A method of tracking a pig for a pipeline

Abstract

The application discloses a pipeline pig tracking method, and relates to the field of pig tracking. The method finds that the existing optical fiber sensing tracking system has the defect of blind area when tracking the pig. The method firstly performs multiple operations of the pig on the pipeline, then collects vibration data, analyzes the vibration data, accurately locates the blind area, installs a calibration box in the corresponding area of the blind area, and obtains the pig final passing time corresponding to the blind area by summing up the pig passing time measured by the calibration box and the pig passing time measured by the optical fiber sensing tracking system, so that the blind area tracking of the pig is realized.

Description

Technical Field

[0001] This invention relates to the field of pipeline pig positioning, and more specifically, to a tracking method for a pipeline pig. Background Technology

[0002] A pipeline pig is a specialized tool propelled by gas, liquid, or pipeline transport media, used to clean pipelines. It can carry an electromagnetic transmitter and ground receiving instrument to form an electronic tracking system, and can also be equipped with other accessories to complete various complex pipeline operations.

[0003] During pipeline operation, pigs need to be located. Existing pig location methods rely on distributed fiber optic sensing for tracking. This method utilizes optical fibers laid in the same trench as the pipeline to transmit vibration signals from the pig as it passes through the pipeline. These signals are then processed by a computer to obtain the pig's real-time location. However, this method has several drawbacks: while the fiber optic tracking system can measure vibration signals over a large portion of the pipeline length, limitations in on-site fiber optic installation, signal attenuation with increasing fiber length, and pipeline separation can all lead to weak or nonexistent signals in some areas. Furthermore, in high-noise areas, the pig's signal may be submerged, rendering existing fiber optic tracking systems ineffective in these regions. Summary of the Invention

[0004] The purpose of this invention is to achieve real-time, blind-spot-free tracking of the entire pipeline by a pig.

[0005] To achieve the above objectives, the present invention provides a method for tracking a pipeline pig, wherein optical fibers are laid in the same trench as the pipeline, and an optical fiber sensing tracking system is provided based on the optical fibers. The method includes:

[0006] Step 1: Run the pig multiple times from the pigging start point to the pigging end point. During each run, use the fiber optic sensing tracking system to collect vibration data corresponding to the pipe skin length, and obtain a set of vibration data for each run, for a total of multiple sets of vibration data.

[0007] Step 2: Analyze multiple sets of vibration data, draw a vibration time-domain diagram based on the multiple sets of vibration data, obtain the pipeline pig's running trajectory based on the vibration time-domain diagram, name the pipeline skin length region where the vibration signal value corresponding to the pipeline pig's running trajectory is less than or equal to the first threshold as the first test area, and name the pipeline skin length region where the environmental interference signal in multiple sets of vibration data is greater than or equal to the second threshold as the second test area.

[0008] Step 3: Install calibration boxes in both the first and second test areas. The calibration boxes are used to measure the vibration signal data of the pig in the corresponding areas.

[0009] Step 4: Start pig tracking. When the pig runs to the first or second test area, use the calibration box to collect the time t1 of the pig passing through the corresponding pipe length of the area, and use the fiber optic sensor tracking system to collect the time t2 of the pig passing through the corresponding pipe length of the area. Calculate the final time of the pig passing through the corresponding pipe length of the area based on t1 and t2.

[0010] The applicant discovered that existing fiber optic sensing tracking systems have blind spots when tracking pipeline pigs. This method first runs the pipeline pig multiple times, then collects vibration data. By analyzing the vibration data, the aforementioned blind spots are accurately located. Then, a calibration box is installed in the corresponding area of ​​the blind spot. The final transit time of the pig corresponding to the aforementioned blind spot is calculated by summing the transit time of the pig measured by the calibration box and the transit time of the pig measured by the fiber optic sensing tracking system. This achieves real-time, blind-spot-free tracking of the pipeline pig throughout the entire pipeline.

[0011] Furthermore, the method also includes:

[0012] Step 5: When the pig moves to an area outside the first and second test areas, the time taken for the pig to pass through the corresponding pipe length in that area is collected using the fiber optic sensing tracking system.

[0013] By combining the time the pig traverses the blind zone obtained in step 4 with the time the pig traverses the remaining pipeline length collected by the fiber optic sensing tracking system, full-line monitoring of the pig is achieved.

