Global energy consumption optimization method for compressor unit of mixed transportation pipe network of offshore gas field

Abstract

The invention relates to an offshore gas field mixed transportation pipe network compressor unit global energy consumption optimization method, which comprises the steps of S1, acquiring a pipe network structure and pipeline parameters of an offshore gas field mixed transportation pipe network and a pressure operation interval of each compressor unit, and establishing an offshore gas field pipe network simulation model; s2, acquiring operation parameters of a single-day upper gas point gas compression station, wherein the operation parameters comprise the gas compression amount and the gas inlet pressure and temperature value of the gas compression station; s3, establishing a pipe network global compressor unit energy consumption optimization model according to the pressure operation interval of the compressor unit, the pipeline design pressure and the inlet parameters of the compressor at the upper gas point, and calculating a total energy consumption optimal starting scheme of the compressor according to a preset optimization solution algorithm under set constraint conditions by taking the minimum daily compressor energy consumption accumulated total amount as a target; wherein the energy consumption of the compressor is determined according to the inlet pressure, the final-stage outlet pressure, the inlet temperature and the air compression amount of the compressor; the numerical relationship among the parameters of the pipe network is obtained through simulation according to the offshore gas field pipe network simulation model.

Description

TECHNICAL FIELD

[0001] The present application relates to the offshore gas field mixed transportation pipe network operation and energy saving technical field, and particularly relates to a global compressor energy consumption optimization method for offshore oil and gas mixed transportation pipe network considering the cooperative work of different compressors. BACKGROUND

[0002] The offshore gas field mixed transportation pipe network is a complex network system composed of multiple different compressor stations, offshore platforms, oil and gas pipelines and distribution nodes. Compared with the long-distance pipeline network on land, due to the high-pressure operation condition, short transportation distance and complex pipe network topology, coupled with the phase change heat transfer in the pipeline medium flow process, the hydraulic and thermal coupling simulation modeling has higher complexity. The energy consumption of the compressor as the core power equipment of the pipe network operation accounts for 60%-70% of the total operation cost of the pipe network.

[0003] Research shows that the existing pipe network compressor optimization research mainly focuses on the improvement of single machine efficiency, and lacks systematic optimization of the cooperative operation of multiple stations in the pipe network.

[0004] In addition, the operation scheme of the compressor in the prior art is usually set based on experience or adjusted based on local pressure demand, which also leads to the following problems:

[0005] Energy waste: lack of pressure coordination between compressor stations, invalid throttling loss when high-pressure stations coexist with low-pressure stations;

[0006] Equipment wear: low compressor efficiency under non-optimal working conditions, and long-term operation aggravates equipment wear.

[0007] The compressor energy consumption optimization problem has a non-convex feasible region, and the variables have continuous, discrete and mixed characteristics, which is a mixed integer nonlinear programming problem. Traditional algorithms are relatively accurate in solving such problems but time-consuming and low in efficiency, while intelligent random algorithms have better effects in solving such optimization models.

[0008] Therefore, there is an urgent need for a multi-compressor energy consumption optimization method based on the global pressure coordination of the pipe network. SUMMARY

[0009] The present application provides a global energy consumption optimization method for a compressor unit of an offshore gas field mixed transportation pipe network, which solves the problems of the prior art.

[0010] To achieve the above purpose, the present application adopts the following technical scheme:

[0011] In a first aspect, the present application provides a global energy consumption optimization method for a compressor unit of an offshore gas field mixed transportation pipe network, comprising:

[0012] S1, obtaining the pipe network structure, pipeline parameters and pressure operation interval of each compressor unit of the offshore gas field mixed transportation pipe network, and establishing a simulation model of the offshore gas field pipe network;

[0013] S2, acquiring operation parameters of the upper gas point compressor station, including the compression amount and the inlet pressure and temperature values of the compressor station;

[0014] S3, establishing a global compressor group energy consumption optimization model of the pipeline network according to the pressure operation interval of the compressor group and the pipeline parameters and the operation parameters of the upper gas point compressor station, taking the minimum daily cumulative total amount of compressor energy consumption as the target, and calculating the optimal start-up scheme of the total energy consumption of the compressor under the set constraint conditions and according to a preset optimization solving algorithm;

[0015] The compressor energy consumption is determined according to the inlet pressure, inlet temperature, compression amount and outlet pressure of the compressor; the inlet pressure, inlet temperature and inlet flow of the compressor at the upper gas point are specified by the user, the inlet pressure, inlet temperature and inlet flow of the compressor at other positions are simulated according to the simulation model of the offshore gas field pipeline network, the outlet pressure of all compressors is a decision variable, and is obtained according to the optimization solving algorithm.

