Prediction and evaluation method for corrosion fatigue residual life of oil pipeline

Abstract

The invention discloses a method for predicting and evaluating corrosion fatigue residual life of an oil pipeline, and belongs to the technical field of structural integrity management and safety engineering of oil and gas conveying pipelines. Comprising the following steps: acquiring pipeline corrosion defect original data, carrying out defect merging and identification to obtain a defect sample set, selecting a target defect sample, and obtaining the defect depth of the target defect sample at the detection moment; based on the defect sample set, statistical modeling is carried out on the corrosion defect depth, the maximum defect depth in a preset service period is obtained through prediction, and safety margin evaluation is carried out; constructing an electrochemical corrosion life model fused with a temperature-soil resistivity correction factor to predict the residual life; and carrying out pipeline residual strength evaluation and decision making by adopting an optimized ASME B31G standard and an SY / T 6151 standard. According to the method, all links are seamlessly connected, a complete solution from theory to practice and from technology to economy is provided, the operation and maintenance cost is effectively reduced, and the pipeline safety management level is improved.

Description

A method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines Technical Field

[0001] This invention relates to the field of oil and gas pipeline integrity management and safety evaluation technology, and in particular to a method for predicting and evaluating the remaining service life of oil pipelines due to corrosion fatigue. Background Technology

[0002] Oil pipelines are critical infrastructure in the energy supply chain. During long-term service, the combined effects of internal fluid pressure and external soil corrosion can easily lead to corrosion defects, resulting in thinner walls, reduced load-bearing capacity, and even catastrophic accidents such as leaks and explosions. Therefore, accurate prediction of the remaining life and strength assessment of pipelines are of paramount importance.

[0003] Oil pipelines in service operate under complex soil and environmental conditions for extended periods, making them susceptible to electrochemical corrosion, which can lead to localized wall thinning or even perforation failure. Existing methods primarily focus on evaluating the residual strength of a single time section, making it difficult to simultaneously reflect the future development trend of corrosion defects and the level of extreme risks. Furthermore, they fail to effectively distinguish the physical boundary between risk prediction and life prediction, thus limiting the reliability of engineering applications.

[0004] Furthermore, in terms of lifespan prediction, traditional methods often rely on simple linear corrosion rate extrapolation or laboratory plate tests, failing to fully consider the profound impact of dynamic changes in the soil environment (such as resistivity and temperature) on the electrochemical corrosion process, and failing to effectively utilize in-service testing data for extreme value statistical inference, resulting in insufficient reliability of prediction results. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a comprehensive evaluation method for predicting the remaining corrosion life and classifying the operational risk of oil pipelines without relying on long-term monitoring data.

[0006] To achieve the above objectives, the present invention provides a method for predicting and evaluating the remaining service life of corrosion fatigue in oil pipelines, comprising the following steps: (1) acquiring the original data of pipeline corrosion defects, merging and identifying defects to obtain a defect sample set, selecting target defect samples, and obtaining the defect depth at the detection time. (2) Based on the defect sample set, statistical modeling is performed on the corrosion defect depth to predict the maximum defect depth within the preset service period. And perform a safety margin assessment. If the safety margin is less than zero, the target defect is determined to be in a failure risk state and a maintenance decision signal is output. Otherwise, step (3) is executed. (3) Construct an electrochemical corrosion lifetime model with fused temperature-soil resistivity correction factor, based on the initial defect depth of the defect sample to be detected. and the predicted maximum defect depth (4) Use the optimized ASME B31G standard and SY / T 6151 standard to evaluate the remaining strength of the pipeline, and combine the maximum defect depth and remaining life to form the final maintenance strategy.

[0007] Further, step (1) specifically involves: (1.1) obtaining raw data of pipeline corrosion defects through in-pipe inspection (ILI), ultrasonic thickness measurement (UT), or radiographic inspection (RT); (1.2) classifying the defects and merging adjacent defects according to the interaction criterion based on defect spacing to obtain a defect sample set; (1.3) selecting a target defect sample and obtaining its geometric parameters, where the vertical depth is the defect depth at the time of detection. .

