Oil and gas pipeline quantitative risk calculation method

Abstract

The application discloses an oil and gas pipeline quantitative risk calculation method, and particularly relates to the technical field of pipeline risk calculation; based on multi-source monitoring data collected during the operation of an oil and gas pipeline, a multi-source monitoring data set is constructed, fluid disturbance characteristic data and corrosion defect topography data are extracted through frequency domain transformation and spatial geometry analysis, and an oil and gas pipeline inner wall shear stress intensity sequence and a corrosion defect evolution sequence are respectively established; a scour-corrosion coupling feedback equation is constructed based on the oil and gas pipeline inner wall shear stress intensity sequence and the corrosion defect evolution sequence, and is iteratively solved to obtain a scour-corrosion coupling risk factor; in combination with the limit design stress of the oil and gas pipeline, the operation risk index of the oil and gas pipeline is calculated, and the risk grade of the oil and gas pipeline is output, so that the precision and adaptability of quantitative risk calculation under the operation state of the oil and gas pipeline are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline risk calculation, and more particularly to an oil and gas pipeline quantitative risk calculation method. BACKGROUND

[0002] During long-term operation, the inner wall of the oil and gas pipeline will gradually form corrosion defects of different degrees due to the corrosion of the transported medium and the influence of complex flow state. The existing oil and gas pipeline quantitative risk calculation method is generally based on the structural static integrity and the flow model under stable working conditions, and usually ignores the interactive coupling effect between the unsteady flow state and the local corrosion defects of the inner wall of the pipeline. SUMMARY

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an oil and gas pipeline quantitative risk calculation method to solve the problems raised in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0005] An oil and gas pipeline quantitative risk calculation method, comprising the following steps:

[0006] S1: Real-time acquisition of multi-source pipeline monitoring data during operation of the oil and gas pipeline, and addition of a unified time stamp label to construct a time-synchronized multi-source monitoring data set;

[0007] S2: Fluid disturbance spectrum feature extraction and corrosion morphology feature extraction are performed on the multi-source monitoring data set to generate fluid disturbance feature data and corrosion defect morphology data;

[0008] S3: Based on the fluid disturbance feature data, the spatial distribution of the shear stress of the inner wall of the oil and gas pipeline is analyzed, and a shear stress intensity sequence is outputted;

[0009] S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defect of the inner wall of the oil and gas pipeline, and a corrosion defect evolution sequence is outputted;

[0010] S5: According to the shear stress intensity sequence and the corrosion defect evolution sequence, a scour-corrosion coupling feedback equation is constructed and iteratively solved to output a scour-corrosion coupling risk factor;

[0011] S6: According to the scour-corrosion coupling risk factor and the limit design stress of the oil and gas pipeline, the operation risk index of the oil and gas pipeline is calculated, and the risk level of the oil and gas pipeline is outputted.

[0012] In a preferred embodiment, S1, specifically:

[0013] Real-time acquisition of multi-source pipeline monitoring data during the operation of the oil and gas pipeline, including transient flow rate data of the fluid inside the pipeline, transient pressure data of the fluid, depth distribution data of the corrosion defects on the inner wall of the oil and gas pipeline, and point cloud data of the geometric morphology of the corrosion defects on the inner wall of the oil and gas pipeline;

[0014] A uniform time stamp label is attached to each type of pipeline monitoring data to form multi-source pipeline monitoring data with a uniform time marker.

[0015] Based on the multi-source pipeline monitoring data with a uniform time marker, each type of pipeline monitoring data is time-synchronized according to the uniform time stamp label to generate a multi-source monitoring data set after time synchronization.

[0016] In a preferred embodiment, S2 specifically includes:

[0017] The transient flow rate data and transient pressure data of the fluid inside the oil and gas pipeline in the multi-source monitoring data set are subjected to frequency domain transformation to extract disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters of the fluid, thereby generating fluid disturbance feature data of the oil and gas pipeline.

[0018] The depth distribution data of the corrosion defects on the inner wall of the oil and gas pipeline and the point cloud data of the geometric morphology of the corrosion defects on the inner wall of the oil and gas pipeline in the multi-source monitoring data set are subjected to spatial geometric analysis to extract depth feature parameters, geometric shape feature parameters, and spatial distribution feature parameters of the corrosion defects on the inner wall of the oil and gas pipeline, thereby generating corrosion defect morphology data of the inner wall of the oil and gas pipeline.

[0019] In a preferred embodiment, S3 specifically includes:

[0020] Based on the fluid disturbance feature data of the oil and gas pipeline, the shear stress generated by fluid disturbance at different spatial positions of the inner wall of the oil and gas pipeline is calculated.

[0021] According to the shear stress generated by fluid disturbance at different spatial positions of the inner wall of the oil and gas pipeline, the spatial distribution law of the shear stress of the inner wall of the oil and gas pipeline along the axial and circumferential directions of the pipeline is determined.

[0022] According to the spatial distribution law of the shear stress of the inner wall of the oil and gas pipeline, the shear stress intensity of the inner wall of the oil and gas pipeline is time-series statistically analyzed to generate a shear stress intensity sequence of the inner wall of the oil and gas pipeline.

