Multi-defect pipeline evaluation method, device, equipment, storage medium and program product

Abstract

The invention provides a multi-defect pipeline evaluation method, device and equipment, a storage medium and a program product, and relates to the technical field of energy. The method comprises the steps of determining a plurality of pipeline limit state equations; determining a probability distribution function corresponding to each defect and a pipeline failure probability corresponding to each defect based on a Monte Carlo method according to the plurality of pipeline limit state equations; based on each probability distribution function and a Vine Copula function, constructing a multi-dimensional joint probability distribution model; and based on the multi-dimensional joint probability distribution model, evaluating the to-be-evaluated pipeline, and determining the reliability of the to-be-evaluated pipeline. Through the mode, the reliability of the multi-defect pipeline can be effectively represented, the evaluation result is more accurate, and data reference can be provided for daily maintenance of the pipeline.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a multi-defect pipeline evaluation method, device, equipment, storage medium and program product. Background Art

[0002] Oil and gas resources are important resources that promote the progress of civilization and meet the needs of modern social development and production and life. Therefore, the safe operation of oil and gas pipelines is of vital importance. Due to the unstable foundation of the pipeline, medium corrosion and other accidents, as the operation time of the pipeline increases, the problem of pipeline aging will become more serious. The pipeline will inevitably experience corrosion and other phenomena. These corrosions can be considered as pipeline defects. The final pipeline leakage is one of the important safety hazards of long-distance oil and gas pipeline transportation. Therefore, it is crucial to accurately evaluate the reliability of multi-defect pipelines based on the basic data of pipelines and various defects to ensure the safety of in-service pipelines.

[0003] The failure process of a pipeline with multiple defects is usually not caused by a single failure mode, but the result of the combined action of multiple different failure modes. There is a certain correlation between these failure modes. If this correlation is ignored, the accuracy of reliability assessment may be reduced. Measuring the correlation between failure modes is a key and challenging part of reliability research. Before studying the correlation between multiple failure modes, researchers mostly used the "weakest link principle" or "independence assumption principle" to evaluate the reliability of pipelines with multiple failure modes: the weakest link principle assumes that all failure modes are completely correlated, and that the reliability of the pipeline is determined by the weakest part of it. The reliability of the pipeline is equal to the one with the lowest reliability among all pipeline subsystems; the independence assumption principle assumes that each failure mode is independent of each other, and the reliability of the pipeline is equal to the product of the reliabilities of all pipeline subsystems, which is the traditional series reliability method.

[0004] However, these two extreme assumption methods have their own shortcomings: the weakest link principle often overestimates pipeline reliability, while the independence assumption principle may underestimate pipeline reliability. In contrast, the reliability assessment method that considers the correlation of multiple defect failures is more in line with the actual situation. Therefore, it is necessary to explore the reliability research based on the correlation of multiple failure modes.

[0005] In some related technologies, the correlation between defects can be considered by describing the correlation between pipeline parameter variables, thereby performing reliability assessment on the pipeline.

[0006] However, the evaluation results of existing multi-defect pipeline evaluation methods are poor, and it is difficult to accurately evaluate the reliability of multi-defect pipelines. Summary of the invention

[0007] The present invention provides a multi-defect pipeline assessment method, device, equipment, storage medium and program product, which are used to solve the defects of the multi-defect pipeline assessment method in the prior art, that is, the assessment result is poor and it is difficult to accurately assess the reliability of the multi-defect pipeline.

[0008] The present invention provides a multi-defect pipeline assessment method, comprising: determining multiple pipeline limit state equations; one pipeline limit state equation is determined according to a defect of a pipeline to be assessed, and the pipeline to be assessed is a pipeline containing multiple defects; according to the multiple pipeline limit state equations, based on the Monte Carlo method, a probability distribution function corresponding to each defect and a pipeline failure probability corresponding to each defect are determined; one probability distribution function uses one pipeline limit state equation as a random variable; based on each probability distribution function and a Vine Copula function, a multidimensional joint probability distribution model is constructed; based on the multidimensional joint probability distribution model, the pipeline to be assessed is assessed to determine the reliability of the pipeline to be assessed.

[0009] According to a multi-defect pipeline assessment method provided by the present invention, multiple pipeline limit state equations are determined, including: obtaining pipeline body parameters of each defect and defect size parameters of each defect; the pipeline body parameters include pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline materials and pipeline outer diameter, and the defect size parameters include corrosion depth; based on the pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline materials, pipeline outer diameter and corrosion depth of each defect, multiple pipeline limit state equations are determined.

[0010] According to a multi-defect pipeline assessment method provided by the present invention, the calculation formula of the pipeline limit state equation is: ; in, represents the pipeline limit state equation; is the failure pressure; is the pipe wall thickness; is the yield strength of the pipeline material; is the outer diameter of the pipe; is the corrosion depth; is the operating pressure; is a constant.

[0011] According to a multi-defect pipeline assessment method provided by the present invention, according to multiple pipeline limit state equations, based on the Monte Carlo method, a probability distribution function corresponding to each defect and a pipeline failure probability corresponding to each defect are determined, including: based on the pipeline body parameters of each defect and the defect size parameters of each defect, a plurality of samples are generated; according to the plurality of samples and the plurality of pipeline limit state equations, based on the law of large numbers, a probability distribution function corresponding to each defect is fitted, and the pipeline failure probability corresponding to each defect is determined.

