A method, system, device, and medium for predicting corrosion rates of subsea pipelines

Abstract

The application provides a submarine pipeline corrosion rate prediction method, system, device and medium, wherein the method comprises: acquiring monitoring data and pipeline body data of a target submarine pipeline along a line; constructing a random dynamics model of microbial corrosion based on the monitoring data, simulating a random microbial corrosion current component; calculating a non-uniformly distributed pipe wall temperature field along the pipeline axial direction based on the monitoring data and the pipeline body data; calculating an electrochemical corrosion current component based on the pipe wall temperature field and the electrochemical parameters of the pipeline material; performing multi-physical field coupling calculation on the random microbial corrosion current component and the electrochemical corrosion current component to generate a corrosion space-time distribution field; and performing corrosion risk analysis based on the corrosion space-time distribution field to obtain a corrosion risk prediction result. The method can improve the accuracy of submarine pipeline corrosion prediction.

Description

Technical Field

[0001] This invention belongs to the field of marine oil and gas engineering construction technology, and in particular relates to a method, system, equipment and medium for predicting the corrosion rate of subsea pipelines. Background Technology

[0002] With the expansion of submarine pipeline construction and the increase in service life, their safe operation faces severe corrosion threats. To implement predictive maintenance, corrosion rate prediction technology based on a combination of data-driven and electrochemical mechanisms has become a research hotspot in the industry. This type of method integrates pipeline operating parameters, environmental monitoring data, and basic corrosion kinetic models to effectively assess uniform or steady-state corrosion trends.

[0003] However, subsea pipeline corrosion is a dynamic stochastic process driven by complex coupling of multiple physical and biological fields. Existing mainstream prediction models have significant limitations in dealing with two types of deep uncertainties: First, they struggle to quantify the nonlinear spikes in localized corrosion rates and the risk of sudden perforation caused by the random growth of biofilm communities and abrupt changes in metabolic activity in microbially induced corrosion (MIC); second, they generally neglect the differences in soil thermal resistance caused by complex seabed topography and burial depth variations over long distances of subsea pipelines. These differences alter the temperature field distribution within the pipe body, significantly affecting the kinetics of the corrosion electrochemical reaction and causing biases in models based on the constant temperature assumption. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, system, equipment, and medium for predicting the corrosion rate of subsea pipelines, which can improve the accuracy of subsea pipeline corrosion prediction, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for predicting the corrosion rate of subsea pipelines, including:

[0006] Acquire monitoring data along the target subsea pipeline and pipeline body data, including time-series data on microbial activity and environmental parameters;

[0007] A stochastic dynamics model of microbial corrosion was constructed based on monitoring data to simulate and generate stochastic microbial corrosion current components.

[0008] Based on monitoring data and pipeline body data, the pipe wall temperature field that is non-uniformly distributed along the pipeline axis is calculated.

[0009] Based on the pipe wall temperature field and the electrochemical parameters of the pipe material, the electrochemical corrosion current component is calculated.

[0010] The random microbial corrosion current component and the electrochemical corrosion current component are coupled in a multiphysics field to generate a corrosion spatiotemporal distribution field.

[0011] Corrosion risk analysis is conducted based on the spatiotemporal distribution field of corrosion to obtain corrosion risk prediction results, which include high-risk corrosion sections, risk development time, and risk probability.

[0012] In one embodiment, a stochastic dynamics model of microbial corrosion is constructed based on monitoring data to simulate and generate stochastic microbial corrosion current components, including:

[0013] The main corrosive microbial populations were identified based on time-series data of microbial activity, and the growth kinetic parameters of the main corrosive microbial populations were calibrated based on the correspondence between time-series data of environmental parameters and time-series data of microbial activity.

[0014] Based on growth kinetic parameters and real-time environmental parameters, a stochastic differential equation is constructed to describe the evolution of microbial metabolic activity intensity. The drift term of the stochastic differential equation is determined based on growth kinetic parameters and real-time environmental parameters, and its diffusion term is used to characterize random environmental disturbances.

[0015] Numerical solutions are used to solve stochastic differential equations to simulate and generate stochastic time-series curves of metabolic activity intensity within a preset future time period;

[0016] Based on the random time-series curve of metabolic activity intensity, the random microbial corrosion current component is calculated through a preset electrochemical conversion relationship.

[0017] In one embodiment, a stochastic differential equation describing the evolution of microbial metabolic activity intensity is constructed based on growth kinetic parameters and real-time environmental parameters, including:

[0018] Based on growth kinetic parameters, time-series data of microbial biomass density were calculated using a microbial growth model.

[0019] Acquire time-series data of local pipe wall temperature corresponding to the microbial activity monitoring location in the pipe wall temperature field;

[0020] Construct a stochastic differential equation, the expression of which is as follows:

[0021]

[0022] in, for The intensity of microbial metabolic activity at any given time. The mean regression rate constant; Let be the objective function, representing that in biomass density at time and local pipe wall temperature Expected metabolic activity levels under certain conditions; This is a volatility parameter used to quantify the intensity of the impact of random environmental disturbances on metabolic activity; This is the increment of the standard Wiener process, used to introduce random noise.

[0023] In one embodiment, based on monitoring data and pipeline body data, the non-uniformly distributed pipe wall temperature field along the pipeline axis is calculated, including:

[0024] Extract the outer diameter, burial depth, and spatial coordinates of the pipeline from the pipeline body data, and obtain the soil type and thermal properties parameters corresponding to the spatial coordinates from the seabed geological survey data;

[0025] Based on the burial depth data, pipe outer diameter, and thermal property parameters, calculate the soil thermal resistance per unit length at each point along the pipe axis. The soil thermal resistance is a function of the axial position.

[0026] The thermal resistance of the anti-corrosion layer, calculated based on the pipeline structural parameters in the pipeline body data, is superimposed with the thermal resistance of the soil to obtain the radial comprehensive thermal resistance that varies along the pipeline axis.

[0027] A one-dimensional axial unsteady-state pipeline heat conduction control equation was constructed with radial composite thermal resistance, in-pipe fluid temperature and velocity as key parameters; the in-pipe fluid temperature and velocity were obtained from monitoring data.

[0028] Numerical solution of the heat conduction control equations is used to output the pipe wall temperature field that is non-uniformly distributed along the pipe axis.

[0029] In one embodiment, corrosion risk analysis is performed based on the spatiotemporal distribution field of corrosion to obtain corrosion risk prediction results, including:

[0030] The spatiotemporal distribution field of corrosion is transformed according to Faraday's law of electrolysis to obtain the spatiotemporal distribution field of instantaneous corrosion rate.

[0031] The spatiotemporal distribution field of instantaneous corrosion rate is numerically integrated along the time dimension to obtain the spatiotemporal distribution field of cumulative corrosion depth.

[0032] The spatiotemporal distribution field of cumulative corrosion depth is subtracted from the initial wall thickness distribution of the pipeline body data to obtain the spatiotemporal distribution field of the remaining wall thickness.

[0033] Based on the preset risk threshold, the spatiotemporal distribution field of the remaining wall thickness is scanned and compared to identify high-risk corrosion sections and their corresponding risk development time.

[0034] Based on the results of multiple random simulations of the random microbial corrosion current components, the high-risk corrosion sections identified multiple times are subjected to probabilistic statistical processing to generate risk probabilities.

[0035] In one embodiment, the method further includes:

[0036] Multiple independent stochastic simulations were performed on the stochastic dynamics model of microbial corrosion to generate multiple stochastic microbial corrosion current components.

[0037] Based on multiple random microbial corrosion current components, a multi-physics field coupling calculation is repeatedly performed a preset number of times to generate multiple corrosion spatiotemporal distribution fields.

[0038] For each spatiotemporal point, statistical analysis was performed on the corrosion current density values ​​of multiple spatiotemporal corrosion distribution fields at the spatiotemporal point to obtain the statistical characteristics of the corrosion current density at the spatiotemporal point.

[0039] Based on the statistical characteristics of corrosion current density, the corrosion rate statistical characteristics at spatiotemporal points are obtained by conversion using Faraday's law of electrolysis.

