Arrayed old oil pipe wall defect morphology depth evaluation algorithm, system and detection device

Abstract

The application relates to the technical field of oil pipe detection, and discloses an array type old oil pipe wall defect morphology depth evaluation algorithm, a system and a detection device.The algorithm comprises the following steps: acquiring detection signals of each sampling point; extracting first signal features and second signal features of the detection signals of each sampling point, and acquiring defect imaging results based on the first signal features and the second signal features; identifying a defect contour based on the acquired defect imaging results; establishing a defect depth quantitative compensation evaluation algorithm based on the identified defect contour; and performing defect depth quantitative evaluation based on the established defect depth quantitative compensation evaluation algorithm.The signal features are used to associate defect sizes, so that nondestructive, quantitative and visual evaluation of old oil pipe wall thickness reduction defect morphology and defect depth can be realized.

Description

Technical Field

[0001] This invention belongs to the field of nondestructive testing technology, and specifically relates to an array-type old oil pipe wall defect morphology depth evaluation algorithm, system and detection device. Background Technology

[0002] With the continuous expansion of unconventional oil and gas field development, pipelines are easily damaged by environmental or other factors due to long-term operation, such as corrosion and debris impact. Once pipeline damage occurs, the investigation and inspection work is a labor-intensive and costly task, and the accuracy of the inspection is inconsistent. Therefore, improving pipeline inspection and investigation is of great significance. Every year, many oil pipes are scrapped due to corrosion and other problems, resulting in huge resource waste and increasing production costs and environmental burden for enterprises. Although a large number of old oil pipes have repair and reuse value, the lack of efficient and accurate on-site quantitative detection technology for corrosion defects restricts the large-scale development of the green remanufacturing industry for old oil pipes. Currently, oil well pipe inspection methods include magnetic flux leakage testing, magnetic memory non-destructive testing, and ultrasonic non-destructive testing. Among these methods, magnetic flux leakage (MF) testing is the most widely used for oil well pipe inspection. For example, Chinese patent (CN111912899A) discloses an online non-destructive testing method and device for oil well pipes. This method uses a transverse MF testing channel and a longitudinal MF testing channel, applying mutually orthogonal magnetizing fields. The two-dimensional MF testing technology can distinguish various defects. However, MF testing systems are large, complex to operate, require magnetization of the test piece, and have low accuracy. Furthermore, magnetic memory non-destructive testing is greatly affected by environmental magnetic fields, making it unsuitable for oil and gas field applications. Ultrasonic non-destructive testing requires applying a coupling agent to the pipe surface, has high requirements for the surface of old pipes, is unsuitable for oil field applications, and is complex to operate, requiring highly skilled operators.

[0003] Therefore, existing non-destructive testing methods suffer from low efficiency and insufficient accuracy in the inspection of old oil pipes, making it difficult to achieve rapid, visual, and quantitative assessment in oil and gas fields. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides an array-based algorithm, system, and detection device for evaluating the morphology and depth of defects in old oil pipe walls, enabling non-destructive, quantitative, and visual evaluation of defect morphology and depth.

[0005] The purpose of this invention is to provide an array-based algorithm for evaluating the morphology and depth of defects in old oil pipe walls, including:

[0006] Acquire the detection signals at each sampling point;

[0007] Extract the first and second signal features of the detection signal at each sampling point, and obtain the defect imaging results based on the first and second signal features;

[0008] Based on the acquired defect imaging results, the defect contour is identified;

[0009] Based on the identified defect contours, a quantitative compensation and evaluation algorithm for defect depth is established.

[0010] Based on the established quantitative compensation evaluation algorithm for defect depth, a quantitative evaluation of defect depth is performed.

[0011] Furthermore, the first and second signal features of the detection signal at each sampling point are extracted, and the defect imaging results are obtained based on the first and second signal features.

[0012] The first and second signal features are normalized.

[0013] Based on the normalization results and preset thresholds, the defect region DA and the non-defect region NDA are determined.

[0014] Based on the determined defect region DA and non-defect region NDA, the characteristics of the fused signal are obtained.

