Quantitative risk calculation method for oil and gas pipeline

Abstract

The invention discloses an oil and gas pipeline quantitative risk calculation method, and particularly relates to the technical field of pipeline risk calculation. The method comprises the following steps: constructing a multi-source monitoring data set based on multi-source monitoring data acquired during the operation period of an oil and gas pipeline, extracting fluid disturbance characteristic data and corrosion defect morphology data through frequency domain transformation and spatial geometric analysis, and respectively establishing an oil and gas pipeline inner wall shear stress intensity sequence and a corrosion defect evolution sequence; constructing a scour-corrosion coupling feedback equation based on the shearing stress intensity sequence of the inner wall of the oil and gas pipeline and the corrosion defect evolution sequence, and performing iterative solution to obtain scour-corrosion coupling risk factors; in combination with the limit design stress of the oil-gas pipeline, the operation risk index of the oil-gas pipeline is calculated, the risk level of the oil-gas pipeline is output, and the precision and adaptability of quantitative risk calculation in the oil-gas pipeline operation state are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline risk calculation, and more particularly to a method for calculating quantitative risk of an oil and gas pipeline. Background Art

[0002] During long-term operation, oil and gas pipelines are subject to the corrosive effects of the transported media and the complex flow patterns, leading to the gradual formation of varying degrees of corrosion defects on the inner walls of the pipelines. Existing quantitative risk calculation methods for oil and gas pipelines are generally based on static structural integrity and flow models under stable operating conditions, often ignoring the interactive coupling between unsteady flow conditions and localized corrosion defects on the inner walls of the pipelines. Summary of the Invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for calculating the quantitative risk of an oil and gas pipeline to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for calculating quantitative risk of an oil and gas pipeline comprises the following steps: S1: Collect multi-source pipeline monitoring data in real time during the operation of oil and gas pipelines, add unified timestamp tags, and construct a time-synchronized multi-source monitoring dataset; S2: Extract fluid disturbance spectrum features and corrosion morphology features from multi-source monitoring data sets to generate fluid disturbance feature data and corrosion defect morphology data; S3: Based on the fluid disturbance characteristic data, the spatial distribution of shear stress on the inner wall of the oil and gas pipeline is analyzed and the shear stress intensity sequence is output; S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline and output the corrosion defect evolution sequence; S5: Based on the shear stress intensity sequence and the corrosion defect evolution sequence, the erosion-corrosion coupling feedback equation is constructed and solved iteratively to output the erosion-corrosion coupling risk factor; S6: Based on the erosion-corrosion coupling risk factor and the ultimate 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.

[0005] In a preferred embodiment, S1 is specifically: Real-time collection of multi-source pipeline monitoring data during oil and gas pipeline operation, including transient flow velocity data of the fluid inside the pipeline, transient pressure data of the fluid, depth distribution data of corrosion defects on the inner wall of the oil and gas pipeline, and point cloud data of the geometric morphology of corrosion defects on the inner wall of the oil and gas pipeline; A unified timestamp tag is added to each type of pipeline monitoring data to form multi-source pipeline monitoring data with unified time stamp; Based on the multi-source pipeline monitoring data with unified time tags, each type of pipeline monitoring data is time synchronized according to the unified timestamp tag to generate a time-synchronized multi-source monitoring data set.

[0006] In a preferred embodiment, S2 is specifically: Perform frequency domain transformation on the transient velocity data and transient pressure data of the fluid inside the oil and gas pipeline in the multi-source monitoring data set, extract the disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters of the fluid, and generate the disturbance characteristic data of the fluid inside the oil and gas pipeline; Spatial geometric analysis is performed on the depth distribution data of oil and gas pipeline inner wall corrosion defects and the point cloud data of the geometric morphology of oil and gas pipeline inner wall corrosion defects in the multi-source monitoring data set. The depth characteristic parameters, geometric shape characteristic parameters and spatial distribution characteristic parameters of the oil and gas pipeline inner wall corrosion defects are extracted to generate the oil and gas pipeline inner wall corrosion defect morphology data.

[0007] In a preferred embodiment, S3 is specifically: Based on the fluid disturbance characteristic data inside the oil and gas pipeline, the shear stress generated by the fluid disturbance at different pipeline spatial locations is calculated; According to the shear stress generated by fluid disturbance at different spatial locations of the oil and gas pipeline inner wall, the spatial distribution law of the shear stress along the axial and circumferential directions of the pipeline inner wall is determined; According to the spatial distribution law of shear stress on the inner wall of oil and gas pipelines, the time series statistics of the shear stress intensity on the inner wall of oil and gas pipelines are performed to generate the shear stress intensity sequence on the inner wall of oil and gas pipelines.

