Pipeline pitting corrosion prediction method, system, equipment and medium

Abstract

The invention discloses a pipeline pitting corrosion prediction method, system and device and a medium, the method comprises the steps that initial corrosion pit parameters of a pipeline and environment parameters of the environment where the pipeline is located are obtained, the initial corrosion pit parameters comprise the pipeline material, the initial corrosion pit radius and the initial corrosion pit position, and the environment parameters comprise the environment state quantity and corrosion solution flow parameters; constructing a pitting corrosion model of an initial corrosion pit of the pipeline based on the initial corrosion pit parameters and the environmental parameters; and dynamic pitting prediction is conducted on the initial corrosion pit based on the pitting model, target corrosion pit parameters are obtained, and the target corrosion pit parameters comprise the corrosion rate and the corrosion pit change condition. The problem that an existing pitting corrosion prediction model is only suitable for static conditions, and consequently prediction accuracy is low is solved.

Description

Technical Field

[0001] This application relates to the field of pipeline corrosion technology, and in particular to a method, system, equipment and medium for predicting pipeline pitting corrosion. Background Technology

[0002] Pitting corrosion is one of the most common forms of corrosion in pipelines. It is a highly localized form of corrosion, where the vast majority of the metal surface remains passive while a very small area is affected by pitting. Pitting typically occurs in locally activated areas of the metal surface, forming tiny holes or cavities, with corrosion rates much higher than the average corrosion rate on the metal surface. In the surrounding passive areas, corrosion occurs almost entirely or at a much lower rate than in the pitted areas. The pitting area is usually small, and the surface is often covered by corrosion products, making it difficult to detect. Although the overall mass loss caused by pitting corrosion is relatively low, by the time it is detected, it may have penetrated deep into the metal, leading to perforation of the metal pipe and causing significant damage. Especially under flowing conditions, on the one hand, fluid flow accelerates the mass transfer rate of corrosive species; on the other hand, fluid shear stress causes the removal of corrosion products from inside the pit. Therefore, accurately predicting the expansion of pits and changes in corrosion rate under flowing conditions, and establishing a pitting prediction model under flowing conditions, is particularly important for pitting corrosion protection of oil and gas pipelines, providing necessary guarantees for the safe transportation of oil and gas pipelines.

[0003] In existing technologies, numerous corrosion prediction models have been established based on electrochemical theory for pitting corrosion prediction, and various modeling methods have been proposed to describe and predict the pitting corrosion process, including moving interface models, peridynamic models, cellular automata models, and phase-field models. However, existing pitting corrosion prediction models are only applicable to static conditions, and pitting corrosion prediction models under static conditions cannot accurately predict the development of pitting corrosion under pipeline flow conditions, resulting in low accuracy in pipeline pitting corrosion prediction. Summary of the Invention

[0004] To overcome the problem that existing pitting corrosion prediction models are only applicable to static conditions, resulting in low prediction accuracy, this application provides a method, system, equipment, and medium for predicting pipeline pitting corrosion.

[0005] In a first aspect, in order to solve the above-mentioned technical problems, this application provides a method for predicting pitting corrosion in pipelines, including: obtaining initial pitting parameters of the pipeline and environmental parameters of the environment in which the pipeline is located. The initial pitting parameters include the pipeline material, the initial pitting radius and the initial pitting location, and the environmental parameters include environmental state parameters and corrosive solution flow parameters. A pitting corrosion model of the pipeline's initial pitting is constructed based on the initial pitting parameters and environmental parameters. Based on the pitting corrosion model, dynamic pitting corrosion prediction is performed on the initial pit to obtain the target pit parameters, which include corrosion rate and pit change.

[0006] Furthermore, a pitting corrosion model of the initial pitting of the pipeline is constructed based on the initial pitting parameters and electrolyte solution parameters, including: A two-dimensional geometric-physical model of the initial corrosion pits in the pipeline is constructed based on the initial corrosion pit parameters. The target tertiary current distribution physical field is constructed based on environmental parameters. The target tertiary current distribution physical field is a flow physical field. The target tertiary current distribution physical field is determined as the physical field of a two-dimensional geometric physical model to generate the pitting corrosion model of the initial corrosion pit.

[0007] Furthermore, the environmental state parameters include pipeline temperature and pipeline pressure, and the corrosion solution flow parameters include corrosion solution type, corrosion solution flow location, corrosion solution flow velocity, corrosion solution flow direction and corrosion solution temperature, with the corrosion solution flow location corresponding to the initial corrosion pit location; The physical field for the target cubic current distribution is constructed based on environmental parameters, including: The initial three-dimensional current distribution physical field was constructed based on the pipe temperature and pipe pressure. Based on the type and flow rate of the corrosive solution, the target fluid flow pattern for the two-dimensional geometric physical model is determined. Based on the target fluid flow pattern, corrosive solution type, corrosive solution flow location, corrosive solution flow velocity, corrosive solution flow direction, and corrosive solution temperature, target flow conditions are formed for the two-dimensional geometric physical model; The target flow conditions are determined as the flow parameters of the initial cubic current distribution physical field, and the target cubic current distribution physical field is obtained.

[0008] Furthermore, the target fluid flow pattern for the two-dimensional geophysical model is determined based on the type and velocity of the corrosive solution, including: The Reynolds number for the pipeline is determined based on the type and flow rate of the corrosive solution. Find the alternative flow patterns corresponding to the Reynolds number from the preset flow pattern type table; The alternative flow patterns are determined as the target fluid flow patterns for the two-dimensional geometric physical model.