[0016] In an implementation manner, in the S1, the pressure operation interval of the compressor group includes the minimum inlet pressure and pressure ratio range of each compressor, the compression amount range of the compressor, and the design pressure range of the connected pipeline; the compressors in the pipeline network include centrifugal compressors.

[0017] In an implementation manner, in the S3, the mathematical formula of the objective function taking the minimum daily cumulative total amount of compressor energy consumption as the target is:

[0018] minW=N s t

[0019]

[0020] In the formula, W represents the daily compressor energy consumption in the whole period, the unit is kWh, t represents the daily operation time of the compressor, the unit is h, N s represents the shaft power of the compressor, the unit is kW, n represents the rotation speed of the compressor, the unit is rad / min, N SX is an intermediate variable, X represents the first stage or the last stage, Z represents the first stage and the last stage, k represents the adiabatic index of the compression medium of the compressor, P 1X represents the suction pressure of the Xth stage of the compressor, the unit is MPa, P 2X represents the discharge pressure of the Xth stage of the compressor, the unit is MPa, λ vX represents the volume coefficient of the Xth stage, ξ 1X represents the suction compression coefficient of the Xth stage, ξ 2X represents the discharge compression coefficient of the Xth stage.

[0021] In an implementation manner, in step S3, the constraint conditions are set, including: the operation interval constraint of each compressor unit, and the design pressure constraint and the export pressure constraint of each pipeline.

[0022] In an implementation manner, in step S3, the preset optimization solving algorithm includes a particle swarm optimization algorithm.

[0023] In a second aspect, a computer readable storage medium is provided, and the computer readable storage medium stores at least one computer program.

[0024] The offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization method provided by the present application has the following beneficial effects:

[0025] The offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization method provided by the present application has the following beneficial effects: BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 FIG. 1 is a flowchart of an offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization method according to an embodiment of the present application;

[0027] Figure 2 FIG. 2 is a structural diagram of an offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization system according to an embodiment of the present application;

[0028] Figure 3 FIG. 3 is a structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described below in conjunction with the drawings of the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0030] In view of the defects and problems of the prior art, the present application provides an offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization method, which comprises the following steps:

[0031] S1. Obtain the pipeline structure, pipeline parameters, and pressure operating range of each compressor unit of the offshore gas field mixed transmission pipeline network, and establish a simulation model of the offshore gas field pipeline network.

[0032] S2, obtain the daily operating parameters of the compressor station at the gas supply point, including the gas volume entering the station and the gas pressure value of the compressor station;

[0033] S3. Based on the pressure operating range of the compressor unit, pipeline parameters, and operating parameters of the compressor station at the gas supply point, establish a global compressor unit energy consumption optimization model for the pipeline network. With the goal of minimizing the cumulative daily compressor energy consumption, under set constraints, calculate the optimal start-up scheme for the compressor's total energy consumption according to the preset optimization solution algorithm.

[0034] The compressor energy consumption is determined based on the compressor's inlet pressure, inlet temperature, compressed gas, and final stage outlet pressure. The inlet pressure, inlet temperature, and inlet flow rate of the compressor at the gas supply point are specified by the user, while the inlet pressure, inlet temperature, and inlet flow rate of the compressors at other locations are obtained through simulation based on the offshore gas field pipeline network simulation model. The final stage outlet pressure of all compressors is the decision variable, which is obtained based on the optimization solution algorithm.

[0035] The above method is described below in one or more more detailed embodiments with reference to more accompanying drawings.

[0036] Detailed Implementation Examples

[0037] like Figure 1 As shown in the figure, a method for optimizing the global energy consumption of compressor units in an offshore gas field mixed-transmission pipeline network according to an embodiment of the present invention includes the following steps:

[0038] S1. Obtain the pressure operating range of each compressor unit in the pipeline network, as well as the pipeline network structure and parameters of each pipeline, to establish a simulation model of the offshore gas field pipeline network.