[0008] In a preferred embodiment, S4 is specifically: Based on the morphology data of the corrosion defects on 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 by using the corrosion defect evolution model of the inner wall of the oil and gas pipeline; According to the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline, the expansion trend of the corrosion defects on the inner wall of the oil and gas pipeline along the axial and circumferential directions of the pipeline is determined; According to the expansion trend of corrosion defects on the inner wall of oil and gas pipelines along the axial and circumferential directions of the pipeline, the evolution sequence of corrosion defects on the inner wall of oil and gas pipelines is generated.

[0009] In a preferred embodiment, S5 is specifically: Based on the shear stress intensity sequence and the corrosion defect evolution sequence of the inner wall of the oil and gas pipeline, the erosion-corrosion coupling feedback equation is established; The erosion-corrosion coupling risk factor of oil and gas pipelines is generated by iteratively solving the erosion-corrosion coupling feedback equation.

[0010] In a preferred embodiment, S6 is specifically: Based on the erosion-corrosion coupling risk factor of oil and gas pipelines and the ultimate design stress of oil and gas pipelines, the operation risk index of oil and gas pipelines is calculated using the strength verification calculation method; A risk level classification threshold is preset, and the operation 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.

[0011] The technical effects and advantages of the quantitative risk calculation method for oil and gas pipelines of the present invention are as follows: Through the real-time collection and unified time synchronization of multi-source pipeline monitoring data, a comprehensive and timely grasp of the pipeline operation status is ensured; spectral analysis and spatial geometry extraction are used to effectively separate fluid disturbance characteristic data and corrosion defect morphology data, thereby improving the accuracy of feature expression; shear stress intensity series are calculated based on fluid disturbance characteristic data, providing a quantitative basis for revealing the local scouring effect under the non-steady-state action of the fluid; corrosion defect morphology data are predicted time-varyingly through the corrosion evolution model, accurately simulating the expansion rate of corrosion defects and improving the predictability of defect development; by constructing and iteratively solving the scouring-corrosion coupling feedback equation, a quantitative description of the nonlinear relationship between shear stress and corrosion expansion that reinforces each other is achieved; based on the scouring-corrosion coupling risk factor and the ultimate 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, which improves the reliability and accuracy of quantitative risk calculation and effectively reduces the uncertainty of oil and gas pipeline operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of a quantitative risk calculation method for oil and gas pipelines according to the present invention. DETAILED DESCRIPTION

[0013] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0014] Example Figure 1 The present invention provides a method for calculating the quantitative risk of an oil and gas pipeline, which includes the following steps: S1: Collect multi-source pipeline monitoring data in real time during the operation of oil and gas pipelines, add unified timestamp tags, and construct a time-synchronized multi-source monitoring dataset; S2: Extract fluid disturbance spectrum features and corrosion morphology features from multi-source monitoring data sets to generate fluid disturbance feature data and corrosion defect morphology data; S3: Based on the fluid disturbance characteristic data, the spatial distribution of shear stress on the inner wall of the oil and gas pipeline is analyzed and the shear stress intensity sequence is output; S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline and output the corrosion defect evolution sequence; S5: Based on the shear stress intensity sequence and the corrosion defect evolution sequence, the erosion-corrosion coupling feedback equation is constructed and solved iteratively to output the erosion-corrosion coupling risk factor; S6: Based on the erosion-corrosion coupling risk factor and the ultimate 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.

[0015] S1: Collect multi-source pipeline monitoring data in real time during oil and gas pipeline operation, attach a unified timestamp tag, and construct a time-synchronized multi-source monitoring dataset, including: Real-time collection of multi-source pipeline monitoring data during oil and gas pipeline operation; Multi-source pipeline monitoring data includes transient flow velocity data of the fluid inside the pipeline, transient pressure data of the fluid, depth distribution data of corrosion defects on the inner wall of the oil and gas pipeline, and point cloud data of the geometric morphology of corrosion defects on the inner wall of the oil and gas pipeline. Transient flow velocity data of the fluid inside the pipeline refers to the instantaneous fluid flow velocity measured in real time by flow velocity sensors at different spatial locations within the pipeline during operation. Transient pressure data of the fluid inside the pipeline refers to the real-time measurement of the fluid pressure within the pipeline by pressure sensors installed on the inner wall of the pipeline during operation. Depth distribution data of corrosion defects on the inner wall of the oil and gas pipeline refers to the depth distribution of corrosion pits or corrosion areas formed locally on the inner wall of the pipeline due to long-term corrosion by the oil and gas medium. For example, by scanning and inspecting the inner wall of the oil and gas pipeline using high-precision ultrasonic sensors or electromagnetic nondestructive testing equipment, the depth of corrosion defects at different locations on the inner wall of the pipeline can be collected in real time to form the corrosion defect depth distribution data. Point cloud data of the geometric morphology of corrosion defects on the inner walls of oil and gas pipelines is spatial geometric information collected in real time using 3D laser scanning equipment or high-resolution optical measurement equipment. This data contains spatial characteristics such as the geometric dimensions, spatial position coordinates, and 3D structure of the corrosion defects. For example, if there are multiple corrosion pits on the inner wall of a certain section of an oil and gas pipeline, a high-resolution laser scanner can be used to scan the pits and obtain a 3D coordinate point cloud dataset for each pit, accurately reflecting the morphological characteristics of the corrosion defects.

