; on the other hand, they do not adequately consider the downhole environment, leading to insufficient cooling effects. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, and storage medium for determining bottom hole cooling parameters with dual-objective optimization.

[0004] To achieve the above objectives, the first aspect of this application provides a method for determining bottom hole cooling parameters through dual-objective optimization, comprising: Acquire the operating condition data of the well to be processed, including formation thermal characteristic parameters, drilling fluid physical property parameters, and wellhead ambient temperature parameters; Identify the decision variables to be optimized for the well to be treated. The decision variables to be optimized are one or more adjustable engineering parameters used to control bottom hole cooling measures. A dual-objective optimization function is established with the objectives of maximizing the bottom-hole cooling efficiency and minimizing the economic cost of implementing cooling measures. Based on the wellhead ambient temperature parameters and the engineering safety range, allowable value ranges are set for the decision variables to be optimized. A multi-objective optimization algorithm is used to solve the bi-objective optimization function within the allowed value range to obtain the Pareto front solution set; Based on engineering requirements, an optimal solution is selected from the Pareto front solution set as the best bottomhole cooling scheme. The bottomhole cooling operation for the well to be processed is then guided by the parameters of the decision variables corresponding to the optimal bottomhole cooling scheme.

[0005] In this embodiment, the objectives are to maximize the bottom-hole cooling efficiency and minimize the economic cost of implementing cooling measures. The dual-objective optimization function is established by: constructing a wellbore temperature field calculation model based on operating data and fundamental laws of heat transfer; obtaining the economic cost of the cooling equipment and establishing a cost calculation model for the cooling equipment based on this cost; and establishing the dual-objective optimization function based on a multi-objective optimization algorithm, combining the wellbore temperature field calculation model and the cost calculation model. The expression for the bi-objective optimization function is as follows:

[0006] in, It is a bi-objective optimization function. The target temperature for cooling measures, This refers to the bottom-hole temperature, which is calculated based on a wellbore temperature field model. The cost calculation model for the first cooling device. For the first Cost calculation model for a cooling device. In this embodiment of the application, the wellbore temperature field calculation model is constructed based on operating data and the basic laws of heat transfer, including: establishing the energy equation, momentum equation, continuity equation, and formation heat conduction control equation of drilling fluid flow in the wellbore based on drilling fluid physical property parameters, formation thermal characteristic parameters, and the basic laws of thermodynamics; and discretizing and solving the energy equation, momentum equation, continuity equation, and formation heat conduction control equation based on the effective volume method to construct the wellbore temperature field calculation model.

[0007] In this embodiment of the application, obtaining the economic cost of the cooling equipment and establishing a cost calculation model for the cooling equipment based on the economic cost includes: obtaining the unit price, quantity, operating power, energy unit price, average service life, average failure rate, and maintenance cost of the cooling equipment; constructing a purchase cost model based on the unit price and quantity of the cooling equipment; constructing an operating energy consumption cost model based on the operating power and energy unit price of the cooling equipment; constructing a maintenance cost model based on the average service life, average failure rate, and maintenance cost of the cooling equipment; and fitting the purchase cost model, operating energy consumption cost model, and maintenance cost model to obtain a cost calculation model for the cooling equipment.

[0008] In this embodiment, a multi-objective optimization algorithm is used to solve the bi-objective optimization function within the allowed value range, obtaining the Pareto front solution set, including: establishing constraint equations based on the allowed value range of the decision variables to be optimized: The expression for the constraint equation is as follows:

[0009] in, For the constraint equations, For the first One decision variable to be optimized For the first The minimum value allowed within the range of values ​​for each decision variable to be optimized. For the first The maximum value of the allowed range of values ​​for each decision variable to be optimized is determined; the decision variables to be optimized are encoded to obtain a set of codes for each decision variable to be optimized; based on a non-dominated sorting genetic algorithm, selection, crossover, and mutation operations are performed on the codes in each set of codes to obtain multiple combinations of candidate parameters; based on the wellbore temperature field calculation model and cost calculation model, the cooling efficiency and economic cost for each combination of candidate parameters are calculated respectively; a multi-objective optimization algorithm is used to generate a Pareto optimal solution set based on the cooling efficiency, economic cost, and constraint equations.

