Performance testing method and system for deep well drill rod

Abstract

The invention discloses a performance testing method and system for a deep well drill rod, and relates to the field of deep well drilling. The method is applied to a drill rod performance testing system, and comprises the following steps: performing frequency sweeping treatment on a drill rod to be detected by adopting multi-frequency coupling excitation, and detecting to obtain multi-waveform data of the drill rod to be detected; based on the multi-waveform data, calculating to obtain performance evaluation indexes of a plurality of structural points of the to-be-detected drill rod; performing anomaly evaluation on the performance evaluation indexes of the plurality of structural points by adopting a preset temperature-performance evaluation index reference table, and determining a plurality of abnormal structural points and abnormal modes of the plurality of abnormal structural points; determining defect information of the plurality of abnormal structure points according to the abnormal modes of the plurality of abnormal structure points; and according to the defect information of the plurality of abnormal structure points, evaluating to obtain the performance grade of the to-be-detected drill rod. By implementing the technical scheme provided by the invention, the problem of low detection precision caused by the influence of temperature on the performance detection of the drill rod at present is solved.

Description

Technical Field

[0001] This application relates to the field of deep well drilling, specifically to a method and system for testing the performance of deep well drill pipe. Background Technology

[0002] During deep well drilling operations, drill pipes are subjected to high pressure, high temperature, torque impact, and formation medium corrosion for extended periods, making them highly susceptible to defects such as surface cracks, internal inclusions, and thinning of the wall. These defects significantly increase the probability of serious accidents such as drill pipe breakage and stuck pipe. Therefore, it is necessary to test the performance of drill pipes during drilling intervals to determine whether they are suitable for continued service, thereby ensuring the safety of deep well drilling.

[0003] Currently, non-destructive testing (NDT) technology is the main method for testing drill pipe performance during drilling operations. Among these, electromagnetic ultrasonic testing technology has become the mainstream testing method in the industry due to its advantages such as not requiring coupling agents, being suitable for oily and rough conditions on drill pipe surfaces, and being able to detect both surface and internal defects simultaneously. Its core principle is to generate a static magnetic field through a permanent magnet inside the probe, which excites a coil to pass a high-frequency alternating current to induce eddy currents on the surface of the drill pipe. The eddy currents are excited by the Lorentz force in the static magnetic field, which generates ultrasonic waves. When the ultrasonic waves propagate inside the drill pipe, they interact with defects to generate reflected / scattered signals. By analyzing the signal characteristics, defect identification and performance evaluation can be achieved.

[0004] However, in actual drilling scenarios, after the drill pipe completes deep well drilling operations, its surface temperature rises significantly due to the high temperature of the formation and frictional heat generated downhole. This causes a decrease in the magnetic flux of the permanent magnet and an increase in the resistance of the excitation coil, resulting in a significant decrease in signal detection intensity and making it impossible to accurately determine the actual performance status of the drill pipe. Summary of the Invention

[0005] To address the problem of low accuracy in drill pipe performance testing due to temperature variations, this application provides a performance testing method and system for deep well drill pipes.

[0006] In a first aspect, this application provides a performance testing method for deep well drill pipe, applied in a drill pipe performance testing system, the method comprising:

[0007] Multi-frequency coupled excitation is used to perform frequency sweeping processing on the drill pipe under test, and multi-waveform data of the drill pipe under test is obtained. The multi-waveform data includes surface waves, transverse waves and longitudinal waves.

[0008] Based on the multi-waveform data, performance evaluation indices for multiple structural points of the drill pipe under test are calculated.

[0009] Anomaly assessment of the performance evaluation indices of multiple structural points is performed using a preset temperature-performance evaluation index benchmark table to identify multiple abnormal structural points and their abnormal patterns.

[0010] Based on the abnormal patterns of the multiple abnormal structural points, the defect information of the multiple abnormal structural points is determined;

[0011] The performance level of the drill pipe to be tested is evaluated based on the defect information of multiple abnormal structural points.

