General optimization design method and system for layout scheme of natural gas field gathering and transportation network

Abstract

The application discloses a general optimization design method and system for a natural gas field gathering pipeline network layout scheme, relates to the technical field of gathering pipeline network planning and design, and comprises the following steps: firstly, a general optimization design model of the natural gas field gathering pipeline network layout scheme under different decision preferences is constructed; and then, the general optimization design model is solved by using an optimization algorithm to obtain the layout scheme of the natural gas field gathering pipeline network under each decision preference, so as to be selected by a user. Therefore, the general optimization design model under different decision preferences is established, the general optimization design model is solved by using the optimization algorithm, and the optimization design of the natural gas field gathering pipeline network layout scheme can be automatically completed, so that the demand of different users for the design preference of the gathering pipeline network is met, and problems, such as large engineering quantity, high construction investment and long scheme design period, caused by manual design are solved.

Description

Technical Field

[0001] This invention relates to the field of gathering and transmission pipeline planning and design technology, and in particular to a general optimization design method and system for natural gas field gathering and transmission pipeline layout schemes. Background Technology

[0002] The general optimization design of natural gas field gathering and transmission pipeline network layout schemes refers to determining the optimal pipeline network layout scheme through optimization design methods. This involves determining the best pipeline connections and station geometric locations to minimize pipeline investment and total length. However, the optimization models currently used in natural gas field gathering and transmission pipeline network layout planning lack universality and cannot meet the different decision-making preferences of planners and designers. To satisfy these preferences, manual design is necessary. However, manual design is easily influenced by human experience, and the resulting scheme cannot guarantee optimality, potentially leading to problems such as large engineering workload, high construction investment, and long design cycles. Summary of the Invention

[0003] The purpose of this invention is to provide a general optimization design method and system for the layout scheme of natural gas field gathering and transmission pipeline network. By establishing a general optimization design model under different decision preferences and solving the general optimization design model using optimization algorithms, the optimization design of the layout scheme of natural gas field gathering and transmission pipeline network can be automatically completed to meet the needs of different users for the design preferences of gathering and transmission pipeline network, and solve the problems of large engineering workload, high construction investment and long scheme design cycle caused by manual design.

[0004] To achieve the above objectives, the present invention provides the following solution: A general optimization design method for the layout of a natural gas field gathering and transmission pipeline network, the general optimization design method comprising: Construct a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision-making preferences; The general optimization design model is solved using an optimization algorithm to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference. The layout scheme includes the number of gas gathering stations and the location of each gas gathering station, the number of gas gathering trunk pipeline inlets and the location of each gas gathering trunk pipeline inlet, the number of stations, and the pipeline connection relationship between gas wells, gas gathering stations, and gas gathering trunk pipeline inlets.

[0005] A general optimization design system for natural gas field gathering and transmission pipeline network layout schemes, the general optimization design system comprising: The model building module is used to construct a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision preferences; The model solving module is used to solve the general optimization design model using optimization algorithms to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference. The layout scheme includes the number of gas gathering stations and the location of each gas gathering station, the number of gas gathering trunk pipeline inlets and the location of each gas gathering trunk pipeline inlet, the number of stations, and the pipeline connection relationship between gas wells, gas gathering stations and gas gathering trunk pipeline inlets.

[0006] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention provides a general optimization design method and system for natural gas field gathering and transmission pipeline network layout schemes. First, a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision preferences is constructed. Then, an optimization algorithm is used to solve the general optimization design model to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference, for users to choose from. Thus, by establishing a general optimization design model under different decision preferences and using an optimization algorithm to solve the general optimization design model, the optimization design of the natural gas field gathering and transmission pipeline network layout scheme can be automatically completed to meet the needs of different users for gathering and transmission pipeline network design preferences, solving problems such as large engineering workload, high construction investment, and long scheme design cycle that are easily caused by manual design. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a flowchart of the general optimization design method provided in Embodiment 1 of the present invention; Figure 2 The flowchart for solving the general optimization design model provided in Embodiment 1 of the present invention is shown. Figure 3 This is a topology diagram of a ring-star gathering and transmission pipeline network layout scheme obtained by artificial design method when considering structure, gas volume, and pipeline specifications simultaneously, as provided in Embodiment 1 of the present invention. Figure 4 This is a topology diagram of the ring-star gathering and transportation network layout scheme obtained by solving the structural preference evaluation model provided in Embodiment 1 of the present invention. Figure 5 This is a topology diagram of the ring-star gathering and transmission pipeline layout scheme obtained by solving the structure-gas volume preference evaluation model provided in Embodiment 1 of the present invention. Figure 6This is a topology diagram of the ring-star gathering and transmission pipeline network layout scheme obtained by solving the structure-gas volume-pipeline specification preference evaluation model provided in Embodiment 1 of the present invention. Figure 7 This is a comparative diagram of the solution results of the structure-gas volume-pipeline specification preference evaluation model using the HRPSO algorithm, SPSO algorithm, MeanPSO algorithm, PHSPSO algorithm and LDWPSO algorithm provided in Embodiment 1 of the present invention. Figure 8 This is a system block diagram of the general optimization design system provided in Embodiment 2 of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] The purpose of this invention is to provide a general optimization design method and system for the layout scheme of natural gas field gathering and transmission pipeline network. By establishing a general optimization design model under different decision preferences and solving the general optimization design model using optimization algorithms, the optimization design of the layout scheme of natural gas field gathering and transmission pipeline network can be automatically completed to meet the needs of different users for the design preferences of gathering and transmission pipeline network, and solve the problems of large engineering workload, high construction investment and long scheme design cycle caused by manual design.

