Multi-objective optimization method and device for operating parameters of an electric submersible pump system

Abstract

The application relates to the technical field of electric submersible pumps, and provides a multi-target optimization method and device for operation parameters of an electric submersible pump system. The method comprises the following steps: obtaining a plurality of candidate operation parameter combinations of the electric submersible pump system; performing physical feasibility screening on the candidate operation parameter combinations based on a coordinated working point condition of an oil well production system to obtain a feasible operation parameter combination set; calculating the values of at least two mutually conflicting operation performance indexes corresponding to each group of operation parameter combinations in the feasible operation parameter combination set; performing optimization based on a multi-target optimization algorithm in the feasible operation parameter combination set according to the values to obtain a Pareto solution set composed of a plurality of mutually non-dominated operation parameter combinations and output the Pareto solution set. In the electric submersible pump system operation optimization process, the embodiment of the application can realize the collaborative optimization between a plurality of conflicting operation indexes, and avoid the equipment safety risk caused by the single pursuit of system efficiency.

Description

Technical Field

[0001] This application relates to the field of electric submersible pump technology, and in particular to a multi-objective optimization method and apparatus for the operating parameters of an electric submersible pump system. Background Technology

[0002] Electric submersible pump (ESP) systems are widely used in artificial lift operations in offshore and onshore oil and gas fields due to their large displacement, high head, and ease of automation. The operation of an ESP system involves multiple energy conversion and transfer stages, including ground power supply equipment, cable transmission, motor power conversion, and pump unit liquid lifting. Its overall energy consumption level directly affects oilfield production costs and equipment operational safety. Therefore, optimizing the energy consumption of ESP systems has significant engineering value.

[0003] Existing energy consumption optimization technologies for electric submersible pumps (ESPs) primarily aim to improve system efficiency by adjusting pump operating frequency, nozzle size, or wellhead parameters to achieve localized energy efficiency gains. These methods typically begin by establishing a system efficiency or unit fluid production energy consumption calculation model for the ESP system. This model comprehensively considers pump hydraulic output, wellbore flow characteristics, and system input power to calculate energy consumption levels under different combinations of operating parameters. Then, it selects "maximizing system efficiency" or "minimizing unit fluid production energy consumption" as the sole optimization objective and employs intelligent optimization methods such as genetic algorithms and particle swarm optimization to find the optimal set of operating parameters. However, in actual operation, there are often significant coupling and constraints between system efficiency, motor efficiency, and cable current. For example, under certain operating conditions, improving system efficiency may be accompanied by increased motor load or cable current, thereby increasing the risk of motor overheating or cable overload. The aforementioned single-objective optimization methods, focusing only on system efficiency, fail to consider both motor efficiency and electrical safety, easily leading to problems such as excessive equipment load and shortened equipment lifespan.

[0004] Another existing technology optimizes the operating parameters of electric submersible pump systems using a combination of "energy consumption model calculation + fixed-weight comprehensive scoring + parameter search". This approach first establishes an energy consumption calculation model that includes system efficiency, motor efficiency, and cable current. Then, it linearly weights multiple indicators into a single comprehensive score using fixed weights, and optimizes parameters with the goal of maximizing this score, ultimately outputting a set of recommended operating parameters. While this method considers multiple indicators simultaneously, the use of fixed weights to compress multiple objectives into a single objective fails to characterize the nonlinear conflicting relationships between system efficiency, motor efficiency, and cable current. This can easily lead to situations where one indicator is overemphasized while others deteriorate significantly.

[0005] In summary, the key challenge is to achieve coordinated optimization among multiple conflicting operational indicators during the operation optimization process of an electric submersible pump system, thereby avoiding equipment safety risks such as excessive motor load and excessive cable current caused by solely pursuing system efficiency. Summary of the Invention

[0006] The purpose of this application is to provide a multi-objective optimization method and apparatus for the operating parameters of an electric submersible pump system, so as to achieve synergistic optimization among multiple conflicting operating indicators during the operation optimization process of the electric submersible pump system, and avoid equipment safety risks such as excessive motor load and excessive cable current caused by solely pursuing system efficiency.

[0007] To achieve the above objectives, one aspect of this application is to provide a multi-objective optimization method for the operating parameters of an electric submersible pump system, comprising the following steps: Obtain multiple candidate combinations of operating parameters for the electric submersible pump system; Based on the coordinated working point conditions of the oil well production system, the physical feasibility of the candidate operating parameter combinations is screened to obtain a set of feasible operating parameter combinations. Calculate the values ​​of at least two conflicting performance indicators for each set of feasible operating parameters in the set of feasible operating parameter combinations; Based on the aforementioned values, an optimization algorithm is used to find the Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations within the set of feasible operating parameter combinations. Output the Pareto solution set.

[0008] In the method of this application embodiment, the operating performance indicators include: The first indicator characterizing the overall energy efficiency of the system, the second indicator characterizing the energy conversion efficiency of the motor, and the third indicator characterizing the load level of the downhole electrical system.

[0009] In the method of this application embodiment, the first indicator is the efficiency of the electric submersible pump system, the second indicator is the motor efficiency, and the third indicator is the cable current.

[0010] In the method of this application embodiment, the multi-objective optimization algorithm includes a non-dominated sorting genetic algorithm, and the step of searching for optimization based on the multi-objective optimization algorithm in the feasible set of operating parameter combinations includes: Based on the current actual operating parameters, multiple sets of operating parameters are selected from the set of feasible operating parameter combinations, and these multiple sets of operating parameters are used to construct an initial population. Perform a non-dominated ranking of the individuals in the current population, and map the individuals in the current population to multiple Pareto levels based on the ranking results; Within the same Pareto level, calculate the crowding distance for each individual; Parent individuals are selected from the current population based on the Pareto level and crowding distance to generate a parent population; wherein the selection priority is negatively correlated with the Pareto level to which the individual belongs and positively correlated with the crowding distance of individuals within the same Pareto level. Perform crossover and / or mutation operations on the parent individuals to generate the offspring population; The parent population is merged with the offspring population to generate a new population; The process iterates until the convergence condition is met, thus obtaining the Pareto solution set.

