A method for assessing the resilience of buried oil and gas pipeline systems under climate change

Abstract

The application discloses a kind of buried corrosion oil and gas pipeline system toughness evaluation method under climate change, it is related to oil and gas pipeline toughness evaluation field, including collection multi-source data and do standardization processing;Predict the failure start time of the pipeline to be evaluated, obtain resistance stage reliability-time curve;Predict the corrosion failure probability of the pipeline to be evaluated, obtain the relationship between corrosion failure probability and time, obtain absorption stage reliability-time curve;Predict the recovery time required for artificial repair when the pipeline to be evaluated is at failure probability threshold, obtain recovery stage reliability-time curve;Obtain the toughness evolution curve of the pipeline to be evaluated, calculate the comprehensive toughness index of the pipeline to be evaluated.The application is used to solve the problem of lack of analysis of the climate-toughness relationship of buried pipeline system in the prior art, to realize accurate evaluation of the influence of climate change on the toughness of buried pipeline system, to provide a scientific basis for regional infrastructure planning and management under the condition of climate change.

Description

Technical Field

[0001] This invention relates to the field of toughness assessment of oil and gas pipelines, and specifically to a method for assessing the toughness of buried oil and gas pipeline systems under climate change corrosion. Background Technology

[0002] Pipelines are the core pillar of global energy supply, carrying the majority of oil and gas transportation. The operational stability of pipelines is crucial for energy security, regional economies, and public welfare. However, pipelines are vulnerable to external disturbances during their service life, potentially disrupting energy transmission and triggering cascading accidents (such as leaks and explosions), leading to substantial economic losses, casualties, and environmental pollution. Therefore, ensuring the safety and reliability of pipeline systems has long been a core focus of engineering. Although pipeline failures are low-probability, high-consequence events, with the continuous expansion of pipeline networks and the increasing global energy demand, society's tolerance for energy supply disruptions has significantly decreased. Even short-term outages can severely impact industrial production, residential lives, and regional energy security. Therefore, the goal of pipeline safety assessment and management is not only to prevent failures within reasonable limits but also to ensure rapid recovery after an outage. In this context, resilience, as a measure of a system's ability to withstand disturbances, absorb impacts, and restore normal function, has become a core framework for infrastructure safety assessment. Pipeline system resilience can be understood as the pipeline's ability to withstand and absorb disturbances and restore transportation performance.

[0003] Global warming exacerbates sea-level rise and extreme weather events such as droughts and floods, threatening natural ecosystems, infrastructure, and human societies. Against the backdrop of climate change, changes in soil temperature, humidity, and salinity will accelerate pipeline degradation, while extreme events such as floods and heat waves may delay recovery and restoration processes, further weakening pipeline resilience.

[0004] However, current technologies still lack sufficient analysis of the scope, mechanisms, and pathways of climate change's impact on the resilience of buried oil and gas pipeline systems; the lack of a comprehensive understanding of the climate-resilience relationship of buried pipeline systems restricts resilience prediction and decision-making in climate-adaptive pipeline management. Summary of the Invention

[0005] This invention provides a method for assessing the toughness of buried oil and gas pipeline systems under climate change, in order to address the lack of analysis on the climate-toughness relationship of buried pipeline systems in existing technologies. This method aims to accurately assess the impact of climate change on the toughness of buried pipeline systems and provide a scientific basis for regional infrastructure planning and management under climate change conditions.

[0006] This invention is achieved through the following technical solution:

[0007] A method for assessing the toughness of buried oil and gas pipeline systems under climate change corrosion includes the following steps:

[0008] S1. Collect multi-source data and perform standardization processing;

[0009] The multi-source data includes: pipeline attribute data, climate data, environmental data, and socioeconomic data;

[0010] The climate data includes: historical climate data and future climate scenario prediction data;

[0011] S2. Predict the failure initiation time of the pipeline being evaluated and obtain the reliability-time curve of the resistance phase;

[0012] S3. Based on the predicted future climate scenarios, predict the corrosion failure probability of the pipeline being evaluated and obtain the relationship between the corrosion failure probability and time; based on the preset failure probability threshold, obtain the reliability-time curve of the absorption stage.

[0013] S4. Based on the multi-source data, predict the recovery time required for manual repair of the evaluated pipeline when it is at the failure probability threshold, and obtain the reliability-time curve of the recovery stage.

[0014] S5. Connect the reliability-time curves of the resistance stage, absorption stage, and recovery stage in sequence to obtain the toughness evolution curve of the pipeline being evaluated, and calculate the comprehensive toughness index of the pipeline being evaluated.

[0015] To address the lack of analysis on the climate-resilience relationship of buried pipeline systems in existing technologies, this invention proposes a method for assessing the resilience of buried oil and gas pipeline systems under climate change. This method first collects and standardizes multi-source data, including pipeline attribute data of the pipeline being assessed, as well as climate, environmental, and socioeconomic data of the region where the pipeline is located. The climate data further includes historical climate data and future climate scenario predictions for the region where the pipeline is located.