[0011] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] Example 1: Before introducing the optimized design method of this embodiment, the composition of the natural gas field gathering and transmission pipeline network is explained: The gathering and transmission pipeline network is a large-area network pipeline structure composed of metal or non-metal pipes of different diameters and wall thicknesses. It covers all gas wells in the natural gas field area and provides a channel for the natural gas produced by the gas wells to reach various stations and finally to the natural gas processing plant. The gathering and transmission pipeline network includes: (1) Gas gathering pipeline, which is the connecting pipeline between the gas well and the gas gathering station. Its function is to collect the natural gas produced by a single well or a group of adjacent gas wells to the gas gathering station. (2) Gas gathering branch line, which is the connecting pipeline between the gas gathering station and the gas gathering trunk pipeline inlet. Its function is to transport the pre-treated natural gas at the gas gathering station to the gas gathering trunk line. (3) Gas gathering trunk line, which is the connecting pipeline between the inlets of two gas gathering trunk lines. Its function is to receive the natural gas transported from each gas gathering branch line and finally collect them to the natural gas processing plant / purification plant. In this embodiment, the natural gas processing plant / purification plant is referred to as the main station.

[0013] This embodiment provides a general optimization design method for the layout of a natural gas field gathering and transmission pipeline network. Its purpose is to determine the number and location of gathering stations, the number and location of main gathering pipeline inlets, the number of stations, and the pipeline connections between wells, gathering stations, and main gathering pipeline inlets, given the number of gas wells, their locations, and their production rates. This allows for the determination of the topological structure of the natural gas field gathering and transmission pipeline network. Figure 1 As shown, the optimization design method includes: S1: Construct a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision preferences; S2: Solve the general optimization design model using an optimization algorithm to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference; the layout scheme includes the number of gas gathering stations and the location of each gas gathering station, the number of gas gathering trunk pipeline inlets and the location of each gas gathering trunk pipeline inlet, the number of stations, and the pipeline connection relationship between gas wells, gas gathering stations and gas gathering trunk pipeline inlets.

[0014] This embodiment first constructs a general optimization design model for the layout scheme of natural gas field gathering and transmission pipeline under different decision preferences, and then uses an optimization algorithm to solve the general optimization design model to obtain the layout scheme of natural gas field gathering and transmission pipeline under each decision preference. It can automatically complete the optimization design of the layout scheme of natural gas field gathering and transmission pipeline to meet the needs of different users for the design preferences of gathering and transmission pipeline, and solve the problems that manual design is prone to cause large engineering workload, high construction investment, and long scheme design cycle.

[0015] The general optimization design model in this embodiment uses the location and number of gas gathering stations, the location and number of gas gathering trunk pipeline inlets, the number of stations, and the connection relationships between gas wells, gas gathering stations, and gas gathering trunk pipeline inlets as decision variables. It considers the impact of construction costs and actual operating conditions on the overall layout of the gas gathering and transmission network. With node flow balance, gas gathering station processing capacity, and ring network size as constraints, the model establishes a general optimization design model with the objective function of minimizing total construction costs. Node flow balance is reflected in the fact that in the natural gas field gas gathering and transmission network, the inflow and outflow of each node are equal, the outflow of each level of node is equal to the inflow of the next level of node, and the flow rate of each pipeline segment in the ring network formed by the gas gathering trunk lines is equal.

[0016] The general optimization design model in this embodiment includes: ; in, F The fitness value represents the total construction cost; N The number of gas wells; M g The number of gas gathering stations; To indicate a gas well i with gas gathering station j The binary variable representing the pipeline connection relationship, when the gas well i with gas gathering station j When connected by pipes, Select 1 if the value is 1, otherwise select 0. gas well i with gas gathering station j The equivalent length of the connecting pipes; gas well i with gas gathering station j The cost of pipes used to connect the pipelines between them; K The number of inlets for the gas collection trunk pipeline; To indicate the gas gathering station i Inlet of the gas gathering trunk pipeline j The binary variable representing the pipeline connection relationship, when the gas gathering station i Inlet of the gas gathering trunk pipeline j When connected by pipes, Select 1 if the value is 1, otherwise select 0. For gas gathering station i Inlet of the gas gathering trunk pipeline j The equivalent length of the connecting pipes; For gas gathering station i Inlet of the gas gathering trunk pipeline j The cost of pipes used to connect the pipelines between them; To indicate the inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline jThe binary variable representing the pipeline connection relationship, when the gas gathering trunk pipeline inlet... i Inlet of the gas gathering trunk pipeline j When connected by pipes, Select 1 if the value is 1, otherwise select 0. Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The equivalent length of the connecting pipes; Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The cost of pipes used to connect the pipelines between them; Costs required for the construction of the station; The first penalty coefficient is... ,but Take infinity, otherwise Set to 0; The second penalty coefficient, if ,but Take infinity, otherwise Set to 0; The third penalty coefficient, if ,but Take infinity, otherwise Set to 0; A The third penalty coefficient, representing the maximum number of gas wells a single gas gathering station can connect to, is used to reflect the gas gathering station's processing capacity constraint; each gas gathering station can only manage a maximum of [number missing]. A One gas well; The fourth penalty coefficient, if ,but Take infinity, otherwise Set to 0; The relevant terms of this fourth penalty coefficient, which represents the minimum ring network length, are used to reflect the ring network size constraint. The fifth penalty coefficient, if ,but Take infinity, otherwise Set to 0; For gas gathering station i Processing capacity; For gas gathering station i The lower limit of processing capacity; The sixth penalty coefficient, if ,but Take infinity, otherwise Set to 0; For gas gathering station i The maximum processing capacity.