[0014] Furthermore, in step 4, based on t1 and t2, the final time t4 for the pig to traverse the corresponding pipe length in that area is calculated using the following formula:

[0015] t4 = t2*a + t1*b, where a is the fiber weighting factor, b is the calibration box weighting factor, and a + b = 1.

[0016] In this invention, the monitoring results of the calibration box and the fiber optic sensing tracking system are integrated and calculated in real time using the above formula. Thus, at a location where both distributed fiber optic equipment and calibration box monitoring are present, when the pig passes by, the system can perform a real-time integrated calculation based on the vibration time obtained by the distributed fiber optic equipment and the signal time obtained by the calibration box, according to the formula. This allows the system to obtain the integrated calculation time of the pig. Because this time integrates the time of the two signals, it is more accurate and reliable than relying on only one (distributed fiber optic equipment or calibration box).

[0017] Furthermore, the first test area includes: a first sub-test area and a second sub-test area. The first sub-test area is the region where the vibration signal value corresponding to the pig in multiple sets of vibration data is less than or equal to the first threshold and greater than the third threshold. The second sub-test area is the region where the vibration signal value corresponding to the pig in multiple sets of vibration data is less than or equal to the third threshold. For the first sub-test area, a = f * 50%; b = 1 - f * 50%. For the second sub-test area, a = f * 10%; b = 1 - f * 10%. For the second test area, a = f * 30%; b = 1 - f * 30%. Where f is the audio intensity factor.

[0018] Furthermore, the audio intensity factor is calculated as follows: the relevant audio time-domain graph signal at the time when the pig vibrates is called, the maximum signal value corresponding to the time period when the pig passes through is extracted from the audio time-domain graph signal, and the ratio between the maximum signal value and the audio threshold is calculated to obtain the audio intensity factor.

[0019] Furthermore, this method can also monitor key areas. The method also includes setting key monitoring areas along the pipeline skin length, installing calibration boxes in the key monitoring areas, and calculating the final time of the pig passing through the key monitoring areas based on the time data of the pig passing through the key monitoring areas collected by the calibration boxes and the time data of the pig passing through the key monitoring areas collected by the fiber optic sensor tracking system.

[0020] Furthermore, step 3 also includes testing the calibration box and the fiber optic sensing and tracking system, and proceeding to subsequent steps after the tests are passed.

[0021] Furthermore, the calibration box is installed at the corresponding position of the pipe bend or tee, because the ball is prone to get stuck at bends and tees, so these positions are the focus of monitoring.

[0022] Furthermore, in order to accurately discover historical vibration data and pinpoint the blind spots of existing fiber optic sensing and tracking systems, the vibration time-domain plotting method in this invention is as follows:

[0023] Vibration data collected per second per meter of skin length during the pigging process are used to form vibration data per second over the entire skin length.

[0024] After filtering the vibration data per second across the entire skin length, a vibration time-domain graph is plotted using the horizontal axis representing skin length and the vertical axis representing time.

[0025] Step 4, pig tracking, specifically includes:

[0026] Before the pig enters the first test area, the vibration signal of the pig is tracked to obtain the vibration trajectory of the pig. Based on the vibration trajectory of the pig and the pattern recognition model of the vibration time domain diagram, the passage of the pig is identified and the identification result is obtained. Based on the identification result, the time t2 of the pig's most recent passage through the first test area is obtained.

[0027] Based on the identification results, it is determined that after the pig enters the first test area, the estimated time for the next slack length point is calculated according to the current speed of the pig, combined with t2. Before and after the estimated time, the vibration time domain diagram is used to check whether the pig has passed. After confirming that the pig has passed, a vibration elapsed time is generated, and the elapsed time detected by the calibration box deployed on the slack length is recorded to obtain the time t1.

[0028] The actual elapsed time of the pig is calculated based on t1 and t2;

[0029] The pattern recognition model is used to identify whether a pig has passed through. The pattern recognition model is obtained as follows:

[0030] Data acquisition: Audio signals are captured using fiber optic sensing modules and converted into vibration time-domain plots to represent the frequency content of the signal over time;

[0031] Feature extraction: Relevant features are extracted from the vibration time-domain plot to capture features related to the pipeline passing through the pig, and a labeled training set is obtained;

[0032] Training and model development: The pattern recognition model is trained using a labeled training set to obtain the pattern recognition model.