[0009] Furthermore, dynamic pitting prediction is performed on the initial pit based on the pitting model to obtain the target pit parameters, including: dynamic pitting simulation is performed on the initial pit based on the pitting model to simulate the chemical reaction that occurs in the initial pit in the environment corresponding to the environmental parameters, and the dynamic pit corresponding to the initial pit is obtained. Based on the preset dilute substance transport method and corrosion solution flow parameters, the corrosion rate of dynamic pits is determined. Based on the preset Lapss smoothing method and the initial pit location, the changes in dynamic pits are determined. Target pit parameters are determined based on corrosion rate and pitting variation.

[0010] Furthermore, the chemical reactions include anodic reactions; based on a pre-defined dilute mass transfer method and corrosion solution flow parameters, the corrosion rate of dynamic pits is determined, including: Using a pre-defined dilute substance transport method, the dynamic flux of ions participating in the anodic reaction in the dynamic corrosion pit is determined based on the corrosion solution flow rate, which is included in the corrosion solution flow parameters. Anode current density is determined based on multiple dynamic fluxes; The corrosion rate of dynamic pits is determined based on the target current density.

[0011] Furthermore, based on the preset Lapss smoothing method and the initial pit location, the changes in dynamic pits are determined, including: Using the pre-defined Lapss smoothing method, the transient degrees of freedom of the pit edge of the dynamic pit are determined based on the initial pit location; By comparing the transient degrees of freedom at the pit edge with the initial pit position, the dynamic changes in the pit are obtained, including both longitudinal and lateral changes.

[0012] Secondly, this application also provides a pipeline pitting corrosion prediction system, comprising: The acquisition module is used to acquire the initial pitting parameters of the pipeline and the environmental parameters of the environment in which the pipeline is located. The initial pitting parameters include the pipeline material, the initial pitting radius, and the initial pitting location. The environmental parameters include environmental state variables and corrosive solution flow parameters. The construction module is used to construct a pitting corrosion model of the initial pitting of the pipeline based on the initial pitting parameters and environmental parameters. The pitting prediction module is used to dynamically predict the initial pitting based on the pitting model, and obtain the target pitting parameters, including the corrosion rate and the changes in the pitting.

[0013] Thirdly, this application also provides a computing device, including a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the pipeline pitting prediction method described above.

[0014] Fourthly, this application also provides a computer-readable storage medium storing instructions that, when executed on a terminal device, cause the terminal device to perform the steps of a pipeline pitting prediction method.

[0015] The beneficial effects of this application are as follows: A pitting corrosion model for the initial pitting on the pipeline is constructed using the initial pitting parameters and environmental parameters of the environment in which the pipeline is located. The initial pitting parameters include the pipeline material, the radius of the initial pit, and the location of the initial pit. The environmental parameters include environmental state variables and corrosive solution flow parameters. Based on the pitting corrosion model, dynamic pitting corrosion prediction is performed on the initial pit to obtain the target pitting parameters. In this way, the fluid dynamics characteristics of the pipeline's environment are considered when constructing the pitting corrosion model. This allows for the dynamic pitting corrosion prediction based on the model to take into account the influence of fluid dynamics on species mass transfer during the pitting process and the destructive effect on corrosion products within the pit, thus achieving pipeline pitting corrosion prediction under flow conditions and improving the accuracy of the target pitting parameters obtained from the pitting corrosion prediction. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart illustrating a pipeline pitting corrosion prediction method as an exemplary embodiment of this application; Figure 2 This is a schematic diagram of a two-dimensional geometrical physical model of the initial pitting pit in an exemplary embodiment of this application; Figure 3 This is an exemplary embodiment of the present application, showing the electrolyte potential distribution cloud map of the corrosive solution at different flow rates; Figure 4 This is a diagram showing the expansion and variation of dynamic erosion pits at different flow rates in an exemplary embodiment of this application; Figure 5 This is a corrosion rate diagram of dynamic pits at different flow rates in an exemplary embodiment of this application; Figure 6 This is a schematic diagram illustrating the structure of a pipeline pitting prediction system, which is an exemplary embodiment of this application. Detailed Implementation

[0017] The following embodiments are further explanations and supplements to this application and do not constitute any limitation on this application.

[0018] The following describes a method, system, device, and medium for predicting pipeline pitting corrosion according to embodiments of this application, in conjunction with the accompanying drawings.

[0019] The pipeline pitting corrosion prediction method provided in this application can be executed by a server. It should be noted that the server can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. No limitation is imposed here.

[0020] Please see Figure 1 , Figure 1 An exemplary embodiment of this application illustrates a method for predicting pitting corrosion in pipelines, such as... Figure 1 As shown, this application provides a method for predicting pitting corrosion in pipelines, including: S11, obtain the initial pitting parameters of the pipeline and the environmental parameters of the environment in which the pipeline is located. The initial pitting parameters include the pipeline material, the initial pitting radius and the initial pitting location. The environmental parameters include environmental state parameters and corrosive solution flow parameters. S12, The pitting corrosion model of the initial pitting of the pipeline is constructed based on the initial pitting parameters and environmental parameters; S13. Based on the pitting corrosion model, dynamic pitting corrosion prediction is performed on the initial pit to obtain the target pit parameters, which include corrosion rate and pit change.

[0021] The pipeline pitting corrosion prediction method provided in this embodiment constructs a pitting corrosion model of the initial pit on the pipeline using the initial pit parameters and environmental parameters of the environment in which the pipeline is located. The initial pit parameters include the pipeline material, the radius of the initial pit, and the location of the initial pit. The environmental parameters include environmental state variables and corrosive solution flow parameters. Based on the pitting corrosion model, the method performs dynamic pitting corrosion prediction to obtain target pit parameters. In this way, the fluid dynamics characteristics of the pipeline's environment are considered when constructing the pitting corrosion model. This allows for dynamic pitting corrosion prediction based on the model, taking into account the influence of fluid dynamics on species mass transfer during the pitting process and the destructive effect on corrosion products within the pit. This enables pipeline pitting corrosion prediction under flow conditions, thereby improving the accuracy of the target pit parameters obtained from the pitting corrosion prediction.