[0015] Further, the first signal feature is the falling edge logarithmic slope SK, and the second signal feature is the normalized differential peak value SP, wherein normalizing the falling edge logarithmic slope SK and the normalized differential peak value SP satisfies the following:

[0016]

[0017] In the formula, the signal feature NSK represents the result after normalizing the logarithmic slope SK of the falling edge; the signal feature NSP represents the result after normalizing the differential peak value SP. The values ​​of both the signal feature NSK and the signal feature NSP are between 0 and 1.

[0018] Furthermore, based on the normalization results and preset thresholds, the defective region DA and the non-defective region NDA are determined, and based on the determined defective region DA and non-defective region NDA, the fused signal features are obtained, including...

[0019] The preset threshold is between 0 and 1. If the first signal feature or the second signal feature is greater than the preset threshold, it is regarded as a defect region DA. If the first signal feature or the second signal feature is less than the threshold, it is regarded as a non-defect region NDA.

[0020] The fused signal characteristics satisfy:

[0021]

[0022] In the formula, NSF ( m, n ) represents the feature matrix of the fused signal. mand n These represent the row and column numbers in the feature matrix of the fused signal, respectively.

[0023] Based on the fused signal feature matrix, a median filter is used to smooth the signal in the noisy region of the non-defect NDA, finally forming the fused signal feature. NSF, The fused signal feature NSF is the defect imaging result.

[0024] Furthermore, based on the defect imaging results, the defect contours are identified, including:

[0025] Based on the defect imaging results, an initial contour region is set;

[0026] In each iteration, the internal and external energies of the initial contour region are calculated;

[0027] The final contour region is obtained through multiple iterations.

[0028] Furthermore, based on the identified defect contours, a quantitative compensation and evaluation algorithm for defect depth is established, including:

[0029] By fitting the signal characteristic-depth power function relationship of the stepped tube specimen, a calibration curve is established to determine the defect depth. h Quantitative assessment satisfies:

[0030] SK = A·h SK m , SP = B·h SP n

[0031] In the formula, A, B, m, and n are all coefficients;

[0032] Based on the area of ​​the local wall thickness reduction defect and the eddy current coverage area at the sampling point, a depth compensation coefficient β is established:

[0033]

[0034] In the formula, S CA S represents the size of the overlapping area between the local wall thickness reduction defect area and the eddy current coverage area at the sampling point. EA Indicates the eddy current coverage area at the sampling point;

[0035] Establish a quantitative compensation evaluation algorithm for defect depth:

[0036] SK = A·(βh SK ） m SP = B·(βh SP ） n

[0037] h SK =( SK / A ·β m ) 1 / m , h SP =(SP / B ·β n ) 1 / n

[0038] Another object of the present invention is to provide an array-type old oil pipe wall defect morphology depth assessment system, comprising,

[0039] The acquisition module is used to acquire the detection signals at each sampling point;

[0040] The processing module is used to extract the first signal feature and the second signal feature of the detection signal at each sampling point, and to obtain the defect imaging result based on the first signal feature and the second signal feature;

[0041] The identification module is used to identify defect contours based on defect imaging results;

[0042] The compensation module is used to establish a quantitative compensation evaluation algorithm for defect depth based on the identified defect contours.

[0043] The evaluation module is used to perform quantitative evaluation of defect depth based on the established quantitative compensation evaluation algorithm for defect depth.

[0044] Another objective of this invention is to provide an array-type old oil pipe wall defect morphology depth assessment and detection device, comprising a probe, multiple ring array probe groups, and a computer control system; wherein,

[0045] Multiple ring array probe groups are equipped with an array of probes evenly distributed along the circumference. Each probe includes: an excitation coil, a Ni-Zn ferrite magnet core, and two coaxial TMR magnetic field sensors. The probes detect wall thickness reduction defects at the sampling points and transmit the detection signals to the computer control system.

[0046] A computer control system is used to execute the array-type old oil pipe wall defect morphology depth evaluation algorithm described above.

