Method, device, medium and equipment for analyzing low carbon potential of crude oil transportation system

Abstract

The present application relates to a kind of crude oil transportation system low-carbon potential analysis method, device, medium and equipment, method includes: obtaining pipeline design data, crude oil physical data, equipment parameter, environmental data and carbon emission coefficient, to crude oil transportation system add virtual node;According to the life cycle characteristics of crude oil transportation system, carbon emission stage is divided, and the life cycle environmental load evaluation model of crude oil transportation system is established;According to the solid deposition pigging, safety shutdown constraint, determine the safe transportation temperature limit of each transportation pipeline;With the principle of safe and smooth operation of crude oil transportation system, constraint condition is generated, with economic, low carbon as double target, establish the operation optimization model of crude oil transportation system, generate pareto frontier;According to sensitivity analysis, the operating cost and carbon emissions under different key parameters are calculated, and the low-carbon potential of the transportation system is judged;The present application can determine the economic low-carbon operation scheme of crude oil transportation system, tap the low-carbon potential of transportation system, and promote the green low-carbon transformation of oil field.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for analyzing the low-carbon potential of crude oil transportation systems, belonging to the field of oil and gas extraction technology. Background Technology

[0002] For long-distance crude oil transportation systems, there are often crude oil transfer systems at the joint stations along the route. It is a multi-inlet, single-outlet system. The pumps inside and outside the joint station consume electrical energy to provide pressure energy for the crude oil to overcome friction loss and potential energy loss. The transfer pumps inside the joint station provide pressure energy for the crude oil to ensure that the oil products of this station are smoothly integrated into the system. The heating furnaces of each joint station consume fuel to provide heat energy for the crude oil to overcome heat loss along the route.

[0003] On the one hand, the crude oil extracted from most oilfields in my country has high viscosity, high pour point, and high wax content, resulting in poor fluidity. It is often transported using high-temperature heating methods. High-temperature transportation and storage lead to the volatilization of a large amount of light components, resulting in high system operating costs and carbon emissions. Lowering the crude oil transportation temperature increases pump operating costs and causes a large amount of wax precipitation, shortening the downtime of the transportation pipeline and reducing the reliability of crude oil flow. For operating units, reducing the operating costs of the transportation system is particularly important. However, the optimization problem of crude oil transportation system operation belongs to the MINLP model mathematically, which is a very complex optimization problem. Its purpose is to achieve optimal economic benefits under multiple constraints, such as strictly satisfying flow assurance, hydrothermal constraints, and flow balance.

[0004] On the other hand, for crude oil transportation systems that rely on heating along the pipeline, their lifecycle carbon emissions account for a significant proportion of carbon emissions in the oil and gas storage and transportation industry. However, due to the unique characteristics of crude oil transportation systems, their lifecycle carbon emissions have not yet been assessed, and operational optimization often sacrifices low-carbon performance to achieve economic optimality. When budgeting energy consumption for transportation systems, electricity and crude oil prices are often dynamically changing; furthermore, during oil and gas field development, new wells are frequently added, increasing the throughput of transportation pipelines. Therefore, it is necessary to analyze the low-carbon potential of crude oil transportation systems to promote energy conservation and emission reduction, reduce CO2e emissions, evaluate the low-carbon potential of crude oil transportation systems, and achieve the green transformation of oilfields. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method, apparatus, medium, and equipment for analyzing the low-carbon potential of crude oil transportation systems. This method can calculate the carbon emissions at each stage of the crude oil transportation system, determine an economical and low-carbon operation plan for the crude oil transportation system, explore the low-carbon potential of the transportation system, and promote the green and low-carbon transformation of oilfields.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for analyzing the low-carbon potential of crude oil transportation systems includes:

[0008] Acquire basic data of the crude oil transportation system and add virtual nodes to divide the crude oil transportation system into station connection units and pipeline connection units;

[0009] Based on the life cycle characteristics of crude oil transportation systems, carbon emission stages are divided, a life cycle environmental load assessment model for crude oil transportation systems is established, and the carbon emissions of crude oil transportation systems at each stage are calculated.

[0010] Based on the constraints of solid phase deposition pigging and safe shutdown, the safe transport temperature limits for each transport pipeline are determined.

[0011] Based on the principle of safe and stable operation of the crude oil transportation system, constraints including the safe transportation temperature limits of each pipeline are generated according to the basic data of the crude oil transportation system. With the operating cost and carbon emissions of the crude oil transportation system as dual objectives, an operation optimization model of the crude oil transportation system is established, and the Pareto front is generated.

[0012] Based on the operation optimization model of the crude oil transportation system, this study analyzes the sensitivity of electricity price, crude oil price, and transportation volume, calculates the operating costs and carbon emissions under different key parameters, and assesses the low-carbon potential of the crude oil transportation system.

[0013] The method for analyzing the low-carbon potential of crude oil transportation systems, preferably, involves dividing the crude oil transportation system into carbon emission stages based on its life cycle characteristics, establishing a life cycle environmental load assessment model for the crude oil transportation system, and calculating the carbon emissions at each stage of the crude oil transportation system. Specifically, this includes:

[0014] Carbon emission phases include: carbon emissions during the production and construction phase, carbon emissions during the operation phase, carbon emissions during the liquidity support phase, and carbon emissions during the recycling phase;

[0015] The life-cycle environmental load assessment model for crude oil transportation systems uses the carbon emission factor method to calculate the carbon emissions at each stage of the crude oil transportation system, expressed as follows:

[0016] E = AD × EF (1)

[0017] In the formula: E is the CO2e emission; AD is the material consumption; EF is the carbon emission intensity coefficient.

[0018] The method for analyzing the low-carbon potential of the crude oil transportation system, preferably, determines the safe transportation temperature limits for each pipeline based on solid phase deposition pigging constraints and safe shutdown constraints, specifically including:

[0019] Solid phase deposition pigging constraint specifically refers to: calculating the volume of solid phase deposits in the pipeline during the on-site pigging cycle at different operating outlet oil temperatures based on the solid phase deposition model; determining the minimum operating outlet oil temperature for each pipeline section under the solid phase deposition pigging constraint, with the pipeline solid phase deposit volume not exceeding the receiver volume as a constraint; expressed as:

[0020] V wax (T out,wax ,t wax )≤V pig (2)

[0021] T wax =min(T) out,wax (3)

[0022] In the formula, V wax (T out,wax ,t wax (The value is T) is the operating outlet oil temperature. out,wax On-site cleaning cycle t wax Volume of solid deposits in the pipe at that time; V pig T represents the receiver volume; wax This is the minimum operating outlet oil temperature under the constraint of solid phase deposition pigging;

[0023] Specifically, the safe shutdown constraint refers to: calculating the pipeline outlet oil temperature at the safe shutdown time under different operating outlet oil temperatures based on the shutdown temperature drop model, and determining the minimum operating outlet oil temperature for each pipeline section under the safe shutdown constraint, with the pipeline outlet oil temperature not being less than 3°C below the crude oil pour point as a constraint. This is expressed as:

[0024] T stop (T out,stop ,t stop )≥T cp +3(4)

[0025] T st op = min(T) out,stop (5)

[0026] In the formula, T stop (T out,stop ,t stop (The value is T) is the operating outlet oil temperature. out,stop Safe shutdown time t stop The oil temperature at the pipeline outlet at that time; T cp T is the pour point of crude oil; stop Minimum operating outlet oil temperature under safe shutdown constraints;

[0027] The safe transport temperature limits for each transport pipeline are generated based on the constraints that the safe transport temperature limit of the pipeline is higher than the minimum operating outlet oil temperature under the constraint of solid phase deposition cleaning of the pipeline section, and the safe transport temperature limit of the pipeline is higher than the minimum operating outlet oil temperature under the constraint of safe shutdown of the pipeline section.