[0017] Preferably, the decision preferences in this embodiment include structural preferences, structural-gas volume preferences, and structural-gas volume-pipeline specification preferences. In this case, the general optimization design model includes a structural preference evaluation model, a structural-gas volume preference evaluation model, and a structural-gas volume-pipeline specification preference evaluation model.

[0018] Specifically, in the structural preference assessment model, the cost of pipe materials is calculated based on the equivalent length of the pipe. ;in, M Z This represents the total number of stations; For the construction of the station i The first construction cost fitting coefficient; For the station i Processing capacity; For the construction of the station i The second construction cost fitting coefficient; and Take 0.

[0019] In the structure-gas volume preference assessment model, pipe material costs are calculated based on the equivalent length of the pipe. ;in, M Z This represents the total number of stations; For the station i The gas flow rate.

[0020] In the structure-gas volume-pipe specification preference evaluation model, pipe material costs are calculated based on pipe wall thickness, pipe diameter, and equivalent pipe length. ;in, M Z The total number of stations; gas represents the number of stations. i The gas flow rate.

[0021] More specifically, the structural preference evaluation model in this embodiment includes: ; In the structural preference assessment model, , and All figures are calculated based on the equivalent length of the pipeline. The formula for calculating pipe material costs based on the equivalent length of the pipeline is as follows: ; in, For pipe material costs, X Pick A , B , C ; This is the fitting coefficient for pipe construction costs. Since the unit cost of branch and main pipelines is an estimate, therefore... Take the estimated value, that is It is a constant; For the equivalent length of the pipe, X Pick A , B , C。

[0022] In the structural preference assessment model, M Z The total number of stations is denoted as , which includes gas gathering stations and main stations. If the number of gas gathering stations and main stations is not fixed, it will increase the computational complexity. Therefore, the total number of stations is obtained by enumeration. For the construction of the station i The first construction cost fitting coefficient; For the station i The processing capacity, and the station i The sum of the gas production of all connected gas wells is the station's gas production. i Processing capacity; For the construction of the station i The second construction cost fitting coefficient. Since the station construction cost is only related to the well type constraint, and Take the estimated value, that is and It is a constant.

[0023] Specifically, during the iteration process, when calculating the fitness value of a particle using the structural preference evaluation model, if the first penalty coefficient, second penalty coefficient, third penalty coefficient, or fourth penalty coefficient is infinite, the particle is eliminated and will not enter the subsequent iteration process.

[0024] Unlike the structure preference assessment model, the structure-gas volume preference assessment model in this embodiment further considers gas volume. In this case, the station construction cost is related to the gas volume. Therefore, the structure-gas volume preference assessment model in this embodiment includes: ; In the structure-gas volume preference assessment model, , and The cost of pipes is calculated based on the equivalent length of the pipe, and it uses the same formula as the pipe cost calculation used in the structural preference assessment model.

[0025] In the structure-gas volume preference assessment model, M Z This represents the total number of stations; For the station i The gas flow rate is equal to that of the station. i The sum of gas production from all connected gas wells, in m³. 3 / d.

[0026] Specifically, during the iteration process, when calculating the fitness value of a particle using the structure-gas-volume preference evaluation model, if the first, second, third, fourth, fifth, or sixth penalty coefficients are infinite, the particle is eliminated and will not enter the subsequent iteration process.