[0047] Furthermore, it also includes oil pipes, a test system support, multiple adjustable bases, a motor, a turntable, and a transmission mechanism, among which,

[0048] The oil pipe is placed on the turntable. The motor is connected to the transmission mechanism and the turntable respectively. The motor drives the transmission mechanism to transport the oil pipe through the array probe group and move along the axial direction.

[0049] Multiple adjustable bases are used to hold the ring array probe group;

[0050] The test system support bracket is used to support the transmission mechanism and multiple adjustable bases.

[0051] Furthermore, the distance between each adjustable base is greater than or equal to a preset distance; the ring array probe group is distributed along the circumference, and the angle between individual probes is greater than or equal to a preset angle.

[0052] The evaluation algorithm of this invention utilizes signal features to correlate defect size, enabling three-dimensional reconstruction and visualization of complex wall thickness reduction defects. This allows for non-destructive, quantitative, and visual evaluation of the morphology and depth of wall thickness reduction defects in old oil pipes on-site. Furthermore, the quantitative evaluation results are applied to conduct intelligent evaluation of old oil pipe remanufacturing, achieving efficient and accurate on-site detection of wall thickness reduction defects in old oil pipes.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart of an array-based old oil pipe wall defect morphology depth evaluation algorithm is shown in an embodiment of the present invention;

[0056] Figure 2 A flowchart of another array-based old oil pipe wall defect morphology depth evaluation algorithm is shown in an embodiment of the present invention;

[0057] Figure 3 A contour recognition algorithm diagram is shown in an embodiment of the present invention;

[0058] Figure 4 A contour diagram of defect #1-2 in an embodiment of the present invention is shown;

[0059] Figure 5 A contour diagram of defect #2-2 in an embodiment of the present invention is shown;

[0060] Figure 6 A contour diagram of defect #3-2 in an embodiment of the present invention is shown;

[0061] Figure 7a The image shows the defect imaging result of defect #1-1 of the tubing sample #1 in the embodiment of the present invention;

[0062] Figure 7b The image shows the defect imaging results of defects #1-2 in the tubing sample #1 in the embodiment of the present invention;

[0063] Figure 7c The image shows the defect imaging results of defects #1-3 in the tubing sample #1 in the embodiment of the present invention;

[0064] Figure 7d The image shows the defect imaging results of defects #1-4 in the tubing sample #1 in the embodiment of the present invention;

[0065] Figure 8a The image shows the defect imaging result of defect #2-1 in the tubing sample #2 in the embodiment of the present invention;

[0066] Figure 8b The image shows the defect imaging result of defect #2-2 of the tubing sample #2 in the embodiment of the present invention;

[0067] Figure 8c The image shows the defect imaging results of defects #2-3 in the tubing sample #2 in the embodiment of the present invention;

[0068] Figure 8d The image shows the defect imaging results of defects #2-4 in the tubing sample #2 in the embodiment of the present invention;

[0069] Figure 9a The image shows the defect imaging result of defect #3-1 of the tubing sample #3 in the embodiment of the present invention;

[0070] Figure 9b The image shows the defect imaging result of defect #3-2 of the tubing sample #3 in the embodiment of the present invention;

[0071] Figure 9c The image shows the defect imaging results of defect #3-3 in the tubing sample #3 in the embodiment of the present invention;

[0072] Figure 9d The image shows the defect imaging results of defects #3-4 in the tubing sample #3 in the embodiment of the present invention;

[0073] Figure 10 A flowchart illustrating a quantitative compensation process for defect depth in an embodiment of the present invention is shown.

[0074] Figure 11 A structural diagram of an array-type old oil pipe wall defect morphology depth assessment system according to an embodiment of the present invention is shown;

[0075] Figure 12 A diagram of an array-type old oil pipe wall defect morphology depth assessment and detection device is shown in an embodiment of the present invention;

[0076] Figure 13 A support diagram of the array-type old oil pipe wall thickness reduction defect morphology detection device in an embodiment of the present invention is shown;

[0077] Figure 14 The diagram shows the probe distribution of the array-type old oil pipe wall thickness reduction defect morphology detection device in an embodiment of the present invention.