[0039] The pressure operating range of the compressor unit includes: the minimum inlet pressure and pressure ratio range of each compressor, the compressor's compressed air volume range, and the design pressure range of the connected pipelines.

[0040] Among them, the compressor is a centrifugal compressor, etc.

[0041] S2. Obtain the daily compressed air volume and the inlet pressure value of the compressor station at the gas supply point.

[0042] S3, a global compressor group energy consumption optimization model of the pipeline network is established according to the pressure operation range of the compressor group and the pipeline design pressure range and the operation parameters of the upper gas point compressor station, and the total energy consumption of the compressor is calculated according to the preset optimization solving algorithm under multiple constraint conditions to obtain an optimal start-up scheme of the compressor. The energy consumption of the compressor is determined according to the inlet pressure, inlet temperature, inlet flow rate and outlet pressure of the last stage of the compressor; the inlet pressure, inlet temperature and inlet flow rate of the compressor at the upper gas point are specified by the user, and the inlet pressure, inlet temperature and inlet flow rate of the compressor at other positions are simulated according to the simulation model of the offshore gas field pipeline network; the outlet pressure of all compressors is a decision variable and is obtained according to the optimization solving algorithm.

[0043] In the formula, the actual operation range of the compressor outlet pressure for optimization calculation is simulated according to the pressure operation range of the compressor and the pipeline design pressure, that is, the upper and lower bounds of the decision variable.

[0044] minW = N s t

[0045]

[0046] In the formula, W represents the daily energy consumption of the compressor in the whole cycle, t represents the daily operation time of the compressor, N represents the shaft power of the compressor, n represents the rotation speed of the compressor, P represents the suction pressure of the compressor, P represents the discharge pressure of the compressor, λ represents the volume coefficient of the compressor, and ξ represents the compression coefficient of the compressor. s SX 1X 2X vX 1X 2X

[0047] The specific objective function is:

[0048] minW = N s t

[0049] In the formula, minW represents the minimization of W.

[0050] In the formula, the specific process of determining the daily energy consumption of the compressor according to the inlet pressure, inlet temperature and outlet pressure of the first stage of the compressor is as follows:

[0051] ​​​​​​​According to the compressor's first stage inlet pressure, outlet pressure and last stage outlet pressure, the compressor is simulated to determine the compressor's daily energy consumption, specifically:

[0052] According to the parameters of the compressor station, a compressor simulation model is established by combining theoretical formulas, the daily first stage inlet pressure, inlet temperature and last stage outlet pressure are input into the centrifugal compressor simulation model, the daily compressor shaft power is output, and the daily compressor energy consumption is calculated in combination with the compressor running time.

[0053] The parameters of the centrifugal compressor include the compressor's stroke, cylinder stage number, each stage cylinder diameter, design speed and each stage cylinder inlet temperature, etc.

[0054] Among them, the first stage inlet pressure and inlet temperature are determined by the gas supply trunk line gas supply situation, and the last stage outlet pressure is obtained by pipeline simulation.

[0055] Optionally, in the above technical solution, according to the gas intake of each compressor station, the actual operating pressure range of the compressor is determined, including:

[0056] According to the gas intake of each compressor station, the gas supply amount of all pipelines in the pipeline network can be determined, and the actual operating range of the compressor outlet pressure for optimization calculation is obtained by pipeline simulation according to the pressure operating range of the compressor and the design pressure of the pipeline:

[0057] 1) The first limit value: the maximum outlet pressure of the compressor station can be obtained according to the design pressure of the downstream pipeline and the pipeline gas supply amount;

[0058] 2) The second limit value: the minimum outlet pressure of the compressor station can be obtained according to the minimum inlet pressure of the downstream compressor station and the pipeline gas supply amount.

