CCUS Cluster Pipeline Network Matching Model System and Method Based on Geological Database

Abstract

This invention provides a CCUS cluster pipeline matching model system and method based on a geological database, belonging to the field of cluster pipeline matching technology. The system includes: a pipeline generation module connected to an emission source screening module and a geological storage site screening module; the cluster pipeline generation module generates a pipeline layout from the emission source set to the geological storage site set based on the principle of the lowest cost route along the cost surface; the emission source screening module screens and optimizes emission sources to obtain target emission sources; and the geological storage site screening module screens geological storage sites. A full-process techno-economic evaluation module is used to conduct a comprehensive economic evaluation of the pipeline layout and CCUS cluster between the target geological storage site and the target emission source. This invention solves problems related to national and regional CCUS cluster costs, cluster planning, and pipeline route optimization.

Description

Technical Field

[0001] This invention relates to the field of clustered pipeline network matching technology, and in particular to a CCUS clustered pipeline network matching model system and method based on a geological database. Background Technology

[0002] Carbon dioxide (CO2) capture, utilization, and storage (CCUS) refers to the process of separating CO2 from industrial processes, energy use, or the atmosphere and injecting it directly into the ground to achieve CO2 emission reduction. CCUS is a technology capable of achieving low-carbon utilization of high-carbon energy and negative emissions. In the context of a global effort to address climate change, CCUS technology has been regarded as a safety net for achieving deep carbon reduction and carbon neutrality from concentrated CO2 emission sources such as power plants, fossil fuels, and industries like cement and chemicals. The commercial development of CCUS will involve large-scale infrastructure deployment. Therefore, before large-scale CCUS projects, careful and comprehensive planning of infrastructure (pipelines) is necessary to ensure the most cost-effective selection of capture sites (carbon emission sources), storage sites (storage sinks), backbone pipelines, and CO2 distribution branches. Thus, the matching of numerous CO2 emission sources with storage sinks is one of the key issues for the large-scale application of CCUS. Faced with a number of sources with varying emissions and capture costs, and a number of sinks with varying storage potential and storage costs, designing source-sink matching schemes and planning corresponding transportation networks from a globally optimal perspective has become the primary issue that needs to be addressed for the large-scale implementation of CCUS.

[0003] Source-sink matching plays a crucial pivotal role in the entire CCUS technology chain. Adopting a reasonable matching scheme can effectively reduce CCUS project costs and enable large-scale commercial application of CCUS technology. Some scholars have already conducted related research: for example, using a continuous-time uncertain mixed-integer linear programming (MILP) model with physical and time constraints to explore robust optimal source-sink matching problems in the CCS supply chain under uncertainty conditions; developing a CO2 transportation optimization model to reduce CO2 transportation costs; constructing a CCUS source-sink matching evaluation process and related index calculation methods for factors such as pipeline layout and oilfield succession development planning schemes; and establishing a CCUS technology source-sink matching pipeline model to achieve optimized supply allocation from different emission sources to oilfields, coal seams, and brackish water storage.

[0004] These studies still have some shortcomings. First, current research on source-sink matching decision factors mainly focuses on transportation distance, with less consideration given to the nature and cost of sources and sinks. Second, past studies have lacked a systemic approach to the interaction between various technical links within the system, failing to conduct minimum-cost system optimization of the entire project. Therefore, it is difficult to reflect the actual cost of the entire CCUS project, the minimum cost after global optimization, and the corresponding matching scheme. Third, existing research emphasizes theoretical model building, with less integration with actual technology and engineering conditions. As CCUS technology matures, the focus will shift from technical research to application promotion. At that time, facing sources with varying emissions and capture costs, and sinks with varying storage potential and storage costs, how to design source-sink matching schemes and plan corresponding transportation networks from a globally optimal perspective will become the primary problem that urgently needs to be solved for the large-scale implementation of CCUS. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a CCUS cluster pipeline matching model system and method based on a geological database. This invention solves the problem that the source-sink matching in existing technologies is not fully considered and cannot design a source-sink matching scheme from a globally optimal perspective.

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

[0007] A CCUS cluster pipeline matching model system based on a geological database includes:

[0008] The technical and economic assessment module, along with the cluster pipeline generation module, emission source screening module, and geological storage site screening module, all connected to the technical and economic assessment module;

[0009] The cluster pipeline generation module is connected to the emission source screening module and the geological storage site screening module, respectively.

[0010] The cluster pipeline generation module is used to generate a pipeline layout from the emission source set to the geological storage site set based on the principle of the lowest cost route along the cost surface. The emission source screening module is used to screen the emission source set according to the pipeline layout to obtain target emission sources. The geological storage site screening module is used to screen the geological storage site set according to the pipeline layout to obtain target geological storage sites. The techno-economic evaluation module is used to perform an economic evaluation on the pipeline layout between the target geological storage site and the target emission source to obtain the evaluation result.

[0011] Preferably, the cluster network generation module includes:

[0012] Cost surface generation submodule, automatic pipeline optimization submodule, and CO2 pipeline economics submodule;

[0013] The cost surface generation submodule is used to determine the basic variables of the minimum cost path and, based on the basic variables, assign values ​​to various pipeline cost influencing factors in the GIS database according to transportation risk and relative transportation cost to each GIS layer, calculate the cost surface, and rasterize it to obtain the cost surface. Using the cost surface as the analysis base map, the corresponding pressure-flow relationship and cost-flow relationship are calculated using pipeline seepage mechanics and the derived cost-flow model. The optimal source-sink combination is obtained by naturally finding the route with the lowest resistance during pipeline iteration. The automatic pipeline optimization submodule is used to solve the cost-pipe flow coupling model to obtain cost data and determine the target pipeline network in the optimal source-sink combination based on the cost data. The CO2 pipeline economic submodule is used to construct the pipeline network layout from the emission source set to the geological storage site set based on the target pipeline network and provide a CO2 pipeline economic model.

