Shale gas gathering and transportation system two-stage operation optimization method considering well opening and equipment allocation

Abstract

The invention provides a shale gas gathering and transportation system two-stage operation optimization method considering well opening and equipment allocation. The method comprises the following steps: acquiring basic data of a shale gas gathering and transportation system and skid-mounted equipment; a shale gas gathering and transportation system well opening scheme and non-supercharging equipment allocation optimization model and a shale gas gathering and transportation system compressor skid-mounted allocation optimization model are established respectively, a shale gas gathering and transportation system two-stage operation optimization model considering well opening and equipment allocation is formed, and the optimization model is solved by adopting a zebra optimization algorithm. And a shale gas gathering and transportation system operation scheme is formed. The optimization method provided by the invention can effectively carry out joint optimization research on allocation and well opening schemes of the skid-mounted equipment of the shale gas gathering and transportation system, has remarkable guiding significance on operation and management of the shale gas gathering and transportation system, and greatly improves the overall efficiency and flexibility of system operation.

Description

Technical Field

[0001] The present invention relates to the field of shale gas gathering and transportation system operation optimization, and in particular to a two-stage operation optimization method for a shale gas gathering and transportation system taking well drilling and equipment deployment into consideration. Background Art

[0002] my country has rich shale gas resources and huge mining potential. During the development of shale gas, as shale gas production capacity rapidly decreases, equipment load rate and various operating conditions are constantly changing, thus affecting the selection of equipment type, equipment installation location and equipment installation timing. The rapid growth of natural gas consumption has led to an increasing intensity of shale gas development. While vigorously developing shale gas, we must face the problems of redundant equipment processing capacity, low equipment utilization, and poor matching brought about by the characteristics of shale gas production capacity.

[0003] The use of skid-mounted equipment can effectively cope with the impact of changes in shale gas production parameters. Skid-mounted equipment has the characteristics of small footprint, easy installation and disassembly, high degree of integrated automation, and can be produced as a single module and then transported to the site for rapid assembly. Once the pressure and flow at the current location change significantly, the skid-mounted equipment can be easily disassembled and moved to the next location where it needs to be installed.

[0004] Compared with traditional fixed equipment, skid-mounted equipment is more flexible, so it is more suitable for use in shale gas production, but this also increases the complexity of the decision-making process of purchasing, deploying or selling skid-mounted equipment. Decision makers must weigh the cost and effectiveness of continuous production at a certain location and decide how to deploy skid-mounted equipment according to changes in supply conditions and market demand over time.

[0005] Optimizing the purchase, movement and sale of skid-mounted equipment during the long-term operation of shale gas gathering and transportation systems is conducive to the efficient use of skid-mounted equipment in shale gas gathering and transportation systems and ensuring the normal operation of shale gas gathering and transportation systems. A two-stage operation optimization method for shale gas gathering and transportation systems that takes into account well opening and equipment deployment has played a positive role in promoting the orderly, rapid, efficient and controllable development of shale gas in my country, solving key problems in shale gas surface gathering and transportation systems, accelerating the development of shale gas in my country, and ensuring energy security for national economic and social development. Summary of the invention

[0006] In view of the problems of redundant equipment processing capacity, low equipment utilization, poor matching degree, etc. caused by the characteristics of shale gas production capacity, the present invention proposes a two-stage operation optimization method for shale gas gathering and transportation system considering well drilling and equipment deployment.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] A two-stage operation optimization method for a shale gas gathering and transportation system considering well opening and equipment deployment, characterized in that it comprises the following steps:

[0009] S1: Obtain basic data of shale gas gathering and transportation system and skid-mounted equipment;

[0010] S2: In the first optimization stage, the well opening plan and non-boosting equipment deployment optimization model of the shale gas gathering and transportation system are established;

[0011] S3: In the second optimization stage, a skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system is established;

[0012] S4: The zebra optimization algorithm is used to solve the two-stage optimization model established in S2 and S3 to form an operation plan for the shale gas gathering and transportation system that takes into account the well opening plan and equipment deployment.

[0013] Furthermore, the skid-mounted equipment described in step S1 includes a separation and metering skid, a dehydration skid and a compression unit skid;

[0014] The basic data of the shale gas gathering and transportation system include the number of dehydration stations and gas gathering station platforms and the corresponding connection relationship pipeline diameter, pipeline connection relationship, pipeline length, design pressure, production data, production time, shale gas sales price and second-hand equipment sales price;

[0015] The basic data of the skid-mounted equipment includes equipment model, equipment processing capacity, purchase cost, operation cost, installation and disassembly cost and moving cost;

[0016] Furthermore, the shale gas gathering and transportation system well opening plan and non-boosting equipment deployment optimization model described in step S2 takes the maximum net present value of the shale gas field as the objective function;

[0017] The net present value of the shale gas field is determined by the total cash flow of the shale gas field in period t, and the calculation formula is:

[0018]

[0019] Where NPV is the net present value; C t is the cash flow in period t; r is the discount rate;

[0020] The cash flow includes shale gas sales revenue, second-hand skid-mounted equipment sales revenue, non-boosting equipment purchase cost, non-boosting equipment installation cost, non-boosting equipment disassembly cost, non-boosting equipment operation cost and non-boosting equipment deployment cost, and the calculation formula is:

[0021] C t =S G，t +S E,t -F B，t -F I,t -FD,t -F O,t -F M,t

[0022] In the formula, S G，t is the revenue from shale gas sales; S E,t F is the sales income of second-hand non-boosting equipment; B，t Purchase cost for non-boosting equipment; F I,t Installation cost for non-pressurized equipment; F D,t F is the loading and unloading cost of non-pressurized equipment; O,t F is the operating cost of non-boosting equipment; M,t Allocate costs for non-boosting equipment;

[0023] The calculation formula for shale gas sales revenue is:

[0024] S G，t =P G，t Q t τ t

[0025] Where P G，t is the shale gas price in period t; Q t is the total output of shale gas production blocks in period t; τ t is the production time of shale gas field in period t;

[0026] The sales revenue calculation formula for second-hand skid-mounted equipment is:

[0027] S E,t =P E,k (1-α·τ t′,t )·Bs i,k,e,t,t′

[0028] Where P E,k is the purchase price of each device; a is the depreciation coefficient; τ t't Bs is the actual use time of the skid-mounted equipment; i,k,e,t,t′ is a binary 0-1 variable for equipment sales. If type k equipment of type e is purchased at node i during period t′ and sold during period t, then Bs i,k,e,t,t′ =1, otherwise Bs i,k,e,t,t′ =0;