[0033] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0034] This invention, based on the existing fiber optic sensing tracking pig operation, introduces calibration boxes in areas with weak signals, high noise levels, and pipeline separation. The calibration results of these boxes are then rationally combined with the fiber optic vibration tracking signal to correct the pig tracking and positioning, thereby achieving real-time, blind-spot-free tracking of the entire pipeline. This reduces the need for personnel deployment along the pig tracking route and improves pig monitoring efficiency. It also enables real-time, blind-spot-free tracking of the pig, allowing users to adjust parameters promptly in case of impending pig jamming, further mitigating this risk. Furthermore, it enables full monitoring of the subsequent unblocking process after pig jamming, improving unblocking efficiency and allowing real-time evaluation of the unblocking effect for real-time decision-making, further enhancing unblocking efficiency. The real-time, blind-spot-free tracking of the entire pipeline lays a crucial foundation for continuous optimization of pigging process parameters. Users can use historical pigging data as a reference, combined with relevant pigging gas volume and pressure records, to flexibly formulate pigging process parameters for different periods, such as peak and off-peak gas times. Attached Figure Description

[0035] The accompanying drawings, which are provided to further illustrate embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention.

[0036] Figure 1 This is a schematic diagram illustrating the principle of the pipeline pig tracking method in this invention;

[0037] Figure 2 This is a flowchart illustrating the tracking method for pipeline pigs in this invention. Detailed Implementation

[0038] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, where there is no conflict, the embodiments of the present invention and the features thereof can be combined with each other.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0040] Example 1

[0041] Please refer to Figures 1-2 This invention provides a method for tracking a pipeline pig, wherein optical fibers are laid in the same trench as the pipeline, and an optical fiber sensing tracking system is provided based on the optical fibers. The method includes:

[0042] Step 1: Run the pig multiple times from the pigging start point to the pigging end point. During each run, use the fiber optic sensing tracking system to collect vibration data corresponding to the pipe skin length, and obtain a set of vibration data for each run, for a total of multiple sets of vibration data.

[0043] Step 2: Analyze multiple sets of vibration data, plot vibration time-domain diagrams based on the multiple sets of vibration data, obtain the pig's running trajectory based on the vibration time-domain diagrams, name the pipe length region where the vibration signal values ​​corresponding to the pig's running trajectory are all less than or equal to the first threshold as the first test region, and name the pipe length region where the environmental interference signals in multiple sets of vibration data are all greater than or equal to the second threshold as the second test region. The first threshold is 0.5, the second threshold is 0.7, and the third threshold is 0.1.

[0044] The vibration time-domain plot is a type of heatmap, with skin length and time as the x and y axes, respectively. Each row represents the full skin length vibration data at the corresponding time (second). (If each meter of skin length corresponds to one vibration data point, the full skin length vibration data can be presented as an array.) The specific processing method for plotting is as follows: After the front-end obtains the vibration data for each row (second) via a request, it performs normalization processing as follows: The maximum value of the data is taken as the divisor, and the data per meter is taken as the dividend. After this division operation, the normalized data for that row (in the form of an array of numbers between 0 and 1) is obtained. The first sub-test area (weak signal area) can be defined as a normalized data value less than or equal to 0.5; the second sub-test area (complete pipeline separation area) can be defined as a normalized data value less than or equal to 0.2; and the high noise floor area can be defined as a normalized data value greater than or equal to 0.7 and a duration exceeding 24 hours.

[0045] Step 3: Install calibration boxes in both the first test area (corresponding to the weak signal area, the partial pipeline classification area, and the pipeline completely separated area) and the second test area (corresponding to the high noise area). The calibration boxes are used to measure the vibration signal data of the pig in the corresponding area.

[0046] Step 4: Start pig tracking. When the pig runs to the first or second test area, use the calibration box to collect the time t1 of the pig passing through the corresponding pipe length of the area, and use the fiber optic sensor tracking system to collect the time t2 of the pig passing through the corresponding pipe length of the area. Calculate the final time of the pig passing through the corresponding pipe length of the area based on t1 and t2.