[0022] Optionally, a pitting corrosion model of the initial pitting of the pipeline is constructed based on the initial pitting parameters and electrolyte solution parameters, including: A two-dimensional geometric-physical model of the initial corrosion pits in the pipeline is constructed based on the initial corrosion pit parameters. The target tertiary current distribution physical field is constructed based on environmental parameters. The target tertiary current distribution physical field is a flow physical field. The target tertiary current distribution physical field is determined as the physical field of a two-dimensional geometric physical model to generate the pitting corrosion model of the initial corrosion pit.

[0023] In this embodiment, the physical field of the target cubic current distribution under flow conditions, constructed based on environmental parameters, is determined as the physical field of the two-dimensional geometric physical model of the initial pit of the pipeline, constructed based on the initial pit parameters, to generate the pitting corrosion model of the initial pit. In this way, the fluid dynamic characteristics of the initial pit of the pipeline under real flow conditions can be quantified into the pitting corrosion model, thereby improving the prediction accuracy of pitting corrosion based on the pitting corrosion model.

[0024] In one exemplary embodiment provided in this application, a two-dimensional geometric-physical model of the initial erosion pit is constructed using COMSOL Multiphysics (a multiphysics simulation software based on the finite element method). Specifically, for example: In COMSOL Multiphysics, the computational domain corresponding to the initial pitting is first established. A free triangular mesh is used to divide the computational domain, facilitating the spatial discretization of the continuous pitting expansion and corrosion rate changes in the pitting corrosion model. This transforms the continuous physical problem into a discrete algebraic equation system solvable by a computer, providing an accurate and feasible method for pitting corrosion prediction. The basic mesh element size is 0.15 μm, the maximum mesh element size is 0.15 μm, and the minimum mesh element size is 0.002 μm. Local mesh refinement is applied to the edge region of the initial pitting to improve computational accuracy. Ultimately, a total of 3311 triangular elements, 225 edge elements, and 7 vertex elements are generated within the computational domain, with the minimum element quality reaching 0.6173.

[0025] Secondly, within the computational domain, a two-dimensional geometric-physical model of the initial pitting of the pipeline is constructed based on the initial pitting parameters, to set the material physical parameters of the two-dimensional geometric-physical model. For example, the initial pitting parameters include the pipeline material being stainless steel, the initial pitting radius being R = 1.5 μm, and the initial pitting location being on the pipe wall. Then, within the computational domain, a model is constructed as follows... Figure 2 The illustrated two-dimensional geometrical model includes an initial pit and a corresponding section of pipe. The radius of the initial pit is R = 1.5 μm, and the length of the pipe is greater than 1.5 μm and less than or equal to 100 μm. A rectangular region with a width W = 100 μm and a height H = 50 μm is constructed as the electrolyte domain (corrosive solution region) for pitting corrosion, allowing the corrosive solution in the electrolyte domain (corrosive solution region) to cover the outer wall of the pipe and the initial pit. Simultaneously, the left end face of the rectangle is designated as the turbulent inlet, and the right end face as the turbulent outlet, enabling the corrosive solution fluid to fully develop and flow.

[0026] Optionally, the environmental state parameters include pipeline temperature and pipeline pressure, and the corrosion solution flow parameters include corrosion solution type, corrosion solution flow location, corrosion solution flow velocity, corrosion solution flow direction and corrosion solution temperature, with the corrosion solution flow location corresponding to the initial pit location; The physical field for the target cubic current distribution is constructed based on environmental parameters, including: The initial three-dimensional current distribution physical field was constructed based on the pipe temperature and pipe pressure. Based on the type and flow rate of the corrosive solution, the target fluid flow pattern for the two-dimensional geometric physical model is determined. Based on the target fluid flow pattern, corrosive solution type, corrosive solution flow location, corrosive solution flow velocity, corrosive solution flow direction, and corrosive solution temperature, target flow conditions are formed for the two-dimensional geometric physical model; The target flow conditions are determined as the flow parameters of the initial cubic current distribution physical field, and the target cubic current distribution physical field is obtained.

[0027] In this embodiment, firstly, an initial tertiary current distribution physical field is constructed based on pipe temperature and pressure. This initial tertiary current distribution physical field comprehensively considers the effects of solution resistance, electrode dynamics, and changes in electrolyte concentration in the solution, making it closer to reality and suitable for situations with complex electrode reactions and mass transfer processes. Secondly, based on the type and velocity of the corrosive solution, the target fluid flow pattern for the two-dimensional geometric physical model is determined. Based on the target fluid flow pattern, corrosive solution type, flow location, velocity, direction, and temperature, target flow conditions for the two-dimensional geometric physical model are formed. These target flow conditions are used as flow parameters of the initial tertiary current distribution physical field, quantifying the fluid dynamic characteristics of the initial pitting of the pipe under real flow conditions into the initial tertiary current distribution physical field. This results in a higher degree of matching between the obtained target tertiary current distribution physical field and the real flow conditions, thereby improving the realism of the pitting corrosion model generated based on the target tertiary current distribution physical field and ultimately improving the accuracy of pitting corrosion prediction.

