Submarine pipeline life prediction and life extension decision-making methods, systems, equipment and media

Abstract

The invention discloses a method, system, equipment and medium for predicting the life of a submarine pipeline and making a life extension decision. The method comprises: collecting operating state data and pipeline test data of a submarine pipeline to be predicted, determining the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted, and simplifying to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; adopting a dynamic Bayesian network to construct a submarine pipeline crack propagation model and obtain a submarine pipeline crack failure probability curve; according to the crack propagation limit state equation, adopting a Monte Carlo method to calculate and obtain a submarine pipeline reliability degradation curve, and determining the operating life and key failure time node of the pipeline to be predicted, that is, obtaining the submarine pipeline life prediction and life extension decision result; the invention realizes the submarine pipeline life extension decision under the reliability degradation model, effectively reduces the probability of failure accidents during the life extension period of the submarine pipeline, and provides technical guarantee for the integrity management and life extension decision of the submarine pipeline.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrity management of aged submarine pipelines, and in particular relates to a submarine pipeline life prediction and life extension decision-making method, system, equipment and medium. Background Art

[0002] Submarine pipelines are an important part of marine oil and gas transportation. With the increase of service life, many submarine pipelines are approaching the end of their design life or have exceeded their design life. At present, most of the old submarine pipelines have serious corrosion fatigue cracks and structural damage due to poor management, unreasonable early design and harsh operating environment, and some have even reached their working life ahead of schedule. However, due to the continued exploitation of marginal oil fields, many submarine pipelines still need to continue to serve for 5-15 years, which is very likely to cause submarine pipeline leakage failure accidents, and even cause serious fires and explosions, bringing huge economic losses and ecological disasters. Therefore, in order to ensure the safe operation of submarine pipelines, it is urgent to predict the life of submarine pipelines and make life extension decisions.

[0003] In the existing technology, the reliability degradation process is the essential performance of characterizing and predicting the service life of the pipeline. In the random process models, such as: Wiener model, Gamma model and Gauss-Poisson joint distribution model, they are widely used to capture the uncertainty of operating environment, measurement and variability in the degradation process. However, although the reliability degradation state is a monotonically decreasing process, the life extension assessment needs to be dynamically updated to adapt to the complex seabed environment, and the single linear damage accumulation method is simple to calculate and lacks clear physical failure criteria.

[0004] Due to the cross-influence of electrochemistry and mechanics, the corrosion fatigue crack propagation mechanism is more complex and unique; however, the early process models for corrosion fatigue crack propagation in life extension decisions are still relatively complex and redundant; and the initial Paris formula is often used in the crack propagation stage, and the uncertainty of structural parameters makes the prediction results have great randomness. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a method, system, equipment and medium for predicting the life of a submarine pipeline and making a life extension decision, so as to solve the technical problems that in the existing submarine pipeline life extension decision-making process, the fatigue process model is complex, the reliability of the evaluation result is low, and the decision result has a large error.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] The present invention provides a method for predicting the life of a submarine pipeline and making a life extension decision, comprising the following steps:

[0008] Select the submarine pipeline to be predicted, and collect the operation status data and pipeline test data of the submarine pipeline to be predicted;

[0009] According to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is determined;

[0010] Based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, the EIFS distribution function is introduced to determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline;

[0011] Combining the equivalent initial defect size and the Paris formula, according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, a dynamic Bayesian network is used to construct a submarine pipeline crack growth model; and based on the submarine pipeline crack growth model, the submarine pipeline crack failure probability curve is obtained;

[0012] Based on the crack failure probability curve of submarine pipeline and the number of environmental load cycles, the crack extension limit state equation is constructed; and based on the crack extension limit state equation, the Monte Carlo method is used to calculate the submarine pipeline reliability degradation curve;

[0013] According to the preset reliability threshold of the pipeline to be predicted and the reliability degradation curve of the submarine pipeline, the operating life and key failure time nodes of the pipeline to be predicted are determined, that is, the submarine pipeline life prediction and life extension decision results are obtained.

[0014] Furthermore, the operating status of the submarine pipeline to be predicted includes submarine pipeline design parameters, pipeline operation routes and pipeline maintenance data; the pipeline test data includes pipeline material mechanical property test results and fatigue crack growth test results.

[0015] Furthermore, the corrosion-fatigue dynamic degradation process states of the submarine pipeline to be predicted include the corrosion pit initiation state, the corrosion pit extension state to the short crack extension state, the short crack extension state to the long crack extension state and the long crack extension state.

[0016] Furthermore, according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted, the process of determining the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is as follows:

[0017] According to the corrosion environment of submarine pipelines and the thickness of pipeline corrosion layer, the initial anti-corrosion layer rupture and corrosion pit formation stage of pipelines are generally divided into the corrosion pit initiation state;

[0018] Based on the corrosion pit initiation state and combined with the wave and current effect in the marine environment of the submarine pipeline to be predicted, the lateral growth stage of the pipeline corrosion pit and the short crack development stage of the submarine pipeline to be predicted are divided into the corrosion pit to short crack extension state.

[0019] Based on the state of corrosion pits extending into short cracks and considering the crack extension mode, the development stage of crack length from short to long is divided into the state of short cracks extending into long cracks.

[0020] Based on the state of short crack extension to long crack and considering the final pipeline failure state, the long crack extension stage is divided into the long crack extension state as a whole.