[0029] The formula for calculating the purchase cost of non-boosting equipment is:

[0030]

[0031] Where, X i,k,e,t is the number of type e non-boosting equipment at node i at time t;

[0032] The calculation formula for the installation cost of non-boosting equipment is:

[0033]

[0034] In the formula, e I,k,e,t The unit installation cost for type K equipment;

[0035] The calculation formula for the disassembly cost of non-pressurized equipment is:

[0036]

[0037] In the formula, e D,i,k,e,t Unit disassembly cost for type K equipment;

[0038] The calculation formula for the skid-mounted equipment operating cost is:

[0039]

[0040] In the formula, β k is the unit processing cost of shale gas; Q E，i,k,e,t is the processing capacity of the equipment in period t;

[0041] The calculation formula for the skid-mounted equipment deployment cost is:

[0042]

[0043] In the formula, B M,h,h′,k,t Allocate base costs for K-type equipment.

[0044] Furthermore, the constraints of the shale gas gathering and transportation system well opening plan and non-boosting equipment deployment optimization model described in step S2 include equipment processing capacity constraints, equipment deployment constraints, well opening timing constraints and well opening quantity constraints;

[0045] The equipment processing capacity constraint means that the processing capacity of each equipment is less than or equal to the rated processing capacity, and the sum of the actual processing capacity of all production platform equipment is equal to the shale gas field production. The constraint relationship is as follows:

[0046] Q E,h,k,t ≤aq max ·qe h,k

[0047] Q E,h,k,t ≥aq min ·qe h,k

[0048]

[0049] In the formula, Q E,h,k,t is the shale gas processing capacity of each k-type equipment in period t; qe h,k is the rated processing capacity of the H platform K type equipment; aq maxis the maximum flow coefficient within the rated capacity fluctuation range; aq min is the minimum flow coefficient within the rated capacity fluctuation range; Q t is the total output of shale gas production blocks in period t;

[0050] The equipment deployment constraints include equipment purchase, sale and movement constraints, and current equipment quantity constraints;

[0051] The purchase constraint requires that when the platform first starts to purchase equipment, the number of equipment is the purchase quantity, and the constraint relationship is:

[0052] B h,k,t,t′ =Bb h,k,t

[0053] In the formula, Bb h,k,t is a binary variable for purchase. When type k equipment is purchased on platform h during period t and sold during period t, Bb h,k,t =1, otherwise Bb h,k,t =0;

[0054] The sales constraint requires that the equipment cannot be sold before the equipment is purchased. The constraint relationship is:

[0055] Bs h,k,t,t′ =0

[0056] In the formula, Bs h,k,t,t′ is a binary variable that determines whether to sell type k equipment on platform h. If type k equipment is purchased on platform h during period t and sold during period t, then Bs h,k,t,t′ =1, otherwise Bs h,k,t,t′ =0;

[0057] Each device can only be purchased and sold once during its entire life cycle. The constraint is:

[0058]

[0059] In the first period, there is no equipment to sell, and the constraint relationship is:

[0060] Bs h,k,t,t′ =0

[0061] The equipment deployment constraint relation is:

[0062] B h,k,t-1,t′ ≥Bs h,k,t,t′

[0063] The current device quantity constraint of the platform is:

[0064] B h,k,t,t′ =B h,k,t-1,t′ +Bm h′,h,k,t,t′ -Bmh,h′,k,t,t′ -Bs h,k,t,t′

[0065] In the formula, Bm h′,h,k,t,t′ is a disassembly binary variable. At time t, if the k-type skid-mounted equipment moves from node h to node h', then Bm h′,h,k,t,t′ =1, otherwise Bm h′,h,k,t,t′ =0.

[0066] The well opening timing constraint requires that each shale gas well can only be opened once during the production period. The constraint relationship is:

[0067]

[0068] In the formula, Bopen h,w,t′ is a binary variable for well opening; at time t, gas well w in platform h is opened, then Bopen h,w,t′ =1, otherwise Bopen h,w,t′ =0.

[0069] Furthermore, the skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system described in step S3 takes the minimization of the total cost of the compressors as the objective function;

[0070] The total cost of the compressor includes the compressor purchase cost, compressor installation cost, compressor disassembly cost, compressor operation cost and compressor deployment cost, and the calculation formula is as follows:

[0071] minF=F b +F i +F d +F o +F e

[0072] Where, F is the total cost of the compressor; F b The cost of purchasing the compressor; F i F is the compressor installation cost; d F is the compressor disassembly cost; o is the compressor operating cost; F e Allocate costs for compressors;

[0073] The compressor purchase cost calculation formula is:

[0074]

[0075] In the formula, B D,i,e,t For compressor purchase binary variables, at time t, if a compressor of type e is purchased at node i, then B D,i,e,t =1, otherwise B D,i,e,t =0;X c,i,e,tis the number of type e compressors at node i at time t; P c,e Purchase cost for e-type compressor;

[0076] The compressor installation cost calculation formula is:

[0077]

[0078] In the formula, Bb i,e,t is a binary variable for compressor installation. At time t, if the e-type compressor moves from node i to node i′, then Bb i,e,t =1, otherwise Bb i,e,t =0;β c,e Cost of installing an e-type compressor;

[0079] The compressor disassembly cost calculation formula is:

[0080]

[0081] In the formula, Bs i,e,t is the compressor removal binary variable. At time t, if the e-type compressor moves from node i to node i′, then Bs i,e,t =1, otherwise Bs i,e,t =0;γ c,e Cost of disassembling the e-type compressor;

[0082] The compressor operating cost calculation formula is:

[0083]

[0084] Where N i,t is the operating power of the compressor at node i at time t; e c,r is the unit operating cost of the e-type compressor;

[0085] The compressor deployment cost calculation formula is:

[0086]

[0087] In the formula, eu i,t is the number of compressors deployed at node i at time t; is the unit cost of deploying the compressor at node i at time t.