[0078] Figure descriptions: 1. Oil pipe, 2. Test system support, 3. Adjustable base, 4. Probe, 5. Ring array probe group, 6. Motor, 7. Turntable, 8. Transmission mechanism. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] like Figure 1 As shown in the figure, an array-based algorithm for evaluating the morphology and depth of defects in the wall of old oil pipes is disclosed in this embodiment of the invention. The algorithm includes: first, acquiring the detection signals of each sampling point; second, extracting the first signal feature and the second signal feature of the detection signal of each sampling point, and acquiring the defect imaging result based on the first signal feature and the second signal feature; then, identifying the defect contour based on the acquired defect imaging result; then, establishing a quantitative compensation evaluation algorithm for defect depth based on the identified defect contour; and finally, performing a quantitative evaluation of defect depth based on the established quantitative compensation evaluation algorithm for defect depth. By using signal features to correlate defect size, the above algorithm is used to achieve three-dimensional reconstruction and visualization of complex wall thickness reduction defects, realizing non-destructive, quantitative, and visual evaluation of the morphology and depth of wall thickness reduction defects in old oil pipes on site. Furthermore, the quantitative evaluation results are applied to perform intelligent evaluation of old oil pipe remanufacturing, achieving efficient and accurate on-site detection of wall thickness reduction defects in old oil pipes.

[0081] Specifically, such as Figure 2As shown, the detection signals of each sampling point are first input, and then the first signal feature and the second signal feature of the detection signal of each sampling point are extracted to image the defect. The first signal feature is the falling edge logarithmic slope SK, and the second signal feature is the normalized differential peak value SP. Normalization of the first and second signal features satisfies the following:

[0082]

[0083] In the formula, the signal feature NSK represents the result after normalizing the logarithmic slope SK of the falling edge; the signal feature NSP represents the result after normalizing the differential peak value SP. The values ​​of both the signal feature NSK and the signal feature NSP are between 0 and 1.

[0084] Then, a preset threshold between 0 and 1 can be selected. The portion of the signal feature NSK or NSP greater than the preset threshold is regarded as the defect region, and the portion less than the preset threshold is regarded as the non-defect region. The signal feature matrix is ​​divided into defect region (DA) and non-defect region (NDA), so that the signal feature fusion algorithm satisfies:

[0085]

[0086] In the formula, NSF ( m, n ) represents the feature matrix of the fused signal. m and n These represent the row and column numbers in the fused signal feature matrix, respectively; that is, during the fusion process, signal features are extracted from the defect region. NSK and NSP The larger value in the signal is used to identify the signal characteristics in non-defect areas. NSK and NSP The smaller value in the range.

[0087] Finally, based on the fused signal feature matrix, a median filter is used to smooth the signal in the noisy region, ultimately forming the fused signal features. NSF fusion signal features NSF This refers to the defect imaging results. To limit noise fluctuations in non-defect areas to a relatively small noise mean, a median filter is used to smooth the signal in the noisy region, ultimately forming a fused signal feature. Imaging based on the fused signal feature better highlights the defect, and after filtering, the image noise fluctuations are smaller. The aforementioned fused signal feature... NSF Improve the imaging signal-to-noise ratio. Examples of the results from defects formed, as shown in Tables 1 and 2, are as follows: Figures 7a-7d As shown in 8a-8d and 9a-9d.

[0088] Table 1 Defect data of tubing sample #1

[0089]

[0090] Table 2 Defect data for tubing sample #2

[0091]

[0092] Table 3 Defect data for tubing sample #3

[0093]

[0094] In this embodiment of the invention, based on the acquired defect imaging results, the defect contour is identified, including using MATLAB to implement Active Contour Models to accurately segment the defect edges and identify the defect contour in order to assess the defect opening area, such as... Figure 3 As shown, the contour recognition process is as follows:

[0095] Based on the defect imaging results, an initial contour region is set. This initial contour is based on a preliminary estimate of the approximate location and shape of the defect and is mainly set manually.