[0047] This invention provides a method for tracking pipeline pigs using a distributed optical fiber device and calibration box. Addressing the limitations of on-site optical fiber laying conditions, signal attenuation with increasing fiber length, and partial pipeline separation, which result in weak or even no signal in relevant areas, the invention allows the pipeline pig, captured by the optical fiber, to be calibrated using a calibration box under these limited signal strength conditions, achieving better pipeline pig tracking performance. The specific implementation steps are as follows:

[0048] For areas with weak signals, this method specifically includes:

[0049] 1. Firstly, for areas with weak signals, historical pigging vibration data from the same pipe section is imported into the system. This allows for comparison to identify areas where weak signals consistently exist at different speeds. Alternatively, during on-site calibration, some areas may be difficult to calibrate, thus identifying these as areas with weak signals. Currently, calibration involves manual double-hammer striking on-site. The fiber optic system monitors vibration-related signals in real time, identifying the skin length of the striking vibration signal and recording the corresponding system skin length for each strike, thus completing the calibration. However, due to variations in pipeline and fiber optic deployment methods, burial depths, and surrounding environmental conditions at different locations, calibration in some areas is difficult. This difficulty manifests in the inability to clearly identify the corresponding system skin length after double-hammer striking within the system.

[0050] 2. After confirming the weak signal areas through the above steps, and in conjunction with the on-site construction drawings, deploy calibration boxes in the corresponding areas, such as elbows and tees, where the ball may get stuck.

[0051] 3. After the calibration boxes are deployed, simultaneously monitor the fiber optic sensing module and the calibration box module in the monitoring system. For the fiber optic sensing module, the calibration information mentioned in step 1 needs to be entered into the fiber optic sensing module, and the optical path quality and vibration signal need to be checked. If the optical path quality, vibration signal intensity, etc., do not meet the requirements, contact the on-site technical personnel for handling in a timely manner. For the calibration box module, the calibration boxes deployed on-site need to be entered into the system, and the information of the on-site calibration boxes needs to be viewed in the system. Check the relevant calibration box information such as power, GPS location information, magnetic signal, and low-frequency signal. If these signals are inaccurate, contact the on-site technical personnel for handling in a timely manner.

[0052] 4. During the pig tracking process, when the pig runs near the area with weak signal, the fiber optic sensing module listens to the vibration signal at the current location in real time, and the calibration box module monitors the judgment result of the corresponding calibration box on the signal of the pig passing through in real time.

[0053] 5. Based on step 4, during the actual operation of the pipeline pig, the system determines the specific time it takes for the pig to traverse a certain pipeline length using the following formula:

[0054] t 某皮长经过时间 =t 光纤振动信号判断通过时间 *a+t 定标盒信号判断通过时间 *b, where a is the fiber weighting factor, b is the calibration box weighting factor, a+b=1, a=f*50%; b=1-50%;

[0055] The system's determination of the pig's location is processed comprehensively, and the relevant steps are as follows:

[0056] 5.1 Before the pig enters the area with weak signal, the system tracks the vibration signal of the pig as it passes through, and generates corresponding system records based on the latest pig vibration trajectory and the pattern recognition of the real-time audio time domain graph.

[0057] Pattern recognition is the process of identifying and classifying patterns or features in data based on predefined criteria or models. It involves analyzing data to extract meaningful patterns and then using these patterns for decision-making or prediction.

[0058] Pattern recognition plays a crucial role in determining the passage of a pipeline cleaning machine using audio time-domain graphs. The determination process typically includes the following steps:

[0059] a. Data acquisition: Audio signals are captured using an optical fiber sensing module, and then the signals are converted into a time-domain graph to represent the frequency content of the signal over time;

[0060] b. Feature extraction: Extract relevant features from the time domain plot to capture unique patterns or features related to the pipeline passing through the pig. These features may include frequency peaks, spectral shape, time variations, and other characteristics related to the pig's passage.

[0061] c. Training and Model Development: The pattern recognition model is trained using a labeled dataset, such as a machine learning algorithm or a rule-based classifier. The dataset includes a time-domain graph and corresponding labels indicating the presence of a pig.

[0062] d. Classification: Once the model is trained, it can be used to classify new time-domain maps. The model applies the learned patterns or rules based on the features extracted from the time-domain map and predicts the presence of the pig. The output can be a binary decision (present or absent) or a probability score, representing the likelihood of the pig's presence.

[0063] e. Evaluation and Improvement: Use validation or test data to evaluate the accuracy and performance of the pattern recognition system. If necessary, the system can be improved by adjusting parameters, improving feature extraction methods, or using more advanced classification algorithms.

[0064] 5.2 After the pig enters a weak signal area, the system combines the most recent confirmed elapsed time in that area with the estimated time of the next pelvis point based on the current speed of the pig. Before and after the estimated time, the user reviews the time using an audio time-domain graph. After confirmation, the system generates a vibration elapsed time. At the same time, the system records the elapsed time detected by the calibration box deployed on that pelvis.