[0021] Furthermore, based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, the EIFS distribution function is introduced to determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the process of the corrosion-fatigue reliability degradation process state of the submarine pipeline, as follows:

[0022] According to the collected pipeline test data of the submarine pipeline to be predicted, the number of stress fracture cycles under each pipeline test environment condition is fitted to obtain the fatigue limit test value and stress intensity factor threshold test value under the corresponding test environment condition;

[0023] Determine the functional distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted according to the fatigue limit test value and the stress intensity factor threshold test value under the corresponding test environment conditions;

[0024] According to the function distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; wherein the corrosion-fatigue reliability degradation process state of the submarine pipeline is the crack propagation process state based on the initial defect size.

[0025] Furthermore, combining the equivalent initial defect size and the Paris formula, according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, a submarine pipeline crack growth model is constructed using a dynamic Bayesian network; and according to the submarine pipeline crack growth model, the process of obtaining the submarine pipeline crack failure probability curve is as follows:

[0026] According to the equivalent initial defect size, the Paris formula is simplified and the function variables are extracted to obtain the simplified Paris formula;

[0027] The Bayesian function nodes of the dynamic Bayesian network are selected, and the submarine pipeline crack propagation model is constructed according to the simplified Paris formula;

[0028] According to the submarine pipeline crack growth model, the submarine pipeline reliability degradation curve is calculated to obtain the pipeline crack growth size probability under the time coordinate, that is, the submarine pipeline crack failure probability defect is obtained.

[0029] Furthermore, the simplified Paris formula is as follows:

[0030]

[0031] Among them, da / dN is the crack extension; a is the crack depth; N is the number of external load cycles; ΔK is the range of stress intensity factor variation; C and m are material parameters, which obey lognormal distribution and normal distribution respectively;

[0032] Among them, the stress intensity factor variation range ΔK is related to the geometric shape of the submarine pipeline to be predicted; for the flat plate model with a crack in the center, the stress intensity factor variation range ΔK is described as:

[0033]

[0034] Where Δσ is the far-field stress amplitude, and Y(a) is the geometric function related to the crack depth;

[0035] Assuming that the geometric function Y(a) related to the crack depth is independent of the crack depth a, and the far-field stress amplitude Δσ obeys the Weibull distribution, the crack depth a is expressed as:

[0036]

[0037] Where a0 is the initial crack size; A is the scale parameter of the Weibull distribution; B is the shape parameter of the Weibull distribution; Γ(·) is the gamma function; the far-field stress amplitude Δσ is described by the Weibull distribution, which is expressed as:

[0038]

[0039] The present invention also provides a submarine pipeline prediction and life extension decision system, comprising:

[0040] A collection module is used to select the submarine pipeline to be predicted and collect the operation status data and pipeline test data of the submarine pipeline to be predicted;

[0041] A state description module is used to determine the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted;

[0042] The degradation process simplification module is used to introduce the EIFS distribution function based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, determine the equivalent initial defect size and function fitting parameters; and simplify the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the determined equivalent initial defect size and function fitting parameters, and obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline;

[0043] The failure probability curve module is used to combine the equivalent initial defect size and the Paris formula, and use the dynamic Bayesian network to build a submarine pipeline crack growth model according to the submarine pipeline corrosion-fatigue reliability degradation process state; and obtain the submarine pipeline crack failure probability curve based on the submarine pipeline crack growth model;

[0044] The reliability curve module is used to construct the crack extension limit state equation based on the crack failure probability curve of the submarine pipeline and the number of environmental load cycles; and according to the crack extension limit state equation, the Monte Carlo method is used to calculate the submarine pipeline reliability degradation curve;

[0045] The result output module is used to determine the operating life and key failure time nodes of the pipeline to be predicted according to the preset reliability threshold of the pipeline to be predicted and the reliability degradation curve of the submarine pipeline, that is, to obtain the submarine pipeline life prediction and life extension decision results.

[0046] The present invention also provides a submarine pipeline life prediction and life extension decision-making device, comprising:

[0047] Memory for storing computer programs;

[0048] A processor is used to implement the steps of predicting the life of a submarine pipeline and making a life extension decision when executing the computer program.

[0049] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of predicting the life of a submarine pipeline and making a life extension decision are implemented.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a method for predicting the life of a submarine pipeline and making a life extension decision. Based on a dynamic Bayesian model, a crack growth model of an aged submarine pipeline is established by using distribution function nodes, and an intuitive description of the pipeline corrosion-fatigue crack growth structure by using graph theory language and probability principle is realized, thereby reducing the computational complexity of reasoning. The corrosion-fatigue process is effectively simplified by using the concept of equivalent crack size (EIFS), and an effective and reliable quantitative evaluation is performed on the original corrosion amount, thereby realizing model analysis and optimization of the complex pit initiation and short crack growth process. Based on the limit state equation, a life extension decision of an aged submarine pipeline under a reliability degradation model is realized, thereby effectively reducing the probability of failure accidents during the life extension period of the submarine pipeline, and providing technical guarantee for the integrity management and life extension decision of the submarine pipeline.

[0052] Furthermore, as an improved probability model, dynamic Bayesian network (DBH) can effectively avoid the dilemma of static reliability degradation analysis, effectively improve the reliability of evaluation results, and reduce the error of decision results.