[0016] This application divides the resilience evolution process of buried oil and gas pipelines into three stages: the resistance stage, the absorption stage, and the recovery stage. Specifically: the resistance stage refers to the period from when the pipeline is put into operation until the first degradation of its reliability; the absorption stage refers to the period from the moment the pipeline's reliability first degrades until it reaches its maximum permissible value; and the recovery stage refers to the period from when the pipeline's reliability degrades to its maximum permissible value, to when human intervention repairs it, until the pipeline's reliability returns to its initial state.

[0017] This application first predicts the failure onset time of the pipeline being evaluated. Before the failure onset time, the pipeline reliability remains in its initial state, thus yielding a resistance phase reliability-time curve, which is a straight line.

[0018] Subsequently, this application considers the impact of climate change on the performance of buried pipelines. Based on future climate scenario prediction data in the climate data, it predicts the corrosion failure probability of the pipeline being evaluated and obtains the relationship between the corrosion failure probability and time. Then, combined with a preset failure probability threshold, the corrosion failure probability change curve can be obtained from the failure start time to the corrosion failure probability reaching the failure probability threshold. This curve is then converted into a reliability-time curve, which is the absorption stage reliability-time curve required by this application.

[0019] Then, based on the historical climate data and other multi-source data, this application predicts the recovery time required for the evaluated pipeline to be manually repaired from the point of failure probability threshold, thereby obtaining the reliability-time curve of the recovery phase.

[0020] Finally, by sequentially connecting the reliability-time curves of the resistance phase, the absorption phase, and the recovery phase, the toughness evolution curve of the pipeline throughout its entire life cycle can be obtained. Furthermore, this application can also calculate the comprehensive toughness index of the pipeline being evaluated, providing a quantitative assessment of the corrosion toughness of buried oil and gas pipeline systems under climate change.

[0021] Those skilled in the art should understand that, for buried pipeline systems, reliability = 1 - probability of failure.

[0022] As can be seen, this application creatively proposes a corrosion toughness assessment framework for buried oil and gas pipeline systems oriented towards climate change. Combining climate change predictions, it systematically assesses the dynamic toughness performance of pipeline systems under long-term climate disturbances, achieving accurate assessment of pipeline toughness under different climate scenarios. This provides scientific and reasonable technical support for pipeline climate adaptability planning, operation and maintenance, and infrastructure planning and management. The toughness assessment method constructed in this application not only considers the influence of traditional corrosion failure modes but also introduces changes in future climate scenarios as a driving force, realizing dynamic simulation of toughness throughout the entire life cycle from resistance and absorption to recovery. This provides a scientific decision-making tool and quantitative basis for the climate adaptability planning and operation management of pipeline systems. For example, in engineering practice, the assessment results of this application can be used to optimize investment strategies for protective measures; rationally allocate emergency maintenance resources; and formulate differentiated maintenance and recovery plans for different climate scenarios, effectively improving the operational reliability of pipeline systems under climate uncertainty and ensuring the safe and stable transmission of energy.

[0023] Furthermore, the pipeline attribute data includes any one or more of the following: pipeline material, wall thickness, anti-corrosion measures, design pressure, and service life;

[0024] The environmental data includes any one or more of the following: soil parameters along the route, hydrological data, topographic data, and land use types;

[0025] The socioeconomic data include any one or more of the following: population density along the route, GDP density, road density, and impervious surface coverage.

[0026] Furthermore, in step S2, the failure onset time of the pipeline being evaluated is predicted using the following method:

[0027] S201. Establish the limit state equation for the failure of buried oil and gas pipelines due to corrosion defects: In the formula, denoted by the first limit state equation; SF is the safety factor; δ is the initial wall thickness of the pipe; d(t) is the pipe defect depth varying with time t.

[0028] S202. Based on the anti-corrosion measures taken for the pipeline being evaluated, determine the equation for the change of pipeline defect depth with time t: In the formula, d0 is the initial defect depth of the pipeline; k is the corrosion prevention measure effectiveness parameter; α is the proportionality factor; β is the exponential factor; and t0 is the failure initiation time.

[0029] S203. Substitute the equation of the pipeline defect depth as a function of time t into the limit state equation of the failure of the buried oil and gas pipeline caused by the corrosion defect, and let g(t) = 0 to solve for the failure start time t0.

[0030] In this scheme, the safety factor SF is used to characterize the corrosion allowance, and its value can be selected according to specific operating conditions, typically between 75% and 85%. Furthermore, the proportionality factor α and the exponential factor β are both related to soil and pipeline characteristics, and both are adaptively set according to specific operating conditions; no specific limitations are imposed here.

[0031] This scheme also introduces a corrosion prevention measure effectiveness parameter k, which is used to quantify the effectiveness of the corrosion prevention measures taken by the pipeline being evaluated. It is adaptively assigned based on the corrosion prevention measures taken by the pipeline being evaluated; for example, it can be quantified through expert scoring, the mapping relationship between preset corrosion prevention measures and k, etc.; it is only necessary to satisfy that the larger the value of k, the weaker the effectiveness of the corrosion prevention measures.

[0032] Furthermore, in step S3, the relationship between corrosion failure probability and time is obtained using the following method:

[0033] S301. Establish the pipeline failure limit state equation under the orifice leakage failure mode: In the formula, Represents the second limit state equation; w t d represents the remaining wall thickness of the pipe. n d0 represents the maximum corrosion depth after n years; d0 represents the initial defect depth of the pipeline.