[0008] Furthermore, step (2) specifically involves: (2.1) calculating the minimum axial wall thickness using the axial and circumferential stress formulas respectively. and minimum circumferential wall thickness The maximum value between the two is taken as the minimum allowable wall thickness of the pipe. ; in: This refers to the internal pressure of the pipeline during operation. This refers to the inner diameter of the pipe. The average of the outer and inner diameters of the pipe is denoted as the pipe mean diameter. Poisson's ratio; The resultant force in the axial direction of the pipeline is denoted as the pipeline axial force. If the stress is pressure, the value is negative. For pipe bending moment, if the stress is pressure, the value is negative; This refers to the weld coefficient; To calculate the attenuation coefficient for pipeline safety and load-bearing capacity; The yield strength of the material; (2.2) Divide the entire pipeline into N blocks according to its length, and extract the maximum defect depth as a sample in each block according to the defect sample set; Based on the Gumbel extreme value distribution theory, construct the distribution function of the maximum corrosion depth of the defect, and combine it with the recurrence time theory to map the recurrence time and the remaining service life into the equivalent number of samplings, and predict the maximum corrosion depth of the entire pipeline in the remaining service life; The Gumbel extreme value distribution function is: in: For position parameters, For scale parameters, A random variable representing the maximum corrosion depth. This indicates that the maximum corrosion depth does not exceed [a certain value]. Probability; the Gumbel extreme value distribution predicts the maximum corrosion depth by correlating reliability with reproducibility time, where reproducibility time is the key factor. Set to: Therefore, the maximum corrosion depth for: Based on the defect sample set, the parameters of the Gumbel extreme value distribution function are obtained by fitting, and then the maximum corrosion depth is predicted according to the reproduction time. (2.3) Calculate the safety margin for the remaining defect depth; calculate the critical depth of the defect: Calculate the safety margin for remaining defect depth: in: This represents the safety margin for the remaining defect depth. The original wall thickness of the pipe. (2.4) If the remaining defect depth safety margin is <0, the pipeline is in an unsafe state; otherwise, the pipeline is in a safe state.

[0009] Furthermore, step (3) specifically involves: (3.1) collecting soil resistivity data at the pipeline burial site. and ambient temperature data Based on the temperature-soil resistivity correction factor, the soil resistivity corrected to the predefined standard temperature is obtained; in: For a predefined standard temperature; These are the fitting coefficients, obtained by fitting experimental data. The soil resistivity is given at a predefined standard temperature; (3.2) The pipeline life is obtained by coupling the defect geometry, equivalent circuit model, differential equation of metal loss, and soil resistivity corrected to the predefined standard temperature; when the defect is elliptical, the pipeline life calculation formula is: When the defect is uniform corrosion, the formula for calculating the pipeline's lifespan is: When the defect is a cone-shaped corrosion defect, the formula for calculating the pipeline's life is: in: A coefficient related to the geometry of the defect. This represents the number of defects. For metal density, Soil resistivity at standard temperature H represents the potential difference; H represents the defect depth. For lifespan; It is the electrochemical equivalent constant; The angle between the generatrix of the cone side and the axis; (3.3) Based on the defect depth at the time of detection of the target defect sample. Substituting the lifetime formula from step (3.2) into the equation, we obtain the lifetime of the target defect at the detection time. Based on the predicted maximum defect depth obtained in step (2), the lifetime at the moment of maximum defect depth is obtained by substituting it into the lifetime formula in step (3.2). Then the remaining lifespan is: in: The remaining lifespan.

[0010] Furthermore, the aforementioned They are respectively: in: The radius of the major axis of the defect; The radius of the minor axis of the defect.

[0011] Furthermore, the optimized ASME B31G standard in step (4) is as follows: (a) the material base yield stress is modified by adding 68.95 MPa to the raw material base yield stress, which is used as the material base yield stress in the optimized ASME B31G standard to be closer to the actual flow stress; (b) the Folis coefficient is modified by modifying the Folis coefficient according to the corrosion defect characteristics. in: Let be the axial length of the defect. The outer diameter of the pipe. (c) The projected area of ​​the defect sample is calculated using the trapezoidal integral method.

[0012] Furthermore, step (4) specifically involves: (4.1) evaluating the remaining strength of the pipeline using the optimized ASME B31G standard and SY / T 6151 standard respectively; (4.2) combining the maximum defect depth, remaining life, failure pressure and remaining strength factor to form an optimized maintenance strategy of "immediate repair, time-limited repair and monitoring operation".