[0028] In an exemplary embodiment provided in this application, among the environmental parameters, the pipeline temperature can be 60°C and the pipeline pressure can be 1 MPa. Among the corrosion solution flow parameters, the corrosion solution type can be simulated oilfield formation water, and its chemical composition consists of Na... + ,K + Ca 2+ Mg 2+ ,Cl - SO4 2- and HCO3 - The composition of the corrosive solution matches this type of corrosive solution, and the physical parameters include an electrolyte conductivity of 5.5 e.g., -6 [S / m], solution density is 983.51 kg / m³, and hydrodynamic viscosity is 4.6901 E. - 4 kg / (m·s); the flow location of the corrosive solution is the surface of the initial pit; the flow velocity of the corrosive solution can be 2m / s, 4m / s or 6m / s; the flow direction of the corrosive solution is from left to right along the pipeline extension direction, or from right to left along the pipeline extension direction; the temperature of the corrosive solution is 293.15K, i.e. 20℃.

[0029] In this way, based on the initial tertiary current distribution physical field and the target flow conditions (target fluid flow pattern, corrosive solution type, corrosive solution flow location, corrosive solution flow velocity, corrosive solution flow direction and corrosive solution temperature) the flow parameters of the initial tertiary current distribution physical field are determined, and the target tertiary current distribution physical field is obtained, so as to set the environmental related parameters for the two-dimensional geometric physical model.

[0030] Optionally, the target fluid flow pattern for the two-dimensional geophysical model is determined based on the type and velocity of the corrosive solution, including: The Reynolds number for the pipeline is determined based on the type and flow rate of the corrosive solution. The formula for calculating the Reynolds number is as follows: Among them, R e ρ is the Reynolds number; v is the flow rate of the corrosive solution, in m / s; ρ is the solution density corresponding to the type of corrosive solution, in kg / m³. 3 ; d is the hydraulic diameter corresponding to the type of corrosive solution, in meters; μ is the hydrodynamic viscosity corresponding to the type of corrosive solution, in Pa·s; Find the alternative flow patterns corresponding to the Reynolds number from the preset flow pattern type table; The alternative flow patterns are determined as the target fluid flow patterns for the two-dimensional geometric physical model.

[0031] In this embodiment, the Reynolds number for the pipeline is determined based on the type and flow rate of the corrosive solution. A pre-defined flow pattern type table is then used to find the corresponding candidate flow pattern, resulting in the target fluid flow pattern for the two-dimensional geometrical physical model. This selection of a suitable target fluid flow pattern based on the Reynolds number ensures a higher similarity between the target flow conditions and the actual flow conditions, thereby improving the accuracy of the subsequently constructed pitting corrosion model and ultimately enhancing its predictive accuracy.

[0032] In this embodiment, if the Reynolds number is less than 2000, it indicates that the fluid in the corrosive solution flows in layers, and the fluid particles move in an orderly manner without mixing with each other. The corresponding target fluid flow pattern is laminar flow.

[0033] If the Reynolds number is greater than or equal to 2000 and less than or equal to 4000, it indicates that the inertial force and viscous force of the fluid in the corrosive solution are roughly equal, and the flow field is very sensitive to external disturbances. Even a small external disturbance can break the original flow equilibrium and cause the fluid to change from laminar flow to turbulent flow. Therefore, the flow state of the fluid in the corrosive solution is unstable at this time. It may be laminar flow, turbulent flow, or alternating between laminar and turbulent flow. The corresponding target fluid flow pattern is a transition state.

[0034] If the Reynolds number is greater than 4000, it means that the fluid streamlines are no longer clearly distinguishable, there are many small vortices in the flow field, the fluid particles move irregularly, mix with each other, and have tortuous and chaotic trajectories. At this time, the inertial force in the corrosive solution plays a dominant role, and the flow of the fluid becomes irregular. The corresponding target fluid flow pattern is k-ε turbulent flow.

[0035] Optionally, dynamic pitting prediction is performed on the initial pit based on the pitting model to obtain the target pit parameters, including: dynamic pitting simulation is performed on the initial pit based on the pitting model to simulate the chemical reaction that occurs in the initial pit in the environment corresponding to the environmental parameters, and the dynamic pit corresponding to the initial pit is obtained. Based on the preset dilute substance transport method and corrosion solution flow parameters, the corrosion rate of dynamic pits is determined. Based on the preset Lapss smoothing method and the initial pit location, the changes in dynamic pits are determined. Target pit parameters are determined based on corrosion rate and pitting variation.

[0036] In this embodiment, firstly, based on a pitting corrosion model that considers the fluid dynamics of the pipeline environment, dynamic pitting corrosion simulation of the initial pit can simulate the chemical reactions occurring in the initial pit under corresponding environmental parameters, obtaining each dynamic pit during the pitting process. This allows for understanding the dynamic changes of the initial pit under flow conditions based on all dynamic pits. Secondly, based on the corrosion rate of the dynamic pit determined by a preset dilute mass transfer method and corrosion solution flow parameters, and the pit changes of the dynamic pit determined by a preset Lapss smoothing method and the initial pit location, target pit parameters are formed. This allows for dynamic quantification of the initial pit during the pitting process in the form of data, facilitating an intuitive understanding of the pitting changes of the initial pit under flow conditions based on each target pit parameter. This not only improves the accuracy of pitting corrosion prediction but also enhances the feasibility and accuracy of quantitative analysis of pitting corrosion changes.