[0023] In a preferred embodiment, S4 specifically includes:

[0024] Based on the corrosion defect morphology data of the inner wall of the oil and gas pipeline, the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline is calculated through a corrosion defect evolution model of the inner wall of the oil and gas pipeline.

[0025] According to the expansion rate of the corrosion defect on the inner wall of the oil and gas pipeline, the expansion trend of the corrosion defect on the inner wall of the oil and gas pipeline along the axial and circumferential directions of the pipeline is determined;

[0026] According to the expansion trend of the corrosion defect on the inner wall of the oil and gas pipeline along the axial and circumferential directions of the pipeline, an evolution sequence of the corrosion defect on the inner wall of the oil and gas pipeline is generated.

[0027] In a preferred embodiment, S5, specifically:

[0028] Based on the shear stress intensity sequence of the inner wall of the oil and gas pipeline and the evolution sequence of the corrosion defect on the inner wall of the oil and gas pipeline, a scour-corrosion coupling feedback equation is established.

[0029] By iteratively solving the scour-corrosion coupling feedback equation, a scour-corrosion coupling risk factor of the oil and gas pipeline is generated.

[0030] In a preferred embodiment, S6, specifically:

[0031] Based on the scour-corrosion coupling risk factor of the oil and gas pipeline and the limit design stress of the oil and gas pipeline, a strength check calculation method is used to calculate the operation risk index of the oil and gas pipeline.

[0032] A risk level division threshold is preset, and the operation risk index of the oil and gas pipeline is compared with the risk level division threshold to determine the risk level of the oil and gas pipeline.

[0033] The technical effects and advantages of the oil and gas pipeline quantitative risk calculation method of the present application are:

[0034] Through real-time acquisition and unified time synchronization of multi-source pipeline monitoring data, the overall and timely grasp of the pipeline operation state is ensured; spectrum analysis and spatial geometry extraction are used to effectively separate fluid disturbance characteristic data and corrosion defect morphology data, improving the accuracy of feature expression; based on the fluid disturbance characteristic data, a shear stress intensity sequence is calculated, providing a quantitative basis for revealing the local scouring effect under the action of fluid non-steady state; the corrosion evolution model is used to perform time-varying prediction on the corrosion defect morphology data, accurately simulating the expansion rate of the corrosion defect and improving the predictability of the defect development; by constructing and iteratively solving the scour-corrosion coupling feedback equation, the nonlinear relationship between shear stress and corrosion expansion is quantitatively described; based on the scour-corrosion coupling risk factor and the limit design stress of the oil and gas pipeline, the operation risk index of the oil and gas pipeline is calculated, and the risk level of the oil and gas pipeline is output, improving the reliability and accuracy of quantitative risk calculation and effectively reducing the uncertainty of oil and gas pipeline operation and maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 The schematic diagram of the oil and gas pipeline quantitative risk calculation method of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0037] Embodiment

[0038] Figure 1 A quantitative risk calculation method for oil and gas pipelines is given, which comprises the following steps:

[0039] S1: Real-time acquisition of multi-source pipeline monitoring data during the operation of the oil and gas pipeline, and addition of a unified time stamp label to construct a time-synchronized multi-source monitoring data set;

[0040] S2: Fluid disturbance spectrum feature extraction and corrosion morphology feature extraction are performed on the multi-source monitoring data set to generate fluid disturbance feature data and corrosion defect morphology data;

[0041] S3: Based on the fluid disturbance feature data, the spatial distribution of the shear stress of the inner wall of the oil and gas pipeline is analyzed, and a shear stress intensity sequence is output;

[0042] S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defect of the inner wall of the oil and gas pipeline, and a corrosion defect evolution sequence is output;

[0043] S5: According to the shear stress intensity sequence and the corrosion defect evolution sequence, a scouring-corrosion coupling feedback equation is constructed and iteratively solved to output a scouring-corrosion coupling risk factor;

[0044] S6: According to the scouring-corrosion coupling risk factor and the limit design stress of the oil and gas pipeline, the operation risk index of the oil and gas pipeline is calculated and the risk level of the oil and gas pipeline is output.

[0045] S1: Real-time acquisition of multi-source pipeline monitoring data during the operation of the oil and gas pipeline, and addition of a unified time stamp label to construct a time-synchronized multi-source monitoring data set, comprising:

[0046] Real-time acquisition of multi-source pipeline monitoring data during the operation of the oil and gas pipeline;

[0047] The multi-source pipeline monitoring data includes pipeline internal fluid transient flow rate data, fluid transient pressure data, oil and gas pipeline inner wall corrosion defect depth distribution data, and oil and gas pipeline inner wall corrosion defect geometry point cloud data. The pipeline internal fluid transient flow rate data refers to the instantaneous fluid flow rate measured by a flow rate sensor at different pipeline spatial positions during the operation of the oil and gas pipeline. The fluid transient pressure data refers to the pipeline internal fluid pressure measured by a pressure sensor installed on the inner wall of the pipeline in real time during the operation of the oil and gas pipeline. The oil and gas pipeline inner wall corrosion defect depth distribution data refers to the depth distribution of the corrosion pits or corrosion regions formed on the surface of the inner wall of the oil and gas pipeline due to the long-term corrosion of the oil and gas medium. For example, a high-precision ultrasonic sensor or an electromagnetic non-destructive testing device is used to scan and detect the inner wall of the oil and gas pipeline, and the corrosion defect depth of the inner wall of the pipeline at different positions is collected in real time to form the corrosion defect depth distribution data. The point cloud data of the geometry of the oil and gas pipeline inner wall corrosion defect is the spatial geometry information of the corrosion pits or corrosion regions of the inner wall of the oil and gas pipeline collected in real time by a three-dimensional laser scanning device or a high-resolution optical measurement device, including the geometric size, spatial position coordinates and three-dimensional structure of the corrosion defect. For example, there are multiple corrosion pits on the inner wall of a certain pipe section of the oil and gas pipeline, and a high-resolution laser scanner is used to scan the corrosion pits to obtain the three-dimensional coordinate point cloud data set of each corrosion pit to accurately reflect the topographic features of the corrosion defect.