[0013] The beneficial effects of this invention are as follows: 1. Significantly improves accuracy: This invention can significantly improve the accuracy of defect quantification and depth prediction by using the precise area calculation method and Gumbel extreme value distribution prediction.

[0014] 2. Enhanced model adaptability: This invention introduces a temperature-soil resistivity correction factor and a lifetime model based on defect shape, enabling the method to adapt to pipelines with different soil environments and different corrosion morphologies, thus making it more universal.

[0015] 3. Achieve accurate dynamic prediction: The lifetime model built based on the electrochemical corrosion mechanism can dynamically respond to environmental changes and achieve accurate prediction of long-term lifetime, rather than simple extrapolation.

[0016] 4. Forming a closed-loop decision-making process: This invention seamlessly connects the detection, prediction, evaluation, and decision-making processes, providing pipeline operators with a complete solution from theory to practice and from technology to economics, which can effectively reduce operation and maintenance costs and improve pipeline safety management. Attached Figure Description

[0017] Figure 1 is a schematic flowchart of the method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to an embodiment of the present invention.

[0018] Figure 2 is a schematic diagram of corrosion defect classification in an embodiment of the present invention.

[0019] Figure 3 is a schematic diagram of the equivalent circuit model of pipeline corrosion defects in an embodiment of the present invention.

[0020] Figure 4 is a geometrical schematic diagram of an elliptical corrosion defect according to an embodiment of the present invention.

[0021] Figure 5 is a geometrical schematic diagram of uniform corrosion defects in an embodiment of the present invention.

[0022] Figure 6 is a geometrical schematic diagram of the conical corrosion defect in an embodiment of the present invention.

[0023] Figure 7 is a schematic diagram of the accurate area calculation method according to an embodiment of the present invention. Detailed Implementation

[0024] The present invention will be further explained and described below with reference to the accompanying drawings and embodiments.

[0025] As shown in Figure 1, this embodiment of the invention provides a method for predicting and evaluating the remaining service life of oil pipeline corrosion fatigue, including the following steps: S101, acquiring raw data of pipeline corrosion defects, merging and identifying defects to obtain a defect sample set, selecting target defect samples, and obtaining the defect depth at the detection time. .

[0026] (1) Obtain the original data of pipeline corrosion defects by in-pipeline inspection (ILI), ultrasonic thickness measurement (UT), or radiographic inspection (RT).

[0027] (2) Classify the defects and merge adjacent defects according to the interaction criterion based on the defect spacing to obtain a defect sample set.

[0028] As shown in Figure 2, based on the interaction criterion of defect spacing, corrosion defects are divided into independent defects and interactive defects. According to the positional relationship between defects, interactive defects are further divided into axial cluster defects and circumferential cluster defects. Based on the depth of corrosion defects, independent defects are divided into deep defects and shallow defects. Note that interactive defects require consideration of defect merging, while independent defects do not.

[0029] (a) In axial cluster defects, the platform between adjacent defects is a boss, and when the boss length is... When the length is less than or equal to 25.4 mm, defect one and defect two are considered as a single unit. In this case, the length of the overall defect... The depth is based on the deepest point of the defect, i.e. .when For defects larger than 25.4 mm, the length and depth of the defect are based on their respective lengths and depths.

[0030] (b) Circumferential Cluster Defects: Corrosion defects are continuously distributed along the pipe axis, forming a banded arrangement, reflecting the concentration trend of corrosion within a certain range, but corrosion only occurs in the circumferential direction of the pipe. When the distance between adjacent defects is less than 1 / 6 of the pipe wall thickness, they are included in the defect interaction analysis. When the circumferential distance between two defects reaches 1 / 6 or more of the wall thickness, they are treated as independent defects, and the length and depth of the defects are calculated separately.

[0031] (3) Select the target defect sample and obtain its geometric parameters. The vertical depth at this time is the defect depth at the detection time. .

[0032] S102. Based on Gumbel's extreme value distribution theory and reproduction time theory, and based on the defect sample set, predict the maximum defect depth. Then, a safety margin assessment is performed. If the safety margin is less than 0, the test is immediately stopped and maintenance is started; otherwise, step S103 is executed.

[0033] (1) Calculate the minimum axial wall thickness using the axial and circumferential stress formulas respectively. and minimum circumferential wall thickness The maximum value between the two is taken as the minimum allowable wall thickness of the pipe. .

