Safety state assessment method and system for submarine pipeline, medium and equipment
By integrating multi-source acoustic wave detection data and edge detection algorithms, a 3D model of the subsea pipeline was constructed, which solved the problem of low accuracy in subsea pipeline condition detection and achieved high-precision safety status assessment and three-dimensional visualization.
Patent Information
- Application Number
- CN202511290744.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-10
- Publication Date
- 2025-12-12
AI Technical Summary
Existing technologies for detecting the condition of subsea pipelines have low accuracy, making it difficult to fully reflect the actual condition of the pipeline and affecting the accuracy of detection.
By integrating acoustic feature data, geometric feature data, and spatial location data, edge detection algorithms are used to extract pipeline edge features, construct a 3D model of the subsea pipeline, obtain bending curvature morphology parameters and span length, and conduct safety status assessment by combining critical curvature and critical span length.
It improves the accuracy of subsea pipeline condition detection, enables three-dimensional visualization and quantitative assessment of pipeline condition, and enhances the reliability and stability of detection results.
Smart Images
Figure CN121117954A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of energy pipeline safety assessment, in particular to a safety state assessment method, system, medium and equipment for a submarine pipeline. BACKGROUND
[0002] The submarine pipeline is an important part of offshore oil and gas development and a weak link in marine oil and gas development engineering. Once it fails, it will cause serious accident consequences and secondary disasters, and it is difficult and costly to repair. However, according to the submarine pipeline accident investigation, one of the main reasons for the offshore submarine pipeline accident is that the pipeline safety state cannot be correctly detected and identified. Therefore, it is of great practical significance to develop a safety state assessment method for the in-service submarine pipeline to ensure the effective identification of the submarine pipeline operating state for the integrity management of the submarine pipeline.
[0003] The acquisition of submarine pipeline state detection data is an important prerequisite for the safety state assessment of the submarine pipeline. Due to the characteristics of long-distance transmission and easy control of sound waves, and the good correlation between the echo characteristics and the physical properties of the target object, it has become an effective tool for underwater detection. The side scan sonar system (SSS), multi-beam system (MBS) and shallow profile instrument (SBP) based on acoustic detection can realize wide coverage and high resolution detection of submarine topography and geomorphology, and have been widely used in submarine topography and seabed condition detection, submarine geological disaster detection and submarine structure detection. Among them, the high-resolution sonar image of SSS and MBS can obtain the real-time in-situ state of the non-buried submarine pipeline, and the SBP monitoring data can be used to judge the relative spatial position relationship of the pipeline on the seabed, that is, to determine the burial depth and overhanging height of the pipeline. However, for the small target detection object of the submarine pipeline, the detection image of SSS and MBS is easily affected by the transducer beam opening angle, the sound wave grazing angle and the submarine topography, and it is difficult to ensure the continuous output of high-quality and high-precision detection data. In addition, the complex sea conditions and the large amount of detection data bring serious challenges to the processing of submarine pipeline monitoring data and the judgment of safety state. The SBP detection data only obtain the spatial position of the pipeline cross section relative to the submarine topography, and are not suitable for large-scale submarine pipeline detection and identification. In summary, for each detection system, a single detection method is limited by the working principle and can only determine part of the characteristics of the submarine pipeline, which makes it difficult to fully reflect the actual state of the pipeline, and further affects the accuracy of the pipeline state detection. SUMMARY
[0004] The present application provides a safety state assessment method, system, medium and equipment for a submarine pipeline to solve the above problems existing in the prior art, that is, how to improve the pipeline state detection accuracy in the prior art. The present application provides a safety state assessment method for a submarine pipeline, which comprises: fuse the acquired acoustic feature data, geometric feature data and spatial position data of the submarine pipeline to determine sound wave detection data; perform multi-directional adaptive edge detection on the sound wave detection data by using an edge detection algorithm to preliminarily extract pipeline edge features; identify the pipeline edge features to determine submarine pipeline features, extract elevation coordinates according to the submarine pipeline features, and determine horizontal coordinates of pipeline ridge points on a pipeline central axis by performing coordinate transformation on pixel positions corresponding to the submarine pipeline features; generate a circular pipeline cross section based on a preset pipeline diameter, determine a three-dimensional spline curve path of the pipeline central axis in combination with the horizontal coordinates and the elevation coordinates, and construct a submarine pipeline 3D model based on the circular pipeline cross section and the three-dimensional spline curve path; acquire a distance between any three adjacent sampling points at similar distances on a central axis of the submarine pipeline 3D model, determine a bending curvature form parameter of the pipeline according to the distance between the three adjacent sampling points, determine a span length of the pipeline in combination with the elevation coordinates according to the horizontal coordinates, and compare the bending curvature form parameter and the span length with corresponding critical curvatures and critical span lengths to obtain a safety state evaluation result of the submarine pipeline.