[0028] The aforementioned method for analyzing the low-carbon potential of crude oil transportation systems, preferably, takes the safe and stable operation of the crude oil transportation system as its principle. Based on the acquired basic data of the crude oil transportation system, it generates constraints including the safe transportation temperature limits of each pipeline. With the operating cost and carbon emissions of the crude oil transportation system as dual objectives, it establishes an operational optimization model for the crude oil transportation system and generates a Pareto front. Specifically, this includes:

[0029] Define the objective function, with the lowest operating cost of the crude oil transportation system as the economic objective and the lowest carbon emissions from the operation of the crude oil transportation system as the low-carbon objective.

[0030] Based on the principle of safe and stable operation of the crude oil transportation system, constraints including the safe transportation temperature limits of each pipeline are generated according to the basic data of the crude oil transportation system.

[0031] An operational optimization model for the crude oil transportation system is generated based on the objective function and constraints, including the safe transportation temperature limits of each pipeline. The model is then solved using a GUROBI optimization solver based on the branch and bound method, generating a Pareto front and yielding an economical and low-carbon operation scheme.

[0032] The aforementioned method for analyzing the low-carbon potential of crude oil transportation systems preferably defines an objective function, with the lowest operating cost of the crude oil transportation system as the economic objective and the lowest carbon emissions from the operation of the crude oil transportation system as the low-carbon objective, specifically including:

[0033] The operating cost of the crude oil transportation system is the sum of the operating costs of each external pump, each transfer pump, each heater, and the depreciation and maintenance costs of each piece of equipment, expressed as:

[0034] minf eco =N p1 +N p2 +N f +N rep (6)

[0035] In the formula, f eco Operating costs for the crude oil transportation system; N p1 Operating costs for each external pump; N p2 Operating costs for each pump; N f Operating costs for each heating furnace; N rep For equipment depreciation and maintenance costs;

[0036] The carbon emissions from the crude oil transportation system are the sum of the carbon emissions from each external pump, each transfer pump, and each heating furnace, expressed as:

[0037]

[0038] In the formula, f car Carbon emissions from the operation of the crude oil transportation system; P j The pressure at node j; T j P represents the temperature of node j; i The pressure at node i; T i The temperature of node i; These are the sets of inlet and outlet nodes for the station connection unit, respectively; BP i,j This represents the connection relationship between nodes i and j; 0-1 decision variables; q is a 0-1 decision variable; i denoted as _c_, where _c_ is the intermediate station feed rate at node i; _c_ is the specific heat capacity of crude oil; _Q_ is the input flow rate at node i. i,j ρ represents the flow rate of the pipe segment connection unit between nodes i and j. i,j η represents the crude oil density of the pipe segment connecting nodes i and j. p1 η p2 η f These represent the operating efficiencies of the external pump, the feed pump, and the heating furnace, respectively; t represents the annual operating time of the system; EF ele CO2e emission factor for power grid supply; EF oil CO2e emission factor for energy supplied by crude oil.

[0039] The aforementioned method for analyzing the low-carbon potential of crude oil transportation systems, preferably, is based on the principle of safe and stable operation of the crude oil transportation system. It generates constraints, including safe transportation temperature limits for each pipeline, based on the acquired basic data of the crude oil transportation system. Specifically, these constraints include:

[0040] Flow assurance constraints, flow balance constraints, hydraulic constraints, thermal constraints, boundary constraints, and piecewise linearization constraints;

[0041] The flow assurance constraint is: the oil temperature at the outlet of the pipeline is not lower than the safe delivery temperature limit of the pipeline; the flow balance constraint is: the outflow from a node is equal to the sum of the inflow from other nodes and the delivery flow; the hydraulic constraints include: hydraulic constraints of the pipe section connection unit, start-up constraints of pumps inside and outside the station connection unit, and pressure bypass constraints of the station connection unit; the thermal constraints include: thermal constraints of the pipe section connection unit, start-up constraints of the heating furnace inside the station connection unit, and thermal bypass constraints of the station connection unit; the boundary constraints include temperature boundary constraints and pressure boundary constraints.

[0042] The method for analyzing the low-carbon potential of the crude oil transportation system, preferably, includes the following: the hydraulic constraint of the pipe section connection unit is that the pressure drop of the pipe section connection unit satisfies Darcy's formula; the start-up constraint of the internal and external pumps of the station connection unit is that the pressure rise value inside the station is equal to the pressure rise value of the external pump when the external pump of the station connection unit starts up; and the pressure bypass constraint of the station connection unit is that the pressure rise value inside the station is equal to 0 when the pressure of the external pump of the station connection unit bypasses the station.

[0043] The thermal constraint of the pipe section connection unit is that the temperature drop of the pipe section connection unit satisfies the Sukhov temperature drop formula; the start-up constraint of the station connection unit heater is that when the station connection unit heater is started, the temperature rise in the station is equal to the sum of the heater temperature rise and the temperature rise caused by oil injection; the thermal bypass constraint of the station connection unit is that when the station connection unit thermal bypasses the station, the temperature rise in the station is equal to the temperature rise caused by oil injection.

[0044] The aforementioned method for analyzing the low-carbon potential of crude oil transportation systems preferably involves generating an operational optimization model of the crude oil transportation system based on an objective function and constraints including the safe transportation temperature limits of each pipeline. The model is then solved using a GUROBI optimization solver based on the branch-and-bound method to generate a Pareto front, yielding an economical low-carbon operation scheme. Specifically, this includes:

[0045] The piecewise linear method was used to linearize the crude oil viscosity-temperature relationship curve and pressure drop equation, and the MOMINLP model was converted into the MOMILP model. The Pareto front was obtained by solving the MOMILP model through the augmented ε-constraint method and the branch and bound method.

[0046] The economic and low-carbon operation plan includes: bypassing stations, external pump pressure increase, heating furnace temperature increase, feed pump feed pressure, water and thermal conditions along the pipeline, transportation system operating costs, and transportation system carbon emissions.

[0047] The aforementioned method for analyzing the low-carbon potential of crude oil transportation systems, preferably, is based on an operational optimization model of the crude oil transportation system. It analyzes electricity prices, crude oil prices, and the sensitivity of transportation volume, calculates operating costs and carbon emissions under different key parameters, and determines the low-carbon potential of the crude oil transportation system. Specifically, this includes:

[0048] Based on the optimization model of crude oil transportation system operation, and with the minimum operating cost of crude oil transportation system as the objective function, this paper analyzes the sensitivity of electricity price and crude oil price, gives the optimization results under variable electricity price and variable crude oil price, and judges the low-carbon potential of crude oil transportation system.

[0049] The optimization results include the number of heating stations, the number of external pumping stations, the operating cost of heating furnaces, the operating cost of external pumps, the operating cost of transfer pumps, the depreciation and maintenance cost of equipment, the operating cost of the conveying system, the carbon emissions of heating furnaces, the carbon emissions of external pumps, the carbon emissions of transfer pumps, and the carbon emissions of the conveying system.

[0050] The unit operating cost and unit carbon emissions are defined as follows:

[0051]

[0052]

[0053] In the formula, Q change This is the coefficient for the change in output. Operating cost per unit of transmission volume; Carbon emissions per unit of transmission volume.