[0012] Optionally, the multi-frequency coupling excitation includes low-frequency excitation, medium-frequency excitation, and high-frequency excitation, which are used to excite surface waves, transverse waves, and longitudinal waves of the drill pipe under test, respectively.

[0013] Optionally, the step of calculating the performance evaluation index of multiple structural points of the drill pipe under test based on the multi-waveform data further includes:

[0014] Wavelet decomposition is performed on the waveform data to be evaluated of the first structural point to obtain multiple wavelet detail coefficients. The waveform data to be evaluated is any one of the multiple waveform data, and the first structural point is any one of the multiple structural points.

[0015] Thresholding is performed on the multiple wavelet detail coefficients to obtain multiple effective wavelet detail coefficients;

[0016] Based on the multiple effective wavelet detail coefficients, the waveform data to be evaluated is reconstructed to obtain the corrected waveform data;

[0017] Calculate the effective signal energy in the corrected waveform data;

[0018] Based on the effective signal energy of the correction waveform data corresponding to each of the multiple waveform data, the performance evaluation index of the first structural point is calculated.

[0019] Optionally, a preset temperature-performance evaluation index benchmark table is used to perform anomaly evaluation on the performance evaluation indexes of multiple structural points, thereby identifying multiple abnormal structural points, specifically:

[0020] Obtain the surface temperature of multiple structural points;

[0021] The surface temperatures of multiple structural points are matched with the preset temperature-performance evaluation index benchmark table to obtain the performance evaluation index benchmarks corresponding to each of the multiple structural points.

[0022] The performance evaluation index of the structural point to be evaluated is compared with the performance evaluation index benchmark of the structural point to be evaluated, wherein the structural point to be evaluated is any one of the multiple structural points;

[0023] If the performance evaluation index of the structure point to be evaluated does not meet the performance evaluation index benchmark of the structure point to be evaluated, then the structure point to be evaluated is determined to be an abnormal structure point.

[0024] Optionally, after comparing the performance evaluation index of the structural point to be evaluated with the performance evaluation index benchmark of the structural point to be evaluated, the method further includes:

[0025] If the performance evaluation index of the structure point to be evaluated meets the performance evaluation index benchmark of the structure point to be evaluated, then the evaluation dataset of the structure point to be evaluated is selected based on the preset evaluation window with the structure point to be evaluated as the center point.

[0026] The evaluation dataset is constructed into a window performance curve, and the window performance curve is converted into a window difference curve;

[0027] Calculate the curve fluctuation coefficient of the window difference curve;

[0028] The curve fluctuation coefficient is compared with a preset curve fluctuation coefficient threshold.

[0029] If the curve fluctuation coefficient is greater than or equal to the preset curve fluctuation coefficient threshold, then the structural point to be evaluated is determined to be an abnormal structural point.

[0030] Optionally, after comparing the curve fluctuation coefficient with a preset curve fluctuation coefficient threshold, the method further includes:

[0031] If the curve fluctuation coefficient is less than the preset curve fluctuation coefficient threshold, then the preset evaluation window of the structure point to be evaluated is divided into a first evaluation window and a second evaluation window, with the structure point to be evaluated as the boundary.

[0032] Calculate the first curve fluctuation coefficient of the first evaluation window and the second curve fluctuation coefficient of the second evaluation window;

[0033] Calculate the difference in curve fluctuation coefficient between the first curve fluctuation coefficient and the second curve fluctuation coefficient;

[0034] If the difference in the curve fluctuation coefficient is greater than or equal to the preset threshold for the difference in the curve fluctuation coefficient, then the structural point to be evaluated is determined to be an abnormal structural point.