[0010] In this embodiment of the application, the determination method further includes: when guiding the bottom-hole cooling operation for the well to be processed based on the parameters of the decision variables corresponding to the optimal bottom-hole cooling scheme, obtaining the actual bottom-hole cooling efficiency and the actual economic cost of implementing the cooling measures; verifying the optimal bottom-hole cooling scheme based on the actual bottom-hole cooling efficiency and the actual economic cost, and determining the reliability of the optimal bottom-hole cooling scheme if the actual bottom-hole cooling efficiency and the actual economic cost match the theoretical calculation results based on the optimal bottom-hole cooling scheme.

[0011] In this embodiment of the application, the determination method further includes: establishing a drilling simulation numerical model based on operating condition data; simulating and calculating the simulated cooling efficiency and simulated economic cost for each Pareto front solution set based on the drilling simulation numerical model to verify the optimal bottom hole cooling scheme; and determining the reliability of the optimal bottom hole cooling scheme when the Pareto front solution set with the highest simulated cooling efficiency and the lowest simulated economic cost matches the optimal bottom hole cooling scheme.

[0012] In this embodiment of the application, the determination method further includes: selecting different cooling methods based on the seasonal temperature fluctuation patterns and the formation thermal characteristics variation patterns at different well depths, and determining the optimal bottom hole cooling parameters based on the cooling methods.

[0013] A second aspect of this application provides a dual-objective optimized device for determining bottom hole cooling parameters, comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the aforementioned dual-objective optimization method for determining bottom hole cooling parameters.

[0014] A third aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned dual-objective optimized bottom hole cooling parameter determination method.

[0015] The above technical solution comprehensively considers the formation thermal characteristics, the impact of the drilling fluid circulation system on the downhole temperature field, and the economic cost of implementing bottom hole cooling measures. A dual-objective optimization function is established, and the dual-objective optimization function is constrained by the allowable value range of the decision variables to be optimized. Based on the multi-objective optimization algorithm and engineering requirements, the optimal bottom hole cooling scheme is obtained, realizing the synergistic optimization of cooling effect and economic cost.

[0016] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The schematic diagram illustrates a flowchart of a dual-objective optimized method for determining bottom hole cooling parameters according to an embodiment of this application; Figure 2 This illustration schematically shows a diagram illustrating the construction of a bi-objective optimization equation according to an embodiment of this application; Figure 3 This illustration schematically shows a solution diagram of a biobjective optimization equation according to an embodiment of this application; Figure 4 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Figure 1 A schematic flowchart illustrating a dual-objective optimized method for determining bottom-hole cooling parameters according to an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a method for determining bottom hole cooling parameters through dual-objective optimization is provided, comprising the following steps: Step 101: Obtain the operating condition data of the well to be processed. The operating condition data includes formation thermal characteristic parameters, drilling fluid physical property parameters, and wellhead ambient temperature parameters.

[0020] Step 102: Determine the decision variables to be optimized for the well to be treated. The decision variables to be optimized are one or more adjustable engineering parameters used to control bottom hole cooling measures.

[0021] Step 103: Establish a dual-objective optimization function with the objectives of maximizing the bottom-hole cooling efficiency and minimizing the economic cost of implementing cooling measures.

[0022] Step 104: Based on the wellhead ambient temperature parameters and the engineering safety range, set the allowable value range for the decision variables to be optimized.

[0023] Step 105: Using a multi-objective optimization algorithm, the bi-objective optimization function is solved within the allowed value range to obtain the Pareto front solution set.

[0024] Step 106: Based on engineering requirements, select an optimal solution from the Pareto front solution set as the optimal bottom hole cooling scheme, so as to guide the bottom hole cooling operation for the well to be processed according to the parameters of the decision variables corresponding to the optimal bottom hole cooling scheme.

[0025] The well to be processed refers to a well requiring drilling operations and for which bottomhole cooling parameters need to be determined. Operating data includes formation thermal characteristics, drilling fluid properties, and wellhead ambient temperature. Optimization decision variables are one or more engineering parameters used to control bottomhole cooling measures, including at least the thermal conductivity of the insulated drill pipe, the length of the insulated drill pipe, the drilling fluid inlet temperature, the drilling fluid specific heat, and the drilling fluid thermal conductivity. The processor can acquire the operating data of the well to be processed and the optimization decision variables. Then, the processor can establish a bi-objective optimization function with the objectives of maximizing bottomhole cooling efficiency and minimizing the economic cost of implementing cooling measures. Further, the processor can set the allowable value range of the optimization decision variables based on the wellhead temperature ambient parameters and engineering safety range, and constrain the bi-objective optimization function based on the allowable value range of the optimization decision variables. A multi-objective optimization algorithm is then used to solve the upper objective optimization function to obtain the Pareto front solution set. Furthermore, based on engineering requirements, an optimal solution can be selected from the Pareto front solution set as the optimal bottom hole cooling scheme, so as to guide the bottom hole cooling operation for the well to be processed according to the parameters of the decision variables corresponding to the optimal bottom hole cooling scheme.