[0016] A unified timestamp tag is added to each type of pipeline monitoring data to form multi-source pipeline monitoring data with unified time stamp; Unified timestamp tagging means that when collecting each type of pipeline monitoring data, a high-precision time synchronization device is used to record the exact time of collection for each data point. For example, if a velocity data collection point measures a velocity of 3.5 meters per second at 14:01:00:000 milliseconds, it will be recorded as "Flow velocity 3.5 meters per second, Collection time 14:01:00:000 milliseconds." If a corrosion depth collection point measures a depth of 1.25 millimeters at 14:01:00:005 milliseconds, it will be recorded as "Depth 1.25 millimeters, Collection time 14:01:00:005 milliseconds." Each type of data is uniformly labeled using the same timestamp recording method, achieving unified data recording across the time dimension.

[0017] Based on the multi-source pipeline monitoring data with unified time tags, each type of pipeline monitoring data is time synchronized according to the unified timestamp tag to generate a time-synchronized multi-source monitoring data set; Time synchronization involves using a unified timestamp tag for each type of pipeline monitoring data to align it with a set, unified time interval (e.g., 0.01 seconds). For example, using a unified time base of 0.01-second intervals, interpolation, resampling, or interpolation processing is performed on the transient flow velocity data, transient pressure data, depth distribution data of oil and gas pipeline inner wall corrosion defects, and point cloud data of the geometric morphology of oil and gas pipeline inner wall corrosion defects to obtain multi-source pipeline monitoring data with a consistent time step at the same time point, forming a time-synchronized multi-source monitoring dataset with fully aligned data moments.

[0018] S2: Extract fluid disturbance spectrum features and corrosion morphology features from multi-source monitoring data sets to generate fluid disturbance feature data and corrosion defect morphology data, including: Perform frequency domain transformation on the transient velocity data and transient pressure data of the fluid inside the oil and gas pipeline in the multi-source monitoring data set, extract the disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters of the fluid, and generate the disturbance characteristic data of the fluid inside the oil and gas pipeline; Frequency domain transformation refers to the use of Fourier transform method to transform the time-synchronized transient flow velocity data and transient pressure data of the fluid inside the oil and gas pipeline from a time domain numerical sequence to a frequency domain numerical sequence, so as to extract the frequency characteristics hidden in the transient changes of the fluid inside the pipeline.

[0019] For example, frequency domain analysis is performed on a 100-second segment of transient fluid velocity and pressure data measured by a velocity sensor and pressure sensor installed within a specific section of an oil and gas pipeline. First, a total of 10,000 data points, sampled at 0.01-second intervals over 100 seconds, are subjected to a fast Fourier transform algorithm for numerical transformation, yielding the corresponding frequency spectrum. By processing these frequency spectrum results, the disturbance characteristics of the fluid within the oil and gas pipeline at different frequency points can be identified, with the frequency spectrum analysis results presented as a frequency spectrogram.

[0020] The frequency spectrum can show the relationship between frequency distribution and amplitude, where each frequency point corresponds to a disturbance intensity. For example, during the actual operation of an oil and gas pipeline for a certain period of time, after frequency domain transformation, it was found that there were disturbance energy peaks at the frequency points of 0.2 Hz, 1.0 Hz, and 5.0 Hz. 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. The disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters are then used to fully express the disturbance characteristic state of the fluid inside the pipeline. The frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters extracted from the collected transient flow velocity data and transient pressure data of the fluid inside the pipeline constitute the disturbance characteristic data of the fluid inside the oil and gas pipeline.

[0021] Perform spatial geometric analysis on the depth distribution data of oil and gas pipeline inner wall corrosion defects and the point cloud data of the geometric morphology of oil and gas pipeline inner wall corrosion defects in the multi-source monitoring data set, extract the depth characteristic parameters, geometric shape characteristic parameters and spatial distribution characteristic parameters of the oil and gas pipeline inner wall corrosion defects, and generate the oil and gas pipeline inner wall corrosion defect morphology data; Spatial geometric analysis uses 3D geometric analysis methods to analyze the geometric features of the depth distribution data and geometric morphology of corrosion defects on the inner walls of oil and gas pipelines using point cloud data. This allows the extraction of depth, geometry, and spatial distribution parameters of corrosion defects on the inner walls of oil and gas pipelines. 3D geometric analysis methods include point cloud spatial reconstruction, surface fitting analysis, and spatial statistical analysis.