[0012] According to a multi-defect pipeline evaluation method provided by the present invention, the reliability of the pipeline to be evaluated is expressed as: ; in, is the reliability of the pipeline to be evaluated; For the The pipeline limit state equation corresponding to each defect; For the The probability that the pipeline limit state equation corresponding to a defect is greater than or equal to zero; For the The probability of pipeline failure corresponding to each defect; It is a multi-dimensional joint probability distribution model; The value range of is [1, n], where n is the total number of defects in the pipeline to be evaluated.

[0013] According to a multi-defect pipeline assessment method provided by the present invention, based on a multi-dimensional joint probability distribution model, the pipeline to be assessed is assessed, and after the reliability of the pipeline to be assessed is determined, the method further includes: determining a setting area of ​​the pipeline to be assessed; determining a target reliability based on the setting area; and determining the remaining service life of the pipeline to be assessed based on the reliability of the pipeline to be assessed and the target reliability.

[0014] The present invention also provides a multi-defect pipeline evaluation device, comprising: a limit state equation determination module, used to determine multiple pipeline limit state equations; one pipeline limit state equation is determined according to a defect of the pipeline to be evaluated, and the pipeline to be evaluated is a pipeline containing multiple defects; a probability distribution determination module, used to determine the probability distribution function corresponding to each defect and the pipeline failure probability corresponding to each defect based on the Monte Carlo method according to the multiple pipeline limit state equations; one probability distribution function uses one pipeline limit state equation as a random variable; a model construction module, used to construct a multi-dimensional joint probability distribution model based on each probability distribution function and the Vine Copula function; an evaluation module, used to evaluate the pipeline to be evaluated based on the multi-dimensional joint probability distribution model, and determine the reliability of the pipeline to be evaluated.

[0015] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, any of the above-mentioned multi-defect pipeline evaluation methods is implemented.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the computer program implements any of the above-mentioned multi-defect pipeline evaluation methods.

[0017] The present invention also provides a computer program product, comprising a computer program, which implements any of the above-mentioned multi-defect pipeline evaluation methods when executed by a processor.

[0018] The multi-defect pipeline assessment method, device, equipment, storage medium and program product provided by the present invention determine the probability distribution function corresponding to each defect according to the pipeline limit state equation corresponding to each defect, and introduce the Vine Copula function in the Vine Copula theory to construct a multi-dimensional joint probability distribution model. The pipeline to be assessed is evaluated through the multi-dimensional joint probability distribution model to characterize the reliability of the pipeline to be assessed. The assessment result is more accurate, and the reliability of the multi-defect pipeline can be effectively characterized, thereby providing data reference for the daily maintenance of the pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is one of the flow charts of the multi-defect pipeline evaluation method provided by the present invention.

[0021] Figure 2 It is a decomposition schematic diagram of the four-dimensional Vine Copula structure provided by the present invention.

[0022] Figure 3 This is the second flow chart of the multi-defect pipeline evaluation method provided by the present invention.

[0023] Figure 4 It is a schematic diagram of the remaining service life curve of the pipeline provided by the present invention.

[0024] Figure 5 It is a structural schematic diagram of the multi-defect pipeline evaluation device provided by the present invention.

[0025] Figure 6 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 , Figure 1 FIG. 1 is one of the flow charts of the multi-defect pipeline evaluation method provided by the present invention. Figure 1 As shown, in this embodiment, the multi-defect pipeline assessment method includes steps S110 to S140, and each step is specifically as follows: S110: Determine multiple pipeline limit state equations.

[0028] A pipeline limit state equation is determined according to a defect of the pipeline to be evaluated, and the pipeline to be evaluated is a pipeline containing multiple defects.

[0029] Generally speaking, the reliability of pipeline structure is usually affected by factors such as pipeline load, material strength, geometric dimensions, calculation formula errors, etc. These influencing factors are random and can be regarded as random variables.

[0030] Specifically, if a pipeline to be evaluated that contains multiple defects is to be evaluated, then for each defect of the pipeline to be evaluated, it is necessary to first obtain the pipeline body parameters and defect size parameters corresponding to the defect.

[0031] Among them, the pipeline body parameters (also known as the basic parameters of the pipeline body) include pipeline wall thickness, failure pressure, operating pressure, pipeline internal pressure, yield strength of pipeline material and pipeline outer diameter; defect size parameters include corrosion depth (i.e. defect depth), corrosion length (i.e. defect length), defect depth growth rate and defect length growth rate.