[0005] Optionally, the critical curvatures and the critical span lengths are acquired by specifically including: determine a pipeline wall equivalent stress based on Mises equivalent stress criterion according to acquired longitudinal and circumferential stresses borne by the submarine pipeline 3D model, and determine a critical curvature of pipeline bending based on the pipeline wall equivalent stress and a pipeline material yield stress; establish a differential control equation of the submarine pipeline based on acquired hydrodynamic forces, seabed resistances and self-gravities borne by the submarine pipeline, and obtain critical span lengths of the non-buried pipeline based on the differential control equation of the submarine pipeline; the critical span lengths include a critical span length of the non-buried pipeline and a critical span length of the suspended pipeline.
[0006] Optionally, the horizontal coordinates of the pipeline ridge points on the pipeline central axis are determined by performing coordinate transformation on pixel positions corresponding to the submarine pipeline features, specifically including: the horizontal coordinates of the pipeline ridge points are acquired by using the following formula: in the formula, (x i, y i) is the horizontal coordinates of the i th detection point on the ridge line; (x i-1, y i-1) and (x i+1, y i+1) are coordinates of the i th detection point and the (i+1) th detection point on the ridge line. X i , Y i x i , y i X i ,Y i (x0, y0) corresponds to the coordinates of the feature point of the pipeline; (x0, y0) is the known reference coordinate of the acoustic map; scale is the scale between the actual coordinates and the pixel coordinates.
[0007] Optionally, the acoustic feature data, geometric feature data, and spatial location data can be acquired using a side-scan sonar system (SSS), a multi-beam sonar system (MBS), and a shallow profiler (SBP), respectively.
[0008] Optionally, a B-spline function can be used to smooth the horizontal coordinates of the pipeline ridge points to obtain a smooth ridge line for the subsea pipeline.
[0009] Optionally, obtaining the bending curvature morphology parameters of the pipeline specifically includes: The bending curvature morphology parameters are obtained using the following formula: in, P 01 , P 02 , P 12 These are continuous sampling points on the 3D model of the subsea pipeline. P 1 , P 0 , P 2 The spacing between them; k P for P The curvature of the pipe at the point.
[0010] Optionally, the edge detection algorithm is the Kirsch operator edge detection algorithm; the pipe edge features are identified by using an Active Contour Model (ACM); wherein the Active Contour Model (ACM) is the Snake model.
[0011] This invention provides a safety status assessment system for subsea pipelines, comprising: The fusion module is used to fuse the acquired acoustic feature data, geometric feature data, and spatial location data of the subsea pipeline to determine the acoustic detection data; The pipeline edge feature extraction module is used to perform multi-directional adaptive edge detection on acoustic wave detection data using an edge detection algorithm to initially extract pipeline edge features. The coordinate confirmation module is used to identify pipeline edge features, determine the features of the subsea pipeline, extract elevation coordinates based on the features of the subsea pipeline, and determine the horizontal coordinates of the pipeline ridge point on the central axis of the pipeline by performing coordinate transformation on the pixel positions corresponding to the features of the subsea pipeline. The construction module is configured to generate a circular pipeline section based on a preset pipeline diameter, determine a three-dimensional space spline curve path of a pipeline central axis in combination with a horizontal coordinate and an elevation coordinate, and construct a submarine pipeline 3D model based on the circular pipeline section and the three-dimensional space spline curve path. The evaluation module is configured to acquire a distance between any three adjacent sampling points on the central axis of the submarine pipeline 3D model, determine a bending curvature form parameter of the pipeline according to the distance between the three adjacent sampling points, determine a span length of the pipeline in combination with the elevation coordinate based on the horizontal coordinate, and compare the bending curvature form parameter and the span length with corresponding critical curvatures and critical span lengths respectively to obtain a safety state evaluation result of the submarine pipeline.
[0012] The application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the safety state evaluation method of the submarine pipeline.
[0013] The application provides a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the safety state evaluation method of the submarine pipeline when executing the program.