[0053] Furthermore, by introducing the equivalent initial crack size (EIFS) and simplifying the Paris formula, the complex initial fatigue crack initiation and propagation process is effectively replaced, achieving a complete model description of the corrosion fatigue failure process and effectively reducing the complexity of the fatigue process model. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flowchart of the submarine pipeline life prediction and life extension decision-making method described in the embodiment;

[0055] Figure 2 is a flow chart of corrosion-fatigue process analysis in the embodiment;

[0056] Figure 3 is a dynamic Bayesian network crack propagation model in an embodiment;

[0057] Figure 4 Graph showing the calculation result of the crack growth probability of the submarine pipeline in the embodiment.

[0058] Figure 5 It is a probability distribution diagram of crack size at different time periods in the embodiment;

[0059] Figure 6 It is a curve diagram of submarine pipeline life prediction and life extension decision results in the embodiment. DETAILED DESCRIPTION

[0060] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail in the following specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] The present invention provides a method for predicting the life of a submarine pipeline and making a life extension decision, comprising the following steps:

[0062] Step 1: Select the submarine pipeline to be predicted, and collect the operation status data and pipeline test data of the submarine pipeline to be predicted; the operation status of the submarine pipeline to be predicted includes submarine pipeline design parameters, pipeline operation route and pipeline maintenance data; the pipeline test data includes pipeline material mechanical property test results and fatigue crack growth test results.

[0063] Step 2: According to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is determined; the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted includes the corrosion pit initiation state, the corrosion pit to short crack extension state, the short crack to long crack extension state and the long crack extension state; the details are as follows:

[0064] According to the corrosion environment of submarine pipelines and the thickness of pipeline corrosion layer, the initial anti-corrosion layer rupture and corrosion pit formation stage of pipelines are generally divided into the corrosion pit initiation state;

[0065] Based on the corrosion pit initiation state and combined with the wave and current effect in the marine environment of the submarine pipeline to be predicted, the lateral growth stage of the pipeline corrosion pit and the short crack development stage of the submarine pipeline to be predicted are divided into the corrosion pit to short crack extension state.

[0066] Based on the state of corrosion pits extending into short cracks and considering the crack extension mode, the development stage of crack length from short to long is divided into the state of short cracks extending into long cracks.

[0067] Based on the state of short crack extension to long crack and considering the final pipeline failure state, the long crack extension stage is divided into the long crack extension state as a whole.

[0068] Step 3: Based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, the EIFS distribution function is introduced to determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; the details are as follows:

[0069] According to the collected pipeline test data of the submarine pipeline to be predicted, the number of stress fracture cycles under each pipeline test environment condition is fitted to obtain the fatigue limit test value and stress intensity factor threshold test value under the corresponding test environment condition;

[0070] Determine the functional distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted according to the fatigue limit test value and the stress intensity factor threshold test value under the corresponding test environment conditions;

[0071] According to the function distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; wherein the corrosion-fatigue reliability degradation process state of the submarine pipeline is the crack propagation process state based on the initial defect size.

[0072] Step 4: Combine the equivalent initial defect size and the Paris formula, and according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, use the dynamic Bayesian network to build a submarine pipeline crack growth model; and obtain the submarine pipeline crack failure probability curve based on the submarine pipeline crack growth model; the specific process is as follows:

[0073] According to the equivalent initial defect size, the Paris formula is simplified and the function variables are extracted to obtain the simplified Paris formula;

[0074] The Bayesian function nodes of the dynamic Bayesian network are selected, and the submarine pipeline crack propagation model is constructed according to the simplified Paris formula;

[0075] According to the submarine pipeline crack growth model, the submarine pipeline reliability degradation curve is calculated to obtain the pipeline crack growth size probability under the time coordinate, that is, the submarine pipeline crack failure probability defect is obtained.

[0076] The simplified Paris formula is as follows:

[0077]

[0078] Among them, da / dN is the crack extension; a is the crack depth; N is the number of external load cycles; ΔK is the range of stress intensity factor variation; C and m are material parameters, which obey lognormal distribution and normal distribution respectively;

[0079] Among them, the stress intensity factor variation range ΔK is related to the geometric shape of the submarine pipeline to be predicted; for the flat plate model with a crack in the center, the stress intensity factor variation range ΔK is described as:

[0080]

[0081] Where Δσ is the far-field stress amplitude, and Y(a) is the geometric function related to the crack depth;

[0082] Assuming that the geometric function Y(a) related to the crack depth is independent of the crack depth a, and the far-field stress amplitude Δσ obeys the Weibull distribution, the crack depth a is expressed as:

[0083]

[0084] Where a0 is the initial crack size; A is the scale parameter of the Weibull distribution; B is the shape parameter of the Weibull distribution; Γ(·) is the gamma function; the far-field stress amplitude Δσ is described by the Weibull distribution, which is expressed as:

[0085]

[0086] Step 5: Based on the crack failure probability curve of the submarine pipeline and the number of environmental load cycles, a crack extension limit state equation is constructed; and based on the crack extension limit state equation, the Monte Carlo method is used to calculate the submarine pipeline reliability degradation curve; in the present invention, the size of the pipeline penetration crack is considered, the final size of the fatigue failure crack is set, and the crack extension limit state equation is constructed; the number of submarine cyclic loads of the pipeline is determined, and the submarine pipeline reliability degradation curve is calculated based on the Monte Carlo method.