[0032] For each defect in the pipeline to be evaluated, the pipeline body parameters and defect size parameters corresponding to the defect can be recorded as ,in, Indicates Components, one component represents a pipeline body parameter or a defect size parameter, and each component is independent of each other. The mean value of , The standard deviation of , then for the various functions required by the pipeline to be evaluated, a relationship including these basic random variables can be established to obtain the functional function (i.e., pipeline limit state equation): ; in, is a random variable representing the safety margin of the pipeline structure. Indicates that the pipeline structure is in a reliable state. Indicates that the pipeline structure is in the limit state. Indicates that the pipeline structure is in a failure state. For the ultimate state of pipeline bearing capacity, the random variable Indicates the safety margin of a certain function of the pipeline structure. It is called the pipeline limit state equation, which is an important basis for pipeline structure reliability analysis; in addition, the definition is the safety index, and the reliability is expressed as , is the cumulative function of the standard normal distribution.

[0033] In this embodiment, the above-mentioned function needs to be improved, and the B31G criterion is introduced to perform reliability analysis on the pipeline to be evaluated with multiple defects. At this time, the function function (i.e., the pipeline limit state equation) can be expressed as: ; in, represents the pipeline limit state equation; is the failure pressure, in MPa; is the pipe wall thickness, in mm; is the yield strength of the pipeline material, in MPa; is the outer diameter of the pipe, in mm; is the corrosion depth, in mm; is the operating pressure, in MPa; is a constant, The calculation method is as follows: (1) When hour, , Indicates the corrosion length (in mm), is a constant determined based on the corrosion length, pipe outer diameter and pipe wall thickness.

[0034] (2) When hour, , Indicates the corrosion length (in mm), is a constant determined based on the corrosion length, pipe outer diameter and pipe wall thickness.

[0035] For each defect, the above method can be used to determine the pipeline limit state equation corresponding to the defect, and multiple pipeline limit state equations can be obtained: Assume that the pipeline to be evaluated is a pipeline containing n defects, that is, the total number of defects in the pipeline to be evaluated is n, Indicates The pipeline limit state equations corresponding to defects, then multiple pipeline limit state equations can be expressed as .

[0036] S120: According to the plurality of pipeline limit state equations and based on the Monte Carlo method, a probability distribution function corresponding to each defect and a pipeline failure probability corresponding to each defect are determined.

[0037] A probability distribution function takes a pipeline limit state equation as a random variable.

[0038] After determining multiple pipeline limit state equations, the Monte Carlo method can be used to construct a probability distribution (CDF) model of the pipeline under the action of a single defect, and the probability distribution function (denoted as ,in Indicates The probability distribution function corresponding to each defect) and the pipeline failure probability corresponding to each defect (denoted as , Indicates The probability of pipeline failure corresponding to each defect).

[0039] For ease of explanation, the pipeline limit state equation can be simplified as This formula shows that the pipeline limit state equation is related to the pipeline wall thickness , Pipe outer diameter , Operating pressure , corrosion length , corrosion depth and the constant ( represents a constant).

[0040] Specifically, for each defect, the Monte Carlo method is used to generate a set of samples that conform to the random variable distribution based on the pipeline body parameters and defect size parameters, and substitute them into the pipeline limit state equation , a random number can be calculated. By repeating this method for n times, n random numbers can be obtained. According to the corresponding probability distribution, the corresponding random variables can be generated to obtain the sample of the solution. ; Substitute multiple samples into the pipeline limit state equation corresponding to the defect, and count the number of pipeline failures based on the law of large numbers. The pipeline failure probability corresponding to the defect can be determined according to the frequency approximate probability theory. The probability distribution function corresponding to the defect with the pipeline limit state equation as the random variable can be further obtained by fitting.

[0041] Among them, the probability distribution function corresponding to all defects is , and its distribution form is mostly normal distribution.

[0042] S130: Constructing a multi-dimensional joint probability distribution model based on each probability distribution function and the Vine Copula function.

[0043] For the reliability assessment of the pipeline to be evaluated containing multiple defects, the core problem is that the failure of the pipeline to be evaluated is caused by the joint action of multiple defects. By constructing the joint probability distribution of each defect in the pipeline system, the failure state of the pipeline can be more accurately reflected. Therefore, based on this idea, this embodiment proposes to regard the probability distribution corresponding to the pipeline limit state equation of each defect as a random variable, thereby transforming the problem into: how to derive the multidimensional joint probability distribution of the pipeline as a whole from the failure probability distribution of each defect. In recent years, the Copula function in the Copula theory has become an effective tool for modeling the correlation between random variables and constructing a multidimensional joint probability distribution. It can be regarded as a connection function between the marginal distribution and the joint distribution. This theory can solve the difficulty of the reliability evaluation problem of pipelines containing multiple defects. Therefore, this embodiment introduces the Copula theory, takes the pipeline limit state equation corresponding to each defect as a random variable, uses the Copula function to describe the correlation between each defect, and uses the probability distribution function corresponding to each defect obtained by the Monte Carlo method to construct a multidimensional joint probability distribution model for characterizing the reliability and failure probability of the pipeline.

[0044] Assuming that the density functions of the joint distribution, marginal distribution and conditional distribution of the pipeline limit state equations of all defects in a pipeline to be evaluated are continuous, and the number of defects that can be constructed by the Copula joint distribution is n when the correlation condition is satisfied, consider the n-dimensional random variables and their corresponding joint distribution functions composed of the pipeline limit state equations of n defects in the same period. According to Sklar's theorem, the multidimensional joint probability distribution function can be expressed by the Copula function and the marginal distribution function of the random variable, that is, the expression of the multidimensional joint distribution function is: ; Correspondingly, the multivariate joint density function is expressed as: ; in, Represents n-dimensional Copula function; is the marginal density function of each random variable.