[0034] The formula for solving axial stress is as follows: When the pipe stress reaches its limit: Therefore, the minimum axial wall thickness is: The formula for solving circumferential stress is as follows: When the circumferential stress reaches its maximum value: Therefore, the minimum circumferential wall thickness is: Therefore, the minimum allowable wall thickness is: in: This refers to the internal pressure of the pipeline during operation. This refers to the inner diameter of the pipe. The average of the outer and inner diameters of the pipe is denoted as the pipe mean diameter. Poisson's ratio; The resultant force in the axial direction of the pipeline is denoted as the pipeline axial force. If the stress is pressure, the value is negative. For pipe bending moment, if the stress is pressure, the value is negative; For pipe wall thickness; This refers to the weld coefficient; To calculate the attenuation coefficient for pipeline safety and load-bearing capacity; The yield strength of the material.

[0035] (2) Divide the entire pipeline into N blocks according to its length. Based on the defect sample set, extract the maximum defect depth in each block as a sample. Based on the Gumbel extreme value distribution theory, construct the distribution function of the maximum corrosion depth of the defect. Combine the reproduction time theory to map the reproduction time and the remaining service life into the equivalent number of samplings. Predict the maximum corrosion depth of the entire pipeline in the remaining service life.

[0036] The maximum defect depth of a pipeline typically follows a Gumbel first-kind extremum distribution, and the Gumbel extremum distribution function is: in: For position parameters, For scale parameters, A random variable representing the maximum corrosion depth. This indicates that the maximum corrosion depth does not exceed [a certain value]. Probability.

[0037] To infer overall corrosion conditions based on limited data, the concept of recurrence time is introduced, and the maximum defect depth of the pipeline is estimated. The Gumbel extreme value distribution predicts the maximum corrosion depth by correlating reliability with recurrence time. It is the expected value of the observation sequence number of a certain measured value X. By drawing N samples from the Gumbel extreme value distribution and taking the maximum value, the maximum possible defect depth in a local area can be estimated from the maximum defect depth of a small number of samples.

[0038] When the reproduction time Set to: Therefore, the maximum corrosion depth for: Based on the defect sample set, the parameters of the Gumbel extreme value distribution function are obtained by fitting, and then the predicted value of the maximum corrosion depth is obtained according to the reproduction time. .

[0039] (3) Calculate the safety margin for the remaining defect depth; first, calculate the critical depth of the defect: Then, calculate the safety margin for the remaining defect depth: in: This represents the safety margin for the remaining defect depth. The original wall thickness of the pipe. This represents the critical depth of the defect.

[0040] (4) If the remaining defect depth safety margin is <0, the pipeline is in an unsafe state; otherwise, the pipeline is in a safe state.

[0041] S103. Construct an electrochemical corrosion lifetime model incorporating temperature-soil resistivity correction factors, based on the initial defect depth of the obtained defect sample to be tested. and the predicted maximum defect depth Perform remaining life prediction.

[0042] (1) Soil resistivity at the pipeline laying location was collected. and ambient temperature data Based on the temperature-soil resistivity correction factor, the soil resistivity corrected to the predefined standard temperature is obtained.

[0043] Soil is a medium with a microscopic capillary structure, containing solid, liquid, and gaseous substances simultaneously.

[63] Soil micropores contain abundant gases and water, as well as electrolyte ions dissolved in the water. The directional movement of positive and negative ions in the soil contributes to its conductivity, creating conditions conducive to corrosion. Generally, an increase in soil conductivity is positively correlated with an increase in the rate of electrochemical corrosion. From a microscopic perspective, high resistivity indicates reduced ion mobility, hindering the movement of positive and negative ions. Therefore, the electrochemical corrosion reaction on metal surfaces is less active, and the rate is slowed down. Conversely, low resistivity or high conductivity favors ion movement, enhances the electrochemical corrosion reaction, and may accelerate the corrosion process. To better assess the corrosion rate, a correction parameter is introduced. This is used to correct for conductivity measured at different temperatures. (Based on research findings from the U.S. Salinity Laboratory.)

[65] The conductivity measured at a specific temperature can be expressed by the following formula.