[0087] Step 6: According to the preset reliability threshold of the pipeline to be predicted and the reliability degradation curve of the submarine pipeline, the dynamic change result of the pipeline reliability during operation is obtained based on the time coordinate axis, and the operating life and key failure time nodes of the pipeline to be predicted are determined, that is, the submarine pipeline life prediction and life extension decision results are obtained.

[0088] The present invention also provides a submarine pipeline life prediction and life extension decision system, including an acquisition module, a state description module, a degradation process simplification module, a failure probability curve module, a reliability curve module and a result output module; the acquisition module is used to select a submarine pipeline to be predicted and collect the operating state data and pipeline test data of the submarine pipeline to be predicted; the state description module is used to determine the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted; the degradation process simplification module is used to introduce the EIFS distribution function based on the collected operating state data and pipeline test data of the submarine pipeline to be predicted, and determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, simplify the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted to obtain to the submarine pipeline corrosion-fatigue reliability degradation process state; the failure probability curve module is used to combine the equivalent initial defect size and the Paris formula, and according to the submarine pipeline corrosion-fatigue reliability degradation process state, use the dynamic Bayesian network to build a submarine pipeline crack growth model; and according to the submarine pipeline crack growth model, obtain the submarine pipeline crack failure probability curve; the reliability curve module is used to build the crack growth limit state equation based on the submarine pipeline crack failure probability curve and the number of environmental load cycles; and according to the crack growth limit state equation, use the Monte Carlo method to calculate the submarine pipeline reliability degradation curve; the result output module is used to determine the operating life and key failure time nodes of the pipeline to be predicted according to the preset reliability threshold of the pipeline to be predicted and the submarine pipeline reliability degradation curve, that is, to obtain the submarine pipeline life prediction and life extension decision results.

[0089] The present invention also provides a submarine pipeline life prediction and life extension decision-making device, comprising: a memory for storing a computer program; a processor for implementing the steps of the submarine pipeline life prediction and life extension decision-making method when executing the computer program.

[0090] When the processor executes the computer program, the steps of the above-mentioned submarine pipeline life prediction and life extension decision method are implemented, for example: selecting a submarine pipeline to be predicted, collecting operating status data and pipeline test data of the submarine pipeline to be predicted; determining the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted; introducing the EIFS distribution function based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, determining the equivalent initial defect size and function fitting parameters; and simplifying the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the determined equivalent initial defect size and function fitting parameters to obtain the submarine pipeline corrosion-fatigue reliability degradation process state. The invention relates to a method for determining the state of the submarine pipeline corrosion-fatigue reliability degradation process; combining the equivalent initial defect size and the Paris formula, and adopting the dynamic Bayesian network according to the state of the submarine pipeline corrosion-fatigue reliability degradation process; and obtaining the submarine pipeline crack failure probability curve according to the submarine pipeline crack growth model; and constructing the crack growth limit state equation based on the submarine pipeline crack failure probability curve and the number of environmental load cycles; and according to the crack growth limit state equation, adopting the Monte Carlo method, calculating the submarine pipeline reliability degradation curve; and determining the operating life and key failure time node of the pipeline to be predicted according to the preset reliability threshold of the pipeline to be predicted and the submarine pipeline reliability degradation curve, so as to obtain the submarine pipeline life prediction and life extension decision result.

[0091] Alternatively, the processor implements the functions of each module in the above system when executing the computer program, for example: an acquisition module, used to select a submarine pipeline to be predicted, and collect the operating status data and pipeline test data of the submarine pipeline to be predicted; a state description module, used to determine the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted; a degradation process simplification module, used to introduce the EIFS distribution function based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, and determine the equivalent initial defect size and function fitting parameters; and simplify the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the determined equivalent initial defect size and function fitting parameters to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline. ; The failure probability curve module is used to combine the equivalent initial defect size and the Paris formula, and according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, use the dynamic Bayesian network to build a submarine pipeline crack growth model; and according to the submarine pipeline crack growth model, obtain the submarine pipeline crack failure probability curve; The reliability curve module is used to construct the crack growth limit state equation based on the submarine pipeline crack failure probability curve and the number of environmental load cycles; and according to the crack growth limit state equation, use the Monte Carlo method to calculate the submarine pipeline reliability degradation curve; The result output module is used to determine the operating life and key failure time nodes of the pipeline to be predicted according to the preset reliability threshold of the pipeline to be predicted and the submarine pipeline reliability degradation curve, that is, to obtain the submarine pipeline life prediction and life extension decision results.

[0092] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the submarine pipeline life prediction and life extension decision-making device. For example, the computer program can be divided into an acquisition module, a state description module, a degradation process simplification module, a failure probability curve module, a reliability curve module and a result output module, and the specific functions of each module are as follows: an acquisition module is used to select a submarine pipeline to be predicted and collect the operating state data and pipeline test data of the submarine pipeline to be predicted; a state description module is used to determine the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted; a degradation process simplification module is used to introduce the EIFS distribution function based on the collected operating state data and pipeline test data of the submarine pipeline to be predicted, and determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain The submarine pipeline corrosion-fatigue reliability degradation process state; the failure probability curve module is used to combine the equivalent initial defect size and the Paris formula, and according to the submarine pipeline corrosion-fatigue reliability degradation process state, use the dynamic Bayesian network to build a submarine pipeline crack growth model; and according to the submarine pipeline crack growth model, obtain the submarine pipeline crack failure probability curve; the reliability curve module is used to build the crack growth limit state equation based on the submarine pipeline crack failure probability curve and the number of environmental load cycles; and according to the crack growth limit state equation, use the Monte Carlo method to calculate the submarine pipeline reliability degradation curve; the result output module is used to determine the operating life and key failure time nodes of the pipeline to be predicted according to the preset reliability threshold of the pipeline to be predicted and the submarine pipeline reliability degradation curve, that is, to obtain the submarine pipeline life prediction and life extension decision results.