[0045] See also Figure 2 , Figure 2 It is a decomposition schematic diagram of the four-dimensional Vine Copula structure provided by the present invention.

[0046] Bedford and Cooke proposed a graph structure called Regular Vine to visualize the construction process of multidimensional distributions. Regular Vine is defined as follows: an n-dimensional vine is a sequence of n−1 trees with the following properties: (1) tree j has n+1−j nodes and n−j edges; (2) the edges in tree j are nodes in tree j+1; (3) the proximity condition: if the corresponding edges in tree j share a node, then two nodes in tree j+1 are connected by an edge. According to this definition, the joint density represented by the vine is given by the density of the entire graph. The Pair-Copula (binary Copula) density on the edge and the edge density of n nodes in the first-level tree.

[0047] Among the many different PCC methods, there are two special types of Regular Vine: Canonical Vine (C-vine) and Stretchable Vine (D-vine). Generally, when there is a main variable that guides other variables, the C-vine structure is chosen to describe the variable. Figure 2 As shown, for C-vine, each tree has a unique node connected to all other nodes. Figure 2 The decomposition diagram of the C-vine structure of four variables is shown. At this time, the joint density distribution of C-vine and probability distribution The calculation formula is as follows: ; Depend on Figure 2 It can be seen that each tree in the C-vine structure has a main variable connected to the remaining variables. The two types of combinations are merged layer by layer, and finally the joint probability function of the multi-dimensional random variables can be obtained. Different selections of main variables in the C-vine structure will lead to the diversification of models. In practical applications, the correlation between the random variables is often calculated, and the variable with the largest sum of correlation coefficients with other variables is selected as the main variable. In this embodiment, the Kendall Rank correlation coefficient is used. The Kendall Rank correlation coefficient is another rank correlation statistic. It judges the degree of correlation between variables based on the consistency of the order of two variable pairs: for variable pairs , and ,when hour , or when When , the two variable pairs are considered to be consistent order pairs, otherwise they are inconsistent order pairs. The calculation formula of Kendall Rank correlation coefficient is as follows: ; in, is the total number of consistent sequence pairs; is the total number of inconsistent variable pairs; is the number of random variables.

[0048] When the value of the Kendall Rank correlation coefficient is greater than 0, it means that the two variables are positively correlated, and the closer the value of the Kendall Rank correlation coefficient is to 1, the stronger the degree of correlation is; when the value of the Kendall Rank correlation coefficient is equal to 0, it means that the two variables are independent of each other; when the value of the Kendall Rank correlation coefficient is less than 0, it means that the two variables are negatively correlated, and the closer the value of the Kendall Rank correlation coefficient is to -1, the stronger the degree of correlation is.

[0049] There are many types of binary Copula functions in Vine Copula theory. Currently, the two most commonly used types of Copula functions are elliptic Copula and Archimedean Copula. Among them, Gaussian Copula and binary Student t Copula are elliptic Copula functions. The two marginal distributions of these two functions satisfy the characteristics of origin symmetry, while Student t Copula introduces more degrees of freedom parameters. ,when When Student t Copula is close to Gaussian Copula. Archimedean Copula is a cluster of functions constructed by a "generating function", and the commonly used ones are Frank Copula, Clayton Copula and Gumbel Copula. This embodiment intends to use these five Vine Copula functions to establish a multi-dimensional joint probability distribution model between multiple defects, and the form of each function is as follows: (1) The functional form of the Gaussian Copula function is: ; In the above formula, The parameter range is .

[0050] (2) The functional form of the T Copula function is: ; In the above formula, The parameter range is , The parameter range is .

[0051] (3) The functional form of the Frank Copula function is: ; In the above formula, The parameter range is .

[0052] (4) The functional form of the Clayton Copula function is: ; In the above formula, The parameter range is .

[0053] (5) The functional form of the Gumbel Copula function is: ; In the above formula, The parameter range is .

[0054] After determining the correlation of the canonical vine (i.e., C-vine) Copula function, it is necessary to select the Copula function family of each binary Copula function in the Vine Copula theory, and use the maximum likelihood estimation method to estimate the parameters of the C-vine Copula function, so as to determine the multidimensional joint probability distribution function (i.e., multidimensional joint probability distribution model) that represents the correlation between the defects of the pipeline to be evaluated. The main steps are as follows: (1) Select the function form of each binary Copula function that constitutes the Vine Copula function: Use the information criterion quantitative calculation method to evaluate the function form of the binary Copula function selected from the two perspectives of goodness of fit and complexity. Commonly used information criterion quantitative calculation methods include Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Compared with BIC, AIC focuses on the balance between model goodness of fit and complexity, so it has a lower penalty for complexity and is more sensitive to the model. Therefore, the binary Copula function form with the smallest AIC is selected for parameter estimation.

[0055] The calculation formula of AIC is as follows: ; in, is the number of parameters in the Copula function; is the value of the maximum likelihood function of the Copula function.

