Rock debris cleaning method based on ocean platform

Abstract

The invention relates to the technical field of cleaning of rock debris at flat pile legs on the sea, in particular to a rock debris cleaning method based on an ocean platform. The method comprises the following steps: scanning an area outside a pile leg of a WHPB wellhead platform in the Bohai Sea area, obtaining data of a rock debris scattering area and a stacking height thereof, and generating a three-dimensional model of rock debris distribution; calculating an optimal obstacle avoidance path of the main workboat and the tug based on the three-dimensional model; the height of a ditcher nozzle and the height of the surface of the rock debris are matched in real time, and the ditcher nozzle is controlled to conduct sweeping and blowing based on the optimal obstacle avoidance path; after the purging process is completed, scanning is conducted again, the rock debris scattering area and stacking height data before and after purging are compared, and whether the purging effect reaches the expected standard or not is evaluated. According to the design, the problem that rock debris accumulates near a platform pile leg carrying an inherent drilling machine module is solved, existing equipment is ingeniously used within the reasonable cost reduction range, and the influence on subsequent later-stage mixed transportation, water injection vertical pipe and cable protection pipe installation is avoided.

Description

Technical Field

[0001] This invention relates to the field of rock debris removal technology at flat pile legs at sea, and more specifically, to a rock debris removal method based on an offshore platform. Background Technology

[0002] During offshore oil drilling and production, a large amount of rock cuttings (fragments generated during drilling) are produced. If these rock cuttings are not properly handled, they will pollute the surrounding marine ecosystem. Traditional cleanup methods may require sending divers for manual observation and cleanup, which poses a considerable risk when dealing with rock cuttings accumulations of a certain height that could cause landslides or other dangerous situations. In addition, the complex and variable seabed environment increases the difficulty of cleanup work. Therefore, this paper proposes a rock cuttings cleanup method based on an offshore platform. Summary of the Invention

[0003] The purpose of this invention is to provide a method for cleaning rock cuttings from an offshore platform, in order to solve the problems of complexity and high risk of traditional rock cutting methods in the above-mentioned background art.

[0004] To achieve the above objectives, the present invention aims to provide a method for cleaning rock cuttings from an offshore platform, comprising the following steps:

[0005] S1. Scan the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea region to obtain data on the rock cuttings distribution area and its accumulation height, and generate a three-dimensional model of the rock cuttings distribution.

[0006] S2. Based on the three-dimensional model, calculate the optimal obstacle avoidance path between the main working vessel and the tugboat;

[0007] S3. Real-time matching of the trencher nozzle height and the rock cutting surface height, and control of the trencher nozzle for sweeping and blowing based on the optimal obstacle avoidance path;

[0008] S4. After the purging process is completed, a second scan is performed to compare the data on the area of ​​rock debris dispersion and its accumulation height before and after purging, and to evaluate whether the purging effect has met the expected standards.

[0009] As a further improvement to this technical solution, in step S1, the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea region is scanned to obtain data on the rock cuttings distribution area and its accumulation height, generating a three-dimensional model of the rock cuttings distribution and a preliminary cleanup plan, including the following steps:

[0010] S1.1 Use three-dimensional sonar to perform a full-range scan of the predetermined area to generate point cloud data;

[0011] S1.2 Import the data acquired by the three-dimensional sonar into the point cloud processing software;

[0012] S1.3. Use filtering algorithms to remove noise points generated during the scanning process;

[0013] S1.4 Determine the accumulation height of rock debris using point cloud data after removing noise points;

[0014] S1.5 Using the triangulation method, a three-dimensional surface model of the rock debris dispersion area is created based on point cloud data and the accumulation height of rock debris.

[0015] As a further improvement to this technical solution, step S1.3 involves using a filtering algorithm to remove noise points generated during the scanning process, including the following steps:

[0016] S1.31, Determine the neighborhood radius;

[0017] S1.32. For each point in the point cloud data, calculate the average distance between it and all other points in its neighborhood;

[0018] S1.33. Based on the average distance, further calculate the standard deviation of the average distance;

[0019] S1.34. Set a threshold a; points exceeding this threshold are considered noise points.

[0020] S1.35. Compare the average distance of each point with its corresponding threshold. If the average distance of a point is greater than the average distance of its neighboring points, then mark it as a noise point.

[0021] S1.36. Remove all points marked as noise points from the original point cloud data.

