A quantitative inversion method for composite magnetic flux leakage signals of oil and gas pipeline defects

Abstract

The present invention discloses a quantitative inversion method for composite magnetic leakage signals of oil and gas pipeline defects. The method comprises the following steps: data collection, including basic pipeline information and site conditions; formulation of a simulation plan, and establishment of a pipeline defect magnetic leakage model; statistical analysis of the relationships between various defect characteristics and different components of the magnetic leakage signal based on different magnetic leakage signal characteristics, and construction of weight coefficients for these components to form a composite inversion model for defect magnetic leakage signals; acquisition of magnetic leakage signals under actual operating conditions; and inversion of the size and depth of pipeline defects under actual operating conditions based on the acquired magnetic leakage signals. The method can establish corresponding defect detection models for different pipelines based on actual on-site conditions, is highly efficient, has practical application value, provides new detection strategies for the operational status of oil and gas pipelines, and is of great significance for ensuring the safe and normal operation of oil and gas pipelines.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas pipeline detection, and specifically embodies a quantitative inversion method for composite magnetic leakage signals of oil and gas pipeline defects. Background Art

[0002] In the long-distance transportation and gathering sectors of oil and gas pipelines, corrosion pits inevitably develop during service due to various factors, impacting safe production operations. Magnetic flux leakage (MFL) internal inspection is widely used as a common internal inspection method to detect defects in oil and gas pipelines after a period of service. However, current quantitative inversion methods for defects in oil and gas pipelines mostly calculate defect signatures based on a single MFL signal, and the accuracy of the resulting defect signatures needs to be improved. Summary of the Invention

[0003] To address the above issues, the present invention provides a quantitative inversion method for composite magnetic flux leakage signals from oil and gas pipeline defects. By acquiring basic information about the oil and gas pipeline and the magnetic flux leakage detector, and establishing a magnetic flux leakage model for the oil and gas pipeline defect, the magnetic flux leakage signals at different defect radii and depths are simulated. Based on the characteristics of the different magnetic flux leakage signals, statistical analysis is performed on the relationships between the various defect characteristics and the different components of the magnetic flux leakage signal. Weight coefficients are then constructed for these components to form a composite inversion model for the defect magnetic flux leakage signal. Using this composite inversion model, the actual defect characteristics can be inverted from the actual magnetic flux leakage signal.

[0004] This method can infer the characteristics of defects based on the magnetic flux leakage signals collected from pipelines of different working conditions and materials. It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as those commonly understood by those skilled in the art to which this application belongs.

[0005] The main steps are as follows:

[0006] Step 1: Data collection, collect basic pipeline information and on-site information, including but not limited to: pipe diameter, wall thickness, pipe material, etc.

[0007] Step 2: Develop a simulation plan based on on-site working conditions and establish a magnetic flux leakage model for oil and gas pipeline defects.

[0008] Step 3: Based on the oil and gas pipeline defect magnetic flux leakage model, simulate the pipeline's axial and radial magnetic flux leakage signals at different defect radii and depths. Based on the different magnetic flux leakage signal characteristics, statistically analyze the patterns between the various defect characteristics and the different components of the magnetic flux leakage signal. Weight coefficients for these components are then constructed to form a composite inversion model for the defect magnetic flux leakage signal. Axial refers to the length of the pipeline, and radial refers to the radius of the pipeline.

[0009] Inversion calculation model of defect radius and axial magnetic flux leakage signal:

[0010] r1=a1ΔT a1 +b1 (1)

[0011] Where: r1 is the axial inversion defect radius, mm; ΔT a1 is the amplitude of the axial magnetic induction intensity, mT; a1, b1 are unknown coefficients, and the unknown coefficients of different pipelines are determined by fitting the simulated data of the leakage magnetic signal.

[0012] Defect radius and radial magnetic flux leakage signal inversion calculation model:

[0013] r2=c1ΔT r1 +d1 (2)

[0014] Where: r2 is the radial inversion defect radius, mm; ΔT r1 is the peak-to-valley value of radial magnetic induction intensity, mT; c1, d1 are unknown coefficients, which are determined by fitting the simulated data of magnetic flux leakage signals for different pipelines.

[0015] Construct a composite inversion model, defect radius:

[0016] r=mr1+nr2 (3)

[0017] Where: r is the defect radius; m and n are weight coefficients. The weight coefficients of different pipelines are determined by fitting the magnetic flux leakage signal simulation data.