[0040] Based on the statistical characteristics of corrosion rates at all spatiotemporal points, a corrosion rate statistical prediction report is generated, which includes a corrosion rate expectation field and a corrosion rate uncertainty field.

[0041] Secondly, this application also provides a subsea pipeline corrosion rate prediction system for implementing the method proposed in the first aspect, comprising:

[0042] The data acquisition module is used to acquire monitoring data along the target subsea pipeline and pipeline body data. The monitoring data includes time-series data of microbial activity and time-series data of environmental parameters.

[0043] The microbial corrosion simulation module is used to build a stochastic dynamics model of microbial corrosion based on monitoring data and simulate the generation of stochastic microbial corrosion current components.

[0044] The pipe wall temperature calculation module is used to calculate the non-uniformly distributed pipe wall temperature field along the pipe axis based on monitoring data and pipe body data.

[0045] The electrochemical corrosion calculation module is used to calculate the electrochemical corrosion current component based on the pipe wall temperature field and the electrochemical parameters of the pipe material.

[0046] The multi-field coupling calculation module is used to perform multi-physics field coupling calculations on the random microbial corrosion current component and the electrochemical corrosion current component to generate the spatiotemporal distribution field of corrosion.

[0047] The corrosion risk analysis module is used to perform corrosion risk analysis based on the spatiotemporal distribution field of corrosion and obtain corrosion risk prediction results. The corrosion risk prediction results include high-risk corrosion sections, risk development time, and risk probability.

[0048] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-mentioned method for predicting the corrosion rate of a subsea pipeline.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for predicting the corrosion rate of a subsea pipeline.

[0050] The proposed method, system, equipment, and medium for predicting corrosion rates of subsea pipelines acquire monitoring data along the target subsea pipeline and pipeline body data. Based on the monitoring data, a stochastic dynamic model of microbial corrosion is constructed to simulate and generate stochastic microbial corrosion current components. Simultaneously, a non-uniformly distributed pipe wall temperature field along the pipeline axis is calculated based on the monitoring data and pipeline body data. Then, the electrochemical corrosion current component is calculated using this temperature field and the electrochemical parameters of the pipeline material. The stochastic microbial corrosion current component and the electrochemical corrosion current component are coupled in a multiphysics field calculation to generate a spatiotemporal corrosion distribution field. Finally, corrosion risk analysis is performed based on this distribution field to obtain prediction results including high-risk corrosion sections, risk development time, and risk probability. This achieves synergistic quantification and coupled prediction of the stochastic dynamics of microbial corrosion and the spatial heterogeneity of the pipe body temperature field, improving the accuracy of subsea pipeline corrosion rate prediction and the comprehensiveness of risk warning. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a method for predicting the corrosion rate of a subsea pipeline provided in an embodiment of the present invention;

[0053] Figure 2 This is a schematic diagram of a subsea pipeline corrosion rate prediction system provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] First, a brief introduction to the terms used in the embodiments of this application will be given.

[0056] In the prediction of corrosion in subsea pipelines, monitoring data refers to time-series information collected through sensors or other detection methods that reflects the environmental conditions and microbial activity along the pipeline route. This data includes time-series data on microbial activity, such as the metabolic activity change sequences of corrosion-related microorganisms like sulfate-reducing bacteria, and time-series data on environmental parameters, such as physicochemical parameters like temperature, pH, and dissolved oxygen concentration that change over time. This data serves as the fundamental input for constructing predictive models, characterizing the driving factors of the corrosion process.

[0057] Pipeline body data refers to information describing the inherent properties and geometric characteristics of a subsea pipeline, typically derived from engineering design documents, construction records, or databases. This data includes pipeline specifications (such as diameter and wall thickness), material composition (such as steel grade and corrosion protection type), spatial layout (such as three-dimensional coordinates and burial depth), and operating parameters (such as design pressure and flow history). This data provides the structural background and boundary conditions for calculating the pipe wall temperature field and corrosion response.

[0058] The stochastic microbial corrosion current component is a time-series variable generated by a stochastic dynamics model of microbial corrosion, characterizing the contribution of corrosion current caused by microbial metabolic activity. This component exhibits random fluctuations, with its value varying over time and not being entirely deterministic. It reflects the unpredictable fluctuations in microbial activity caused by factors such as nutrient supply and sudden changes in environmental pressure. In corrosion prediction, this component, as a part of the total corrosion current, is directly related to the potential sudden risk of microbial-induced corrosion, and its randomness provides a basis for quantifying uncertainty.

[0059] Based on the above definitions, the implementation environment of the subsea pipeline corrosion rate prediction method provided in this application embodiment will be described. Indicatively, the implementation environment includes: sensors, terminals, and processors. The sensors may include electrochemical microbial probes for acquiring time-series data of microbial activity, such as microbial activity monitoring sensors based on linear polarization resistance or electrochemical noise principles; and multi-parameter water quality sensors for acquiring time-series data of environmental parameters, such as underwater in-situ monitoring instruments capable of simultaneously measuring temperature, pH, redox potential, dissolved oxygen concentration, and conductivity. Furthermore, the implementation environment also includes pressure transmitters, temperature transmitters, and flow meters integrated into the pipeline monitoring and data acquisition system; the processor may be a central processing unit, an artificial intelligence chip, or a multi-core processor, without limitation here.

[0060] Based on the above explanations of terms and implementation environments, the application scenarios of the embodiments of this application are described. The method for predicting the corrosion rate of subsea pipelines provided in the embodiments of this application can be applied to scenarios including but not limited to the following:

[0061] In deepwater oil and gas field development scenarios, subsea pipelines often endure extreme environments of high pressure and low temperature, making frequent manual inspections and maintenance difficult. Traditional corrosion prediction models struggle to accurately quantify the unique activity and random fluctuations of microbial communities in deepwater environments, and cannot accurately reflect the complex temperature changes along the pipeline from the platform outlet to the distant wellhead. This method integrates long-term environmental and microbial data collected by sensors deployed in deepwater areas, combined with the actual pipeline route and burial conditions, to achieve early identification of corrosion "hot spots" and quantitative assessment of risk probabilities throughout the pipeline. This provides a scientific basis for developing economical and reasonable deepwater pipeline inspection and maintenance strategies, effectively preventing sudden leaks caused by localized corrosion.

[0062] In areas with densely established pipeline networks on the nearshore continental shelf, multiple subsea pipelines and cables of varying ages, materials, and transport media often intersect and cross, forming a complex network of subsea infrastructure. Corrosion assessment of newly constructed or existing pipelines must consider not only their own operating conditions but also the potential interactive effects from adjacent facilities. This method can precisely calculate local temperature field anomalies caused by route undulations and special backfill materials at crossings of specific pipelines, and assess complex electrochemical environments such as stray current interference from cathodic protection systems of nearby pipelines. This high spatial resolution prediction can accurately locate the pipeline segments with the highest corrosion risk under the constraints of dense pipeline networks, providing decision support for targeted interventions within limited operating windows and strict safety restrictions.

[0063] For aging subsea oil and gas pipeline management projects that have been in service for many years, the anti-corrosion coating may age and break down as the pipeline's service life increases, significantly increasing the risk of microbial corrosion and localized corrosion. Traditional assessment methods based on the uniform assumptions made during the design phase are often too conservative or deviate significantly from reality. This method combines historical internal inspection data, direct corrosion assessment results, and the latest environmental monitoring data to calibrate and update corrosion model parameters. By importing "pipeline body data" reflecting the current true state of the pipeline (such as updated local wall thickness and coating defect locations based on internal inspections) and using real-time "monitoring data" to drive the model, dynamic and probabilistic predictions of the pipeline's remaining lifespan can be achieved. This provides core technical support for determining whether the pipeline meets the conditions for life extension and for identifying economically reliable maintenance and reinforcement solutions.

[0064] This is illustrative; the method for predicting the corrosion rate of a subsea pipeline provided in this application embodiment can also be applied to other application scenarios. It is only used as an example and is not limited to specific application scenarios.