[0022] As a further improvement to this technical solution, in step S1.5, a three-dimensional surface model of the rock debris dispersion area is created based on point cloud data and the accumulation height of rock debris using a triangulation method, including the following steps:

[0023] S1.51. Select a point in the point cloud data and find a certain number n neighboring points in its neighborhood.

[0024] S1.52 Calculate the covariance matrix of the nearest points, and solve for the eigenvalues ​​and eigenvectors of the covariance matrix;

[0025] S1.53. Select the eigenvector corresponding to the smallest eigenvalue as the normal vector of that point;

[0026] S1.54. Connect adjacent points into triangles using Delaunay triangulation to form a surface grid for the rock debris region.

[0027] As a further improvement to this technical solution, in step S2, the optimal obstacle avoidance path between the main working vessel and the tugboat is calculated based on the three-dimensional model, including the following steps:

[0028] S2.1. Use GPS technology to determine the current coordinates and heading angle of the main working vessel, and obtain the current position of the tugboat and its direction and distance relative to the main working vessel;

[0029] S2.2 Determine the target location where the trencher needs to be lowered;

[0030] S2.3. Convert the 3D model into a discretized mesh;

[0031] S2.4 Calculate the optimal obstacle avoidance path between the main working vessel and the tugboat using Dijkstra's algorithm;

[0032] S2.5 Decompose the calculated optimal obstacle avoidance path into movement commands and send them to the control systems of the main work vessel and the tugboat.

[0033] As a further improvement to this technical solution, in step S2.3, the optimal obstacle avoidance path between the main working vessel and the tugboat is calculated using Dijkstra's algorithm, including the following steps:

[0034] S2.31. For all nodes in the discretized mesh, set the initial distance and set the predecessor node for all nodes;

[0035] S2.32. Create a priority queue and add all nodes to it, sorting them according to their distance values.

[0036] S2.33. Select the node with the smallest distance from the set of unprocessed nodes and mark it as processed;

[0037] S2.34. For each neighboring node of a node, calculate the new distance to the neighboring node through the node, and optimize the calculation process of the new distance from the node to the neighboring node for the multi-constraint path planning problem in the scenario of rock cuttings cleaning on an offshore platform.

[0038] S2.35. After the endpoint is processed, the optimal obstacle avoidance path from the starting point to the endpoint is reconstructed by backtracking the predecessor node array.

[0039] As a further improvement to this technical solution, in S2.34, the new distance from a node to its neighboring node is:

[0040] alt = d[u] + weight(u, v);

[0041] Where alt represents the new distance from a node to its neighboring nodes; u represents a node; v represents the neighboring nodes of node u; and d[u] represents the distance of the currently known shortest path from the starting point to node u.

[0042] To address the multi-constraint path planning problem in the scenario of cuttings removal on offshore platforms, the process of calculating the new distance from a node to its neighboring nodes is optimized by decomposing weight(u, v) into a three-dimensional composite function:

[0043] weight(u,v)=α·db+β·fc+γ·fr;

[0044] Where db represents the basic geometric distance; fc represents the ocean current drag coefficient; fr represents the safety distance penalty term; α represents the weighting coefficient of the basic geometric distance; β represents the weighting coefficient of the ocean current drag coefficient; and γ represents the weighting coefficient of the safety distance penalty term.

[0045] In summary, the optimized new distance alt1 from the node to its neighboring nodes is:

[0046] alt1=d[u]+α·db+β·fc+γ·fr.

[0047] As a further improvement to this technical solution, step S3, which involves real-time matching of the trencher nozzle height with the rock cuttings surface height, includes the following steps:

[0048] S3.1 Divide the entire area to be swept and bleed into a grid with dimensions of 2m×2m, and each grid represents an independent cleaning unit;

[0049] S3.2 Select the height of the trencher nozzle relative to the rock cutting surface as the control variable, and initialize the PID parameters;

[0050] S3.3 Use sensors to monitor the distance between the trencher nozzle and the rock cutting surface in real time;

[0051] S3.4 Compare the target height with the actual measured height to obtain the error value;

[0052] S3.5. Based on the error value, calculate the control variable that needs to be adjusted using the PID controller.