[0011] In the method of this application embodiment, the step of selecting multiple sets of operating parameters from the feasible operating parameter combination set based on the current actual operating parameters specifically includes: Based on the current actual operating parameters, Gaussian sampling is performed on individuals in the set of feasible operating parameter combinations that satisfy the conditions of the coordinated working point.

[0012] In the method of this application embodiment, the physical feasibility screening based on the coordinated working point conditions of the oil well production system specifically includes: For each set of candidate operating parameters, confirm whether there is a coordinated operating point that simultaneously satisfies the wellbore flow relationship, pump characteristic relationship, and nozzle throttling relationship; If such a combination of candidate operating parameters exists, it will be included in the set of feasible operating parameter combinations.

[0013] In the method of this application embodiment, calculating the values ​​of at least two conflicting operational performance indicators corresponding to each set of operational parameter combinations in the feasible operational parameter combination set includes: An energy consumption calculation model is established to characterize the energy transfer process of the electric submersible pump system. The energy transfer process includes sequentially associated energy input nodes, energy transfer nodes, energy conversion nodes, and pump unit work nodes. Using the energy consumption calculation model, the corresponding system efficiency, motor efficiency, and cable current are calculated for each combination of operating parameters.

[0014] In the method of this application embodiment, after outputting the Pareto solution set, it further includes: Based on actual production targets or equipment status constraints, a suitable combination of operating parameters is selected from the Pareto solution set for on-site control.

[0015] On the other hand, embodiments of this application also provide a multi-objective optimization device for the operating parameters of an electric submersible pump system, comprising: The acquisition module is used to acquire multiple candidate combinations of operating parameters for the electric submersible pump system; The screening module is used to perform physical feasibility screening on the candidate operating parameter combinations based on the coordinated working point conditions of the oil well production system, so as to obtain a set of feasible operating parameter combinations. The calculation module is used to calculate the values ​​of at least two conflicting operating performance indicators corresponding to each set of operating parameters in the set of feasible operating parameter combinations. The optimization module is used to perform optimization based on the numerical values ​​in the set of feasible operating parameter combinations using a multi-objective optimization algorithm to obtain a Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations. The output module is used to output the Pareto solution set.

[0016] On the other hand, embodiments of this application also provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the computer program, when run by the processor, executes instructions of the above-described method.

[0017] As can be seen from the technical solutions provided in the embodiments of this application above, in the embodiments of this application, physical feasibility screening of multiple candidate operating parameter combinations of the electric submersible pump system can be performed based on the coordinated working point conditions of the oil well production system to obtain a set of feasible operating parameter combinations, thereby avoiding invalid searches. On this basis, based on the values ​​of at least two conflicting operating performance indicators corresponding to each set of operating parameter combinations in the set of feasible operating parameter combinations, optimization is performed on the set of feasible operating parameter combinations using a multi-objective optimization algorithm to obtain a Pareto solution set composed of multiple sets of non-dominant operating parameter combinations. This achieves collaborative optimization among multiple conflicting operating indicators during the operation optimization process of the electric submersible pump system, avoiding equipment safety risks such as excessive motor load and excessive cable current caused by solely pursuing system efficiency. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This application illustrates a schematic diagram of the application environment for multi-objective optimization of the operating parameters of an electric submersible pump system in some embodiments of this application; Figure 2 A flowchart of a multi-objective optimization method for the operating parameters of an electric submersible pump system in some embodiments of this application is shown; Figure 3 A schematic diagram of the energy consumption calculation model of the energy transfer process of the electric submersible pump system in some embodiments of this application is shown; Figure 4 It shows Figure 2 The flowchart shown illustrates the optimization process based on a multi-objective optimization algorithm (non-dominated sorting genetic algorithm). Figure 5 A flowchart of a multi-objective optimization method for the operating parameters of an electric submersible pump system is shown in some other embodiments of this application; Figure 6 The diagram shows a structural block diagram of a multi-objective optimization method apparatus for operating parameters of an electric submersible pump system in some embodiments of this application; Figure 7 A structural block diagram of a computer device in some embodiments of this application is shown.

[0019] [Explanation of Labels in the Attached Image]

[0020] 10. Electric Submersible Pump System; 20. Host computer; 61. Acquisition Module; 62. Filtering module; 63. Calculation module; 64. Optimization module; 65. Output module; 702. Computer equipment; 704, Processor; 706. Memory; 708. Drive mechanism; 710. Input / output interfaces; 712. Input devices; 714. Output devices; 716. Presentation equipment; 718. Graphical User Interface; 720. Network interface; 722. Communication link; 724. Communication bus. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0022] It should be noted that in the embodiments of this application, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved are all information and data authorized and agreed upon by the user and fully authorized by all parties. That is, the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0023] In the optimization of electric submersible pump (ESP) systems, system efficiency is typically used as a core indicator for measuring energy efficiency. Improving system efficiency or reducing energy consumption per unit of produced fluid can, to some extent, reduce energy consumption and enhance production economics. However, in the complex ESP system, which involves multiple coupled energy-consuming nodes, system efficiency, motor efficiency, and cable current are not entirely consistent; rather, there are clear constraints and trade-offs (i.e., mutual conflicts).