[0065] In one exemplary embodiment, such as Figure 1As shown, a method for predicting the corrosion rate of subsea pipelines is provided, including the following steps 101 to 106, wherein:

[0066] Step 101: Obtain monitoring data along the target subsea pipeline and pipeline body data. The monitoring data includes time-series data of microbial activity and time-series data of environmental parameters.

[0067] Specifically, monitoring data is primarily obtained through online sensors deployed along the pipeline or at key nodes, and through periodic sampling and analysis. Microbial activity time-series data can be acquired using pre-installed microbial sensor arrays along the pipeline. Examples include continuous monitoring of corrosive microbial metabolic activity signal sequences using online probes based on electrochemical noise or bioelectrochemical principles, or specific microbial population abundance change sequences obtained through periodic collection of seawater and sediment samples for molecular biological analysis such as quantitative PCR. Environmental parameter time-series data can be acquired through deployed multi-parameter water quality sensors, continuously recording dynamic changes in parameters such as temperature, pH, dissolved oxygen, redox potential, and salinity of the seawater surrounding the pipeline. Pipeline body data originates from engineering design, construction completion data, and the pipeline integrity management database, covering pipeline material, wall thickness, outer diameter, anti-corrosion layer structure and thickness, precise three-dimensional spatial coordinates and burial depth data, as well as historical and real-time operating pressure, flow rate, and medium temperature records. This method performs time alignment, outlier removal, and normalization preprocessing on all raw data, and maps them uniformly onto a spatiotemporal computing grid that is discrete along the axial direction with the pipeline centerline as the reference, thereby forming a structured and standardized input dataset required for subsequent modeling.

[0068] Step 102: Construct a stochastic dynamic model of microbial corrosion based on monitoring data, and simulate the generation of stochastic microbial corrosion current components.

[0069] For example, trend and random fluctuation terms in microbial activity time series data can be extracted using time series analysis methods to identify the lag effect of biofilm community growth and abrupt changes in metabolic activity. By combining the Monte Carlo stochastic simulation algorithm, a dynamic equation containing random parameters such as microbial population reproduction rate and metabolite diffusion coefficient can be constructed. This equation quantifies the impact of the randomness of microbial activity on the corrosion process, thereby simulating the generation of random microbial corrosion current components at different times and in different pipeline sections. This model breaks through the limitations of conventional steady-state corrosion models and can accurately capture the nonlinear fluctuation characteristics of local corrosion rate in microbial-induced corrosion.

[0070] Step 103: Based on the monitoring data and the pipeline body data, calculate the non-uniformly distributed pipe wall temperature field along the pipeline axis.

[0071] Specifically, a mathematical model of pipe heat conduction can be established based on seawater temperature and sediment thermal conductivity data from environmental parameter time series data, combined with burial depth, pipe diameter, and insulation layer parameters from pipe body data. The model is then solved using the finite element analysis method. Simultaneously, considering the differences in soil thermal resistance caused by seabed topography, the thermal conductivity coefficient at different axial positions is dynamically corrected to obtain pipe wall temperature field data continuously distributed along the pipe axis. This calculation method overcomes the shortcomings of the constant temperature assumption in conventional models, making the temperature field distribution more consistent with actual working conditions.

[0072] Step 104: Calculate the electrochemical corrosion current component based on the pipe wall temperature field and the electrochemical parameters of the pipe material.

[0073] For example, electrochemical parameters such as the anodic polarization curve, cathodic polarization curve, and exchange current density of the pipe material can be experimentally determined to establish an electrochemical corrosion kinetic equation. Then, the temperature data of each axial position in the pipe wall temperature field can be substituted into the equation, and the influence of temperature on the corrosion reaction rate constant can be corrected by the Arrhenius formula. In this way, the electrochemical corrosion current component corresponding to each position can be calculated, realizing the coupling relationship between the temperature field and the electrochemical corrosion process, and improving the accuracy of the calculation of the electrochemical corrosion component.

[0074] Step 105: Perform multi-physics field coupling calculations on the random microbial corrosion current component and the electrochemical corrosion current component to generate the spatiotemporal distribution field of corrosion.

[0075] Specifically, a field coupling superposition algorithm is adopted to synthesize two types of corrosion current components at the same pipeline location and time according to the principle of vector superposition. At the same time, a corrosion current diffusion coefficient is introduced to correct the interaction between different fields. Combined with dynamic change data in the time dimension, a three-dimensional corrosion spatiotemporal distribution field model is constructed. This model can intuitively reflect the distribution characteristics of corrosion rate in the axial, radial and time dimensions of the pipeline, realizing the leap from single-point static prediction to global dynamic prediction.

[0076] Step 106: Conduct corrosion risk analysis based on the spatiotemporal distribution field of corrosion to obtain corrosion risk prediction results, including high-risk corrosion sections, risk development time, and risk probability.

[0077] Specifically, this method performs corrosion risk analysis based on the spatiotemporal distribution field of corrosion to obtain corrosion risk prediction results. The high-risk corrosion section can be determined by setting a corrosion rate threshold and screening out the axial range of the pipeline that exceeds the threshold in the spatiotemporal distribution field. The risk development time is calculated by analyzing the trend of corrosion rate change over time and combining it with the minimum allowable wall thickness of the pipeline to obtain the time when corrosion reaches the critical state. The risk probability is obtained by statistically analyzing the frequency of corrosion exceeding the standard under different operating conditions and fitting it with a probability density function. The final output corrosion risk prediction results comprehensively cover the high-risk corrosion section, risk development time and risk probability, providing an accurate basis for predictive maintenance of pipelines.

[0078] The proposed method for predicting the corrosion rate of subsea pipelines acquires monitoring data along the pipeline route and pipeline body data. Based on the monitoring data, a stochastic dynamics model of microbial corrosion is constructed to simulate and generate stochastic microbial corrosion current components. Simultaneously, a non-uniformly distributed pipe wall temperature field along the pipeline axis is calculated based on the monitoring data and pipeline body data. Then, the electrochemical corrosion current component is calculated using this temperature field and the electrochemical parameters of the pipeline material. The stochastic microbial corrosion current component and the electrochemical corrosion current component are coupled in a multiphysics calculation to generate a spatiotemporal corrosion distribution field. Finally, corrosion risk analysis is performed based on this distribution field to obtain prediction results including high-risk corrosion sections, risk development time, and risk probability. This achieves synergistic quantification and coupled prediction of the stochastic dynamics of microbial corrosion and the spatial heterogeneity of the pipe body temperature field, improving the accuracy of subsea pipeline corrosion rate prediction and the comprehensiveness of risk warning.

[0079] In one embodiment, a stochastic dynamics model of microbial corrosion is constructed based on monitoring data to simulate and generate stochastic microbial corrosion current components, including:

[0080] The main corrosive microbial populations were identified based on time-series data of microbial activity, and the growth kinetic parameters of the main corrosive microbial populations were calibrated based on the correspondence between time-series data of environmental parameters and time-series data of microbial activity.

[0081] Specifically, the microbial activity time-series data includes key indicators such as the population abundance and metabolic enzyme activity of various microorganisms during different monitoring periods. This method preprocesses the microbial activity time-series data by noise reduction, removes outliers caused by equipment interference and instantaneous environmental fluctuations during data acquisition through wavelet transform, and then uses principal component analysis combined with Pearson correlation coefficient calculation to screen out microbial species whose population abundance changes are significantly positively correlated with previous pipeline corrosion monitoring data, thereby determining the main corrosion microbial populations. At the same time, based on the correspondence between environmental parameter time-series data and microbial activity time-series data, this method calibrates the growth kinetic parameters of the main corrosion microbial populations. The environmental parameter time-series data comes from multi-parameter water quality monitors and sediment characteristic analyzers deployed along the pipeline. This method encompasses parameters such as seawater pH, dissolved oxygen concentration, nutrient content, and temperature. It aligns the time-series data of environmental parameters collected concurrently with the time-series data of microbial activity, and uses the nonlinear least squares method to fit a Logistic growth model. The results are obtained by solving for growth kinetic parameters such as the maximum specific growth rate, half-saturation constant, and environmental inhibition coefficient of the main corrosive microbial populations. The purpose of these operations is to identify the types of microorganisms that play a dominant role in corrosion and to establish a quantitative correlation between microbial growth and environmental conditions. This provides core parameter support for the subsequent construction of a microbial metabolic activity model. The calibrated growth kinetic parameters can be directly used to describe the growth and reproduction patterns of microorganisms under different environmental conditions and are key basic data reflecting the corrosive ability of microorganisms.