[0053] As a further improvement to this technical solution, in step S3, the trenching machine nozzle is controlled to perform sweeping and purging based on the optimal obstacle avoidance path, including the following steps:

[0054] S3.6 Starting from the predetermined starting point, select the first 2m×2m grid as the first cleaning unit;

[0055] S3.7 Use a PID controller to dynamically adjust the nozzle height to ensure that the optimal spray distance is maintained throughout the purging process;

[0056] S3.8. Purge the selected grid area until the rock debris height is reduced to below the threshold b.

[0057] S3.9 After completing the purging of the current grid, move to the next adjacent grid according to the pre-set optimal obstacle avoidance path planning, and repeat the above purging process.

[0058] As a further improvement to this technical solution, in step S4, after the purging process is completed, a second scan is performed to compare the data on the rock debris dispersion area and its accumulation height before and after purging, and to evaluate whether the purging effect has met the expected standard. This includes the following steps:

[0059] S4.1. Use a three-dimensional sonar system to perform a comprehensive scan of the area that has been purged;

[0060] S4.2 Compare the height difference before and after purging at the same location;

[0061] S4.3 Calculate the change in the average height of rock cuttings before and after purging in the entire area, as a measure of the overall purging effect;

[0062] S4.4, and judge the actual sweeping effect based on the measurement criteria.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0064] This method for cleaning cuttings from offshore platforms utilizes advanced 3D sonar scanning technology to accurately acquire data on the distribution and accumulation height of cuttings in the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea, generating a detailed 3D model. This not only improves the planning efficiency of cuttings cleaning operations but also significantly reduces the risks associated with human underwater observation. It involves fewer pieces of equipment, and the operation process is simple and clear, demonstrating high efficiency and ease of maintenance. While reasonably controlling costs, it avoids impacting subsequent operations, resolving the problem of cuttings accumulation near the platform legs carrying inherent drilling rig modules. It cleverly utilizes existing equipment within a reasonable cost-reduction range, avoiding any impact on subsequent mixed-transport, water injection riser, and cable conduit installation. Cuttings cleaning of the affected area needs to be completed before these other tasks are carried out to ensure the smooth operation of subsequent work. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the overall method of the present invention;

[0066] Figure 2 This is a schematic diagram of the overall underwater construction process in an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of an underwater partial construction method in an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram showing the distribution of vessels operating on water in an embodiment of the present invention.

[0069] Figure 5 This is a flowchart of the WHPB cave cleaning process in an embodiment of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0071] Example: Please refer to Figure 1-5 As shown, this embodiment provides a method for cleaning rock cuttings from an offshore platform, including the following steps:

[0072] S1. Scan the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea region to obtain data on the rock cuttings distribution area and its accumulation height, and generate a three-dimensional model of the rock cuttings distribution.

[0073] This embodiment includes a series of vessels, a non-contact trenching machine, and 3D sonar equipment. First, sonar scanning is used to determine the area and height of rock debris accumulation. Through coordinated operation of the vessels and the trenching machine, the accumulated rock debris near the pile legs is blown away. After a period of time, the sonar is used again to scan the rock debris height, and this process is repeated until all rock debris is cleared. During construction, the progress of rock debris clearing needs to be monitored in real time to adjust the trenching machine's trajectory. However, because the rock debris has reached a certain height, it is prone to collapse and other problems. Sending divers to manually observe the debris is quite dangerous; therefore, 3D sonar is used for monitoring throughout the entire construction process.

[0074] A scan was performed on the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea region to obtain data on the area of ​​rock cuttings dispersion and their accumulation height. A three-dimensional model of the rock cuttings distribution and a preliminary cleanup plan were generated, including the following steps:

[0075] S1.1 Use three-dimensional sonar to perform a full-range scan of the predetermined area to generate point cloud data;

[0076] S1.2 Import the data acquired by the three-dimensional sonar into the point cloud processing software;

[0077] S1.3. Use filtering algorithms (such as statistical outlier removal) to remove noise points generated during the scanning process to improve data quality. If the same area is scanned multiple times, these point cloud data need to be registered to ensure that they can be accurately aligned in space.