[0027] Unlike the structure-gas volume preference assessment model, the structure-gas volume-pipe specification preference assessment model in this embodiment further considers pipe specifications. In this case, pipe costs are related to pipe wall thickness and pipe diameter. Therefore, the structure-gas volume-pipe specification preference assessment model in this embodiment includes: ; In the structure-gas volume-pipeline specification preference evaluation model, , and The calculation formula for pipe material costs, based on pipe wall thickness and pipe diameter, is as follows: ; in, This is the fitting coefficient for pipe construction costs. Since the unit cost of branch and main pipelines is related to the pipe diameter and wall thickness, therefore... Take the actual value; gas well i with gas gathering station j The wall thickness of the connecting pipes; gas well i with gas gathering station j The diameter of the connecting pipe. This embodiment is based on the gas well. i To determine the appropriate gas production capacity, a pipeline of corresponding specifications should be selected. This pipeline is a gas transmission industrial pipeline, and its wall thickness should meet the requirements of the national standard GB 50251-2015 "Code for Design of Gas Transmission Pipeline Engineering". After determining the pipeline specifications, the upper and lower limits of the pipeline wall thickness can be determined according to the above regulations. A wall thickness between the upper and lower limits should be selected as the appropriate thickness. The pipe diameter should conform to the national pipe diameter standards and actual needs. After determining the pipe specifications, the upper and lower limits of the pipe diameter can be determined according to the above regulations. A pipe diameter between the upper and lower limits should be selected as the standard. It should be noted that the above process is completed automatically during the model solving process and does not require manual intervention.

[0028] ; in, For gas gathering station i Inlet of the gas gathering trunk pipeline j The wall thickness of the connecting pipes; For gas gathering station i Inlet of the gas gathering trunk pipeline j The diameter of the connecting pipe. The method for determining the pipe wall thickness and diameter is the same as above, except it needs to be based on the gas gathering station. i The system automatically selects the appropriate pipe size based on the gas volume.

[0029] ; in, Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The wall thickness of the connecting pipes; Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The diameter of the connecting pipe. The method for determining the pipe wall thickness and diameter is the same as above, except that it needs to be based on the inlet of the gas gathering main pipe. i The system automatically selects the appropriate pipe size based on the gas volume.

[0030] Specifically, during the iteration process, when calculating the fitness value of a particle using the structure-gas volume-pipeline specification preference evaluation model, if the first penalty coefficient, second penalty coefficient, third penalty coefficient, fourth penalty coefficient, fifth penalty coefficient, or sixth penalty coefficient is infinite, the particle is eliminated and will not enter the subsequent iteration process.

[0031] Considering that existing optimization design methods for natural gas field gathering and transmission pipeline network layouts can only achieve optimization design under a single decision preference, planners and designers cannot select the best layout scheme under multiple decision preferences, thus failing to meet their optimization design needs. Consequently, the optimization design methods for natural gas field gathering and transmission pipeline network layouts cannot solve practical planning and design problems. This embodiment's general optimization design model includes evaluation models under different decision preferences. The layout schemes obtained after solving the general optimization design model include layout schemes under each decision preference. A layout scheme can then be selected as the final layout scheme according to user needs, enabling planners and designers to optimize the gathering and transmission pipeline network layout scheme and select the best layout scheme under the three decision preferences, thereby achieving a general, high-quality, cost-reducing, and efficiency-enhancing optimization design.

[0032] In S2, this embodiment can use any existing optimization algorithm to solve the optimization design model.

[0033] Particle swarm optimization (PSO) is a primary intelligent algorithm for solving engineering optimization problems. However, PSO is prone to getting trapped in local optima during iterative solving, making it difficult to find the optimal solution. Therefore, this embodiment improves the PSO algorithm by using the improved version to solve the optimization design model. In other words, the optimization algorithm in this embodiment can employ the improved PSO. Unlike the standard PSO algorithm, the improved PSO in this embodiment uses a hormone regulation operator and a neighborhood exploration operator to update the particles.

[0034] The update formula for the hormone regulation operator includes: ; ; In the above formula, the first term is the velocity update formula, and the second term is the position update formula. For the first t +1 generation particle velocity; w The inertial weight of the particle swarm is relatively large. w It is beneficial for global search, escaping local optima, and smaller... w It facilitates local search, allowing the algorithm to converge quickly to the optimal solution; For the first t The velocity of particles in a generation; For individual learning factors in the particle swarm; These are constant coefficients, as shown in this embodiment. Option 2 is acceptable; D As a threshold, in this embodiment ; n It is a constant, in this embodiment ; t This represents the number of iterations. Hormones y The basal secretion rate in this embodiment 0 can be taken; r 1 is the first random number; For the first t The optimal value of an individual in a generation; For the first t The position of the particles in the generation; The social learning factor of particle swarm; r 2 is the second random number; For the first t The global optimum of the generation; For the first t +1 generation particle position.

[0035] The update formula for the neighborhood exploration operator includes: ; ; In the above formula, For the first t+ The position of the first-generation particle; For the first t The position of the particles in the generation; To determine the exploration step size, in this embodiment... 0.6 is acceptable.

[0036] like Figure 2 As shown, the process of solving the optimization design model based on the improved particle swarm optimization algorithm in this embodiment is as follows: (1) Initialize and generate initial populations under three decision preferences in parallel. Each initial population includes multiple particles, and each particle represents a layout scheme.

[0037] In this embodiment, particles are initialized through random assignment to obtain an initial population. Each particle in this embodiment carries layout information including the location of each gas gathering station, the location of each gas gathering trunk pipeline inlet, the total number of stations, the pipeline connection relationship between gas wells and gas gathering stations, the pipeline connection relationship between gas gathering stations and gas gathering trunk pipeline inlets, and the pipeline connection relationship between gas gathering trunk pipeline inlets. In practical applications, the above-mentioned layout information can be encoded using a mixture of integer and real number encoding to obtain the particle.