[0024] In actual operation, when solely pursuing improved system efficiency, it is often necessary to increase the operating frequency of the electric submersible pump or increase the system load. This inevitably leads to increased motor load and increased cable current. While these changes may improve system efficiency in the short term, they significantly increase the operational burden on the motor and cables, accelerate motor heating and cable losses, thereby increasing the risk of equipment failure and shortening the overall service life of the electric submersible pump system. Therefore, using system efficiency as the sole optimization objective makes it difficult to balance system energy efficiency with long-term safe operation of the equipment.

[0025] Therefore, how to achieve coordinated optimization among multiple conflicting operating indicators during the operation optimization process of electric submersible pump systems, and avoid equipment safety risks such as excessive motor load and excessive cable current caused by solely pursuing system efficiency, has become an urgent technical problem to be solved.

[0026] In view of this, the embodiments of this application introduce a multi-objective optimization algorithm in the operation optimization of the electric submersible pump system. The algorithm takes multiple conflicting performance indicators of the electric submersible pump system, such as system efficiency, motor efficiency, and cable current, as co-optimization objectives. By using a multi-objective optimization method, the operating parameters of the electric submersible pump system are comprehensively optimized to obtain a Pareto solution set that reflects the trade-off relationship between different objectives. This provides a multi-dimensional and weighted optimization basis for the reasonable selection of operating parameters of the electric submersible pump system.

[0027] Figure 1The diagram illustrates the application environment in some embodiments of this application; this application environment includes an electric submersible pump system 10 and a host computer 20. The host computer 20 can acquire multiple candidate operating parameter combinations of the electric submersible pump system 10; based on the coordinated operating point conditions of the oil well production system, it performs physical feasibility screening on the candidate operating parameter combinations to obtain a set of feasible operating parameter combinations; it calculates the values ​​of at least two conflicting operating performance indicators corresponding to each set of operating parameter combinations in the set of feasible operating parameter combinations; based on the values, it performs optimization on the set of feasible operating parameter combinations using a multi-objective optimization algorithm to obtain a Pareto solution set composed of multiple sets of non-dominant operating parameter combinations; and it outputs the Pareto solution set. The output Pareto solution set allows the electric submersible pump system 10 to select suitable operating parameter combinations from the Pareto solution set for on-site control based on actual production goals or equipment state constraints. Through the embodiments of this application, collaborative optimization among multiple conflicting operating indicators can be achieved during the operation optimization process of the electric submersible pump system, avoiding equipment safety risks such as excessive motor load and excessive cable current caused by solely pursuing system efficiency.

[0028] In some embodiments of this application, the electric submersible pump system 10 is an electric submersible pump system, which refers to a fluid transport device that can directly submerge in fluids such as water, consisting of a ground power supply system, a frequency converter, cables, an electric submersible motor, a protector, a separator, and an electric submersible pump.

[0029] In some embodiments of this application, the host computer 20 can be an electronic device with computing and network interaction functions, such as a desktop computer, laptop computer, or workstation. Of course, the host computer 20 is not limited to the aforementioned physical electronic devices; it can also be software running on those devices.

[0030] This application provides a multi-objective optimization method for the operating parameters of an electric submersible pump system, which can be applied to the aforementioned host computer side. (Refer to...) Figure 2 As shown in some embodiments of this application, the multi-objective optimization method for the operating parameters of an electric submersible pump system may include the following steps: Step 201: Obtain multiple candidate combinations of operating parameters for the electric submersible pump system.

[0031] Step 202: Based on the coordinated working point conditions of the oil well production system, perform physical feasibility screening on the candidate operating parameter combinations to obtain a set of feasible operating parameter combinations.

[0032] Step 203: Calculate the values ​​of at least two conflicting operating performance indicators for each set of feasible operating parameter combinations.

[0033] Step 204: Based on the stated values, perform optimization using a multi-objective optimization algorithm within the set of feasible operating parameter combinations to obtain a Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations.

[0034] Step 205: Output the Pareto solution set.

[0035] In this embodiment, based on the coordinated operating point conditions of the oil well production system, the physical feasibility of multiple candidate operating parameter combinations of the electric submersible pump system can be screened to obtain a set of feasible operating parameter combinations, thus avoiding invalid searches. On this basis, based on the values ​​of at least two conflicting operating performance indicators corresponding to each set of operating parameter combinations in the set of feasible operating parameter combinations, optimization is performed on the set of feasible operating parameter combinations using a multi-objective optimization algorithm to obtain a Pareto solution set composed of multiple sets of non-dominant operating parameter combinations. This achieves collaborative optimization among multiple conflicting operating indicators during the operation optimization process of the electric submersible pump system, avoiding equipment safety risks such as excessive motor load and excessive cable current caused by solely pursuing system efficiency.

[0036] In some embodiments of this application, obtaining multiple candidate combinations of operating parameters for an electric submersible pump system may include: Parameters that are adjustable and have a significant impact on system energy consumption during the operation of the electric submersible pump system are selected as operating parameter variables. For example, candidate operating parameters may include, but are not limited to: Operating frequency f of the electric submersible pump (unit: Hz); Oil nozzle size D (unit: mm).

[0037] Taking the above-mentioned electric submersible pump operating frequency f and nozzle size D as examples, the physical value range of the operating parameters can be set according to the equipment capacity and on-site operating constraints: f min ≤f≤f max D min ≤D≤D max (1) Within the above value range, several sets of candidate operating parameter combinations (f, D) are generated by traversal or sampling to serve as inputs for subsequent feasibility determination and performance calculation.

[0038] In the embodiments of this application, the coordination work point condition refers to the condition that the coordination work point must meet.

[0039] Oil well production is a dynamic equilibrium process, mainly including the following stages: a. Formation fluid supply capacity (from reservoir to bottom of well): Fluid flows from the reservoir into the bottom of the well, following an inflow dynamic curve; the higher the production, the lower the bottom-hole flowing pressure.