[0014] Preferably, the cost surface generation sub-model includes:

[0015] Variable determination unit, cost aspect determination unit, and source-sink combination determination unit;

[0016] The variable determination unit is used to determine the basic variables of the minimum cost path. The basic variables include: topography, landform, land cover, land use type, transportation, city, industry, land ownership, and population density surface data. The cost surface determination unit is used to assign values ​​to various pipeline cost influencing factors in the GIS database according to transportation risk and relative transportation cost, calculate cost surfaces, and rasterize them to obtain cost surfaces. The source-sink combination determination unit is used to use the cost surface as the analysis base map, calculate the corresponding pressure-flow relationship and cost-flow relationship using pipeline seepage mechanics and derived cost-flow model, and find the route with the lowest resistance naturally during pipeline iteration to obtain the optimal source-sink combination.

[0017] Preferably, the source-sink combination determination unit includes:

[0018] Initial pipeline determination subunit, cost path calculation subunit, and candidate pipeline determination subunit;

[0019] The initial pipeline determination subunit is used to create an initial and idealized pipeline network between the source and the sink. The cost path calculation subunit is used to iteratively solve the cost path between the emission source and the storage site in the initial and idealized pipeline network using a cost minimization path algorithm to obtain a path set. The cost minimization path algorithm is used to solve the seepage mechanics of the pipeline and calculate the corresponding pressure-flow relationship and cost-flow relationship based on the derived cost-flow model. The candidate pipeline determination subunit is used to find the route with the lowest resistance naturally during the pipeline iteration process, determine the pipeline network corresponding to the lowest path in the path set, use the current pipeline network as candidate pipelines, and obtain the optimal source-sink combination based on the candidate pipelines.

[0020] Preferably, the automatic pipeline optimization submodule includes:

[0021] Solving element and pipeline determination element;

[0022] The solution unit is used to establish a cost model and a pipeline flow model based on the cost-pipeline flow coupling model and solve for cost data using an iterative method, or to correct the cost coefficient through a cost surface and introduce key coefficients into the pipeline flow and solve for cost data. The key coefficients are equivalent permeability coefficients or cost-flow relationship coefficients. The pipeline network determination unit is used to determine the target pipeline network in the optimal source-sink combination based on the cost data.

[0023] Preferably, the pipeline parameters of the CO2 pipeline economic submodule include:

[0024] Pipeline diameter, wall thickness, pressure drop, inlet pressure, outlet pressure, operating temperature, CO2 density, CO2 standard density, CO2 viscosity, compressibility factor, roughness, and flow rate.

[0025] Preferably, the formula for calculating the pipe diameter is:

[0026]

[0027] Where D is the pipe diameter, F f Let ρ be the friction coefficient, L be the transport distance, ρ be the density, and ΔP be the pressure drop.

[0028] Preferably, the formula for calculating the friction coefficient is:

[0029]

[0030] Where ε is the pipe roughness and Re is the Reynolds coefficient.

[0031] A CCUS cluster pipeline matching model method based on a geological database includes:

[0032] The pipeline layout from emission source set to geological storage site set is generated based on the principle of CO2 flowing along the minimum cost path;

[0033] Based on the pipeline network layout, the emission source set is screened and optimized to obtain the target emission source;

[0034] Based on the pipeline network layout, the set of geological storage sites is screened to obtain the target geological storage sites;

[0035] An economic assessment was conducted on the pipeline network layout between the target geological storage site and the target emission source, and the assessment results were obtained.

[0036] Preferably, the pipeline layout for generating emission source sets to geological storage site sets based on the principle of CO2 flowing along the minimum cost path includes:

[0037] The basic variables of the minimum cost path are determined, and based on the basic variables, the data of various pipeline cost influencing factors in the GIS database are assigned to each GIS layer according to transportation risk and relative transportation cost, and the cost surface is calculated and rasterized to obtain the cost surface;

[0038] Using the cost surface as the analysis base map, the corresponding pressure-flow relationship and cost-flow relationship are calculated by using the seepage mechanics of the pipeline and the derived cost-flow model. The optimal source-sink combination is obtained by naturally finding the route with the lowest resistance during the pipeline iteration process.

[0039] The cost-pipe-flow coupling model is solved to obtain cost data, and the target pipeline network in the optimal source-sink combination is determined based on the cost data.

[0040] Based on the target pipeline network, an economic model for CO2 pipelines is provided to construct the pipeline network layout from the emission source set to the geological storage site set.

[0041] The present invention discloses the following technical effects:

[0042] This invention provides a CCUS cluster pipeline network matching model system and method based on a geological database. The system includes: a techno-economic evaluation module and cluster pipeline network generation module, emission source screening module, and geological storage site screening module, all connected to the techno-economic evaluation module. The cluster pipeline network generation module is connected to both the emission source screening module and the geological storage site screening module. The cluster pipeline network generation module generates a pipeline network layout from the emission source set to the geological storage site set based on the principle of the lowest cost route along the cost surface. The emission source screening module screens the emission source set according to the pipeline network layout to obtain target emission sources. The geological storage site screening module screens the geological storage site set according to the pipeline network layout to obtain target geological storage sites. The techno-economic evaluation module performs an economic evaluation on the pipeline network layout between the target geological storage site and the target emission source to obtain the evaluation result. This invention introduces a scalable pipeline infrastructure model for CCUS clusters, which can generate fully integrated, cost-minimized CCUS clusters. CCUS cluster layout determines where and how much CO2 is captured and stored, as well as where pipelines with different flow rates are built and connected to reduce the levelized cost of CCS. The CCUS cluster method can be used to analyze the region, scale (local, regional, national) and cost of CCUS deployment, significantly reducing the overall CCUS cost, optimizing the current source-sink mismatch situation in my country, and steadily promoting the commercialization of CCUS. This invention, based on a geological database, presents a CCUS cluster source-sink matching model suitable for large-scale calculations involving multiple sources and sinks. The model enables CCUS deployment (pipeline network) planning, significantly improving the accuracy of source-sink matching considering source-sink conditions, reducing CCUS investment risk, providing a basis for CCUS industrialization decisions and development paths, and laying the foundation for nationwide CCUS decision-making research. The CCUS cluster pipeline matching model system solves the existing source-sink matching problem. The core of the model is the pipeline generation method, with the following main advantages: 1) It forms a high-precision full-process CCUS techno-economic model; 2) The pipeline optimization theory is a real pipeline flow model; 3) The candidate pipeline selection process is automatically generated, suitable for large-scale calculations involving multiple sources and sinks. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1A schematic diagram of a CCUS cluster pipeline matching model system based on a geological database is provided for an embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram of the CCUS pipeline network solution method provided in this embodiment of the invention;