[0082] Based on growth kinetic parameters and real-time environmental parameters, a stochastic differential equation is constructed to describe the evolution of microbial metabolic activity intensity. The drift term of the stochastic differential equation is determined based on the growth kinetic parameters and real-time environmental parameters, and its diffusion term is used to characterize random environmental disturbances.

[0083] Specifically, the method constructs a stochastic differential equation with microbial metabolic activity intensity as the dependent variable and time as the independent variable. The drift term of the equation is determined by coupling growth kinetic parameters with real-time environmental parameters. Specifically, the real-time environmental parameters are substituted into the Logistic growth model to calculate the deterministic evolution trend of microbial metabolic activity intensity. The diffusion term of the equation is determined by introducing the standard Wiener process and combining it with the historical fluctuation variance of environmental parameters. It is used to characterize the impact of uncontrollable environmental random disturbances caused by ocean current disturbances, random nutrient transport, and other factors on microbial metabolic activity. The construction of this equation overcomes the technical deficiency of traditional deterministic kinetic models in being unable to characterize the random fluctuations of microbial metabolic activity. Its core purpose is to achieve a dynamic and accurate description of the evolution process of microbial metabolic activity, solving the technical pain point that existing models are unable to quantify the randomness of microbial-induced corrosion. The constructed stochastic differential equation can be directly used to simulate the change process of microbial metabolic activity intensity in future time periods, providing a dynamic input basis for the subsequent calculation of corrosion current components.

[0084] Numerical solutions are used to solve stochastic differential equations to simulate and generate stochastic time-series curves of metabolic activity intensity within a preset future time period.

[0085] Specifically, the constructed stochastic differential equations are numerically solved to simulate and generate stochastic time-series curves of metabolic activity intensity within a preset future time period. The solution process can employ the Euler-Markov method or the Milstin method, both of which are suitable for the numerical discretization of stochastic differential equations and can effectively preserve the stochastic characteristics of the equations. This method sets the time step for the future time period, and the selection of the time step needs to balance computational accuracy and efficiency, usually determined based on the acquisition interval of actual monitoring data. Then, the predicted values ​​of real-time environmental parameters and growth kinetic parameters are substituted into the discretized iterative formula. Multiple sets of initial values ​​for iteration under different random perturbation scenarios are generated through multiple random samplings. Subsequently, iterative calculations are performed to obtain multiple sets of stochastic time-series curves of metabolic activity intensity covering different probability scenarios. The purpose of this step is to transform the abstract stochastic differential equations into intuitive time-series data through numerical solutions, fully presenting the stochastic variation trend of microbial metabolic activity intensity within a future time period. The generated stochastic time-series curves of metabolic activity intensity can directly reflect the fluctuation of microbial metabolic activity at different times, providing dynamic activity intensity input data for subsequent electrochemical conversion.

[0086] Based on the random time-series curve of metabolic activity intensity, the random microbial corrosion current component is calculated through a preset electrochemical conversion relationship.

[0087] Specifically, the pre-established electrochemical conversion relationship is established through indoor accelerated corrosion experiments. This involves simulating seabed environmental conditions in the laboratory, cultivating major corrosive microbial populations, and measuring the microbial corrosion current density corresponding to different metabolic activity intensities. A nonlinear fitting method is then used to establish a quantitative mapping model between metabolic activity intensity and corrosion current density. This mapping model represents the electrochemical conversion relationship. The method substitutes the activity intensity values ​​at each moment in the random time-series curve of metabolic activity intensity into the mapping model to calculate the microbial corrosion current density at the corresponding moment. Combined with the corrosion reaction area of ​​the pipeline, the random microbial corrosion current component is calculated. The core purpose of this step is to achieve a quantitative conversion of microbial metabolic activity intensity into corrosion current components, establishing a direct correlation between microbial activity and corrosion rate. The calculated random microbial corrosion current component can accurately reflect the corrosion current changes caused by random fluctuations in microbial metabolic activity and can be directly used for subsequent multiphysics coupling calculations, providing crucial data support for a comprehensive assessment of the risk of microbial-induced corrosion in subsea pipelines.

[0088] In one embodiment, a stochastic differential equation describing the evolution of microbial metabolic activity intensity is constructed based on growth kinetic parameters and real-time environmental parameters, including:

[0089] Based on growth kinetic parameters, time-series data of microbial biomass density were calculated using a microbial growth model.

[0090] For example, the microbial growth model adopts a Logistic model modified by environmental factors. The growth kinetic parameters (maximum specific growth rate, half-saturation constant, inhibition coefficient, etc.) are the core input parameters of the model. This method substitutes real-time environmental parameters (such as seawater pH, dissolved oxygen concentration, nutrient content) into the model and obtains microbial biomass density data at different times through continuous iterative calculation, forming complete biomass density time series data. This data directly reflects the dynamic change of microbial population size over time and is the key basis for subsequent calculation of the expected metabolic activity level. Its role is to establish a quantitative correlation between microbial population size and metabolic activity, and to provide core state variables for stochastic differential equations.

[0091] Acquire time-series data of local pipe wall temperature corresponding to the microbial activity monitoring location in the pipe wall temperature field.

[0092] Specifically, by matching spatial coordinates, temperature data at the pipe wall locations corresponding to the microbial activity monitoring points are extracted, and then sorted by time dimension to form time-series data of local pipe wall temperature. The purpose of obtaining this data is to consider the significant impact of temperature on microbial metabolic activity. Both corrosion electrochemical reactions and microbial metabolic processes are highly sensitive to temperature. Dynamic changes in local pipe wall temperature will directly change the metabolic rate of microorganisms. Incorporating this into the model can overcome the limitation of traditional models that ignore the dynamic influence of temperature, and ensure that the calculation of metabolic activity intensity is more in line with actual working conditions.

[0093] Construct a stochastic differential equation, the expression of which is as follows:

[0094]

[0095] in, for The intensity of microbial metabolic activity at any given time. The mean regression rate constant; Let be the objective function, representing that in biomass density at time and local pipe wall temperature Expected metabolic activity levels under certain conditions; This is a volatility parameter used to quantify the intensity of the impact of random environmental disturbances on metabolic activity; This is the increment of the standard Wiener process, used to introduce random noise.

[0096] In the formula, for The intensity of microbial metabolic activity at time t is the core dependent variable of the equation, directly reflecting the intensity of the effect of microorganisms on pipeline corrosion at that time, and is a key intermediate variable for subsequent calculation of the random microbial corrosion current component. The mean regression rate constant is obtained through indoor experiments. Its function is to control the rate at which the intensity of microbial metabolic activity converges to the desired level, ensuring that the model can reasonably reflect the dynamic adjustment process of metabolic activity and avoid drastic fluctuations that do not match reality. Let be the objective function, representing that in biomass density at time and local pipe wall temperature The expected metabolic activity level under certain conditions is established by multivariate nonlinear fitting, which incorporates time-series biomass density data. Local pipe wall temperature time series data By substituting the values, the expected metabolic activity at the corresponding time point can be obtained. Its core function is to quantify the average evolutionary trend of metabolic activity under the combined influence of microbial population size and temperature. This is a volatility parameter used to quantify the intensity of the impact of random environmental disturbances on metabolic activity. It is calibrated by statistically analyzing the random fluctuation amplitude of historical environmental parameters and is specifically used to quantify the intensity of the impact of random environmental disturbances such as abrupt temperature changes and random fluctuations in nutrient concentration on metabolic activity. A higher value indicates a more significant impact of environmental disturbances on metabolic activity; As an increment of the standard Wiener process, it is used to introduce random noise that conforms to a normal distribution, realistically simulating unpredictable random disturbances in the environment, and achieving accurate characterization of the randomness of metabolic activity evolution.