[0059] Optionally, in the above technical solution, the multiple constraint conditions include: pipeline water thermal force constraint, compressor feasible region constraint and compressor characteristic constraint, and pipeline design pressure upper limit constraint, specifically:

[0060] 1) Compressor feasible region constraint and compressor characteristic constraint

[0061] (Q 1,i ,P 1,i ,T 1,i ,P 1,i )∈D ci

[0062]

[0063] In the formula, D ci represents the operating feasible region of the compressor unit, Q i,l is the flow rate of the lth compressor in the compressor unit, m 3 / s; The lower and upper limits of the flow rate through the 1th compressor in the compressor set, m 3 / s; P i1,l , The inlet and outlet pressures of the 1th compressor in the compressor set, MPa; The lower and upper limits of the inlet and outlet pressures of the 1th compressor in the compressor set, MPa; T 1i,l The inlet temperature of the 1th compressor in the compressor set, K; The lower and upper limits of the inlet temperature of the 1th compressor in the compressor set, K; n i,l The rotating speed of the 1th compressor in the compressor set, RPM; The lower and upper limits of the rotating speed of the 1th compressor in the compressor set, RPM; N I,l The power of the 1th compressor in the compressor set, kW; The lower and upper limits of the power of the 1th compressor in the compressor set, kW; T 2i,l The outlet temperature of the 1th compressor in the compressor set, K; The lower and upper limits of the outlet temperature of the 1th compressor in the compressor set, K; h i,l The energy head of the 1th compressor in the compressor set, J / kg; k i,l The on-off state of the 1th compressor in the compressor set, k i,l =0 indicates that the compressor is in the off state, k i,l =1 indicates that the compressor is in the on state. G i,l The mass flow rate of the gas through the 1th compressor in the compressor set, kg / s; η i,l The operating efficiency of the compressor in the compressor set.

[0064] 2) Pipe water thermal force constraints

[0065] Mass conservation equation:

[0066]

[0067] Energy conservation equation:

[0068]

[0069] Momentum conservation equation:

[0070]

[0071] In the formula, p is the average pressure of the calculated pipe section, Pa; L is the length of the calculated pipe section, m; ρ O , ρ g are the liquid phase and gas phase densities, kg / m3; H gis the cross-sectional liquid holdup; g is the gravitational acceleration, m / s 2 ; β is the pipe inclination, °; M is the mass flow rate of the gas-liquid mixture, kg / s; w is the average flow velocity of the gas-liquid mixture, m / s; w sg is the gas phase reduced flow velocity, m / s.

[0072] 3) The upper limit of the pipe design pressure constraint is: P i ≤ P i,max , P i represents the starting pressure of the i-th pipe, MPa, P i,max represents: the upper limit of the pressure set for the i-th pipe (i.e. the maximum value of the starting pressure).

[0073] The decision variables of the global compressor unit energy consumption optimization mathematical model are:

[0074] P' = (p1, p2, …, p n′-1 , p n′ )

[0075] In the above formula, P' represents the compressor unit outlet pressure solution vector.

[0076] Optionally, in the above technical scheme, the preset optimization solving algorithm is an improved particle swarm optimization algorithm.

[0077] Taking the data of a certain offshore gas field mixed transportation pipe network on a certain day as an example, there are 5 compressor units, the compressor unit 1 has a discharge capacity of 9.67 million cubic meters, the inlet pressure is 10.5 MPa, the compressor unit 2 has a discharge capacity of 5.07 million cubic meters, the inlet pressure is 4.15 MPa, the compressor unit 3 has a discharge capacity of 1.14 million cubic meters, the inlet pressure is 4 MPa, the compressor unit 4 has a discharge capacity of 14.24 million cubic meters, the inlet pressure range is 3.4-7.46 MPa, the outlet pressure range is 6.74-9.9 MPa, the compressor unit 5 has a discharge capacity of 14.24 million cubic meters, the inlet pressure range is 2.25-7.18 MPa, the improved particle swarm optimization algorithm is adopted, and the global energy consumption optimization mathematical model of the offshore gas field mixed transportation pipe network compressor unit is solved, and the discharge pressure and total energy consumption of each compressor in the optimized pipe network are obtained.

[0078] Particle swarm algorithm is a global optimization algorithm of swarm intelligence behavior simulation. In the particle swarm algorithm, each particle moves in the search space, constantly updates its speed and position, in order to find the optimal solution. In order to avoid falling into local optimum, the speed update formula of the particle swarm algorithm is improved, the inertia weight is increased, the global search ability of the particle is increased, and the inertia weight decreasing strategy is adopted to increase the global search ability of the particle, speed up the convergence speed, and reduce the value of inertia weight in the later period, realize the function of increasing the local search precision. The setting parameters of the improved particle swarm optimization algorithm are as follows: the population size is set to 100 (generally 20-50 times of the dimension number), which can avoid premature or overload; the iteration number is set to 200, which can basically cover 99% of the convergence probability, the particle dimension is 2, the maximum movement speed of each dimension is set to [-0.1, 0.1], the inertia factor adopts linear decreasing strategy, and is set to [0.4, 0.9], which can avoid falling into local optimum as much as possible; compared with setting to a constant value, the individual learning factor is set to [0.5, 2.5], and the group learning factor is set to [0.5, 2.5], which can reduce the probability of falling into local optimum.