[0014] Compared with the prior art, the application has the following beneficial effects: the application provides a safety state evaluation method of a submarine pipeline, which fuses multi-source acoustic wave detection data to improve the expression accuracy of pipeline real-time state detection results, extracts pipeline edge features by using an edge detection algorithm, and then identifies the pipeline edge features, so that the completeness of submarine pipeline feature acquisition is improved, a submarine pipeline 3D model is constructed based on submarine pipeline features, so that three-dimensional visual fine expression of submarine pipeline detection results is realized, key geometric form parameters such as a bending curvature form parameter and a span length are extracted from the submarine pipeline 3D model, the bending curvature form parameter and the span length are compared with corresponding critical curvatures and critical span lengths respectively, so that quantitative evaluation of the safety state of the submarine pipeline is realized, and the pipeline state detection accuracy is improved. BRIEF DESCRIPTION OF DRAWINGS
[0015] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate an embodiment consistent with the application and, together with the description, serve to explain the principles of the application.
[0016] Figure 1 A flowchart of a safety state evaluation method of a submarine pipeline provided by an embodiment of the application is shown in the figure. Figure 2 A submarine pipeline position coordinate extraction and pipeline central axis acquisition schematic diagram provided by an embodiment of the application is shown in the figure. Figure 3A schematic diagram of the detection results of a subsea pipeline based on MBS provided in an embodiment of the present invention; Figure 4 A schematic diagram of the subsea pipeline detection results based on a 3D model of the subsea pipeline provided in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the analysis of factors affecting pipe curvature provided in an embodiment of the present invention; in, Figure 5 (a) shows the relationship between critical curvature and pipe diameter. Figure 5 (b) shows the relationship between critical curvature and wall thickness. Figure 5 (c) represents the relationship between the critical curvature and temperature; Figure 6 A schematic diagram of a computer device for assessing the safety status of a subsea pipeline as provided in an embodiment of the present invention. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0018] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.
[0019] Figure 1 This is a flowchart of a safety status assessment method for subsea pipelines provided in an embodiment of the present invention, such as... Figure 1 As shown in this embodiment, a method for assessing the safety status of a subsea pipeline includes: S1: The acoustic feature data, geometric feature data, and spatial location data of the acquired subsea pipeline are fused to determine the acoustic wave detection data.
[0020] For example, side-scan sonar (SSS), multibeam sonar (MBS), and shallow profiler (SBP) can be used to detect the subsea pipeline to obtain the corresponding acoustic feature data, geometric feature data, and spatial location data of the subsea pipeline. The three data can then be fused to determine the acoustic detection data. Then, the acquired acoustic detection data can be preprocessed by noise reduction, filtering, and other operations to improve the data quality.
[0021] S2: An edge detection algorithm is used to perform multi-directional adaptive edge detection on the acoustic wave detection data to initially extract the edge features of the pipeline.
[0022] For example, the strong changes in grayscale values in the acoustic image of a seabed pipeline represent obvious edge features between the target pipeline and the seabed background. Edge detection technology can extract these pipeline edge features from the acoustic image, thereby enabling the identification and location of the target object in the image. This invention can perform edge detection on seabed acoustic wave detection data by employing the Kirsch operator as a multi-directional adaptive detection operator, including but not limited to this algorithm. Assuming the 3×3 pixel intensity at any point in the image is: The gray gradient can be represented as: in, s k = a k + a k+1 + a k+2 , t k = a k+3 + a k+4 +…+ a k+7 .
[0023] The image convolves the neighborhood of each pixel with eight templates, outputting the maximum value to detect all edge directions. However, due to image data resolution and noise, the edge detection results contain many short lines with large curvature variations. Furthermore, the loss of weak edges leads to poor edge image continuity, making the edge detection results coarse and insufficient for extracting complete pipe features. Therefore, to extract and identify complete pipe feature information, further processing of the edge detection results is necessary to organize these fragmented feature information. This further processing will be performed in subsequent steps.
[0024] S3: Identify the pipeline edge features, determine the subsea pipeline features, extract the elevation coordinates based on the subsea pipeline features, and determine the horizontal coordinates of the pipeline ridge point on the pipeline central axis by performing coordinate transformation on the pixel positions corresponding to the subsea pipeline features.