[0056] (2) For the Copula function, the maximum likelihood method can generally be used to estimate the relevant parameters of the Copula function: , , .

[0057] Assuming that the pipeline limit state equation group with the same defect is given, the sample matrix of the pipeline limit state equation for each defect in a single period is: , dimension is the total number of defects, is the sample size, then the maximum likelihood function of the Cvine Copula function can be constructed : in, is the parameter vector of the Cvine Copula function; before performing maximum likelihood estimation, the initial parameter values ​​of each binary Copula function must be solved.

[0058] For a pipeline to be evaluated that contains multiple defects, its failure depends on the defect that fails first, and all defects have the possibility of failing first. Based on this, the failure probability of the pipeline to be evaluated depends on the joint probability distribution of the limit state (stress-load) of all defects. Therefore, after determining the functional form of each binary Copula function that constitutes the Vine Copula function and calculating the relevant parameters of each binary Copula function, the probability distribution function corresponding to each defect obtained based on the above Monte Carlo method is ,The Vine Copula function is used to build a multi-dimensional joint probability distribution model to ,evaluate the pipeline to be evaluated and characterize the reliability and ,failure probability of the pipeline to be evaluated.

[0059] It should be noted that the binary Copula function is two-dimensional, while the multidimensional joint probability distribution model has a higher dimension. In the Vine Copula theory, the transformation principle of the multidimensional joint probability distribution is as follows: Given n variables , the corresponding thresholds of each variable are , define the function , , then according to Sklar's theorem, the Copula function satisfies: ; According to Nelsen (2006), the survival copula satisfies: ; The relationship between the survival copula and the copula function is: .

[0060] S140: Based on the multi-dimensional joint probability distribution model, the pipeline to be evaluated is evaluated to determine the reliability of the pipeline to be evaluated.

[0061] Generally speaking, for the The process of the defect, if The pipeline limit state equation corresponding to the defect satisfy , then the defect is determined as a failure event of the pipeline to be evaluated. is the failure probability of the pipeline to be evaluated under the effect of the defect, corresponding to The cumulative failure probability (CDF) of the pipeline to be evaluated is defined as The reliability under the effect of defects is .

[0062] After understanding the reliability evaluation principle of the pipeline to be evaluated under the action of a single defect, for the pipeline to be evaluated containing multiple defects in the series system, the reliability expression of the pipeline to be evaluated containing multiple defects is: ; Right now ; in, is the reliability of the pipeline to be evaluated; For the The pipeline limit state equation corresponding to each defect; For the The probability that the pipeline limit state equation corresponding to a defect is greater than or equal to zero; For the The probability of pipeline failure corresponding to each defect; It is a multi-dimensional joint probability distribution model; The value range of is [1, n], where n is the total number of defects in the pipeline to be evaluated.

[0063] The multi-defect pipeline assessment method provided in this embodiment determines the probability distribution function corresponding to each defect according to the pipeline limit state equation corresponding to each defect, introduces the Vine Copula function in the Vine Copula theory, and constructs a multi-dimensional joint probability distribution model. The pipeline to be assessed is evaluated through the multi-dimensional joint probability distribution model to characterize the reliability of the pipeline to be assessed. The assessment result is more accurate, and the reliability of the multi-defect pipeline can be effectively characterized, thereby providing data reference for the daily maintenance of the pipeline.

[0064] In some embodiments, determining multiple pipeline limit state equations includes: obtaining pipeline body parameters of each defect and defect size parameters of each defect; the pipeline body parameters include pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline material and pipeline outer diameter, and the defect size parameters include corrosion depth; based on the pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline material, pipeline outer diameter and corrosion depth of each defect, determining multiple pipeline limit state equations.

[0065] Specifically, if a pipeline to be evaluated that contains multiple defects is to be evaluated, then for each defect of the pipeline to be evaluated, it is necessary to first obtain the pipeline body parameters and defect size parameters corresponding to the defect.

[0066] Among them, the pipeline body parameters (also known as the basic parameters of the pipeline body) include pipeline wall thickness, failure pressure, operating pressure, pipeline internal pressure, yield strength of pipeline material and pipeline outer diameter; defect size parameters include corrosion depth (i.e. defect depth), corrosion length (i.e. defect length), defect depth growth rate and defect length growth rate.

[0067] For each defect in the pipeline to be evaluated, the pipeline body parameters and defect size parameters corresponding to the defect can be recorded as ,in, Indicates Components, one component represents a pipeline body parameter or a defect size parameter, and each component is independent of each other. The mean value of , The standard deviation of , then for the various functions required by the pipeline to be evaluated, a relationship including these basic random variables can be established to obtain the functional function (i.e., pipeline limit state equation): ; in, is a random variable representing the safety margin of the pipeline structure. Indicates that the pipeline structure is in a reliable state. Indicates that the pipeline structure is in the limit state. Indicates that the pipeline structure is in a failure state. For the ultimate state of pipeline bearing capacity, the random variable Indicates the safety margin of a certain function of the pipeline structure. It is called the pipeline limit state equation, which is an important basis for pipeline structure reliability analysis; in addition, the definition is the safety index, and the reliability is expressed as , is the cumulative function of the standard normal distribution.