[0040] b. Lifting and flow capacity (from bottom of the well to the wellhead via ESP): An electric submersible pump lifts fluid from the bottom of a well to the wellhead. At a given operating frequency, there is a definite functional relationship between the pump's head and its output (pump characteristic curve).

[0041] As fluid flows from the bottom of the well through the tubing to the wellhead, frictional losses and gravity pressure drops occur along the way.

[0042] c. Ground control capability (flowing into ground pipelines via nozzles): When fluid passes through the wellhead nozzle, the throttling effect of the nozzle determines the wellhead pressure (nozzle flow curve) that needs to be maintained at a given production rate.

[0043] A coordinated working point is a unique working point that simultaneously meets the following three conditions: 1. Coordination between wellbore flow and pump characteristics: At a given frequency f, the pump provides sufficient pressure to lift a fluid production rate of Q to the wellhead and overcome friction losses. That is, the pump outlet pressure, calculated using a wellbore flow model, precisely yields the wellhead pressure Pwh.

[0044] 2. Coordination of nozzle throttling: Given a nozzle size D, when a fluid with a production rate of Q passes through the nozzle, the upstream pressure Pwh needs to be maintained.

[0045] 3. Unified production volume and pressure: The two processes mentioned above involve the same production rate Q and the same wellhead pressure Pwh.

[0046] In short, the equilibrium operating point is the point at which the amount of fluid pumped to the surface is exactly equal to the amount of fluid flowing out of the nozzle. At this point, the entire production system (from the reservoir to the nozzle) is in a state of dynamic equilibrium and operates stably.

[0047] As mentioned above, not any combination of operating frequency and nozzle size can guarantee stable production conditions. To avoid performing optimization calculations in physically unfeasible regions, this application introduces a coordinated operating point determination step before performing multi-objective optimization. This step screens multiple candidate combinations of operating parameters for the electric submersible pump system for physical feasibility, resulting in a set of feasible operating parameter combinations. This avoids invalid searches and improves optimization efficiency.

[0048] In some embodiments of this application, based on the coordinated operating point conditions of the oil well production system, the candidate operating parameter combinations are physically screened to obtain a set of feasible operating parameter combinations. This may include: for each set of candidate operating parameter combinations, confirming whether there exists a coordinated operating point that simultaneously satisfies the wellbore flow relationship, pump characteristic relationship, and nozzle throttling relationship; if so, then the set of candidate operating parameter combinations is included in the set of feasible operating parameter combinations. The wellbore flow relationship describes the dependence between pressure and production rate as fluid flows upward from the bottom of the well along the tubing to the wellhead. The pump characteristic relationship describes the inherent relationship between the head (or outlet pressure), power, efficiency, and displacement (production rate) of the electric submersible pump at a given speed (or frequency). The nozzle throttling relationship describes the quantitative relationship between the flow rate through the nozzle and the pressure upstream and downstream of the nozzle when fluid passes through this throttling device at the wellhead.

[0049] The specific steps for the above-mentioned physical feasibility screening may include: 1. Under a given production rate, calculate the relationship between bottom hole flowing pressure and wellhead oil pressure based on the wellbore flow model; 2. Under a given operating frequency f, by combining the pump characteristic curve and the pipeline pressure drop model, the relationship curve of wellhead oil pressure versus production is formed by calculating step by step from the bottom of the well to the wellhead. 3. Given the nozzle size D, calculate the relationship between wellhead oil pressure and production rate based on the nozzle throttling model; 4. Determine whether the two types of curves intersect. If they intersect, the operating state corresponding to that intersection is defined as the coordinated working point.

[0050] A candidate combination of operating parameters (f, D) is considered physically feasible only if a coordinated operating point exists. All operating parameter combinations that satisfy the coordinated operating point condition together constitute the initial set of feasible operating parameter combinations, which serves as the search space for subsequent multi-objective optimization.

[0051] In some embodiments of this application, calculating the values ​​of at least two conflicting performance indicators corresponding to each set of feasible operating parameter combinations may include: Step 1: Establish an energy consumption calculation model characterizing the energy transfer process of the electric submersible pump system. The energy transfer process includes sequentially associated energy input nodes, energy transfer nodes, energy conversion nodes, and pump unit work nodes.

[0052] The electric submersible pump system involved in this application embodiment is a typical multi-energy-consumption node coupled system. Its energy is transferred step-by-step from the surface power supply side to the wellbore, and ultimately converted into effective work for liquid lifting. The energy transfer path is as follows: surface power grid, transformer, frequency converter, wellbore cable, electric submersible motor, and pump unit. During the transfer process, energy input nodes, energy transfer nodes, energy conversion nodes, and pump unit work nodes are formed, and each node experiences significant energy loss. Therefore, this application embodiment provides a complete and calculable energy consumption basis for subsequent operating parameter optimization by quantitatively modeling the entire energy transfer process. The constructed energy consumption calculation model can be as follows: Figure 3 As shown, in Figure 3 In the model, the energy consumption calculation includes energy input nodes, energy transfer nodes, energy conversion nodes, and pump unit work nodes. The nodes are sequentially associated to form an energy transfer path. P1 to P5 correspond to the total input power P1 of the electric submersible pump system, the input power P2 of the energy transfer node, the motor input power P3, the pump unit input power P4, and the pump unit effective power P5.

[0053] Step 2: Using the energy consumption calculation model, calculate the corresponding system efficiency, motor efficiency, and cable current for each combination of operating parameters.