[0022] For example, an analysis of the corrosion pits within a specific section of an oil and gas pipeline reveals that ultrasonic testing equipment measures the distribution of corrosion pit depths ranging from 0.5 mm to 5 mm, generating corrosion defect depth distribution data. A 3D laser scanner collects 3D point cloud data of the corrosion area, including the 3D spatial coordinates, contours, and bottom shape of the pits. Spatial geometric analysis methods are used to process the 3D point cloud data and corrosion depth data. For example, surface fitting is used to fit the pit contours. The fitting results can be used to extract the pit's geometric shape characteristic parameters, including its depth, width, length, bottom radius, or taper angle.

[0023] Spatial statistical analysis methods are used to quantitatively analyze the three-dimensional positional distribution of corrosion pits to determine the spatial distribution pattern of corrosion pits on the pipeline inner wall, such as concentration parameters along the pipeline's axial or circumferential directions, and the density or sparseness of corrosion defect areas. Parameters obtained through three-dimensional geometric analysis include depth characteristic parameters (e.g., the maximum depth of corrosion pits is 5 mm, and the average depth is 2 mm), geometric shape characteristic parameters (e.g., the average length of corrosion pits is 20 mm, and the average width is 10 mm), and spatial distribution characteristic parameters (e.g., the axial distribution interval of corrosion pits is 50 mm). These parameters constitute the morphological data of corrosion defects on the inner wall of oil and gas pipelines, accurately describing the state of the corrosion pits on the pipeline inner wall.

[0024] S3: Based on the fluid disturbance characteristic data, the spatial distribution of shear stress on the inner wall of the oil and gas pipeline is analyzed and a shear stress intensity sequence is output, including: Based on the fluid disturbance characteristic data inside the oil and gas pipeline, the shear stress generated by the fluid disturbance at different pipeline spatial locations is calculated; The characteristic data for fluid disturbances within oil and gas pipelines includes disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters. The disturbance frequency spectrum distribution parameters record the multiple frequencies at which disturbances occur within the pipeline; the disturbance intensity spectrum distribution parameters record the energy of the fluid disturbance at the corresponding frequency point, indicating the intensity of the fluid disturbance. This reflects the non-steady-state and non-uniform nature of the fluid state within the pipeline.

[0025] Fluid dynamics methods are used to analyze and calculate the shear stress generated by fluid disturbances on the inner wall of oil and gas pipelines at different spatial locations. These methods, including finite element analysis, finite volume analysis, and computational fluid dynamics analysis, are used to calculate the tangential force, or shear stress, generated by fluid disturbances on the inner wall of oil and gas pipelines. For example, a 50-meter section of an oil and gas pipeline is divided into several computational grids along the axial and circumferential directions, each with a spatial resolution of 0.1 m x 0.1 m. Within the fluid region near the inner wall of the pipeline, the disturbance frequency and intensity spectral distribution parameters are input into computational fluid dynamics analysis software. A flow field model is then established and simulations are performed to determine the shear stress generated by the fluid on the inner wall of the pipeline at each spatial grid location, thereby obtaining an accurate spatial distribution of the shear stress. For example, calculations show that at a 90° angle, 20 meters from the pipeline inlet, the shear stress generated by fluid disturbance is 3.5 Pascals; at a 270° angle, 40 meters from the pipeline inlet, the shear stress is 4.2 Pascals. This calculation allows us to determine the shear stress on the inner wall of an oil and gas pipeline at all locations of interest.

[0026] According to the shear stress generated by fluid disturbance at different spatial locations of the oil and gas pipeline inner wall, the spatial distribution law of the shear stress along the axial and circumferential directions of the pipeline inner wall is determined; The spatial distribution of shear stress along the axial and circumferential directions of an oil and gas pipeline's inner wall refers to the changing trends and patterns of shear stress at different axial and circumferential locations along the pipeline. By analyzing the calculated spatial statistics and distribution patterns of the shear stress along the pipeline's inner wall, we can determine the shear stress trends along the pipeline's length (axial) and along the circumference (circumferential) of the pipeline cross section. For example, a 50-meter length of the pipeline is divided into 5-meter axial intervals. Within each interval, the shear stress at all circumferential locations is averaged and statistically analyzed to produce an axial distribution curve. For example, the average shear stress within the first 5-meter interval is 2.5 Pa, the average shear stress within the next 5-meter interval is 3.2 Pa, and so on, to the end of the pipeline section. Similarly, the circumferential distribution pattern is determined by dividing the 360° circumference of the pipeline into a number of equally divided circumferential intervals (for example, 30° intervals) and averaging and statistically analyzing the shear stress at all axial locations within each circumferential interval to produce a circumferential distribution curve. For example, the average shear stress in the circumferential direction of 0°-30° is 3.0 Pascals, and the average shear stress in the 180°-210° range is 4.1 Pascals. Through the above method, the spatial distribution of shear stress along the axial and circumferential directions on the inner wall of the oil and gas pipeline is obtained.