[0093] The submarine pipeline life prediction and life extension decision-making device can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The submarine pipeline life prediction and life extension decision-making device can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is only an example of a submarine pipeline life prediction and life extension decision-making device, and does not constitute a limitation on the submarine pipeline life prediction and life extension decision-making device, and can include more components than the above, or combine certain components, or different components. For example, the submarine pipeline life prediction and life extension decision-making device can also include input and output devices, network access devices, buses, etc.

[0094] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the submarine pipeline life prediction and extension decision-making device, and uses various interfaces and lines to connect various parts of the entire submarine pipeline life prediction and extension decision-making device.

[0095] The memory may be used to store the computer program and / or module, and the processor implements various functions of the submarine pipeline life prediction and life extension decision-making device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0096] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0097] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting and extending the life of a submarine pipeline are implemented.

[0098] If the modules / units integrated in the submarine pipeline life prediction and life extension decision system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0099] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned submarine pipeline life prediction and life extension decision method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned submarine pipeline life prediction and life extension decision method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0100] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0101] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0102] Example

[0103] This embodiment takes the life prediction and life extension decision optimization process of an old submarine pipeline as an example. Figure 1 As shown, this embodiment provides a method for predicting the life of a submarine pipeline and making a life extension decision, which specifically includes the following steps:

[0104] Step 1: Select the submarine pipeline to be predicted, and collect the operation status data and pipeline test data of the submarine pipeline to be predicted; the operation status of the submarine pipeline to be predicted includes submarine pipeline design parameters, pipeline operation route and pipeline maintenance data; the pipeline test data includes pipeline material mechanical property test results and fatigue crack growth test results; specifically, the submarine pipeline design parameters of the submarine pipeline to be predicted are shown in Table 1 below.

[0105] Table 1 Design parameters of submarine pipelines to be predicted

[0106]

[0107] Step 2: According to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted, the pipeline corrosion-fatigue dynamic degradation process is divided into four basic states; among them, the submarine pipeline corrosion-fatigue mechanism is clarified, and according to the material state change process, the pipeline corrosion-fatigue dynamic degradation process is divided into four basic states, including the corrosion pit initiation state, the corrosion pit to short crack extension state, the short crack to long crack extension state and the long crack extension state.

[0108] As attached Figure 2 As shown, in this embodiment, the specific division process of the four states includes the following steps:

[0109] Step 21: Considering the corrosion environment of the submarine pipeline and the thickness of the pipeline corrosion layer, the initial anti-corrosion layer rupture and corrosion pit formation stage of the pipeline are divided into the corrosion pit initiation state.

[0110] Step 22: Based on the corrosion pit initiation state and considering the wave and current effects in the marine environment, the lateral growth stage of the pipeline corrosion pit and the short crack development stage are divided into the corrosion pit to short crack extension state.

[0111] Step 23: Based on the state of corrosion pits extending into short cracks, the crack extension mode is considered and the development stage of crack length from short to long is divided into the state of short cracks extending into long cracks.

[0112] Step 24: Based on the state of short crack extension to long crack, the entire long crack extension stage is divided into the long crack extension state in consideration of the final pipeline failure state.

[0113] Step 3: Based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, the EIFS distribution function is introduced to determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline.

[0114] The specific steps include:

[0115] Step 31, design multiple fatigue tests, and fit the number of stress-fracture cycles under each test environment condition according to the collected pipeline test data of the submarine pipeline to be predicted, so as to obtain the fatigue limit test value and stress intensity factor threshold test value under the corresponding test environment condition.

[0116] Step 32: Determine the functional distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted according to the fatigue limit test value and the stress intensity factor threshold test value.

[0117] Step 33: According to the function distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; wherein the corrosion-fatigue reliability degradation process state of the submarine pipeline is the crack propagation process state based on the initial defect size.

[0118] Step 4: Combining the equivalent initial defect size and the Paris formula, according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, a dynamic Bayesian network is used to construct a submarine pipeline crack propagation model; and based on the submarine pipeline crack propagation model, the submarine pipeline crack failure probability curve is obtained.

[0119] The specific steps include:

[0120] Step 41, simplifying the Paris formula and extracting function variables according to the equivalent initial defect size to obtain a simplified Paris formula; wherein the equivalent initial defect size is an equivalent initial defect size EIFS function distribution.