[0025] For example, this invention employs an Active Contour Model (ACM), such as selecting the Snake model (including but not limited to this model) within the ACM, to further process the edge detection results (pipeline edge features). Specifically, this includes defining a closed parametric contour curve in the image spatial domain:
[0026] Where s∈[0,1] is the normalized curve length; v(0)=v(1), and x(s) and y(s) are the coordinates of the curve control points in the image; Total energy of the profile curve E t Internal energy determined by the curve's own characteristics E int and external energy determined by image target features E ext Composition. Contour curve in E int The generated internal forces and E ext The resulting image deforms under the combined action of internal and external forces. The internal forces maintain the smoothness and continuity of the contour, while the external forces guide the contour curve to move toward the image features. E t , E int , E ext It can be represented as:
[0027] Where dv / ds is the rate of change of the profile curve length; d 2 v / ds 2 κ is the curvature force vector; A and B are weighting parameters used to control the elasticity and rigidity of the active contour in the Snake model, respectively. By adjusting the weighting parameters A and B, the contour curve moves towards the target boundary in the image; κ is the weighting parameter; ∇ is the gradient operator [∂ / ∂x, ∂ / ∂y], and ∇I is the gradient of the image edge features. Treating v(s) as a curve that changes with time, according to the variational principle, E ext The solution must satisfy the Euler-Lagrange equations:
[0028] In the formula, γ(s) is the damping coefficient. When ∂v(s,t) / ∂t=0, the energy function... E snakeThe minimum contour curve converges to the target edge. After edge detection using the Kirsch operator, the extracted edges are heuristically connected using the Snake model, with continuity and curvature used as constraint parameters for path tracking. Among all short linear features, the longest and continuous path is identified as the subsea pipeline feature.
[0029] Optionally, the horizontal coordinates of the pipe ridge point on the central axis of the pipe can be determined by performing coordinate transformation on the pixel positions corresponding to the pipe features. Specifically, this includes: The horizontal coordinates of the pipe ridge point are obtained using the following formula: In the formula, ( X i , Y i The horizontal coordinate of the i-th detection point on the ridge line; x i , y i ) is the coordinate ( X i , Y i (x0, y0) corresponds to the coordinates of the feature point of the pipeline; (x0, y0) is the known reference coordinate of the acoustic map; scale is the scale between the actual coordinates and the pixel coordinates.
[0030] For example, since MBS and SBP can acquire pipeline acoustic images and elevation coordinates, the elevation coordinates Z of the subsea pipeline features in the MBS and SBP acoustic images are... i It can be extracted directly, such as Figure 2 As shown.
[0031] For example, a B-spline function can be used to smooth the horizontal coordinates of the pipeline ridge points to obtain a smooth ridge line for the subsea pipeline, specifically including: Among them, (X) r ,Y r Z r The coordinates of the smoothed pipe ridge line are shown below; ξ represents the relative position of the ridge point in the curve, 0 < ξ < 1. Given a pipe diameter of D, the coordinates of the pipe's central axis (X) are shown below. c ,Y c Z c )for:
[0032] .
[0033] S4: Based on the preset pipe diameter, generate a circular pipe cross-section. Combine the horizontal and vertical coordinates to determine the three-dimensional spline curve path of the pipe's central axis. Based on the circular pipe cross-section and the three-dimensional spline curve path, construct a 3D model of the subsea pipeline.
[0034] For example, the central axis can be transformed into a smooth spline curve based on the horizontal coordinates to determine the three-dimensional spline curve path of the pipeline's central axis. Then, guided by this path, a circular sketch is drawn near one end of the central line with a given pipeline diameter as the size for scanning operations to construct a 3D model of the subsea pipeline.
[0035] S5: Obtain the distance between any three adjacent sampling points on the central axis of the 3D model of the subsea pipeline. Based on the distance between the three adjacent sampling points, determine the bending curvature morphology parameters of the pipeline. Based on the horizontal coordinates and the elevation coordinates, determine the span length of the pipeline. Compare the bending curvature morphology parameters and span length with the corresponding critical curvature and critical span length to obtain the safety status assessment results of the subsea pipeline.
[0036] Optionally, the acquisition of the pipe's bending curvature morphology parameters specifically includes: The bending curvature morphology parameters are obtained using the following formula: in, P 01 , P 02 , P 12 These are continuous sampling points on the 3D model of the subsea pipeline. P 1 , P 0 , P 2 The spacing between them; k P for P The curvature of the pipe at the point.