[0065] 5.3 After obtaining the vibration elapsed time and the calibration box elapsed time, the system starts to use the pig elapsed time calculation module to calculate the actual elapsed time of the pig: The system calls the relevant audio time-domain graph signal at the vibration elapsed time, extracts the corresponding signal maximum value in the pig elapsed time period, and calculates the ratio between it and the audio threshold (i.e., the audio intensity factor f). The audio threshold is 32768. Then, the current parameters a and b can be calculated: a = f * 50%; b = 1 - f * 50%; and then the elapsed time of the pig when it passes through this length can be calculated.

[0066] The vibration data includes vibration data collected per second per meter of diaphragm length during the pigging process, forming vibration data per second across the entire diaphragm length (which can be represented in array form). After filtering and other processing of the vibration data per second across the entire diaphragm length, a vibration time-domain graph can be plotted using diaphragm length and time. From the vibration time-domain graph, the trajectory of the pig can be identified for each pigging operation. From the vibration time-domain graph, within a certain diaphragm length, the average speed of the pig can be represented by the slope of the trajectory within that segment. For the same diaphragm length segment, the slope may not be the same for different pigging operations, and therefore the pig speed may not be the same. Therefore, the different speeds here refer to the different pig speeds represented by different trajectory slopes within the same diaphragm length segment during different pigging operations. In the comparison of historical vibration time-domain graphs from multiple pigging operations, the weak signal areas have the following characteristics: regardless of the speed of the pig in this area, the trajectory formed by the pig when passing through this area is not obvious; the normalized vibration data in this area is less than or equal to 0.5 and greater than 0.1.

[0067] This method does not simply combine distributed fiber optic equipment with calibration boxes. Instead, it integrates the monitoring results of both using the formula described herein, and performs real-time comprehensive calculations through the system. Therefore, at a location simultaneously monitored by both distributed fiber optic equipment and calibration boxes, when a pig passes by, this method can perform real-time comprehensive calculations based on the vibration time acquired by the distributed fiber optic equipment and the signal time acquired by the calibration box, according to the formula described above. This allows for the acquisition of the pig's comprehensive elapsed time, which, because it integrates the elapsed times of both signals, is more accurate and reliable than relying on only one (distributed fiber optic equipment or calibration box).

[0068] This method incorporates the elapsed time of distributed fiber optic equipment monitoring and the monitoring time of the calibration box into the calculation. The sum of the fiber weighting factor 'a' and the calibration box weighting factor 'b' is 1. In areas with normal fiber optic signals, the fiber weighting factor 'a' is 50%, and therefore 'b' is also 50%. This reflects that in these areas, the system fully considers the monitoring results of the distributed fiber optic equipment and the calibration box, and the weights are evenly distributed. For areas with weak signals, complete pipeline separation, and high noise floor, the fiber weighting factor 'a' is designed to be 50%, 10%, and 30%, respectively.

[0069] This weighting ratio is derived from a comprehensive analysis of historical data on different pipelines and pigging operations. It accurately reflects the accuracy of the elapsed time obtained by the distributed fiber optic equipment in the corresponding area. In other words, this method fully considers the situation in each area of ​​the pigging site and sets different proportions of fiber optic weighting factors and calibration box weighting factors for different areas, thereby accurately obtaining the system's comprehensive calculation elapsed time when the pig passes through different areas.

[0070] For areas with complete pipeline separation, this method specifically includes:

[0071] This method utilizes a distributed optical fiber device and calibration box for pig tracking. It addresses the limitations of on-site optical fiber deployment, signal attenuation with increasing fiber length, and areas of complete pipeline separation, where weak or nonexistent signals may occur. By combining the pig captured by the optical fiber with the calibration box under these limited signal strength conditions, better pig tracking performance is achieved. The specific implementation steps are as follows:

[0072] 1. First, for areas where pipelines are separated, historical pigging vibration data for the same pipeline segment is imported into the system. Areas with persistently low values ​​within the same skin length can be considered as completely separated pipeline areas. In addition, areas where calibration cannot be completed on-site starting from a certain skin length can be considered as completely separated pipeline areas.

[0073] 2. After confirming the pipeline separation area through the above steps, and in conjunction with the on-site construction drawings, deploy calibration boxes in the corresponding areas, such as elbows and tees, where the ball is prone to get stuck.