[0046] Figure 3 This is a schematic diagram of an edge weight calculation method provided in an embodiment of the present invention, wherein, Figure 3 (a) is a schematic diagram of the first allocation edge cost. Figure 3 (b) is a schematic diagram of the second allocation edge cost;

[0047] Figure 4 This is a schematic diagram illustrating the construction of a pipeline network model provided in an embodiment of the present invention, wherein, Figure 4 (a) is a schematic diagram of the cost coefficient distribution. Figure 4 (b) is a schematic diagram of the pipeline network connection. Figure 4 (c) Schematic diagram of pipeline levelized cost distribution;

[0048] Figure 5 This is a schematic diagram of the CO2 pipeline transportation implementation process provided in an embodiment of the present invention.

[0049] Explanation of reference numerals in the attached figures:

[0050] 1-Technical and economic assessment module, 2-Emission source screening module, 3-Cluster pipeline network generation module, 4-Geological storage site screening module. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] like Figure 1 As shown, this invention provides a CCUS cluster pipeline network matching model system based on a geological database, comprising:

[0054] Technical and economic assessment module 1, cluster pipeline generation module 3 (which is connected to technical and economic assessment module 1), emission source screening module 2, and geological storage site screening module 4;

[0055] The cluster pipeline generation module 3 is connected to the emission source screening module 2 and the geological storage site screening module 4, respectively.

[0056] The cluster pipeline generation module 3 is used to generate a pipeline layout from the emission source set to the geological storage site set based on the principle of CO2 flowing along the minimum cost path. The emission source screening module 2 is used to screen the emission source set according to the pipeline layout to obtain target emission sources. The geological storage site screening module 4 is used to screen the geological storage site set according to the pipeline layout to obtain target geological storage sites. The techno-economic evaluation module 1 is used to perform an economic evaluation on the pipeline layout between the target geological storage site and the target emission source to obtain the evaluation result.

[0057] Specifically, the clustered pipe network generation module simulates the natural phenomenon of water flowing along the path of least resistance and expanding to form dominant channels. Utilizing the similarity in data equations between the two, a pipe network optimization method is constructed.

[0058] Specifically, the CCUS cluster pipeline matching model, based on a geological database, uses a cluster pipeline generation method to complete the cluster source-sink matching between CO2 emission sources and geological storage sites. The model includes four modules: cluster pipeline generation, emission source screening, geological storage site screening, and technical and economic evaluation of the entire CCUS project.

[0059] Furthermore, the cluster network generation module 3 includes:

[0060] Cost surface generation submodule, automatic pipeline optimization submodule, and CO2 pipeline economics submodule;

[0061] The cost surface generation submodule is used to determine the basic variables of the minimum cost path and, based on the basic variables, assign values ​​to various pipeline cost influencing factors in the GIS database according to transportation risk and relative transportation cost to each GIS layer, calculate the cost surface, and rasterize it to obtain the cost surface. Using the cost surface as the analysis base map, the corresponding pressure-flow relationship and cost-flow relationship are calculated using pipeline seepage mechanics and the derived cost-flow model. The optimal source-sink combination is obtained by naturally finding the route with the lowest resistance during pipeline iteration. The automatic pipeline optimization submodule is used to solve the cost-pipe flow coupling model to obtain cost data and determine the target pipeline network in the optimal source-sink combination based on the cost data. The CO2 pipeline economic submodule is used to construct the pipeline network layout from the emission source set to the geological storage site set based on the target pipeline network and provide a CO2 pipeline economic model.

[0062] Specifically, the method corresponding to cluster pipeline generation module 3 is based on the principle that CO2 flows along the minimum cost path in nature. It iteratively adjusts the pipe diameter according to the CO2 flow rate to generate the optimal pipeline layout. This is achieved through iterative solutions using pipeline flow physics calculations and cost calculations. Figure 2 As shown, the solution methods for pipeline networks mainly include: preprocessing (integration of cost surface and source / sink data), physical flow (pipe diameter-pressure-flow relationship), cost-flow model (pipe diameter-cost-flow relationship), and flow-techno-economic model of the actual pipeline network.

[0063] More specifically, the detailed process of solving the pipeline network involves generating source and sink data and the pipeline network. Based on pipeline cost parameters, GIS database, and cost surface generation, the cost-flow relationship is determined and calculated. The changes in the flow space distribution are observed by comparing them with the initial grid. If the change is less than 1%, the target pipeline network is determined and the CCUS cluster is determined based on the technical and economic model. If the change is greater than or equal to 1%, the initial pipeline network is reset and the logistics flow calculation, cost-flow relationship calculation, and flow space distribution are observed.

[0064] Furthermore, the cost surface generation sub-model includes:

[0065] Variable determination unit, cost aspect determination unit, and source-sink combination determination unit;

[0066] The variable determination unit is used to determine the basic variables of the minimum cost path. The basic variables include: topography, landform, land cover, land use type, transportation, city, industry, land ownership, and population density surface data. The cost surface determination unit is used to assign values ​​to various pipeline cost influencing factors in the GIS database according to transportation risk and relative transportation cost, calculate cost surfaces, and rasterize them to obtain cost surfaces. The source-sink combination determination unit is used to use the cost surface as the analysis base map, calculate the corresponding pressure-flow relationship and cost-flow relationship using pipeline seepage mechanics and derived cost-flow model, and find the route with the lowest resistance naturally during pipeline iteration to obtain the optimal source-sink combination.