[0068] In this embodiment, the above-mentioned function needs to be improved, and the B31G criterion is introduced to perform reliability analysis on the pipeline to be evaluated with multiple defects. At this time, the function function (i.e., the pipeline limit state equation) can be expressed as: ; in, represents the pipeline limit state equation; is the failure pressure, in MPa; is the pipe wall thickness, in mm; is the yield strength of the pipeline material, in MPa; is the outer diameter of the pipe, in mm; is the corrosion depth, in mm; is the operating pressure, in MPa; is a constant, The calculation method is as follows: (1) When hour, , Indicates the corrosion length (in mm), is a constant determined based on the corrosion length, pipe outer diameter and pipe wall thickness.

[0069] (2) When hour, , Indicates the corrosion length (in mm), is a constant determined based on the corrosion length, pipe outer diameter and pipe wall thickness.

[0070] For each defect, the above method can be used to determine the pipeline limit state equation corresponding to the defect, and multiple pipeline limit state equations can be obtained: Assume that the pipeline to be evaluated is a pipeline containing n defects, that is, the total number of defects in the pipeline to be evaluated is n, Indicates The pipeline limit state equations corresponding to defects, then multiple pipeline limit state equations can be expressed as .

[0071] In some embodiments, the calculation formula of the pipeline limit state equation is: ; in, represents the pipeline limit state equation; is the failure pressure; is the pipe wall thickness; is the yield strength of the pipeline material; is the outer diameter of the pipe; is the corrosion depth; is the operating pressure; is a constant.

[0072] In some embodiments, according to multiple pipeline limit state equations, based on the Monte Carlo method, the probability distribution function corresponding to each defect and the pipeline failure probability corresponding to each defect are determined, including: generating multiple samples based on the pipeline body parameters of each defect and the defect size parameters of each defect; according to the multiple samples and the multiple pipeline limit state equations, based on the law of large numbers, fitting the probability distribution function corresponding to each defect, and determining the pipeline failure probability corresponding to each defect.

[0073] After determining multiple pipeline limit state equations, the Monte Carlo method can be used to construct a probability distribution (CDF) model of the pipeline under the action of a single defect, and the probability distribution function (denoted as ,in Indicates The probability distribution function corresponding to each defect) and the pipeline failure probability corresponding to each defect (denoted as , Indicates The probability of pipeline failure corresponding to each defect).

[0074] For ease of explanation, the pipeline limit state equation can be simplified as This formula shows that the pipeline limit state equation is related to the pipeline wall thickness , Pipe outer diameter , Operating pressure , corrosion length , corrosion depth and the constant ( represents a constant).

[0075] Specifically, for each defect, the Monte Carlo method is used to generate a set of samples that conform to the random variable distribution based on the pipeline body parameters and defect size parameters, and substitute them into the pipeline limit state equation , a random number can be calculated. By repeating this method for n times, n random numbers can be obtained. According to the corresponding probability distribution, the corresponding random variables can be generated to obtain the sample of the solution. ; Substitute multiple samples into the pipeline limit state equation corresponding to the defect, and count the number of pipeline failures based on the law of large numbers. The pipeline failure probability corresponding to the defect can be determined according to the frequency approximate probability theory. The probability distribution function corresponding to the defect with the pipeline limit state equation as the random variable can be further obtained by fitting.

[0076] Among them, the probability distribution function corresponding to all defects is , and its distribution form is mostly normal distribution.

[0077] In some embodiments, the reliability of the pipeline to be evaluated is expressed as: ; in, is the reliability of the pipeline to be evaluated; For the The pipeline limit state equation corresponding to each defect; For the The probability that the pipeline limit state equation corresponding to a defect is greater than or equal to zero; For the The probability of pipeline failure corresponding to each defect; It is a multi-dimensional joint probability distribution model; The value range of is [1, n], where n is the total number of defects in the pipeline to be evaluated.

[0078] In some embodiments, after evaluating the pipeline to be evaluated based on the multi-dimensional joint probability distribution model and determining the reliability of the pipeline to be evaluated, it also includes: determining the setting area of ​​the pipeline to be evaluated; determining the target reliability based on the setting area; and determining the remaining service life of the pipeline to be evaluated based on the reliability of the pipeline to be evaluated and the target reliability.

[0079] Generally, there are two analytical methods for evaluating pipeline reliability and remaining service life, namely, a calculation method based on reliability theory and a method based on historical data prediction. This embodiment will adopt a calculation method based on reliability theory: Generally, different pipeline installation areas have different target reliability (target reliability is the judgment threshold for evaluating whether the pipeline is reliable); according to API579 standard, the target reliability of different installation areas is shown in Table 1.

[0080] Table 1

[0081] Specifically, after determining the setting area of ​​the pipeline to be evaluated, the target reliability can be determined according to the setting area, and the reliability of the pipeline to be evaluated is compared with the target reliability to determine whether the pipeline to be evaluated is reliable, and then determine the remaining service life of the pipeline to be evaluated.