[0044] in: is the temperature correction factor, used to adjust for the effect of soil conductivity changing with temperature; EC is the soil's ability to conduct current, denoted as soil conductivity. The soil conductivity at a certain random temperature; The soil conductivity at 25℃.

[0045] The ratio model is one of the commonly used standardization models.

[0046] in: The effect of temperature change on the measurement results is denoted as the temperature slope parameter; The ambient temperature.

[0047] From the two formulas above, the expression for the temperature correction factor of conductivity can be derived as follows: in: For a predefined standard temperature; The fitting coefficients are obtained by fitting experimental data.

[0048] The embodiments of the present invention adopt Temperature-resistivity tests were conducted on five soil types, with three samples taken from each type, and the results were calculated. It is 0.0254.

[0049] Resistivity and conductivity are reciprocals of each other; an increase in one will lead to a decrease in the other.

[0050] in: Soil resistivity at a predefined standard temperature.

[0051] (2) The pipeline life is obtained by using the geometry of the coupling defect, the equivalent circuit model, the differential equation of metal loss, and the soil resistivity corrected to a predefined standard temperature.

[0052] In electrochemical corrosion lifetime models, Faraday's law is a key theoretical foundation. The mass lost by a metal due to corrosion is closely related to the intensity and duration of the current generated during the corrosion reaction. The stronger the current and the longer the duration, the greater the mass loss of the metal.

[0053] in: The mass of the precipitated or dissolved substance; It is the electrochemical equivalent constant; The total charge flowing through the electrodes is denoted as the total charge; I is the magnitude of the current, denoted as the current intensity; and T is the duration of the current action, denoted as the energizing time. atomic weight; The valence of an element; It is a Faraday constant.

[0054] In electrochemical corrosion processes, the corrosion current in pipe metal is typically variable and cannot be directly solved using simple current equations. To more accurately describe the corrosion process, the differential form of the corrosion current is used. This differential form reveals minute changes in the corrosion current over extremely short periods, providing a more precise mathematical description for analyzing corrosion rates and reaction mechanisms. According to Faraday's law, the differential of the metal loss... It can be represented as: in: The rate of mass change per unit time during the corrosion process is denoted as the differential of the loss. The minute time intervals during the corrosion process are denoted as the time derivative; It represents electric current.

[0055] In practical applications, to calculate the leakage current of pipelines, an equivalent circuit method is used for simplified analysis. As shown in Figure 3, the pipeline is simplified into an ungrounded equivalent circuit, assuming that there is only a single leakage point along its length, which facilitates the quantitative calculation of the distribution and intensity of the leakage current. Based on the equivalent circuit shown in Figure 3, according to Kirchhoff's current law and voltage law, and combined with Ohm's law, a loop equation is established for the pipeline section resistance, the equivalent resistance at the defect, and the leakage resistance to ground, and the expression for the current at the leakage point is obtained by solving the equation. in: The potential of the non-leaking part of the pipe; The potential of the leaking part of the pipe; The resistance of the underground portion of the pipeline; For pipe resistance; The resistance at the corrosion defect; For potential difference, .

[0056] Soil resistivity is typically measured using the four-electrode method, and the calculation is as follows: in: Soil resistivity represents the degree to which the soil impedes the conduction of electric current. The distance between electrodes or the equivalent measurement distance; The voltage actually measured between the electrodes is denoted as voltage; The current intensity injected through the soil is denoted as current intensity.

[0057] The pipeline lifespan is obtained by using the geometry of the coupling defects, the equivalent circuit model, the differential equation for metal loss, and the soil resistivity corrected to a predefined standard temperature.

[0058] When the defect is elliptical, as shown in Figure 4, a parameter is introduced to simplify the calculation formula. and , , . and Rewritten as , , The radius of the major axis of the defect, Let be the radius of the minor axis of the defect. The loss at the defect is as follows: The shaded area in the geometric diagram is obtained by the following formula: in: It is the equivalent semi-axis that varies with depth. When hour , when hour, The distance from the shaded area to the center line in the geometric diagram is expressed as: Introduction : The resistance at the defect is: Assuming the resistance of each corrosion point is in series, that is... Therefore: Assuming there are n defects distributed along the same direction in the pipeline, the current at each defect is calculated as follows: Metal loss: Will Substituting into the above formula, we obtain the formula for calculating the lifespan of the pipeline: When the defect is a uniform corrosion defect, as shown in Figure 5, the formula for calculating the pipeline's lifespan is: When the defect is a cone-shaped corrosion defect, as shown in Figure 6, the formula for calculating the pipeline's lifespan is: in: This represents the number of defects. For metal density, Soil resistivity at standard temperature It is the electrochemical equivalent constant. H represents the potential difference; H represents the defect depth. For lifespan; The angle between the generatrix of the cone and the axis (half the vertex angle / half the cone angle). , (where is the radius of the cone base).