[0035] Optionally, the step of evaluating the performance level of the drill pipe to be tested based on the defect information of multiple abnormal structural points specifically involves:

[0036] Obtain the inherent parameters and drilling scenario parameters of the drill pipe to be tested;

[0037] Based on the inherent parameters and drilling scenario parameters, the stress risk zone of the drill pipe to be tested is determined;

[0038] Based on the defect information and inherent parameters of the multiple abnormal structural points, the stress concentration areas of the multiple abnormal structural points are determined;

[0039] The stress risk coefficient of the drill pipe to be tested is determined based on the stress risk area and the stress concentration area.

[0040] Based on the stress risk coefficient, the performance level of the drill pipe to be tested is obtained by querying a preset performance level table.

[0041] Secondly, this application provides a performance testing system for deep well drill pipes. The system comprises a testing module, a processing module, and an output module, wherein:

[0042] The test module is used to perform frequency sweeping processing on the drill pipe under test using multi-frequency coupling excitation, and to detect the multi-waveform data of the drill pipe under test, including surface waves, transverse waves and longitudinal waves.

[0043] The processing module is used to calculate the performance evaluation index of multiple structural points of the drill pipe to be tested based on the multi-waveform data.

[0044] Anomaly assessment of the performance evaluation indices of multiple structural points is performed using a preset temperature-performance evaluation index benchmark table to identify multiple abnormal structural points and their abnormal patterns.

[0045] Based on the abnormal patterns of the multiple abnormal structural points, the defect information of the multiple abnormal structural points is determined;

[0046] The output module is used to evaluate the performance level of the drill pipe to be tested based on the defect information of multiple abnormal structural points.

[0047] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0048] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0049] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0050] When performing performance testing on drill pipes, this application employs low-frequency, medium-frequency, and high-frequency excitation to sweep the frequency of the drill pipe under test, obtaining waveform data of the drill pipe under test at different excitation frequencies. These waveforms include surface waves, transverse waves, and longitudinal waves. Surface waves reflect the surface structure of the drill pipe, transverse waves reflect its internal structure, and longitudinal waves reflect its wall thickness. Then, corresponding performance parameter data are extracted from the multi-waveform data. However, due to the high temperature of the drill pipe, the detection intensity of the electromagnetic ultrasonic testing equipment decreases during testing, making it difficult to accurately distinguish between noise and defect signals in the monitoring signal. Consequently, it is difficult to directly judge the drill pipe performance using the performance parameter data. Therefore, this application... The application considers that noise signals, being global interference signals, will simultaneously and uniformly affect all waveform data, while defect signals have different and selective effects on different waveform data. Therefore, the surface waves, transverse waves, and longitudinal waves are first converted into energy probability distributions. Since energy corresponds to the integral intensity of the signal in the time or frequency domain, it is not sensitive to random noise and instantaneous fluctuations, thus weakening the interference. Then, the difference between the measured energy probability distribution and the theoretical energy probability distribution at the current temperature is calculated. If defects exist, the difference will be significant. Based on this, the defect information of the drill pipe is determined according to the preset temperature-performance parameter index benchmark table, and the performance level of the drill pipe is further determined, thereby improving the accuracy of drill pipe performance testing in complex temperature environments. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a performance testing method for deep well drill pipe provided in an embodiment of this application.

[0052] Figure 2 This is a schematic diagram of the structure of a performance testing system for deep well drill pipe provided in an embodiment of this application.