[0037] In this embodiment, dynamic pitting refers to pitting obtained by focusing only on the steady-state pitting process of the initial pit under flowing conditions. The steady-state pitting process has the following characteristics: the chemical environment within the pit reaches dynamic equilibrium, at which point the concentration of each corrosive species within the pit no longer changes; the generation and dissolution rates of corrosion products remain constant; the passivation film state around the pit tends to be stable, at which point the repassivation process of the passivation film around the pit reaches equilibrium, the thickness of the passivation film no longer changes significantly due to continuous dissolution or repassivation, and is usually maintained at the nanometer level (1-5 nm); the surface morphology of the passivation film (such as porosity and defect density) tends to be constant; the film layer is dense without new cracks; and the open circuit potential or polarization resistance of the chemical reaction in the corrosion solution remains constant, which also indicates that the hindering effect of the passivation film on charge transfer no longer changes.

[0038] When the pipe material is stainless steel, the chemical reactions that occur in the initial corrosion pits under the corresponding environmental parameters are as follows: The cathode reaction is the reduction of oxygen: O₂ + 2H₂O + 4e⁻ - →4OH - ; The anodic reaction is the iron dissolution reaction: Fe → F 2+ +2e - .

[0039] Optionally, the chemical reaction includes anodizing; the corrosion rate of the dynamic pit is determined based on a pre-defined dilute mass transfer method and corrosion solution flow parameters, including: Using a pre-defined dilute substance transport method, the dynamic flux of ions participating in the anodic reaction in the dynamic corrosion pit is determined based on the corrosion solution flow rate, which is included in the corrosion solution flow parameters. The formula for calculating dynamic flux is as follows: Where, N i D represents the dynamic flux of ion i participating in the anodic reaction. i The molecular diffusion coefficient (m) of ion i participating in the anodic reaction 2 / s), c i The concentration (molm) of ion i participating in the anodic reaction -3 ), z i u represents the charge number of ion i participating in the anodic reaction. i The electromobility (mol / kg) of ion i participating in the anodic reaction -1 F is the Faraday constant (96485 C / mol), Φ is the potential of the corrosive solution (V), v is the flow rate of the corrosive solution (m / s), t is the corrosion time, and R is the corrosion rate. i This refers to the chemical reaction rate (which has a corresponding relationship with the temperature of the corrosive solution and the pipe temperature); Anode current density is determined based on multiple dynamic fluxes; The formula for calculating the anode current density is as follows: i a =FΣ i z i N i ; Among them, i a Anode current density (A / m) 2 ), F is the Radius constant (96485 C / mol), z i N represents the charge number of ion i participating in the anodic reaction. i The dynamic flux (mol / (m2·s)) of ion i participating in the anodic reaction; The corrosion rate of dynamic pits is determined based on the target current density; The formula for calculating the corrosion rate is as follows: Among them, V corr M represents the corrosion rate. Fe ρ is the molar mass of iron (kg / mol), n is the number of electrons transferred in the iron dissolution half-cell reaction (n=2), F is the Faraday constant (96485C / mol), and ρ is the molar mass of iron. Fe Let i be the density of iron (g / m3). a This represents the anode current density.

[0040] In this embodiment, a preset dilute substance transport method is used to determine the dynamic flux of ions participating in the anodic reaction in the dynamic pit based on the flow rate of the corrosive solution. The corrosion rate of the dynamic pit is determined based on the anodic current density determined by multiple dynamic fluxes. This allows for the dynamic quantification of the corrosion rate of the initial pit during the pitting process in the form of corrosion rate data. This facilitates an intuitive understanding of the corrosion rate of the initial pit under flow conditions based on each corrosion rate. It not only improves the accuracy of the corrosion rate predicted by pitting corrosion, but also enhances the feasibility and accuracy of the quantitative analysis of corrosion rate changes.

[0041] In an exemplary embodiment provided in this application, the method for calculating the dynamic flux of ions participating in the cathode reaction is the same as that for the anode reaction, and the method for calculating the cathode current density of ions participating in the cathode reaction is the same as that for the anode current density of ions participating in the anode reaction.

[0042] Combining the formulas for calculating dynamic flux and current density, we obtain: Among them, i a / c This refers to the anode current density or the cathode current density.

[0043] Consider the charge conservation during chemical reactions in corrosive solutions: Among them, Q i This represents the electrolyte phase current source term (A / m³) in the corrosive solution, which is zero under conditions of no applied current. An electroneutrality constraint Σz is introduced. i c i =0, Coupled ( and Solve for the anode current density i a With cathode current density i c The formula for calculating the corrosion rate is obtained as follows:

[0044] Optionally, based on a preset Lapss smoothing method and initial pit locations, the changes in dynamic pits are determined, including: Using the pre-defined Lapss smoothing method, the transient degrees of freedom of the pit edge of the dynamic pit are determined based on the initial pit location; The formula for calculating the transient degrees of freedom at the edge of the pit is as follows: Among them, the distribution of multiple (x, y) represents the transient degrees of freedom of the pit edge in the pitting model, that is, the spatial coordinates of the pit edge, and the distribution of multiple (X, Y) represents the initial pit position and the corresponding spatial coordinates of the pipeline in the pitting model. By comparing the transient degrees of freedom at the pit edge with the initial pit position, the dynamic changes in the pit are obtained, including both longitudinal and lateral changes.

[0045] In this embodiment, a preset Lapss smoothing method is used to determine the transient degrees of freedom of the pit edge of the dynamic pit based on the initial pit position. By comparing the transient degrees of freedom of the pit edge with the initial pit position, the longitudinal and lateral changes of the dynamic pit are obtained, forming the pit change situation. In this way, the expansion of the pit edge during the pitting process can be dynamically quantified in the form of pit change data. This makes it easy to intuitively understand the expansion of the pit edge under flow conditions based on the changes of each pit. This not only improves the accuracy of the pit change situation obtained from pitting prediction, but also improves the feasibility and accuracy of the quantitative analysis of pit change situation.