[0078] The process of removing noise points generated during the scanning process using filtering algorithms (such as statistical outlier removal) includes the following steps:

[0079] Filtering algorithms are statistical outlier removal methods used in point cloud data processing to remove noisy points. This method first determines the neighborhood radius of each point and calculates the average distance and standard deviation between that point and all other points in its neighborhood to measure the distance variation within the region. Then, a threshold is set; points exceeding this threshold are considered noise and removed from the original dataset. This method effectively improves the quality of point cloud data, ensuring that the subsequently generated 3D surface models are more accurate and reliable. During 3D scanning, inaccurate data points (i.e., noise) may be generated due to equipment errors, environmental interference, or object surface characteristics. Removing these noise points through filtering algorithms can significantly improve the accuracy of the dataset; 3D surface models created based on cleaner data will be closer to reality, reducing geometric distortions or erroneous features caused by noise. This is crucial for generating high-quality 3D models of debris-strewn areas.

[0080] S1.31 Determine the neighborhood radius. This radius determines the range within which each point searches for its neighbors. It usually needs to be adjusted based on the actual data density and the desired smoothness.

[0081] S1.32. For each point in the point cloud data, calculate the average distance between it and all other points in its neighborhood. This can be done by iterating through the neighborhood of each point, calculating the sum of the distances, and then dividing by the number of neighborhood points.

[0082] S1.33. Based on the average distance, further calculate the standard deviation of the average distance to measure the distance variation of points in the region;

[0083] S1.34. Set a threshold a; points exceeding this threshold are considered noise points.

[0084] S1.35. Compare the average distance of each point with its corresponding threshold. If the average distance of a point is greater than the average distance of its neighboring points, it is marked as a noise point.

[0085] S1.36. Remove all points marked as noise from the original point cloud data to obtain a cleaner and more accurate dataset;

[0086] S1.4 Determine the accumulation height of rock debris using point cloud data after removing noise points;

[0087] S1.5 Using the triangulation method, a three-dimensional surface model of the rock debris dispersion area is created based on point cloud data and the accumulation height of rock debris.

[0088] Triangulation is a technique for creating 3D surface models based on point cloud data. It involves selecting a point and several points in its neighborhood within the point cloud, calculating the covariance matrix of these points, and solving for their eigenvalues ​​and eigenvectors to determine the normal vector. Then, the Delaunay triangulation algorithm is used to connect adjacent points into triangles, forming a surface mesh. This method aims to maximize the minimum angle and avoid generating overly long triangles, thus ensuring mesh quality and ultimately constructing an accurate and smooth 3D surface model. Triangulation can construct high-precision 3D surface models based on actual measurements in point cloud data, accurately reproducing the real terrain features of debris-strewn areas, which is crucial for understanding debris distribution. This method can flexibly handle arbitrarily complex geometries; by converting point cloud data into a mesh composed of numerous small triangles, even irregular or steep terrain can be effectively represented.

[0089] Using triangulation methods, a 3D surface model of the debris dispersion area is created based on point cloud data and the accumulation height of the debris, including the following steps:

[0090] S1.51. Select a point in the point cloud data and find a certain number of n neighboring points in its neighborhood (determined by KD tree or octree).

[0091] S1.52 Calculate the covariance matrix of neighboring points (the calculation of the covariance matrix first requires determining the centroid of a point and other points in its neighborhood, then calculating the deviation vector of each point relative to the centroid, and then constructing the covariance matrix through these deviation vectors), and solve for the eigenvalues ​​and eigenvectors of the covariance matrix.

[0092] S1.53. Select the eigenvector corresponding to the smallest eigenvalue as the normal vector of that point;

[0093] S1.54. Connect adjacent points into triangles using Delaunay triangulation to form a surface mesh for the rock debris area (this algorithm aims to maximize the minimum angle, thereby avoiding the generation of overly long triangles and ensuring mesh quality). During this process, the software will automatically construct a surface mesh composed of triangles based on the input points and their normal vector information.

[0094] S2. Based on the three-dimensional model, calculate the optimal obstacle avoidance path between the main working vessel and the tugboat;

[0095] In this embodiment, the optimal obstacle avoidance path between the main working vessel and the tugboat is calculated based on a three-dimensional model, including the following steps:

[0096] Considering the interference between the ship's structure and the platform's structure, the auxiliary tugboat cooperated with the main working vessel to successfully lower the trencher to a suitable dredging position. At the same time, two limiting steel wire ropes were added during the lowering process of the trencher to prevent the trencher from rotating and getting tangled with the power cable while blowing away rock debris.