[0027] 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 statistically analyzed in time series to generate the shear stress intensity sequence on the inner wall of the oil and gas pipeline; A shear stress intensity series for the inner wall of an oil and gas pipeline is a data sequence formed by chronologically arranging the shear stress intensity of the inner wall of the pipeline. Based on the spatial distribution of shear stress, key spatial locations with large shear stress values ​​are selected for time series analysis. For example, locations every 5 meters along the pipeline axis and corresponding key circumferential locations are selected. By recording and analyzing the shear stress at these locations over a long period of continuous monitoring (e.g., one hour), a data series of shear stress intensity over time is generated. For example, at an axial position of 10 meters and a circumferential position of 90°, shear stress is recorded at 1-second intervals for one hour, resulting in 3,600 shear stress data points. Arranging these data points sequentially forms a shear stress intensity series. Similarly, a corresponding shear stress intensity series can be obtained at an axial position of 30 meters and a circumferential position of 270°.

[0028] S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline, and the corrosion defect evolution sequence is output, including: Based on the morphology data of the corrosion defects on 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 by using the corrosion defect evolution model of the inner wall of the oil and gas pipeline; The morphological data of corrosion defects on the inner wall of oil and gas pipelines consists of characteristic parameters for corrosion defect depth, geometric shape, and spatial distribution. The depth characteristic parameter refers to the depth of the corrosion pit or corrosion area, such as the maximum depth and average depth. The geometric shape characteristic parameters include the pit length, width, pit bottom radius, profile, and bottom cone angle. The spatial distribution characteristic parameters include the location, spacing, and pattern of the corrosion pits in the axial and circumferential directions of the pipeline.

[0029] The corrosion defect evolution model for oil and gas pipeline inner walls is a mathematical model based on the fundamental principles of the corrosion process, used to describe the growth and evolution of corrosion defects on the inner walls of oil and gas pipelines. The model is based on the principles of electrochemical corrosion kinetics, material corrosion mechanisms, and the interaction between fluids and pipe walls. By establishing a mathematical relationship between the corrosion rate and external environmental conditions (such as temperature, flow rate, and fluid composition) and internal material conditions (such as pipe material and surface treatment), it quantitatively calculates the corrosion defect growth rate. For example, taking the pitting corrosion pits on the inner wall of a pipeline as an example, the corrosion defect evolution model can be based on the pitting kinetic equation. Using a mathematical expression for the time-varying pit depth, combined with the initial pit depth and geometric parameters measured from the corrosion defect topography data, the model can calculate the pit depth growth rate over time. For example, if the pit depth at the initial measurement time is 2 mm, the corrosion defect evolution model calculates that the pit depth increases by 0.02 mm per hour at a temperature of 40°C and a fluid flow rate of 3 meters per second, indicating a corrosion defect growth rate of 0.02 mm per hour.

[0030] According to the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline, the expansion trend of the corrosion defects on the inner wall of the oil and gas pipeline along the axial and circumferential directions of the pipeline is determined; The axial and circumferential expansion trends of corrosion defects on the inner wall of an oil and gas pipeline refer to the spatial expansion patterns and trends of each corrosion defect, including locations with faster and slower expansion rates, the changing patterns of expansion rates, and the spatial variation patterns of the corrosion defects. For example, taking the axial expansion trend as an example, a 100-meter section of the oil and gas pipeline is first divided into multiple axial position intervals, each 10 meters long. The expansion rates of all corrosion pits in each axial interval are statistically analyzed. Assume that the average expansion rate of corrosion defects in the 0-10 meter interval is 0.015 mm / h, the average expansion rate in the 10-20 meter interval is 0.022 mm / h, and so on, to the end of the pipeline section. By comparing the expansion rates in different intervals, the axial expansion trend of the pipeline inner wall corrosion defects can be determined, such as whether the expansion rate gradually increases or decreases from the pipeline inlet to the outlet. Similarly, the analysis of the circumferential expansion trend can also be carried out by segmenting the pipeline cross-section in the circumferential direction. Assuming that the pipeline is divided into 8 equal intervals, each circumferential interval is 45 degrees, then the corrosion defect expansion rate of each circumferential interval is counted to obtain the distribution of the corrosion expansion rate in each circumferential interval. For example, the average expansion rate in the 0-45 degree interval is 0.018 mm / h, and the average expansion rate in the 135-180 degree interval is 0.025 mm / h, thereby determining the corrosion defect expansion trend along the circumferential direction.