[0037] For example, the distance between three adjacent sampling points that are close to each other on the pipeline ridge point or pipeline central axis can be obtained by using the horizontal coordinates of the pipeline ridge point or pipeline central axis. Then, the bending curvature morphology parameters of the pipeline can be calculated based on the distance between these three sampling points. Secondly, by combining the results of the pipeline 3D model, the non-buried pipeline section can be directly identified. Then, the span length of the non-buried pipeline section can be calculated based on the horizontal and elevation coordinates of the pipeline ridge point or pipeline central axis.
[0038] During operation, the subsea pipeline is subjected to forces from internal pressure, temperature, overlying soil, and hydrodynamic forces. By analyzing the longitudinal and circumferential stresses of the pipeline wall and based on the Mises equivalent stress criterion, the equivalent stress of the pipeline wall is calculated. To ensure the safe operation of the pipeline, the equivalent stress of the pipeline wall must not exceed the yield stress of the pipe material. Based on this, the critical curvature of the pipeline bending is determined.
[0039] For unburied pipelines, the main forces are hydrodynamics, seabed resistance, and their own weight. Stability analysis of the pipeline under wave and current conditions is used to calculate the maximum internal stress at mid-span. To ensure safe operation, this maximum internal stress must not exceed the yield stress of the pipe material. This allows for the deduction of the critical span length. Alternatively, using a 3D model of the subsea pipeline, the maximum lateral displacement can be determined by comparing the horizontal and vertical coordinates of the pipeline's ridge point or central axis with the original horizontal and vertical coordinates of the pipeline. To ensure safe operation, this maximum lateral displacement must not exceed the allowable value specified in the standards. This allows for the deduction of the critical span length.
[0040] For suspended pipes, it is generally believed that vortex-induced resonance will occur when the excitation force frequency reaches 0.7 to 1.3 times the natural frequency of the suspended pipe. In order to avoid resonance of the suspended pipe under the action of wave flow, the excitation force frequency should be less than 0.7 times the natural frequency of the suspended pipe. The excitation force frequency and the natural frequency of the suspended pipe can be found in the relevant technical specifications, and the critical span length can be determined by reverse calculation.
[0041] For example, when the curvature parameter exceeds the critical value (critical curvature) k th When the critical curvature parameter reaches its limit, it indicates that the pipeline is in an unsafe stress state, posing a potential risk that requires appropriate maintenance measures. In actual engineering, considering the influence of uncertain factors, the critical curvature parameter is affected by the pipeline structure, service environment, and in-situ condition, exhibiting a certain degree of randomness. Therefore, the potential risk of the pipeline is best expressed through failure probability analysis, defining the state where the pipeline curvature reaches the allowable curvature as the limit state, whose function can be expressed as:
[0042] Where x is a random variable vector. Therefore, the probability of failure of the pipe section can be obtained as:
[0043] Optional, the acquisition of critical curvature and critical span length includes: Based on the longitudinal and circumferential stresses borne by the obtained 3D model of the subsea pipeline, and combined with the Mises equivalent stress criterion, the equivalent stress of the pipeline wall is determined. Based on the equivalent stress of the pipeline wall and the yield stress of the pipe material, the critical curvature of the pipeline bending is determined. Based on the hydrodynamic forces, seabed resistance, and gravity experienced by the subsea pipeline, differential governing equations for the subsea pipeline are established. Based on these differential governing equations, the critical span length of the unburied pipeline is obtained. The critical span length includes the critical span length of the unburied pipeline and the critical span length of the suspended pipeline.
[0044] The loads on the subsea pipeline during the operation period mainly include internal pipeline pressure, temperature, overlying soil pressure, and hydrodynamic forces. The resulting pipe wall stress model is shown in Table 1.
[0045] Table 1. Pipe Wall Stress Analysis Model For pipe structures, which mainly bear longitudinal and circumferential stresses, according to the Mises equivalent stress criterion: In the formula, S eq S l S c These represent the Mises equivalent stress, axial stress, and circumferential stress within the pipe wall, respectively. Considering the Poisson effect, for an exposed pipe, the stress S within the pipe wall... l and S c It can be represented as:
[0046] For buried pipelines, the overburden pressure must also be considered, resulting in: To ensure the safe operation of the pipeline, the following applies: Among them, S y Pipe yield stress; η This is the utilization factor.