[0067] Specifically, such as Figure 3 As shown, where, Figure 3 (a) is a schematic diagram of the first allocation of marginal costs, showing the input values ​​for the cumulative costs of building, environmental, and social factors. Figure 3(b) Schematic diagram of the second allocation edge cost, route selection. Cost surface calculation considers different geographical features or attributes, such as topography, land cover, land ownership, and population density, and is a fundamental variable for determining the minimum cost path. In GIS shortest path algorithms, the shortest path between vertices or nodes is calculated by minimizing the weight of connecting lines; the most widely used is the lowest cost or shortest path algorithm. Edge weights are typically calculated using raster-based cumulative cost surfaces, which quantify the cost of movement across grid cells by incorporating social and environmental factors. With these cumulative environmental and social factors, the optimal minimum cost path also explains the minimum distance between node pairs. The center of each cell in the raster cost surface is defined as a node, and the line segment between two nodes is defined as an edge. Linear features refer to rivers, roads, or pipelines; cells refer to grid cells established during the GIS model building process; low-suitable cells refer to things like rivers and roads; suitable cells refer to existing right-of-way, such as pipelines. The cost of linear features is measured as low-suitable or suitable cells.

[0068] If adjacent units contain linear features like roads, the standard procedure is to increase the cost of that unit, regardless of whether it crosses a low-suitability unit. In pipeline infrastructure construction, a road can simultaneously serve as both a low-suitability unit and a suitable unit (a right-of-way or easement for transport infrastructure). Building a pipeline along a highway is significantly less expensive than tunneling under a highway because many costs, such as land acquisition and leveling, are already incurred during highway construction.

[0069] Furthermore, the source-sink combination determination unit includes:

[0070] Initial pipeline determination subunit, cost path calculation subunit, and candidate pipeline determination subunit;

[0071] The initial pipeline determination subunit is used to create an initial and idealized pipeline network between the source and the sink. The cost path calculation subunit is used to iteratively solve the cost path between the emission source and the storage site in the initial and idealized pipeline network using a cost minimization path algorithm to obtain a path set. The cost minimization path algorithm is used to solve the seepage mechanics of the pipeline and calculate the corresponding pressure-flow relationship and cost-flow relationship based on the derived cost-flow model. The candidate pipeline determination subunit is used to find the route with the lowest resistance naturally during the pipeline iteration process, determine the pipeline network corresponding to the lowest path in the path set, use the current pipeline network as candidate pipelines, and obtain the optimal source-sink combination based on the candidate pipelines.

[0072] Specifically, data on various pipeline cost influencing factors in the GIS database are assigned to different GIS layers based on transportation risk and relative transportation cost, and cost surfaces are calculated and rasterized to obtain cost surfaces. Using the cost surface as the analysis base map, the corresponding pressure-flow relationships and cost-flow relationships are calculated using pipeline seepage mechanics and derived cost-flow models, ultimately obtaining the optimal total cost or total benefit for the entire pipeline network. One-to-one cost minimization can be achieved using the minimum cost path analysis tool in GIS software's spatial analysis to perform relative cost analysis of the transmission routes of emission sources within a certain range around the containment target area. The route with the lowest resistance is naturally sought during pipeline iteration to form the optimal source-sink combination. For multi-source and multi-sink combinations involving main pipelines and branch pipelines, the optimal pipeline network is also naturally formed through iteration.

[0073] Specifically, an initial, idealized pipeline network is created between the source and the sink, unaffected by real-world environmental or social costs. Figure 4 (a is the cost coefficient distribution map). Then, the minimum cost path between the emission source and the storage site is iteratively solved using a cost-minimizing path algorithm. Figure 4 (b) In this calculation, each grid represents a space of approximately 4.5 x 4.5 square kilometers, and there is an estimated cost for constructing pipelines within that area. A subset of the lowest-cost paths is selected as the candidate pipeline network, corresponding to a set of possible pipeline routes influenced by factors such as CO2 prices and transportation costs.

[0074] Furthermore, a weighted cost surface and a weighted cost curve are constructed. The cost surface includes preprocessed default data and weights for land cover, slope, lateral features, population density, protected land, rivers, roads, railways, and pipeline networks. The cost factor considers the actual pipeline construction cost under different terrain and geomorphological conditions, comparing it to the cost ratio of similar pipelines under reference terrain and geomorphological conditions. Then, the cost surface is rasterized and used for pipeline optimization calculations.

[0075] Cost surface data is used to construct the reference cost of the pipeline network. Generating a weighted cost surface involves laying a mesh on the modeling domain and determining the cost from one cell to an adjacent cell. This is a functional underlying terrain (slope and aspect) including population density distribution, land use type (e.g., desert, forest, farmland, grassland, shrubland, water body, wetland, etc.), surface slope, intersections (railways, rivers, and roads), existing pipeline road rights (roads), and land use rights. Because these costs are independent, construction costs and road costs are both represented by separate weighted cost surfaces. For example, steep slopes will affect construction costs but not road costs (pipelines are typically buried in ditches), and the acquisition costs for farmland and grassland are different. Table 1 shows the boundary condition settings for the source / sink and pipeline network, as shown below:

[0076] Table 1. Boundary Condition Setting Table for Source / Sink and Pipeline Network

[0077]

[0078]

[0079] Furthermore, the automatic pipeline optimization submodule includes:

[0080] Solving element and pipeline determination element;

[0081] The solution unit is used to establish a cost model and a pipeline flow model based on the cost-pipeline flow coupling model and solve for cost data using an iterative method, or to correct the cost coefficient through a cost surface and introduce key coefficients into the pipeline flow and solve for cost data. The key coefficients are equivalent permeability coefficients or cost-flow relationship coefficients. The pipeline network determination unit is used to determine the target pipeline network in the optimal source-sink combination based on the cost data.