[0048] A uniform time stamp label is attached to each type of pipeline monitoring data to form multi-source pipeline monitoring data with uniform time markers.

[0049] The uniform time stamp label refers to recording accurate collection time information for each collection data point by using a high-precision time synchronization device at the same time when collecting each type of pipeline monitoring data. For example, the flow rate value measured by the flow rate data collection point at 14:01:00,000 milliseconds is 3.5 meters per second, which is recorded as "flow rate value 3.5 meters per second, collection time 14:01:00,000 milliseconds"; the depth value measured by the corrosion depth collection point at 14:01:00,005 milliseconds is 1.25 millimeters, which is recorded as "depth value 1.25 millimeters, collection time 14:01:00,005 milliseconds". Each type of data is uniformly marked by using the same time stamp recording method, thereby realizing the unified recording of the data in the time dimension.

[0050] Based on the multi-source pipeline monitoring data with uniform time markers, each type of pipeline monitoring data is time-synchronized according to the uniform time stamp label to generate a multi-source monitoring data set after time synchronization.

[0051] Time synchronization refers to using the unified timestamp label of each pipeline monitoring data to time-align each pipeline monitoring data according to the set unified time interval (such as 0.01 seconds). For example, under the unified time reference of 0.01 second interval, the interpolation, resampling or interpolation processing is respectively performed on the pipeline internal fluid transient flow rate data, the fluid transient pressure data, the oil and gas pipeline internal wall corrosion defect depth distribution data and the oil and gas pipeline internal wall corrosion defect geometry topography point cloud data to obtain the multi-source pipeline monitoring data with consistent time step at the same time point, and to form the time-synchronized multi-source monitoring data set with completely aligned data time.

[0052] S2: fluid disturbance frequency spectrum feature extraction and corrosion topography feature extraction are performed on the multi-source monitoring data set to generate fluid disturbance feature data and corrosion defect topography data, including:

[0053] The oil and gas pipeline internal fluid transient flow rate data and the fluid transient pressure data in the multi-source monitoring data set are subjected to frequency domain transformation to extract the fluid disturbance frequency spectrum distribution parameters and the disturbance intensity spectrum distribution parameters, and to generate the oil and gas pipeline internal fluid disturbance feature data;

[0054] The frequency domain transformation refers to using the Fourier transform method to convert the time-synchronized oil and gas pipeline internal fluid transient flow rate data and the fluid transient pressure data from the time domain numerical sequence to the frequency domain numerical sequence to extract the frequency features hidden in the pipeline internal fluid transient changes.

[0055] For example, the fluid transient flow rate data and the fluid transient pressure data measured by the flow rate sensor and the pressure sensor installed in a certain pipe section of the oil and gas pipeline for a length of 100 seconds are subjected to frequency domain analysis. First, the total of 10,000 data points obtained at a sampling interval of 0.01 seconds within 100 seconds are subjected to fast Fourier transform algorithm for numerical transformation to obtain the corresponding frequency spectrum results. By processing the frequency spectrum results, the disturbance characteristics of the oil and gas pipeline internal fluid at different frequency points can be identified, wherein the frequency spectrum analysis results are represented in the form of frequency spectrum graph.

[0056] The frequency spectrum graph can show the relationship between frequency distribution and amplitude size, wherein each frequency point corresponds to a disturbance intensity. For example, during the actual operation of a certain section of the oil and gas pipeline, it is found through frequency domain transformation that there are disturbance energy peaks at 0.2 Hz, 1.0 Hz and 5.0 Hz frequency points. The three frequency points are recorded as disturbance frequency spectrum distribution parameters, and the corresponding energy peaks are recorded as disturbance intensity spectrum distribution parameters, and then the disturbance frequency spectrum distribution parameters and the disturbance intensity spectrum distribution parameters are used to completely express the disturbance characteristic state of the pipeline internal fluid. The frequency spectrum distribution parameters and the disturbance intensity spectrum distribution parameters extracted from the collected pipeline internal fluid transient flow rate data and the fluid transient pressure data constitute the oil and gas pipeline internal fluid disturbance feature data.

[0057] The spatial geometry analysis is performed on the oil and gas pipeline inner wall corrosion defect depth distribution data and the point cloud data of the oil and gas pipeline inner wall corrosion defect geometry to extract the depth feature parameters, the geometric shape feature parameters and the spatial distribution feature parameters of the oil and gas pipeline inner wall corrosion defect, and to generate the oil and gas pipeline inner wall corrosion defect morphology data.