[0054] In some embodiments of this application, the operating performance indicators may include: a first indicator characterizing the overall energy efficiency level of the system, a second indicator characterizing the energy conversion efficiency of the motor, and a third indicator characterizing the load level of the downhole electrical system. For example, the first indicator is the efficiency of the electric submersible pump system, the second indicator is the motor efficiency, and the third indicator is the cable current. Therefore, step 2 is an exemplary description using system efficiency, motor efficiency, and cable current as examples.

[0055] 1. Calculation of pump unit work nodes

[0056] For each set (f, D) in the initial feasible operating parameter combination set, first calculate the effective power P5 of the pump unit for liquid lifting, which is expressed as: (2) in: P5 is the effective power of the pump unit (kW); ρ is the fluid density (kg / m³) 3 ); g is the acceleration due to gravity (m / s²). 2 ); Q ESP The displacement of the electric submersible pump (m³) 3 / d); P t Wellhead oil pressure (Pa); h lThe depth of the moving liquid surface (m).

[0057] After obtaining the effective power P5, and considering the pump efficiency and losses of auxiliary components, calculate the pump unit input power (i.e., motor output power) P4: (3) in: P4 is the input power of the pump unit (kW). η pump For electric submersible pump efficiency; ΔP prot Power loss of the protector (kW); ΔP sep The power loss of the separator is kW.

[0058] In addition, under high viscosity or emulsification conditions, the pump efficiency can be corrected according to formula (4): η vis =k η ×η pump (4) in: η vis The corrected pump efficiency of the electric submersible pump; k η This is the viscosity correction factor.

[0059] Viscosity correction factor k η It can be calculated using the following formula: (5) In the formula, Re is the Reynolds number, which can be calculated using the following formula: (6) in: g is the acceleration due to gravity, taken as 9.8 m / s². 2 q is the pump's rated displacement (m³ / s). 3 / d) h represents the pump's single-stage head (m). v is the kinematic viscosity of the fluid (m / s) 2 ) The effective output power of the electric submersible pump system is quantified and used as the numerator in the system efficiency calculation.

[0060] 2. Calculation of pump unit work nodes

[0061] Calculate the motor input power P3 based on the motor load status: (7) Where, η SEM This is the efficiency of the motor under the current load rate. The efficiency under the current load rate can be calculated by the following formula: (8) In the formula, K L For load factor; η SEMnp Here, represents the motor's nameplate efficiency; a, b, c, d, and e are polynomial coefficients. Due to differences in motor manufacturers and models, efficiencies vary. The polynomial coefficients can be determined based on the motor characteristic curves provided by the motor manufacturer, thus allowing the calculation of the motor efficiency under different load rates. The motor's load rate can be calculated using the following formula: (9) Among them, P SEMnp The rated power of the motor (kW).

[0062] 3. Calculation of energy transfer nodes

[0063] The input power P2 at the energy transfer node is the power transferred from the frequency converter cabinet to the cable. It can be expressed as the motor input power and cable loss power. P2 = P3 + ΔP2 (10) In the formula, ΔP2 represents the cable loss power (kW). Cable loss power can be expressed using the cable's internal resistance and the motor's operating current. (11) in: I SEM The motor operating current (A); R CL The internal resistance of the cable ( ).

[0064] The motor current can be calculated using the following formula: (12) in: U SEMnp The rated voltage of the motor is (kV). f is the current operating frequency of the motor (Hz); f np This refers to the rated frequency of the electric motor. SEM It is the power factor of the motor under the current load factor.

[0065] The internal resistance of the cable is calculated by the following formula: (13) in: ρ is the resistivity of the cable core at 20℃ (Ω·m); L is the cable length (m); A is the cross-sectional area of ​​the cable (m²) 2); α is the temperature coefficient of resistance, which represents the degree to which the resistance of the cable core changes with temperature (°C). -1 ); T E The current ambient temperature (°C).

[0066] By establishing a quantitative relationship between the power demand at the motor end and the output power of the frequency converter cabinet, an accurate power basis is provided for subsequent energy input sections.

[0067] 4. Energy Input Node Calculation

[0068] The total input power P1 of the electric submersible pump system can be expressed by the output power of the frequency converter cabinet and the power loss of the transformer and the frequency converter cabinet, as shown in the following formula: P1 = P2 + ΔP T +ΔP CS (14) in: ΔP T Transformer power loss, unit: kW; ΔP CS The power loss of the frequency converter cabinet is expressed in kW.

[0069] Transformer power loss is expressed by the transformer's iron loss and copper loss, as shown in the following formula: ΔP T = P core +P cu (15) In the formula, P core This refers to the iron loss of the transformer. Iron loss is generally the rated value of the transformer and can be found on the transformer's nameplate or technical manual. cu The copper loss of the transformer can be calculated using the following formula: (16) Among them, R T The resistance of the transformer winding is in Ω, which can be calculated by referring to formula (13).

[0070] The power loss of the frequency converter cabinet can be calculated by the following formula: (17) In the formula, η CSnp The efficiency is indicated on the nameplate of the frequency converter cabinet.

[0071] The system efficiency of an electric submersible pump well can be expressed as the total input power and the effective power of the electric submersible pump, as shown in the following formula: (18) 5. Calculation of Three-Target Performance Indicators For each group (f, D), the following calculation steps are performed: (1) System efficiency η sys = P5 / P1; (2) Motor efficiency η SEM ; (3) Motor operating current (cable current) I.

[0072] System efficiency characterizes the overall energy utilization level of the electric submersible pump system from the surface power supply end to the liquid lifting end. This indicator reflects the direct economic benefits of system energy consumption optimization. Motor efficiency characterizes the efficiency level of the motor energy conversion process. This indicator is closely related to motor heating, insulation aging, and operational reliability. Cable current characterizes the load level of the downhole electrical system. This indicator is directly related to cable loss, electrical safety margin, and equipment life. These three indicators together constitute a comprehensive evaluation vector for the combination of operating parameters (f, D), which is used for subsequent multi-objective non-dominated ranking and evolutionary calculations.