[0046] In this embodiment, the initial pitting is defined as the deformable region, while other areas on the pipe surface are considered non-deformable regions. In the numerical simulation of pitting corrosion, the geometric evolution of the active boundary of the pipe's metal electrode surface deforms with corrosion, and the electrolyte domain (corrosive solution region) also undergoes corresponding displacement. This dynamic deformation process causes distortion of the mesh within the computational domain, shifting the mapping relationship between mesh coordinates and spatial coordinates, thus leading to divergence in the numerical solution. Therefore, a dynamic coordination mechanism between the mesh and the active boundary must be established. The Laplace smoothing method is used to handle mesh deformation and solve for the transient degrees of freedom of the deformed pitting.

[0047] In an exemplary embodiment provided in this application, the initial pitting parameters include a pipe material of stainless steel, an initial pit radius of R = 1.5 μm, and an initial pit location on the pipe wall. The corrosive solution region is a rectangular area with a width W = 100 μm and a height H = 50 μm, serving as the electrolyte domain for pitting corrosion.

[0048] Among the environmental parameters, the pipeline temperature is 60℃ and the pipeline pressure is 1 MPa. Among the environmental parameters, the corrosion solution flow parameters indicate that the corrosion solution type is simulated oilfield formation water, and its chemical composition consists of Na... + ,K + Ca 2+ Mg 2+ ,Cl - SO4 2- and HCO3 - The composition of the corrosive solution matches this type of corrosive solution, and the physical parameters include an electrolyte conductivity of 5.5 e.g., -6 [S / m], solution density is 983.51 kg / m³ 3 The hydrodynamic viscosity is 4.6901E. -4 kg / (m·s); the flow location of the corrosive solution is the surface of the initial pit; the flow velocity of the corrosive solution is 2m / s, 4m / s or 6m / s; the flow direction of the corrosive solution is from left to right along the extension direction of the pipe, that is, the left end face of the rectangle is the turbulent inlet and the right end face of the rectangle is the turbulent outlet; the temperature of the corrosive solution is 293.15K, that is, 20℃.

[0049] The target fluid flow pattern obtained based on the above-mentioned corrosive solution type and flow rate is a turbulent flow pattern.

[0050] Based on the initial pitting parameters and environmental parameters mentioned above, a pitting corrosion model for the initial pitting of the pipeline is constructed. Dynamic pitting corrosion prediction is performed on the initial pitting to obtain the target pitting parameters, including the corrosion rate and pitting changes.

[0051] Therefore, by analyzing the changes in corrosion rate and pitting patterns obtained from dynamic pitting corrosion, the following results were obtained: Figure 3 The electrolyte potential distribution of the corrosive solution at different flow rates is shown below. Figure 4 The expansion and changes of dynamic erosion pits under different flow velocities are shown, such as Figure 5 The corrosion rate of dynamic pits is shown at different flow rates.

[0052] Figure 3 In the dynamic corrosion pits, at flow rates of 2 m / s, 4 m / s, and 6 m / s, the electrolyte potential of the corrosive solution gradually increases along the pit growth direction, with the maximum electrolyte potential occurring at the bottom of the pit. Furthermore, the electrolyte potential gradually increases with the increase of the flow rate of the corrosive solution outside the dynamic corrosion pit. At higher flow rates, the electrolyte potential changes more drastically inside the dynamic corrosion pit, thus accelerating localized corrosion. The current density vector field distribution shows that the highest current density is located at the bottom of the pit, and the current density gradient increases with increasing flow rate. The change in current density is consistent with the change in electrolyte potential.

[0053] Figure 4 The diagram illustrates the longitudinal and transverse variations of dynamic corrosion pits at flow rates of 2 m / s, 4 m / s, and 6 m / s. The variations show that the expansion patterns are similar across the three flow rates, with all pits expanding uniformly outwards. The expansion rate increases with increasing flow rate. At a flow rate of 2 m / s, the corrosion pit expansion is minimal, with a longitudinal variation of only 0.2 μm and a transverse variation of 0.8 μm. At a flow rate of 6 m / s, the corrosion pit expansion is maximum, with a longitudinal variation reaching 0.3 μm and a transverse variation reaching 1 μm.

[0054] Figure 5 The paper presents the corrosion rate changes of the edge corrosion in a dynamic pit on days 0, 10, 20, and 30 at flow rates of 2 m / s, 4 m / s, and 6 m / s. These changes indicate that the corrosion rate at the bottom of the dynamic pit is higher than that at the outer edge, and the corrosion rate gradually increases with the progress of the reaction. When the corrosive solution flow rate is 2 m / s, the corrosion rate is at its minimum of 0.00128 mm / y on day 0 and 0.00133 mm / y on day 30. When the flow rate is 4 m / s, the corrosion rate is 0.00175 mm / y on day 0 and 0.00186 mm / y on day 30. When the flow rate is 6 m / s, the corrosion rate is 0.00197 mm / y on day 0 and reaches its maximum of 0.00232 mm / y on day 30, showing that the edge corrosion rate increases with increasing flow rate.

[0055] In summary, the pipeline pitting corrosion prediction method of this application establishes a two-dimensional geometric model of the pitting pit in the pipeline, combines chemical reactions, electrochemical reactions and species mass transfer processes, couples the three-dimensional current distribution, deformation geometry and flow field multi-physics fields, and calculates and solves key data such as the expansion and change of pitting pits under flow conditions and the corrosion rate within the pit, thereby realizing the prediction of pitting corrosion expansion and corrosion rate changes under flow conditions, thus improving the accuracy of pitting corrosion prediction.