[0058] The spatial geometry analysis is performed on the spatial geometry analysis is performed on the oil and gas pipeline inner wall corrosion defect depth distribution data and the point cloud data of the oil and gas pipeline inner wall corrosion defect geometry to extract the depth feature parameters, the geometric shape feature parameters and the spatial distribution feature parameters of the oil and gas pipeline inner wall corrosion defect, and to generate the oil and gas pipeline inner wall corrosion defect morphology data.

[0059] For example, the corrosion pit area existing in a certain section of the oil and gas pipeline is analyzed, the different distribution conditions of the corrosion pit depth between 0.5 mm and 5 mm are measured by the ultrasonic detection equipment, and the corrosion defect depth distribution data are formed. The three-dimensional point cloud data of the corrosion area are collected by the three-dimensional laser scanner, including the three-dimensional spatial coordinates, the contour and the pit bottom shape information of the corrosion pit. The three-dimensional point cloud data and the corrosion depth data are processed by using the spatial geometry analysis method, such as fitting the corrosion pit contour by using the surface fitting method, and the geometric shape feature parameters of the corrosion pit are extracted from the fitting result, including the depth, the width, the length, the bottom radius or the taper angle of the corrosion pit.

[0060] The three-dimensional position distribution of the corrosion pit is quantitatively analyzed by using the spatial statistical analysis method to determine the spatial position distribution mode of the corrosion pit in the inner wall of the pipeline, such as the concentration parameters along the axial direction or the circumferential direction of the pipeline, the density or the sparsity of the corrosion defect area, etc. The parameters obtained by the three-dimensional geometry analysis include the depth feature parameters (such as the maximum depth of the corrosion pit is 5 mm, and the average depth is 2 mm), the geometric shape feature parameters (such as the average length of the corrosion pit is 20 mm, and the average width is 10 mm) and the spatial distribution feature parameters (such as the corrosion pit is distributed with an interval of 50 mm along the axial direction), which constitute the oil and gas pipeline inner wall corrosion defect morphology data, and accurately describe the state of the corrosion pit in the inner wall of the pipeline.

[0061] S3: Based on the fluid disturbance feature data, the spatial distribution of the shear stress of the oil and gas pipeline inner wall is analyzed, and a shear stress intensity sequence is output, including:

[0062] Based on the fluid disturbance feature data in the oil and gas pipeline, the shear stress of the oil and gas pipeline inner wall at different spatial positions of the pipeline due to the fluid disturbance is calculated;

[0063] The fluid disturbance feature data of the oil and gas pipeline interior includes disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters. The disturbance frequency spectrum distribution parameters record multiple frequency values of the fluid disturbance phenomenon in the oil and gas pipeline interior; the disturbance intensity spectrum distribution parameters record the energy size of the fluid disturbance at the corresponding frequency points, and represent the fluid disturbance intensity. The fluid state in the pipeline interior is non-steady and non-uniform.

[0064] The shear stress generated by the fluid disturbance at different spatial positions of the oil and gas pipeline inner wall is calculated, and fluid mechanics methods are used for analysis and calculation. The fluid mechanics methods include finite element calculation methods, finite volume calculation methods and computational fluid dynamics analysis methods, etc. to calculate the tangential force, i.e. shear stress, formed on the surface of the oil and gas pipeline inner wall due to fluid disturbance. For example, taking a 50-meter long section of the oil and gas pipeline as an example, the pipeline section is divided into several calculation grids along the axial and circumferential directions, and the spatial resolution of each grid is 0.1m x 0.1m. In the fluid region near the pipeline inner wall, the disturbance frequency spectrum distribution parameters and the disturbance intensity spectrum distribution parameters are input into the computational fluid dynamics analysis software, the flow field model is established, and the simulation calculation is performed to obtain the shear stress generated by the fluid action on the pipeline inner wall at each spatial grid position, and thus the accurate shear stress spatial distribution is obtained. For example, the calculation results show that the shear stress generated by the fluid disturbance is 3.5 Pa at a distance of 20 meters from the pipeline inlet and at a circumferential position of 90°, and the shear stress is 4.2 Pa at a distance of 40 meters from the pipeline inlet and at a circumferential position of 270°. Through calculation, the shear stress of the oil and gas pipeline inner wall at all concerned positions can be obtained.

[0065] According to the shear stress generated by the fluid disturbance at different spatial positions of the oil and gas pipeline inner wall, the spatial distribution law of the oil and gas pipeline inner wall shear stress along the axial and circumferential directions of the pipeline is determined;

[0066] The spatial distribution law of the shear stress on the inner wall of the oil and gas pipeline refers to the variation trend and law of the shear stress on the inner wall of the oil and gas pipeline at different axial positions and circumferential positions of the pipeline. By performing spatial statistics and distribution law analysis on the shear stress on the inner wall of the oil and gas pipeline obtained through calculation, the variation trend of the shear stress along the length direction (axial direction) of the pipeline and the circumferential direction (circumferential direction) of the pipeline cross section can be obtained. Taking the axial direction as an example, the 50-meter length of the oil and gas pipeline is divided into an axial interval of 5 meters each, and the shear stress at all circumferential positions in each interval is averaged and counted to obtain an axial distribution curve. For example, the average shear stress in the first 5-meter interval is 2.5 pascals, the average shear stress in the next 5-meter interval is 3.2 pascals, and so on, until the end of the pipeline section. Similarly, the circumferential distribution law is obtained by dividing the pipeline circumference of 360° into several equal circumferential intervals (for example, an interval of 30° each), and averaging and counting the shear stress at all axial positions in each circumferential interval to obtain a circumferential distribution curve. For example, the average shear stress at the circumferential position of 0°-30° is 3.0 pascals, and the average shear stress at the circumferential position of 180°-210° is 4.1 pascals. In this way, the spatial distribution law of the shear stress on the inner wall of the oil and gas pipeline along the axial and circumferential directions is obtained.