[0096] In each iteration, the internal energy (i.e., internal energy used for smoothing) and external energy (i.e., external energy used to attract to the edge) of the initial contour region are calculated.

[0097] The total energy is obtained by combining the internal and external energies of the initial contour region.

[0098] Move the points on the contour according to the energy gradient;

[0099] The final contour region is obtained through multiple iterations, and it is determined whether the energy has converged or whether the maximum number of iterations has been reached. Ten iterations are selected as the standard. An appropriate number of iterations can achieve good contour segmentation results while ensuring computational efficiency. However, this is not a limitation; 15 iterations, etc., are also applicable to this invention.

[0100] The final output is the optimized, precise contour.

[0101] Contour recognition is performed using the aforementioned active contour models to obtain the contour images of the exemplary defects #1-2, #2-2, and #3-2, as shown below. Figure 4-6 As shown. The specific algorithms described above are all based on existing active contour models, which are classic contour recognition algorithms in computer image processing, and will not be elaborated further here.

[0102] In embodiments of the present invention, such as Figure 2 , 10As shown, based on the identified defect contours, the algorithm for quantitative compensation and evaluation of defect depth includes:

[0103] By fitting the signal characteristic-depth power function relationship of the stepped tube specimen, a calibration curve is established to determine the defect depth. h Quantitative evaluation can be performed because the signal characteristic-thinning amount correlation curve crosses zero, therefore it can be used... f (x)=a x b Fitting a power function of the form satisfies:

[0104] SK = A·h SK m , SP = B·h SP n

[0105] In the formula: h SK This indicates the defect depth corresponding to the first signal feature. h SP The expression represents the defect depth corresponding to the second signal feature. A, B, m, and n are all coefficients. The expression is obtained under the condition of uniform thinning of the inner wall around the entire circumference. When using this function expression to predict the depth of local wall thickness thinning defects on the inner wall, it is also necessary to compensate for the defect depth prediction result.

[0106] Furthermore, based on the identified defect contours, the area of ​​the local wall thinning defect is calculated using the shoelace formula, and the eddy current coverage area is calculated using the major and minor axes of the eddy current ellipse distribution. The compensation coefficient is then calculated from this. It should be noted that the area covered by the eddy current probe on the surface of the object being measured is the area within the object where the eddy currents excited by the probe can generate an effective electromagnetic effect.

[0107] Establish the depth compensation coefficient β:

[0108]

[0109] In the formula, S CA The area represented by S is the coincidence area between the local wall thickness reduction defect area and the eddy current coverage area at the sampling point. EA This indicates the size of the eddy current coverage area at the sampling point. When detecting uniform thinning defects, the eddy current covers the entire defect area, and the compensation coefficient β is defined as 1 at this time; the compensation coefficient is used to resolve the quantitative deviation between local defects and uniform thinning.

[0110] Establish a quantitative compensation evaluation algorithm for defect depth:

[0111] SK = A·(βhSK) m SP = B·(βhSP) n

[0112] hSK =( SK / A ·β m ) 1 / m , hSP =(SP / B ·β n ) 1 / n

[0113] That is, the defect depth formulas of the first signal feature SK and the second signal feature SP are obtained respectively. In subsequent applications, based on the defect depths of the first signal feature SK and the second signal feature SP calculated by the calibration defect (one or more defects of preset depth are designed and calibrated before the calculation of a certain old oil pipe), the depth of the signal feature with the smallest error (such as the smallest difference compared with the preset depth) is selected as the calibration defect depth for calculation, and the depth value corresponding to the signal feature with the smallest error is selected in subsequent calculations.

[0114] Quantitative assessment of defect depth using interpolation:

[0115] d x = d 1 + ( d 2 - d 1) ×( h x - h 1) / ( h 2 - h 1)

[0116] In the formula, dx is the depth of the actual defect (the defect to be measured), d1 is the known first depth (the known depth of the calibration defect, through which the actual depth is calculated), h1 is the reference wave height corresponding to the known first depth (the calibration defect depth calculated using the above algorithm), d2 is the known second depth (the known depth of another calibration defect), h2 is the reference wave height corresponding to the known second depth (the calibration defect depth calculated using the above algorithm), and hx is the wave height corresponding to the x-th depth of the actual defect.