[0059] (3) Based on the defect depth at the time of detection of the target defect sample Substituting the lifetime formula from step (2) into the equation, we obtain the lifetime of the target defect at the detection time. Based on the predicted maximum defect depth obtained in step S102, the lifetime at the moment of maximum defect depth is obtained by substituting it into the lifetime formula in step (2). Then the remaining lifespan is: in: The remaining lifespan.

[0060] S104. The remaining strength of the pipeline is evaluated and decided upon using the optimized ASME B31G standard and SY / T 6151 standard.

[0061] (1) The remaining strength of the pipeline was evaluated using the optimized ASME B31G standard and SY / T 6151 standard respectively.

[0062] The optimized ASME B31G standard includes: a) modifying the material basis yield stress by adding 68.95 MPa to the raw material basis yield stress. This 68.95 MPa is an equivalent increment of 10 ksi, used to approximate the difference between flow stress and yield stress, making the B31G calculations closer to reality. This material basis yield stress is used in the optimized ASME B31G standard to more closely approximate actual flow stress.

[0063] b. Modify the Folis coefficient: Based on the characteristics of corrosion defects, modify the Folis coefficient.

[0064] in: Let be the axial length of the defect. The outer diameter of the pipe. For pipe wall thickness, This is the corrected Folis coefficient.

[0065] c. The projected area of ​​the defect sample is calculated using the trapezoidal integral method.

[0066] As shown in Figure 7, the continuous corrosion defect profile is decomposed into multiple geometric infinitesimal elements, and high-precision area calculation is achieved based on numerical integration theory. The defect region is divided into n equally spaced infinitesimal segments along the corrosion propagation direction, with the width of each segment being x. The defect depth sequence at each node is obtained. The area of ​​each trapezoidal segment is calculated, and the areas of all trapezoids are summed to obtain the approximate total area of ​​the defect. Let the total defect area be A, the calculation equation can be written as: Typically, the depth of the first and last infinitesimal elements in a defect is 0, i.e. The above formula can be rewritten as: in: This represents the total area of ​​the defects. The length of the defect; The average depth of the defect is the average depth of the depth measured at each point.

[0067] (2) Combining the maximum defect depth, remaining life, failure pressure and remaining strength factor, an optimized maintenance strategy of "immediate repair, time-limited repair and monitoring operation" is formed.

[0068] Example 1: Following the above method and steps, the remaining corrosion life of an oil pipeline is predicted and evaluated.

[0069] With an outer diameter D = 813 mm and an initial wall thickness of... This invention is implemented using an oil pipeline with a diameter of 11.1 mm and material of X65 as an example.

[0070] Data Acquisition: Internal inspection revealed a circumferential cluster corrosion defect. After merging and processing, the measured... =5.2mm, L=150mm, the defect area A=735mm² is calculated using the trapezoidal integration method. The minimum allowable wall thickness is then calculated. =7.2mm. The critical depth of the defect is .because > This indicates that the defect has exceeded the allowable corrosion limit.

[0071] Twenty sets of corrosion depth data were collected, and the parameters λ=1.1 and α=0.34 were obtained by fitting the data using the Gumbel distribution. The maximum depth within the next 20 years was then predicted. =6.0mm. Substituting into the life model, the remaining life ΔT is calculated to be 2.5 years, and the overall evaluation result is "Immediate Repair Level". Evaluation and Decision: Optimized ASME B31G evaluation calculation yields P_f = 9.8MPa, which is lower than 1.25 times MAOP. SY / T 6151 evaluation is "Immediate Repair Level". Based on the comprehensive life prediction results, it is recommended that this pipe section be repaired and replaced immediately.