[0073] In some embodiments of this application, multi-objective optimization algorithms include, for example, the Non-Dominated Sorting Genetic Algorithm (NSGA-II), the Decomposition-Based Multi-Objective Evolutionary Algorithm (MOEA / D), the Multi-Objective Differential Evolutionary Algorithm (MODE), and the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO). The following uses the Non-Dominated Sorting Genetic Algorithm as an example to describe a specific implementation of optimization based on multi-objective optimization algorithms within the set of feasible operating parameter combinations.

[0074] refer to Figure 4 As shown, in some embodiments of this application, taking system efficiency, motor efficiency, and cable current as examples, the optimization based on a non-dominated sorting genetic algorithm within the set of feasible operating parameter combinations may include the following steps: Step 401: Select multiple sets of operating parameters from the set of feasible operating parameter combinations based on the current actual operating parameters, and construct the multiple sets of operating parameters into an initial population.

[0075] In some embodiments of this application, Gaussian sampling can be performed on individuals in the set of feasible operating parameter combinations that satisfy the coordinated working point condition, centered on the current actual operating parameters (f0, D0), and the sampled multiple sets of operating parameters can be used to construct an initial population. Specifically, for the sampled individuals: (1) If its operating parameters exceed the physical value range, then it should be trimmed or discarded; (2) If it does not meet the conditions for coordinating working points, it will be directly eliminated.

[0076] By using the above methods, it is ensured that all individuals in the initial population correspond to physically achievable and engineering-significant operating conditions.

[0077] Step 402: Perform a non-dominated sort on the individuals in the current population, and map the individuals in the current population to multiple Pareto levels based on the sorting results.

[0078] For all individuals in the population, non-dominant relationships are determined based on three indicators: system efficiency, motor efficiency, and cable current. If a combination of operating parameters is superior to another combination in at least one objective and is not inferior to another combination in the remaining objectives, then it is considered to dominate the latter. Individuals not dominated by any other combination constitute the first non-dominated layer.

[0079] The population is divided into multiple Pareto levels by the above non-dominated sorting, which reflects the relative superiority or inferiority of different combinations of operating parameters in the multi-objective space.

[0080] Step 403: Within the same Pareto level, calculate the crowding distance for each individual.

[0081] Within the same non-dominated layer, the crowding distance of each individual in the target space is calculated to measure the degree of sparse distribution between it and its neighboring individuals.

[0082] The congestion distance is obtained by normalizing the difference between each objective function, and the calculation formula is as follows: (19) Where M is the number of objective functions, f j (X i+1 ) and f j (X i-1 ) represent the values ​​of the objective function j for individuals i+1 and i-1, respectively, and f j (X max ) and f j (X min ) are the maximum and minimum values ​​of the objective function j, respectively.

[0083] Step 404: Select parent individuals from the current population based on the Pareto level and crowding distance to generate a parent population; wherein the selection priority is negatively correlated with the Pareto level to which the individual belongs and positively correlated with the crowding distance of individuals within the same Pareto level.

[0084] During the selection process, for individuals within the same Pareto level, individuals with larger crowding distances are preferred (i.e., the crowding distances of individuals within the same Pareto level are positively correlated) to ensure the uniform distribution of the Pareto solution set in the target space and avoid Pareto solution set concentration in local areas; for individuals within different Pareto levels, individuals with lower Pareto levels are preferred (i.e., the priority is negatively correlated with the Pareto level to which the individual belongs).

[0085] Step 405: Perform crossover and / or mutation operations on the parent individuals to generate the offspring population.

[0086] Performing a crossover operation on the parent individuals means: performing crossover on the selected parent individuals to generate a new combination of operating parameters; Performing mutation operations on the parent individual refers to making small perturbations to the operating frequency and nozzle size to enhance the search capability.

[0087] Step 406: Merge the parent population with the offspring population to generate a new population.

[0088] By merging the parent population with the offspring population, a new population can be generated, which will then be used for the next round of evolution.

[0089] Step 407: Iterate and evolve until the convergence condition is met to obtain the Pareto solution set.

[0090] After multiple generations of iterative evolution, the final (optimal) Pareto solution set is obtained. This solution set consists of multiple sets of non-dominant combinations of operating parameters. Each combination satisfies the coordinated operating point condition and corresponds to different trade-offs between system efficiency, motor efficiency, and cable current.

[0091] For each Pareto optimal solution in the Pareto solution set, the system simultaneously outputs: The corresponding operating frequency f and nozzle size D; System efficiency η sys ; Motor efficiency η SEM ; Cable current I.

[0092] As can be seen, this Pareto solution set no longer provides a single optimal solution, but rather offers multiple sets of optional operating schemes for the system or operators.

[0093] This application provides another multi-objective optimization method for the operating parameters of an electric submersible pump system, which can be applied to the aforementioned host computer side. (Refer to...) Figure 5 As shown in some embodiments of this application, the multi-objective optimization method for the operating parameters of an electric submersible pump system may include the following steps: Step 501: Obtain multiple candidate operating parameter combinations for the electric submersible pump system.

[0094] Step 502: Based on the coordinated working point conditions of the oil well production system, perform physical feasibility screening on the candidate operating parameter combinations to obtain a set of feasible operating parameter combinations.

[0095] Step 503: Calculate the values ​​of at least two conflicting operating performance indicators for each set of feasible operating parameter combinations in the set of feasible operating parameter combinations.

[0096] Step 504: Based on the stated values, perform optimization using a multi-objective optimization algorithm within the set of feasible operating parameter combinations to obtain a Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations.