[0034] S302. Establish a corrosion rate calculation equation; correlate the corrosion rate calculation equation with one or more parameters in the future climate scenario prediction data;

[0035] S303, Based on the corrosion rate calculation equation, d is obtained. n And substitute it into the pipeline failure limit state equation under the small hole leakage failure mode;

[0036] S304. The probability distribution of parameters related to the corrosion rate calculation equation in the future climate scenario prediction data is determined by using the Akaike information content criterion, and the corrosion failure probability curve of the pipeline being evaluated is obtained by Monte Carlo simulation.

[0037] This scheme ensures that the established corrosion rate calculation equation is correlated with at least one parameter in future climate scenario prediction data, thus fully considering the impact of climate change on pipeline toughness during the absorption phase. Climate change affects the corrosion process by altering the corrosion rate. This scheme employs the Akaike information criterion and Monte Carlo simulation to consider the uncertainty of long-term reliability of oil and gas pipelines in the soil environment under the influence of climate change. Based on the probability distribution simulation of the aforementioned relevant parameters, it predicts the probability of pipeline corrosion failure during the absorption phase. The application of the Akaike information criterion and Monte Carlo simulation technology in this scheme can be achieved using existing mature algorithms, and will not be elaborated upon here.

[0038] It should be noted that the limit state equation established in this scheme is the pipeline failure limit state equation under the orifice leakage failure mode. For pipeline corrosion defects, possible failure modes include orifice leakage or rupture failure. This scheme only considers the orifice leakage failure mode because orifice leakage is the most common failure mode of underground pipelines affected by corrosion. Electrochemical corrosion driven by climatic factors such as soil temperature, humidity, and salinity often develops in the form of local pitting corrosion, eventually forming orifice leakage. The probability of orifice leakage is much higher than that of overall rupture failure, and it better reflects the general law of pipeline corrosion failure under climate change.

[0039] Furthermore, the corrosion rate calculation equation is as follows:

[0040] ;

[0041] In the formula: v i Corr represents the annual corrosion rate in year i, in mm / year. i,climate ρ is the corrosion current density in year i, which is related to climate change; M is the molar mass of the pipe material being evaluated; z is the number of exchanged electrons in the pipe material being evaluated; ρ is the density of the pipe material being evaluated.

[0042] Based on the corrosion rate calculation equation, d is obtained. n The method is as follows: .

[0043] This method calculates the corrosion rate on an annual basis, thus obtaining the corrosion depth for each year. The annual corrosion depths are then summed to obtain the maximum corrosion depth after n years. The annual corrosion rate is related to the predicted corrosion current density for that year.

[0044] Furthermore, the number of exchanged electrons in this scheme refers to the number of electrons transferred when the atoms of the metal pipe material lose electrons and are oxidized during the electrochemical corrosion process in the soil, and its value is 2. For example, for steel pipes, the number of exchanged electrons is 2.

[0045] Furthermore, the corrosion current density is calculated using the following formula:

[0046] ;

[0047] Where: pH i T represents the soil pH value in year i. i,climate Let Cl be the ambient temperature in year i, which is related to climate change; i Let be the soil chloride ion concentration in year i.

[0048] This scheme clearly defines the calculation method for the annual corrosion current density. Based on the predicted future climate scenario data, it obtains the corresponding soil pH value, ambient temperature and soil chloride ion concentration, and then predicts the corrosion behavior under different climate change and carbon emission scenarios, which is used to quantitatively assess the reliability degradation process of the absorption stage.

[0049] Furthermore, the parameters related to the corrosion rate calculation equation in the future climate scenario prediction data include soil pH, ambient temperature, and soil chloride ion concentration.

[0050] Furthermore, step S4 specifically includes:

[0051] S401. Extract the extreme climate events that caused the pipeline damage from the historical climate data, obtain the multi-source data corresponding to the extreme climate events, and the arrival time and repair operation time of the artificial repair after the pipeline damage caused by the extreme climate events.

[0052] S402. Using the arrival time of artificial repair and the repair operation time after pipeline damage caused by extreme weather events as outputs, and using several modeling parameters from the multi-source data corresponding to the extreme weather events as inputs, establish a recovery time machine learning model.

[0053] S403. Based on the future climate scenario prediction data, predict the extreme climate events that the pipeline being evaluated may encounter, extract the modeling parameters corresponding to the extreme climate events encountered by the pipeline being evaluated, and input the modeling parameters into the recovery time machine learning model to obtain the artificial repair arrival time and repair operation time of the pipeline being evaluated.

[0054] S404. The total repair time of the pipeline being evaluated is obtained by superimposing the arrival time of manual repair and the repair operation time of the pipeline being evaluated.

[0055] S405. Based on the total repair time, plot the reliability-time curve for the recovery phase.

[0056] The recovery phase is a critical process for restoring transport capacity and system reliability after pipeline failure, with the core objective of restoring pipeline performance to pre-failure levels as quickly as possible. In the context of climate change, the frequency and intensity of extreme weather events are increasing. For example, floods can reduce accessibility and timeliness by disrupting transportation networks, inundating passageways, and temporarily closing areas, increasing the time required for personnel mobilization and equipment transport to the site. Floods also prolong repair operations by complicating key on-site tasks (such as increased difficulty in pit drainage, extended site cleanup time, delayed coating curing, and reduced accuracy of trenchless repair tools).