[0088] Furthermore, the constraints of the skid-mounted deployment optimization model for the compressor of the shale gas gathering and transportation system described in step S3 include pipeline pressure constraints, compressor constraints, throttling constraints, node pressure balance constraints, node flow constraints and compressor deployment constraints;

[0089] The pipeline pressure constraint specifies the pipeline operating pressure range, and the constraint relationship is:

[0090] p i|(i,j),t ≤p max

[0091] p j|(i,j),t ≥p min

[0092] In the formula, p i|(i,j),t is the pressure of node i connected to pipeline (i, j) at time t; p j|(i,j),t is the pressure of node j connected to pipeline (i, j) at time t; p max is the maximum pressure value that the pipeline (i, j) can withstand at time t; p min is the minimum pressure value at the end point of pipeline (i, j) at time t;

[0093] The compressor constraint relationship is:

[0094]

[0095] ε min ≤ε i,t ≤ε max

[0096]

[0097] In the formula, p i,t is the pressure at node i at time t; is the compressor inlet pressure at node i at time t; is the compressor outlet pressure at node i at time t; ε i,t is the pressure ratio of the compressor at node i at time t; ε min is the minimum pressure ratio of the compressor; ε max is the maximum pressure ratio of the compressor; N i,t is the operating power of the compressor at node i at time t; q i,t is the volume flow rate of shale gas at node i at time t; k is the specific heat ratio of gas; is the gas compression factor at the compressor inlet condition at node i at time t; is the gas compression factor at the compressor outlet at node i at time t; η is the compressor efficiency; N c,e is the rated operating power of the e-type compressor;

[0098] The throttling constraint relation is:

[0099]

[0100] In the formula, δ w,t is the throttling ratio at the gas well w at time t; p w,t is the pressure at point w in the gas well at time t; pw|(w,h),t is the starting pressure of the pipeline (w, h) at time t; δ h,t is the throttling ratio at platform h at time t; p h,t is the pressure at platform h at time t; p h|(h,j),t is the starting pressure of the pipeline (h, j) at time t;

[0101] The node pressure balance constraint specifies the pressure balance relationship between various nodes, and the constraint relationship is:

[0102]

[0103] In the formula, is the minimum pressure of all gas wells in the platform at time t; p n|(i,n),t is the terminal pressure of pipeline (i, n) at time t; p n,t is the pressure at point n in the region at time t; p s|(i,s),t is the starting pressure of the pipeline (i, s) at time t; p s,t is the pressure of the gas gathering station at time t;

[0104] The node flow constraint specifies the flow relationship between various nodes, and the constraint relationship is:

[0105] q h,w,t =q w|(w,h),t

[0106] q i|(i,j),t =Q i,j,t =q j|(i,j),t

[0107] Q t ≤Qm t

[0108]

[0109] In the formula, q h,w,t is the flow rate of gas well w in platform h at time t; q w|(w,h),t is the flow rate of the pipeline connected to well w at time t; Qm t is the maximum shale gas demand in the market at time t; Qpad h,t is the shale gas production of platform h at time t; q h,w,t is the flow rate of gas well w in platform h at time t;

[0110] The compressor constraint stipulates the quantitative relationship satisfied during compressor allocation, and the constraint relationship is:

[0111] X c,i,e,t =X c,i,e,t-1 +Bm i′,i,e,t -Bm i,i′,e,t -Bsi,e,t

[0112] Furthermore, the two-stage operation optimization model solution process described in S4 includes:

[0113] Phase I: The zebra optimization algorithm is used to solve the well opening plan and non-boosting equipment deployment optimization model of the shale gas gathering and transportation system, and the well opening plan, gas well production, non-boosting equipment purchase, operation and deployment plan and shale gas field net present value are obtained;

[0114] Phase II: The well opening plan and gas well production determined in Phase I are used as known parameters to input into the skid-mounted compressor deployment optimization model of the shale gas gathering and transportation system, and the Zebra optimization algorithm is used to solve the problem to obtain node pressure data, compressor deployment plan, and compressor operating cost;

[0115] The zebra optimization algorithm solution process includes:

[0116] S401: Set the initial zebra population size N and the maximum number of iterations T. According to the known parameters and constraints of the model, randomly initialize the initial zebra population in the search space and construct the ZOA matrix. The expression is as follows:

[0117]

[0118] Where X is the zebra population, X i is the zebra individual; m is the number of model decision variables;

[0119] S402: Calculate the fitness of each zebra individual in the population according to the model objective function, determine the optimal zebra individual according to the fitness, and update the position of the optimal zebra individual;

[0120] S403: Entering the foraging behavior phase, generating random numbers r and I in the range of [0,1] and [1,2] respectively, simulating the foraging behavior of zebras to update the individual positions of zebras, comparing the fitness value of the best individual zebras, and updating the final positions of the individuals. The expression is:

[0121]

[0122] In the formula, is the updated position of the zebra individual; PZ j is the jth dimension value of the optimal zebra individual; F i new,p1 is the fitness of the zebra individual after update; F i is the optimal individual zebra fitness;

[0123] S404: Enter the stage of resisting predators, generate a random P in the range of [0,1] s and R;

[0124] If P s ≤0.5, the escape strategy is used to update the individual position, and the update formula is:

[0125]

[0126] Where t is the current iteration number;

[0127] If P s > 0.5, the counterattack strategy is used to update the individual position, and the update formula is:

[0128]

[0129] In the formula, AZ j is the j-th dimension value of the attacked zebra individual;

[0130] Compare the fitness value of the best zebra individual and update the final position of the individual. The expression is:

[0131]

[0132] S405: If t≤T, return to S402 and continue to iterate to solve the model; if t>T, terminate the algorithm and output the optimal zebra individual as the optimal solution of the model.

[0133] In summary, the present invention provides a two-stage operation optimization method for a shale gas gathering and transportation system that takes into account well opening and equipment deployment. This method aims at the flexible planning of equipment skid-mounted and well opening plans, and establishes a two-stage model for the joint optimization of skid-mounted equipment deployment and well opening plans for the shale gas gathering and transportation system. The first stage is the optimization of the well opening plan and non-boosting equipment deployment of the shale gas gathering and transportation system, and the second stage is the optimization of skid-mounted deployment of compressors in the shale gas gathering and transportation system. Through this method, it is possible to effectively carry out joint optimization research on skid-mounted equipment deployment and well opening plans for shale gas gathering and transportation systems, which has significant guiding significance for the operation and management of shale gas gathering and transportation systems, and greatly improves the overall efficiency and flexibility of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] Figure 1 Flow chart of the present invention

[0135] Figure 2 Flowchart of solving the two-stage optimization model for the zebra optimization algorithm

[0136] Figure 3 Pipeline network structure for a shale gas field production block

[0137] Figure 4 The shale gas production decline curve of the block

[0138] Figure 5 Optimized well opening plan

[0139] Figure 6 To optimize the deployment plan of skid-mounted equipment

[0140] Figure 7 is the cumulative net present value of shale gas production blocks in each period

[0141] Figure 8 The optimized compressor allocation scheme is shown in FIG. DETAILED DESCRIPTION

[0142] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0143] A two-stage operation optimization method for a shale gas gathering and transportation system considering well opening and equipment deployment, characterized in that it comprises the following steps:

[0144] S1: Obtain basic data of shale gas gathering and transportation system and skid-mounted equipment;

[0145] S2: In the first optimization stage, the well opening plan and non-boosting equipment deployment optimization model of the shale gas gathering and transportation system are established;

[0146] S3: In the second optimization stage, a skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system is established;

[0147] S4: The zebra optimization algorithm is used to solve the two-stage optimization model established in S2 and S3 to form an operation plan for the shale gas gathering and transportation system that takes into account the well opening plan and equipment deployment.