[0097] S2.1. Use GPS technology to determine the current coordinates and heading angle of the main working vessel, and obtain the current position of the tugboat and its direction and distance relative to the main working vessel;

[0098] S2.2 Determine the target location where the trencher needs to be lowered;

[0099] S2.3. Convert the 3D model into a discretized mesh or graph structure, where each node represents a reachable location and an edge indicates whether there is an unobstructed direct connection between two locations.

[0100] S2.4 Calculate the optimal obstacle avoidance path between the main working vessel and the tugboat using Dijkstra's algorithm;

[0101] Dijkstra's algorithm is a classic algorithm for calculating the single-source shortest path in a weighted graph. Starting from the initial node, it progressively determines the shortest path to every other node in the graph, prioritizing the expansion to the node with the closest current distance and updating the shortest path estimates of its neighbors, until the target node is found or the entire graph is traversed. With all edge weights non-negative, Dijkstra's algorithm guarantees finding the shortest path from the starting point to any other node (including the target location). This is crucial for offshore platform debris removal tasks that require precise control and efficient operation. Many potential obstacles exist around offshore platforms, such as platform legs, other equipment, and seabed topography. Using Dijkstra's algorithm can help identify safe paths that avoid these obstacles, reducing collision risks and ensuring operational safety. The algorithm finds the shortest path (or the path with minimum cost) from the starting point to the target point, which not only reduces travel time but also saves fuel consumption, improving the overall operational efficiency of the debris removal task.

[0102] The optimal obstacle avoidance path between the main work vessel and the tugboat is calculated using Dijkstra's algorithm, including the following steps:

[0103] S2.31. For all nodes in the discretized mesh, set the initial distance and set the predecessor node for all nodes;

[0104] S2.32. Create a priority queue and add all nodes to it, sorting them according to their distance values.

[0105] S2.33. Select the node with the smallest distance from the set of unprocessed nodes and mark it as processed;

[0106] S2.34. For each neighboring node of a node, calculate the new distance to the neighboring node through the node, and optimize the calculation process of the new distance from the node to the neighboring node for the multi-constraint path planning problem in the scenario of rock cuttings cleaning on an offshore platform.

[0107] The new distance from a node to its neighboring nodes is:

[0108] alt = d[u] + weight(u, v);

[0109] Where alt represents the new distance from a node to its neighboring nodes; u represents a node; v represents the neighboring nodes of node u; and d[u] represents the distance of the currently known shortest path from the starting point to node u.

[0110] Ocean currents affect ship movement, with different directions and speeds increasing or decreasing navigation difficulty. By introducing a current resistance coefficient and adjusting weights based on the angle between the ship's speed vector and the current's speed vector, energy consumption under actual navigation conditions can be more accurately reflected, allowing for the selection of safe and energy-efficient routes. When operating on offshore platforms, avoiding collisions with other structures (such as platform legs, routing areas, etc.) is crucial. Setting a safety distance penalty and dynamically adjusting weights based on the distance from a node to the nearest obstacle can automatically avoid potentially hazardous areas during route planning, ensuring operational safety.

[0111] To address the multi-constraint path planning problem in the scenario of cuttings removal on offshore platforms, the process of calculating the new distance from a node to its neighboring nodes is optimized by decomposing weight(u, v) into a three-dimensional composite function:

[0112] weight(u,v)=α·db+β·fc+γ·fr;

[0113]

[0114] fr=k·e (20-do) / 5 ;

[0115] Where db represents the basic geometric distance; fc represents the ocean current drag coefficient; fr represents the safety distance penalty term; α represents the weighting coefficient of the basic geometric distance; β represents the weighting coefficient of the ocean current drag coefficient; γ represents the weighting coefficient of the safety distance penalty term; vc represents the real-time ocean current velocity vector (from the ocean forecasting system); vs represents the ship speed vector; θ represents the angle between the ship's heading and the direction of the ocean current; do represents the distance from node v to the nearest obstacle (platform leg / routing area); k represents the penalty coefficient (default k = 10);

[0116] In summary, the optimized new distance alt1 from the node to its neighboring nodes is:

[0117] alt1=d[u]+α·db+β·fc+γ·fr.

[0118] S2.35. After the endpoint is processed, the optimal obstacle avoidance path from the starting point to the endpoint is reconstructed by backtracking the predecessor node array.

[0119] S2.5 Decompose the calculated optimal obstacle avoidance path into movement commands and send them to the control systems of the main work vessel and the tugboat.