[0082] Specifically, the seepage-pipe flow model is used to analyze the flow process of CO2 in pipelines, thereby completing the site selection for pipeline laying. The seepage-pipe flow model is a commonly used model in pipeline flow and seepage mechanics. For solving the "cost-pipe flow coupling model," two methods are proposed: "The first is to establish a cost model and a pipe flow model separately, and then solve them using an iterative method"; the second is to correct the cost coefficient (cost) through a cost surface and introduce an "equivalent permeability coefficient" or cost-flow relationship coefficient that formally obeys Darcy's law into the pipe flow, thus treating the cost-pipe flow model as a coupled system of "seepage" and "cost" including cost. For this new "seepage" system, the key issue is how to determine the "equivalent permeability coefficient" that includes cost. Since the physical properties of CO2 in the pipeline change with pressure, to simplify the pipeline optimization process, CO2 is simplified as an incompressible fluid for global optimization during the pipeline network optimization process. Starting from the equation for the head loss ΔH(m) of incompressible fluid flow in a cylindrical pipe (hydraulic pipeline):

[0083]

[0084] Where: L - pipe length (m); d - pipe inner diameter (m); P - pressure difference between the two ends of the pipe (MPa); μ - fluid dynamic viscosity (Pa.s); Q - fluid flow rate (m³ / s). 3 / s or t / s); when the flow in the pipe is laminar, the average velocity inside the pipe can be obtained (equal to the seepage velocity V):

[0085]

[0086] Where: J—hydraulic gradient; γ—fluid specific weight; μ—fluid dynamic viscosity.

[0087] This formula is consistent with Darcy's law V=K n By comparing J, we can obtain the equivalent "permeability coefficient" expression under laminar flow conditions:

[0088]

[0089] Where: K n —Equivalent permeability coefficient for pipe flow under laminar flow conditions. When the pipe flow is turbulent, the expression for the flow velocity is:

[0090]

[0091] The equivalent permeability coefficient K of pipe flow under turbulent conditions n for:

[0092]

[0093] Using an optimal flow regime (laminar flow) for the pipeline simplifies the iterative process, thus the flow equation in the pipeline is:

[0094]

[0095] Furthermore, there is a certain functional relationship between the unit CO2 transportation cost (levelized cost) of the pipeline and the flow rate. The levelized cost of the pipeline is inversely proportional to the flow rate, and the levelized cost decreases as the flow rate increases.

[0096] C = f cost (Q)→Q=f′ cost (C) (7)

[0097] Relationships can be simplified:

[0098]

[0099] In the formula, C represents the CO2 transportation cost [USD / t], α is a cost parameter, typically 0.00056 USD / t / km [USD / t / km], β is an exponent, typically between -0.7 and -0.8 [-], and Q represents the flow rate [t / s]. This cost-flow relationship holds true for every section of the pipeline network. For a single pipeline, the comprehensive CO2 transportation cost is (directly related to Q and L):

[0100] E flow =Q·C=α·Q β+1 ·L (9)

[0101] In the formula, E flow This represents the cumulative levelized cost or total cost of the CO2 pipeline per unit time [USD / s] or [USD / a]. Therefore, the pipeline cluster problem becomes E flowThe problem is minimization. For pipeline networks, the levelized cost needs to be accumulated across all pipelines.

[0102]

[0103] Total C flow =∑Q k ·C' k =∑α k ·Q k β+1 ·L k (11)

[0104] In the formula, k is the index of multiple sources or sinks; Q k The flow rate [t / s] of the source or sink with sequence number k; C k The cost or value [USD / t or CNY / t] for multiple sources and sinks. The summation over the entire discretized domain is:

[0105]

[0106] The above discretization formulas are integrated across the entire region to calculate the total cost for the entire region, and E... flow The minimum value corresponds to one of its extreme values. Therefore, the location of the extreme value is C. flow The derivative is zero, i.e., C' flow =0. The boundary conditions for the source and sink are shown in Table 2. Table 2 is the boundary condition setting table for the source, sink, and pipeline network, as shown below:

[0107] Table 2 Boundary Condition Setting Table for Source / Sink and Pipeline Network

[0108]

[0109]

[0110] In the formula, C s C p C r Fixed costs (USD) for emission sources, pipelines, or opening of storage sites;

[0111] V s V p V r The variable costs of capturing CO2 range from the source, through pipeline transport, or into storage facilities (per tonne in USD).

[0112] Q s Q p Q r CO2 flow rate (tons) at source nodes, pipelines, and storage sites;

[0113] Q totalThe target amount (tons) of carbon dioxide to be stored;

[0114] The total value (cost data) of the CCUS project is:

[0115] R = P carbon -C s -C p -C r (13)

[0116] Where R represents the revenue from conducting CCUS on carbon dioxide (USD / ton or CNY / t).

[0117] Furthermore, the implementation of CO2 pipelines is similar to that of natural gas pipelines, and the implementation process generally includes several stages: project identification, design, construction, operation and management, closure, and post-closure. Figure 5 The paper introduces the implementation process of CO2 pipelines. From the project concept design to the final investment decision stage, it generally takes 2-5 years. Depending on the pipeline length and site complexity, the construction time is generally 1-3 years. The technical and economic model covers the entire process of CO2 pipeline implementation.

[0118] In principle, CO2 can be transported via pipelines in gaseous, liquid, supercritical, or two-phase states. The physical properties of CO2 have a significant impact on technical design and cost evaluation. Two-phase fluids can cause cavitation corrosion, turbulence due to foam pressure, low flow rates, vibration, and other problems. Currently, most pipelines transport supercritical CO2. The minimum operating pressure for supercritical CO2 pipeline transport is 6.0-10.3 MPa, with an average temperature of 40°C. The most abundant impurities in CO2 streams are H2O, N2, O2, H2S, and CO. Operating pressure and temperature are adjusted according to the phase diagram of the gas mixture to avoid phase transitions (especially between gaseous and liquid states).