[0031] Generate the evolution sequence of corrosion defects on the inner wall of oil and gas pipelines based on the expansion trend of corrosion defects along the axial and circumferential directions of the pipelines; The evolution sequence of corrosion defects on the inner wall of an oil and gas pipeline is a time series data generated by recording and arranging the corrosion defect growth rate at different spatial locations on the inner wall of the pipeline over a continuous time period based on the spatial expansion trend of the corrosion defects. After determining the corrosion defect growth trend, several representative key corrosion locations are selected (for example, at 20 meters, 50 meters, and 80 meters axially, as well as specific circumferential locations). The expansion of the corrosion defects at each location over a specific timeframe is continuously monitored and recorded. For example, a corrosion pit at 20 meters axially is monitored for a period of time (for example, 100 hours), with the corrosion depth recorded every hour. Corrosion depth data is obtained at 100 consecutive time points (e.g., initial depth 2 mm, depth 2.02 mm at the first hour, depth 2.04 mm at the second hour, etc.). The corrosion depth data is then arranged in chronological order to form a spatial evolution sequence of the corrosion defects. The same monitoring and data recording process is repeated at other key spatial locations on the pipeline to form a complete set of corrosion defect evolution sequence data. The corrosion defect evolution sequence data can intuitively show the evolution process of corrosion defects on the inner wall of the pipeline over time and reflect the dynamic law of corrosion defect expansion.

[0032] S5: Based on the shear stress intensity sequence and the corrosion defect evolution sequence, the erosion-corrosion coupling feedback equation is constructed and solved iteratively to output the erosion-corrosion coupling risk factor, including: Based on the shear stress intensity sequence and the corrosion defect evolution sequence of the inner wall of the oil and gas pipeline, the erosion-corrosion coupling feedback equation is established; The erosion-corrosion coupled feedback equation is established by using a series of shear stress intensity and an evolutionary series of corrosion defects on the inner wall of an oil and gas pipeline as initial input data. Based on the theoretical derivation of the mechanism by which fluid disturbances on the inner wall of the pipeline affect corrosion growth, a mathematical expression is established that describes the interaction between the growth of corrosion defects on the inner wall of the oil and gas pipeline and the fluid disturbances within the pipe. Specifically, the erosion-corrosion coupled feedback equation describes the direct effect of the shear stress intensity on the inner wall of the oil and gas pipeline on the rate of corrosion pit growth, as well as the inverse relationship between changes in the depth of the corrosion pit and changes in the shear stress intensity on the inner wall of the pipeline. For example, the erosion-corrosion coupled feedback equation can be expressed as follows: the rate of change of the corrosion defect depth is equal to the functional relationship between the corrosion growth rate under shear stress and the corrosion defect morphology factor. The corrosion defect morphology factor is determined by characteristic parameters such as the depth, width, and bottom shape of the corrosion pit, while the corrosion growth rate under shear stress is directly controlled by the shear stress intensity. The erosion-corrosion coupled feedback equation also includes the influence of the change in shear stress intensity on the change in corrosion pit depth. That is, as the corrosion pit depth increases, the local flow field turbulence intensifies, causing the local shear stress intensity to change, thus forming a bidirectional coupled feedback relationship.

[0033] By iteratively solving the erosion-corrosion coupling feedback equation, the erosion-corrosion coupling risk factor of the oil and gas pipeline is generated; Using the initial data of the shear stress intensity series and the corrosion defect evolution series on the inner wall of the oil and gas pipeline as boundary conditions and initial conditions, numerical calculation methods are used to gradually approximate the exact solution of the erosion-corrosion coupled feedback equation. For example, the finite difference iterative method is used to discretize the erosion-corrosion coupled feedback equation and perform the calculations step by step with a certain time step (for example, each time step is 1 second). By substituting the initial corrosion defect depth and the shear stress at the corresponding moment into the erosion-corrosion coupled feedback equation, the corrosion defect depth prediction at the next moment is first calculated. Then, using the predicted corrosion defect depth as a condition, the shear stress prediction is updated. The calculation is then continued in a continuous iterative process to solve the problem. During the calculation process, the calculation step size is continuously adjusted based on the error calculated in the iterative process to ensure the accuracy and stability of the calculation results. For example, at the initial time 0, the corrosion pit depth is 2 mm and the local shear stress intensity is 3.5 Pascals. The first iterative calculation shows that the corrosion pit depth in the next second is 2.000005 mm, and the corresponding shear stress intensity is updated to 3.5001 Pascals. After continuous iterations, stable and reliable corrosion defect depth prediction sequences and shear stress intensity prediction sequences are obtained.