[0053] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0054] Explanation of reference numerals in the attached diagram: 1. Test module; 2. Processing module; 3. Output module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0056] During deep well drilling operations, drill pipes deteriorate rapidly due to the harsh working environment. Therefore, to ensure the safety of deep well drilling, drill pipe performance must be tested periodically to determine its suitability for continued service. Currently, there are various testing methods for drill pipes, such as magnetic particle testing, radiographic testing, and electromagnetic ultrasonic testing. However, in actual working conditions, the defect patterns of drill pipes are very complex, and the surface is usually covered with oil and mud. Electromagnetic ultrasonic testing technology has become the mainstream testing method in the industry due to its advantages such as not requiring coupling agents, adaptability to oily and rough drill pipe surfaces, and the ability to simultaneously detect surface and internal defects. However, in actual drilling scenarios, after the drill pipe completes deep well drilling operations, its surface temperature rises significantly due to the high temperature of the formation and frictional heat. The detection accuracy of electromagnetic ultrasonic testing technology is also highly sensitive to temperature. As the temperature rises, the magnetic flux of the permanent magnet decreases significantly, and the resistance of the excitation coil also increases continuously, resulting in a significant reduction in signal detection strength. Consequently, it becomes impossible to accurately determine the actual performance status of the drill pipe. Therefore, forced air cooling is currently used to accelerate cooling before testing, but this still requires a long cooling time. Furthermore, the drilling operation schedule is continuous and tight, leading to some drill pipes being hastily resumed due to insufficient testing, increasing safety risks.

[0057] Therefore, this application provides a performance testing method for deep well drill pipe, which is applied to a drill pipe performance testing system, such as... Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0058] S101. Multi-frequency coupling excitation is used to perform frequency sweep processing on the drill pipe to be tested, and multi-waveform data of the drill pipe to be tested is obtained. The multi-waveform data includes surface wave, shear wave and longitudinal wave.

[0059] In the above steps, during the drilling operation interval, this application first designs a multi-frequency coupled excitation frequency sweep scheme based on the material characteristics and defect types of the drill pipe to be inspected. Specifically, it includes low-frequency excitation, medium-frequency excitation, and high-frequency excitation. Among them, low-frequency excitation mainly excites the surface waves of the drill pipe for detecting surface and near-surface defects of the drill pipe, medium-frequency excitation mainly excites the transverse waves of the drill pipe for detecting internal defects of the drill pipe, and high-frequency excitation mainly excites the longitudinal waves of the drill pipe for detecting wall thickness wear defects of the drill pipe.

[0060] Then, the coupled signals of the three independent frequency band excitation signals are applied to the surface of the drill rod to be tested by scanning along the length of the drill rod through the excitation coil of the electromagnetic ultrasonic probe, and the ultrasonic signals returned from the drill rod are received by the receiving probe. At this time, the received ultrasonic signals are converted from analog to digital, and then the amplitude curves corresponding to each waveform are separated and extracted from the signals according to the characteristics of different waveforms in the time and frequency domains, thus obtaining the multi-waveform data of the drill rod to be tested.

[0061] S102. Based on multi-waveform data, the performance evaluation indexes of multiple structural points of the drill pipe to be tested are calculated.

[0062] In the above steps, the multi-waveform data covers the structural features of multiple equidistant structural points along the length of the drill pipe. At this time, each structural point corresponds to a set of waveform data: (surface wave, transverse wave, longitudinal wave). Then, in actual high-temperature testing scenarios, the magnetic flux of the permanent magnet of the electromagnetic ultrasonic probe decreases due to the increase in drill rod surface temperature, and the resistance of the excitation coil also increases accordingly. This causes a global and trend-like attenuation of the absolute amplitude of all waveform data. At this time, noise signals and defect signals will be amplified in amplitude. If the amplitude of a single waveform is directly used as the performance evaluation basis, it is difficult to distinguish whether the amplitude shift is caused by noise interference or by defects, thus leading to misjudgment of the evaluation results. Therefore, for multi-waveform data of a certain structural point, this application first performs wavelet decomposition on each waveform data to obtain the wavelet detail coefficients of each waveform data. The wavelet detail coefficients characterize the abrupt change intensity of the signal in a specific frequency band. Each waveform data corresponds to multiple wavelet detail coefficients, which characterize the abrupt change intensity of the signal in multiple specific frequency bands. It should be noted that noise generally presents as a global, continuous, small disturbance to the signal, while the defect signal presents as a local, directional, strong disturbance. Therefore, the abrupt change intensity of the defect signal in a specific frequency band is higher than that of the noise signal. Thus, this application thresholds multiple wavelet detail coefficients. Value processing involves comparing multiple wavelet detail coefficients with a preset wavelet detail coefficient threshold. Wavelet detail coefficients greater than or equal to the threshold are considered valid wavelet coefficients. Based on these valid wavelet detail coefficients, the waveform data to be evaluated is reconstructed to obtain corrected waveform data, thus achieving initial noise reduction. Next, the effective signal energy of the corrected waveform data for each waveform is calculated. Then, the effective signal energy of each waveform is normalized to obtain the energy probability distribution of the structural point. It should be noted that since the signal energy of the waveform data corresponds to the integral intensity of the signal in the time or frequency domain, and the integral intensity is mainly related to the physical properties of the material itself, converting the waveform data into an energy probability distribution can reduce the influence of random noise and instantaneous fluctuations while retaining the signal energy of structural defects, thus further improving signal accuracy. Finally, the relative deviation between the measured energy probability distribution and the theoretical energy probability distribution at the current temperature (performance evaluation index) is calculated, which can significantly improve the identification accuracy of defects. The specific calculation method is as follows:

[0063]

[0064] in, Let i be the performance evaluation index for the i-th structural point. Let be the measured energy distribution of the k-th waveform at the i-th structural point. Let be the theoretical energy distribution of the k-th waveform at the i-th structural point. These correspond to surface waves, transverse waves, and longitudinal waves, respectively.

[0065] In the above formula, The information difference between the measured energy distribution of a structural point and the energy distribution that a healthy system should have at the same temperature was quantified. At this time, if the waveform data of a certain structural point is distorted due to high temperature interference, but as long as the measured energy distribution is highly consistent with the theoretical energy distribution at the current temperature, the point is still judged to be a normal point. Otherwise, the structural point is judged to be a defective structural point.

[0066] S103. Using a preset temperature-performance evaluation index benchmark table, perform anomaly evaluation on the performance evaluation index of multiple structural points to identify multiple abnormal structural points and their abnormal patterns.

[0067] In the above steps, the preset temperature-performance evaluation index benchmark table can be understood as the regular mapping relationship between temperature and performance evaluation indexes, which has been determined through numerous experiments for defect-free drill pipes. At this point, anomaly identification can be performed on multiple defect points to determine the abnormal structural points and abnormal patterns. Specifically:

[0068] First, the surface temperatures of multiple structural points are obtained. Then, the surface temperatures of each structural point are matched with a preset temperature-performance evaluation index benchmark table to obtain the performance evaluation index benchmarks corresponding to each structural point. At this point, for any structural point to be evaluated, the calibration performance evaluation index of the structural point to be evaluated is compared with the performance evaluation index benchmark of the structural point to be evaluated. If the performance evaluation index of the structural point to be evaluated does not meet the performance evaluation index benchmark of the structural point to be evaluated, the structural point to be evaluated can be determined as an abnormal structural point, and the abnormal model of the abnormal structural point is recorded. For example, the performance evaluation index of the abnormal structural point deviates from the benchmark S.

[0069] In one possible implementation, since some structural points exhibit potential defect patterns, i.e., the current performance evaluation index meets its corresponding performance evaluation index benchmark, but shows a significant deviation trend, in order to discover these potential defect structural points, this application also performs spatial continuity analysis on structural points that meet their corresponding performance evaluation index benchmark to capture hidden anomalies. Specifically, based on a preset evaluation window with the structural point to be evaluated as the center point, an evaluation dataset of the structural point to be evaluated is selected. For example, for the i-th structural point, if the preset evaluation window is 5, then the evaluation dataset includes the (i-2)-th structural point, the (i-1)-th structural point, the i-th structural point, the (i+1)-th structural point, and the (i+2)-th structural point. At this point, the performance evaluation indicators of all points within the window are constructed into a window performance curve. The horizontal axis of the window performance curve is the distance between the structural point and the drill pipe starting point, and the vertical axis is the performance evaluation indicator. Then, the window performance curve is converted into a window difference curve to amplify small trend changes. Then, the curve fluctuation coefficient of the window difference curve is calculated (e.g., variance, standard deviation, coefficient of variation, etc., determined according to the actual business scenario). If the curve fluctuation coefficient is greater than the preset curve fluctuation coefficient threshold, it indicates that the structure in this area has a sharp trend change and has a high probability of developing into a structural defect in subsequent drilling operations. At this time, it is also identified as an abnormal structural point, and its corresponding abnormal mode is recorded. For example, the performance evaluation indicator of the abnormal structural point shows a significant unstable change.