[0119] The selection of CO2 pipeline routes should follow the principles of safety, economy, convenience, and optimization, meeting project needs while minimizing transportation costs. The general principles for CO2 pipeline route selection are as follows:

[0120] (1) The selection of pipeline laying areas should comply with the current relevant regulations and should avoid urban planning areas, historical sites and cultural relics, scenic spots, nature reserves, etc.;

[0121] (2) The route should be as straight as possible to shorten the route length;

[0122] (3) The route should be laid along existing highways as much as possible to facilitate construction and management;

[0123] (4) The power supply for the line should be made as much as possible from the existing national power grid in order to reduce costs;

[0124] (5) The route should avoid areas with high-intensity earthquakes, deserts, swamps, landslides, debris flows and other areas with poor engineering geology and difficult construction as much as possible;

[0125] (6) The route should avoid crossing or crossing obstacles such as rivers, highways, and railways as much as possible.

[0126] Pipeline technical parameters affect economic costs. These parameters include pipe diameter, wall thickness, pressure drop, inlet pressure, outlet pressure, operating temperature, CO2 density, CO2 standard density, CO2 viscosity, compressibility factor, roughness, and flow velocity. Pipeline diameter is a key parameter in the techno-economic model. Methods for calculating pipe diameter include rate equations and hydraulic equations. This paper uses McCoy's equation to calculate the CO2 pipeline diameter. For more complex calculation methods, please refer to the corresponding calculation software. This report recommends a simplified formula for calculating the CO2 pipeline transportation diameter:

[0127]

[0128] The formula for calculating the Fanning friction coefficient is as follows:

[0129] The formula for calculating the Reynolds coefficient (Re) is:

[0130]

[0131] In the above formula:

[0132] D – Pipe diameter (in); m – CO2 mass flow rate (t / d); ε – Pipe roughness (ft); ρ – Density (kg / m³) 3 L – Transport distance (km); μ – Kinematic viscosity (Pa·s);

[0133] The formula for calculating pipeline pressure drop is:

[0134]

[0135] In the formula, the Moody friction factor f F The calculation formula is:

[0136] The Darcy-Weisbach friction factor f is calculated as follows:

[0137]

[0138] In the above formula: - Pressure drop per unit pipe length (N / m) 3 D – Pipe diameter (m); ν – Flow velocity (m / s);

[0139] According to the "Code for Design of Gas Transmission Pipeline Engineering" GB50251-2003, the formula for calculating the wall thickness of straight pipe sections is as follows:

[0140]

[0141] Where: δ - calculated wall thickness, mm; P - pipe design pressure, MPa; D - pipe outer diameter, mm; σs - pipe yield strength, MPa; — Weld coefficient, taken as 1; F- Strength design coefficient, for general line sections, 0.72 for Level 1 areas, 0.6 for Level 2 areas, 0.5 for Level 3 areas, and 0.4 for Level 4 areas; t- Temperature reduction coefficient, taken as 1.

[0142] Furthermore, compressor stations in CO2 pipeline systems can be divided into two categories: starting stations located at the pipeline inlet and booster stations located along the pipeline, used to compensate for pressure drops caused by friction and elevation loss. In principle, the longer the pipeline and the more significant the terrain undulations, the greater the compressor power required to reach the required output pressure at the destination.

[0143] Determine the number of pumping stations N:

[0144] N booster =INT[P drop / (P c -p c )]=INT[(i.L+AZ) / (P c -p c )] (twenty one)

[0145] In the formula P drop It is the total pressure loss of the pipeline, including frictional pressure loss and height pressure loss [MPa], P drop =i·L+AZ·g·ρ CO2 ;P c It is the pressure increased by a single booster station; p c This refers to the pressure loss of a single booster station, including oil and gas pipelines and pumps, [MPa]. c and p c This is a key factor provided by the equipment supplier. The number of booster stations is designed based on an average spacing of 150km-250km, P c -p c According to P drop / N booster Selection. The total power consumption of the booster station, which transports CO2 with a flow rate of Q from the starting point to the ending point, is calculated according to the following formula:

[0146] E station =∑Q·(P final -P cut-off ) / (ρ CO2 ·ηpump ) (twenty two)

[0147] In the formula E station This refers to the power consumption of the booster pump [kW.h], Pc ut-off P is the inlet pressure of the CO2 pump [Pa]. final η is the outlet pressure of the CO2 pump [Pa]. booster The pump's compression efficiency is [-]. Other power consumption at the booster station is for auxiliary equipment and daily needs, calculated at 5%-10% of the pump's power consumption. The main technical components of a booster station include a plant, work area, power supply system, CO2 pump, automation control, heating and ventilation system, streamlined connections, valve system, and other devices. The first station of the booster station also needs a pig sending device, and the last station needs a pig receiving device.

[0148] Furthermore, pipeline project construction includes actual pipeline route selection, land acquisition (ROW), civil engineering (earthwork, pipeline installation, booster station installation, ground equipment installation, land reclamation), and other construction work. The technology for constructing CO2 booster pump stations is similar to that for natural gas booster stations; therefore, existing guidelines and standards for natural gas pipelines can be referenced for CO2 pipeline construction.

[0149] Furthermore, pipeline operation is divided into three aspects: daily operation, routine operations, and management and maintenance. Pipeline operation involves health, safety, and environmental assessments. Generally, overall operation and maintenance management needs to consider personnel training, pipeline inspection, integrated safety, pipeline signage, public education, awareness campaigns, damage prevention plans, communication, facility safety, and leak detection. Typically, the pipeline control center directs the daily operation of the pipeline. Routine operations include: maintaining CO2 receiving conditions (pressure, temperature, composition, flow rate) at a stable state, maintaining operating pressure at the minimum required level, minimizing transient conditions, daily hydraulic calculations and equipment performance evaluations, and equipment performance checks.

[0150] Furthermore, pipeline decommissioning includes pipeline and booster station dismantling, equipment disposal, and land reclamation. Refer to natural gas pipeline guidelines and standards.

[0151] Furthermore, the CO2 pipeline project adopts a budgetary techno-economic model, defined as the IRSM model. Costs mainly include investment costs and operation and maintenance (O&M) costs. Pipeline investment costs are mainly divided into five categories: material costs, civil engineering costs, ROW costs, labor costs, and other miscellaneous expenses. Material costs include pipe materials, pipe coatings (insulation, corrosion protection), cathodic protection, etc.; civil engineering costs include pipe installation, pipe protection, auxiliary works, etc.; ROW costs include land acquisition and compensation; labor costs include wages, benefits, insurance, etc. O&M costs include electricity costs, equipment and pipeline maintenance costs, labor costs, monitoring and evaluation costs, risk prediction and management costs, etc. Other miscellaneous expenses include site survey fees, design fees, evaluation fees, approval fees, construction costs, supervision fees, temporary expenses, allowances, etc.