[0026] In one embodiment, the dual-objective optimization function is established with the objectives of maximizing the bottom-hole cooling efficiency and minimizing the economic cost of implementing cooling measures. This includes: constructing a wellbore temperature field calculation model based on operating data and fundamental laws of heat transfer; obtaining the economic cost of the cooling equipment and establishing a cost calculation model for the cooling equipment based on the economic cost; and establishing a dual-objective optimization function based on a multi-objective optimization algorithm, combining the wellbore temperature field calculation model and the cost calculation model.

[0027] Specifically, in one embodiment, the wellbore temperature field calculation model is constructed based on operating data and fundamental laws of heat transfer. This includes: establishing energy equations, momentum equations, continuity equations, and control equations for formation heat conduction of drilling fluid flowing in the wellbore based on drilling fluid physical properties, formation thermal properties, and fundamental laws of thermodynamics; and discretizing and solving the energy equations, momentum equations, continuity equations, and control equations for formation heat conduction using the effective volume method to construct the wellbore temperature field calculation model. The wellbore temperature field calculation model considers the influence of various physical properties of drilling fluid and formation heat conduction on the heat exchange process, and can quantitatively analyze the influence of different factors on the bottom hole cooling efficiency. Obtaining the economic cost of cooling equipment and establishing a cost calculation model for it based on that cost includes: obtaining the unit price, quantity, operating power, energy unit price, average service life, average failure rate, and maintenance costs of the cooling equipment; constructing a purchase cost model based on the unit price and quantity; constructing an operating energy consumption cost model based on the operating power and energy unit price; constructing a maintenance cost model based on the average service life, average failure rate, and maintenance costs of the cooling equipment; and fitting the purchase cost model, operating energy consumption cost model, and maintenance cost model to obtain a cost calculation model for the cooling equipment. For example, in one embodiment, the cooling cost model for the heat-insulated drill rod, fitted based on the purchase cost, operating energy consumption cost, and maintenance cost, is as follows:

[0028] in, To reduce the cost of cooling the heat-insulated drill rod, This refers to the length of the heat-insulated drill rod. Let be the thermal conductivity of the insulated drill pipe. Based on the purchase cost, operating energy cost, and maintenance cost of the wellhead cooling equipment, a cooling cost model for wellhead cooling is obtained as follows:

[0029] in A cost model for cooling the wellhead. The drilling fluid inlet temperature is used. Based on a multi-objective optimization algorithm, combining a wellbore temperature field calculation model, a cooling cost model for insulated drill pipe, and a cooling cost model for wellhead cooling, a dual-objective optimization function is established:

[0030] in, It is a bi-objective optimization function. The bottom hole temperature is calculated using a wellbore temperature field calculation model. This refers to the length of the heat-insulated drill rod. The thermal conductivity of the insulated drill rod. This refers to the drilling fluid inlet temperature.

[0031] In one embodiment, a multi-objective optimization algorithm is used to solve the bi-objective optimization function within the allowable value range to obtain the Pareto front solution set. This includes establishing constraint equations based on the allowable value range of the decision variables to be optimized. For example, in one embodiment, the decision variables to be optimized include the thermal conductivity of the insulated drill pipe, the length of the insulated drill pipe, the drilling fluid inlet temperature, the specific heat of the drilling fluid, and the thermal conductivity of the drilling fluid. The thermal conductivity of the insulated drill pipe ranges from 0.3 to 10, the length of the insulated drill pipe ranges from 0 to 100, the drilling fluid inlet temperature ranges from 3 to 50, the specific heat of the drilling fluid ranges from 1000 to 4000, and the thermal conductivity of the drilling fluid ranges from 0.73 to 1.73. Then, the constraint equations are:

[0032] in, The thermal conductivity of the insulated drill rod. This refers to the length of the heat-insulated drill rod. This refers to the drilling fluid inlet temperature. For drilling fluid specific heat, Let be the thermal conductivity of the drilling fluid. The decision variables to be optimized are encoded to obtain a set of codes for each variable. Based on a non-dominated sorting genetic algorithm, selection, crossover, and mutation operations are performed on the codes in each set to obtain multiple candidate parameter combinations. Based on the wellbore temperature field calculation model and cost calculation model, the cooling efficiency and economic cost for each candidate parameter combination are calculated respectively. A multi-objective optimization algorithm is used to generate a Pareto optimal solution set based on the cooling efficiency, economic cost, and constraint equations.