[0067] According to the spatial distribution law of the shear stress on the inner wall of the oil and gas pipeline, the shear stress intensity on the inner wall of the oil and gas pipeline is time-sequentially counted to generate a shear stress intensity sequence on the inner wall of the oil and gas pipeline;

[0068] The shear stress intensity sequence on the inner wall of the oil and gas pipeline refers to a data sequence formed by arranging the shear stress intensity on the inner wall of the oil and gas pipeline in time sequence. Based on the spatial distribution law of the shear stress, key spatial positions with relatively large shear stress values are selected for time-sequential analysis, such as positions every 5 meters along the axial direction of the pipeline and corresponding circumferential key positions. By recording and counting the shear stress at these positions over a relatively long continuous monitoring time (for example, 1 hour), a data sequence of the shear stress intensity changing with time is generated. For example, at the axial position of 10 meters and the circumferential position of 90°, the shear stress is recorded at an interval of 1 second within 1 hour, and 3600 shear stress data points are obtained, which are arranged in sequence to form a shear stress intensity sequence. Similarly, at the axial position of 30 meters and the circumferential position of 270°, a corresponding shear stress intensity sequence can also be obtained.

[0069] S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defect on the inner wall of the oil and gas pipeline, and an evolution sequence of the corrosion defect is output, including:

[0070] Based on the corrosion defect morphology data on the inner wall of the oil and gas pipeline, the expansion rate of the corrosion defect on the inner wall of the oil and gas pipeline is calculated through the corrosion defect evolution model on the inner wall of the oil and gas pipeline;

[0071] The oil and gas pipeline inner wall corrosion defect morphology data is composed of corrosion defect depth characteristic parameters, corrosion defect geometric shape characteristic parameters and corrosion defect spatial distribution characteristic parameters. The corrosion defect depth characteristic parameters refer to the depth of a corrosion pit or a corrosion region, such as the maximum depth and the average depth; the corrosion defect geometric shape characteristic parameters include the length, the width, the pit bottom radius, the profile shape, the bottom taper angle and the like of the corrosion pit; and the corrosion defect spatial distribution characteristic parameters include the distribution position, the distribution interval and the distribution mode of the corrosion pit in the axial direction and the circumferential direction of the pipeline.

[0072] The oil and gas pipeline inner wall corrosion defect evolution model refers to a mathematical model established based on the basic principles of the corrosion process, used to describe the expansion and evolution law of the corrosion defect on the inner wall of the oil and gas pipeline. The model is established based on the principles of electrochemical corrosion dynamics, material corrosion mechanism and the interaction process between the fluid and the pipe wall, and realizes the quantitative calculation of the corrosion defect expansion rate by establishing the mathematical relationship between the corrosion rate and the external environmental conditions (such as temperature, flow rate, fluid composition) and the internal material conditions (such as pipeline material, surface treatment process). For example, taking the pitting corrosion pit on the inner wall of the pipeline as an example, the corrosion defect evolution model can be based on the pitting corrosion kinetic equation, adopt the mathematical expression of the change of the pitting corrosion depth with time, combine the initial depth of the pit and the geometric size parameters of the pit measured in the corrosion defect morphology data, and obtain the expansion rate of the corrosion pit depth with time through calculation. For example, the depth of the corrosion pit is 2 millimeters at the initial measurement time, and the corrosion defect evolution model is calculated under the conditions of a temperature of 40 degrees Celsius and a fluid flow rate of 3 meters per second, and the depth of the corrosion pit is increased by 0.02 millimeters per hour, that is, the corrosion defect expansion rate is 0.02 millimeters per hour.

[0073] According to the expansion rate of the oil and gas pipeline inner wall corrosion defect, the expansion trend of the oil and gas pipeline inner wall corrosion defect in the axial direction and the circumferential direction of the pipeline is determined;