[0097] Specifically, by incorporating the dynamic changes in biomass density and local pipe wall temperature with random environmental disturbances into the same model, this equation not only solves the problem that traditional deterministic models cannot characterize the random fluctuations in metabolic activity, but also overcomes the shortcomings of conventional stochastic models that ignore the coupling effect of key environmental factors (such as temperature) and microbial population size. It can accurately capture the dynamic evolution of microbial metabolic activity intensity, providing a scientific and reliable mathematical model support for the subsequent simulation and generation of random microbial corrosion current components, ensuring that the calculation of subsequent corrosion current components can truly reflect the random characteristics and dynamic trends of microbial-induced corrosion.

[0098] In one embodiment, based on monitoring data and pipeline body data, the non-uniformly distributed pipe wall temperature field along the pipeline axis is calculated, including:

[0099] The pipeline outer diameter, burial depth, and spatial coordinates are extracted from the pipeline body data, and the soil type and thermal properties corresponding to the spatial coordinates are obtained from the seabed geological survey data.

[0100] Specifically, the pipeline body data is derived from the design documents, construction completion data, and periodic inspection reports of the target subsea pipeline. The extracted pipeline outer diameter is used to characterize the contact area between the pipeline and the surrounding soil, and the burial depth data reflects the burial depth of the pipeline in the seabed sediments. Spatial coordinates accurately locate the geographical location of each point along the pipeline axis. At the same time, this method obtains the soil type and thermal property parameters corresponding to the spatial coordinates from the seabed geological survey data. The seabed geological survey data is obtained through previous marine geological drilling, ground-penetrating radar detection, and other exploration methods. The soil types include sandy soil, clay soil, and silty soil, and the thermal property parameters cover soil thermal conductivity, specific heat capacity, density, etc. The core purpose of this step is to collect the basic data required to calculate soil thermal resistance and achieve accurate matching between pipeline parameters and geological parameters through spatial coordinates, laying the data foundation for subsequent calculation of axial non-uniform thermal resistance.

[0101] Based on the burial depth data, pipe outer diameter, and thermal property parameters, the soil thermal resistance per unit length at each point along the pipe axis is calculated. The soil thermal resistance is a function of the axial position.

[0102] Specifically, the pipeline's outer diameter, burial depth, and spatial coordinates are extracted from the pipeline body data. This pipeline body data is typically stored in an engineering database or design file, and the pipeline's outer diameter and spatial coordinates (such as a three-dimensional centerline sequence expressed in longitude, latitude, and elevation depth) can be directly read. The burial depth data refers to the vertical distance between the top of the pipeline and the seabed mudline; it may be an attribute field bound to the spatial coordinates, or it may be calculated by overlaying the pipeline coordinates with a high-precision seabed digital terrain model. Simultaneously, the soil type and thermal properties corresponding to the pipeline's spatial coordinates need to be obtained from an independent seabed geological survey database. For example, spatial association can be performed using a geographic information system (GIS) to discretize the pipeline centerline into a series of calculation points, and match each point with the nearest geological borehole or geophysical profile data to determine the soil type (such as clay, sand, or silt) and its key thermal properties, primarily the soil thermal conductivity. This step ensures that the temperature field calculation model has a realistic geological background input. Based on the burial depth data, pipe outer diameter, and thermal property parameters, the soil thermal resistance per unit length at each point along the pipe axis is calculated. Here Representing the axial distance along the pipe, this indicates that the thermal resistance is a function of position. The calculation employs an engineering formula based on the theory of line heat sources, which describes the thermal resistance of a cylindrical heat source undergoing steady-state heat dissipation in a homogeneous medium. For a specific calculation point, the formula for calculating the soil thermal resistance is:

[0103]

[0104] in, It is the burial depth of the pipeline's centerline at that point. It refers to the outer diameter of the pipe. The physical meaning of this formula lies in the soil thermal resistance. With soil thermal conductivity It is inversely proportional to the soil's thermal conductivity; that is, the worse the soil's thermal conductivity, the greater the thermal resistance. Simultaneously, it is related to the burial depth. The thermal resistance is proportional to the logarithm of the pipe's diameter, meaning that the deeper the pipe is buried, the longer the heat dissipation path and the greater the thermal resistance. By applying this formula to every discrete point on the pipe's axis, a distribution curve of soil thermal resistance varying along the pipe's axis can be obtained, which is the primary physical reason for the non-uniformity of the final temperature field.

[0105] The thermal resistance of the anti-corrosion layer, calculated based on the pipeline structural parameters in the pipeline body data, is superimposed with the thermal resistance of the soil to obtain the radial composite thermal resistance that varies along the pipeline axis.

[0106] Specifically, pipeline structural parameters include the thickness of anti-corrosion coatings (such as FBE), concrete counterweight layers, etc. and the thermal conductivity of each material The thermal resistance per unit length of these structural layers The thermal resistance can be calculated using the formula for the thermal conductivity of a flat wall. For example, for a multilayer structure, its thermal resistance is the sum of the thermal resistances of each layer: It is important to note that, It is usually considered a constant that does not change with axial position. Ultimately, at some point in the pipeline... Total radial thermal resistance It consists of the thermal resistance of the soil and the thermal resistance of the structural layer connected in series:

[0107]

[0108] because Follow Change, therefore It is also a function of axial position. This comprehensive thermal resistance quantitatively characterizes the total resistance of the heat dissipation path from the outer wall of the pipe to the distant isothermal stratum, and its spatial variability directly determines the difference in heat dissipation efficiency in different sections of the pipe.

[0109] A one-dimensional axial unsteady-state pipeline heat conduction control equation was constructed with radial composite thermal resistance, in-pipe fluid temperature and velocity as key parameters; the in-pipe fluid temperature and velocity were obtained from monitoring data.

[0110] Specifically, a one-dimensional axial unsteady-state pipe heat conduction control equation is constructed, with radial composite thermal resistance, in-pipe fluid temperature, and flow velocity as key parameters. The in-pipe fluid temperature... With flow rate It needs to be extracted in real time or according to historical sequences from the monitoring data obtained from the pipeline monitoring and data acquisition system. Considering the energy balance of the pipeline micro-elements, a governing equation is established. A typical form of this equation is as follows, which describes the pipe wall temperature. With axial position and time Changes:

[0111]

[0112] In this equation: These are the density, specific heat, and cross-sectional area of ​​the steel used in the pipe wall; These are the mass flow rate and specific heat of the fluid inside the pipe, respectively. It is the inner diameter of the pipe; It is the convective heat transfer coefficient between the fluid and the pipe wall; The ambient temperature of the deep seabed surrounding the pipeline can be considered a constant. The left side of the equation represents the rate of change of energy within the pipe wall; the first term on the right side is the axial energy transport (convection) caused by fluid flow; the second term is the convective heat transfer between the fluid inside the pipe and the pipe wall; and the third term is the radial thermal resistance. Heat dissipation to the environment is a key aspect of coupling spatially varying thermal resistance into the dynamic thermal model.

[0113] Numerical solution of the heat conduction control equations is used to output the pipe wall temperature field that is non-uniformly distributed along the pipe axis.

[0114] Specifically, the heat conduction control equation is solved numerically to output a non-uniformly distributed pipe wall temperature field along the pipe axis. Because the equation contains variable coefficients. Furthermore, since the condition is unsteady, analytical solutions are difficult to obtain. Therefore, numerical methods can be used to discretize the pipe into multiple grid elements along the axial direction and also into multiple time steps. The partial differential equations are discretized using the finite volume method or the finite difference method, transforming the continuous differential equations into a system of algebraic equations concerning the temperature at each grid point. Given the initial temperature distribution and boundary conditions (such as the fluid temperature at the pipe inlet)... (and temperature gradient), combined with time-varying... , And changes with space The temperature of all grid points at each time step is solved iteratively (e.g., using implicit schemes to ensure stability). After solving, the pipe wall temperature at each axial position at each moment is obtained over the entire prediction time range, representing the required non-uniform pipe wall temperature field. This temperature field, as a crucial fundamental physical field, directly determines the reliability of subsequent calculations of the electrochemical corrosion current components, thus resolving the systematic bias in corrosion prediction caused by neglecting spatial differences in heat dissipation conditions over long-distance pipes.