[0070] In one possible implementation, for structural points where the curve fluctuation coefficient is less than or equal to a curve fluctuation coefficient threshold, if this structural point is a defect initiation point, it will cause a significant difference in the data pattern between one side and the other side of the structural point. This will dilute the data pattern on the abnormal side, leading to false detections. Therefore, for this type of structural point to be evaluated, this application divides the preset evaluation window of the structural point to be evaluated into a first evaluation window and a second evaluation window, centered on the structural point. Then, it calculates the first curve fluctuation coefficient and the second curve fluctuation coefficient of the evaluation datasets of the first and second evaluation windows, respectively. If the difference between the first curve fluctuation coefficient of the first evaluation window and the second curve fluctuation coefficient of the second evaluation window is greater than or equal to a preset curve fluctuation coefficient difference threshold, it indicates that there is a significant asymmetry in the data change trend between the first and second evaluation windows, further indicating that the structural point to be evaluated is a defect initiation point. Therefore, it is identified as an abnormal structural point, and its corresponding abnormal pattern is recorded. For example, an abnormal structural point is a defect initiation point where the performance evaluation index shows significant unstable changes.

[0071] S104. Determine the defect information of multiple abnormal structural points based on the abnormal patterns of multiple abnormal structural points.

[0072] In the above steps, this application queries the corresponding defect identification mode according to the abnormal pattern of the abnormal structural point. The defect identification mode includes, but is not limited to, mapping model identification, simulation model identification, etc. Then, using the defect identification mode corresponding to each abnormal structural point, based on the multi-waveform data and performance test index data of the abnormal structural point, the abnormal structural point is defect identified to obtain the defect information of multiple abnormal structural points. The defect information includes defect type and defect size.

[0073] S105. Based on the defect information of multiple abnormal structural points, the performance level of the drill pipe to be tested is evaluated.

[0074] In the above steps, to ensure that the evaluated drill pipe performance level aligns with the current service scenario, this application comprehensively considers the distribution of structural points on the drill pipe, the inherent parameters of the drill pipe, and drilling scenario parameters to determine whether the drill pipe is suitable for continued operation, thereby improving drilling efficiency and safety. Specifically:

[0075] First, the inherent parameters of the drill pipe to be tested and the drilling scenario parameters are obtained. The inherent parameters of the drill pipe include geometric dimensions and material properties, while the drilling scenario parameters include maximum expected axial tensile force, maximum expected torque, wellbore pressure, and formation pressure. Then, based on these parameters, a finite element analysis model is used to determine the stress risk zones of the drill pipe. Specifically, a three-dimensional parametric model is first established based on the geometric dimensions of the drill pipe, and the model is then meshed. The drilling scenario parameters are then converted into boundary conditions for the model, and the material properties of the drill pipe are assigned to the model. A static finite element method is then performed on the model to obtain the equivalent stress contour map. Finally, areas in the contour map where the equivalent stress value exceeds a certain proportion of the material's yield strength are marked as stress risk zones. The stress concentration zone can be understood as the area of ​​the drill pipe to be tested that is significantly affected by stress. Then, based on the defect information and inherent parameters of multiple abnormal structural points, a stress simulation model is used to determine the stress concentration zones of multiple abnormal structural points. Specifically, based on the defect information of multiple abnormal structural points, the defect geometry of each abnormal structural point is reconstructed in the pre-constructed finite element model, and then stress simulation is performed to obtain a stress map. Then, the area in the stress map where the stress value is greater than the stress value threshold is marked as the stress concentration zone. At this time, the stress risk coefficient of the drill pipe to be tested is determined according to the overlap between the stress risk zone and the stress concentration zone. Finally, the stress risk coefficient of the drill pipe to be tested is matched with a preset performance level table to obtain the performance level of the drill pipe to be tested that fits the current drilling scenario. If the performance level is low, it means that the drill pipe is no longer suitable for subsequent drilling work.