[0034] The erosion-corrosion coupling risk factor is a quantitative indicator generated by statistically analyzing the dynamic relationship between the calculated corrosion defect evolution sequence and the shear stress intensity sequence after iterative calculations. The erosion-corrosion coupling risk factor reflects the dynamic influence of the shear stress intensity on the inner wall of the oil and gas pipeline on the expansion of corrosion defects. Specifically, it quantitatively represents the relationship between the corrosion defect expansion rate and the shear stress change rate caused by fluid disturbance within a specific timeframe. For example, over 100 hours of continuous monitoring and calculation, the depth expansion rate of the corrosion pit showed a strong positive correlation with the amplitude of the local shear stress change. Statistical analysis yielded a numerical indicator. Higher values ​​indicate a greater degree of influence on the corrosion defect expansion rate due to shear stress disturbances, thus reflecting a greater risk of erosion-corrosion coupling failure at that location on the oil and gas pipeline. For example, the risk factor ranges from 0 to 1. Values ​​close to 1 indicate a very high risk of coupled failure, while values ​​close to 0 indicate a lower risk of coupled failure.

[0035] 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 erosion-corrosion coupling risk factor and the ultimate design stress of the oil and gas pipeline, including: Based on the erosion-corrosion coupling risk factor of oil and gas pipelines and the ultimate design stress of oil and gas pipelines, the operation risk index of oil and gas pipelines is calculated using the strength verification calculation method; The ultimate design stress of an oil and gas pipeline refers to the maximum allowable stress level the pipeline can withstand, determined during the pipeline design phase based on the pipeline material's performance parameters, design operating conditions, safety margins, and standard specifications. This ultimate design stress is typically calculated based on the pipeline material's yield strength or tensile strength, reduced by a certain safety factor.

[0036] Strength verification calculation methods utilize mechanical calculation methods to convert the erosion-corrosion coupling risk factor into a pipeline wall thickness loss rate or equivalent stress increase. This is then compared with the ultimate design stress of the oil and gas pipeline to determine the pipeline's operational risk index. These methods include residual wall thickness calculation, stress concentration factor analysis, and finite element numerical analysis. For example, the residual wall thickness calculation method first uses the erosion-corrosion coupling risk factor to calculate the amount of wall thinning or the proportion of wall thickness loss due to corrosion expansion. Stress calculations are then performed based on the remaining wall thickness, internal pressure, axial load, and external load conditions. For example, if the original designed wall thickness of a pipeline is 10 mm, and based on the corrosion pit growth rate, the wall thickness loss reaches 2 mm after a certain number of years of operation, the remaining wall thickness is 8 mm. Pipeline mechanics formulas are then used to calculate the hoop and axial stresses on the pipeline wall at this point. The total stress level actually experienced by the pipeline during operation is then calculated using the stress superposition method or the maximum stress criterion. The calculated actual operating stress level is ratioed to the ultimate design stress of the oil and gas pipeline to obtain the operating risk index of the oil and gas pipeline.

[0037] For example, if the actual total operating stress of a pipeline after strength verification is 310 MPa and the ultimate design stress is 413.3 MPa, the pipeline's operational risk index is defined as 310 divided by 413.3, resulting in an operational risk index of 0.75. The closer the risk index is to 1, the closer the actual stress experienced by the oil and gas pipeline during operation is to the ultimate design stress level, indicating a higher operational risk for the pipeline.

[0038] Preset the risk level classification threshold, compare the oil and gas pipeline operation risk index with the risk level classification threshold, and determine the risk level of the oil and gas pipeline; Risk classification thresholds refer to a set of pre-set numerical critical values ​​or ranges used to classify oil and gas pipeline operational risk levels. These thresholds are determined based on oil and gas pipeline operational safety standards, national or industry regulations, and engineering practice experience, and typically include safe, low-risk, medium-risk, and high-risk. For example, the preset risk classification thresholds are: when the operational risk index is between 0 and 0.4, it is considered safe; between 0.4 and 0.6, it is considered low-risk; between 0.6 and 0.8, it is considered medium-risk; and between 0.8 and 1, it is considered high-risk.

[0039] The operational risk index is compared with the preset risk classification threshold to determine the pipeline's risk level. For example, if the operational risk index of an oil and gas pipeline is 0.75, compared with the preset risk classification threshold, 0.75 falls within the range of 0.6 to 0.8. Therefore, the operational risk level of the oil and gas pipeline is determined to be medium, indicating that the pipeline has certain operational safety risks and requires close monitoring and appropriate maintenance measures to reduce the risk level.