[0074] 3. After the calibration boxes are deployed, simultaneously monitor the fiber optic sensing module and the calibration box module in the monitoring system. For the fiber optic sensing module, the calibration information mentioned in step 1 needs to be entered into the fiber optic sensing module, and the optical path quality and vibration signal need to be checked. If the optical path quality, vibration signal intensity, etc., do not meet the requirements, contact the on-site technical personnel for handling in a timely manner. For the calibration box module, the calibration boxes deployed on-site need to be entered into the system, and the information of the on-site calibration boxes needs to be viewed in the system. Check the relevant calibration box information such as power, GPS location information, magnetic signal, and low-frequency signal. If these signals are inaccurate, contact the on-site technical personnel for handling in a timely manner.

[0075] 4. During the pipeline tracking process, when the pipeline pig runs to the vicinity of the pipeline separation area, the fiber optic sensing module listens to the vibration signal at the current location in real time, and the calibration box module monitors the judgment result of the corresponding calibration box on the signal of the pipeline pig passing through in real time.

[0076] 5. Based on step 4, during the actual operation of the pipeline pig, the system determines the specific time it takes for the pig to traverse a certain pipeline length using the following formula:

[0077] T 某皮长经过时 =t 光纤振动信号判断通过时间 *a+t 定标盒信号判断通过时间 *b, where a is the fiber weighting factor, b is the calibration box weighting factor, a+b=1, and a=f*10%; b=1-f*10%;

[0078] The system's determination of the pig's location is processed comprehensively, and the relevant steps are as follows:

[0079] 5.1 Before the pig enters the pipeline separation area, the system tracks the vibration signal of the pig as it passes through, and generates corresponding system records based on the latest pig vibration trajectory and pattern recognition of the real-time audio time domain graph.

[0080] 5.2 After the pig enters the pipeline separation area, the system combines the most recent confirmed elapsed time in that area with the estimated time of the next pipeline segment based on the current speed of the pig. Before and after the estimated time, the user reviews the time using an audio time-domain graph. After confirmation, the system generates a vibration elapsed time. However, it is possible that there is no obvious vibration or audio signal in this area. In this case, the system will use the estimated time as the time for the pig to pass through this pipeline segment. At the same time, the system will record the elapsed time detected by the calibration box deployed on this pipeline segment.

[0081] 5.3 After obtaining the vibration elapsed time and the calibration box elapsed time, the system starts to use the pig elapsed time calculation module to calculate the actual elapsed time of the pig: The system calls the relevant audio time-domain graph signal at the vibration elapsed time, extracts the corresponding signal maximum value in the pig elapsed time period, and calculates the ratio between it and the audio threshold (i.e., the audio intensity factor f). The audio threshold is 32768. Then, the current parameters a and b can be calculated: a = 10% * f; b = 1 - 10 * f; and then the elapsed time of the pig when it passes through this length can be calculated.

[0082] Among them, the vibration normalized data in the completely pipeline-separated area are all less than or equal to 0.1;

[0083] During a calibration process, the following steps can be used to determine whether calibration can be completed: 1. The system optical path quality is normal; 2. After the on-site calibration personnel monitor the specific location of the pipeline using a pipe probe, they tap the pipeline directly above it with two hammers; 3. If the system does not give a corresponding signal at this time; 4. The on-site calibration personnel can move the pipeline a certain distance back, forth, left, and right and try tapping it again with two hammers, continuing to check the system to see if a corresponding signal appears; 5. If the system still does not give a corresponding signal at this time, then this area can be considered an uncalibrable area.

[0084] For regions with high noise floor, this method specifically includes:

[0085] This method utilizes a distributed optical fiber device and calibration box for pig tracking. Addressing persistently high noise levels or high noise levels caused by factors such as pressurization in the early and late stages of pigging, where the pig's signal may be submerged when passing through such areas, this method combines calibration box calibration with pig tracking, achieving better pig tracking results. The specific implementation steps are as follows:

[0086] 1. First, for high noise floor areas, historical cleaning vibration data of the same pipe section is imported into the system. Areas with sustained high values ​​(highlighted) within the same skin length can be considered high noise floor areas. In addition, during the cleaning process, high noise floor areas may be generated due to the pressure effect of the pipe sections before and after. The corresponding areas can also be considered high noise floor areas.