[0082] For example, if the pipeline to be evaluated is located in a Class II area, when the reliability of the pipeline is <0.999, the pipeline is considered unreliable and measures such as replacing the pipeline or reducing the delivery pressure are required. At the same time, the remaining service life of the pipeline is evaluated based on the comparison between the reliability of the pipeline and the target reliability.

[0083] Generally, when the reliability of the pipeline to be evaluated is less than the target reliability, the greater the gap between the reliability of the pipeline to be evaluated and the target reliability, the shorter the remaining service life of the pipeline to be evaluated is considered to be.

[0084] The present invention also provides a specific example of a multi-defect pipeline evaluation method. Figure 3 and Figure 4 , Figure 3 This is the second flow chart of the multi-defect pipeline evaluation method provided by the present invention. Figure 4 It is a schematic diagram of the remaining service life curve of the pipeline provided by the present invention.

[0085] like Figure 3 As shown in Figure 2, for each pipeline to be evaluated, multiple pipeline limit state equations can be used as input, the relevant parameters of each defect and its distribution are introduced, and the initial pipeline remaining service life is set. (in years), target reliability RS′ and initial corrosion depth .

[0086] Furthermore, according to multiple pipeline limit state equations and based on the Monte Carlo method, the probability distribution function corresponding to each defect and the pipeline failure probability corresponding to each defect are determined, and according to each probability distribution function and the C-vine Copula function, a multidimensional joint probability distribution model is constructed; based on the multidimensional joint probability distribution model, the pipeline to be evaluated is evaluated to determine the reliability RS of the pipeline to be evaluated.

[0087] Furthermore, the reliability RS of the pipeline to be evaluated is compared with the target reliability RS′ to determine whether the reliability RS of the pipeline to be evaluated is less than or equal to the target reliability RS′.

[0088] If the reliability RS of the pipeline to be evaluated is greater than the target reliability RS′, it means that the pipeline is reliable. Then the relevant parameters can be adjusted to solve the problem cyclically until the reliability RS is less than or equal to the target reliability RS′ (i.e., the limit reliability). The output is , which is the remaining service life of the pipeline.

[0089] like Figure 4 As shown, according to the cyclic solution of the above algorithm, the remaining service life curve of the pipeline can be fitted, and the curve can reflect the relationship between the pipeline reliability and the remaining service life of the pipeline.

[0090] The multi-defect pipeline assessment method provided by the present invention innovatively proposes to regard the probability distribution of the pipeline limit state equation corresponding to each defect as a random variable. This method goes beyond the scope of only considering the correlation of input variables and goes deep into the results of the defect effect, that is, examining the correlation between defects from the actual state level of the pipeline structure, which can make the final assessment result more accurate; secondly, by introducing the Vine Copula theory, the present invention successfully constructs a multi-dimensional joint probability distribution model between defects that can characterize the reliability of the pipeline. This model provides a comprehensive reliability evaluation for pipelines containing multiple defects. Compared with the independence principle adopted in the traditional series reliability model, this method is closer to the actual situation, so that the multi-dimensional joint probability distribution of the pipeline system can more accurately reveal the failure state of the pipeline, and can provide data reference for the daily maintenance of the pipeline.

[0091] The present invention also provides a multi-defect pipeline evaluation device. Figure 5 , Figure 5 Schematic diagram of the structure of the multi-defect pipeline evaluation device provided by the present invention. In this embodiment, the multi-defect pipeline evaluation device includes a limit state equation determination module 510, a probability distribution determination module 520, a model construction module 530 and an evaluation module 540.

[0092] The limit state equation determination module 510 is used to determine a plurality of pipeline limit state equations.

[0093] A pipeline limit state equation is determined according to a defect of the pipeline to be evaluated, and the pipeline to be evaluated is a pipeline containing multiple defects.

[0094] The probability distribution determination module 520 is used to determine the probability distribution function corresponding to each defect and the pipeline failure probability corresponding to each defect based on the Monte Carlo method according to multiple pipeline limit state equations.

[0095] A probability distribution function takes a pipeline limit state equation as a random variable.

[0096] The model building module 530 is used to build a multi-dimensional joint probability distribution model based on each probability distribution function and the Vine Copula function.

[0097] The evaluation module 540 is used to evaluate the pipeline to be evaluated based on the multi-dimensional joint probability distribution model to determine the reliability of the pipeline to be evaluated.

[0098] In some embodiments, the limit state equation determination module 510 is used to obtain the pipeline body parameters of each defect and the defect size parameters of each defect; the pipeline body parameters include pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline material and pipeline outer diameter, and the defect size parameters include corrosion depth; based on the pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline material, pipeline outer diameter and corrosion depth of each defect, multiple pipeline limit state equations are determined.

[0099] In some embodiments, the calculation formula of the pipeline limit state equation is: ; in, represents the pipeline limit state equation; is the failure pressure; is the pipe wall thickness; is the yield strength of the pipeline material; is the outer diameter of the pipe; is the corrosion depth; is the operating pressure; is a constant.

[0100] In some embodiments, the probability distribution determination module 520 is used to generate multiple samples based on the pipeline body parameters of each defect and the defect size parameters of each defect; based on the multiple samples and multiple pipeline limit state equations, based on the law of large numbers, the probability distribution function corresponding to each defect is fitted, and the pipeline failure probability corresponding to each defect is determined.