[0054] Based on the operation optimization model of the crude oil transportation system, and with the minimum operating cost of the crude oil transportation system as the objective function, the sensitivity of the transportation volume is analyzed, the optimization results under variable transportation volume are given, and the low-carbon potential of the transportation system is judged.

[0055] A second aspect of the present invention provides a low-carbon potential analysis device for a crude oil transportation system, comprising:

[0056] The first processing unit is used to acquire basic data of the crude oil transportation system and add virtual nodes to divide the crude oil transportation system into station connection units and pipeline connection units.

[0057] The second processing unit is used to divide the carbon emission stages according to the life cycle characteristics of the crude oil transportation system, establish a life cycle environmental load assessment model for the crude oil transportation system, and calculate the carbon emissions of the crude oil transportation system at each stage.

[0058] The third processing unit is used to determine the safe transport temperature limits for each transport pipeline based on solid phase deposition pigging constraints and safe shutdown constraints.

[0059] The fourth processing unit is used to generate constraints, including the safe transport temperature limits of each pipeline, based on the basic data of the crude oil transport system and with the principle of safe and stable operation of the crude oil transport system as the principle. It establishes an operation optimization model for the crude oil transport system with the dual objectives of operating cost and carbon emissions and generates the Pareto front.

[0060] The fifth processing unit is used to analyze electricity prices, crude oil prices, and transmission volume sensitivity based on the crude oil transportation system operation optimization model, calculate operating costs and carbon emissions under different key parameters, and determine the low-carbon potential of the crude oil transportation system.

[0061] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for analyzing the low-carbon potential of crude oil transportation systems.

[0062] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for analyzing the low-carbon potential of crude oil transportation systems.

[0063] The present invention has the following advantages due to the adoption of the above technical solutions:

[0064] 1. This invention evaluates the carbon emissions of crude oil transportation systems during the production, construction, operation, flow assurance, and recycling stages from a life cycle management perspective, identifies key carbon emission stages, and lays the foundation for subsequent carbon emission optimization.

[0065] 2. This invention establishes an optimization model for the operation of a multi-constraint, nonlinear crude oil transportation system. It uses a piecewise linear method to transform the nonlinear model into a linear model, and employs the augmented ε-constraint method and a branch-and-bound algorithm based on global search to solve the optimization model. Under multiple constraints such as flow assurance constraints, hydraulic constraints, thermal constraints, and flow balance constraints, it seeks the optimal economic efficiency and low carbon emissions, and obtains the Pareto front for on-site operators to make decisions, formulate bypass plans, and determine the operating status of each piece of equipment and the water and heat distribution along the transportation system.

[0066] 3. This invention utilizes sensitivity analysis to calculate the operating costs and carbon emissions of crude oil transportation systems under varying electricity prices, crude oil prices, and transportation volumes. It assesses the economic viability and low-carbon nature of operations such as new well access, evaluates the low-carbon potential of the transportation system, and provides suggestions for the green transformation of oilfields. Attached Figure Description

[0067] Figure 1 This is a process flow diagram of a crude oil transportation system provided in an embodiment of the present invention;

[0068] Figure 2 This is a schematic diagram of the low-carbon potential analysis method for crude oil transportation systems provided in this embodiment of the present invention;

[0069] Figure 3 This is a structural diagram illustrating the carbon emission stages of the crude oil transportation system provided in this embodiment of the invention.

[0070] Figure 4 for Figure 2 A flowchart illustrating step 300 in the middle section;

[0071] Figure 5 for Figure 2 A flowchart illustrating step 400 in the middle section;

[0072] Figure 6 for Figure 2 A flowchart of step 500 in the middle section;

[0073] Figure 7 This is a schematic diagram of the Pareto front under dual-target conditions in an embodiment of the present invention;

[0074] Figure 8 a is a system hydraulic field distribution diagram of the frontal points a and d in the embodiment of the present invention; Figure 8 b is a system thermodynamic field distribution diagram of the leading edges a and d in the embodiment of the present invention;

[0075] Figure 9a is a graph showing the number of stations and costs in the electricity price sensitivity analysis of this embodiment of the invention; Figure 9 b is a graph showing carbon emissions from the electricity price sensitivity analysis in this embodiment of the invention;

[0076] Figure 10 a is a graph showing the number of stations and costs for crude oil price sensitivity analysis in this embodiment of the invention; Figure 10 b is a graph showing the carbon emissions from the crude oil price sensitivity analysis in this embodiment of the invention;

[0077] Figure 11 a is a graph showing the number of stations and costs in the throughput sensitivity analysis of this invention; Figure 11 b is a graph of carbon emissions from the throughput sensitivity analysis in this embodiment of the invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0079] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," "third," "fourth," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0080] This invention addresses the problem that crude oil extracted from most oilfields in my country has high viscosity, high pour point, high wax content, and poor fluidity, often requiring high-temperature heating for transportation. High-temperature transportation and storage lead to the volatilization of significant amounts of light components, resulting in high system operating costs and carbon emissions. This invention evaluates the carbon emissions of crude oil transportation systems at each stage from a life-cycle perspective, identifying key carbon-emitting stages; establishes a multi-constraint, nonlinear optimization model for crude oil transportation systems to seek the optimal economic and low-carbon solution, obtaining the Pareto front for decision-making by field operators; conducts sensitivity analysis on key parameters to evaluate the low-carbon potential of the transportation system; and provides a device, medium, and equipment for analyzing the low-carbon potential of crude oil transportation systems.

[0081] The technical solution of the present invention will be described below with reference to specific embodiments.

[0082] Figure 1 This is a process flow diagram of a crude oil transportation system. There are 5 transportation pipelines and 6 joint stations. Currently, the heating furnaces, external pumps and transfer pumps in each joint station are all in operation. Some joint stations receive local oil transfers. The local oil is mixed with the oil from the upstream station, pressurized by pumps, heated by heating furnaces, and then transported.

[0083] The method for analyzing the low-carbon potential of crude oil transportation systems involved in this invention has the following specific process: Figure 2 As shown, the process of step 300 is as follows: Figure 4 As shown, the process of step 400 is as follows: Figure 5 As shown, the process of step 500 is as follows: Figure 6 The specific implementation method includes the following steps:

[0084] Step 100: Obtain basic data of the crude oil transportation system, including pipeline design data, crude oil physical property data, equipment parameters, environmental data and carbon emission coefficients, and add virtual nodes to divide the crude oil transportation system into station connection units and pipeline segment connection units.

[0085] Understandably, the pipeline design data in step 100 includes the length, inner diameter, and design pressure of each pipe segment; the crude oil physical property data includes crude oil density, heat capacity, viscosity-temperature curves, and wax precipitation characteristic curves; the equipment parameters include the maximum heating temperature of the heater, the minimum inlet pressure of the external pump, the minimum allowable inlet pressure of the storage tank, equipment purchase costs, and current equipment operating parameters; the environmental data includes the altitude along the pipeline segment, soil temperature, and overall heat transfer coefficient; and the carbon emission coefficients include CO2e emission coefficients for diesel power supply, steel pipe manufacturing, grid power supply, and crude oil power supply. A single transport pipeline is decomposed into multiple pipe segment connection units, and virtual nodes are used to represent the topology of the transport system, including virtual nodes at the inlet and outlet of each equipment and virtual nodes for each pipe segment.