[0097] Step 505: Output the Pareto solution set.

[0098] Step 506: Based on the actual production target or equipment status constraints, select a suitable combination of operating parameters from the Pareto solution set for on-site control.

[0099] exist Figure 5 In the illustrated embodiment, the host computer can not only output Pareto solutions to provide multiple sets of optional operating schemes for the system or operators, but also automatically recommend combinations of operating parameters that are more inclined towards high-efficiency operation, safe equipment operation, or a compromise between the two, based on actual production goals and equipment status.

[0100] Through the above methods, the embodiments of this application achieve a complete technical closed loop, from energy transfer mechanism modeling to feasible region selection, multi-objective optimization, and engineering decision support. Compared with existing energy consumption optimization methods for electric submersible pump systems, the technical solution proposed in the embodiments of this application has at least the following beneficial effects: (i) It realizes full-link, computable modeling of energy consumption in electric submersible pump systems, improving the completeness and accuracy of energy consumption assessment. This application, starting from the energy transfer mechanism, models the entire energy transfer process of an electric submersible pump system from the surface power supply end to the downhole liquid lifting end, node by node. Quantitative relationships are established sequentially between energy input nodes, energy transfer nodes, energy conversion nodes, and pump unit work nodes. By calculating the total system input power and effective output power step-by-step, this application comprehensively reflects the impact of each energy consumption node on the overall system energy efficiency. This avoids the incomplete energy consumption assessment problem caused by existing technologies that only focus on a single device or local component, thus providing a more accurate and reliable calculation basis for energy consumption optimization.

[0101] (ii) By introducing a coordination working point determination mechanism, the optimization search space is effectively constrained, thereby improving the engineering feasibility of the optimization results.

[0102] This application's embodiments introduce a physical feasibility assessment based on coordinated operating points before multi-objective optimization. Only combinations of operating parameters that satisfy wellbore flow, pump characteristics, and nozzle throttling relationships are considered for optimization, thus eliminating physically unrealizable or unstable operating conditions at the source. This mechanism avoids the problem of traditional optimization methods performing ineffective searches within infeasible regions, significantly improving the engineering feasibility and field application value of the optimization results.

[0103] (III) A multi-objective evaluation system that takes into account energy efficiency, equipment condition and electrical safety has been constructed to avoid over-optimization of a single objective.

[0104] This application embodiment uses system efficiency, motor efficiency, and cable current as optimization targets to construct a multi-objective performance index system, achieving a comprehensive evaluation of the energy efficiency level, equipment operational reliability, and electrical load status of the electric submersible pump system. Compared to existing technologies that only consider system efficiency or energy consumption as a single target, this application embodiment can effectively avoid the problems of decreased motor efficiency, increased current, or increased equipment risk caused by excessive pursuit of energy efficiency, achieving coordinated optimization among multiple indicators.

[0105] (iv) Optimization using a non-dominated sorting genetic algorithm can yield multiple Pareto optimal solutions with engineering significance.

[0106] This application employs a non-dominated sorting genetic algorithm to perform multi-objective collaborative optimization of operating parameters, ultimately outputting a Pareto optimal solution set containing multiple sets of non-dominated operating schemes. This solution set does not force a single optimal solution, but rather provides operators with multiple operating schemes that are reasonable under different objective preferences. This facilitates flexible decision-making based on actual production needs, equipment status, or safety requirements, enhancing the adaptability of the optimization results under complex operating conditions.

[0107] (v) It has good versatility and application value.

[0108] The embodiments of this application do not rely on specific oilfields, specific well types, or specific equipment parameters. Their technical solutions are applicable to ESP systems of different depths, production conditions, and configurations, exhibiting good versatility and scalability. By adjusting the range of operating parameters and equipment characteristic parameters, the embodiments of this application can be extended to energy consumption optimization scenarios for different ESP wells or well groups, demonstrating high engineering promotion value.

[0109] Although the process described above includes multiple operations that occur in a specific order, it should be clearly understood that these processes may include more or fewer operations that can be executed sequentially or in parallel (e.g., using parallel processors or a multithreaded environment).

[0110] Corresponding to the multi-objective optimization of the operating parameters of the electric submersible pump system described above, this application also provides a multi-objective optimization device for the operating parameters of the electric submersible pump system, which can be configured on the aforementioned host computer, for reference. Figure 6 As shown in some embodiments of this application, the multi-objective optimization device for the operating parameters of the electric submersible pump system may include: Module 61 is used to acquire multiple candidate combinations of operating parameters for the electric submersible pump system; The screening module 62 is used to perform physical feasibility screening on the candidate operating parameter combinations based on the coordinated working point conditions of the oil well production system, so as to obtain a set of feasible operating parameter combinations. The calculation module 63 is used to calculate the values ​​of at least two conflicting operating performance indicators corresponding to each set of operating parameters in the set of feasible operating parameter combinations. The optimization module 64 is used to perform optimization based on the numerical values ​​in the set of feasible operating parameter combinations using a multi-objective optimization algorithm to obtain a Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations. Output module 65 is used to output the Pareto solution set.