[0033] In one embodiment, the determination method further includes: when guiding the bottom-hole cooling operation for the well to be processed based on the parameters of the decision variables corresponding to the optimal bottom-hole cooling scheme, obtaining the actual bottom-hole cooling efficiency and the actual economic cost of implementing the cooling measures; verifying the optimal bottom-hole cooling scheme based on the actual bottom-hole cooling efficiency and the actual economic cost; and determining the reliability of the optimal bottom-hole cooling scheme if the actual bottom-hole cooling efficiency and the actual economic cost match the theoretical calculation results based on the optimal bottom-hole cooling scheme.

[0034] In one embodiment, the determination method further includes: establishing a drilling simulation numerical model based on operating data; simulating and calculating the simulated cooling efficiency and simulated economic cost for each Pareto front solution set based on the drilling simulation numerical model to verify the optimal bottom hole cooling scheme; and determining the reliability of the optimal bottom hole cooling scheme when the Pareto front solution set with the highest simulated cooling efficiency and the lowest simulated economic cost matches the optimal bottom hole cooling scheme.

[0035] In one embodiment, the determination method further includes: selecting different cooling methods based on the seasonal fluctuations in ambient temperature and the variations in formation thermal characteristics at different well depths, and determining the optimal bottom-hole cooling parameters based on the cooling methods. For example, in one embodiment, when conducting deep well drilling in summer, a cooling method combining wellhead cooling and insulated drill pipe cooling is selected, and the optimal cooling parameters for wellhead cooling and insulated drill pipe cooling are confirmed; when conducting shallow well drilling in winter, a single wellhead cooling method is selected, and the optimal cooling parameters for the wellhead cooling method are confirmed.

[0036] Through the above embodiments, the influence of formation thermal characteristics, drilling fluid circulation system on downhole temperature field, and economic cost of implementing bottomhole cooling measures were comprehensively considered. A dual-objective optimization function was established, and the dual-objective optimization function was constrained by the allowable value range of the decision variables to be optimized. Based on multi-objective optimization algorithm and engineering requirements, the optimal bottomhole cooling scheme was obtained, realizing the synergistic optimization of cooling effect and economic cost.