[0086] Step 200: Environmental load assessment within the life cycle. Based on the life cycle characteristics of the crude oil transportation system, carbon emission stages are divided, and a life cycle environmental load assessment model for the transportation system is established.

[0087] Specifically, carbon emission stages include carbon emissions during the production and construction phase, the operation phase, the liquidity support phase, and the recycling phase. Detailed classification of carbon emission stages and the meaning of carbon emission symbols are as follows: Figure 3 As shown;

[0088] The life-cycle environmental load assessment model for crude oil transportation systems uses the carbon emission factor method to calculate the carbon emissions at each stage of the crude oil transportation system, expressed as follows:

[0089] E = AD × EF (1)

[0090] In the formula: E is the CO2e emission, kg CO2e; AD is the amount of substance consumed, per unit of substance; EF is the carbon emission intensity coefficient, kg CO2e / unit of substance.

[0091] It is understandable that the carbon emissions from construction excavation and backfilling can be expressed as:

[0092]

[0093] In the formula, n pipe S represents the number of pipes. 1,i Let m be the cross-sectional area of ​​the excavation of the i-th trench. 2 S 2,i Let m be the cross-sectional area of ​​the backfill for the i-th trench. 2 L i W is the length of the i-th pipe, in meters. e Excavator power, W; T e The time required to complete one excavation is s; V e For the volume of earth and rock excavated in one operation, m 3 η exc For the operating efficiency of excavators; EF diesel CO2e emission factor for diesel fuel, kg CO2e / J; E 1,31 Carbon emissions from backfilling by construction excavators, kg CO2e.

[0094] Soil carbon loss during the production phase can be represented as:

[0095]

[0096] In the formula, The proportion of soil organic carbon loss due to large-scale land use; V soil For the large-scale land use volume during pipe laying, m 3 ; Organic carbon storage in unexploited soil, kg SOC / m³ 3 ; Here, SOC is the conversion factor between SOC and CO2e, expressed as kg CO2e / kg SOC; ΔC v The reduction in the rate of soil organic carbon sequestration caused by pipeline laying, expressed in kg SOC / (m³). 3 ·a); T life For the design life of the oil transportation system, a; E 1,32 Soil erosion during the production phase, kgCO2e; E 1,321 Carbon loss from soil excavation, kgCO2e; E 1,322This refers to carbon loss caused by the weakening of soil carbon sequestration capacity, expressed as kgCO2e.

[0097] Carbon emissions from solid phase deposition pigging can be expressed as:

[0098]

[0099] In the formula, t wax To plan the wax removal cycle, a; EF wax CO2e generated during a single pigging operation, kg CO2e; E 3，1 The carbon emissions during the solid-phase deposition pigging process are expressed in kg CO2e.

[0100] Evaporation loss and leakage diffusion can be expressed as:

[0101]

[0102] In the formula, EF fe1 EF fe2 These are the CO2e emission coefficient and CH4e emission coefficient during evaporation leakage, respectively, in kgCO2e / m³. 3 oil, kg CH4e / m 3 oil; Q a,i Let m be the annual transport capacity of the i-th pipeline. 3 oil / a; E represents the global warming potential conversion factor of methane based on CO2, 21 kg CO2e / kg CH4e; 3,2 Carbon emissions from evaporation loss and leakage diffusion processes are expressed in kg CO2e.

[0103] Understandably, carbon emissions are calculated using the carbon emission factor method in all other stages.

[0104] Table 1. Carbon emissions of crude oil transportation system at different stages

[0105]

[0106] Table 1 shows the carbon emissions of the crude oil transportation system at different stages. The main energy consumption in the life cycle of the transportation system comes from the operation stage, accounting for about 76.64%. Optimizing carbon emissions during the operation stage is particularly important.

[0107] Step 300: Flow assurance analysis. Based on the constraints of solid phase deposition pigging and safe shutdown, determine the safe transport temperature limits for each transport pipeline.

[0108] Specifically, it includes the following steps:

[0109] (I) Step 301:

[0110] The volume of solid deposits in the pipeline during the on-site cleaning cycle at different operating outlet oil temperatures was calculated based on the solid phase deposition model. The solid phase deposition model is exemplified by the molecular diffusion model.

[0111]

[0112] in, The thickness of the sedimentary layer changes over time, in mm / s; k is a sedimentation kinetic parameter; F w This refers to the wax content of the sedimentary layer. is the solubility coefficient of wax crystals in oil, 1 / ℃; D represents the radial temperature gradient of the pipe wall, in °C / m. w Let m be the diffusion coefficient of wax molecules in the oil. 2 / s.

[0113] With the constraint that the volume of solid deposits in the pipeline should not exceed the volume of the receiver, the minimum operating outlet oil temperature under the solid deposit cleaning constraint for each pipeline section is determined as follows:

[0114] V wax (T out,wax ,t wax )≤V pig (2)

[0115] T wax =min(T) out,wax (3)

[0116] In the formula, V wax (T out,wax ,t wax (The value is T) is the operating outlet oil temperature. out,wax On-site cleaning cycle t wax The volume of solid deposits in the pipeline at that time, m 3 V pig For the receiver volume, m 3 ;T wax The minimum operating outlet oil temperature under solid phase deposition pigging constraints is ℃.

[0117] Table 2. Wax deposition volume during different inlet oil temperatures and pigging cycles in the pipelines from Joint Station 1 to Joint Station 2.

[0118]

[0119]

[0120] Table 2 shows the wax deposition volume of the pipeline from Joint Station 1 to Joint Station 2 at different inlet oil temperatures and during the cleaning cycle. It can be seen that the minimum operating outlet oil temperature of the pipeline from Joint Station 1 to Joint Station 2 under the constraint of solid phase deposition cleaning is 51.58℃.

[0121] (II) Step 302:

[0122] Based on the shutdown temperature drop model, the pipeline outlet oil temperature at the safe shutdown time under different operating outlet oil temperatures is calculated as follows:

[0123]

[0124] In the formula, T stop (T out,stop ,t stop (The value is T) is the operating outlet oil temperature. out,stop Safe shutdown time t stop Pipeline outlet oil temperature at time, °C; T G is the ambient temperature, in °C; b is a coefficient related to the pipe's specific heat capacity, inner diameter, outer diameter, specific heat capacity, and density.

[0125] With the constraint that the pipeline outlet oil temperature at the safe shutdown time should not be less than 3°C of the crude oil's pour point, the minimum operating outlet oil temperature for each pipeline section under the safe shutdown constraint is determined as follows:

[0126] T stop (T out,stop ,t stop )≥T cp +3 (4)

[0127] T stop =min(T) out,stop (5)

[0128] In the formula, T cp T is the pour point of crude oil, in °C; stop The minimum operating outlet oil temperature under safe shutdown constraints is ℃.

[0129] Table 3. Outlet oil temperature of pipelines from Joint Station 1 to Joint Station 2 at different inlet oil temperatures and safe shutdown times.

[0130]

[0131] Table 3 shows the outlet oil temperature of the pipeline from Joint Station 1 to Joint Station 2 under different inlet oil temperatures and safe shutdown times. It can be seen that the minimum operating outlet oil temperature of the pipeline from Joint Station 1 to Joint Station 2 under the safe shutdown constraint is 48.43℃.

[0132] (III) Step 303:

[0133] The safe transport temperature limits for each pipeline are determined by two constraints: the safe transport temperature limit for the pipeline is higher than the minimum operating outlet oil temperature under the constraint of solid phase deposition pigging in the pipeline section, and the safe transport temperature limit for the pipeline is higher than the minimum operating outlet oil temperature under the constraint of safe shutdown in the pipeline section. Therefore, the safe transport temperature limit for the pipelines from Joint Station 1 to Joint Station 2 is 51.58℃. The safe transport temperature limits for each pipeline are shown in Table 4.