[0057] Therefore, this solution extracts multi-source data corresponding to extreme climate events that caused pipeline damage from historical climate data, and extracts some parameters related to pipeline repair as modeling parameters, while also extracting the corresponding arrival time and operation time of manual repair. Using this as a dataset, a recovery time machine learning model is established based on deep learning technology. Then, based on future climate scenario prediction data, the extreme climate events that the assessed pipeline may encounter can be predicted, and the corresponding prediction modeling parameters for these future extreme climate events can be obtained. Based on the predicted modeling parameters of the assessed pipeline, the arrival time and operation time of manual repair are predicted, thus obtaining the total repair time and plotting the reliability-time curve of the recovery phase.

[0058] This study proposes a machine learning-based quantitative assessment framework for resilience during the recovery phase of pipeline systems, designed to evaluate the resilience evolution of these systems under climate change. Existing machine learning models can be used, and no specific limitations are imposed here.

[0059] Furthermore, the extreme weather events mentioned are floods, landslides, or wildfires;

[0060] When the extreme climate event is a flood, several modeling parameters in the multi-source data corresponding to the extreme climate event include: elevation, slope, river density, road density, population density along the route, impervious surface coverage, GDP density, land use type, annual average temperature, and annual average rainfall.

[0061] When an extreme climate event is a landslide, several modeling parameters in the multi-source data corresponding to the extreme climate event include: elevation, slope, aspect, topographic relief, lithology, geological structure density, fault distance, annual average rainfall, rainfall intensity, vegetation cover, land use type, river density, road density, soil moisture content, and soil shear strength.

[0062] When the extreme weather event is a wildfire, several modeling parameters in the multi-source data corresponding to the extreme weather event include: elevation, slope, vegetation cover, vegetation type, annual average temperature, annual average rainfall, wind speed, land use type, road density, population density along the route, GDP density, pipeline material, and soil moisture.

[0063] This solution presents three different extreme climate events and their corresponding modeling parameters, which is helpful for assessing the toughness of buried oil and gas pipelines under extreme climate conditions and fills a gap in existing technologies.

[0064] Furthermore, the overall toughness index of the evaluated pipeline is calculated using the following formula:

[0065] ;

[0066] Where: R is the comprehensive resilience index considering climate change; t0 is the failure onset time; t2 is the recovery time; S0 is the initial reliability of the pipeline being evaluated; S climate (t) represents the reliability of the pipeline being evaluated, taking into account climate change, at time t.

[0067] This plan uses a comprehensive resilience index R. climate This index quantifies the overall resilience of buried oil and gas pipelines under climate disturbances. A higher index indicates stronger resistance to corrosion and degradation under climate disturbances, higher tolerance to climate-related disturbances, faster recovery of transport performance after failure, and better overall stability and reliability in maintaining normal transport functions, with less negative impact from climate factors (such as temperature changes and floods). The S value is a key indicator of this resilience. climate (t) represents the reliability in the resilience evolution curve of the pipeline being evaluated obtained from the aforementioned steps; climate represents consideration of climate change.

[0068] Compared with the prior art, the present invention has at least the following advantages and beneficial effects:

[0069] 1. This invention provides a method for assessing the toughness of buried oil and gas pipeline systems under climate change. It creatively proposes a framework for assessing the corrosion toughness of buried oil and gas pipeline systems in response to climate change. Combined with climate change prediction, it systematically assesses the dynamic toughness performance of pipeline systems under long-term climate disturbances, achieving accurate assessment of pipeline toughness under different climate scenarios. This provides scientific and reasonable technical support for pipeline climate adaptability planning, operation and maintenance, and infrastructure planning and management.

[0070] 2. This invention provides a toughness assessment method for buried corrosion oil and gas pipeline systems under climate change. The constructed toughness assessment method not only considers the influence of traditional corrosion failure modes, but also introduces changes in future climate scenarios as a driving force, realizing dynamic simulation of toughness throughout the entire life cycle from resistance, absorption to recovery. This provides a scientific decision-making tool and quantitative basis for the climate adaptability planning and operation management of pipeline systems. Attached Figure Description

[0071] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0072] Figure 1 This is a flowchart illustrating a specific embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram illustrating the toughness evolution of a buried oil and gas pipeline in a specific embodiment of the present invention.

[0074] Figure 3 This is the reliability-time curve of the resistance phase in a specific embodiment of the present invention;

[0075] Figure 4 This is the reliability-time curve of the absorption phase in a specific embodiment of the present invention;

[0076] Figure 5 This is a spatial distribution diagram of the comprehensive toughness index in a specific embodiment of the present invention. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0078] Example 1:

[0079] like Figure 1 The method for assessing the toughness of buried oil and gas pipeline systems under climate change includes the following steps:

[0080] Step S1: Collect multi-source data and perform standardization processing;

[0081] The multi-source data includes: pipeline attribute data, climate data, environmental data, and socioeconomic data;

[0082] The climate data includes: historical climate data and future climate scenario prediction data.

[0083] The pipeline attribute data includes any one or more of the following: pipeline material, wall thickness, anti-corrosion measures (such as coating type, cathodic protection parameters, etc.), design pressure, and service life.