[0148] In one embodiment, the skid-mounted equipment described in step S1 includes a separation and metering skid, a dehydration skid, and a compression unit skid;

[0149] The basic data of the shale gas gathering and transportation system include the number of dehydration stations and gas gathering station platforms and the corresponding connection relationship pipeline diameter, pipeline connection relationship, pipeline length, design pressure, production data, production time, shale gas sales price and second-hand equipment sales price;

[0150] The basic data of the skid-mounted equipment includes equipment model, equipment processing capacity, purchase cost, operation cost, installation and disassembly cost and moving cost;

[0151] Furthermore, the shale gas gathering and transportation system well opening plan and non-boosting equipment deployment optimization model described in step S2 takes the maximum net present value of the shale gas field as the objective function;

[0152] The net present value of the shale gas field is determined by the total cash flow of the shale gas field in period t, and the calculation formula is:

[0153]

[0154] Where NPV is the net present value; C t is the cash flow in period t; r is the discount rate;

[0155] The cash flow includes shale gas sales revenue, second-hand skid-mounted equipment sales revenue, non-boosting equipment purchase cost, non-boosting equipment installation cost, non-boosting equipment disassembly cost, non-boosting equipment operation cost and non-boosting equipment deployment cost, and the calculation formula is:

[0156] C t =S G，t +S E,t -F B，t -F I,t -F D,t -F O,t -F M,t

[0157] In the formula, S G，t is the revenue from shale gas sales; S E,t F is the sales income of second-hand non-boosting equipment; B，t Purchase cost for non-boosting equipment; F I,t Installation cost for non-pressurized equipment; F D,t F is the loading and unloading cost of non-pressurized equipment; O,t F is the operating cost of non-boosting equipment; M,t Allocate costs for non-boosting equipment;

[0158] The calculation formula for shale gas sales revenue is:

[0159] S G，t =P G，t Q t τ t

[0160] Where P G，t is the shale gas price in period t; Q t is the total output of shale gas production blocks in period t; τ t is the production time of shale gas field in period t;

[0161] The sales revenue calculation formula for second-hand skid-mounted equipment is:

[0162] S E,t =P E,k (1-α·τ t′,t )·Bs i,k,e,t,t′

[0163] Where PE,k is the purchase price of each device; a is the depreciation coefficient; τ t't Bs is the actual use time of the skid-mounted equipment; i,k,e,t,t′ is a binary 0-1 variable for equipment sales. If type k equipment of type e is purchased at node i during period t′ and sold during period t, then Bs i,k,e,t,t′ =1, otherwise Bs i,k,e,t,t′ =0;

[0164] The formula for calculating the purchase cost of non-boosting equipment is:

[0165]

[0166] Where, X i,k,e,t is the number of type e non-boosting equipment at node i at time t;

[0167] The calculation formula for the installation cost of non-boosting equipment is:

[0168]

[0169] In the formula, e I,k,e,t The unit installation cost for type K equipment;

[0170] The calculation formula for the disassembly cost of non-pressurized equipment is:

[0171]

[0172] In the formula, e D,i,k,e,t Unit disassembly cost for type K equipment;

[0173] The calculation formula for the skid-mounted equipment operating cost is:

[0174]

[0175] In the formula, β k is the unit processing cost of shale gas; Q E，i,k,e,t is the processing capacity of the equipment in period t;

[0176] The calculation formula for the skid-mounted equipment deployment cost is:

[0177]

[0178] In the formula, B M,h,h′,k,t Allocate base costs for K-type equipment.

[0179] In one embodiment, the constraints of the shale gas gathering and transportation system well opening plan and non-boosting equipment deployment optimization model described in step S2 include equipment processing capacity constraints, equipment deployment constraints, well opening timing constraints and well opening quantity constraints;

[0180] The equipment processing capacity constraint means that the processing capacity of each equipment is less than or equal to the rated processing capacity, and the sum of the actual processing capacity of all production platform equipment is equal to the shale gas field production. The constraint relationship is as follows:

[0181] Q E,h,k,t ≤aq max ·qe h,k

[0182] Q E,h,k,t ≥aq min ·qe h,k

[0183]

[0184] In the formula, Q E,h,k,t is the shale gas processing capacity of each k-type equipment in period t; qe h,k is the rated processing capacity of the H platform K type equipment; aq max is the maximum flow coefficient within the rated capacity fluctuation range; aq min is the minimum flow coefficient within the rated capacity fluctuation range; Q t is the total output of shale gas production blocks in period t;

[0185] The equipment deployment constraints include equipment purchase, sale and movement constraints, and current equipment quantity constraints;

[0186] The purchase constraint requires that when the platform first starts to purchase equipment, the number of equipment is the purchase quantity, and the constraint relationship is:

[0187] B h,k,t,t′ =Bb h,k,t

[0188] In the formula, Bb h,k,t is a binary variable for purchase. When type k equipment is purchased on platform h during period t and sold during period t, Bb h,k,t =1, otherwise Bb h,k,t =0;

[0189] The sales constraint requires that the equipment cannot be sold before the equipment is purchased. The constraint relationship is:

[0190] Bs h,k,t,t′ =0

[0191] In the formula, Bs h,k,t,t′ is a binary variable that determines whether to sell type k equipment on platform h. If type k equipment is purchased on platform h during period t and sold during period t, then Bs h,k,t,t′ =1, otherwise Bs h,k,t,t′ =0;

[0192] Each device can only be purchased and sold once during its entire life cycle. The constraint is:

[0193]

[0194] In the first period, there is no equipment to sell, and the constraint relationship is:

[0195] Bs h,k,t,t′ =0

[0196] The equipment deployment constraint relation is:

[0197] B h,k,t-1,t′ ≥Bs h,k,t,t′

[0198] The current device quantity constraint of the platform is:

[0199] B h,k,t,t′ =B h,k,t-1,t′ +Bm h′,h,k,t,t′ -Bm h,h′,k,t,t′ -Bs h,k,t,t′

[0200] In the formula, Bm h′,h,k,t,t′ is a disassembly binary variable. At time t, if the k-type skid-mounted equipment moves from node h to node h', then Bm h′,h,k,t,t′ =1, otherwise Bm h′,h,k,t,t′ =0.