[0037] Figure 1 This is a flowchart illustrating a method for determining bottom-hole cooling parameters using dual-objective optimization in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0038] In one embodiment, the method for determining bottomhole cooling parameters through dual-objective optimization includes: acquiring operating condition data of the well to be treated, including drilling fluid physical property parameters and wellhead ambient temperature parameters; determining the decision variables to be optimized for the well to be treated, which are one or more adjustable engineering parameters used to control bottomhole cooling measures; and establishing energy equations, momentum equations, continuity equations, and control equations for formation heat conduction of drilling fluid flowing in the wellbore based on drilling fluid physical property parameters, formation thermal characteristic parameters, and fundamental laws of thermodynamics; and performing discrete solutions to the energy equations, momentum equations, continuity equations, and control equations for formation heat conduction based on the effective volume method to construct a wellbore temperature field calculation model. The unit price, quantity, operating power, energy unit price, average service life, average failure rate, and maintenance cost of the insulated drill pipe and wellhead cooling equipment are obtained. A purchase cost model is constructed based on the unit price and quantity of the cooling equipment. An operating energy consumption cost model is constructed based on the operating power and energy unit price of the cooling equipment. A maintenance cost model is constructed based on the average service life, average failure rate, and maintenance cost of the cooling equipment. Based on the purchase cost model, operating energy consumption cost model, and maintenance cost model, cooling cost calculation models for the insulated drill pipe and wellhead cooling equipment are fitted to obtain their respective calculations. According to the wellhead ambient temperature parameters and engineering safety range, allowable value ranges are set for the decision variables to be optimized, and constraint equations are established based on these allowable value ranges. Further, such as... Figure 2 As shown, a bi-objective optimization function is constructed based on the wellbore temperature field calculation model, the thermal insulation drill pipe cooling cost calculation model, and the wellhead cooling cost calculation model, and constraints are applied to the bi-objective optimization function based on constraint equations. Then, the decision variables to be optimized are encoded to obtain a set of codes for each decision variable. Based on a non-dominated sorting genetic algorithm, selection, crossover, and mutation operations are performed on the codes in each set to obtain multiple candidate parameter combinations. Based on the wellbore temperature field calculation model and the cost calculation model, the cooling efficiency and economic cost for each candidate parameter combination are calculated respectively. A multi-objective optimization algorithm is used to generate a Pareto optimal solution set based on the cooling efficiency, economic cost, and constraint equations. Specifically, as shown... Figure 3As shown, after inputting the well depth, the bottom hole temperature and cooling efficiency for single wellhead cooling or single insulated drill pipe cooling under different candidate parameter combinations are calculated based on the wellbore temperature field calculation model. When the bottom hole temperature and cooling efficiency reach the cooling target, the cooling cost of the candidate parameter combinations for single wellhead cooling or single insulated drill pipe cooling is calculated based on the cost calculation model and constraint equations. When the bottom hole temperature or cooling efficiency cannot reach the cooling target, the bottom hole temperature and cooling efficiency for wellhead cooling combined with insulated drill pipe cooling under different candidate parameter combinations are calculated based on the wellbore temperature field calculation model. When the bottom hole temperature and cooling efficiency reach the cooling target, the cooling cost of the candidate parameter combinations for wellhead cooling combined with insulated drill pipe cooling is calculated based on the cost calculation model and constraint equations, and the candidate parameter combination with the lowest cooling cost is selected to generate the Pareto optimal solution set.

[0039] In one embodiment, a dual-objective optimization device for determining bottom hole cooling parameters is provided, the device comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the aforementioned dual-objective optimization method for determining bottom hole cooling parameters.

[0040] The dual-objective optimized bottomhole cooling parameter determination device includes a processor and a memory. The processor contains a kernel, which retrieves the corresponding program units from the memory. One or more kernels can be configured, and the dual-objective optimized bottomhole cooling parameter determination method can be implemented by adjusting the kernel parameters.

[0041] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0042] This application provides a storage medium storing a program that, when executed by a processor, implements the aforementioned dual-objective optimization method for determining bottom hole cooling parameters.

[0043] This application provides a processor for running a program, wherein the program executes the above-described dual-objective optimized method for determining bottom hole cooling parameters.

[0044] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor A01, a network interface A02, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The network interface A02 is used for communication with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements a dual-objective optimized method for determining bottom hole cooling parameters.

[0045] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0046] This application provides a computer (electronic) device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of any of the above-mentioned dual-objective optimization methods for determining bottom hole cooling parameters.

[0047] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing the steps of a method for determining bottom hole cooling parameters with dual-objective optimization.

[0048] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0050] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0051] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0052] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0053] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0054] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0055] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0056] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining bottom hole cooling parameters through dual-objective optimization, characterized in that, The determination method includes: Acquire the operating condition data of the well to be processed, including formation thermal characteristic parameters, drilling fluid physical property parameters, and wellhead ambient temperature parameters; Determine the decision variables to be optimized for the well to be processed, wherein the decision variables to be optimized are one or more adjustable engineering parameters used to control bottom hole cooling measures; A dual-objective optimization function is established with the objectives of maximizing the bottom-hole cooling efficiency and minimizing the economic cost of implementing cooling measures. Based on the wellhead ambient temperature parameters and the engineering safety range, an allowable value range is set for the decision variable to be optimized. A multi-objective optimization algorithm is used to solve the bi-objective optimization function within the allowed value range to obtain the Pareto front solution set; Based on engineering requirements, an optimal solution is selected from the Pareto front solution set as the optimal bottom hole cooling scheme, so as to guide the bottom hole cooling operation for the well to be processed according to the parameters of the decision variables corresponding to the optimal bottom hole cooling scheme.

2. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 1, characterized in that, The dual-objective optimization function, which aims to maximize wellbore cooling efficiency and minimize the economic cost of implementing cooling measures, includes: Based on the aforementioned operating data and the fundamental laws of heat transfer, a calculation model for the wellbore temperature field is constructed. Obtain the economic cost of the cooling equipment, and establish a cost calculation model for the cooling equipment based on the economic cost; Based on a multi-objective optimization algorithm, and combining the wellbore temperature field calculation model and the cost calculation model, the bi-objective optimization function is established: The expression for the bi-objective optimization function is as follows: in, Let be the bi-objective optimization function. The target temperature for cooling measures, The bottom hole temperature is calculated based on the wellbore temperature field calculation model. The cost calculation model for the first cooling device. For the first Cost calculation model for a cooling device.

3. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 2, characterized in that, The construction of the wellbore temperature field calculation model based on the operating data and the basic laws of heat transfer includes: Based on the drilling fluid physical properties, the formation thermal properties, and the basic laws of thermodynamics, the energy equation, momentum equation, continuity equation, and control equation for formation heat conduction of the drilling fluid flowing in the wellbore are established. The energy equation, momentum equation, continuity equation, and formation heat conduction control equation are discretely solved using the effective volume method to construct the wellbore temperature field calculation model.

4. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 2, characterized in that, The process of obtaining the economic cost of the cooling equipment and establishing a cost calculation model for the cooling equipment based on the economic cost includes: Obtain the unit price, quantity, operating power, energy unit price, average service life, average failure rate, and maintenance cost of the cooling equipment; A purchase cost model is constructed based on the unit price and quantity of the cooling equipment; A cost model for operating energy consumption is constructed based on the operating power and energy unit price of the cooling equipment. A maintenance cost model is constructed based on the average service life, average failure rate, and repair costs of the cooling equipment. The cost calculation model for the cooling equipment is obtained by fitting the purchase cost model, the operating energy consumption cost model, and the maintenance cost model.

5. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 1, characterized in that, The method employs a multi-objective optimization algorithm to solve the bi-objective optimization function within the allowed value range, obtaining the Pareto front solution set, including: Constraint equations are established based on the allowable range of values ​​for the decision variables to be optimized: The expression for the constraint equation is as follows: in, The constraint equation is as follows: For the first The aforementioned decision variables to be optimized For the first The minimum value allowed within the range of values ​​for each decision variable to be optimized. For the first The maximum value allowed within the range of values ​​for each decision variable to be optimized; The decision variables to be optimized are encoded to obtain a set of codes for each of the decision variables to be optimized; Based on the non-dominated sorting genetic algorithm, selection, crossover, and mutation operations are performed on the codes in each of the coding sets to obtain multiple candidate parameter combinations; Based on the wellbore temperature field calculation model and cost calculation model, the cooling efficiency and economic cost for each of the candidate parameter combinations are calculated respectively. A multi-objective optimization algorithm is used to generate the Pareto optimal solution set based on the cooling efficiency, economic cost, and the constraint equations.

6. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 1, characterized in that, The determination method further includes: Based on the parameters of the decision variables corresponding to the optimal bottom hole cooling scheme, the actual bottom hole cooling efficiency and the actual economic cost of implementing the cooling measures are obtained when conducting bottom hole cooling operations on the well to be processed. The optimal bottom-hole cooling scheme is verified based on the actual bottom-hole cooling efficiency and the actual economic cost. If the actual bottom-hole cooling efficiency and the actual economic cost match the theoretical calculation results based on the optimal bottom-hole cooling scheme, the optimal bottom-hole cooling scheme is determined to be reliable.

7. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 1, characterized in that, The determination method further includes: Based on the aforementioned operating data, a drilling simulation numerical model is established; Based on the drilling simulation numerical model, the simulated cooling efficiency and simulated economic cost for each Pareto front solution set are calculated to verify the optimal bottom hole cooling scheme. If the Pareto front solution set with the highest simulated cooling efficiency and the lowest simulated economic cost matches the optimal bottom-hole cooling scheme, then the optimal bottom-hole cooling scheme is determined to be reliable.

8. The method for determining bottom hole cooling parameters through dual-objective optimization according to claim 1, characterized in that, The determination method further includes: Based on the seasonal temperature fluctuation patterns and the formation thermal characteristics variation patterns at different well depths, different cooling methods are selected, and the optimal bottom hole cooling parameters are determined based on the cooling methods.

9. A device for determining bottom hole cooling parameters through dual-objective optimization, characterized in that, include: The memory is configured to store instructions; The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the dual-objective optimized method for determining bottom hole cooling parameters according to any one of claims 1 to 8.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the dual-objective optimized method for determining bottom hole cooling parameters according to any one of claims 1 to 8.

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