[0134] Table 4 Safe transport temperature limits for each transport pipeline

[0135] Safe transport temperature limits, °C Joint Station 1 - Joint Station 2 51.58 Joint Station 2 - Joint Station 3 48.45 Joint Station 3 - Joint Station 4 48.64 Joint Station 4 - Joint Station 5 48.01 Joint Station 5 - Joint Station 6 46.74

[0136] (iv) Step 400:

[0137] The optimization of crude oil transportation system operation is based on the principle of safe and stable operation of the crude oil transportation system. Constraints, including the safe transportation temperature limits of each pipeline, are generated based on the basic data of the crude oil transportation system. With economy and low carbon as the dual objectives, an operation optimization model of crude oil transportation system is established, and a Pareto front is generated. Decision-makers can select the operation scheme corresponding to the Pareto point that meets the management requirements from the Pareto front.

[0138] The Pareto front refers to the following: Given two objective functions, if for solution A, no other solution in the variable space is superior to solution A (note that "superior" here means that both objective function values ​​are superior to the corresponding function values ​​of A), then solution A is the Pareto optimal solution, and the set of Pareto optimal solutions is called the Pareto front. The Pareto front of this problem is... Figure 7 Points a, b, c, and d in the diagram.

[0139] Specifically, it includes the following steps:

[0140] Step 401: Define the objective function, with the lowest operating cost of the crude oil transportation system as the economic objective and the lowest carbon emissions from the operation of the crude oil transportation system as the low-carbon objective.

[0141] The operating cost of the crude oil transportation system is the sum of the operating costs of each external pump, each transfer pump, each heater, and the depreciation and maintenance costs of each piece of equipment, expressed as:

[0142] min f eco =N p1 +N p2 +N f +N rep (6)

[0143] The operating cost of the external pump is expressed as follows:

[0144]

[0145] The annual operating cost of the transfer pump is expressed as follows:

[0146]

[0147] The annual operating cost of the heating furnace is expressed as follows:

[0148]

[0149] The depreciation and maintenance costs of each piece of equipment are expressed as follows:

[0150]

[0151] In the formula: f eco Operating costs for the crude oil transportation system, CNY; N p1 Operating costs for each external pump, CNY; N p2 Operating costs for each pumping unit, CNY; N f Operating costs for each heating furnace, CNY; N rep Equipment depreciation and maintenance costs, CNY; These are the sets of inlet and outlet nodes for the station connection unit, respectively; P j Let Pa be the pressure at node j; T be the pressure at node j. j Let P be the temperature of node j, in °C; i Let T be the pressure at node i, Pa; i Let be the temperature of node i, in °C; variable BP i,j This represents the connection relationship between nodes i and j. If nodes i and j are the entrance and exit of the connection unit, the value is 1; otherwise, it is 0. i For the combined station insertion flow at node i, m 3 / s;Q i,j m represents the flow rate of the pipe segment connection unit between nodes i and j. 3 / s;ρ i,j The crude oil density of the pipe segment connecting nodes i and j, in kg / m³. 3 η p1 η p2 η f These represent the operating efficiencies of the external pump, the feed pump, and the heating furnace, respectively; t is the annual operating time of the system, in seconds; σ e Electricity price, CNY / (kW·h); σ f CNY / m 3 ;c H c is the lower heating value of crude oil, J / kg; c is the specific heat capacity of crude oil, J / (kg·K); The costs for purchasing and constructing the heating furnace, external pump, and transfer pump are respectively in CNY; σ bThe depreciation and maintenance rate for equipment; to prevent accidents from causing the continuous operation of the conveyor system to stop, each joint station will add backup heaters and backup pumps, n f n p1 n p2 The number of heating furnaces, external pumps, and heating furnaces at each station; For 0-1 decision variables, if the node When the external pump in the inter-station connection unit is turned on, the value is 1; otherwise, it is 0. For 0-1 decision variables, if the node When the heating station in the intermediate station connection unit is turned on, the value is 1; otherwise, it is 0.

[0152] The carbon emissions from the crude oil transportation system are the sum of the carbon emissions from each external pump, each transfer pump, and each heating furnace, expressed as:

[0153]

[0154] In the formula, f car Carbon emissions from the operation of the transport system, kg CO2e; EF ele CO2e emission factor for power grid supply, kgCO2e / (kW·h); EF oil CO2e emission factor for crude oil energy supply, kg CO2e / J.

[0155] Step 402: Based on the principle of safe and stable operation of the crude oil transportation system, generate the constraints of the crude oil transportation system operation optimization model according to the basic data of the crude oil transportation system obtained.

[0156] The constraints include: flow assurance constraints, flow balance constraints, hydraulic constraints, thermal constraints, boundary constraints, and piecewise linearization constraints;

[0157] (1) The flow protection constraint is that the oil temperature at the outlet of the pipeline is not lower than the safe delivery temperature limit of the pipeline:

[0158]

[0159] In the formula, T lim,i , ℃, represents the safe transport temperature limit of the pipeline where node i is located.

[0160] (2) The flow balance constraint is that the outflow from a node is equal to the sum of the inflow from other nodes and the plugged-in flow:

[0161]

[0162] (3) Hydraulic constraints

[0163] ① Hydraulic constraints of pipe section connection units:

[0164]

[0165]

[0166]

[0167]

[0168] In the formula, M is an infinite number; v i Let m be the kinematic viscosity of the crude oil at node i. 2 / s;d i,j H represents the inner diameter (m) of the pipe segment connection unit between nodes i and j. i,j Let m be the height difference between the pipe segment connection units between nodes i and j; F i,j The pressure drop of the pipe segment connecting nodes i and j is Pa; g is the acceleration due to gravity, m / s². 2 Equations (22) and (23) are used to calculate the pressure difference F between the pipe segment connection units between nodes i and j. i,j The first term on the right represents the friction loss along the pipe, and the second term on the right represents the gravitational potential energy loss. Equations (24) and (25) are used to ensure that the pressure difference between the pipe section connection units between nodes i and j is equal to the sum of the friction loss along the pipe and the gravitational potential energy loss. These are the inlet and outlet node sets for the pipe segment connection unit, respectively.

[0169] ② Start-up constraints of internal and external pumps in the station connection unit:

[0170]

[0171]

[0172] In the formula, ΔP i,j Let be the boost pressure value of the external pump of the station connection unit between nodes i and j, in Pa. Equations (26) and (27) ensure that if the internal and external pumps of the station connection unit between nodes i and j are started, then the pressure difference between nodes i and j is equal to the boost pressure value of the external pump.

[0173] ③ Station connection unit pressure bypass constraint:

[0174]

[0175]

[0176] Equations (28) and (29) ensure that if the pressure of the station connection unit between nodes i and j exceeds that of the station, then the pressure of nodes i and j is equal.

[0177] (4) Thermal confinement:

[0178] ① Thermal constraints on pipe connection units:

[0179] The diameter, length, and other parameters of different pipe segment connection units may vary; μ is defined. i,j e represents the pipe segment connection unit -αL :

[0180]

[0181]

[0182] Equations (30) and (31) ensure that the temperature drop of the pipe segment connection unit between nodes i and j satisfies the Sukhov formula.