[0201] The well opening timing constraint requires that each shale gas well can only be opened once during the production period. The constraint relationship is:

[0202]

[0203] In the formula, Bopen h,w,t′ is a binary variable for well opening; at time t, gas well w in platform h is opened, then Bopen h,w,t′ =1, otherwise Bopen h,w,t′ =0.

[0204] In one embodiment, the skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system described in step S3 takes the minimization of the total cost of the compressors as the objective function;

[0205] The total cost of the compressor includes the compressor purchase cost, compressor installation cost, compressor disassembly cost, compressor operation cost and compressor deployment cost, and the calculation formula is as follows:

[0206] minF=F b +F i +F d +F o +F e

[0207] Where, F is the total cost of the compressor; F b The cost of purchasing the compressor; F i F is the compressor installation cost; d F is the compressor disassembly cost; o is the compressor operating cost; F e Allocate costs for compressors;

[0208] The compressor purchase cost calculation formula is:

[0209]

[0210] In the formula, B D,i,e,t For compressor purchase binary variables, at time t, if a compressor of type e is purchased at node i, then B D,i,e,t =1, otherwise B D,i,e,t =0;X c,i,e,t is the number of type e compressors at node i at time t; P c,e Purchase cost for e-type compressor;

[0211] The compressor installation cost calculation formula is:

[0212]

[0213] In the formula, Bb i,e,t is a binary variable for compressor installation. At time t, if the e-type compressor moves from node i to node i′, then Bb i,e,t =1, otherwise Bb i,e,t =0;β c,e Cost of installing an e-type compressor;

[0214] The compressor disassembly cost calculation formula is:

[0215]

[0216] In the formula, Bs i,e,t is the compressor removal binary variable. At time t, if the e-type compressor moves from node i to node i′, then Bs i,e,t =1, otherwise Bs i,e,t =0;γ c,e Cost of disassembling the e-type compressor;

[0217] The compressor operating cost calculation formula is:

[0218]

[0219] Where N i,t is the operating power of the compressor at node i at time t; e c,ris the unit operating cost of the e-type compressor;

[0220] The compressor deployment cost calculation formula is:

[0221]

[0222] In the formula, eu i,t is the number of compressors deployed at node i at time t; is the unit cost of deploying the compressor at node i at time t.

[0223] In one embodiment, the constraints of the skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system described in step S3 include pipeline pressure constraints, compressor constraints, throttling constraints, node pressure balance constraints, node flow constraints, and compressor deployment constraints;

[0224] The pipeline pressure constraint specifies the pipeline operating pressure range, and the constraint relationship is:

[0225] p i|(i,j),t ≤p max

[0226] p j|(i,j),t ≥p min

[0227] In the formula, p i|(i,j),t is the pressure of node i connected to pipeline (i, j) at time t; p j|(i,j),t is the pressure of node j connected to pipeline (i, j) at time t; p max is the maximum pressure value that the pipeline (i, j) can withstand at time t; p min is the minimum pressure value at the end point of pipeline (i, j) at time t;

[0228] The compressor constraint relationship is:

[0229]

[0230] ε min ≤ε i,t ≤ε max

[0231]

[0232] In the formula, p i,t is the pressure at node i at time t; is the compressor inlet pressure at node i at time t; is the compressor outlet pressure at node i at time t; ε i,t is the pressure ratio of the compressor at node i at time t; ε min is the minimum pressure ratio of the compressor; ε maxis the maximum pressure ratio of the compressor; N i,t is the operating power of the compressor at node i at time t; q i,t is the volume flow rate of shale gas at node i at time t; k is the specific heat ratio of gas; is the gas compression factor at the compressor inlet condition at node i at time t; is the gas compression factor at the compressor outlet at node i at time t; η is the compressor efficiency; N c,e is the rated operating power of the e-type compressor;

[0233] The throttling constraint relation is:

[0234]

[0235] In the formula, δ w,t is the throttling ratio at the gas well w at time t; p w,t is the pressure at point w in the gas well at time t; p w|(w,h),t is the starting pressure of the pipeline (w, h) at time t; δ h,t is the throttling ratio at platform h at time t; p h,t is the pressure at platform h at time t; p h|(h,j),t is the starting pressure of the pipeline (h, j) at time t;

[0236] The node pressure balance constraint specifies the pressure balance relationship between various nodes, and the constraint relationship is:

[0237]

[0238] In the formula, is the minimum pressure of all gas wells in the platform at time t; p n|(i,n),t is the terminal pressure of pipeline (i, n) at time t; p n,t is the pressure at point n in the region at time t; p s|(i,s),t is the starting pressure of the pipeline (i, s) at time t; p s,t is the pressure of the gas gathering station at time t;

[0239] The node flow constraint specifies the flow relationship between various nodes, and the constraint relationship is:

[0240] q h,w,t =q w|(w,h),t

[0241] q i|(i,j),t =Q i,j,t =q j|(i,j),t

[0242] Q t ≤Qm t

[0243]

[0244] In the formula, q h,w,t is the flow rate of gas well w in platform h at time t; q w|(w,h),t is the flow rate of the pipeline connected to well w at time t; Qm t is the maximum shale gas demand in the market at time t; Qpad h,t is the shale gas production of platform h at time t; q h,w,t is the flow rate of gas well w in platform h at time t;

[0245] The compressor constraint stipulates the quantitative relationship satisfied during compressor allocation, and the constraint relationship is:

[0246] X c,i,e,t =X c,i,e,t-1 +Bm i′,i,e,t -Bm i,i′,e,t -Bs i,e,t

[0247] In one embodiment, the two-stage operation optimization model solution process described in S4 includes:

[0248] Phase I: The zebra optimization algorithm is used to solve the well opening plan and non-boosting equipment deployment optimization model of the shale gas gathering and transportation system, and the well opening plan, gas well production, non-boosting equipment purchase, operation and deployment plan and shale gas field net present value are obtained;