[0117] Based on the defect depth quantitative compensation evaluation algorithm and the first and second signal features of each sampling point, the defect depth is quantitatively evaluated through depth quantitative compensation. Combined with the defect shape size, the final output is a three-dimensional shape, which consists of the contour (position and shape) and the defect depth.

[0118] The 3D reconstruction parameters of the defects corresponding to Table 1-3 are shown in Table 4-6:

[0119] Table 4. Three-dimensional reconstruction results of defects on the inner wall of #1

[0120]

[0121] Table 5. Three-dimensional reconstruction results of defects on the inner wall of #2

[0122]

[0123] Table 6. Three-dimensional reconstruction results of defects on the inner wall of #3

[0124]

[0125] In the table, h SK、 h SP To measure depth, SK , SP for Error in depth measurement.

[0126] By combining quantitative evaluation results with the oil casing evaluation criteria (GB / T19830 or manufacturing enterprise standards), intelligent evaluation is provided for the remanufacturing of old oil pipes, replacing manual screening and improving the automation and intelligence level of old oil pipe remanufacturing.

[0127] like Figure 11 As shown in the figure, an array-type old oil pipe wall defect morphology depth assessment system capable of executing the above-described algorithm is disclosed in this embodiment of the invention. The system includes an acquisition module, a processing module, an identification module, a compensation module, and an evaluation module. The acquisition module is used to acquire the detection signals of each sampling point; the processing module is used to extract the first signal feature and the second signal feature of the detection signal of each sampling point, and to acquire the defect imaging result based on the first signal feature and the second signal feature; the identification module is used to identify the defect contour based on the acquired defect imaging result; the compensation module is used to establish a quantitative defect depth compensation evaluation algorithm based on the identified defect contour; and the evaluation module is used to perform a quantitative defect depth evaluation based on the established quantitative defect depth compensation evaluation algorithm.

[0128] like Figure 12As shown in the illustration, this embodiment of the invention also introduces an array-type old oil pipe wall defect morphology depth assessment and detection device. The detection device includes a probe 4, multiple annular array probe groups 5, and a computer control system. The multiple annular array probe groups 5 are equipped with an array of sensors 4 evenly distributed circumferentially. Each probe 4 includes: an excitation coil, a Ni-Zn ferrite magnet core, and two coaxial TMR magnetic field sensors. The probe 4 detects wall thickness reduction defects at sampling points and transmits the detection signal to the computer control system. The computer control system executes the aforementioned array-type old oil pipe wall defect morphology depth assessment algorithm. By detecting wall thickness reduction defects through the probes and transmitting the detection signal to the computer control system, rapid circumferential scanning of the old oil pipe is achieved.

[0129] In embodiments of the present invention, such as Figure 12 , 13 As shown in Figure 14, the device also includes an oil pipe 1, a test system support 2, multiple adjustable bases 3, a motor 6, a turntable 7, and a transmission mechanism 8, wherein...

[0130] Oil pipe 1 is placed on turntable 7. Motor 6 is connected to transmission mechanism 8 and turntable 7 respectively. Motor 6 drives transmission mechanism 8 to transport oil pipe 1 through array probe group 5 and move along the axial direction.

[0131] Multiple adjustable bases 3 are used to place the ring array probe group 5;

[0132] The test system bracket 2 supports the transmission mechanism 8 and multiple adjustable bases 3. The height of the adjustable bases 3 can be adjusted according to the oil pipe size, taking into account the mutual influence between probes.

[0133] In embodiments of the present invention, such as Figure 12 As shown, the distance between each adjustable base is greater than or equal to a preset distance; the preset distance can be 100 mm, but is not limited to this, other distance values, such as 80 mm, are also applicable to this invention. Probes 4 are distributed circumferentially on the array probe group 5, and the angle between individual probes 4 is greater than or equal to a preset angle, ensuring coverage of the circumferential area of ​​the oil pipe while reducing interference between probes. The preset angle can be 120°, but is not limited to this, other angle values, such as 130°, are also applicable to this invention. Furthermore, the probe 4 is an eddy current probe.