[0038] Specifically, if all the gas gathering trunk pipeline inlets in this embodiment are interconnected to form a ring, then the gas gathering trunk pipeline inlets can also be called ring nodes. The layout information carried by the particles includes the location of each gas gathering station, the location of the ring center point, the total number of stations, the pipeline connection relationship between gas wells and gas gathering stations, the pipeline connection relationship between gas gathering stations and gas gathering trunk pipeline inlets, and the pipeline connection relationship between gas gathering trunk pipeline inlets. In this embodiment, the location of each ring node can be calculated based on the location of the ring center point and the location of each gas gathering station. The method for determining the location of the ring node can include: connecting the ring center point to each gas gathering station, and further taking a point on the connection line as the ring node, thus determining the location of each ring node.

[0039] For each initial group, the following steps are performed: (2) Calculate the fitness value of each particle in the initial population using the general optimization design model, and determine the global optimum and the individual optimum of each particle.

[0040] For each particle, by substituting it into the general optimization design model, the fitness value corresponding to the particle can be calculated. This value is then directly set as the individual optimal value of the particle, and the particle with the smallest fitness value in the initial population is taken as the global optimal value.

[0041] (3) For each particle in the initial population, the hormone regulation operator and mutation operator are used to update the particle in turn, and all the updated particles form the updated population.

[0042] When updating particles using the hormone regulation operator, the particle is the first particle in the update formula of the hormone regulation operator. t The particle position in the next iteration, and the updated particle is the one in the update formula of the hormone regulation operator. t The particle position after +1 iterations.

[0043] After determining the updated particles using the hormone regulation operator, the mutation operator is used to update the particles again. All the updated particles at this point form the updated population. This embodiment introduces a genetic algorithm to mutate chromosomes, ensuring that the improved particle swarm optimization algorithm maintains population diversity during iterative search. For each particle, a random number is assigned, and this random number is then compared with the mutation probability. Comparison: If random numbers Then, a global mutation is performed on the particle position information. At this time, every dimension of the particle position information (referring to the position of each gas gathering station, the position of each gas gathering trunk pipeline inlet, etc. in the layout scheme information carried by the particle) is updated; if random number If this is done, then the particle position information is subjected to local random mutation, at which point several dimensions of the particle position information are randomly selected for updating.

[0044] The update formula for the mutation operator is as follows: ; in, r For update volume; rand It is a random number; For variable asynchronous length; For the first t+ The position of the first-generation particle; For the first t The position of the particles in the generation.

[0045] (4) Calculate the fitness value of each particle in the updated population using the general optimization design model; for each particle in the updated population, determine whether the fitness value of the particle is infinite. If so, remove the particle and use the new updated particle population as the initial population for the next iteration. Return to the step of "for each particle in the initial population, update the particle in turn using the hormone regulation operator and the mutation operator, and all updated particles form the updated population"; if not, determine whether T iterations have been reached and obtain the first judgment result.

[0046] If the fitness value of a particle is infinite, it proves that the penalty coefficient of the particle is infinite, indicating that the particle does not satisfy any constraint condition. In this case, the particle should be removed.

[0047] Determining whether T iterations have been reached involves checking if the current iteration count is T. If yes, then T iterations have been reached; otherwise, T iterations have not been reached. Upon reaching T iterations, the iteration count for the next iteration is set to 1, meaning the iterations restart from step 1.

[0048] (5) If the first judgment result is yes, then determine whether the fitness value corresponding to the global optimal value of the current iteration has decreased; if yes or if the first judgment result is no, then update the global optimal value and the individual optimal value of each particle, determine whether the iteration termination condition is met, and obtain the second judgment result; if no, then use the neighborhood exploration operator to update the particles of the updated population, obtain a new updated population, and use the new updated population as the initial population of the next iteration, and return to the step of "for each particle in the initial population, use the hormone regulation operator and mutation operator to update the particle in turn, and all updated particles form the updated population".

[0049] Determining whether the fitness value corresponding to the global optimum in the current iteration has decreased specifically means determining whether the fitness value corresponding to the global optimum in the T-th iteration is less than the fitness value corresponding to the global optimum in the 1st iteration (where the 1st iteration refers to the iteration that was updated to 1 after T iterations). If so, it indicates that the fitness value corresponding to the global optimum in the current iteration has decreased. In this embodiment, the T dimension can be set to 30.

[0050] Updating the global optimum and the individual optimum for each particle involves: For each particle in the updated population, determining whether its fitness value is less than the fitness value corresponding to its individual optimum; if so, adopting that particle as its individual optimum; otherwise, leaving its individual optimum unchanged. It also involves determining whether the minimum fitness value of the updated population is less than the fitness value corresponding to the global optimum; if so, adopting the particle with the minimum fitness value as the global optimum; otherwise, leaving the global optimum unchanged.