[0047] For example, the critical span length of a subsea pipeline includes the critical span length of the unburied pipeline and the critical span length of the suspended pipeline. The lateral wave force acting on the unburied pipeline increases with the increase of the pipeline's exposed portion, leading to lateral displacement. When the span length of the unburied pipeline reaches this critical value, it indicates a significant risk to the pipeline. Considering the uncertainties of various influencing factors, the state where the pipeline span reaches the critical span length is defined as the limit state, and its function can be expressed as:
[0048] Where x is a random variable vector. Therefore, the probability that the pipe section has a risk can be obtained as:
[0049] For example, the forces acting on unburied pipelines mainly include: lateral wave current force FD, inertial force FI, wave current lift FL, buoyancy Ff, vertical contact force N between the pipeline and the seabed, seabed soil resistance P, and the weight W of the pipeline itself and the internal liquid. Under these stress conditions, unburied pipelines are prone to lateral instability and failure. The criteria for judging their failure include: (1) stiffness failure criterion, i.e., |ymax|≤[y]; (2) strength failure criterion, i.e., |Smax|≤[S], where [S] is the allowable stress. The maximum internal stress at mid-span of an unburied pipeline under wave current action can be expressed as:
[0050] For suspended pipes, it is generally believed that vortex-induced resonance will occur when the excitation force frequency reaches 0.7 to 1.3 times the natural frequency of the suspended pipe. In order to avoid resonance of the suspended pipe under the action of wave flow, the excitation force frequency is less than 0.7 times the natural frequency of the suspended pipe, and the critical span length is determined accordingly.
[0051] For example, comparing the detection results obtained based on the 3D model of the subsea pipeline with those obtained individually using SSS, MBS, and SBP detection methods reveals that SSS can only indirectly obtain the state of the unburied pipeline through the geometric relationship between the seabed and the pipeline and high-resolution acoustic images; SBP can only obtain the positional relationship of each cross-section of the pipeline, but cannot continuously detect the entire pipeline. Therefore, compared with the expression of SSS and SBP detection results, MBS detection results and detection results based on the 3D model of the subsea pipeline can directly determine the spatial location of the pipeline, and both have significant advantages. Figure 3 and Figure 4 The results are shown in the 3D model of the subsea pipeline and the MBS detection results. Comparative analysis reveals that the MBS detection results can provide a preliminary assessment of the pipeline's operational status; however, its resolution is lower, and compared to the 3D model detection results, the acoustic images obtained by MBS have more detection blind spots. Figure 3 (a) to Figure 3 of (d), where, Figure 3 Figure 3 (b) to Figure 3 (d) are respectively Figure 3 (a) shows a magnified view of points A, B, and C; the expression of the detection results is clearly insufficient; the 3D model of the subsea pipeline integrates multi-source detection information from SSS, MBS, and SBP, which significantly improves the reliability and stability of the detection results in terms of result expression, see... Figure 4 (a) to Figure 4 of (d), where, Figure 4 Figure 4 (b) to Figure 4 (d) are respectively Figure 4A magnified view of points A, B, and C in (a).
[0052] Table 2 shows the comparison between the calculated length of the submarine pipeline based on the 3D model and the actual pipeline length.
[0053] Table 2 Comparison of calculated length and actual length of the 3D model of the subsea pipeline The results show that the calculated pipeline length based on the 3D model of the subsea pipeline is basically consistent with the actual length (R0). 2 =0.999, with a relative error of less than 1%. Therefore, the accuracy of constructing a 3D model of a subsea pipeline based on multi-source detection information can be considered reliable.
[0054] For example, taking a certain pipe section as an example, the calculation section is selected with a cross-sectional interval of 10 meters. The pipeline operating parameters are shown in Table 3, and the calculation results of the pipe section curvature are shown in Table 4. The critical curvatures of the pipeline in the unburied state and the buried state are 0.0113 and 0.0123, respectively. Figure 5 (a) to Figure 5 (c) represents the critical curvature k. th And respectively with pipe diameter D, wall thickness w t The relationship between temperature difference ∆T and [other factors]. Figure 5 It can be seen that the critical curvature k th And respectively with pipe diameter D, wall thickness w t It is inversely proportional to the temperature difference ∆T. The calculation results of the probability of failure risk of the pipeline section are shown in Table 4.