[0121] Among them, the simplified Paris formula is as follows:

[0122]

[0123] Among them, da / dN is the crack extension; a is the crack depth; N is the number of external load cycles; ΔK is the range of stress intensity factor variation; C and m are material parameters, which obey lognormal distribution and normal distribution respectively;

[0124] Among them, the stress intensity factor variation range ΔK is related to the geometric shape of the submarine pipeline to be predicted; for the flat plate model with a crack in the center, the stress intensity factor variation range ΔK is described as:

[0125]

[0126] Where Δσ is the far-field stress amplitude, and Y(a) is the geometric function related to the crack depth;

[0127] Assuming that the geometric function Y(a) related to the crack depth is independent of the crack depth a, and the far-field stress amplitude Δσ obeys the Weibull distribution, the crack depth a is expressed as:

[0128]

[0129] Where a0 is the initial crack size; A is the scale parameter of the Weibull distribution; B is the shape parameter of the Weibull distribution; Γ(·) is the gamma function; the far-field stress amplitude Δσ is described by the Weibull distribution, which is expressed as:

[0130]

[0131] Step 42: Select the Bayesian function node of the dynamic Bayesian network and construct a submarine pipeline crack propagation model according to the simplified Paris formula, as shown in the attached figure. Figure 3 shown.

[0132] Step 43: According to the submarine pipeline crack growth model, the submarine pipeline reliability degradation curve is calculated to obtain the pipeline crack growth size probability under the time coordinate, that is, the submarine pipeline crack failure probability defect is obtained, as shown in the attached figure. Figure 4 As shown; among them, Figure 4 The calculation result diagram of the crack propagation probability of the submarine pipeline in the embodiment is given in FIG. Figure 4 It can be seen that in the fourth year of operation, the expansion crack will expand at a higher rate, and the long crack expansion rate will accelerate significantly in the eighth year; after 10 years of operation, the degree of damage will increase significantly, and the failure probability will be about 0.1; if no repair is carried out, the failure probability will increase to nearly 0.5 in about the 15th year of operation; after the 20th year of operation, the structure is very close to a failure event. In addition, the probability of smaller crack size gradually decreases with time. In contrast, the crack size grows faster from the 6th to the 8th year; the main reason is that this period is the competition stage between corrosion pit expansion and fatigue crack expansion. The growth rate of corrosion pits gradually decreases due to the accumulation of corrosion products, and then gradually increases due to the severe cracking of the anti-corrosion layer; with the expansion of corrosion cracks and the cyclic action of sea waves, the increase in fatigue crack growth rate exceeds the corrosion growth rate in a short time; in addition, with the increase of erosion depth, the fatigue crack growth rate is faster than the corrosion crack growth rate, and the time for short cracks to transform into long cracks is earlier.

[0133] In this embodiment, a Bayesian function node is selected, and a submarine pipeline crack propagation model is constructed according to the distribution of the equivalent initial defect size EIFS function and the parameters of the corresponding pipeline; wherein the distribution of the equivalent initial defect size EIFS function and the parameters of the corresponding pipeline are shown in Table 2 below.

[0134] Table 2 Equivalent initial defect size EIFS function distribution and corresponding pipeline parameters

[0135]

[0136] Step 5: Based on the EIFS parameter distribution function and the simplified Paris formula, select the Bayesian function node, discretize the crack extension size and select the Paris formula parameter distribution to build the crack extension model of the aged submarine pipeline, as shown in the attached figure. Figure 5 As shown; among them, Figure 5 The probability distribution diagram of crack size in different time periods in the embodiment is given in FIG. Figure 5It can be seen that with the expansion of cracks and the increase of service time, the probability of smaller cracks gradually decreases, and the probability of larger cracks gradually increases; the probability of cracks around 2.13 mm does not change with time, and the critical crack size does not change much with time. This is because when the crack growth rate is high, the hydrogen supply in the crack tip area is insufficient, resulting in a short-term recovery of the material's fracture resistance; in addition, the large crack stage is more sensitive to time, and its growth rate is much greater than that of small cracks; in the low-speed crack growth stage, crack growth is mainly affected by microstructure and corrosive environment; under the action of stress concentration, the later cracks will rapidly expand until they are destroyed, indicating that the effect of the corrosive environment on crack growth is not very important compared with the environmental load.

[0137] Step 6: Based on the crack failure probability curve of the submarine pipeline and the number of environmental load cycles, the crack extension limit state equation is constructed; and based on the crack extension limit state equation, the Monte Carlo method is used to calculate the submarine pipeline reliability degradation curve; specifically, considering the size of the pipeline penetration crack, the final size of the fatigue failure crack is set, and the crack extension limit state equation is constructed; the number of submarine cyclic loads of the pipeline is determined, and the submarine pipeline reliability degradation curve is calculated based on the Monte Carlo method, as shown in the attached figure. Figure 5 shown.