[0056] Please see Figure 6 , Figure 6 An exemplary embodiment of this application illustrates a pipeline pitting corrosion prediction system, such as... Figure 6 As shown, this application provides a pipeline pitting corrosion prediction system 600, comprising: The acquisition module 601 is used to acquire the initial corrosion pit parameters of the pipeline and the environmental parameters of the environment in which the pipeline is located. The initial corrosion pit parameters include the pipeline material, the initial corrosion pit radius and the initial corrosion pit location. The environmental parameters include environmental state quantities and corrosive solution flow parameters. The construction module 602 is used to construct a pitting corrosion model of the initial pitting of the pipeline based on the initial pitting parameters and environmental parameters; the pitting corrosion prediction module 603 is used to perform dynamic pitting corrosion prediction on the initial pitting based on the pitting corrosion model to obtain the target pitting parameters, which include corrosion rate and pitting change.

[0057] The pipeline pitting corrosion prediction system 600 provided in this embodiment utilizes a construction module 602 to construct a pitting corrosion model of the initial pits on the pipeline by acquiring the initial pitting parameters of the pipeline and the environmental parameters of the environment in which the pipeline is located, obtained by the acquisition module 601. The initial pitting parameters include the pipeline material, the radius of the initial pit, and the location of the initial pit. The environmental parameters include environmental state variables and corrosive solution flow parameters. The pitting corrosion prediction module 603 then uses the pitting corrosion model to perform dynamic pitting corrosion prediction to obtain target pitting parameters. In this way, the fluid dynamics characteristics of the pipeline's environment are considered when constructing the pitting corrosion model. This allows for dynamic pitting corrosion prediction of the initial pit based on the model, taking into account the influence of fluid dynamics on species mass transfer during the pitting process and the destructive effect on corrosion products within the pit. This enables pipeline pitting corrosion prediction under flow conditions, thereby improving the accuracy of the target pitting parameters obtained from the pitting corrosion prediction.

[0058] Optionally, module 602 is constructed specifically for: A two-dimensional geometric-physical model of the initial corrosion pits in the pipeline is constructed based on the initial corrosion pit parameters. The target tertiary current distribution physical field is constructed based on environmental parameters. The target tertiary current distribution physical field is a flow physical field. The target tertiary current distribution physical field is determined as the physical field of a two-dimensional geometric physical model to generate the pitting corrosion model of the initial corrosion pit.

[0059] Optionally, the environmental state parameters include pipeline temperature and pipeline pressure, and the corrosion solution flow parameters include corrosion solution type, corrosion solution flow location, corrosion solution flow velocity, corrosion solution flow direction and corrosion solution temperature, with the corrosion solution flow location corresponding to the initial pit location; Module 602 is specifically used for: The initial three-dimensional current distribution physical field was constructed based on the pipe temperature and pipe pressure. Based on the type and flow rate of the corrosive solution, the target fluid flow pattern for the two-dimensional geometric physical model is determined. Based on the target fluid flow pattern, corrosive solution type, corrosive solution flow location, corrosive solution flow velocity, corrosive solution flow direction, and corrosive solution temperature, target flow conditions are formed for the two-dimensional geometric physical model; The target flow conditions are determined as the flow parameters of the initial cubic current distribution physical field, and the target cubic current distribution physical field is obtained.

[0060] Optionally, module 602 is constructed specifically for: The Reynolds number for the pipeline is determined based on the type and flow rate of the corrosive solution. Find the alternative flow patterns corresponding to the Reynolds number from the preset flow pattern type table; The alternative flow patterns are determined as the target fluid flow patterns for the two-dimensional geometric physical model.

[0061] Optionally, the pitting prediction module 603 is specifically used for: Dynamic pitting corrosion simulation is performed on the initial pit based on the pitting corrosion model to simulate the chemical reaction that occurs in the initial pit under the corresponding environmental parameters, and to obtain the dynamic pit corresponding to the initial pit. Based on the preset dilute substance transport method and corrosion solution flow parameters, the corrosion rate of dynamic pits is determined. Based on the preset Lapss smoothing method and the initial pit location, the changes in dynamic pits are determined. Target pit parameters are determined based on corrosion rate and pitting variation.

[0062] Optionally, the chemical reaction includes an anodic reaction; the pitting corrosion prediction module 603 is specifically used for: Using a pre-defined dilute substance transport method, the dynamic flux of ions participating in the anodic reaction in the dynamic corrosion pit is determined based on the corrosion solution flow rate, which is included in the corrosion solution flow parameters. Anode current density is determined based on multiple dynamic fluxes; The corrosion rate of dynamic pits is determined based on the target current density.

[0063] Optionally, the pitting prediction module 603 is specifically used for: Using the pre-defined Lapss smoothing method, the transient degrees of freedom of the pit edge of the dynamic pit are determined based on the initial pit location; By comparing the transient degrees of freedom at the pit edge with the initial pit position, the dynamic changes in the pit are obtained, including both longitudinal and lateral changes.

[0064] It should be noted that the pipeline pitting corrosion prediction system and the pipeline pitting corrosion prediction method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the pipeline pitting corrosion prediction system provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0065] A computing device according to an embodiment of this application includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements some or all of the steps of the above-described method for predicting pipeline pitting corrosion.

[0066] The computing device can be a computer, and the corresponding program is computer software. The parameters and steps in the computing device described above can be referred to the parameters and steps in the embodiment of the pipeline pitting prediction method above, and will not be repeated here.

[0067] This application embodiment provides a computer-readable storage medium storing instructions that, when executed, perform the steps of the aforementioned pipeline pitting prediction method.