[0183] ② Start-up constraints of the heating furnace within the station connection unit:

[0184] When the joint station along the line is built together with the intermediate station, the produced fluid from the oil wells along the line is processed by the joint station and then directly injected into the transportation system. The injected oil mixes with the oil coming from the upstream station, causing the oil temperature to change.

[0185]

[0186]

[0187]

[0188]

[0189] In the formula, The temperature change of oil caused by oil transfer is measured in °C; T. i in For nodes Oil delivery temperature, °C; ΔT i,j Let be the temperature rise value of the heating furnace in the station connection unit between nodes i and j, in °C. Equations (32) and (33) are used to calculate the temperature change caused by oil injection at intermediate stations, in °C; Equations (34) and (35) ensure that if the heating furnace in the station connection unit between nodes i and j is turned on, the temperature change of nodes i and j consists of two parts: the oil temperature change caused by oil injection and the heating furnace temperature rise value.

[0190] ③ Thermal bypass constraint of station connection unit:

[0191]

[0192]

[0193] Equations (36) and (37) ensure that if the station connection unit between nodes i and j achieves thermal bypass, the temperature change of nodes i and j is equal to the oil temperature change caused by the oil transfer.

[0194] (4) Boundary constraints:

[0195] ① Temperature boundary constraints:

[0196]

[0197]

[0198] In the formula, T o The known fundamental node temperature; OT i It is a 0-1 variable. If the temperature of node i is known, the value is 1, otherwise it is 0.

[0199] node The temperature is below the upper limit temperature of the heating furnace, that is:

[0200]

[0201] In the formula, T max This represents the upper limit temperature of the heating furnace, in °C.

[0202] ② Pressure boundary constraints:

[0203]

[0204]

[0205] In the formula, P o Given the known basic node pressures; OP i It is a 0-1 variable. If the pressure at node i is known, the value is 1, otherwise it is 0.

[0206] The pressure at node i ∈ I satisfies the upper and lower pressure limits of the conveying system, that is:

[0207]

[0208] In the formula, P min P max These represent the upper and lower pressure limits of the conveying system, in Pa.

[0209] Step 403:

[0210] Based on the objective function and the constraints of the crude oil transportation system operation optimization model, an operation optimization model for the transportation system is generated. The model is then solved using a GUROBI optimization solver based on the branch and bound method, generating a Pareto front and obtaining an economical and low-carbon operation scheme.

[0211] Among them, the piecewise linear method was used to linearize the crude oil viscosity-temperature relationship curve and pressure drop equation, and the MOMINLP model was converted into the MOMILP model.

[0212] Linearization constraints:

[0213]

[0214]

[0215]

[0216]

[0217]

[0218]

[0219]

[0220]

[0221]

[0222]

[0223] In the formula, Q Ai,j v represents the characteristic flow rate value of the pipe segment connection unit between nodes i and j in the pipeline characteristic equation; Ai K represents the characteristic viscosity value of node i in the viscosity-temperature curve. T K Q These are sets of temperature range and flow rate range numbers, respectively; T max,k T min,k Let k be the upper and lower limits of the temperature range, in °C; Q max,k Q min,k Let m be the upper and lower limits of the flow range k. 3 / s; for The corresponding flow range has a flow rate of 1.75 to the power of 1. for The viscosity of the corresponding temperature range is 0.25. The decision variable is 0-1. If the crude oil temperature at node i is within the temperature range k, then it is 1; otherwise, it is 0. It is a 0-1 decision variable. If the flow rate of the pipe segment connection unit between nodes i and j is within the flow rate interval k, it is 1; otherwise, it is 0.

[0224] Equations (44) and (45) are the linearized pipe section pressure drop equations, which can be used to calculate the pressure drop of the pipe section connection unit between nodes i and j; Equations (46) and (47) determine that the temperature of node i is in the temperature range k; Equation (48) indicates that the temperature of node i can only be in one temperature range; Equation (49) determines the characteristic viscosity value of node i; Equations (50) and (51) determine the flow range k of the pipe section connection unit between nodes i and j; Equation (52) indicates that the flow of the pipe section connection unit between nodes i and j can only be in one flow range; Equation (53) determines the characteristic flow value of the pipe section connection unit between nodes i and j.

[0225] The Pareto front was obtained by solving the MOMILP model using the augmented ε-constraint method and the branch-and-bound method. Table 5 shows the operating cost and carbon emissions of the transport system at each front point. Figure 7 This is a schematic diagram of the Pareto front under dual objectives. Figure 8 The system hydrothermal distribution diagram at the frontier points of a (economic goal) and d (low-carbon goal).

[0226] Table 5. Operating costs and carbon emissions of the front-end point conveying system

[0227]

[0228] According to the Pareto frontier results, operating carbon emissions can be reduced by up to 24.5%, and operating costs by up to 30%. When economically optimal, [the following data is presented:] ... Figure 8 (a) It can be seen that joint stations 2 and 5 achieve pressure bypassing, reducing the difficulty of operation and management of the conveying system; Figure 8 (b) It can be seen that the operating temperature of the optimized scheme is significantly reduced. It is worth noting that the heating furnace in Joint Station 2 is in a closed state, achieving thermal bypass. The sudden temperature increase at Joint Station 2 is due to the large oil throughput and high temperature at this station, which provides a large amount of heat energy to the transportation system, causing the oil temperature to increase from 53.10℃ to 60.33℃. The carbon emissions of the low-carbon optimal scheme are not significantly different from those of the economic optimal scheme, decreasing by only 0.41%, but the operating costs increase by 2.2%.

[0229] Step 500:

[0230] Low-carbon potential analysis is based on the operation optimization model of crude oil transportation system. It analyzes the sensitivity of electricity price, crude oil price, and transportation volume, calculates the operating costs and carbon emissions under different key parameters, and judges the low-carbon potential of crude oil transportation system.

[0231] Specifically, it includes the following steps:

[0232] Step 501: Based on the crude oil transportation system operation optimization model, with the minimum transportation system operating cost as the objective function, analyze the sensitivity of electricity price and crude oil price, give the optimization results under variable electricity price and variable crude oil price, and judge the low carbon potential of the transportation system.

[0233] Figure 9 The graph shows the results of the electricity price sensitivity analysis. As the electricity price increases, the operating costs of the transfer pump / external transfer pump and the transportation system increase in an approximately linear manner, while other indicators remain stable. The bypass scheme has not changed, and the electricity price has little impact on the formulation of the operation plan. In terms of electricity price, this crude oil transportation system has no low-carbon potential. Figure 10 The graph shows the results of the crude oil price sensitivity analysis. As crude oil prices increase, the operating costs of the transportation system increase linearly. The carbon emissions from operation at a crude oil price of $110 / barrel are 5.2% lower than those at $21 / barrel. This crude oil transportation system has certain low-carbon potential in response to crude oil prices.

[0234] Step 502: Define the unit transmission capacity operating cost and unit transmission capacity carbon emissions, expressed as follows:

[0235]

[0236]

[0237] In the formula, Q change This is the coefficient for the change in output. Operating cost per unit of throughput, CNY; Carbon emissions per unit of transport volume, expressed as kg CO2e.

[0238] Step 503: Based on the crude oil transportation system operation optimization model, with the minimum transportation system operating cost as the objective function, analyze the throughput sensitivity, give the optimization results under variable throughput, and judge the low-carbon potential of the transportation system.