[0087] 2. After confirming the high noise level areas through the above steps, and in conjunction with the on-site construction drawings, deploy calibration boxes in the corresponding areas, such as elbows and tees, where the ball is prone to getting stuck.

[0088] 3. After the calibration boxes are deployed, simultaneously monitor the fiber optic sensing module and the calibration box module in the monitoring system. For the fiber optic sensing module, the calibration information mentioned in step 1 needs to be entered into the fiber optic sensing module, and the optical path quality and vibration signal need to be checked. If the optical path quality, vibration signal intensity, etc., do not meet the requirements, contact the on-site technical personnel for handling in a timely manner. For the calibration box module, the calibration boxes deployed on-site need to be entered into the system, and the information of the on-site calibration boxes needs to be viewed in the system. Check the relevant calibration box information such as power, GPS location information, magnetic signal, and low-frequency signal. If these signals are inaccurate, contact the on-site technical personnel for handling in a timely manner.

[0089] 4. During the pipeline tracking process, when the pipeline pig runs to the vicinity of the pipeline separation area, the fiber optic sensing module listens to the vibration signal at the current location in real time, and the calibration box module monitors the judgment result of the corresponding calibration box on the signal of the pipeline pig passing through in real time.

[0090] 5. Based on step 4, during the actual operation of the pipeline pig, the system determines the specific time it takes for the pig to traverse a certain pipeline length using the following formula:

[0091] t 某皮长经过时间 =t 光纤振动信号判断通过时间 *a+t 定标盒信号判断通过时间 *b, where a is the fiber weighting factor, b is the calibration box weighting factor, a+b=1, a=f*30%; b=1-f*30%;

[0092] The system's determination of the pig's location is processed comprehensively, and the relevant steps are as follows:

[0093] 5.1 Before the pig enters the high noise level area, the system tracks the vibration signal of the pig as it passes through, and generates corresponding system records based on the latest pig vibration trajectory and pattern recognition of the real-time audio time-domain graph.

[0094] 5.2 After the pig enters a high-noise area, the system combines the most recent confirmed elapsed time in that area with the estimated time of the next pig crossing point based on the pig's current speed. Before and after the estimated time, the user reviews the data using an audio time-domain graph. After confirmation, the system generates a vibration elapsed time. In this area, there is likely no obvious audio signal. At this time, the system uses the estimated time as the time for the pig to pass through this pig crossing point. Simultaneously, the system records the elapsed time detected by the calibration box deployed on this pig crossing point.

[0095] 5.3 After obtaining the vibration elapsed time and the calibration box elapsed time, the system starts to use the pig elapsed time calculation module to calculate the actual elapsed time of the pig: The system calls the relevant audio time-domain graph signal at the vibration elapsed time, extracts the corresponding signal maximum value in the pig elapsed time period, and calculates the ratio between it and the audio threshold (i.e., the audio intensity factor f). The audio threshold is 32768. Then, the current parameters a and b can be calculated: a = f * 30%; b = 1 - f * 30%; and then the elapsed time of the pig when it passes through this length can be calculated.

[0096] Among them, by analyzing the vibration time domain map through the system, the high value (highlighted) area where the vibration normalized data are all greater than or equal to 0.7 and the duration exceeds 24 hours is the high noise area.

[0097] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0098] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for tracking a pipeline pig, characterized in that, The pipeline is laid with optical fibers in the same trench, and an optical fiber sensing and tracking system is installed based on the optical fibers. The method includes: Step 1: Run the pig multiple times from the pigging start point to the pigging end point. During each run, use the fiber optic sensing tracking system to collect vibration data corresponding to the pipe skin length, and obtain a set of vibration data for each run, for a total of multiple sets of vibration data. Step 2: Analyze multiple sets of vibration data, draw a vibration time-domain diagram based on the multiple sets of vibration data, obtain the pipeline pig's running trajectory based on the vibration time-domain diagram, name the pipeline skin length region where the vibration signal value corresponding to the pipeline pig's running trajectory is less than or equal to the first threshold as the first test area, and name the pipeline skin length region where the environmental interference signal in multiple sets of vibration data is greater than or equal to the second threshold as the second test area. Step 3: Install calibration boxes in both the first and second test areas. The calibration boxes are used to measure the vibration signal data of the pig in the corresponding areas. Step 4: Start pig tracking. When the pig runs to the first or second test area, use the calibration box to collect the time t1 of the pig passing through the corresponding pipe length of the area, and use the fiber optic sensor tracking system to collect the time t2 of the pig passing through the corresponding pipe length of the area. Calculate the final time of the pig passing through the corresponding pipe length of the area based on t1 and t2.