[0249] Phase II: The well opening plan and gas well production determined in Phase I are used as known parameters to input into the skid-mounted compressor deployment optimization model of the shale gas gathering and transportation system, and the Zebra optimization algorithm is used to solve the problem to obtain node pressure data, compressor deployment plan, and compressor operating cost;

[0250] The zebra optimization algorithm solution process includes:

[0251] S401: Set the initial zebra population size N and the maximum number of iterations T. According to the known parameters and constraints of the model, randomly initialize the initial zebra population in the search space and construct the ZOA matrix. The expression is as follows:

[0252]

[0253] Where X is the zebra population, X i is the zebra individual; m is the number of model decision variables;

[0254] S402: Calculate the fitness of each zebra individual in the population according to the model objective function, determine the optimal zebra individual according to the fitness, and update the position of the optimal zebra individual;

[0255] S403: Entering the foraging behavior phase, generating random numbers r and I in the range of [0,1] and [1,2] respectively, simulating the foraging behavior of zebras to update the individual positions of zebras, comparing the fitness value of the best individual zebras, and updating the final positions of the individuals. The expression is:

[0256]

[0257] In the formula, is the updated position of the zebra individual; PZ j is the jth dimension value of the optimal zebra individual; F i new,p1 is the fitness of the zebra individual after update; F i is the optimal individual zebra fitness;

[0258] S404: Enter the stage of resisting predators, generate a random P in the range of [0,1] s and R;

[0259] If P s ≤0.5, the escape strategy is used to update the individual position, and the update formula is:

[0260]

[0261] Where t is the current iteration number;

[0262] If P s > 0.5, the counterattack strategy is used to update the individual position, and the update formula is:

[0263]

[0264] In the formula, AZ j is the j-th dimension value of the attacked zebra individual;

[0265] Compare the fitness value of the best zebra individual and update the final position of the individual. The expression is:

[0266]

[0267] S405: If t≤T, return to S402 and continue to iterate to solve the model; if t>T, terminate the algorithm and output the optimal zebra individual as the optimal solution of the model.

[0268] To further illustrate this solution, the present invention takes a shale gas field production block as a specific application example to carry out a study on the optimization of shale gas gathering and transportation system skid-mounted equipment deployment. The block includes 1 gas gathering station, 7 platforms, 7 pipelines and 54 wells. Figure 3 As shown in Figure 2, the shale gas production decline curve of the block is as follows: Figure 4The pipeline parameters of the block are shown in Table 1, the commissioning time of each platform in the block is shown in Table 2, the equipment parameters are shown in Table 3 (the equipment residual value rate is generally 5%, and the prescribed service life is 20 years), and the shale gas sales price is shown in Table 4.

[0269] Table 1 Pipeline parameters of shale gas production blocks

[0270]

[0271] Table 2 Platform commissioning time

[0272]

[0273] Table 3 Equipment parameters

[0274]

[0275]

[0276] Table 4 Shale gas sales unit price

[0277]

[0278] The well opening plan after the first phase of optimization is as follows: Figure 5 In the optimized well opening plan, all shale gas wells are divided into 20 batches and put into production from the first to the eighth year. The production time of each platform has changed, and the production time and number of shale gas wells in the platform are different. More platforms and shale gas wells are put into production at an earlier time. This is mainly because the sales unit price of shale gas is higher in the first three periods. Producing more shale gas at this time can effectively increase gas sales revenue, thereby maximizing the net present value.

[0279] The optimized skid-mounted equipment deployment plan is as follows: Figure 6 As shown in the figure, four types of separation and metering skids and three types of dehydration skids are deployed in the optimized well opening plan. There is no i4 separation and metering skid in the optimized well opening plan. This is because the i4 separation and metering skid is used on the least platforms and in the least period during the specified production period. After the well opening plan is optimized, it can be replaced by other types of separation and metering skids.

[0280] The cumulative net present value of shale gas production blocks in each period is as follows: Figure 7 As shown in the figure, by optimizing the well opening plan, the cumulative net present value after optimization during the specified production period is higher than the cumulative net present value under the actual well opening plan. The cumulative net present value after optimization increases by 3.44% compared with the cumulative net present value obtained under the actual well opening plan.

[0281] The optimized compressor allocation plan is as follows Figure 8As shown. Four K8 compressor skids were purchased at t1 and all were installed at the gas gathering station; one was purchased at t3 and t4 and installed at the gas gathering station; three were purchased at t7 and installed at regional point JH14; at t8, the K8 compressor skid at JH14 was deployed to platform H19, thus completing the deployment of all equipment of this type.

[0282] The cost composition of the compressor in each period is shown in Table 5. The total purchase cost of the compressor is 43.21 million yuan, the total installation cost is 1.28 million yuan, the total disassembly cost is 660,000 yuan, the total deployment cost is 1.65 million yuan, and the total operating cost is 506.9127 million yuan. From the amount of each cost of the compressor, it can be seen that the total cost of the compressor is mainly affected by the operating cost, followed by the purchase cost, while the installation, disassembly and deployment costs of the compressor only account for a small part of the total cost. Therefore, reasonable optimization of pressure distribution and reduction of the operating cost of the compressor have a positive effect on the economic development of shale gas.

[0283] Table 5 Cost composition of compressors in different periods

[0284]

[0285] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A two-stage operation optimization method for a shale gas gathering and transportation system considering well opening and equipment deployment, characterized in that: The following steps are involved: S1: Obtain basic data of shale gas gathering and transportation system and skid-mounted equipment; S2: In the first optimization stage, the well opening plan and non-boosting equipment deployment optimization model of the shale gas gathering and transportation system are established; S3: In the second optimization stage, a skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system is established; S4: The zebra optimization algorithm is used to solve the two-stage optimization model established in S2 and S3 to form an operation plan for the shale gas gathering and transportation system that takes into account the well opening plan and equipment deployment.

2. A two-stage operation optimization method for shale gas gathering and transportation system considering well opening and equipment deployment according to claim 1, characterized in that: The skid-mounted equipment includes a separation and metering skid, a dehydration skid and a compressor unit skid; The basic data of the shale gas gathering and transportation system include the number of dehydration stations and gas gathering station platforms and the corresponding connection relationship pipeline diameter, pipeline connection relationship, pipeline length, design pressure, production data, production time, shale gas sales price and second-hand equipment sales price; The basic data of the skid-mounted equipment includes equipment model, equipment processing capacity, purchase cost, operation cost, installation and disassembly cost and moving cost.