[0055] Table 3 Calculation parameters for critical curvature of pipeline Table 4. Results of Pipeline Failure Probability Assessment As shown in Table 4, deterministic analysis reveals that pipe section 6 is at risk of failure, requiring timely inspection and maintenance. Probabilistic analysis shows that sections 5-8 have relatively high risks of failure, with probabilities of 0.324, 1.000, 0.071, and 0.214 respectively. This means that sections 5, 7, and 8, except for section 6, all have a high probability of failure, and corresponding inspection and maintenance measures should be taken promptly for different risk levels.
[0056] Taking a certain pipe section as an example, the critical span length of the pipe in the suspended state is calculated to be 10.62m. The critical span length of the pipe in the exposed state varies with the burial depth of the pipe. The relevant calculation parameters are shown in Table 5. The calculation results of the probability of failure of the pipe section are shown in Table 6.
[0057] Table 5 Calculation parameters for critical span Table 6 Safety assessment results of the pipe section As shown in Table 6, deterministic analysis reveals that sections 1, 6, and 8 of the pipeline are at risk of failure. These sections are all suspended sections and require timely inspection and maintenance. Probabilistic analysis shows that, except for section 2 which has a relatively low risk level, the other sections all have significant risks, as shown in Table 6. Corresponding inspection and maintenance measures should be taken promptly. Therefore, the probabilistic analysis of the safety status of subsea pipelines proposed in this invention is more accurate and reliable than deterministic analysis.
[0058] The above describes one or more embodiments of the safety status assessment method for subsea pipelines provided in this specification. Based on the same approach, this specification also provides a corresponding safety status assessment system for subsea pipelines, including: The fusion module is used to fuse the acquired acoustic feature data, geometric feature data, and spatial location data of the subsea pipeline to determine the acoustic detection data; The pipeline edge feature extraction module is used to perform multi-directional adaptive edge detection on acoustic wave detection data using an edge detection algorithm to initially extract pipeline edge features. The coordinate confirmation module is used to identify pipeline edge features, determine the features of the subsea pipeline, extract elevation coordinates based on the features of the subsea pipeline, and determine the horizontal coordinates of the pipeline ridge point on the central axis of the pipeline by performing coordinate transformation on the pixel positions corresponding to the features of the subsea pipeline. The module is used to generate a circular pipe cross-section based on a preset pipe diameter, and to determine the three-dimensional spline curve path of the pipe's central axis by combining horizontal and vertical coordinates. Based on the circular pipe cross-section and the three-dimensional spline curve path, a 3D model of the subsea pipeline is constructed. The evaluation module is used to obtain the distance between any three adjacent sampling points on the central axis of the 3D model of the subsea pipeline. Based on the distance between the three adjacent sampling points, the bending curvature morphology parameters of the pipeline are determined. Based on the horizontal coordinates and the elevation coordinates, the span length of the pipeline is determined. The bending curvature morphology parameters and span length are compared with the corresponding critical curvature and critical span length to obtain the safety status evaluation results of the subsea pipeline.
[0059] Specific limitations regarding the safety status assessment system for subsea pipelines can be found in the limitations of the safety status assessment method for subsea pipelines mentioned above, and will not be repeated here. Each module in the aforementioned safety status assessment system for subsea pipelines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0060] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described method for assessing the safety status of subsea pipelines.
[0061] The present invention also provides Figure 6 The schematic diagram of the computer device shown is as follows: Figure 6 As shown, at the hardware level, the computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the method provided in the above embodiments.
[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.
Claims
1. A method for assessing the safety status of a subsea pipeline, characterized in that, include: The acoustic feature data, geometric feature data, and spatial location data of the acquired subsea pipeline are fused to determine the acoustic wave detection data; An edge detection algorithm is used to perform multi-directional adaptive edge detection on acoustic wave detection data to initially extract pipeline edge features; The pipeline edge features are identified to determine the characteristics of the subsea pipeline. The elevation coordinates are extracted based on the subsea pipeline features. The horizontal coordinates of the pipeline ridge point on the central axis of the pipeline are determined by performing coordinate transformation on the pixel positions corresponding to the subsea pipeline features. Based on the preset pipe diameter, a circular pipe cross-section is generated. Combining horizontal and vertical coordinates, the three-dimensional spline curve path of the pipe's central axis is determined. Based on the circular pipe cross-section and the three-dimensional spline curve path, a 3D model of the subsea pipeline is constructed. Obtain the distance between any three adjacent sampling points on the central axis of the 3D model of the subsea pipeline. Based on the distance between the three adjacent sampling points, determine the curvature parameters of the pipeline. Based on the horizontal coordinates and the elevation coordinates, determine the span of the pipeline. The bending curvature morphology parameters and span length are compared with the corresponding critical curvature and critical span length to obtain the safety status assessment results of the subsea pipeline.