[0084] The environmental data includes any one or more of the following: soil parameters along the route, hydrological data, topographic data, and land use types. Soil parameters along the route include pH value, chloride ion concentration, humidity, and / or organic matter content. Topographic data includes elevation, slope, and / or aspect. Hydrological data includes river density and / or groundwater level.

[0085] The socioeconomic data include any one or more of the following: population density along the route, GDP, road density, and impervious surface coverage.

[0086] In this embodiment, historical climate data includes: average temperature, rainfall, and records of extreme weather events over the past 30 years.

[0087] In this embodiment, the future climate scenario prediction data can be derived from publicly available data from existing climate prediction models.

[0088] The standardization process in this embodiment includes the following steps:

[0089] Data conversion: Convert multi-source data into a unified raster or vector format, use ArcGIS tools for spatial registration, and unify the coordinate system and resolution;

[0090] Data cleaning: removing outliers, filling in missing values, performing trend smoothing on climate data, and normalizing soil parameters, etc.

[0091] Feature extraction: Based on ArcGIS spatial analysis tools, key feature areas such as areas with high incidence of extreme weather events and areas with high soil corrosion risk are extracted.

[0092] like Figure 2 As shown, this embodiment divides the toughness evolution process of buried oil and gas pipelines into three stages: resistance stage, absorption stage, and recovery stage.

[0093] Step S2: Predict the failure start time of the pipeline being evaluated and obtain the reliability-time curve of the resistance phase.

[0094] The performance of buried oil and gas pipelines is at its optimal state in the initial stage, and the reliability at this point is known as S0. This step aims to obtain the failure initiation time t0.

[0095] The specific process includes:

[0096] S201. Establish the limit state equation for the failure of buried oil and gas pipelines due to corrosion defects: In the formula, δ represents the first limit state equation; SF is the safety factor, which is taken as 75%-85%; δ is the initial wall thickness of the pipeline; d(t) is the pipeline defect depth that varies with time t.

[0097] S202. Based on the anti-corrosion measures taken for the pipeline being evaluated, determine the equation for the change of pipeline defect depth with time t: In the formula, d0 is the initial defect depth of the pipeline; k is the corrosion prevention measure effectiveness parameter; α is the proportional factor; β is the exponential factor; and t0 is the failure initiation time.

[0098] In this embodiment, the value range of the corrosion protection effectiveness parameter k is 0-1. k=1 indicates no protection, and k=0 indicates an ideal corrosion resistance state. In this embodiment, it can be assumed that k follows a uniform distribution [0,1] to avoid subjective bias, and the final value is obtained by random sampling from this distribution.

[0099] Preferred,

[0100] When the anti-corrosion measure is an anti-corrosion coating, k=0.65;

[0101] When the corrosion protection measure is cathodic protection, k=0.5;

[0102] When the corrosion prevention measure is a chemical corrosion inhibitor, k=0.6.

[0103] In this embodiment, the scaling factor α and the exponential factor β are key parameters describing the evolution of pipeline corrosion defect depth and are used in a power-law corrosion growth model. In this embodiment, α = 0.475 and β = 0.592 are selected.

[0104] S203. Substitute the equation of the pipeline defect depth as a function of time t into the limit state equation of the failure of the buried oil and gas pipeline caused by the corrosion defect, and let g(t) = 0 to solve for the failure start time t0.

[0105] After the failure initiation time t0, the reliability-time curve of the resistance phase can be plotted by combining the reliability S0 in the initial state.

[0106] Step S3: Based on the predicted future climate scenario data, predict the corrosion failure probability of the pipeline being evaluated and obtain the relationship between the corrosion failure probability and time; based on the preset failure probability threshold, obtain the reliability-time curve of the absorption stage.

[0107] The specific process includes:

[0108] S301. Establish the pipeline failure limit state equation under the orifice leakage failure mode: In the formula, Represents the second limit state equation; w t d represents the remaining wall thickness of the pipe. n d0 represents the maximum corrosion depth after n years; d0 represents the initial defect depth of the pipeline.

[0109] S302. Establish a corrosion rate calculation equation; correlate the corrosion rate calculation equation with one or more parameters in the future climate scenario prediction data.

[0110] In this embodiment, the corrosion rate calculation equation is as follows:

[0111] ;

[0112] In the formula: v i Let corr be the annual corrosion rate in year i. i,climate ρ is the corrosion current density in year i, which is related to climate change; M is the molar mass of the pipe material being evaluated; z is the number of exchanged electrons in the pipe material being evaluated; ρ is the density of the pipe material being evaluated.

[0113] The corrosion current density is calculated using the following formula:

[0114] ;

[0115] Where: pH i T represents the soil pH value in year i. i,climate Let Cl be the ambient temperature in year i, which is related to climate change; i Let be the soil chloride ion concentration in year i.

[0116] S303, Based on the corrosion rate calculation equation, d is obtained. n : . d n Substitute this into the pipeline failure limit state equation under the orifice leakage failure mode.

[0117] S304. The probability distribution of parameters related to the corrosion rate calculation equation in the future climate scenario prediction data is determined by using the Akaike information content criterion. Then, based on the second limit state equation, the corrosion failure probability curve of the pipeline being evaluated can be obtained by Monte Carlo simulation.