[0134] In this embodiment of the invention, the parameters of the excitation coil are: outer diameter 14mm~30mm, inner diameter 5mm~12mm, and height 10mm~16mm; the parameters of the Ni-Zn ferrite magnet core are: outer diameter 9mm~20mm, inner diameter 4mm~10mm, and height 10mm~16mm; and the parameters of the two coaxial TMR (Tunnel MagnetoResistance) magnetic field sensors are: axial spacing 10mm~20mm. The differential design of the dual sensors (two TMR magnetic field sensors) suppresses common-mode noise and improves the signal-to-noise ratio.

[0135] In this embodiment of the invention, the computer control system includes an excitation module and an acquisition module. The excitation module includes a signal generator and a power amplifier for outputting a bipolar square wave. The acquisition module includes a multi-channel filter amplifier, an oscilloscope, and a computer control module for real-time processing of magnetic field signals.

[0136] The detection uses a rectangular pulse as the excitation signal, which is highly sensitive to local wall thinning defects in the inner wall of the oil pipe. It is suitable for the harsh environment of oil and gas fields, has no special requirements for the surface of old oil pipes, and does not require coupling agent in the detection process. At the same time, the above algorithm is low in cost and high in security.

[0137] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An arrayed old oil pipe wall defect topography depth evaluation algorithm, characterized in that, comprising, obtaining detection signals of each sampling point; extracting a first signal feature and a second signal feature of the detection signal of each sampling point, and obtaining a defect imaging result based on the first signal feature and the second signal feature, wherein the first signal feature is a falling edge logarithmic slope SK, and the second signal feature is a normalized differential peak value SP; identifying a defect contour based on the obtained defect imaging result; based on the identified defect contour, establishing a defect depth quantitative compensation evaluation algorithm, comprising, The signal characteristics-depth power function relationship is fitted through the stepped pipe test piece, a calibration curve is established, and the defect depth is quantitatively evaluated h satisfies: SK = A·h SK m , SP = B·h SP n wherein h SK represents the defect depth corresponding to the first signal feature, h SP represents the defect depth corresponding to the second signal feature, A, B, m, n are coefficients; based on the local wall thickness thinning defect area and the eddy current coverage area at the sampling point, establishing a depth compensation coefficient β: In the formula, S CA represents the size of the overlapping area of the local wall thickness thinning defect area and the eddy current coverage area at the sampling point, S EA represents the eddy current coverage area at the sampling point; establishing a defect depth quantitative compensation evaluation algorithm: SK = A·(βh SK ） m , SP = B·(βh SP ） n h SK ( SK / A ·β m ) 1 / m , h SP =(SP / B ·β n ) 1 / n ; based on the established defect depth quantitative compensation evaluation algorithm, performing defect depth quantitative evaluation.

2. The array-based old oil pipe wall defect profile depth evaluation algorithm of claim 1, wherein, extracting a first signal feature and a second signal feature of the detection signal of each sampling point, and obtaining a defect imaging result based on the first signal feature and the second signal feature, comprising, normalizing the first signal feature and the second signal feature; based on the normalized processing result and a preset threshold, determining a defect area DA and a non-defect area NDA; based on the determined defect area DA and non-defect area NDA, obtaining a fused signal feature.

3. The array-based old oil pipe wall defect profile depth evaluation algorithm of claim 2, wherein, The normalized processing of the falling edge logarithmic slope SK and the normalized differential peak value SP satisfies: wherein, the signal feature NSK represents the normalized processing result of the falling edge logarithmic slope SK; the signal feature NSP represents the normalized processing result of the normalized differential peak value SP, and the values of the signal feature NSK and the signal feature NSP are both between 0 and 1.