[0120] S3. Real-time matching of the trencher nozzle height and the rock cutting surface height, and control of the trencher nozzle for sweeping and blowing based on the optimal obstacle avoidance path;

[0121] In this embodiment, the PID controller is an automatic control system that adjusts the control output through proportional, integral, and derivative calculations to minimize the error between the target value and the actual measured value. The PID controller can precisely adjust based on the error between the actual measured value (i.e., the distance between the trencher nozzle and the rock debris surface) and the target value (the desired optimal spray height). This ensures that the nozzle remains at an optimal height throughout the cleaning process, thereby improving cleaning efficiency and effectiveness. The marine environment is complex and variable, and the height and density of rock debris accumulation may vary. The PID controller can respond to these changes in real time, automatically adjusting the nozzle height to adapt to different working conditions without manual intervention, enhancing the system's adaptability. By maintaining the optimal spray height, the energy of water flow or other media can be maximized, ensuring both shoveling effectiveness and energy conservation. Simultaneously, the efficient cleaning process also helps shorten project cycles and reduce overall costs.

[0122] Real-time matching of the trencher nozzle height with the rock cuttings surface height includes the following steps:

[0123] S3.1 Divide the entire area to be swept and bleed into a grid with dimensions of 2m×2m, and each grid represents an independent cleaning unit;

[0124] S3.2 Select the height of the trencher nozzle relative to the rock cutting surface as the control variable, and initialize the PID parameters;

[0125] S3.3 Use sensors (laser rangefinder or ultrasonic sensor) to monitor the distance between the trencher nozzle and the rock cutting surface in real time;

[0126] S3.4 Compare the target height (i.e., the optimal spray height to be maintained) with the actual measured height to obtain the error value;

[0127] S3.5. Based on the error value, calculate the control variable that needs to be adjusted using the PID controller.

[0128] Furthermore, the purging is carried out in groups and in batches. After each purging, the rock debris surface is scanned again using sonar to determine the location of the optimal purging point for the next purging. This process is repeated until all the rock debris near the platform legs has been removed.

[0129] The trenching machine's nozzles are controlled based on the optimal obstacle avoidance path for sweeping and purging, including the following steps:

[0130] S3.6 Starting from the predetermined starting point, select the first 2m×2m grid as the first cleaning unit;

[0131] S3.7 Use a PID controller to dynamically adjust the nozzle height to ensure that the optimal spray distance is maintained throughout the purging process;

[0132] S3.8. Purge the selected grid area until the rock debris height is reduced to below the threshold b.

[0133] S3.9 After completing the purging of the current grid, move to the next adjacent grid according to the pre-set optimal obstacle avoidance path planning, and repeat the above purging process.

[0134] S4. After the purging process is completed, a second scan is performed to compare the data on the debris dispersion area and its accumulation height before and after purging, and to evaluate whether the purging effect has met the expected standards.

[0135] In this embodiment, after the purging process is completed, a second scan is performed to compare the data on the rock debris dispersion area and its accumulation height before and after purging, and to evaluate whether the purging effect has met the expected standard. This includes the following steps:

[0136] S4.1. Use a three-dimensional sonar system to perform a comprehensive scan of the area that has been purged;

[0137] S4.2 Compare the height difference before and after purging at the same location;

[0138] S4.3 Calculate the change in the average height of rock cuttings before and after purging in the entire area, as a measure of the overall purging effect;

[0139] S4.4, and judge the actual sweeping effect based on the measurement criteria.

[0140] The specific process in this embodiment is as follows (the flowchart is as follows): Figure 5 As shown):

[0141] The main work vessel anchored and positioned itself northeast of the WHPB platform, away from the platform. It used 3D sonar to conduct a 3D imaging survey of the area outside the platform's legs to determine the area and height of rock debris dispersion, as well as areas affecting the routing of risers, expansion bends, etc. (e.g., Figure 2 (as shown);

[0142] Due to the obstruction of the flare arm, the vessel could not hoist the trenching machine into position. A tugboat was used, positioned alongside the vessel's port side with its stern protruding beyond the vessel's position. The trenching machine was launched from the stern of the tugboat, allowing it to hover as close to the rock cuttings as possible. The tugboat moved forward, and the main work vessel was connected to the platform's legs via cross cables. The tugboat served as the working anchor on the port aft side of the vessel (facilitating tugboat positioning). It then joined the main work vessel to its port side, hoisted the trenching machine onto the tugboat's deck, and, after connecting it to the tugboat's main tow cable, slowly lowered it to the stern of the vessel. Once the tugboat's deck personnel disconnected the main work vessel's rigging, the tugboat's main rigging, under tension, slowly lowered the trenching machine below the waterline (e.g., ...). Figure 4 (as shown);