[0152] Specifically, emission source assessment methods

[0153] CO2 emission source data includes location, operating time, scale, capacity / output, process, product, energy consumption and unit price, and carbon emissions (per process). This data is processed and analyzed to form a database. CO2 emissions are calculated using the "Guidelines for Greenhouse Gas Emission Accounting and Reporting of Thermal Power / Steel / Cement / Chemical Production Enterprises in China," based on EF and existing capacity and output. The formula is as follows:

[0154]

[0155] In the formula, ECO2 refers to the annual cumulative CO2 emissions, (ECO2) ij The CO2 emissions of product j from plant i are assessed. (EF) ij This refers to the CO2 emission factor of product j from factory i. The emission factor is based on national assessment methods or parameters provided by the company. ij This refers to the output / capacity of product j in factory i, where M is the number of factories and N is the total number of products.

[0156] Emission source screening: Specific screening criteria should be established, including technical suitability standards, cost requirements, government regulations, laws and regulations, etc. Technical suitability standards include CO2 storage site screening, location, service life, scale, load, emission reduction requirements, space size, etc. Taking coal-fired power plants as an example, whether to retrofit existing assets with CCUS depends on whether it is feasible not to use CCUS as a realistic alternative during the remaining life cycle of the power plant. For CCUS retrofit assessment, the applicable power plant criteria follow the requirements of IEA (2016). These suitable or selected power plants are classified according to three main characteristics: (1) the unit has been in service for less than 40 years; (2) the unit scale is greater than 600MW, or the total amount of CO2 that can be captured is greater than 10Mt / a; (3) the annual operating time is greater than 4000 hours. The screening criteria for other emission sources are not yet clear.

[0157] Specifically, this invention only evaluates onshore geological storage sites at the sub-basin scale. The site suitability evaluation for CO2 enhanced oil recovery (CO2-EOR) is based on a multi-criteria method (considering crude oil gravity, reservoir depth, temperature, and minimum miscibility pressure) using spatial analysis with GIS. At the sub-basin scale, the site suitability evaluation for CO2 saline aquifer storage and enhanced water production (CO2-EWR) considers three priority objectives: technical site injection and storage capacity, risk minimization, and social and environmental constraints. At the basin and sub-basin scales, GIS data (more than 55 GIS data layers) is used to screen sites based on a multi-criteria method and spatial analysis algorithms, and a CO2-EWR storage site suitability map is constructed on the GIS platform.

[0158] Specifically, based on existing data levels and project phases, the full-process CCUS techno-economic model employs a combination of a budgetary model and an empirical model. All technologies in the technical model utilize the best available technologies (BATs) currently available, meaning those with scalable technological maturity, scalability, equipment manufacturing foundation, and application experience. The techno-economic model includes CO2 capture, compression, pipeline, and CO2-EWR / CO2-EOR modules. Cost assessments for each module include fixed asset investment (CAPEX) and operation and maintenance costs (O&M), expressed as levelized cost (including net cost). All costs are calculated using the comprehensive unit price method.

[0159] Furthermore, the CCUS cluster network matching model is used to lay out the CCUS cluster network in a certain region. The potential paths selected by the model are constrained by the lowest cost. These routes connect each combination of carbon sources and storage sites, and finally generate important candidate pipeline segments (pipeline segments), allowing the model to select source-to-source, storage site-to-storage site, and multi-source CO2 to be aggregated into the main pipeline and then transported to the storage site. Figure 5 This presents the matching analysis results of CCUS clusters in a certain region using an automatic pipeline optimization model. Since the main emission sources are located in the Ningdong area, the resulting CCUS clusters are primarily concentrated within the target region.

[0160] This embodiment also provides a CCUS cluster pipeline network matching model method based on a geological database, including:

[0161] The pipeline layout from emission source set to geological storage site set is generated based on the principle of CO2 flowing along the minimum cost path;

[0162] Based on the pipeline network layout, the emission source set is screened and optimized to obtain the target emission source;

[0163] Based on the pipeline network layout, the set of geological storage sites is screened to obtain the target geological storage sites;

[0164] An economic assessment was conducted on the pipeline network layout between the target geological storage site and the target emission source, and the assessment results were obtained.

[0165] Furthermore, the pipeline network layout for generating emission source sets to geological storage site sets based on the principle of CO2 flowing along the minimum cost path includes:

[0166] The basic variables of the minimum cost path are determined, and based on the basic variables, the data of various pipeline cost influencing factors in the GIS database are assigned to each GIS layer according to transportation risk and relative transportation cost, and the cost surface is calculated and rasterized to obtain the cost surface;

[0167] Using the cost surface as the analysis base map, the corresponding pressure-flow relationship and cost-flow relationship are calculated by using the seepage mechanics of the pipeline and the derived cost-flow model. The optimal source-sink combination is obtained by naturally finding the route with the lowest resistance during the pipeline iteration process.

[0168] The cost-pipe-flow coupling model is solved to obtain cost data, and the target pipeline network in the optimal source-sink combination is determined based on the cost data.

[0169] Based on the target pipeline network, an economic model for CO2 pipelines is provided to construct the pipeline network layout from the emission source set to the geological storage site set.