[0040] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0041] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0042] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0043] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0044] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0045] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0046] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0047] If the functions are implemented 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 solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0048] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0049] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating the quantitative risk of an oil and gas pipeline, characterized in that: The steps include: S1: Collect multi-source pipeline monitoring data in real time during the operation of oil and gas pipelines, add unified timestamp tags, and construct a time-synchronized multi-source monitoring dataset; S2: Extract fluid disturbance spectrum features and corrosion morphology features from multi-source monitoring data sets to generate fluid disturbance feature data and corrosion defect morphology data; S3: Based on the fluid disturbance characteristic data, the spatial distribution of shear stress on the inner wall of the oil and gas pipeline is analyzed and the shear stress intensity sequence is output; S4: Based on the corrosion defect morphology data, the corrosion defect evolution model is used to predict the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline and output the corrosion defect evolution sequence; S5: Based on the shear stress intensity sequence and the corrosion defect evolution sequence, the erosion-corrosion coupling feedback equation is constructed and solved iteratively to output the erosion-corrosion coupling risk factor; S6: Based on the erosion-corrosion coupling risk factor and the ultimate 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.

2. The method for calculating the quantitative risk of an oil and gas pipeline according to claim 1, characterized in that: S1, specifically: Real-time collection of multi-source pipeline monitoring data during oil and gas pipeline operation, including transient flow velocity data of the fluid inside the pipeline, transient pressure data of the fluid, depth distribution data of corrosion defects on the inner wall of the oil and gas pipeline, and point cloud data of the geometric morphology of corrosion defects on the inner wall of the oil and gas pipeline; A unified timestamp tag is added to each type of pipeline monitoring data to form multi-source pipeline monitoring data with unified time stamp; Based on the multi-source pipeline monitoring data with unified time tags, each type of pipeline monitoring data is time synchronized according to the unified timestamp tag to generate a time-synchronized multi-source monitoring data set.

3. The method for calculating the quantitative risk of an oil and gas pipeline according to claim 2, characterized in that: S2, specifically: Perform frequency domain transformation on the transient velocity data and transient pressure data of the fluid inside the oil and gas pipeline in the multi-source monitoring data set, extract the disturbance frequency spectrum distribution parameters and disturbance intensity spectrum distribution parameters of the fluid, and generate the disturbance characteristic data of the fluid inside the oil and gas pipeline; Spatial geometric analysis is performed on the depth distribution data of oil and gas pipeline inner wall corrosion defects and the point cloud data of the geometric morphology of oil and gas pipeline inner wall corrosion defects in the multi-source monitoring data set. The depth characteristic parameters, geometric shape characteristic parameters and spatial distribution characteristic parameters of the oil and gas pipeline inner wall corrosion defects are extracted to generate the oil and gas pipeline inner wall corrosion defect morphology data.

4. The method for calculating the quantitative risk of an oil and gas pipeline according to claim 3, characterized in that: S3, specifically: Based on the fluid disturbance characteristic data inside the oil and gas pipeline, the shear stress generated by the fluid disturbance at different pipeline spatial locations is calculated; According to the shear stress generated by fluid disturbance at different spatial locations of the oil and gas pipeline inner wall, the spatial distribution law of the shear stress along the axial and circumferential directions of the pipeline inner wall is determined; According to the spatial distribution law of shear stress on the inner wall of oil and gas pipelines, the time series statistics of the shear stress intensity on the inner wall of oil and gas pipelines are performed to generate the shear stress intensity sequence on the inner wall of oil and gas pipelines.

5. The method for calculating the quantitative risk of an oil and gas pipeline according to claim 4, characterized in that: S4, specifically: Based on the morphology data of the corrosion defects on 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 by using the corrosion defect evolution model of the inner wall of the oil and gas pipeline; According to the expansion rate of the corrosion defects on the inner wall of the oil and gas pipeline, the expansion trend of the corrosion defects on the inner wall of the oil and gas pipeline along the axial and circumferential directions of the pipeline is determined; According to the expansion trend of corrosion defects on the inner wall of oil and gas pipelines along the axial and circumferential directions of the pipeline, the evolution sequence of corrosion defects on the inner wall of oil and gas pipelines is generated.

6. The method for calculating the quantitative risk of an oil and gas pipeline according to claim 5, characterized in that: S5, specifically: Based on the shear stress intensity sequence and the corrosion defect evolution sequence of the inner wall of the oil and gas pipeline, the erosion-corrosion coupling feedback equation is established; The erosion-corrosion coupling risk factor of oil and gas pipelines is generated by iteratively solving the erosion-corrosion coupling feedback equation.

7. The method for calculating the quantitative risk of an oil and gas pipeline according to claim 6, characterized in that: S6, specifically: Based on the erosion-corrosion coupling risk factor of oil and gas pipelines and the ultimate design stress of oil and gas pipelines, the operation risk index of oil and gas pipelines is calculated using the strength verification calculation method; A risk level classification threshold is preset, and the operation 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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