[0239] Figure 11 The graph shows the results of the throughput sensitivity analysis. As the throughput increases, the rate of decrease in operating cost per unit throughput gradually decreases, and the carbon emissions per unit throughput exhibit a concave shape. The carbon emissions per unit throughput are the lowest at 1.18 times the throughput, and the optimal throughput is 1.18 times the throughput. In terms of throughput, this conveying system has high low-carbon potential.

[0240] A second aspect of the present invention provides a low-carbon potential analysis device for a crude oil transportation system, comprising:

[0241] The first processing unit is used to acquire basic data of the crude oil transportation system and add virtual nodes to divide the crude oil transportation system into station connection units and pipeline connection units.

[0242] The second processing unit is used to divide the carbon emission stages according to the life cycle characteristics of the crude oil transportation system, establish a life cycle environmental load assessment model for the crude oil transportation system, and calculate the carbon emissions of the crude oil transportation system at each stage.

[0243] The third processing unit is used to determine the safe transport temperature limits for each transport pipeline based on solid phase deposition pigging constraints and safe shutdown constraints.

[0244] The fourth processing unit is used to generate constraints, including the safe transport temperature limits of each pipeline, based on the basic data of the crude oil transport system and with the principle of safe and stable operation of the crude oil transport system as the principle. It establishes an operation optimization model for the crude oil transport system with the dual objectives of operating cost and carbon emissions and generates the Pareto front.

[0245] The fifth processing unit is used to analyze electricity prices, crude oil prices, and transmission volume sensitivity based on the crude oil transportation system operation optimization model, calculate operating costs and carbon emissions under different key parameters, and determine the low-carbon potential of the crude oil transportation system.

[0246] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for analyzing the low-carbon potential of crude oil transportation systems.

[0247] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for analyzing the low-carbon potential of crude oil transportation systems.

[0248] This invention is described based on flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0249] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0250] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0251] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing low carbon potential of a crude oil transportation system, characterized by, The method comprises the following steps: acquiring basic data of the crude oil transportation system, adding virtual nodes to divide the crude oil transportation system into station connection units and pipe section connection units; dividing carbon emission stages according to the life cycle characteristics of the crude oil transportation system, establishing a life cycle environmental load evaluation model of the crude oil transportation system, and calculating the carbon emissions of each stage of the crude oil transportation system; determining the safe transportation temperature limits of each transportation pipeline according to the solid deposition pigging constraint and the safe shutdown constraint; generating constraint conditions including the safe transportation temperature limits of each transportation pipeline according to the acquired basic data of the crude oil transportation system, taking the safe and stable operation of the crude oil transportation system as the principle, taking the operation cost and the operation carbon emission of the crude oil transportation system as double targets, establishing an operation optimization model of the crude oil transportation system, and generating a pareto frontier; specifically including: defining a target function, taking the minimum operation cost of the crude oil transportation system as the economic target, and taking the minimum operation carbon emission of the crude oil transportation system as the low-carbon target; generating constraint conditions including the safe transportation temperature limits of each transportation pipeline according to the acquired basic data of the crude oil transportation system, taking the safe and stable operation of the crude oil transportation system as the principle; generating the operation optimization model of the crude oil transportation system according to the target function and the constraint conditions including the safe transportation temperature limits of each transportation pipeline, solving the operation optimization model of the crude oil transportation system by using a GUROBI optimization solver based on a branch and bound method, generating a pareto frontier, and obtaining an economic and low-carbon operation scheme; based on the operation optimization model of the crude oil transportation system, analyzing the sensitivity of electricity price, crude oil price and transportation capacity, accounting for the operation cost and carbon emission under different key parameters, and judging the low-carbon potential of the crude oil transportation system; wherein the target function is defined, the minimum operation cost of the crude oil transportation system is taken as the economic target, and the minimum operation carbon emission of the crude oil transportation system is taken as the low-carbon target, specifically including: the operation cost of the crude oil transportation system is the sum of the operation cost of each external pump, the operation cost of each insertion pump, the operation cost of each heating furnace and the depreciation and maintenance cost of each equipment, and is represented as: （6） wherein is the operating cost of the crude oil transportation system; is the operating cost of each export pump; is the operating cost of each injection pump; is the operating cost of each heater; is the equipment depreciation and maintenance cost; the operation carbon emission of the crude oil transportation system is the sum of the carbon emission of each external pump, the carbon emission of each insertion pump and the carbon emission of each heating furnace, and is represented as: （7） wherein is the carbon emission of the crude oil transportation system operation; is the pressure of node j ; is the temperature of node j ; is the pressure of node i ; is the temperature of node i ; , are the inlet and outlet node sets of the station connection unit, respectively; represents the connection relationship between nodes i , j ; is a 0-1 decision variable; is a 0-1 decision variable; is the intermediate station injection flow at node i ; is the specific heat capacity of crude oil; is the flow of the pipe segment connection unit between nodes i , j ; is the crude oil density of the pipe segment connection unit between nodes i , j ; , , are the operating efficiencies of the export pump, the injection pump, and the heating furnace, respectively; is the annual operation time of the system; is the CO2e emission coefficient of the power grid energy supply; is the CO2e emission coefficient of the crude oil energy supply.

2. The method of claim 1, wherein, according to the life cycle characteristics of the crude oil transportation system, dividing carbon emission stages, establishing a life cycle environmental load evaluation model of the crude oil transportation system, and calculating the carbon emissions of each stage of the crude oil transportation system, specifically including: the carbon emission stages include production and construction stage carbon emission, operation stage carbon emission, flow guarantee stage carbon emission and recovery stage carbon emission; the life cycle environmental load evaluation model of the crude oil transportation system calculates the carbon emissions of each stage of the crude oil transportation system by using the carbon emission coefficient method, and is represented as: （1） In the formula: E is the CO2e emissions; AD is the material consumption; EF is the carbon emission intensity coefficient.

3. The method of claim 1, wherein, determining the safe transportation temperature limits of each transportation pipeline according to the solid deposition pigging constraint and the safe shutdown constraint, specifically including: the solid deposition pigging constraint is specifically that the solid deposition volume of the pipeline at different running outlet oil temperatures is calculated according to the solid deposition model, and the constraint that the solid deposition volume of the pipeline is not greater than the volume of the receiver is taken to determine the minimum running outlet oil temperature under the solid deposition pigging constraint of each pipe section, and is represented as: （2） （3） wherein is the minimum operating outlet oil temperature at the time of the pipeline solids deposition volume; is the field pigging cycle; is the pipeline solids deposition volume at the time of the field pigging cycle; is the receiver volume; is the minimum operating outlet oil temperature under solids deposition pigging constraints; The safe shutdown constraint specifically refers to: calculating the pipeline outlet oil temperature at different running outlet oil temperatures according to the shutdown temperature drop model, taking the pipeline outlet oil temperature being not less than 3 ℃ of the crude oil freezing point as a constraint, determining the minimum running outlet oil temperature of each pipe section under the safe shutdown constraint, and representing as: （4） （5） wherein is the pipeline outlet oil temperature at the operating outlet oil temperature is the safety shutdown time is the pipeline outlet oil temperature at the operating outlet oil temperature; is the crude oil pour point; is the minimum operating outlet oil temperature under the safety shutdown constraint; Taking the safe transportation temperature limit of the transportation pipeline being higher than the minimum running outlet oil temperature under the constraint of the pipe section solid deposition pigging and the safe transportation temperature limit of the transportation pipeline being higher than the minimum running outlet oil temperature under the constraint of the pipe section safe shutdown as constraints, the safe transportation temperature limit of each transportation pipeline is generated.