[0018] Axial defect depth and magnetic flux leakage signal inversion calculation model:

[0019]

[0020] Where: h1 is the axial inversion defect depth, mm; ΔT a2 is the amplitude of the axial magnetic induction intensity, mT; a2, b2 are unknown coefficients, which are determined by fitting the simulated data of the magnetic flux leakage signal for different pipelines.

[0021] Inversion calculation model of radial defect depth and magnetic flux leakage signal:

[0022]

[0023] Where: h2 is the radial inversion defect depth, mm; ΔT r2 is the peak-to-valley value of radial magnetic induction intensity, mT; c2, d2 are unknown coefficients, which are determined by fitting the simulated data of magnetic flux leakage signals for different pipelines.

[0024] Construct a composite inversion model, defect depth:

[0025] h=sh1+th2 (6)

[0026] Where: h is the defect radius; s and t are weight coefficients. The weight coefficients of different pipelines are determined by fitting the magnetic flux leakage signal simulation data.

[0027] Step 4: Place the magnetic flux leakage detector into the actual oil and gas pipeline to be tested and make the necessary preparations. Once the pipeline is pressurized, the magnetic flux leakage detector moves forward, collecting axial and radial magnetic flux leakage signals from the pipeline.

[0028] Step 5: Analyze the axial and radial magnetic leakage signals collected by the magnetic leakage internal detector, and calculate the actual oil and gas pipeline defect size and depth based on the composite inversion calculation model of the defect radius, depth and magnetic leakage signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is an example diagram of the axial magnetic leakage signal of an oil and gas pipeline defect at different defect radii. Figure 2 The following is an example diagram of radial magnetic leakage signal of an oil and gas pipeline defect at different defect radii. Figure 3 This is an example diagram of radial magnetic leakage signals of an oil and gas pipeline defect at different defect depths. Figure 4 The following are examples of radial magnetic flux leakage signals of a defect in an oil and gas pipeline at different defect depths. The above examples are only example data of the present invention. The specific sample data volume is large and is not listed one by one. DETAILED DESCRIPTION

[0030] The present invention will be further described below with reference to the accompanying drawings and examples. It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by those skilled in the art to which this application belongs.

[0031] Step 1: Collect basic information of the on-site experimental pipeline. The pipe diameter is 323.8 mm, the outer layer material of the pipeline is L360QS, and the wall thickness is 12 mm; the inner layer material is Incoloy825, and the wall thickness is 3 mm. The prefabricated defect radius is 4 mm, and the prefabricated defect depth is 2.4 mm.

[0032] Step 2: Based on the basic information of the pipeline, use ABAQUS to establish an oil and gas pipeline defect magnetic leakage detection model.

[0033] Step 3: Based on the oil and gas pipeline defect magnetic flux leakage model, simulate the pipeline axial and radial magnetic flux leakage signals at different defect radius and depth. According to the different magnetic flux leakage signal characteristics, statistically analyze the laws of each defect feature and different components of the magnetic flux leakage signal, and construct weight coefficients for the defect features and different components of the magnetic flux leakage signal to form a composite inversion model of the defect magnetic flux leakage signal:

[0034] Axial defect radius and magnetic flux leakage signal inversion calculation model:

[0035] r1=1.879ΔT a1 +1.330 (7)

[0036] Where: r1 is the axial inversion defect radius, mm; ΔT a1 is the amplitude of the axial magnetic induction intensity, mT.

[0037] Radial defect radius and magnetic flux leakage signal inversion calculation model:

[0038] r2=1.114ΔT r1 +1.534 (8)

[0039] Where: r2 is the radial inversion defect radius, mm; ΔT r1 is the peak-to-peak value of radial magnetic induction intensity, mT.

[0040] Composite inversion model of defect radius:

[0041] r=0.892r1+0.108r2 (9)

[0042] Axial defect depth and magnetic flux leakage signal inversion calculation model:

[0043] h1=2.235ΔT a2 0.817 (10)

[0044] Where: h1 is the axial inversion defect depth, mm; ΔT a2 is the amplitude of the axial magnetic induction intensity, mT.

[0045] Inversion calculation model of radial defect depth and magnetic flux leakage signal:

[0046] h2=1.523ΔT r2 0.824 (11)

[0047] Where: h2 is the radial inversion defect depth, mm; ΔT r2 is the radial magnetic induction intensity value amplitude, mT.

[0048] Defect depth composite inversion model:

[0049] h=0.295h1+0.705h2 (12)

[0050] Step 4: In this specific embodiment, the axial magnetic flux leakage intensity amplitude of a defect detected by the internal magnetic flux leakage detector is 1.372 mT, and the peak-to-peak value of the radial magnetic flux leakage intensity is 2.110 mT. The axial magnetic flux leakage intensity amplitude of a defect detected by the internal magnetic flux leakage detector is 1.052 mT, and the peak-to-peak value of the radial magnetic flux leakage intensity is 1.692 mT.