[0111] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0112] Embodiments of this application also provide a computer device. For example... Figure 7As shown, in some embodiments of this application, the computer device 702 may include one or more processors 704, such as one or more central processing units (CPUs) or graphics processing units (GPUs), each of which may implement one or more hardware threads. The computer device 702 may also include any memory 706 for storing any kind of information such as code, settings, data, etc. In one specific embodiment, a computer program is stored on the memory 706 and can run on the processor 704. When the processor 704 executes the program, it can perform instructions for the multi-objective optimization method for submersible pump system operating parameters described in any of the above embodiments. Non-limitingly, for example, the memory 706 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 702. In one case, when the processor 704 executes associated instructions stored in any memory or combination of memories, the computer device 702 can perform any operation of the associated instructions. The computer device 702 also includes one or more drive mechanisms 708 for interacting with any memory, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0113] Computer device 702 may also include an input / output interface 710 (I / O) for receiving various inputs (via input device 712) and providing various outputs (via output device 714). A specific output mechanism may include a presentation device 716 and an associated graphical user interface 718 (GUI). In other embodiments, the input / output interface 710 (I / O), input device 712, and output device 714 may be omitted, and the device may function solely as a computer device within a network. Computer device 702 may also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the components described above together.

[0114] Communication link 722 can be implemented in any way, such as via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0115] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), computer-readable storage media, and computer program products according to some 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 processor to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processor, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processor 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.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processor, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device 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.

[0118] In a typical configuration, a computer device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0119] Memory may include non-persistent storage 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.

[0120] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, 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, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by computer equipment. As defined in this application, computer-readable media does not include transient media, such as modulated data signals and carrier waves.

[0121] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented 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.

[0122] The embodiments of this application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. The embodiments of this application can also be practiced in distributed computing environments where tasks are performed by remote processors connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0123] It should also be understood that, in the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0124] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0125] In the description of this application, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this application. In this application, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this application, as well as the features of different embodiments or examples.

[0126] The above description is merely an embodiment of this application and is 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 multi-objective optimization method for operating parameters of an electric submersible pump system, characterized in that, Includes the following steps: Obtain multiple candidate combinations of operating parameters for the electric submersible pump system; Based on the coordinated working point conditions of the oil well production system, the physical feasibility of the candidate operating parameter combinations is screened to obtain a set of feasible operating parameter combinations. Calculate the values ​​of at least two conflicting performance indicators for each set of feasible operating parameters in the set of feasible operating parameter combinations; Based on the aforementioned values, an optimization algorithm is used to find the Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations within the set of feasible operating parameter combinations. Output the Pareto solution set.

2. The method according to claim 1, characterized in that, The operational performance indicators include: The first indicator characterizing the overall energy efficiency of the system, the second indicator characterizing the energy conversion efficiency of the motor, and the third indicator characterizing the load level of the downhole electrical system.

3. The method according to claim 2, characterized in that, The first indicator is the efficiency of the electric submersible pump system, the second indicator is the motor efficiency, and the third indicator is the cable current.

4. The method according to claim 1, characterized in that, The multi-objective optimization algorithm includes a non-dominated sorting genetic algorithm, and the optimization based on the multi-objective optimization algorithm in the set of feasible operating parameter combinations includes: Based on the current actual operating parameters, multiple sets of operating parameters are selected from the set of feasible operating parameter combinations, and these multiple sets of operating parameters are used to construct an initial population. Perform a non-dominated ranking of the individuals in the current population, and map the individuals in the current population to multiple Pareto levels based on the ranking results; Within the same Pareto level, calculate the crowding distance for each individual; Parent individuals are selected from the current population based on the Pareto level and crowding distance to generate a parent population; wherein the selection priority is negatively correlated with the Pareto level to which the individual belongs and positively correlated with the crowding distance of individuals within the same Pareto level. Perform crossover and / or mutation operations on the parent individuals to generate the offspring population; The parent population is merged with the offspring population to generate a new population; The process iterates until the convergence condition is met, thus obtaining the Pareto solution set.

5. The method according to claim 4, characterized in that, The selection of multiple sets of operating parameters from the set of feasible operating parameter combinations based on the current actual operating parameters specifically includes: Based on the current actual operating parameters, Gaussian sampling is performed on individuals in the set of feasible operating parameter combinations that satisfy the conditions of the coordinated working point.

6. The method according to claim 1, characterized in that, The physical feasibility screening based on the conditions of the coordinated working point of the oil well production system specifically includes: For each set of candidate operating parameters, confirm whether there is a coordinated operating point that simultaneously satisfies the wellbore flow relationship, pump characteristic relationship, and nozzle throttling relationship; If such a combination of candidate operating parameters exists, it will be included in the set of feasible operating parameter combinations.

7. The method according to claim 3, characterized in that, The calculation of the values ​​of at least two conflicting operational performance indicators corresponding to each set of feasible operational parameter combinations includes: An energy consumption calculation model is established to characterize the energy transfer process of the electric submersible pump system. The energy transfer process includes sequentially associated energy input nodes, energy transfer nodes, energy conversion nodes, and pump unit work nodes. Using the energy consumption calculation model, the corresponding system efficiency, motor efficiency, and cable current are calculated for each combination of operating parameters.

8. The method according to claim 1, characterized in that, After outputting the Pareto solution set, the method further includes: Based on actual production targets or equipment status constraints, a suitable combination of operating parameters is selected from the Pareto solution set for on-site control.

9. A multi-objective optimization device for the operating parameters of an electric submersible pump system, characterized in that, include: The acquisition module is used to acquire multiple candidate combinations of operating parameters for the electric submersible pump system; The screening module is used to perform physical feasibility screening on the candidate operating parameter combinations based on the coordinated working point conditions of the oil well production system, so as to obtain a set of feasible operating parameter combinations. The calculation module is used to calculate the values ​​of at least two conflicting operating performance indicators corresponding to each set of operating parameters in the set of feasible operating parameter combinations. The optimization module is used to perform optimization based on the numerical values ​​in the set of feasible operating parameter combinations using a multi-objective optimization algorithm to obtain a Pareto solution set consisting of multiple sets of non-dominant operating parameter combinations. The output module is used to output the Pareto solution set.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the computer program is run by the processor, it executes the instructions of the method according to any one of claims 1-8.

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