[0079] The flow of the improved particle swarm optimization algorithm is: initializing the particle swarm parameters, initializing the particle position, speed, optimal fitness value, iteratively updating the particle speed, position, optimal fitness value, particle swarm parameters, and outputting the optimal solution after iteration.

[0080] The improved particle swarm optimization algorithm is used to solve the start-up scheme of all compressors in the offshore gas field mixed transportation pipe network. Under this scheme, the actual daily total compressor energy consumption is 18888.43 KW, and the optimized total energy consumption is 16360.66 KW. By comparing with the actual production daily data, it can be seen that if the optimized start-up scheme is used for gas transmission, the compressor energy consumption is less than the actual production compressor energy consumption.

[0081] The offshore gas field mixed transportation pipe network compressor group global energy consumption optimization method disclosed in the embodiment is calculated with the minimum daily compressor energy consumption as the target, and the compressor inlet and outlet pressure range under the compressor group inlet flow is determined by the constraint of compressor pressure ratio and operating pressure interval and pipeline design pressure. When the inlet and outlet pressures of the compressor group within the range are used for gas transmission, the energy consumption of the compressor is minimized, the gas transmission efficiency is improved, and the energy consumption is reduced. It is beneficial to the safe and stable operation of the pipe network, and can also reduce unnecessary throttling loss, so that the offshore gas field mixed transportation pipe network gas transmission is more economical.

[0082] In the above embodiments, although the steps are numbered S1, S2, etc., it is only a specific embodiment given by the present application, and those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, which is within the protection scope of the present application. It can be understood that in some embodiments, part or all of the above embodiments can be included.

[0083] As shown in Figure 2 The global energy consumption optimization system 200 of the offshore gas field mixed transportation pipe network compressor unit according to the embodiment of the present application comprises a related characteristic parameter acquisition module 201, an initial data acquisition module 202 and a pipe network optimization model determination module 203.

[0084] The related characteristic parameter acquisition module 201 is used to acquire the pressure operation interval of each compressor unit in the offshore gas field mixed transportation pipe network and the pipeline design parameters.

[0085] The initial data acquisition module 202 is used to acquire the inlet gas quantity, inlet gas temperature and inlet gas pressure of each gas point compressor unit in the offshore gas field mixed transportation pipe network.

[0086] The pipe network optimization model determination module 203 is used to input the pressure operation interval of the compressor unit, the pipeline design parameters and the gas point compressor unit operation parameters into the pipe network optimization model, and under multiple constraint conditions, the feasibility start-up scheme of the compressor, the energy consumption of each compressor and the global total energy consumption can be calculated according to a preset solving algorithm.

[0087] Optionally, in the above technical solution, the pipe network optimization model determination module 203 is further specifically used to:

[0088] According to the inlet gas quantity of each compressor unit, the inlet and outlet pressure range of the compressor is determined in combination with the pressure operation interval of the compressor unit and the pipeline design parameters.

[0089] Optionally, in the above technical solution, the multiple constraint conditions include the pipe network inlet gas total quantity constraint, the operation interval constraint of each compressor unit and the design pressure constraint of each pipeline.

[0090] Optionally, in the above technical solution, the preset optimization algorithm is an improved particle swarm optimization algorithm.

[0091] It should be noted that the beneficial effects of the global energy consumption optimization system 200 for offshore gas field mixed-transmission pipeline compressor units provided in the above embodiments are the same as the beneficial effects of the global energy consumption optimization method for offshore gas field mixed-transmission pipeline compressor units with multi-constraint coupling, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0092] like Figure 3 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320 coupled to a memory 310. The memory 310 stores at least one computer program 330, which is loaded and executed by the processor 320 to enable the electronic device 300 to implement any of the above-mentioned methods for global energy consumption optimization of offshore gas field mixed-transmission pipeline compressor units. Specifically:

[0093] The electronic device 300 can vary considerably due to differences in configuration or performance. It may include one or more processors 320 (Central Processing Units, CPUs) and one or more memories 310. The memories 310 store at least one computer program 330, which is loaded and executed by the processors 320 to enable the electronic device 300 to implement any of the global energy consumption optimization methods for offshore gas field mixed-transmission pipeline compressor units provided in the above embodiments. Of course, the electronic device 300 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. It may also include other components for implementing device functions, which will not be elaborated here. Specifically, the electronic device may be a computer, etc.