[0143] The main work vessel slowly moves towards the platform to the rock cuttings removal area. The trenching machine hovers at a height of 6-8 meters above the top of the rock cuttings, adjusting the height of the trenching machine and the rock cuttings in 0.5-meter increments each time to find the optimal hovering height for blowing away the rock cuttings. A trial blowing of the rock cuttings begins, lasting 5 minutes. The blowing effect is then monitored using 3D sonar. If the effect is satisfactory, blowing of other areas continues at this height. The height of the trenching machine is adjusted appropriately according to the height of the rock cuttings accumulation, ensuring a consistent height from the top of the rock cuttings. After the entire blowing process is completed, a post-blowout survey is conducted using 3D sonar to determine the final blowing effect (e.g., ...). Figure 3 (As shown).

[0144] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for cleaning rock cuttings from an offshore platform, characterized in that, Includes the following steps: S1. Scan the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea region to obtain data on the rock cuttings distribution area and its accumulation height, and generate a three-dimensional model of the rock cuttings distribution. S2. Based on the three-dimensional model, calculate the optimal obstacle avoidance path between the main working vessel and the tugboat; S3. Real-time matching of the trencher nozzle height and the rock cutting surface height, and control of the trencher nozzle for sweeping and blowing based on the optimal obstacle avoidance path; S4. After the purging process is completed, a second scan is performed to compare the data on the area of ​​rock debris dispersion and its accumulation height before and after purging, and to evaluate whether the purging effect has met the expected standards.

2. The method for cleaning rock cuttings based on an offshore platform according to claim 1, characterized in that: In step S1, the outer area of ​​the WHPB wellhead platform legs in the Bohai Sea region is scanned to obtain data on the rock cuttings distribution area and its accumulation height, generating a three-dimensional model of the rock cuttings distribution and a preliminary cleanup plan, including the following steps: S1.1 Use three-dimensional sonar to perform a full-range scan of the predetermined area to generate point cloud data; S1.2 Import the data acquired by the three-dimensional sonar into the point cloud processing software; S1.

3. Use filtering algorithms to remove noise points generated during the scanning process; S1.4 Determine the accumulation height of rock debris using point cloud data after removing noise points; S1.5 Using the triangulation method, a three-dimensional surface model of the rock debris dispersion area is created based on point cloud data and the accumulation height of rock debris.

3. The method for cleaning rock cuttings based on an offshore platform according to claim 2, characterized in that: In step S1.3, the noise points generated during the scanning process are removed using a filtering algorithm, including the following steps: S1.31, Determine the neighborhood radius; S1.

32. For each point in the point cloud data, calculate the average distance between it and all other points in its neighborhood; S1.

33. Based on the average distance, further calculate the standard deviation of the average distance; S1.

34. Set a threshold a; points exceeding this threshold are considered noise points. S1.

35. Compare the average distance of each point with its corresponding threshold. If the average distance of a point is greater than the average distance of its neighboring points, then mark it as a noise point. S1.

36. Remove all points marked as noise points from the original point cloud data.

4. The method for cleaning rock cuttings based on an offshore platform according to claim 3, characterized in that: In step S1.5, a three-dimensional surface model of the rock debris dispersion area is created using a triangulation method based on point cloud data and the accumulation height of the rock debris, including the following steps: S1.

51. Select a point in the point cloud data and find a certain number n neighboring points in its neighborhood. S1.52 Calculate the covariance matrix of the nearest points, and solve for the eigenvalues ​​and eigenvectors of the covariance matrix; S1.

53. Select the eigenvector corresponding to the smallest eigenvalue as the normal vector of that point; S1.

54. Connect adjacent points into triangles using Delaunay triangulation to form a surface grid for the rock debris region.

5. The method for cleaning rock cuttings from an offshore platform according to claim 4, characterized in that: In step S2, based on the three-dimensional model, the optimal obstacle avoidance path for the main working vessel and the tugboat is calculated, including the following steps: S2.

1. Use GPS technology to determine the current coordinates and heading angle of the main working vessel, and obtain the current position of the tugboat and its direction and distance relative to the main working vessel; S2.2 Determine the target location where the trencher needs to be lowered; S2.