3. A two-stage operation optimization method for shale gas gathering and transportation system considering well opening and equipment deployment according to claim 1, characterized in that: The shale gas gathering and transportation system well opening plan and non-boosting equipment deployment optimization model described in step S2 takes the maximum net present value of the shale gas field as the objective function; The net present value of the shale gas field is determined by the total cash flow of the shale gas field in period t, and the calculation formula is: Where NPV is the net present value; C t is the cash flow in period t; r is the discount rate; The cash flow includes shale gas sales revenue, second-hand skid-mounted equipment sales revenue, non-boosting equipment purchase cost, non-boosting equipment installation cost, non-boosting equipment disassembly cost, non-boosting equipment operation cost and non-boosting equipment deployment cost, and the calculation formula is: C t =S G，t +S E,t -F B，t -F I,t -F D,t -F O,t -F M,t In the formula, S G，t is the revenue from shale gas sales; S E,t F is the sales income of second-hand non-boosting equipment; B，t Purchase cost for non-boosting equipment; F I,t Installation cost for non-pressurized equipment; F D,t F is the loading and unloading cost of non-pressurized equipment; O,t F is the operating cost of non-boosting equipment; M,t Allocate costs for non-boosting equipment; The calculation formula for shale gas sales revenue is: S G，t =P G，t Q t τ t Where P G，t is the shale gas price in period t; Q t is the total output of shale gas production blocks in period t; τ t is the production time of shale gas field in period t; The sales revenue calculation formula for second-hand skid-mounted equipment is: S E,t =P E,k (1-α·τ t′,t )·Bs i,k,e,t,t′ Where P E,k is the purchase price of each device; a is the depreciation coefficient; τ t't Bs is the actual use time of the skid-mounted equipment; i,k,e,t,t′ is a binary 0-1 variable for equipment sales. If type k equipment of type e is purchased at node i during period t′ and sold during period t, then Bs i,k,e,t,t′ =1, otherwise Bs i,k,e,t,t′ =0; The formula for calculating the purchase cost of non-boosting equipment is: Where, X i,k,e,t is the number of type e non-boosting equipment at node i at time t; The calculation formula for the installation cost of non-boosting equipment is: In the formula, e I,k,e,t The unit installation cost for type K equipment; The calculation formula for the disassembly cost of non-pressurized equipment is: In the formula, e D,i,k,e,t Unit disassembly cost for type K equipment; The calculation formula for the skid-mounted equipment operating cost is: In the formula, β k is the unit processing cost of shale gas; Q E，i,k,e,t is the processing capacity of the equipment in period t; The calculation formula for the skid-mounted equipment deployment cost is: In the formula, B M,h,h′,k,t Allocate base costs for K-type equipment.

4. A two-stage operation optimization method for shale gas gathering and transportation system considering well opening and equipment deployment according to claim 1, characterized in that: The constraints of the shale gas gathering and transportation system well opening plan and non-boosting equipment deployment optimization model described in step S2 include equipment processing capacity constraints, equipment deployment constraints, well opening timing constraints and well opening quantity constraints; The equipment processing capacity constraint means that the processing capacity of each equipment is less than or equal to the rated processing capacity, and the sum of the actual processing capacity of all production platform equipment is equal to the shale gas field production. The constraint relationship is as follows: Q E,h,k,t white max ·qe h,k Q E,h,k,t ≥white min ·qe h,k In the formula, Q E,h,k,t is the shale gas processing capacity of each k-type equipment in period t; qe h,k is the rated processing capacity of the H platform K type equipment; aq max is the maximum flow coefficient within the rated capacity fluctuation range; aq min is the minimum flow coefficient within the rated capacity fluctuation range; Q t is the total output of shale gas production blocks in period t; The equipment deployment constraints include equipment purchase, sale and movement constraints, and current equipment quantity constraints; The purchase constraint requires that when the platform first starts to purchase equipment, the number of equipment is the purchase quantity, and the constraint relationship is: B h,k,t,t′ =Bb h,k,t In the formula, Bb h,k,t is a binary variable for purchase. When type k equipment is purchased on platform h during period t and sold during period t, Bb h,k,t =1, otherwise Bb h,k,t =0; The sales constraint requires that the equipment cannot be sold before the equipment is purchased. The constraint relationship is: Bs h,k,t,t′ =0 In the formula, Bs h,k,t,t′ is a binary variable that determines whether to sell type k equipment on platform h. If type k equipment is purchased on platform h during period t and sold during period t, then Bs h,k,t,t′ =1, otherwise Bs h,k,t,t′ =0; Each device can only be purchased and sold once during its entire life cycle. The constraint is: In the first period, there is no equipment to sell, and the constraint relationship is: Bs h,k,t,t′ =0 The equipment deployment constraint relation is: B h,k,t-1,t′ ≥Bs h,k,t,t′ The current device quantity constraint of the platform is: B h,k,t,t′ =B h,k,t-1,t′ +Bm h′,h,k,t,t′ -Bm h,h′,k,t,t′ -Bs h,k,t,t′ In the formula, Bm h′,h,k,t,t′ is a disassembly binary variable. At time t, if the k-type skid-mounted equipment moves from node h to node h', then Bm h′,h,k,t,t′ =1, otherwise Bm h′,h,k,t,t′ =0. The well opening timing constraint requires that each shale gas well can only be opened once during the production period. The constraint relationship is: In the formula, Bopen h,w,t′ is a binary variable for well opening; at time t, gas well w in platform h is opened, then Bopen h,w,t′ =1, otherwise Bopen h,w,t′ =0.