[0074] The axial and circumferential extension trend of the corrosion defect on the inner wall of the oil and gas pipeline refers to the extension law and trend of each corrosion defect in the spatial position, including the position where the corrosion defect has a faster extension speed, the position where the corrosion defect has a slower extension speed, the variation law of the extension speed, and the variation mode of the corrosion defect in the space. For example, taking the axial extension trend as an example, first, a certain length of 100 meters of the oil and gas pipeline is divided into multiple axial position intervals, and the length of each interval is 10 meters. The extension rate of all corrosion pits in each axial interval is statistically analyzed. Assuming that the average extension rate of the corrosion defect in the 0-10 meter interval is 0.015 millimeters per hour, the average extension rate in the 10-20 meter interval is 0.022 millimeters per hour, and so on, until the end of the pipeline section. By comparing the extension rates in different intervals, the axial extension trend of the corrosion defect on the inner wall of the pipeline is determined, such as the trend that the extension rate gradually increases or decreases from the inlet to the outlet of the pipeline. Similarly, the circumferential extension trend can also be analyzed by segmenting and statistically analyzing the circumferential direction of the pipeline section. Assuming that the pipeline is divided into 8 equal intervals in the circumferential direction, and each circumferential interval is 45 degrees, the extension rate of the corrosion defect in each circumferential interval is counted, and the distribution of the corrosion extension rate in each circumferential interval is obtained, for example, the average extension rate in the 0-45 degree interval is 0.018 millimeters per hour, and the average extension rate in the 135-180 degree interval is 0.025 millimeters per hour, and then the circumferential corrosion defect extension trend is determined.

[0075] According to the axial and circumferential extension trend of the corrosion defect on the inner wall of the oil and gas pipeline, an evolution sequence of the corrosion defect on the inner wall of the oil and gas pipeline is generated;

[0076] The evolution sequence of the corrosion defect on the inner wall of the oil and gas pipeline refers to the time sequence data formed by recording and arranging the corrosion defect extension rate in a continuous time period at different spatial positions on the inner wall of the oil and gas pipeline according to the spatial extension trend of the corrosion defect. After determining the extension trend of the corrosion defect, a plurality of representative key corrosion positions (such as the axial positions of 20 meters, 50 meters, and 80 meters and their circumferential specific positions) are selected, and the extension of the corrosion defect at each position in a certain time range is continuously monitored and recorded. Taking the corrosion pit at the axial position of 20 meters as an example, the corrosion depth is recorded every 1 hour for a certain period of time (such as 100 consecutive hours), and 100 corrosion depth data points are obtained (such as an initial depth of 2 millimeters, a depth of 2.02 millimeters at the 1st hour, a depth of 2.04 millimeters at the 2nd hour, etc.), which are arranged in chronological order to form the evolution sequence of the corrosion defect at the spatial position. The same monitoring and data recording operation is performed for other key spatial positions of the pipeline to form a complete set of corrosion defect evolution sequence data. The corrosion defect evolution sequence data can intuitively represent the evolution process of the corrosion defect on the inner wall of the pipeline with time, and reflect the dynamic law of the corrosion defect extension.

[0077] S5: According to the shear stress intensity sequence and the corrosion defect evolution sequence, a scour-corrosion coupling feedback equation is constructed and iteratively solved, and a scour-corrosion coupling risk factor is output, including:

[0078] Based on the shear stress intensity sequence of the inner wall of the oil and gas pipeline and the corrosion defect evolution sequence of the inner wall of the oil and gas pipeline, a scour-corrosion coupling feedback equation is established;

[0079] The method for establishing the scour-corrosion coupling feedback equation is that the shear stress intensity sequence of the inner wall of the oil and gas pipeline and the corrosion defect evolution sequence of the inner wall of the oil and gas pipeline are used as initial input data, the action mechanism of the fluid disturbance on the corrosion expansion of the pipeline inner wall is theoretically deduced, and thus a mathematical expression capable of describing the interaction relationship between the corrosion defect expansion of the inner wall of the oil and gas pipeline and the fluid disturbance in the pipeline is established. Specifically, the scour-corrosion coupling feedback equation describes the direct influence of the shear stress intensity of the inner wall of the oil and gas pipeline on the corrosion pit expansion rate, and the counteraction relationship of the change of the corrosion pit depth on the change of the shear stress intensity of the inner wall of the pipeline. For example, the scour-corrosion coupling feedback equation can be expressed as: the corrosion defect depth change rate is equal to the function relationship between the corrosion expansion rate under the action of shear stress and the corrosion defect morphology factor, wherein the corrosion defect morphology factor is determined by the characteristic parameters such as the depth, width and bottom shape of the corrosion pit, and the corrosion expansion rate under the action of shear stress is directly controlled by the shear stress intensity. The scour-corrosion coupling feedback equation also includes the influence of the change of the corrosion pit depth on the change of the shear stress intensity, that is, as the corrosion pit depth increases, the local flow field disturbance intensifies, so that the local shear stress intensity also changes, thereby forming a two-way coupling feedback relationship.

[0080] By iteratively solving the scour-corrosion coupling feedback equation, a scour-corrosion coupling risk factor of the oil and gas pipeline is generated;

[0081] With the initial data of the sequence of the shear stress intensity on the inner wall of the oil and gas pipeline and the sequence of the corrosion defect evolution as the boundary conditions and initial conditions, the numerical calculation method is used to gradually approach the accurate solution of the scour-corrosion coupling feedback equation. For example, the finite difference iteration method is used to discretize the scour-corrosion coupling feedback equation, and the calculation is gradually performed at a certain time step (for example, 1 second per time step). By substituting the initial corrosion defect depth and the shear stress at the corresponding time into the scour-corrosion coupling feedback equation, the corrosion defect depth prediction value at the next time is first calculated, and then the corrosion defect depth prediction value is used as a condition to update the shear stress prediction value, and the calculation is gradually pushed forward, so as to perform the solution by iterative calculation. During the calculation process, the calculation step is adjusted according to the error calculated in the iteration process to ensure that the calculation result is accurate and stable. For example, at the initial time 0, the corrosion pit depth is 2 mm, and the local shear stress intensity is 3.5 Pa. The first iteration calculation obtains the corrosion pit depth of 2.000005 mm at the next second, and the corresponding shear stress intensity is updated to 3.5001 Pa. After continuous iteration, the stable and reliable corrosion defect depth prediction sequence and shear stress intensity prediction sequence are obtained.