[0118] In this embodiment, the parameters related to the corrosion rate calculation equation in the future climate scenario prediction data include: soil pH, ambient temperature, and soil chloride ion concentration. In this embodiment, the ambient temperature can be directly obtained from the future climate scenario prediction data; the optimal distribution of soil pH and soil chloride ion concentration can be fitted to existing soil data, and then the failure probability for a future year can be calculated through Monte Carlo simulation.

[0119] It's easy to understand that in the corrosion failure probability curve of the pipeline being evaluated, the initial corrosion probability = 1 - S0. Then, the time corresponding to the preset failure probability threshold is calculated from the corrosion failure probability curve; this time is... Figure 2 In this context, t1 represents the point at which manual intervention and repair are required on the pipeline being evaluated at time t1. The reliability S1 corresponding to time t1 is calculated as 1 - the failure probability threshold.

[0120] By converting the corrosion failure probability curve between time t0 and t1 into a reliability curve, the reliability-time curve of the absorption stage can be obtained.

[0121] Step S4: Based on the historical climate data and the multi-source data, predict the recovery time required for the evaluated pipeline to be manually repaired from the point of failure probability threshold, and obtain the reliability-time curve of the recovery stage.

[0122] Specifically, it includes:

[0123] S401. Extract extreme climate events that caused pipeline damage from the historical climate data. This embodiment uses "flood" as an example of an extreme climate event. Obtain multi-source data corresponding to the flood extreme climate event, and extract corresponding data such as elevation, slope, river density, road density, population density along the pipeline, impervious surface coverage, GDP density, land use type, annual average temperature, and annual average rainfall for later use. In addition, if the extreme climate event is a landslide or wildfire, the corresponding multi-source data types can be adjusted accordingly according to the description in this application.

[0124] In addition, it also obtains the arrival time and repair operation time of artificial repairs after pipeline damage caused by extreme weather events.

[0125] S402. Using the arrival time and repair operation time of artificial repairs after pipeline damage caused by floods and extreme weather events as outputs, and using elevation, slope, river density, road density, population density along the pipeline, impervious surface coverage, GDP density, land use type, annual average temperature and annual average rainfall as inputs, a recovery time machine learning model is established.

[0126] In this embodiment, the recovery time machine learning model is established using a random forest (RF) model. Preferably, the hyperparameters of the model can also be tuned using a randomized search cross-validation (Randomized SearchCV) algorithm. This algorithm efficiently samples the hyperparameter space through cross-validation, finding approximately optimal settings at a computational cost far lower than that of exhaustive grid search.

[0127] S403. Based on the future climate scenario prediction data, predict the extreme climate events that the pipeline being evaluated may encounter, extract the modeling parameters such as elevation, slope, river density, road density, population density along the route, impervious surface coverage, GDP density, land use type, annual average temperature and annual average rainfall corresponding to the extreme climate events encountered by the pipeline being evaluated, and input these modeling parameters into the recovery time machine learning model to obtain the artificial repair arrival time T1 and repair operation time T2 of the pipeline being evaluated.

[0128] S404. By superimposing the arrival time and operation time of manual repair for the pipeline under evaluation, the total repair time of the pipeline under evaluation is obtained as T = T1 + T2.

[0129] S405. Based on the total repair time, plot the reliability-time curve for the recovery phase. Specifically, the pipeline under evaluation is repaired starting at time t1, and the repair is completed in time T. This time is the recovery time, denoted as t2. Therefore, t2 = t1 + T.

[0130] The reliability corresponding to time t1 is S1, and the reliability corresponding to time t2 is S0; by drawing a straight line between the two points, the reliability-time curve of the recovery phase can be obtained.

[0131] Step S5: Connect the reliability-time curves of the resistance stage, absorption stage, and recovery stage in sequence to obtain the toughness evolution curve of the pipeline being evaluated.

[0132] This embodiment can also calculate the comprehensive toughness index of the pipeline being evaluated using the following formula:

[0133] ;

[0134] Where: R is the comprehensive resilience index considering climate change; t0 is the failure onset time; t2 is the recovery time; S0 is the initial reliability of the pipeline being evaluated; S climate (t) represents the reliability of the pipeline being evaluated, taking into account climate change, at time t.

[0135] In a more preferred embodiment, ArcGIS technology can be used to generate spatial distribution maps of the comprehensive resilience index and hotspot maps of total repair time under different climate scenarios, which can intuitively show the spatial differences in resilience.

[0136] Example 2:

[0137] A method for assessing the toughness of buried oil and gas pipeline systems under climate change is presented in this embodiment, which uses a real buried oil pipeline as an example, based on Example 1.

[0138] The pipeline is a 100-kilometer-long buried oil pipeline made of X60 steel with a wall thickness of 13.15 millimeters.

[0139] In this embodiment, the relevant parameters in the multi-source data are obtained from publicly available databases such as the Geospatial Data Cloud, Science Data Bank, National Earth System Science Data Center, National Tibetan Plateau Science Data Center, China Meteorological Disaster Yearbook, and China National Materials Corrosion and Protection Science Data Center. Among them, the future climate scenario prediction data adopts the temperature, precipitation, and other prediction data under the SSP2-4.5 (medium carbon emission scenario) and SSP5-8.5 (high carbon emission scenario) scenarios in the publicly available global climate model CMIP6 (Sixth Coupled Model Intercomparison Project) dataset.