[0051] In this embodiment, the iteration termination condition can be reaching the maximum number of iterations. The number of iterations used when determining whether the iteration termination condition is met is the total number of iterations. T iterations are only a condition for determining whether to use the neighborhood exploration operator for updating. The number of iterations used when determining whether the iteration termination condition is met is not limited by setting the number of iterations to 1 after T iterations.

[0052] Updating the particles in the updated population using the neighborhood reconnaissance operator to obtain the new updated population may include: whenever the fitness value of the global optimum has not decreased after T iterations, then updating the particle position (i.e., the position of the first particle in the updated population) of each particle in the updated population. t In the next iteration, the particle's position is used to explore its left and right neighborhoods. Each particle can obtain two neighboring particles. An elite strategy is applied to compare the fitness values ​​of the two neighboring particles with the fitness value of the global optimum in the T iterations. The particle with the smallest fitness value among the three is selected as the elite particle and assigned to the T-th iteration. t The particle positions of the next iteration, all elite particles, and all particles of the updated population from the 1st to the T-1th iterations together form a new updated population.

[0053] (6) If the second judgment result is yes, then the global optimal value of the current iteration is taken as the optimal layout scheme; if the second judgment result is no, then the updated population is taken as the initial population of the next iteration, and the step of "for each particle in the initial population, the hormone regulation operator and mutation operator are used to update the particle in turn, and all the updated particles form the updated population" is returned.

[0054] When the optimization design model simultaneously includes a structural preference evaluation model, a structural-gas volume preference evaluation model, and a structural-gas volume-pipeline specification preference evaluation model, the solution process of the improved particle swarm optimization algorithm in this embodiment to solve the general optimization design model includes: initializing three particle swarms using a mixture of integer and real number encodings through random assignment. Swarm 1 corresponds to structural preference, Swarm 2 corresponds to structural-gas volume preference, and Swarm 3 corresponds to structural-gas volume-pipeline specification preference. The structural preference evaluation model is applied to evaluate the layout scheme information carried by all particles in Swarm 1, the structural-gas volume preference evaluation model is applied to evaluate the layout scheme information carried by all particles in Swarm 2, and the structural-gas volume-pipeline specification preference evaluation model is applied to evaluate the layout scheme information carried by all particles in Swarm 3. Based on the evaluation results of each particle in the three swarms, the hormone regulation operator, neighborhood exploration operator, and mutation operator are applied to adjust the layout scheme information carried by each particle in the three swarms, updating the three particle swarms. Determine whether the iteration meets the termination condition. If yes, output the optimal layout scheme of the natural gas field gathering and transmission pipeline network under the three decision preferences. If no, continue the iterative calculation.

[0055] This embodiment relates to the field of planning and design technology of oil and gas storage and transportation and oil and gas pipeline network layout. Specifically, it provides a general optimization design method for the layout scheme of natural gas field gathering and transportation pipeline network. It is applicable to the optimization design of layout schemes for large-scale natural gas field gathering and transportation pipeline networks and can assist natural gas field gathering and transportation pipeline network planners and designers in carrying out efficient and high-quality optimization design of layout schemes under three decision preferences. Based on the coordinates and production data of gas wells in natural gas fields, a particle swarm is constructed corresponding to three decision preferences: structure, structure-gas volume, and structure-gas volume-pipeline specifications. An improved particle swarm optimization algorithm, employing hormone regulation, neighborhood exploration, and mutation operators, is used to perform parallel iterative calculations on the three particle swarms. In each iteration, evaluation models for the three decision preferences (structure, structure-gas volume, and structure-gas volume-pipeline specifications) are used to evaluate each particle in the three particle swarms. Based on the evaluation results of each particle, the layout scheme information carried by the particles is adjusted and the particle swarm is updated using hormone regulation, neighborhood exploration, and mutation operators based on bionic principles. This process is repeated until the iteration termination condition is met, resulting in three layout schemes for the natural gas field gathering and transmission pipeline network under the three decision preferences, which are then available for selection by planners and designers. The optimization design method in this embodiment is a general optimization design method that can directly provide planners with information such as the pipeline length, station location, and pipeline specifications to be selected. It avoids the problems of large engineering workload, high construction cost, and long scheme formulation cycle caused by the uncertainty of manual selection. At the same time, it can meet the three decision preferences of planners and designers, realize the comparison and selection of layout schemes under multiple decision preferences in the planning and design stage, thereby achieving efficient and high-quality design of natural gas field gathering and transmission pipeline layout schemes, which is conducive to the economic and sustainable development of natural gas extraction enterprises.

[0056] Here, this embodiment provides an example, which takes a natural gas field gathering and transmission pipeline network that needs to be optimized as the example research object, and performs layout optimization design on the natural gas field gathering and transmission pipeline network.

[0057] (1) Description of the planning problem: A large gas field has 72 gas wells and plans to construct one central export station and 6-9 gathering stations. The economical flow velocities for the gas production pipeline, gathering branch pipelines, and gathering trunk pipeline are 15 m / s, 13 m / s, and 11 m / s, respectively. To ensure the safety of gas field production, the maximum processing capacity of each gathering station is limited to 30,000 m³ / s. 3 / d, with a minimum ring network length of 25km.