[0138] Step 7: According to the preset reliability threshold of the pipeline to be predicted and the reliability degradation curve of the submarine pipeline, the dynamic change result of the pipeline reliability during operation is obtained based on the time coordinate axis, and the operating life and key failure time nodes of the pipeline to be predicted are determined, that is, the submarine pipeline life prediction and life extension decision results are obtained, as shown in the attached figure. Figure 6 As shown; if the operating life and key failure time nodes of the pipeline to be predicted exceed the preset reliability threshold of the pipeline to be predicted, the pipeline to be predicted needs to be replaced, otherwise the submarine pipeline life prediction and life extension decision results are output; in this embodiment, the dynamic change results of pipeline reliability during operation are obtained based on the time coordinate axis, providing theoretical and technical support for the life prediction and life extension decision of aging pipelines; as shown in the attached Figure 6 The following is a graph showing the life prediction and life extension decision results of the submarine pipeline in the embodiment. Figure 6It can be seen that as the pipeline was initially exposed to seawater, a passivation layer formed on its surface to resist further corrosion; but the combined effect of chloride ions and salt layers provided a favorable environment for the prosperity of submarine microbial activities, leading to the formation of metal cracks; free chloride ions in seawater gradually penetrated the protective film, causing cracks and pitting, and further formed corrosion and pitting development. After pitting caused microcracks, the decline trend of pipeline reliability over time accelerated; according to the dynamic prediction results of this embodiment, the pipeline will enter a rapid degradation period after 10 years, and the decline rate will reach a peak after 18 years; although most submarine pipelines are coated to prevent corrosion and stress corrosion cracking, regular inspections are still required after 8 years of operation to ensure that the corrosion protection system continues to work. Once the crack propagation rate accelerates, the probability of pipeline damage will increase greatly; in the later stage of operation, some modifications and additions to the integrity management measures of the pipeline may be required; the design life of the pipeline in this embodiment is 20 years, and the operating reliability threshold is 0.35. It can be seen that although the design life of the pipeline is close, the reliability is high and it can still operate for about 5-8 years. The results of the reliability prediction model are in good agreement with the actual operational reliability degradation process, which proves the effectiveness of the method.

[0139] The submarine pipeline life prediction and life extension decision method described in this embodiment is aimed at aging submarine pipelines, and takes into account the equivalent initial defect size and the simplification of the corrosion-fatigue model, and proposes an aging submarine pipeline life prediction and life extension decision method with pipeline reliability degradation. First, the corrosion-fatigue high-risk pipeline of the aging submarine pipeline to be predicted is selected, and relevant operation status data and pipeline test data are collected; secondly, considering the interaction between corrosion and fatigue of the outer pipe material of the aging submarine pipeline, the pipeline corrosion-fatigue dynamic degradation process is divided into four basic states; based on the EIFS distribution function, the equivalent initial defect size and function fitting parameters are determined, and the aging submarine pipeline is simplified. The corrosion-fatigue reliability degradation process of submarine pipelines; based on the equivalent initial defect size and the Paris formula, the dynamic Bayesian method is used to construct the crack growth model of aged submarine pipelines and obtain the corrosion-fatigue crack failure probability curve; based on the corrosion-fatigue crack failure probability curve and the number of environmental load cycles, the crack growth limit state equation is constructed, and the Monte Carlo method is used to calculate the reliability degradation curve of aged submarine pipelines; finally, based on the reliability threshold and reliability degradation curve of aged submarine pipelines, the pipeline operating life and key failure time nodes are predicted, providing theoretical and technical support for the life prediction and life extension decision of aged pipelines.

[0140] The description of the relevant parts of the submarine pipeline life prediction and life extension decision system, device and computer-readable storage medium provided in this embodiment can refer to the detailed description of the corresponding parts in the submarine pipeline life prediction and life extension decision described in this embodiment, and will not be repeated here.

[0141] The submarine pipeline life prediction and life extension decision-making method described in the present invention establishes a crack growth model for aged submarine pipelines based on fracture mechanics, considers the randomness of variables in the crack growth model, identifies the crack depth probability distribution based on the Bayesian network, calculates the change law of the aged pipeline reliability with the service time, and provides support for the aged submarine pipeline life prediction and life extension decision-making.

[0142] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. A method for predicting the life of a submarine pipeline and making a decision on its life extension, characterized in that: The following steps are involved: Select the submarine pipeline to be predicted, and collect the operation status data and pipeline test data of the submarine pipeline to be predicted; According to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is determined; Based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, the EIFS distribution function is introduced to determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; Combining the equivalent initial defect size and the Paris formula, according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, a dynamic Bayesian network is used to construct a submarine pipeline crack growth model; and based on the submarine pipeline crack growth model, the submarine pipeline crack failure probability curve is obtained; Based on the crack failure probability curve of submarine pipeline and the number of environmental load cycles, the crack extension limit state equation is constructed; and based on the crack extension limit state equation, the Monte Carlo method is used to calculate the submarine pipeline reliability degradation curve; According to the preset reliability threshold of the pipeline to be predicted and the reliability degradation curve of the submarine pipeline, the operating life and key failure time nodes of the pipeline to be predicted are determined, that is, the submarine pipeline life prediction and life extension decision results are obtained.

2. A submarine pipeline life prediction and life extension decision-making method according to claim 1, characterized in that: The operating status of the submarine pipeline to be predicted includes submarine pipeline design parameters, pipeline operation routes and pipeline maintenance data; the pipeline test data includes pipeline material mechanical properties test results and fatigue crack growth test results.

3. A submarine pipeline life prediction and life extension decision-making method according to claim 1, characterized in that: The corrosion-fatigue dynamic degradation process states of the submarine pipeline to be predicted include the corrosion pit initiation state, the corrosion pit extension state to the short crack extension state, the short crack extension state to the long crack extension state and the long crack extension state.