[0101] In some embodiments, the reliability of the pipeline to be evaluated is expressed as: ; in, is the reliability of the pipeline to be evaluated; For the The pipeline limit state equation corresponding to each defect; For the The probability that the pipeline limit state equation corresponding to a defect is greater than or equal to zero; For the The probability of pipeline failure corresponding to each defect; It is a multi-dimensional joint probability distribution model; The value range of is [1, n], where n is the total number of defects in the pipeline to be evaluated.

[0102] In some embodiments, the evaluation module 540 is used to determine a setting area of ​​the pipeline to be evaluated; determine a target reliability based on the setting area; and determine a remaining service life of the pipeline to be evaluated based on the reliability of the pipeline to be evaluated and the target reliability.

[0103] The invention also provides an electronic device. Figure 6 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 6 As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communications bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communications bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the multi-defect pipeline assessment method.

[0104] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0105] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the multi-defect pipeline evaluation method provided by the above methods is implemented.

[0106] The present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the multi-defect pipeline evaluation method provided by the above methods.

[0107] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0108] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-defect pipeline assessment method, characterized in that: include: Determine a plurality of pipeline limit state equations; one of the pipeline limit state equations is determined based on a defect of a pipeline to be evaluated, wherein the pipeline to be evaluated is a pipeline containing a plurality of defects; According to the plurality of pipeline limit state equations, based on the Monte Carlo method, a probability distribution function corresponding to each defect and a pipeline failure probability corresponding to each defect are determined; one probability distribution function takes one pipeline limit state equation as a random variable; Based on each of the probability distribution functions and the Vine Copula function, construct a multidimensional joint probability distribution model; Based on the multi-dimensional joint probability distribution model, the pipeline to be evaluated is evaluated to determine the reliability of the pipeline to be evaluated.

2. The multi-defect pipeline evaluation method according to claim 1, characterized in that: The determining of a plurality of pipeline limit state equations comprises: Obtaining pipeline body parameters of each defect and defect size parameters of each defect; the pipeline body parameters include pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline material and pipeline outer diameter, and the defect size parameters include corrosion depth; Based on the pipeline wall thickness, failure pressure, operating pressure, yield strength of pipeline material, pipeline outer diameter and corrosion depth of each defect, a plurality of pipeline limit state equations are determined.

3. The multi-defect pipeline evaluation method according to claim 2, characterized in that: The calculation formula of the pipeline limit state equation is: ； in, represents the pipeline limit state equation; is the failure pressure; is the pipe wall thickness; is the yield strength of the pipeline material; is the outer diameter of the pipeline; is the corrosion depth; is the operating pressure; is a constant.

4. The multi-defect pipeline evaluation method according to claim 2, characterized in that: The method of determining the probability distribution function corresponding to each defect and the pipeline failure probability corresponding to each defect based on the Monte Carlo method according to the plurality of pipeline limit state equations includes: generating a plurality of samples based on a pipe body parameter of each of the defects and a defect size parameter of each of the defects; According to the plurality of samples and the plurality of pipeline limit state equations, based on the law of large numbers, a probability distribution function corresponding to each defect is fitted, and the pipeline failure probability corresponding to each defect is determined.

5. The multi-defect pipeline evaluation method according to claim 1, characterized in that: The reliability expression of the pipeline to be evaluated is: ； in, is the reliability of the pipeline to be evaluated; For the The pipeline limit state equation corresponding to the defects; For the The probability that the pipeline limit state equation corresponding to the defect is greater than or equal to zero; For the The probability of pipeline failure corresponding to each of the defects; is the multidimensional joint probability distribution model; The value range of is [1, n], where n is the total number of defects of the pipeline to be evaluated.

6. The multi-defect pipeline evaluation method according to claim 1, characterized in that: After evaluating the pipeline to be evaluated based on the multi-dimensional joint probability distribution model and determining the reliability of the pipeline to be evaluated, the method further includes: Determining a setting area of ​​the pipeline to be evaluated; Based on the setting area, determining a target reliability; Based on the reliability of the pipeline to be evaluated and the target reliability, the remaining service life of the pipeline to be evaluated is determined.

7. A multi-defect pipeline evaluation device, characterized in that: include: A limit state equation determination module is used to determine a plurality of pipeline limit state equations; one of the pipeline limit state equations is determined based on a defect of a pipeline to be evaluated, and the pipeline to be evaluated is a pipeline containing a plurality of defects; A probability distribution determination module, used to determine the probability distribution function corresponding to each defect and the pipeline failure probability corresponding to each defect based on the Monte Carlo method according to the plurality of pipeline limit state equations; one probability distribution function takes one pipeline limit state equation as a random variable; A model building module, used to build a multi-dimensional joint probability distribution model based on each of the probability distribution functions and the Vine Copula function; An evaluation module is used to evaluate the pipeline to be evaluated based on the multi-dimensional joint probability distribution model to determine the reliability of the pipeline to be evaluated.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the multi-defect pipeline evaluation method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-defect pipeline evaluation method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-defect pipeline evaluation method according to any one of claims 1 to 6 is implemented.

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