[0115] In one embodiment, corrosion risk analysis is performed based on the spatiotemporal distribution field of corrosion to obtain corrosion risk prediction results, including:

[0116] The spatiotemporal distribution field of corrosion is transformed according to Faraday's law of electrolysis to obtain the spatiotemporal distribution field of instantaneous corrosion rate.

[0117] Specifically, Faraday's law of electrolysis is the core theoretical basis for the quantitative calculation of corrosion rate. Its core principle is that the amount of metal dissolved during corrosion is directly proportional to the amount of electricity passing through the corrosion system. The spatiotemporal distribution field of corrosion already contains corrosion current density data at each time point and pipe location, which directly reflects the intensity of electrical charge transfer in the corrosion reaction. This method establishes a quantitative conversion relationship between corrosion current density and instantaneous corrosion rate using Faraday's law of electrolysis. During the conversion process, basic physical parameters such as the molar mass, valence, and density of the pipe material are considered. The corrosion current density data at each spatiotemporal node in the corrosion spatiotemporal distribution field is converted one by one into the instantaneous corrosion rate of the corresponding node, ultimately forming a spatiotemporal distribution field of instantaneous corrosion rate. This distribution field can intuitively reflect the dynamic changes in corrosion rate at different times and pipe locations, providing accurate instantaneous rate data support for subsequent calculations of cumulative corrosion depth.

[0118] The spatiotemporal distribution field of instantaneous corrosion rate is numerically integrated along the time dimension to obtain the spatiotemporal distribution field of cumulative corrosion depth.

[0119] Specifically, cumulative corrosion depth is a core indicator for pipeline corrosion risk assessment. Its physical essence is the cumulative effect of instantaneous corrosion rates over a specific time period. The core purpose of this step is to transform discrete instantaneous corrosion rate data over time into continuous cumulative corrosion depth data. In implementation, this method first determines the integration time interval (i.e., the preset risk prediction period). Numerical integration algorithms such as the trapezoidal integral method and Simpson's integral method are used to integrate the rate-time curve corresponding to each pipeline location in the spatiotemporal distribution field of the instantaneous corrosion rate. The integration result is the cumulative corrosion depth at that location within the prediction period. By performing time-dimensional integration processing on all pipeline locations one by one, a spatiotemporal distribution field of cumulative corrosion depth containing data on cumulative corrosion depth at each location and time is finally formed. This distribution field solves the problem that a single instantaneous rate cannot reflect the long-term corrosion effect, laying the foundation for calculating the remaining wall thickness.

[0120] The spatiotemporal distribution field of cumulative corrosion depth is obtained by subtracting the initial wall thickness distribution of the pipeline from the pipeline body data.

[0121] Specifically, the initial wall thickness distribution data of the pipeline comes from pipeline design documents and construction inspection reports, including the initial wall thickness benchmark values ​​at various axial positions of the pipeline. Due to differences in manufacturing and laying processes, there may be slight differences in the initial wall thickness at different axial positions. Therefore, distributed data rather than a single fixed value needs to be used for calculation. This method first aligns the spatiotemporal distribution field of cumulative corrosion depth with the initial wall thickness distribution of the pipeline in spatial coordinates, ensuring that the cumulative corrosion depth data at each pipeline location accurately matches the corresponding initial wall thickness data. Then, it performs the subtraction operation of "initial wall thickness - cumulative corrosion depth" at each spatiotemporal node to obtain the remaining wall thickness data at each spatiotemporal node, and integrates them to form the spatiotemporal distribution field of the remaining wall thickness. The core significance of this step is to quantify the actual wall thickness state of the pipeline after corrosion, directly providing core data for risk threshold comparison and avoiding risk assessment bias caused by ignoring differences in the initial wall thickness distribution.

[0122] Based on the preset risk threshold, the spatiotemporal distribution field of the remaining wall thickness is scanned and compared to identify high-risk corrosion zones and their corresponding risk development times.

[0123] Specifically, the preset risk threshold is the minimum allowable remaining wall thickness for safe pipeline operation. This threshold is determined based on parameters such as pipeline design pressure, medium characteristics, and pipe material strength, and is calculated through strength verification according to relevant industry standards (such as API 5L and GB / T 9711) to ensure the scientific and compliant nature of the threshold setting. This method employs a point-by-point scanning approach to dynamically monitor the change curves of the remaining wall thickness over time at all axial positions in the spatiotemporal distribution field. When the remaining wall thickness data at a certain axial position first decreases to the preset risk threshold, this moment is recorded as the risk development time at that position. Simultaneously, all axial positions with risk development times less than or equal to the preset warning period are continuously divided into high-risk corrosion zones. This process, through dynamic comparison and precise judgment, achieves spatial positioning and temporal prediction of corrosion risk, overcoming the limitation of traditional risk analysis, which can only identify static risk areas.

[0124] Based on the results of multiple random simulations of the random microbial corrosion current components, the high-risk corrosion sections identified multiple times are subjected to probabilistic statistical processing to generate risk probabilities.

[0125] Specifically, due to the significant random uncertainties inherent in microbial corrosion processes, a single simulation cannot comprehensively cover all possible corrosion scenarios. Therefore, this method generates multiple sets of random microbial corrosion current components under different random perturbation scenarios by repeatedly constructing and solving the stochastic dynamics model of microbial corrosion, thereby obtaining multiple sets of corresponding high-risk corrosion section identification results. In practice, this method counts the number of times the same axial location of the pipeline is identified as a high-risk section in all simulation scenarios, and uses the ratio of this number to the total number of simulations as the risk probability of that location. For continuous high-risk sections, the average risk probability of all locations within that section is calculated as the overall risk probability of that section. Through probabilistic statistical processing, the corrosion risk caused by random factors is quantified into specific probability values, providing more comprehensive risk information for pipeline maintenance decisions and overcoming the limitation of single deterministic analysis in quantifying risk uncertainty.

[0126] In one embodiment, the method further includes:

[0127] Multiple independent stochastic simulations were performed on the stochastic dynamics model of microbial corrosion to generate multiple stochastic microbial corrosion current components.

[0128] Specifically, multiple independent stochastic simulations are performed on the stochastic kinetic model of microbial corrosion to generate multiple stochastic microbial corrosion current components. Since microbial metabolic activities are significantly uncertain due to random environmental disturbances, a single stochastic simulation cannot fully cover all possible corrosion scenarios. Therefore, multiple independent simulations can adequately characterize this uncertainty. In implementation, this method keeps the structural parameters of the stochastic kinetic model of microbial corrosion (such as growth kinetic parameters and electrochemical conversion relationships) unchanged, and achieves independent simulations only by changing the initial value of the random noise in the diffusion term of the stochastic differential equation (i.e., the incremental initial conditions of the standard Wiener process). The number of simulations can be set according to engineering accuracy requirements (e.g., 100-1000 times). Each simulation outputs a complete set of stochastic microbial corrosion current components corresponding to different random disturbance scenarios. All components together constitute a microbial corrosion current dataset covering multiple stochastic scenarios, providing sufficient sample support for subsequent statistical analysis.

[0129] Based on multiple random microbial corrosion current components, a multi-physics field coupling calculation is repeatedly performed a preset number of times to generate multiple corrosion spatiotemporal distribution fields.

[0130] Specifically, the core logic of multiphysics coupling calculation is to synthesize the random microbial corrosion current component and the electrochemical corrosion current component according to the principle of vector superposition. Each coupling calculation uses a set of independent random microbial corrosion current components as input, combined with fixed electrochemical corrosion current components (because the electrochemical corrosion process is less affected by random disturbances and its accuracy has been ensured by pipe wall temperature field correction) and preset field coupling correction coefficients to complete the synthesis of corrosion current density within the same pipeline spatiotemporal range, ultimately generating multiple spatiotemporal corrosion distribution fields corresponding to the number of random simulations. This step, through repeated coupling calculations, transfers the random uncertainty of microbial corrosion to the global corrosion distribution, solving the problem that a single coupling result cannot reflect risk fluctuations.