3. Convert the 3D model into a discretized mesh; S2.4 Calculate the optimal obstacle avoidance path between the main working vessel and the tugboat using Dijkstra's algorithm; S2.5 Decompose the calculated optimal obstacle avoidance path into movement commands and send them to the control systems of the main work vessel and the tugboat.

6. The method for cleaning rock cuttings based on an offshore platform according to claim 5, characterized in that: In step S2.3, the optimal obstacle avoidance path between the main working vessel and the tugboat is calculated using Dijkstra's algorithm, including the following steps: S2.

31. For all nodes in the discretized mesh, set the initial distance and set the predecessor node for all nodes; S2.

32. Create a priority queue and add all nodes to it, sorting them according to their distance values. S2.

33. Select the node with the smallest distance from the set of unprocessed nodes and mark it as processed; S2.

34. For each neighboring node of a node, calculate the new distance to the neighboring node through the node, and optimize the calculation process of the new distance from the node to the neighboring node for the multi-constraint path planning problem in the scenario of rock cuttings cleaning on an offshore platform. S2.

35. After the endpoint is processed, the optimal obstacle avoidance path from the starting point to the endpoint is reconstructed by backtracking the predecessor node array.

7. The method for cleaning rock cuttings based on an offshore platform according to claim 6, characterized in that: In S2.34, the new distance from a node to its neighboring node is: alt = d[u] + weight(u, v); Where alt represents the new distance from a node to its neighboring nodes; u represents a node; v represents the neighboring nodes of node u; and d[u] represents the distance of the currently known shortest path from the starting point to node u. To address the multi-constraint path planning problem in the scenario of cuttings removal on offshore platforms, the process of calculating the new distance from a node to its neighboring nodes is optimized by decomposing weight(u, v) into a three-dimensional composite function: weight(u,v)=α·db+β·fc+γ·fr; Where db represents the basic geometric distance; fc represents the ocean current drag coefficient; fr represents the safety distance penalty term; α represents the weighting coefficient of the basic geometric distance; β represents the weighting coefficient of the ocean current drag coefficient; and γ represents the weighting coefficient of the safety distance penalty term. In summary, the optimized new distance alt1 from the node to its neighboring nodes is: alt1=d[u]+α·db+β·fc+γ·fr.

8. The method for cleaning rock cuttings from an offshore platform according to claim 7, characterized in that: In step S3, the real-time matching of the trencher nozzle height with the rock cutting surface height includes the following steps: S3.1 Divide the entire area to be swept and bleed into a grid with dimensions of 2m×2m, and each grid represents an independent cleaning unit; S3.2 Select the height of the trencher nozzle relative to the rock cutting surface as the control variable, and initialize the PID parameters; S3.3 Use sensors to monitor the distance between the trencher nozzle and the rock cutting surface in real time; S3.4 Compare the target height with the actual measured height to obtain the error value; S3.

5. Based on the error value, calculate the control variable that needs to be adjusted using the PID controller.

9. The method for cleaning rock cuttings from an offshore platform according to claim 8, characterized in that: In step S3, the trenching machine nozzles are controlled to perform sweeping and purging based on the optimal obstacle avoidance path, including the following steps: S3.6 Starting from the predetermined starting point, select the first 2m×2m grid as the first cleaning unit; S3.7 Use a PID controller to dynamically adjust the nozzle height to ensure that the optimal spray distance is maintained throughout the purging process; S3.

8. Purge the selected grid area until the rock debris height is reduced to below the threshold b. S3.9 After completing the purging of the current grid, move to the next adjacent grid according to the pre-set optimal obstacle avoidance path planning, and repeat the above purging process.

10. The method for cleaning rock cuttings based on an offshore platform according to claim 9, characterized in that: In step S4, after the purging process is completed, a second scan is performed to compare the data on the debris dispersion area and its accumulation height before and after purging, and to evaluate whether the purging effect has met the expected standard. This includes the following steps: S4.

1. Use a three-dimensional sonar system to perform a comprehensive scan of the area that has been purged; S4.2 Compare the height difference before and after purging at the same location; S4.3 Calculate the change in the average height of rock cuttings before and after purging in the entire area, as a measure of the overall purging effect; S4.4, and judge the actual sweeping effect based on the measurement criteria.

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