[0170] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0171] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A CCUS cluster pipeline network matching model system based on a geological database, characterized in that, include: The technical and economic assessment module, along with the cluster pipeline generation module, emission source screening module, and geological storage site screening module, all connected to the technical and economic assessment module; The cluster pipeline generation module is connected to the emission source screening module and the geological storage site screening module, respectively. The cluster pipeline generation module is used to generate a pipeline layout from the emission source set to the geological storage site set based on the principle of the lowest cost route along the cost surface. The emission source screening module is used to screen and optimize the emission source set according to the pipeline layout to obtain the target emission source. The geological storage site screening module is used to screen the geological storage site set according to the pipeline layout to obtain the target geological storage site. The techno-economic evaluation module is used to perform an economic evaluation on the pipeline layout between the target geological storage site and the target emission source to obtain the evaluation result. The cluster network generation module includes: Cost surface generation submodule, automatic pipeline optimization submodule, and CO2 pipeline economics submodule; The cost surface generation submodule is used to determine the basic variables of the minimum cost path and, based on the basic variables, assign values ​​to various pipeline cost influencing factors in the GIS database according to transportation risk and relative transportation cost to each GIS layer, calculate the cost surface, and rasterize it to obtain the cost surface. Using the cost surface as the analysis base map, the corresponding pressure-flow relationship and cost-flow relationship are calculated using pipeline seepage mechanics and the derived cost-flow model. The optimal source-sink combination is obtained by naturally finding the route with the lowest resistance during pipeline iteration. The automatic pipeline optimization submodule is used to solve the cost-pipe flow coupling model to obtain cost data and determine the target pipeline network in the optimal source-sink combination based on the cost data. The CO2 pipeline economic submodule is used to construct the pipeline network layout from the emission source set to the geological storage site set based on the target pipeline network and provide a CO2 pipeline economic model.

2. The CCUS cluster pipeline matching model system based on a geological database according to claim 1, characterized in that, The cost surface generation submodule includes: Variable determination unit, cost aspect determination unit, and source-sink combination determination unit; The variable determination unit is used to determine the basic variables of the minimum cost path. The basic variables include: topography, landform, land cover, land use type, transportation, city, industry, land ownership, and population density surface data. The cost surface determination unit is used to assign values ​​to various pipeline cost influencing factors in the GIS database according to transportation risk and relative transportation cost, calculate cost surfaces, and rasterize them to obtain cost surfaces. The source-sink combination determination unit is used to use the cost surface as the analysis base map, calculate the corresponding pressure-flow relationship and cost-flow relationship using pipeline seepage mechanics and derived cost-flow model, and find the route with the lowest resistance naturally during pipeline iteration to obtain the optimal source-sink combination.

3. The CCUS cluster pipeline matching model system based on a geological database according to claim 2, characterized in that, The source-sink combination determination unit includes: Initial pipeline determination subunit, cost path calculation subunit, and candidate pipeline determination subunit; The initial pipeline determination subunit is used to create an initial and idealized pipeline network between the source and the sink. The cost path calculation subunit is used to iteratively solve the cost path between the emission source and the storage site in the initial and idealized pipeline network using a cost minimization path algorithm to obtain a path set. The cost minimization path algorithm is used to solve the seepage mechanics of the pipeline and calculate the corresponding pressure-flow relationship and cost-flow relationship based on the derived cost-flow model. The candidate pipeline determination subunit is used to find the route with the lowest resistance naturally during the pipeline iteration process, determine the pipeline network corresponding to the lowest path in the path set, use the current pipeline network as candidate pipelines, and obtain the optimal source-sink combination based on the candidate pipelines.

4. The CCUS cluster pipeline matching model system based on a geological database according to claim 3, characterized in that, The automatic pipeline optimization submodule includes: Solving element and pipeline determination element; The solution unit is used to establish a cost model and a pipeline flow model based on the cost-pipeline flow coupling model, and solve for cost data and pipeline flow data using an iterative method. Alternatively, it can correct the pipeline flow coefficients using cost data, correct the cost coefficients using pipeline flow data, and introduce key coefficients into the pipeline flow and solve for cost data. The key coefficients are cost-flow relationship coefficients. The solution unit eventually iterates to a stable result. The pipeline network determination unit is used to determine the target pipeline network in the optimal source-sink combination based on the cost data.

5. The CCUS cluster pipeline matching model system based on a geological database according to claim 1, characterized in that, The pipeline parameters of the CO2 pipeline economic submodule include: Pipeline diameter, wall thickness, pressure drop, inlet pressure, outlet pressure, operating temperature, CO2 density, CO2 standard density, CO2 viscosity, compressibility factor, roughness, and flow rate.

6. The CCUS cluster pipeline matching model system based on a geological database according to claim 5, characterized in that, The formula for calculating the pipe diameter is: Where D is the pipe diameter, F f ρ is the friction coefficient, L is the transport distance, ρ is the density, ΔP is the pressure drop, and m is the CO2 mass flow rate.

7. The CCUS cluster pipeline matching model system based on a geological database according to claim 6, characterized in that, The formula for calculating the friction coefficient is: Where ε is the pipe roughness and Re is the Reynolds coefficient.

8. A CCUS cluster pipeline network matching model method based on a geological database, characterized in that, include: The pipeline layout from emission source set to geological storage site set is generated based on the principle of CO2 flowing along the minimum cost path; Based on the pipeline network layout, the emission source set is screened and optimized to obtain the target emission source; Based on the pipeline network layout, the set of geological storage sites is screened to obtain the target geological storage sites; An economic assessment was conducted on the pipeline network layout between the target geological storage site and the target emission source, and the assessment results were obtained. The pipeline network layout for generating emission source sets to geological storage site sets based on the principle of CO2 flow along the minimum cost path includes: The basic variables of the minimum cost path are determined, and based on the basic variables, the data of various pipeline cost influencing factors in the GIS database are assigned to each GIS layer according to transportation risk and relative transportation cost, and the cost surface is calculated and rasterized to obtain the cost surface; Using the cost surface as the analysis base map, the corresponding pressure-flow relationship and cost-flow relationship are calculated by using the seepage mechanics of the pipeline and the derived cost-flow model. The optimal source-sink combination is obtained by naturally finding the route with the lowest resistance during the pipeline iteration process. The cost-pipe-flow coupling model is solved to obtain cost data, and the target pipeline network in the optimal source-sink combination is determined based on the cost data. Based on the target pipeline network, an economic model for CO2 pipelines is provided to construct the pipeline network layout from the emission source set to the geological storage site set.

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