[0131] For each spatiotemporal point, statistical analysis was performed on the corrosion current density values ​​of multiple spatiotemporal corrosion distribution fields at the spatiotemporal point to obtain the statistical characteristics of corrosion current density at the spatiotemporal point.

[0132] Specifically, a spatiotemporal point refers to the intersection of the three-dimensional coordinates of the pipeline's axial and radial positions with the time dimension. Each spatiotemporal point corresponds to a set of corrosion current density sample data in multiple corrosion spatiotemporal distribution fields. The statistical analysis process employs mathematical statistics methods to calculate the statistical parameters of this set of sample data, including but not limited to mean, standard deviation, variance, and quantiles. These parameters collectively constitute the statistical characteristics of the corrosion current density at the spatiotemporal point—the mean reflects the average level of corrosion current density at that spatiotemporal point, the standard deviation and variance characterize the fluctuation range of corrosion current density at that point under different random scenarios, and the quantiles can clearly identify the peak value of corrosion current density in extreme scenarios. Through multi-dimensional statistical parameters, the deterministic trend and uncertainty range of the corrosion current density at each spatiotemporal point are comprehensively quantified.

[0133] Based on the statistical characteristics of corrosion current density, the corrosion rate statistical characteristics at spatiotemporal points are obtained by converting them using Faraday's law of electrolysis.

[0134] Specifically, the conversion principle is consistent with the instantaneous corrosion rate calculation, both based on Faraday's law of electrolysis to establish a quantitative mapping relationship between corrosion current density and corrosion rate. In implementation, each parameter (such as mean and standard deviation) in the statistical characteristics of corrosion current density at each spatiotemporal point is substituted into the conversion formula of Faraday's law of electrolysis. The mean corrosion current density is converted into the expected corrosion rate value, the standard deviation into the corrosion rate fluctuation standard deviation, and the variance into the corrosion rate variance. This ultimately yields the statistical characteristics of corrosion rate at each spatiotemporal point, achieving accurate transfer from current density statistics to corrosion rate statistics, providing core data for the subsequent generation of statistical prediction reports.

[0135] Based on the statistical characteristics of corrosion rates at all spatiotemporal points, a corrosion rate statistical prediction report is generated, which includes a corrosion rate expectation field and a corrosion rate uncertainty field.

[0136] Specifically, the expected corrosion rate field is obtained by integrating the mean corrosion rate values ​​of all spatiotemporal points and presented in the form of a three-dimensional field model. This intuitively reflects the average distribution trend of corrosion rate across the entire pipeline during the prediction period, providing a basis for identifying high-risk areas for conventional corrosion. The uncertainty field of corrosion rate is obtained by integrating the standard deviation (or variance) of corrosion rate at all spatiotemporal points and is also presented in the form of a three-dimensional field model. This clearly characterizes the intensity of random fluctuations in corrosion rate at different spatiotemporal points, i.e., the magnitude of uncertainty. Areas with higher values ​​in the uncertainty field indicate a greater influence of random microbial disturbances on the corrosion rate, and a higher probability of extreme corrosion risks occurring. This report, through its dual-field presentation mode of "expected value + uncertainty," clarifies both the basic trend of corrosion development and quantifies the range of risk fluctuations, overcoming the limitation of traditional forecast reports that can only provide deterministic results.

[0137] In summary, the corrosion rate prediction method for subsea pipelines provided in this application acquires monitoring data such as microbial activity time-series data and environmental parameter time-series data along the target subsea pipeline, as well as pipeline body data. By identifying the main corrosive microbial populations and calibrating their growth kinetic parameters, a stochastic dynamics model of microbial corrosion with random environmental disturbances is constructed to simulate and generate random microbial corrosion current components. Simultaneously, by combining pipeline body data and seabed geological survey data, the non-uniformly distributed soil thermal resistance and radial comprehensive thermal resistance along the pipeline axis are calculated, and a one-dimensional axial unsteady-state heat conduction control equation is constructed and solved to obtain the pipe wall temperature field. Then, the electrochemical corrosion current components are calculated by combining the electrochemical parameters of the pipeline material. Subsequently, the two types of corrosion current components are coupled through multiphysics calculations to generate a corrosion spatiotemporal distribution field. Based on this distribution field, high-risk corrosion sections and risk development time are identified through Faraday's law conversion, time integration, initial wall thickness subtraction processing, and threshold comparison. The risk probability is obtained through multiple stochastic simulations and probabilistic statistics. At the same time, through multiple independent simulations, repeated coupled calculations, and spatiotemporal point statistical analysis of the microbial corrosion stochastic dynamics model, a statistical prediction report containing the expected value field and uncertainty field of corrosion rate is generated. This scheme achieves multi-field coupling of microbial corrosion and electrochemical corrosion, as well as spatiotemporal location, time prediction, and probability quantification of corrosion risk. It covers the complex coupling factors and deep uncertainties of subsea pipeline corrosion, and improves the accuracy of subsea pipeline corrosion prediction.

[0138] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0139] Based on the same inventive concept, this application also provides a subsea pipeline corrosion rate prediction system 10 for implementing the above-mentioned subsea pipeline corrosion rate prediction method. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the subsea pipeline corrosion rate prediction system 10 provided below can be found in the limitations of the subsea pipeline corrosion rate prediction method above, and will not be repeated here.

[0140] In one exemplary embodiment, such as Figure 2 As shown, a submarine pipeline corrosion rate prediction system 10 is provided to implement the methods in the above-described method embodiments. The system includes:

[0141] The data acquisition module 11 is used to acquire monitoring data along the target subsea pipeline and pipeline body data. The monitoring data includes microbial activity time series data and environmental parameter time series data.

[0142] Microbial corrosion simulation module 12 is used to construct a stochastic dynamics model of microbial corrosion based on monitoring data and simulate the generation of stochastic microbial corrosion current components.

[0143] The pipe wall temperature calculation module 13 is used to calculate the non-uniformly distributed pipe wall temperature field along the pipe axis based on monitoring data and pipe body data.

[0144] Electrochemical corrosion calculation module 14 is used to calculate the electrochemical corrosion current component based on the pipe wall temperature field and the electrochemical parameters of the pipe material;

[0145] The multi-field coupling calculation module 15 is used to perform multi-physics field coupling calculations on the random microbial corrosion current component and the electrochemical corrosion current component to generate the corrosion spatiotemporal distribution field.

[0146] The corrosion risk analysis module 16 is used to perform corrosion risk analysis based on the spatiotemporal distribution field of corrosion and obtain corrosion risk prediction results. The corrosion risk prediction results include high-risk corrosion sections, risk development time, and risk probability.

[0147] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the method for predicting the corrosion rate of a subsea pipeline as described above.

[0148] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0149] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0150] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for predicting the corrosion rate of subsea pipelines, characterized in that, The method includes: Acquire monitoring data along the target subsea pipeline and pipeline body data, wherein the monitoring data includes time-series data of microbial activity and time-series data of environmental parameters; Based on the monitoring data, a stochastic dynamics model of microbial corrosion was constructed to simulate and generate stochastic microbial corrosion current components. Based on the monitoring data and the pipeline body data, the pipe wall temperature field that is non-uniformly distributed along the pipeline axis is calculated. Based on the pipe wall temperature field and the electrochemical parameters of the pipe material, the electrochemical corrosion current component is calculated. The random microbial corrosion current component and the electrochemical corrosion current component are coupled using a multiphysics calculation to generate a corrosion spatiotemporal distribution field. The multiphysics calculation includes: using a field coupling superposition algorithm to synthesize the two types of corrosion current components at the same pipeline location and time according to the vector superposition principle; introducing a corrosion current diffusion coefficient to correct the interaction between different fields; and combining dynamic change data in the time dimension to construct a three-dimensional corrosion spatiotemporal distribution field model. The corrosion spatiotemporal distribution field includes corrosion current density data at each time and each pipeline location. Corrosion risk analysis is performed based on the aforementioned spatiotemporal distribution field to obtain corrosion risk prediction results, wherein the corrosion risk prediction results include high-risk corrosion sections, risk development time, and risk probability. The step of constructing a stochastic dynamic model for microbial corrosion based on the monitoring data, and simulating the generation of stochastic microbial corrosion current components, includes: The main corrosive microbial populations were identified based on the microbial activity time-series data, and the growth kinetic parameters of the main corrosive microbial populations were calibrated based on the correspondence between the environmental parameter time-series data and the microbial activity time-series data. Specifically, the main corrosive microbial populations were identified by preprocessing the microbial activity time-series data for noise reduction, removing outliers caused by equipment interference and instantaneous environmental fluctuations during data acquisition using wavelet transform, and then using principal component analysis combined with Pearson correlation coefficient calculation to screen out microbial species whose population abundance changes were significantly positively correlated with previous pipeline corrosion monitoring data. Based on the growth kinetic parameters and real-time environmental parameters, a stochastic differential equation describing the evolution of microbial metabolic activity intensity is constructed. The drift term of the stochastic differential equation is determined based on the growth kinetic parameters and real-time environmental parameters, and its diffusion term is used to characterize random environmental disturbances. The stochastic differential equation is numerically solved to simulate and generate a stochastic time series curve of metabolic activity intensity within a preset future time period; Based on the random time-series curve of the metabolic activity intensity, the random microbial corrosion current component is calculated through a preset electrochemical conversion relationship.