[0082] The scour-corrosion coupling risk factor is a quantitative index generated by statistical analysis of the dynamic change relationship between the corrosion defect evolution sequence and the shear stress intensity sequence obtained by calculation after the iteration calculation is completed. The scour-corrosion coupling risk factor reflects the dynamic influence degree of the shear stress intensity on the inner wall of the oil and gas pipeline on the corrosion defect expansion, that is, the quantitative relationship between the corrosion defect expansion rate and the shear stress change rate caused by fluid disturbance within a certain time. For example, in 100 hours of continuous monitoring and calculation, the depth expansion speed of the corrosion pit and the local shear stress change amplitude present a high positive correlation, and a numerical index is obtained through statistical analysis. The higher the index value, the greater the influence of the shear stress disturbance on the corrosion defect expansion rate, thereby reflecting the more serious scour-corrosion coupling failure risk of the oil and gas pipeline at the position. For example, the risk factor value is between 0 and 1, and when the value is close to 1, it indicates a high coupling failure risk, and when the value is close to 0, it indicates a low coupling failure risk.

[0083] S6: According to the scour-corrosion coupling risk factor and the ultimate design stress of the oil and gas pipeline, the operating risk index of the oil and gas pipeline is calculated, and the risk level of the oil and gas pipeline is output, including:

[0084] Based on the scour-corrosion coupling risk factor of the oil and gas pipeline and the ultimate design stress of the oil and gas pipeline, the strength checking calculation method is used to calculate the operating risk index of the oil and gas pipeline;

[0085] The limit design stress of the oil and gas pipeline refers to the maximum allowable stress level that the pipeline can withstand during the design stage of the oil and gas pipeline, which is determined according to the performance parameters of the pipeline material, the design working conditions, the safety margin, and the requirements of the standard specifications. The limit design stress is usually determined by reducing the yield strength or tensile strength of the selected pipeline material by a certain safety factor.

[0086] The strength checking calculation method refers to a calculation method that uses mechanical calculation methods to convert the scour-corrosion coupling risk factor into a pipeline wall thickness loss rate or an equivalent stress increase value, and compares it with the limit design stress of the oil and gas pipeline to determine the pipeline operation risk index. The strength checking calculation method includes pipeline wall thickness residual strength calculation method, stress concentration factor method, and finite element numerical analysis method, etc. Taking the pipeline wall thickness residual strength calculation method as an example, first, the pipeline wall thinning amount or wall thickness loss ratio caused by corrosion expansion of the inner wall of the pipeline is calculated using the scour-corrosion coupling risk factor; then, the stress is calculated based on the residual wall thickness of the pipeline and the internal pressure, axial load, and external load conditions. For example, the original wall thickness of the pipeline is 10 mm, and the wall thickness loss reaches 2 mm after a certain period of operation according to the corrosion pit expansion rate calculation, so the residual wall thickness of the pipeline is 8 mm. The circumferential stress value and the axial stress value of the pipeline wall at this time are calculated using the pipeline mechanics formula. The total stress level actually borne by the pipeline during operation is calculated using the stress superposition method or the maximum stress criterion. The actual operating stress level calculated is compared with the limit design stress of the oil and gas pipeline to obtain the operating risk index of the oil and gas pipeline.

[0087] For example, the actual total stress of the pipeline after strength checking calculation is 310 MPa, and the limit design stress of the pipeline is 413.3 MPa, so the operating risk index of the pipeline is defined as 310 divided by 413.3, and the operating risk index obtained is 0.75. The closer the risk index is to 1, the closer the actual stress borne by the oil and gas pipeline during operation is to the limit design stress level, indicating that the operating risk of the oil and gas pipeline is higher.

[0088] The operating risk index of the oil and gas pipeline is compared with the risk level division threshold to determine the risk level of the oil and gas pipeline.

[0089] The risk level division threshold refers to a set of numerical critical values or intervals that are preset for dividing the operating risk level of the oil and gas pipeline. The risk level division threshold is determined according to the operating safety standards of the oil and gas pipeline, national or industry regulations, and engineering practice experience, and usually includes safe, low risk, medium risk, and high risk. For example, the preset risk level division threshold is: when the operating risk index is between 0 and 0.4, it is safe; between 0.4 and 0.6, it is low risk; between 0.6 and 0.8, it is medium risk; and between 0.8 and 1, it is high risk.

[0090] The running risk index is compared with a preset risk level division threshold value to determine the risk level to which the pipeline belongs. For example, the oil and gas pipeline running risk index is 0.75, and compared with the preset risk level division threshold value, according to the preset threshold value, 0.75 is located in the interval between 0.6 and 0.8, and it is determined that the oil and gas pipeline running risk level is a medium risk level, indicating that the pipeline has a certain operation safety risk, which needs to be closely monitored, and corresponding measures are taken in maintenance to reduce the risk level.