[0140] In this embodiment, the pipeline corrosion protection measure is cathodic protection, corresponding to k=0.5. The obtained reliability-time curve of the resistance stage is as follows. Figure 3 As shown in the solid line portion.

[0141] This embodiment considers the reliability evolution process of the absorption phase under two scenarios: SSP2-4.5 (medium emission scenario) and SSP5-8.5 (high emission scenario). The resulting reliability-time curves of the absorption phase are shown below. Figure 4 As shown by the colored lines.

[0142] Under SSP2-4.5 (medium emission scenario), the total remediation time is T=132 days;

[0143] Under SSP5-8.5 (high emission scenario), the total repair time is T=137 days.

[0144] This allows us to obtain the resilience evolution curves of the evaluated pipeline under the two climate scenarios SSP2-4.5 and SSP5-8.5, and then calculate the corresponding comprehensive resilience index.

[0145] This embodiment uses ArcGIS technology to obtain a spatial distribution map of the comprehensive resilience index of a certain region under two climate scenarios, SSP2-4.5 and SSP5-8.5, as shown below. Figure 5 As shown; Figure 5 In the figure, (a) shows the spatial distribution of the comprehensive resilience index under the SSP2-4.5 scenario; (b) shows the spatial distribution of the comprehensive resilience index under the SSP5-8.5 scenario.

[0146] It can be seen that under SSP2-4.5 scenarios, the overall toughness index value is generally high with small spatial differences, indicating that toughness remains relatively stable during the resistance-absorption-recovery process, and the overall toughness can be maintained at a high level. Under SSP5-8.5 scenarios, the overall toughness index value shifts to a lower range, indicating that the combined effects of extreme floods and accelerated corrosion under high carbon emission scenarios significantly reduce the overall toughness level. Increased time costs, increased maintenance difficulty, and the interference of floods on accessibility and operating conditions significantly weaken recovery efficiency. Overall, SSP5-8.5 scenarios have a significant adverse impact on pipeline toughness, highlighting the necessity of targeted corrosion protection upgrades and enhanced flood emergency repair capabilities in high-risk areas. The relative stability of the overall toughness index under SSP2-4.5 scenarios provides a reference for toughness optimization under moderate climate risks.

[0147] Example 3:

[0148] A corrosion toughness assessment system for buried oil and gas pipeline systems under climate change, used to perform the assessment method as described in Example 1. The system includes:

[0149] Data acquisition module: used to collect the multi-source data and perform standardization processing;

[0150] Resistance Phase Assessment Module: Used to predict the failure onset time of the pipeline being assessed and obtain the resistance phase reliability-time curve;

[0151] Absorption phase assessment module: used to predict the corrosion failure probability of the pipeline being assessed, obtain the relationship between corrosion failure probability and time; and based on the preset failure probability threshold, obtain the reliability-time curve of the absorption phase.

[0152] Recovery phase assessment module: used to predict the recovery time required for manual repair of the assessed pipeline from when it is at the failure probability threshold, and to obtain the recovery phase reliability-time curve;

[0153] Output module: Used to output the toughness evolution curve and comprehensive toughness index of the evaluated pipeline.

[0154] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0155] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. A method for assessing the toughness of buried oil and gas pipeline systems under climate change corrosion, characterized in that, Includes the following steps: S1. Collect multi-source data and perform standardization processing; The multi-source data includes: pipeline attribute data, climate data, environmental data, and socioeconomic data; The climate data includes: historical climate data and future climate scenario prediction data; S2. Predict the failure initiation time of the pipeline being evaluated and obtain the reliability-time curve of the resistance phase; S3. Based on the predicted future climate scenarios, predict the corrosion failure probability of the pipeline being evaluated and obtain the relationship between the corrosion failure probability and time; based on the preset failure probability threshold, obtain the reliability-time curve of the absorption stage. S4. Based on the multi-source data, predict the recovery time required for manual repair of the evaluated pipeline when it is at the failure probability threshold, and obtain the reliability-time curve of the recovery stage. S5. Connect the reliability-time curves of the resistance stage, absorption stage, and recovery stage in sequence to obtain the toughness evolution curve of the pipeline being evaluated, and calculate the comprehensive toughness index of the pipeline being evaluated.

2. The method for assessing the toughness of buried oil and gas pipeline systems under climate change according to claim 1, characterized in that, The pipeline attribute data includes any one or more of the following: pipeline material, wall thickness, anti-corrosion measures, design pressure, and service life; The environmental data includes any one or more of the following: soil parameters along the route, hydrological data, topographic data, and land use types; The socioeconomic data include any one or more of the following: population density along the route, GDP, road density, and impervious surface coverage.