[0076] Reference Figure 2This application also provides a performance testing system for deep well drill pipes. The system includes a testing module 1, a processing module 2, and an output module 3, wherein:

[0077] Test module 1 is used to perform frequency sweep processing on the drill pipe under test using multi-frequency coupling excitation to detect multi-waveform data of the drill pipe under test, including surface wave, shear wave and longitudinal wave;

[0078] Processing module 2 is used to calculate the performance evaluation index of multiple structural points of the drill pipe to be tested based on multi-waveform data;

[0079] Anomalies were assessed on the performance evaluation indices of multiple structural points using a preset temperature-performance evaluation index benchmark table, which identified multiple abnormal structural points and their abnormal patterns.

[0080] Based on the abnormal patterns of multiple abnormal structural points, the defect information of multiple abnormal structural points is determined;

[0081] Output module 3 is used to evaluate the performance level of the drill pipe to be tested based on the defect information of multiple abnormal structural points.

[0082] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0083] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0084] The communication bus 302 is used to enable communication between these components.

[0085] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0086] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0087] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0088] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a performance testing method of deep well drill pipe.

[0089] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a deep well drill pipe performance testing method. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0090] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0091] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0095] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0096] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for testing the performance of deep well drill pipe, characterized in that, The method, applied in a drill pipe performance testing system, includes: Multi-frequency coupled excitation is used to perform frequency sweeping processing on the drill pipe under test, and multi-waveform data of the drill pipe under test is obtained. The multi-waveform data includes surface waves, transverse waves and longitudinal waves. Based on the multi-waveform data, performance evaluation indices for multiple structural points of the drill pipe under test are calculated. Anomaly assessment of the performance evaluation indices of multiple structural points is performed using a preset temperature-performance evaluation index benchmark table to identify multiple abnormal structural points and their abnormal patterns. Based on the abnormal patterns of the multiple abnormal structural points, the defect information of the multiple abnormal structural points is determined; The performance level of the drill pipe to be tested is evaluated based on the defect information of multiple abnormal structural points.

2. The method according to claim 1, characterized in that, The multi-frequency coupling excitation includes low-frequency excitation, medium-frequency excitation and high-frequency excitation, which are used to excite surface waves, transverse waves and longitudinal waves of the drill pipe to be tested, respectively.

3. The method according to claim 1, characterized in that, The calculation of performance evaluation indices for multiple structural points of the drill pipe under test based on the multi-waveform data further includes: Wavelet decomposition is performed on the waveform data to be evaluated of the first structural point to obtain multiple wavelet detail coefficients. The waveform data to be evaluated is any one of the multiple waveform data, and the first structural point is any one of the multiple structural points. Thresholding is performed on the multiple wavelet detail coefficients to obtain multiple effective wavelet detail coefficients; Based on the multiple effective wavelet detail coefficients, the waveform data to be evaluated is reconstructed to obtain the corrected waveform data; Calculate the effective signal energy in the corrected waveform data; Based on the effective signal energy of the correction waveform data corresponding to each of the multiple waveform data, the performance evaluation index of the first structural point is calculated.