4. The array-based old oil pipe wall defect profile depth evaluation algorithm of claim 3, wherein, based on the normalized processing result and a preset threshold, determining a defect area DA and a non-defect area NDA, and based on the determined defect area DA and non-defect area NDA, obtaining a fused signal feature, comprising, the preset threshold is between 0 and 1, wherein if the first signal feature or the second signal feature is greater than the preset threshold, it is regarded as a defect area DA, and if the first signal feature or the second signal feature is less than the threshold, it is regarded as a non-defect area NDA; the fused signal feature satisfies: In the formula, NSF m, n ) indicates a fusion signal feature quantity matrix, m and n indicate a row number and a column number in the fusion signal feature quantity matrix, respectively.​ Based on the fusion signal feature quantity matrix, a median filter is used to smooth the signal of the noise area in the non-defect area NDA, and finally the fusion signal feature NSF, The fusion signal feature NSF is the defect imaging result.

5. The array-based old oil pipe wall defect profile depth evaluation algorithm of claim 3, wherein, based on the defect imaging result, identifying a defect contour, comprising, setting an initial contour area according to the defect imaging result; in each iteration, calculating the internal energy and external energy of the initial contour area; obtaining the final contour area through multiple iterations.

6. An arrayed old oil pipe wall defect profile depth evaluation system characterized by, comprising, an acquisition module, configured to obtain detection signals of each sampling point; a processing module, configured to extract a first signal feature and a second signal feature of the detection signal of each sampling point, and obtain a defect imaging result based on the first signal feature and the second signal feature, wherein the first signal feature is a falling edge logarithmic slope SK, and the second signal feature is a normalized differential peak value SP; an identification module, configured to identify a defect contour based on the defect imaging result; a compensation module, configured to establish a defect depth quantitative compensation evaluation algorithm based on the identified defect contour, comprising, The signal characteristics-depth power function relationship is fitted through the stepped pipe test piece, a calibration curve is established, and the defect depth h is quantitatively evaluated to meet: SK = A·h SK m , SP = B·h SP n wherein h SK represents the defect depth corresponding to the first signal feature, h SP represents the defect depth corresponding to the second signal feature, A, B, m, n are coefficients; based on the local wall thickness thinning defect area and the eddy current coverage area at the sampling point, establishing a depth compensation coefficient β: In the formula, S CA represents the size of the overlapping area of the local wall thickness reduction defect area and the eddy current coverage area at the sampling point, S EA represents the eddy current coverage area at the sampling point; establishing a defect depth quantitative compensation evaluation algorithm: SK = A·(βh SK ） m , SP B·(βh = B· (βh SP ） n h SK ( SK / A ·β m ) 1 / m , h SP =(SP / B ·β n ) 1 / n ; The evaluation module is configured to perform quantitative evaluation of the defect depth based on the established quantitative compensation evaluation algorithm for the defect depth.

7. An arrayed old oil pipe wall defect topography depth evaluation detection device, characterized in that, The array probe includes a probe, a plurality of annular array probe groups, and a computer control system. The plurality of annular array probe groups are provided with a plurality of arrays of probes uniformly distributed in the circumferential direction, each probe including an excitation coil, a Ni-Zn ferrite magnetic core, and two coaxial TMR magnetic field sensors. The computer control system is configured to execute the array probe-based old oil pipe wall defect profile depth evaluation algorithm according to any one of claims 1-5.

8. The apparatus according to claim 7, wherein The test system support is further provided with a plurality of adjustable bases, a motor, a rotary table, and a transmission mechanism. The oil pipe is placed on the rotary table, the motor is connected to the transmission mechanism and the rotary table, respectively, and the motor drives the transmission mechanism to transport the oil pipe through the array probe group and move along the axial direction. The plurality of adjustable bases are used to place the annular array probe groups. The test system support is used to support the transmission mechanism and the plurality of adjustable bases.

9. The arrayed used tubing wall defect profile depth evaluation detection device of claim 8, wherein, The distance between each adjustable base is greater than or equal to a preset distance, and the annular array probe groups are distributed along the circumference, and the angle between the single probes is greater than or equal to a preset angle.

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