4. A submarine pipeline life prediction and life extension decision-making method according to claim 3, characterized in that: According to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted, the process of determining the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is as follows: According to the corrosion environment of submarine pipelines and the thickness of pipeline corrosion layer, the initial anti-corrosion layer rupture and corrosion pit formation stage of pipelines are generally divided into the corrosion pit initiation state; Based on the corrosion pit initiation state and combined with the wave and current effect in the marine environment of the submarine pipeline to be predicted, the lateral growth stage of the pipeline corrosion pit and the short crack development stage of the submarine pipeline to be predicted are divided into the corrosion pit to short crack extension state. Based on the state of corrosion pits extending into short cracks and considering the crack extension mode, the development stage of crack length from short to long is divided into the state of short cracks extending into long cracks. Based on the state of short crack extension to long crack and considering the final pipeline failure state, the long crack extension stage is divided into the long crack extension state as a whole.

5. The method for predicting and extending the life of a submarine pipeline according to claim 1, characterized in that: Based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, the EIFS distribution function is introduced to determine the equivalent initial defect size and function fitting parameters; and according to the determined equivalent initial defect size and function fitting parameters, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the process of the corrosion-fatigue reliability degradation process state of the submarine pipeline, as follows: According to the collected pipeline test data of the submarine pipeline to be predicted, the number of stress fracture cycles under each pipeline test environment condition is fitted to obtain the fatigue limit test value and stress intensity factor threshold test value under the corresponding test environment condition; Determine the functional distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted according to the fatigue limit test value and the stress intensity factor threshold test value under the corresponding test environment conditions; According to the function distribution of the equivalent initial defect size EIFS of the submarine pipeline to be predicted, the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted is simplified to obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; wherein the corrosion-fatigue reliability degradation process state of the submarine pipeline is the crack propagation process state based on the initial defect size.

6. A submarine pipeline life prediction and life extension decision-making method according to claim 1, characterized in that: Combining the equivalent initial defect size and the Paris formula, according to the corrosion-fatigue reliability degradation process state of the submarine pipeline, a submarine pipeline crack growth model is constructed using a dynamic Bayesian network; and according to the submarine pipeline crack growth model, the process of obtaining the submarine pipeline crack failure probability curve is as follows: According to the equivalent initial defect size, the Paris formula is simplified and the function variables are extracted to obtain the simplified Paris formula; The Bayesian function nodes of the dynamic Bayesian network are selected, and the submarine pipeline crack propagation model is constructed according to the simplified Paris formula; According to the submarine pipeline crack growth model, the submarine pipeline reliability degradation curve is calculated to obtain the pipeline crack growth size probability under the time coordinate, that is, the submarine pipeline crack failure probability defect is obtained.

7. A submarine pipeline life prediction and life extension decision-making method according to claim 6, characterized in that: The simplified Paris formula is as follows: Among them, da / dN is the crack extension; a is the crack depth; N is the number of external load cycles; ΔK is the range of stress intensity factor variation; C and m are material parameters, which obey lognormal distribution and normal distribution respectively; Among them, the stress intensity factor variation range ΔK is related to the geometric shape of the submarine pipeline to be predicted; for the flat plate model with a crack in the center, the stress intensity factor variation range ΔK is described as: Where Δσ is the far-field stress amplitude, and Y(a) is the geometric function related to the crack depth; Assuming that the geometric function Y(a) related to the crack depth is independent of the crack depth a, and the far-field stress amplitude Δσ obeys the Weibull distribution, the crack depth a is expressed as: Where a0 is the initial crack size; A is the scale parameter of the Weibull distribution; B is the shape parameter of the Weibull distribution; Γ(·) is the gamma function; the far-field stress amplitude Δσ is described by the Weibull distribution, which is expressed as:

8. A submarine pipeline life prediction and life extension decision system, characterized in that: include: A collection module is used to select the submarine pipeline to be predicted and collect the operation status data and pipeline test data of the submarine pipeline to be predicted; A state description module is used to determine the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the interaction between corrosion and fatigue of the outer pipe material of the submarine pipeline to be predicted; The degradation process simplification module is used to introduce the EIFS distribution function based on the collected operating status data and pipeline test data of the submarine pipeline to be predicted, determine the equivalent initial defect size and function fitting parameters; and simplify the corrosion-fatigue dynamic degradation process state of the submarine pipeline to be predicted according to the determined equivalent initial defect size and function fitting parameters, and obtain the corrosion-fatigue reliability degradation process state of the submarine pipeline; The failure probability curve module is used to combine the equivalent initial defect size and the Paris formula, and use the dynamic Bayesian network to build a submarine pipeline crack growth model according to the submarine pipeline corrosion-fatigue reliability degradation process state; and obtain the submarine pipeline crack failure probability curve based on the submarine pipeline crack growth model; The reliability curve module is used to construct the crack extension limit state equation based on the crack failure probability curve of the submarine pipeline and the number of environmental load cycles; and according to the crack extension limit state equation, the Monte Carlo method is used to calculate the submarine pipeline reliability degradation curve; The result output module is used to determine the operating life and key failure time nodes of the pipeline to be predicted according to the preset reliability threshold of the pipeline to be predicted and the reliability degradation curve of the submarine pipeline, that is, to obtain the submarine pipeline life prediction and life extension decision results.

9. A submarine pipeline life prediction and life extension decision-making device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of predicting the life of a submarine pipeline and making a life extension decision as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of predicting the life of a submarine pipeline and making a life extension decision are implemented as described in any one of claims 1 to 7.

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