[0072] Compared to traditional methods, under the aforementioned implementation conditions, this invention significantly improves the accuracy of defect quantification and in-depth prediction, and can dynamically respond to environmental changes, enhancing the model's universality. Furthermore, by forming a complete technical system encompassing detection, prediction, evaluation, and decision-making, it can reduce operation and maintenance costs by more than 15%, providing pipeline operators with a comprehensive solution from theory to practice.

[0073] This embodiment verifies that the method of the present invention has a clear process and accurate results, providing clear guidance for pipeline safety management. The core innovation of this invention lies in the organic integration of precise defect quantification, extreme value statistical prediction, electrochemical mechanism modeling, and multi-standard evaluation. First, in the data acquisition and defect quantification stage, raw data is obtained through technologies such as internal detection (ILI) and dynamically merged according to defect interaction criteria. Precise area calculation methods such as the trapezoidal integral method are used to replace the traditional parabolic model, significantly improving the quantification accuracy of defect projected area, depth, and length. Second, in the assessment of the remaining defect depth safety margin, Gumbel's extreme value distribution theory is introduced. Based on the distribution parameters fitted from on-site corrosion pit depth samples, the maximum corrosion depth of the pipeline during its remaining service life is predicted. Combined with the minimum allowable wall thickness of the pipeline, the safety margin is calculated, providing a critical threshold for life prediction. Third, in the electrochemical corrosion remaining life modeling stage, soil resistivity and ambient temperature data were collected, temperature corrections were applied, and an equivalent circuit model of pipeline corrosion defects (including solution resistance, pipeline resistance, and defect resistance) was established. Based on Faraday's law, a metal loss differential equation was constructed, and finally, an integral expression for remaining life was derived by coupling the defect geometry, achieving dynamic life prediction based on the corrosion mechanism. Finally, in the remaining strength evaluation and decision-making stage, a multi-standard comparative analysis was conducted using the optimized ASME B31G (such as corrected rheological stress and Folis coefficient) and the SY / T 6151 standard. Failure pressure and remaining strength factors were calculated, and combined with risk classification and economic analysis, an optimized maintenance strategy of "immediate repair, time-limited repair, and monitoring operation" was formed.

[0074] In summary, this invention not only addresses the shortcomings of existing technologies in terms of accuracy, adaptability, and dynamism, but also achieves scientific prediction and safety classification evaluation of pipeline corrosion fatigue remaining life through the combination of mechanism modeling and extreme value statistics. It has significant engineering application value and socio-economic benefits, and is of great significance for improving the safe operation level and integrity management capabilities of oil and gas pipelines.

[0075] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and various changes or modifications can be made to these embodiments without departing from the principles and essence of the present invention.

Claims

1. A method for predicting and evaluating the remaining service life of oil pipelines due to corrosion fatigue, characterized in that, include: (1) Obtain the original data of pipeline corrosion defects, merge and identify defects to obtain a defect sample set, select the target defect sample, and obtain the defect depth at the detection time. (2) Based on the defect sample set, statistical modeling is performed on the corrosion defect depth to predict the maximum defect depth within the preset service period. And perform a safety margin assessment. If the safety margin is less than zero, the target defect is determined to be in a failure risk state and a maintenance decision signal is output. Otherwise, step (3) is executed. (3) Construct an electrochemical corrosion lifetime model with fused temperature-soil resistivity correction factor, based on the initial defect depth of the defect sample to be detected. and the predicted maximum defect depth (4) Use the optimized ASME B31G standard and SY / T 6151 standard to evaluate the remaining strength of the pipeline, and combine the maximum defect depth and remaining life to form the final maintenance strategy.