5. According to claim 1, a two-stage operation optimization method for shale gas gathering and transportation system considering well opening and equipment deployment is characterized in that: The skid-mounted deployment optimization model for compressors in the shale gas gathering and transportation system described in step S3 takes the minimization of the total cost of the compressors as the objective function; The total cost of the compressor includes the compressor purchase cost, compressor installation cost, compressor disassembly cost, compressor operation cost and compressor deployment cost, and the calculation formula is as follows: minF=F b +F i +F d +F o +F e Where, F is the total cost of the compressor; F b The cost of purchasing the compressor; F i F is the compressor installation cost; d F is the compressor disassembly cost; o is the compressor operating cost; F e Allocate costs for compressors; The compressor purchase cost calculation formula is: In the formula, B D,i,e,t For compressor purchase binary variables, at time t, if a compressor of type e is purchased at node i, then B D,i,e,t =1, otherwise B D,i,e,t =0;X c,i,e,t is the number of type e compressors at node i at time t; P c,e Purchase cost for e-type compressor; The compressor installation cost calculation formula is: In the formula, Bb i,e,t is a binary variable for compressor installation. At time t, if the e-type compressor moves from node i to node i′, then Bb i,e,t =1, otherwise Bb i,e,t =0;β c,e Cost of installing an e-type compressor; The compressor disassembly cost calculation formula is: In the formula, Bs i,e,t is the compressor removal binary variable. At time t, if the e-type compressor moves from node i to node i′, then Bs i,e,t =1, otherwise Bs i,e,t =0;γ c,e Cost of disassembling the e-type compressor; The compressor operating cost calculation formula is: Where N i,t is the operating power of the compressor at node i at time t; e c,r is the unit operating cost of the e-type compressor; The compressor deployment cost calculation formula is: In the formula, eu i,t is the number of compressors deployed at node i at time t; is the unit cost of deploying the compressor at node i at time t.

6. According to claim 1, a two-stage operation optimization method for shale gas gathering and transportation system considering well opening and equipment deployment is characterized in that: The constraints of the skid-mounted deployment optimization model for the compressor of the shale gas gathering and transportation system described in step S3 include pipeline pressure constraints, compressor constraints, throttling constraints, node pressure balance constraints, node flow constraints and compressor deployment constraints; The pipeline pressure constraint specifies the pipeline operating pressure range, and the constraint relationship is: p i|(i,j),t ≤p max p j|(i,j),t ≥p min In the formula, p i|(i,j),t is the pressure of node i connected to pipeline (i, j) at time t; p j|(i,j),t is the pressure of node j connected to pipeline (i, j) at time t; p max is the maximum pressure value that the pipeline (i, j) can withstand at time t; p min is the minimum pressure value at the end point of pipeline (i, j) at time t; The compressor constraint relationship is: e min ≤e i,t ≤e max In the formula, p i,t is the pressure at node i at time t; is the compressor inlet pressure at node i at time t; is the compressor outlet pressure at node i at time t; ε i,t is the pressure ratio of the compressor at node i at time t; ε min is the minimum pressure ratio of the compressor; ε max is the maximum pressure ratio of the compressor; N i,t is the operating power of the compressor at node i at time t; q i,t is the volume flow rate of shale gas at node i at time t; k is the specific heat ratio of gas; is the gas compression factor at the compressor inlet condition at node i at time t; is the gas compression factor under the compressor outlet condition at node i at time t; η is the compressor efficiency; N c,e is the rated operating power of the e-type compressor; The throttling constraint relation is: In the formula, δ w,t is the throttling ratio at the gas well w at time t; p w,t is the pressure at point w in the gas well at time t; p w|(w,h),t is the starting pressure of the pipeline (w, h) at time t; δ h,t is the throttling ratio at platform h at time t; p h,t is the pressure at platform h at time t; p h|(h,j),t is the starting pressure of the pipeline (h, j) at time t; The node pressure balance constraint specifies the pressure balance relationship between various nodes, and the constraint relationship is: In the formula, is the minimum pressure of all gas wells in the platform at time t; p n|(i,n),t is the terminal pressure of pipeline (i, n) at time t; p n,t is the pressure at point n in the region at time t; p s|(i,s),t is the starting pressure of the pipeline (i, s) at time t; p s,t is the pressure of the gas gathering station at time t; The node flow constraint specifies the flow relationship between various nodes, and the constraint relationship is: q h,w,t =q w|(w,h),t q i|(i,j),t =Q i,j,t =q j|(i,j),t Q t ≤Qm t In the formula, q h,w,t is the flow rate of gas well w in platform h at time t; q w|(w,h),t is the flow rate of the pipeline connected to well w at time t; Qm t is the maximum shale gas demand in the market at time t; Qpad h,t is the shale gas production of platform h at time t; q h,w,t is the flow rate of gas well w in platform h at time t; The compressor constraint stipulates the quantitative relationship satisfied during compressor deployment, and the constraint relationship is: X c,i,e,t =X c,i,e,t-1 +Bm i′,i,e,t -Bm i,i′,e,t -Bs i,e,t 7. A two-stage operation optimization method for shale gas gathering and transportation system considering well opening and equipment deployment according to claim 1, characterized in that: The two-stage operation optimization model solution process described in S4 includes: Phase I: The zebra optimization algorithm is used to solve the well opening plan and non-boosting equipment deployment optimization model of the shale gas gathering and transportation system, and the well opening plan, gas well production, non-boosting equipment purchase, operation and deployment plan and shale gas field net present value are obtained; Phase II: The well opening plan and gas well production determined in Phase I are used as known parameters to input into the skid-mounted compressor deployment optimization model of the shale gas gathering and transportation system, and the Zebra optimization algorithm is used to solve the problem to obtain node pressure data, compressor deployment plan, and compressor operating cost; The zebra optimization algorithm solution process includes: S401: Set the initial zebra population size N and the maximum number of iterations T. According to the known parameters and constraints of the model, randomly initialize the initial zebra population in the search space and construct the ZOA matrix. The expression is as follows: Where X is the zebra population, X i is the zebra individual; m is the number of model decision variables; S402: Calculate the fitness of each zebra individual in the population according to the model objective function, determine the optimal zebra individual according to the fitness, and update the position of the optimal zebra individual; S403: Entering the foraging behavior phase, generating random numbers r and I in the range of [0,1] and [1,2] respectively, simulating the foraging behavior of zebras to update the individual positions of zebras, comparing the fitness value of the best individual zebras, and updating the final positions of the individuals. The expression is: In the formula, is the updated position of the zebra individual; PZ j is the jth dimension value of the optimal zebra individual; is the fitness of the zebra individual after update; F i is the optimal individual zebra fitness; S404: Enter the stage of defending against predators, generate a random P in the range of [0,1] s and R; If P s ≤0.5, the escape strategy is used to update the individual position, and the update formula is: Where t is the current iteration number; If P s > 0.5, the counterattack strategy is used to update the individual position, and the update formula is: In the formula, AZ j is the j-th dimension value of the attacked zebra individual; Compare the fitness value of the best zebra individual and update the final position of the individual. The expression is: S405: If t≤T, return to S402 and continue to iterate to solve the model; if t>T, terminate the algorithm and output the optimal zebra individual as the optimal solution of the model.

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