3. The method for assessing the toughness of buried oil and gas pipeline systems under climate change according to claim 1, characterized in that, In step S2, the failure onset time of the pipeline being evaluated is predicted using the following method: S201. Establish the limit state equation for the failure of buried oil and gas pipelines due to corrosion defects: In the formula, denoted by the first limit state equation; SF is the safety factor; δ is the initial wall thickness of the pipe; d(t) is the pipe defect depth varying with time t. S202. Based on the anti-corrosion measures taken for the pipeline being evaluated, determine the equation for the change of pipeline defect depth with time t: In the formula, d0 is the initial defect depth of the pipeline; k is the corrosion prevention measure effectiveness parameter; α is the proportionality factor; β is the exponential factor; and t0 is the failure initiation time. S203. Substitute the equation of the pipeline defect depth as a function of time t into the limit state equation of the failure of the buried oil and gas pipeline caused by the corrosion defect, and let g(t) = 0 to solve for the failure start time t0.

4. The method for assessing the toughness of buried corrosion oil and gas pipeline systems under climate change as described in claim 1, characterized in that, In step S3, the relationship between corrosion failure probability and time is obtained using the following method: S301. Establish the pipeline failure limit state equation under the orifice leakage failure mode: In the formula, Represents the second limit state equation; w t d represents the remaining wall thickness of the pipe. n d0 represents the maximum corrosion depth after n years; d0 represents the initial defect depth of the pipeline. S302. Establish a corrosion rate calculation equation; correlate the corrosion rate calculation equation with one or more parameters in the future climate scenario prediction data; S303, Based on the corrosion rate calculation equation, d is obtained. n And substitute it into the pipeline failure limit state equation under the small hole leakage failure mode; S304. The probability distribution of parameters related to the corrosion rate calculation equation in the future climate scenario prediction data is determined by using the Akaike information content criterion, and the corrosion failure probability curve of the pipeline being evaluated is obtained by Monte Carlo simulation.

5. The method for assessing the toughness of buried oil and gas pipeline systems under climate change according to claim 4, characterized in that, The equation for calculating the corrosion rate is as follows: ； In the formula: v i Corr represents the annual corrosion rate in year i, in mm / year. i,climate ρ is the corrosion current density in year i; M is the molar mass of the pipe material being evaluated; z is the number of exchanged electrons in the pipe material being evaluated; ρ is the density of the pipe material being evaluated. Based on the corrosion rate calculation equation, d is obtained. n The method is as follows: .

6. The method for assessing the toughness of buried oil and gas pipeline systems under climate change according to claim 5, characterized in that, The corrosion current density is calculated using the following formula: ； Where: pH i T represents the soil pH value in year i. i,climate Let Cl be the ambient temperature in year i; i Let be the soil chloride ion concentration in year i.

7. The method for assessing the toughness of buried corrosion oil and gas pipeline systems under climate change as described in claim 4, characterized in that, The parameters related to the corrosion rate calculation equation in the future climate scenario prediction data include soil pH, ambient temperature, and soil chloride ion concentration.

8. The method for assessing the toughness of buried oil and gas pipeline systems under climate change according to claim 1, characterized in that, Step S4 specifically includes: S401. Extract the extreme climate events that caused the pipeline damage from the historical climate data, obtain the multi-source data corresponding to the extreme climate events, and the arrival time and repair operation time of the artificial repair after the pipeline damage caused by the extreme climate events. S402. Using the arrival time of artificial repair and the repair operation time after pipeline damage caused by extreme weather events as outputs, and using several modeling parameters from the multi-source data corresponding to the extreme weather events as inputs, establish a recovery time machine learning model. S403. Based on the future climate scenario prediction data, predict the extreme climate events that the pipeline being evaluated may encounter, extract the modeling parameters corresponding to the extreme climate events encountered by the pipeline being evaluated, and input the extracted modeling parameters into the recovery time machine learning model to obtain the artificial repair arrival time and repair operation time of the pipeline being evaluated. S404. The total repair time of the pipeline being evaluated is obtained by superimposing the arrival time of manual repair and the repair operation time of the pipeline being evaluated. S405. Based on the total repair time, plot the reliability-time curve for the recovery phase.

9. A method for assessing the toughness of buried corrosion oil and gas pipeline systems under climate change as described in claim 8, characterized in that, The extreme weather events mentioned are floods, landslides, or wildfires; When the extreme climate event is a flood, several modeling parameters in the multi-source data corresponding to the extreme climate event include: elevation, slope, river density, road density, population density along the route, impervious surface coverage, GDP density, land use type, annual average temperature, and annual average rainfall. When an extreme climate event is a landslide, several modeling parameters in the multi-source data corresponding to the extreme climate event include: elevation, slope, aspect, topographic relief, lithology, geological structure density, fault distance, annual average rainfall, rainfall intensity, vegetation cover, land use type, river density, road density, soil moisture content, and soil shear strength. When the extreme weather event is a wildfire, several modeling parameters in the multi-source data corresponding to the extreme weather event include: elevation, slope, vegetation cover, vegetation type, annual average temperature, annual average rainfall, wind speed, land use type, road density, population density along the route, GDP density, pipeline material, and soil moisture.

10. The method for assessing the toughness of buried corrosion oil and gas pipeline systems under climate change as described in claim 1, characterized in that, The overall toughness index of the pipeline being evaluated is calculated using the following formula: ； In the formula: R is the comprehensive resilience index taking into account climate change; t0 is the failure onset time; t2 is the recovery time; S0 is the initial reliability of the pipeline being evaluated; S climate (t) represents the reliability of the pipeline being evaluated, taking into account climate change, at time t.

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