[0068] The computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0069] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of this disclosure. The aforementioned computer-readable storage medium can be a non-transitory computer-readable storage medium, including: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, and other media capable of storing program code; it can also be a transient computer-readable storage medium.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] Those skilled in the art will recognize that this application can be implemented as a system, method, or computer program product. Therefore, this disclosure can be implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "module" or "system." Furthermore, in some embodiments, this application can also be implemented as a computer program product contained in one or more computer-readable media, which contains computer-readable program code. Computer-readable storage media can be, for example, but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof.

[0072] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0073] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting pitting corrosion in pipelines, characterized in that, include: The initial pitting parameters of the pipeline and the environmental parameters of the environment in which the pipeline is located are obtained. The initial pitting parameters include the pipeline material, the initial pit radius, and the initial pit location. The environmental parameters include environmental state variables and corrosive solution flow parameters. Based on the initial pitting parameters and the environmental parameters, a pitting corrosion model of the initial pitting of the pipeline is constructed. Based on the pitting corrosion model, dynamic pitting corrosion prediction is performed on the initial pit to obtain target pit parameters, which include corrosion rate and pit change.

2. The method according to claim 1, characterized in that, The pitting corrosion model of the pipeline, constructed based on the initial pitting parameters and the electrolyte solution parameters, includes: A two-dimensional geometric-physical model of the initial corrosion pits of the pipeline is constructed based on the initial corrosion pit parameters. Based on the environmental parameters, a target tertiary current distribution physical field is constructed, and the target tertiary current distribution physical field is a flow physical field; The physical field of the target three-dimensional current distribution is determined as the physical field of the two-dimensional geometric physical model, and the pitting corrosion model of the initial corrosion pit is generated.

3. The method according to claim 2, characterized in that, The environmental parameters include pipe temperature and pipe pressure. The corrosion solution flow parameters include corrosion solution type, corrosion solution flow location, corrosion solution flow velocity, corrosion solution flow direction, and corrosion solution temperature. The corrosion solution flow location corresponds to the initial pit location. The physical field for constructing the target third current distribution based on the environmental parameters includes: An initial three-dimensional current distribution physical field is constructed based on the pipe temperature and the pipe pressure; Based on the type and flow rate of the corrosive solution, the target fluid flow pattern for the two-dimensional geometric physical model is determined. Based on the target fluid flow pattern, the type of corrosive solution, the flow location of the corrosive solution, the flow velocity of the corrosive solution, the flow direction of the corrosive solution, and the temperature of the corrosive solution, target flow conditions are formed for the two-dimensional geometric physical model; The target flow conditions are determined as the flow parameters of the initial cubic current distribution physical field, thus obtaining the target cubic current distribution physical field.

4. The method according to claim 3, characterized in that, Determining the target fluid flow pattern for the two-dimensional geophysical model based on the type and velocity of the corrosive solution includes: The Reynolds number for the pipeline is determined based on the type and flow rate of the corrosive solution. Find the alternative flow patterns corresponding to the Reynolds number from the preset flow pattern type table; The alternative flow patterns are determined as the target fluid flow patterns for the two-dimensional geophysical model.

5. The method according to any one of claims 1 to 4, characterized in that, The step of dynamically predicting pitting based on the pitting model to obtain target pitting parameters includes: Based on the pitting corrosion model, dynamic pitting corrosion simulation is performed on the initial pit to simulate the chemical reaction that occurs in the initial pit in the environment corresponding to the environmental parameters, so as to obtain the dynamic pit corresponding to the initial pit. The corrosion rate of the dynamic pit is determined based on the preset dilute substance transport method and the flow parameters of the corrosive solution. Based on the preset Lapss smoothing method and the initial pit location, the pit change of the dynamic pit is determined; Target pitting parameters are formed based on the corrosion rate and the changes in the pitting.

6. The method according to claim 5, characterized in that, The chemical reaction includes anodizing; the determination of the corrosion rate of the dynamic pit based on the preset dilute substance transport method and the flow parameters of the corrosion solution includes: Using a pre-defined dilute substance transport method, the dynamic flux of ions participating in the anodic reaction in the dynamic corrosion pit is determined based on the corrosion solution flow rate, which is included in the corrosion solution flow parameters. The anode current density is determined based on multiple dynamic fluxes. The corrosion rate of the dynamic pit is determined based on the target current density.

7. The method according to claim 5, characterized in that, The determination of the dynamic pit changes based on the preset Lapss smoothing method and the initial pit location includes: Using a pre-defined Lapss smoothing method, the transient degrees of freedom of the edge of the dynamic pit are determined based on the initial pit location; By comparing the transient degrees of freedom at the pit edge with the initial pit position, the changes in the dynamic pit are obtained, including longitudinal and lateral changes.

8. A pipeline pitting corrosion prediction system, characterized in that, include: The acquisition module is used to acquire the initial corrosion pit parameters of the pipeline and the environmental parameters of the environment in which the pipeline is located. The initial corrosion pit parameters include the pipeline material, the initial corrosion pit radius and the initial corrosion pit location. The environmental parameters include environmental state quantities and corrosive solution flow parameters. A construction module is used to construct a pitting corrosion model of the initial pitting of the pipeline based on the initial pitting parameters and the environmental parameters; The pitting prediction module is used to perform dynamic pitting prediction on the initial pit based on the pitting model to obtain target pit parameters, which include corrosion rate and pit change.

9. A computing device comprising a memory, a processor, and a program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of a pipeline pitting prediction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a terminal device, cause the terminal device to perform the steps of a pipeline pitting prediction method as described in any one of claims 1 to 7.

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