2. The safety status assessment method for subsea pipelines as described in claim 1, characterized in that, The acquisition of the critical curvature and critical span length specifically includes: Based on the longitudinal and circumferential stresses borne by the obtained 3D model of the subsea pipeline, and combined with the Mises equivalent stress criterion, the equivalent stress of the pipeline wall is determined. Based on the equivalent stress of the pipeline wall and the yield stress of the pipe material, the critical curvature of the pipeline bending is determined. Based on the hydrodynamic forces, seabed resistance, and gravity experienced by the subsea pipeline, differential governing equations for the subsea pipeline are established. Based on these differential governing equations, the critical span length of the unburied pipeline is obtained. The critical span length includes the critical span length of the unburied pipeline and the critical span length of the suspended pipeline.
3. The safety status assessment method for subsea pipelines as described in claim 1, characterized in that, The process of determining the horizontal coordinates of the pipeline ridge point on the pipeline's central axis by performing coordinate transformation on the pixel positions corresponding to the features of the subsea pipeline specifically includes: The horizontal coordinates of the pipe ridge point are obtained using the following formula: In the formula, ( X i , Y i The horizontal coordinate of the i-th detection point on the ridge line; x i , y i ) is the coordinate ( X i , Y i (x0, y0) corresponds to the coordinates of the feature point of the pipeline; (x0, y0) is the known reference coordinate of the acoustic map; scale is the scale between the actual coordinates and the pixel coordinates.
4. The safety status assessment method for subsea pipelines as described in claim 1, characterized in that, The acoustic feature data, geometric feature data, and spatial location data were acquired using a side-scan sonar system (SSS), a multibeam sonar system (MBS), and a shallow profiler (SBP), respectively.
5. The safety status assessment method for subsea pipelines as described in claim 1, characterized in that, The horizontal coordinates of the pipeline ridge points are smoothed using a B-spline function to obtain a smooth ridge line for the subsea pipeline.
6. The safety status assessment method for subsea pipelines as described in claim 1, characterized in that, The acquisition of the bending curvature parameters of the pipeline specifically includes: The bending curvature morphology parameters are obtained using the following formula: in, P 01 , P 02 , P 12 These are continuous sampling points on the 3D model of the subsea pipeline. P 1 , P 0 , P 2 The spacing between them; k P for P The curvature of the pipe at the point.
7. The safety status assessment method for subsea pipelines as described in claim 1, characterized in that, The edge detection algorithm is the Kirsch operator edge detection algorithm; the pipe edge features are identified by using an Active Contour Model (ACM); wherein the Active Contour Model (ACM) is the Snake model.
8. A safety status assessment system for subsea pipelines, characterized in that, include: The fusion module is used to fuse the acquired acoustic feature data, geometric feature data, and spatial location data of the subsea pipeline to determine the acoustic detection data; The pipeline edge feature extraction module is used to perform multi-directional adaptive edge detection on acoustic wave detection data using an edge detection algorithm to initially extract pipeline edge features. The coordinate confirmation module is used to identify pipeline edge features, determine the features of the subsea pipeline, extract elevation coordinates based on the features of the subsea pipeline, and determine the horizontal coordinates of the pipeline ridge point on the central axis of the pipeline by performing coordinate transformation on the pixel positions corresponding to the features of the subsea pipeline. The module is used to generate a circular pipe cross-section based on a preset pipe diameter, and to determine the three-dimensional spline curve path of the pipe's central axis by combining horizontal and vertical coordinates. Based on the circular pipe cross-section and the three-dimensional spline curve path, a 3D model of the subsea pipeline is constructed. The evaluation module is used to obtain the distance between any three adjacent sampling points on the central axis of the 3D model of the subsea pipeline. Based on the distance between the three adjacent sampling points, the bending curvature morphology parameters of the pipeline are determined; and based on the horizontal coordinates and the elevation coordinates, the span length of the pipeline is determined. The bending curvature morphology parameters and span length are compared with the corresponding critical curvature and critical span length to obtain the safety status assessment results of the subsea pipeline.
9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the safety status assessment method for submarine pipelines as described in any one of claims 1-7.
10. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the safety status assessment method for the subsea pipeline according to any one of claims 1-7.