2. The tracking method for a pipeline pig according to claim 1, characterized in that, The method further includes: Step 5: When the pig moves to an area outside the first and second test areas, the time taken for the pig to pass through the corresponding pipe length in that area is collected using the fiber optic sensing tracking system.

3. The tracking method for a pipeline pig according to claim 1, characterized in that, In step 4, based on t1 and t2, the final time t4 for the pig to pass through the corresponding pipe length in this area is calculated using the following formula: t4 = t2*a + t1*b, where a is the fiber weighting factor, b is the calibration box weighting factor, and a + b = 1.

4. The tracking method for a pipeline pig according to claim 3, characterized in that, The first test area includes: a first sub-test area and a second sub-test area. The first sub-test area is the region where the vibration signal value corresponding to the pig in multiple sets of vibration data is less than or equal to the first threshold and greater than the third threshold and is greater than the pipe skin length. The second sub-test area is the region where the vibration signal value corresponding to the pig in multiple sets of vibration data is less than or equal to the third threshold. For the first sub-test area, a = f * 50%; b = 1 - f * 50%. For the second sub-test area, a = f * 10%; b = 1 - f * 10%. For the second sub-test area, a = f * 30%; b = 1 - f * 30%. Where f is the audio intensity factor.

5. A method for tracking a pipeline pig according to claim 4, characterized in that, The audio intensity factor is calculated as follows: the relevant audio time-domain signal of the time when the pig vibrates is called, the maximum value of the signal corresponding to the time period when the pig passes through is extracted from the audio time-domain signal, and the ratio between the maximum value of the signal and the audio threshold is calculated to obtain the audio intensity factor.

6. The tracking method for a pipeline pig according to claim 1, characterized in that, The method also includes setting key monitoring areas along the pipeline skin length, installing calibration boxes in the key monitoring areas, and calculating the final time of the pig passing through the key monitoring areas based on the time data of the pig passing through the key monitoring areas collected by the calibration boxes and the time data of the pig passing through the key monitoring areas collected by the fiber optic sensor tracking system.

7. A method for tracking a pipeline pig according to claim 1, characterized in that, Step 3 also includes testing the calibration box and the fiber optic sensing and tracking system. After the test is passed, the subsequent steps are carried out.

8. A method for tracking a pipeline pig according to any one of claims 1-7, characterized in that, The calibration box is installed at the corresponding position of the pipe bend or tee.

9. A method for tracking a pipeline pig according to any one of claims 1-7, characterized in that, The vibration time-domain plot is drawn as follows: Vibration data collected per second per meter of skin length during the pigging process are used to form vibration data per second over the entire skin length. After filtering the vibration data per second across the entire skin length, a vibration time-domain graph is plotted using the horizontal axis representing skin length and the vertical axis representing time.

10. A method for tracking a pipeline pig according to claim 9, characterized in that, Step 4, pig tracking, specifically includes: Before the pig enters the first test area, the vibration signal of the pig is tracked to obtain the vibration trajectory of the pig. Based on the vibration trajectory of the pig and the pattern recognition model of the vibration time domain diagram, the passage of the pig is identified and the identification result is obtained. Based on the identification result, the time t2 of the pig's most recent passage through the first test area is obtained. Based on the identification results, it is determined that after the pig enters the first test area, the estimated time for the next slack length point is calculated according to the current speed of the pig, combined with t2. Before and after the estimated time, the vibration time domain diagram is used to check whether the pig has passed. After confirming that the pig has passed, a vibration elapsed time is generated, and the elapsed time detected by the calibration box deployed on the slack length is recorded to obtain the time t1. The actual elapsed time of the pig is calculated based on t1 and t2; The pattern recognition model is used to identify whether a pig has passed through. The pattern recognition model is obtained as follows: Data acquisition: Audio signals are captured using fiber optic sensing modules and converted into vibration time-domain plots to represent the frequency content of the signal over time; Feature extraction: Relevant features are extracted from the vibration time-domain plot to capture features related to the pipeline passing through the pig, and a labeled training set is obtained; Training and model development: The pattern recognition model is trained using a labeled training set to obtain the pattern recognition model.

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