[0091] The above formulas are all dimensionless values, and the formulas are obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters and threshold values in the formulas are set by a person skilled in the art according to the actual situation.

[0092] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another by wired (for example, infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center and the like containing one or more available medium sets. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD) or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0093] Those skilled in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and module described above can refer to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0095] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, and can be electrical, mechanical or other forms.

[0096] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on a plurality of network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0097] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module.

[0098] If the functions are realized in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0099] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0100] Finally, the above merely provides the preferred embodiments of the present application, but is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for quantitative risk calculation of oil and gas pipelines, characterized in that, Includes the following steps: S1: Collect multi-source pipeline monitoring data in real time during the operation of oil and gas pipelines, and attach a unified timestamp label to build a time-synchronized multi-source monitoring dataset; S2: Extract fluid disturbance spectrum features and corrosion morphology features from the multi-source monitoring dataset to generate fluid disturbance feature data and corrosion defect morphology data; Frequency domain transformation is performed on the transient flow velocity data and transient pressure data of fluid inside oil and gas pipelines in the multi-source monitoring dataset to extract the disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters of the fluid, and generate disturbance characteristic data of fluid inside oil and gas pipelines. Spatial geometric analysis was performed on the point cloud data of the depth distribution data and geometric morphology data of the corrosion defects on the inner wall of oil and gas pipelines in the multi-source monitoring dataset. The depth feature parameters, geometric feature parameters and spatial distribution feature parameters of the corrosion defects on the inner wall of oil and gas pipelines were extracted to generate the morphology data of the corrosion defects on the inner wall of oil and gas pipelines. S3: Based on fluid disturbance characteristic data, analyze the spatial distribution of shear stress on the inner wall of oil and gas pipelines and output the shear stress intensity sequence; Based on the fluid disturbance characteristic data inside the oil and gas pipeline, the shear stress generated by the fluid disturbance on the inner wall of the oil and gas pipeline at different spatial locations is calculated. Based on the shear stress generated by fluid disturbance at different spatial locations of the oil and gas pipeline inner wall, the spatial distribution law of shear stress along the pipeline axial and circumferential directions is determined. Based on the spatial distribution law of shear stress on the inner wall of oil and gas pipelines, time-series statistics of shear stress intensity on the inner wall of oil and gas pipelines are performed to generate a sequence of shear stress intensity on the inner wall of oil and gas pipelines. S4: Based on corrosion defect morphology data, use the corrosion defect evolution model to predict the propagation rate of corrosion defects on the inner wall of oil and gas pipelines and output the corrosion defect evolution sequence. Based on the morphological data of corrosion defects on the inner wall of oil and gas pipelines, the propagation rate of corrosion defects on the inner wall of oil and gas pipelines is calculated by using an evolution model of corrosion defects on the inner wall of oil and gas pipelines. Based on the propagation rate of corrosion defects on the inner wall of oil and gas pipelines, the propagation trend of corrosion defects on the inner wall of oil and gas pipelines along the axial and circumferential directions of the pipeline is determined. Based on the expansion trend of corrosion defects on the inner wall of oil and gas pipelines along the axial and circumferential directions, an evolution sequence of corrosion defects on the inner wall of oil and gas pipelines is generated. S5: Based on the shear stress intensity sequence and the corrosion defect evolution sequence, construct the scour-corrosion coupling feedback equation and solve iteratively to output the scour-corrosion coupling risk factor; S6: Calculate the operational risk index of the oil and gas pipeline and output the risk level of the oil and gas pipeline based on the scouring-corrosion coupling risk factor and the ultimate design stress of the oil and gas pipeline.

2. The quantitative risk calculation method for oil and gas pipelines according to claim 1, characterized in that, S1, specifically: Real-time acquisition of multi-source pipeline monitoring data during the operation of oil and gas pipelines, including transient fluid velocity data, transient fluid pressure data, depth distribution data of corrosion defects on the inner wall of oil and gas pipelines, and point cloud data of the geometric morphology of corrosion defects on the inner wall of oil and gas pipelines. A unified timestamp label is attached to each type of pipeline monitoring data to form multi-source pipeline monitoring data with a unified timestamp. Based on multi-source pipeline monitoring data with unified timestamps, time synchronization is performed on each type of pipeline monitoring data according to a unified timestamp label to generate a time-synchronized multi-source monitoring dataset.

3. The quantitative risk calculation method for oil and gas pipelines according to claim 2, characterized in that, S5, specifically: Based on the shear stress intensity sequence of the inner wall of oil and gas pipelines and the evolution sequence of corrosion defects in the inner wall of oil and gas pipelines, a scouring-corrosion coupled feedback equation is established. By iteratively solving the scour-corrosion coupling feedback equation, the scour-corrosion coupling risk factor of oil and gas pipelines is generated.

4. The quantitative risk calculation method for oil and gas pipelines according to claim 3, characterized in that, S6, specifically: Based on the scour-corrosion coupling risk factor of oil and gas pipelines and the ultimate design stress of oil and gas pipelines, the operational risk index of oil and gas pipelines is calculated using the strength verification calculation method. A risk level classification threshold is preset, and the operational risk index of the oil and gas pipeline is compared with the risk level classification threshold to determine the risk level of the oil and gas pipeline.

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