4. The method of claim 1, wherein, Taking the safe and stable operation of the crude oil transportation system as a principle, the constraint conditions including the safe transportation temperature limit of each transportation pipeline are generated according to the obtained basic data of the crude oil transportation system, specifically including: Flow guarantee constraint, flow balance constraint, hydraulic constraint, thermal constraint, boundary constraint and piecewise linearization constraint; The flow guarantee constraint is that the outlet oil temperature of the transportation pipeline is not lower than the safe transportation temperature limit of the transportation pipeline; the flow balance constraint is that the flow out of the node is equal to the sum of the flow into the node and the flow inserted; the hydraulic constraint includes the pipe section connection unit hydraulic constraint, the station connection unit internal and external transportation pump start constraint and the station connection unit pressure over-station constraint; the thermal constraint includes the pipe section connection unit thermal constraint, the station connection unit internal heating furnace start constraint and the station connection unit thermal over-station constraint; the boundary constraint includes temperature boundary constraint and pressure boundary constraint.

5. The method of claim 4, wherein, The pipe section connection unit hydraulic constraint is that the pressure drop of the pipe section connection unit satisfies the Darcy formula; the station connection unit internal and external transportation pump start constraint is that when the external transportation pump of the station connection unit is started, the station internal pressure boost value is equal to the external transportation pump pressure boost value; The station connection unit pressure over-station constraint is that when the external transportation pump pressure of the station connection unit over-station, the station internal pressure boost value is equal to 0; The pipe section connection unit thermal constraint is that the temperature drop of the pipe section connection unit satisfies the Sukhov temperature drop formula; the station connection unit heating furnace start constraint is that when the heating furnace of the station connection unit is started, the station internal temperature boost value is equal to the sum of the heating furnace temperature boost value and the inserted oil temperature boost value; the station connection unit thermal over-station constraint is that when the thermal over-station of the station connection unit, the station internal temperature boost value is equal to the inserted oil temperature boost value.

6. The method of claim 1, wherein, The crude oil transportation system operation optimization model is generated according to the objective function and the constraint conditions including the safe transportation temperature limit of each transportation pipeline, and the GUROBI optimization solver based on the branch and bound method is used to solve the crude oil transportation system operation optimization model to generate the pareto frontier and obtain the economic and low-carbon operation scheme, specifically including: The piecewise linear method is used for linearization processing of the crude oil viscosity-temperature relationship curve and the pressure drop equation, the MOMINLP model is converted into the MOMILP model, and the pareto frontier is obtained by solving the MOMILP model through the augmented ε-constraint method and the branch and bound method; The economic and low-carbon operation scheme includes: over-station scheme, external transportation pump pressure boost value, heating furnace temperature boost value, inserted pump inserted pressure, water and thermal state along the pipeline, transportation system operation cost and transportation system carbon emission.

7. The method of claim 1, wherein, Based on the crude oil transportation system operation optimization model, the sensitivity of electricity price, crude oil price and throughput is analyzed, the operation cost and carbon emission under different key parameters are calculated, and the low-carbon potential of the crude oil transportation system is judged, specifically including: Based on the operation optimization model of the crude oil transportation system, the sensitivity of electricity price and crude oil price is analyzed, and the optimization results under variable electricity price and variable crude oil price are given to determine the low-carbon potential of the crude oil transportation system; The optimization results include the number of heating stations, the number of external pump stations, the heating furnace operation cost, the external pump operation cost, the plug-in pump operation cost, the equipment depreciation and maintenance cost, the transportation system operation cost, the heating furnace carbon emission, the external pump carbon emission, the plug-in pump carbon emission, and the transportation system carbon emission; The unit transportation volume operation cost and the unit transportation volume carbon emission are defined as follows: （8） （9） wherein is the throughput change coefficient; is the unit throughput operating cost; is the unit throughput operating carbon emission; Based on the operation optimization model of the crude oil transportation system, the sensitivity of throughput is analyzed, and the optimization results under variable throughput are given to determine the low-carbon potential of the transportation system.

8. A low carbon potential analysis device for a crude oil transportation system, characterized by, It includes: The first processing unit is used to obtain the basic data of the crude oil transportation system, and the crude oil transportation system is divided into station connection units and pipe segment connection units by adding virtual nodes; The second processing unit is used to divide the carbon emission stage according to the life cycle characteristics of the crude oil transportation system, establish a life cycle environmental load evaluation model of the crude oil transportation system, and calculate the carbon emission of each stage of the crude oil transportation system; The third processing unit is used to determine the safe transportation temperature limit of each transportation pipeline according to the solid deposition pigging constraint and the safe shutdown constraint; The fourth processing unit is used to generate constraint conditions including the safe transportation temperature limit of each transportation pipeline based on the obtained basic data of the crude oil transportation system, establish an operation optimization model of the crude oil transportation system with the operation cost and the operation carbon emission as double objectives, and generate a pareto frontier; specifically including: defining the objective function, taking the minimum operation cost of the crude oil transportation system as the economic target and the minimum operation carbon emission of the crude oil transportation system as the low-carbon target; generating constraint conditions including the safe transportation temperature limit of each transportation pipeline based on the obtained basic data of the crude oil transportation system according to the principle of safe and stable operation of the crude oil transportation system; generating the operation optimization model of the crude oil transportation system according to the objective function and the constraint conditions including the safe transportation temperature limit of each transportation pipeline, solving the operation optimization model of the crude oil transportation system by using the GUROBI optimization solver based on the branch and bound method, generating the pareto frontier, and obtaining the economic and low-carbon operation scheme; The fifth processing unit is used to analyze the sensitivity of electricity price, crude oil price, and throughput based on the operation optimization model of the crude oil transportation system, calculate the operation cost and carbon emission under different key parameters, and determine the low-carbon potential of the crude oil transportation system; The objective function is defined, the minimum operation cost of the crude oil transportation system is taken as the economic target, and the minimum operation carbon emission of the crude oil transportation system is taken as the low-carbon target, specifically including: The operation cost of the crude oil transportation system is the sum of the operation cost of each external pump, the operation cost of each plug-in pump, the operation cost of each heating furnace, and the depreciation and maintenance cost of each equipment, which is represented as: （6） wherein is the operating cost of the crude oil transportation system; is the operating cost of each export pump; is the operating cost of each injection pump; is the operating cost of each heater; is the equipment depreciation and maintenance cost; The operation carbon emission of the crude oil transportation system is the sum of the carbon emission of each external pump, the carbon emission of each plug-in pump, and the carbon emission of each heating furnace, which is represented as: （7） wherein is the carbon emission of the crude oil transportation system operation; is the pressure of node j ; is the temperature of node j ; is the pressure of node i ; is the temperature of node i ; , are the inlet and outlet node sets of the station connection unit, respectively; represents the connection relationship between nodes i , j ; is the 0-1 decision variable; is the 0-1 decision variable; is the intermediate station injection flow at node i ; is the specific heat capacity of crude oil; is the flow of the pipe section connection unit between nodes i , j ; is the crude oil density of the pipe section connection unit between nodes i , j ; , , are the operating efficiencies of the export pump, the injection pump, and the heating furnace, respectively; is the system annual operation time; is the CO2e emission coefficient of the power grid energy supply; is the CO2e emission coefficient of the crude oil energy supply.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method for analyzing low-carbon potential of a crude oil transportation system according to any one of claims 1-7.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the method for analyzing low-carbon potential of a crude oil transportation system according to any one of claims 1-7.

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