[0094] An embodiment of the present invention provides a computer-readable storage medium storing at least one computer program, which is loaded and executed by a processor to enable a computer to implement any of the above-mentioned methods for global energy consumption optimization of compressor units in offshore gas field mixed-transmission pipelines.

[0095] Optionally, the computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0096] In the example embodiments, a computer program product or a computer program is also provided, which includes computer instructions stored in a computer readable storage medium. A processor of an electronic device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to make the electronic device perform any one of the above offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization methods.

[0097] In several embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0098] The above are only the preferred embodiments of the present application, and are not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing global energy consumption of a compressor train of a mixed gas pipeline network of an offshore gas field, characterized in that, The method comprises the following steps: S1, obtaining the pipeline network structure, pipeline parameters and operation range of each compressor unit of the offshore gas field mixed transportation pipeline network, and establishing an offshore gas field pipeline network simulation model; S2, obtaining the operation parameters of the single-day gas injection point compressor station, including the compression amount and the inlet pressure value of the gas injection point compressor station; S3, establishing a global compressor unit energy consumption optimization model of the pipeline network according to the pressure operation range of the compressor unit and the pipeline design pressure and the inlet parameter of the gas injection point compressor, taking the minimum cumulative total amount of daily compressor energy consumption as the target, and calculating the optimal start-up scheme of the total energy consumption of the compressor under the set constraint condition according to the preset optimization solving algorithm; Wherein, the compressor energy consumption is determined according to the inlet pressure, inlet temperature, compression and last stage outlet pressure of the compressor; The inlet pressure, inlet temperature and inlet flow rate of the compressor at the gas injection point are specified by the user, and the inlet pressure, inlet temperature and inlet flow rate of the compressor at other positions are simulated according to the offshore gas field pipeline network simulation model, and the last stage outlet pressure of all compressors is obtained according to the optimization solving algorithm.

2. The method of claim 1, wherein, In the S1, the offshore gas field pipeline network simulation model involves mathematical modeling and solving of multiphase pipe flow water-heat coupling and compressor operation characteristic simulation, and the operation range of the compressor unit includes the minimum inlet pressure and pressure ratio range of each compressor, the compression amount range of the compressor, and the design pressure range of the connected pipeline; the compressors in the pipeline network include centrifugal compressors.

3. The method of claim 2, wherein, In the S3, the mathematical formula of the objective function taking the minimum cumulative total amount of daily compressor energy consumption as the target is: minW = N s t wherein W represents the compressor energy consumption per day in the whole cycle, in kWh, t represents the compressor operation time per day, in h, N s represents the compressor shaft power, in kW, n represents the compressor rotation speed, in rad / min, N SX is an intermediate variable, X represents the first stage or the last stage, Z represents the first stage and the last stage, k represents the adiabatic index of the compressed medium of the compressor, P 1X represents the suction pressure of the Xth stage of the compressor, in MPa, P 2X represents the discharge pressure of the Xth stage of the compressor, in MPa, λ vX represents the volume coefficient of the Xth stage, ξ 1X represents the suction compression coefficient of the Xth stage, ξ 2X represents the discharge compression coefficient of the Xth stage.

4. The method for optimizing global energy consumption of a compressor train of a multiphase offshore gas field pipeline network according to claim 3, characterized in that, In step S3, the set constraint conditions include: pipeline inlet total amount constraint, operation range constraint of each compressor unit, design pressure constraint of each pipeline, pipeline network simulation model constraint, and export pressure constraint.

5. The method of claim 3, wherein, In step S3, the preset optimization solving algorithm includes a particle swarm optimization algorithm.

6. A computer readable storage medium characterized by, The computer readable storage medium stores at least one computer program, which is loaded and executed by the processor to enable the computer to implement the offshore gas field mixed transportation pipeline network compressor unit global energy consumption optimization method according to any one of claims 1 to 5.

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