[0058] (2) Parameter settings for planning method application: This embodiment is based on the Matlab r2020b platform for simulation testing, with the population size N=100, the maximum number of iterations MaxIt=500, and the hormone regulation operator in... Take 2, Take 0, Take 2, n Take 2, Take 0.95, mutation probability Take 0.5, variable asynchronous length Set the value to 0.1, the neighborhood exploration dimension T to 30, and the exploration step size to 0.1. Take 0.6.

[0059] (3) Comparative analysis of planning schemes: like Figures 3-6 As shown, the layout optimization design of the natural gas field gathering and transmission pipeline network in this example, due to the adoption of the general optimization design method and solution scheme in this embodiment, saves 9.26% of the total investment and construction cost compared to manual design, effectively avoiding redundant calculations and reducing construction costs. To demonstrate the superiority of the general optimization design method used in this embodiment, the solution results of the hormone-regulated particle swarm optimization algorithm (HRPSO) used in this embodiment are compared with those of the standard particle swarm optimization algorithm (SPSO), the linearly decreasing inertia weighted particle swarm optimization algorithm (LDWPSO), the mean particle swarm optimization algorithm (MeanPSO), and the probability-stratified simplified particle swarm optimization algorithm (PHSPSO). Figure 7 As shown, compared with SPSO, MeanPSO, PHSPSO and LDWPSO algorithms, the total investment and construction costs are reduced by 3.23%, 2.28%, 2.01% and 1.01% respectively, indicating that the HRPSO algorithm proposed in this embodiment has a better optimization effect.

[0060] This embodiment establishes a general optimization mathematical model for gas field gathering and transmission pipelines that conforms to actual planning conditions, based on multiple constraints such as the impact of construction costs and actual operation on the overall layout of the gathering and transmission pipeline network, as well as node flow balance, gas gathering station processing capacity, and ring network size. It then proposes an HRPSO algorithm to solve the general optimization mathematical model, obtaining the optimal layout scheme. This forms a new and efficient general optimization design method for natural gas field gathering and transmission pipeline network layout schemes, aiming to find the planning and design scheme with the minimum total construction cost. This optimization design method is applicable to the optimization design of gathering and transmission pipeline network layout schemes for ring-star-shaped natural gas fields such as natural gas fields and shale gas fields. It can assist natural gas pipeline network planners and designers in optimizing the layout scheme of gathering and transmission pipelines according to decision preferences and comparing design schemes under three decision preferences, achieving a general, high-quality, cost-reducing, and efficiency-enhancing optimization design for natural gas gathering and transmission pipeline network layout schemes.

[0061] Compared with existing optimization design methods, the optimization design method in this embodiment has the following advantages: (1) This embodiment can recommend the best layout scheme under three decision preferences for natural gas gathering and transmission pipeline network planners and designers, meet the actual needs of planners and designers to compare schemes under different preferences, and can be applied to the optimization design of ring-star natural gas gathering and transmission pipeline networks under any requirements.

[0062] (2) This embodiment can automatically provide natural gas gathering and transmission pipeline network planners with multiple decision options through optimized calculations, thus assisting in the construction of smart natural gas fields.

[0063] (3) This embodiment uses random assignment to initialize the particle population, eliminating the need for specific processing of the initial particle population and improving the universality of the method.

[0064] (4) Based on the principle of bionics, this embodiment introduces the regular biological laws of hormone regulation into the updating of individual positions in the particle swarm algorithm and proposes a hormone regulation operator. It also proposes a neighborhood exploration operator by imitating the characteristics of birds exploring around food. This is a beneficial practice of applying the principle of bionics to solve practical engineering problems.

[0065] Example 2: This embodiment provides a general optimization design system for natural gas field gathering and transmission pipeline network layout schemes, such as... Figure 8 As shown, the optimization design system includes: The model building module M1 is used to build a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision preferences; The model solving module M2 is used to solve the general optimization design model using optimization algorithms to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference. The layout scheme includes the number of gas gathering stations and the location of each gas gathering station, the number of gas gathering trunk pipeline inlets and the location of each gas gathering trunk pipeline inlet, the number of stations, and the pipeline connection relationship between gas wells, gas gathering stations and gas gathering trunk pipeline inlets.

[0066] Each embodiment in this specification focuses on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be found in the method section.