2. The method according to claim 1, characterized in that, The stochastic differential equation describing the evolution of microbial metabolic activity intensity based on the growth kinetic parameters and real-time environmental parameters includes: Based on the aforementioned growth kinetic parameters, time-series data of microbial biomass density were calculated using a microbial growth model. Obtain time-series data of the local pipe wall temperature corresponding to the microbial activity monitoring location in the pipe wall temperature field; The stochastic differential equation is constructed as follows: ； in, for The intensity of microbial metabolic activity at time t, The mean regression rate constant; Let be the objective function, representing that in biomass density at time [time] and the local pipe wall temperature Expected metabolic activity levels under certain conditions; This is a volatility parameter used to quantify the intensity of the impact of random environmental disturbances on metabolic activity; This is the increment of the standard Wiener process, used to introduce random noise.

3. The method according to claim 1, characterized in that, The calculation of the non-uniformly distributed pipe wall temperature field along the pipe axis based on the monitoring data and the pipe body data includes: The outer diameter, burial depth, and spatial coordinates of the pipeline are extracted from the pipeline body data, and the soil type and thermal properties corresponding to the spatial coordinates are obtained from the seabed geological survey data. Based on the burial depth data, the outer diameter of the pipeline, and the thermal property parameters, the soil thermal resistance per unit length at each point along the pipeline axis is calculated, where the soil thermal resistance is a function of the axial position. The thermal resistance of the anti-corrosion layer, calculated based on the pipe structure parameters in the pipe body data, is superimposed with the thermal resistance of the soil to obtain the radial comprehensive thermal resistance that varies along the pipe axis. A one-dimensional axial unsteady-state pipeline heat conduction control equation is constructed with the radial composite thermal resistance, the temperature and velocity of the fluid inside the pipe as key parameters; wherein, the temperature and velocity of the fluid inside the pipe are obtained from the monitoring data; The heat conduction control equation is solved numerically, and the pipe wall temperature field, which is non-uniformly distributed along the pipe axis, is output.

4. The method according to claim 1, characterized in that, The corrosion risk analysis based on the spatiotemporal distribution field of the corrosion, and the resulting corrosion risk prediction, include: The spatiotemporal distribution field of corrosion is transformed according to Faraday's law of electrolysis to obtain the spatiotemporal distribution field of instantaneous corrosion rate. The spatiotemporal distribution field of the instantaneous corrosion rate is numerically integrated along the time dimension to obtain the spatiotemporal distribution field of the cumulative corrosion depth. Based on the initial wall thickness distribution of the pipeline in the pipeline body data, the spatiotemporal distribution field of the cumulative corrosion depth is subtracted to obtain the spatiotemporal distribution field of the remaining wall thickness. The remaining wall thickness spatiotemporal distribution field is scanned and compared according to a preset risk threshold to identify the high-risk corrosion zone and the corresponding risk development time. Based on the results of multiple random simulations of the random microbial corrosion current components, the high-risk corrosion sections identified multiple times are subjected to probability statistical processing to generate the risk probability.

5. The method according to claim 4, characterized in that, The method further includes: Multiple independent stochastic simulations were performed on the aforementioned stochastic dynamics model of microbial corrosion to generate multiple stochastic microbial corrosion current components. Based on multiple random microbial corrosion current components, the multiphysics coupling calculation is repeatedly performed a preset number of times to generate multiple corrosion spatiotemporal distribution fields; For each spatiotemporal point, the corrosion current density values ​​of the multiple spatiotemporal distribution fields of corrosion at the spatiotemporal point are statistically analyzed to obtain the statistical characteristics of the corrosion current density at the spatiotemporal point. Based on the aforementioned statistical characteristics of corrosion current density, the corrosion rate statistical characteristics at the aforementioned spatiotemporal points are obtained by conversion processing using Faraday's law of electrolysis. Based on the statistical characteristics of corrosion rates at all spatiotemporal points, a corrosion rate statistical prediction report is generated, wherein the corrosion rate statistical prediction report includes a corrosion rate expectation field and a corrosion rate uncertainty field.

6. A corrosion rate prediction system for subsea pipelines, used to implement the method according to any one of claims 1 to 5, characterized in that, The system includes: The data acquisition module is used to acquire monitoring data along the target subsea pipeline and pipeline body data, wherein the monitoring data includes microbial activity time-series data and environmental parameter time-series data; The microbial corrosion simulation module is used to construct a stochastic dynamics model of microbial corrosion based on the monitoring data and simulate the generation of stochastic microbial corrosion current components. The pipe wall temperature calculation module is used to calculate the non-uniformly distributed pipe wall temperature field along the pipe axis based on the monitoring data and the pipe body data. The electrochemical corrosion calculation module is used to calculate the electrochemical corrosion current component based on the pipe wall temperature field and the electrochemical parameters of the pipe material. The multi-field coupling calculation module is used to perform multi-physics field coupling calculations on the random microbial corrosion current component and the electrochemical corrosion current component to generate a corrosion spatiotemporal distribution field. The multi-physics field coupling calculation includes: using a field coupling superposition algorithm to synthesize the two types of corrosion current components at the same pipeline location and time according to the principle of vector superposition; at the same time, introducing a corrosion current diffusion coefficient to correct the interaction between different fields; and combining dynamic change data in the time dimension to construct a three-dimensional corrosion spatiotemporal distribution field model. The corrosion spatiotemporal distribution field contains corrosion current density data at each time and each pipeline location. The corrosion risk analysis module is used to perform corrosion risk analysis based on the corrosion spatiotemporal distribution field and obtain corrosion risk prediction results, wherein the corrosion risk prediction results include high-risk corrosion sections, risk development time and risk probability. The step of constructing a stochastic dynamic model for microbial corrosion based on the monitoring data, and simulating the generation of stochastic microbial corrosion current components, includes: The main corrosive microbial populations were identified based on the microbial activity time-series data, and the growth kinetic parameters of the main corrosive microbial populations were calibrated based on the correspondence between the environmental parameter time-series data and the microbial activity time-series data. Specifically, the main corrosive microbial populations were identified by preprocessing the microbial activity time-series data for noise reduction, removing outliers caused by equipment interference and instantaneous environmental fluctuations during data acquisition using wavelet transform, and then using principal component analysis combined with Pearson correlation coefficient calculation to screen out microbial species whose population abundance changes were significantly positively correlated with previous pipeline corrosion monitoring data. Based on the growth kinetic parameters and real-time environmental parameters, a stochastic differential equation describing the evolution of microbial metabolic activity intensity is constructed. The drift term of the stochastic differential equation is determined based on the growth kinetic parameters and real-time environmental parameters, and its diffusion term is used to characterize random environmental disturbances. The stochastic differential equation is numerically solved to simulate and generate a stochastic time series curve of metabolic activity intensity within a preset future time period; Based on the random time-series curve of the metabolic activity intensity, the random microbial corrosion current component is calculated through a preset electrochemical conversion relationship.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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