2. The method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to claim 1, characterized in that, Step (1) specifically involves: (1.1) Obtaining raw data of pipeline corrosion defects through in-pipe inspection (ILI), ultrasonic thickness measurement (UT), or radiographic inspection (RT); (1.2) Classifying the defects and merging adjacent defects according to the interaction criterion based on defect spacing to obtain a defect sample set; (1.3) Selecting a target defect sample and obtaining its geometric parameters, where the vertical depth is the defect depth at the time of detection. 。 3. The method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to claim 1, characterized in that, The specific steps (2) are as follows: (2.1) Calculate the minimum axial wall thickness using the axial and circumferential stress formulas respectively. and minimum circumferential wall thickness The maximum value between the two is taken as the minimum allowable wall thickness of the pipe. ； in: This refers to the internal pressure of the pipeline during operation. This refers to the inner diameter of the pipe. The average of the outer and inner diameters of the pipe is denoted as the pipe mean diameter. Poisson's ratio; The resultant force in the axial direction of the pipeline is denoted as the pipeline axial force. If the stress is pressure, the value is negative. For pipe bending moment, if the stress is pressure, the value is negative; This refers to the weld coefficient; To calculate the attenuation coefficient for pipeline safety and load-bearing capacity; The yield strength of the material; (2.2) Divide the entire pipeline into N blocks according to its length, and extract the maximum defect depth as a sample in each block according to the defect sample set; Based on the Gumbel extreme value distribution theory, construct the distribution function of the maximum corrosion depth of the defect, and combine it with the recurrence time theory to map the recurrence time and the remaining service life into the equivalent number of samplings, and predict the maximum corrosion depth of the entire pipeline in the remaining service life; The Gumbel extreme value distribution function is: in: For position parameters, For scale parameters, A random variable representing the maximum corrosion depth. This indicates that the maximum corrosion depth does not exceed [a certain value]. Probability; the Gumbel extreme value distribution predicts the maximum corrosion depth by correlating reliability with reproducibility time, where reproducibility time is the key factor. Set to: Therefore, the maximum corrosion depth for: Based on the defect sample set, the parameters of the Gumbel extreme value distribution function are obtained by fitting, and then the maximum corrosion depth is predicted according to the reproduction time. (2.3) Calculate the safety margin for the remaining defect depth; calculate the critical depth of the defect: Calculate the safety margin for remaining defect depth: in: This represents the safety margin for the remaining defect depth. The original wall thickness of the pipe. (2.4) If the remaining defect depth safety margin is <0, the pipeline is in an unsafe state; otherwise, the pipeline is in a safe state.

4. The method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to claim 1, characterized in that, The specific steps (3) are as follows: (3.1) Collect the soil resistivity at the pipeline burial site. and ambient temperature data Based on the temperature-soil resistivity correction factor, the soil resistivity corrected to the predefined standard temperature is obtained; in: For a predefined standard temperature; These are the fitting coefficients, obtained by fitting experimental data. The soil resistivity is given at a predefined standard temperature; (3.2) The pipeline life is obtained by coupling the defect geometry, equivalent circuit model, differential equation of metal loss, and soil resistivity corrected to the predefined standard temperature; when the defect is elliptical, the pipeline life calculation formula is: When the defect is uniform corrosion, the formula for calculating the pipeline's lifespan is: When the defect is a cone-shaped corrosion defect, the formula for calculating the pipeline's life is: in: A coefficient related to the geometry of the defect. This represents the number of defects. For metal density, Soil resistivity at standard temperature H represents the potential difference; H represents the defect depth. For lifespan; It is the electrochemical equivalent constant; The angle between the generatrix of the cone side and the axis; (3.3) Based on the defect depth at the time of detection of the target defect sample. Substituting the lifetime formula from step (3.2) into the equation, we obtain the lifetime of the target defect at the detection time. Based on the predicted maximum defect depth obtained in step (2), the lifetime at the moment of maximum defect depth is obtained by substituting it into the lifetime formula in step (3.2). Then the remaining lifespan is: in: The remaining lifespan.

5. The method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to claim 4, characterized in that, The They are respectively: in: The radius of the major axis of the defect; The radius of the minor axis of the defect.

6. The method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to claim 1, characterized in that, The optimized ASME B31G standard in step (4) is: (a) Modify the material base yield stress by adding 68.95 MPa to the raw material base yield stress, which is used as the material base yield stress in the optimized ASME B31G standard to be closer to the actual flow stress. (b) Modify the Folis coefficient by adjusting the Folis coefficient according to the characteristics of corrosion defects; in: Let be the axial length of the defect. The outer diameter of the pipe. (c) The projected area of ​​the defect sample is calculated using the trapezoidal integral method.

7. The method for predicting and evaluating the remaining corrosion fatigue life of oil pipelines according to claim 1, characterized in that, The specific steps (4) are as follows: (4.1) The remaining strength of the pipeline is evaluated by using the optimized ASME B31G standard and SY / T 6151 standard respectively; (4.2) The optimized maintenance strategy of "immediate repair, time-limited repair, and monitoring operation" is formed by combining the maximum defect depth, remaining life, failure pressure and remaining strength factor.

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