[0051] Step 5: Based on the composite inversion calculation model of the defect radius, depth and magnetic flux leakage signal, in a specific embodiment, the inversion value of the defect radius is r=3.91 mm; the inversion value of the defect depth is h=2.34 mm.

[0052] The radius of the prefabricated defect in the pipeline is 4 mm, and the inverted value of the defect radius is r = 3.91 mm, with a relative error of 2.25%. The prefabricated defect depth is 2.4 mm, and the inverted value of the defect depth is h = 2.34 mm, with a relative error of 2.50%.

[0053] By changing the pipeline parameter settings, we can obtain the inversion model of the defect radius and depth of pipelines made of different materials and the leakage magnetic signal. Based on the inversion model, we can calculate the defect radius and depth under different working conditions.

[0054] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

[0055] Technical Effects

[0056] The present invention provides a method for detecting oil and gas pipeline defects. It can infer the size and depth of defects in oil and gas pipelines under actual operating conditions based on magnetic flux leakage signals. During implementation, a defect detection model can be established for each pipeline based on actual field conditions. This method is highly efficient and has practical application value.

Claims

1. A quantitative inversion method for composite magnetic flux leakage signals of oil and gas pipeline defects, characterized by: The following steps are involved: S1. Data collection: Collect basic pipeline information and on-site information, including pipe diameter, wall thickness, and pipe material; S2. Develop a simulation plan based on on-site working conditions and establish a magnetic flux leakage model for oil and gas pipeline defects; S3. Based on the magnetic flux leakage model of oil and gas pipeline defects, simulate the axial and radial magnetic flux leakage signals of pipelines at different defect radii and depths. According to the characteristics of different magnetic flux leakage signals, statistically analyze the laws of various defect characteristics and different components of magnetic flux leakage signals, and construct weight coefficients for the defect characteristics and different components of magnetic flux leakage signals to form a composite inversion model of defect magnetic flux leakage signals. Inversion calculation model of defect radius and axial magnetic flux leakage signal: r1=a1ΔT a1 +b1 (1) Where: r1 is the axial inversion defect radius, mm; ΔT a1 is the amplitude of the axial magnetic induction intensity, mT; a1, b1 are unknown coefficients, which are determined by fitting the simulated data of the magnetic flux leakage signal for different pipelines; Defect radius and radial magnetic flux leakage signal inversion calculation model: r2=c1ΔT r1 +d1 (2) Where: r2 is the radial inversion defect radius, mm; ΔT r1 is the peak-to-valley value of radial magnetic induction intensity, mT; c1, d1 are unknown coefficients, which are determined by fitting the simulated data of magnetic flux leakage signals for different pipelines; Construct a composite inversion model, defect radius: r=mr1+nr2 (3) Where: r is the defect radius; m, n are weight coefficients. The weight coefficients of different pipelines are determined by fitting the magnetic flux leakage signal simulation data. Axial defect depth and magnetic flux leakage signal inversion calculation model: Where: h1 is the axial inversion defect depth, mm; ΔT a2 is the amplitude of the axial magnetic induction intensity, mT; a2, b2 are unknown coefficients, which are determined by fitting the simulated data of the magnetic flux leakage signal for different pipelines; Inversion calculation model of radial defect depth and magnetic flux leakage signal: Where: h2 is the radial inversion defect depth, mm; ΔT r2 is the peak-to-valley value of radial magnetic induction intensity, mT; c2, d2 are unknown coefficients, which are determined by fitting the simulated data of magnetic flux leakage signals for different pipelines; Construct a composite inversion model, defect depth: h=sh1+th2 (6) Where: h is the defect radius; s, t are weight coefficients. The weight coefficients of different pipelines are determined by fitting the magnetic flux leakage signal simulation data. S4. Place the magnetic flux leakage internal detector into the actual oil and gas pipeline to be tested and make relevant preparations. After the preparations are completed, pressurize the pipeline to push the magnetic flux leakage internal detector forward. During the movement, the axial and radial magnetic flux leakage signals of the oil and gas pipeline are collected. S5. Analyze the axial and radial magnetic leakage signals collected by the magnetic leakage internal detector, and calculate the actual oil and gas pipeline defect size and depth based on the composite inversion calculation model of the defect radius, depth and magnetic leakage signal.

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