4. The method according to claim 1, characterized in that, Anomaly assessment was performed on the performance evaluation indices of multiple structural points using a preset temperature-performance evaluation index benchmark table, identifying multiple abnormal structural points, specifically: Obtain the surface temperature of multiple structural points; The surface temperatures of multiple structural points are matched with the preset temperature-performance evaluation index benchmark table to obtain the performance evaluation index benchmarks corresponding to each of the multiple structural points. The performance evaluation index of the structural point to be evaluated is compared with the performance evaluation index benchmark of the structural point to be evaluated, wherein the structural point to be evaluated is any one of the multiple structural points; If the performance evaluation index of the structure point to be evaluated does not meet the performance evaluation index benchmark of the structure point to be evaluated, then the structure point to be evaluated is determined to be an abnormal structure point.

5. The method according to claim 4, characterized in that, After comparing the performance evaluation index of the structural point to be evaluated with the performance evaluation index benchmark of the structural point to be evaluated, the method further includes: If the performance evaluation index of the structure point to be evaluated meets the performance evaluation index benchmark of the structure point to be evaluated, then the evaluation dataset of the structure point to be evaluated is selected based on the preset evaluation window with the structure point to be evaluated as the center point. The evaluation dataset is constructed into a window performance curve, and the window performance curve is converted into a window difference curve; Calculate the curve fluctuation coefficient of the window difference curve; The curve fluctuation coefficient is compared with a preset curve fluctuation coefficient threshold. If the curve fluctuation coefficient is greater than or equal to the preset curve fluctuation coefficient threshold, then the structural point to be evaluated is determined to be an abnormal structural point.

6. The method according to claim 5, characterized in that, After comparing the curve fluctuation coefficient with a preset curve fluctuation coefficient threshold, the method further includes: If the curve fluctuation coefficient is less than the preset curve fluctuation coefficient threshold, then the preset evaluation window of the structure point to be evaluated is divided into a first evaluation window and a second evaluation window, with the structure point to be evaluated as the boundary. Calculate the first curve fluctuation coefficient of the first evaluation window and the second curve fluctuation coefficient of the second evaluation window; Calculate the difference in curve fluctuation coefficient between the first curve fluctuation coefficient and the second curve fluctuation coefficient; If the difference in the curve fluctuation coefficient is greater than or equal to the preset threshold for the difference in the curve fluctuation coefficient, then the structural point to be evaluated is determined to be an abnormal structural point.

7. The method according to claim 1, characterized in that, The performance level of the drill pipe to be tested is evaluated based on the defect information of multiple abnormal structural points, specifically as follows: Obtain the inherent parameters and drilling scenario parameters of the drill pipe to be tested; Based on the inherent parameters and drilling scenario parameters, the stress risk zone of the drill pipe to be tested is determined; Based on the defect information and inherent parameters of the multiple abnormal structural points, the stress concentration areas of the multiple abnormal structural points are determined; The stress risk coefficient of the drill pipe to be tested is determined based on the stress risk area and the stress concentration area. Based on the stress risk coefficient, the performance level of the drill pipe to be tested is obtained by querying a preset performance level table.

8. A performance testing system for deep well drill pipe, characterized in that, The system is a drill pipe performance testing system, which includes a testing module (31), a processing module (301), and an output module (301), wherein: The test module is used to perform frequency sweeping processing on the drill pipe under test using multi-frequency coupling excitation, and to detect the multi-waveform data of the drill pipe under test, including surface waves, transverse waves and longitudinal waves. The processing module is used to calculate the performance evaluation index of multiple structural points of the drill pipe to be tested based on the multi-waveform data. Anomaly assessment of the performance evaluation indices of multiple structural points is performed using a preset temperature-performance evaluation index benchmark table to identify multiple abnormal structural points and their abnormal patterns. Based on the abnormal patterns of the multiple abnormal structural points, the defect information of the multiple abnormal structural points is determined; The output module is used to evaluate the performance level of the drill pipe to be tested based on the defect information of multiple abnormal structural points.

9. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 7.

Images