[0067] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A general optimization design method for the layout scheme of a natural gas field gathering and transmission pipeline network, characterized in that, The general optimization design method includes: Construct a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision-making preferences; The general optimization design model is solved using an optimization algorithm to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference; the layout scheme includes the number of gas gathering stations and the location of each gas gathering station, the number of gas gathering trunk pipeline inlets and the location of each gas gathering trunk pipeline inlet, the number of stations, and the pipeline connection relationship between gas wells, gas gathering stations and gas gathering trunk pipeline inlets. The general optimization design model includes: ； in, F This is the fitness value; N The number of gas wells; M g The number of gas gathering stations; To indicate a gas well i with gas gathering station j A binary variable representing the pipe connection relationship; gas well i with gas gathering station j The equivalent length of the connecting pipes; gas well i with gas gathering station j The cost of pipes used to connect the pipelines between them; K The number of inlets for the gas collection trunk pipeline; To indicate the gas gathering station i Inlet of the gas gathering trunk pipeline j A binary variable representing the pipe connection relationship; For gas gathering station i Inlet of the gas gathering trunk pipeline j The equivalent length of the connecting pipes; For gas gathering station i Inlet of the gas gathering trunk pipeline j The cost of pipes used to connect the pipelines between them; To indicate the inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j A binary variable representing the pipe connection relationship; Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The equivalent length of the connecting pipes; Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The cost of pipes used to connect the pipelines between them; Costs required for the construction of the station; The first penalty coefficient; This is the second penalty coefficient; This is the third penalty coefficient; A This represents the maximum number of gas wells that a single gas gathering station can connect to. This is the fourth penalty coefficient; Minimum ring network length; This is the fifth penalty coefficient; For gas gathering station i Processing capacity; For gas gathering station i The lower limit of processing capacity; This is the sixth penalty coefficient; For gas gathering station i The maximum processing capacity.

2. The general optimization design method according to claim 1, characterized in that, The decision preferences include structural preferences, structural-gas volume preferences, and structural-gas volume-pipeline specification preferences. At this time, the general optimization design model includes a structural preference evaluation model, a structural-gas volume preference evaluation model, and a structural-gas volume-pipeline specification preference evaluation model.

3. The general optimization design method according to claim 2, characterized in that, In the structural preference assessment model, the pipe cost is calculated based on the equivalent length of the pipe; ;in, M Z This represents the total number of stations; For the construction of the station i The first construction cost fitting coefficient; For the station i Processing capacity; For the construction of the station i The second construction cost fitting coefficient; and Set to 0; In the structure-gas volume preference assessment model, the pipe material cost is calculated based on the equivalent length of the pipe. ;in, For the station i Gas flow rate; In the structure-gas volume-pipe specification preference evaluation model, the pipe material cost is calculated based on the pipe wall thickness and pipe diameter; .

4. The general optimization design method according to claim 1, characterized in that, The optimization algorithm is an improved particle swarm optimization algorithm; the improved particle swarm optimization algorithm uses a hormone regulation operator and a neighborhood exploration operator to update the particles.

5. The general optimization design method according to claim 4, characterized in that, The update formula for the hormone regulation operator includes: ； ； in, For the first t +1 generation particle velocity; w The inertial weights of the particle swarm; For the first t The velocity of particles in a generation; For individual learning factors in the particle swarm; These are constant coefficients; D For threshold; n It is a constant; Hormones y Basal secretion rate; r 1 is the first random number; For the first t The optimal value of an individual in a generation; For the first t The position of the particles in the generation; The social learning factor of particle swarm; r 2 is the second random number; For the first t The global optimum of the generation; For the first t +1 generation particle position.

6. A general optimization design system for natural gas field gathering and transmission pipeline network layout, characterized in that, The general optimization design system includes: The model building module is used to construct a general optimization design model for natural gas field gathering and transmission pipeline network layout schemes under different decision preferences; The model solving module is used to solve the general optimization design model using optimization algorithms to obtain the layout scheme of the natural gas field gathering and transmission pipeline network under each decision preference; the layout scheme includes the number of gas gathering stations and the location of each gas gathering station, the number of gas gathering trunk pipeline inlets and the location of each gas gathering trunk pipeline inlet, the number of stations, and the pipeline connection relationship between gas wells, gas gathering stations and gas gathering trunk pipeline inlets. The general optimization design model includes: ； in, F This is the fitness value; N The number of gas wells; M g The number of gas gathering stations; To indicate a gas well i with gas gathering station j A binary variable representing the pipe connection relationship; gas well i with gas gathering station j The equivalent length of the connecting pipes; gas well i with gas gathering station j The cost of pipes used to connect the pipelines between them; K The number of inlets for the gas collection trunk pipeline; To indicate the gas gathering station i Inlet of the gas gathering trunk pipeline j A binary variable representing the pipe connection relationship; For gas gathering station i Inlet of the gas gathering trunk pipeline j The equivalent length of the connecting pipes; For gas gathering station i Inlet of the gas gathering trunk pipeline j The cost of pipes used to connect the pipelines between them; To indicate the inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j A binary variable representing the pipe connection relationship; Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The equivalent length of the connecting pipes; Inlet of the gas gathering trunk pipeline i Inlet of the gas gathering trunk pipeline j The cost of pipes used to connect the pipelines between them; Costs required for the construction of the station; The first penalty coefficient; This is the second penalty coefficient; This is the third penalty coefficient; A This represents the maximum number of gas wells that a single gas gathering station can connect to. This is the fourth penalty coefficient; Minimum ring network length; This is the fifth penalty coefficient; For gas gathering station i Processing capacity; For gas gathering station i The lower limit of processing capacity; This is the sixth penalty coefficient; For gas gathering station i The maximum processing capacity.

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