Abstract: In this paper, the characteristics of acoustic emission technology are combined with the structural characteristics of annular blowout preventers, and a planar positioning scheme for acoustic emission detection is proposed. The preparation work before detection is described; by using two loadings and comparing the collected data in the two experiments, combined with the principle of Kessel effect, the noise source in the detection is comprehensively analyzed, and it is pointed out that effective signals should be extracted from the collected data in the pressure holding stage, and the nature of the defects should be judged based on the effective signals. Finally, the detection results are given and the problems and work directions that need to be urgently solved in the detection of blowout preventers are proposed.
Keywords: acoustic emission; blowout preventer; detection of acoustic emission (AE); non-destructive testing; evaluation of ultrasonic level meter ultrasonic level meter ultrasonic cleaning machine ultrasonic thickness gauge film washing machine
0 Introduction
Acoustic emission (ACOUSTIC EMISSION, referred to as AE) is a non-destructive testing (NDT) method developed in the 1960s and 1970s. In the past one or two decades, this technology has been widely used in petrochemical, aerospace, water conservancy and electricity, transportation, machinery, construction and other industries [1, 2]. And relevant detection standards have been formulated, such as references [5-7]. Its principle is: the material of the stressed component will release plastic strain energy during the initiation and expansion of damage defects, and the strain energy will propagate outward in the form of stress waves. This phenomenon is called acoustic emission. Blowout
preventers are widely used in the oil industry and are the core components of well control equipment. The quality of blowout preventers directly affects the success or failure of oil and gas well pressure control. Once a leak occurs, it will cause serious environmental pollution, bring losses and harm to the social economy, production and people's lives, and directly affect social safety. The annular blowout preventer is named because its sealing element-rubber core-is annular. When sealing the well, the annular rubber core is forced to gather toward the center of the wellbore and embrace the drilling tool. When there is no drilling tool in the well, the annular blowout preventer can be used to completely seal the wellhead. Since the function of the annular blowout preventer is relatively comprehensive and can adapt to various working conditions at the wellhead to quickly seal the well, it is widely used, so the detection of annular blowout preventers is also particularly important.
During the regular inspection, it is difficult to find the defects exceeding the standard by using ultrasonic and radiographic methods. If all defects are repaired, the cost will be too high. Through a large number of experiments, we found that the use of acoustic emission detection can quickly find the active defects in these defects exceeding the standard. Only these active defects need to be repaired, and the pressure vessel can be put into use again.
1 Detection method
1.1 Basic situation of test equipment A
FH28-35 cone-shaped rubber core annular blowout preventer produced in November 1993 in a certain factory has a working pressure of 35MPa. The test medium is water. Before the test, the damaged sealing rubber core, sealing ring, and unqualified bolts, nuts, etc. are replaced.
1.2 Sensor layout plan
According to the stress characteristics of the annular blowout preventer and the actual conditions of our testing equipment, two sets of plane-positioned square arrays are arranged on the annular blowout preventer, one set on the top cover and the other set in the middle of the column shell. Each array consists of four sensors, namely sensors 1-2-3-4 and sensors 5-6-7-8, to ensure the detection of key parts of the blowout preventer. The spacing between adjacent sensors in the two arrays is 560 mm and 900 mm respectively.
1.3 Testing instrument and parameter settings and preparations Host
: DISP-16 channel acoustic emission test system; Software: AEwin for DiSP Version 1.80; Threshold value: 40 dB; Sensor model: R15 piezoelectric sensor; Preamplifier: 2/4/6 type; Gain: 40 dB; Coupling agent: Butter.
On the surface of the blowout preventer where the sensor is installed, first use a grinding wheel to grind off the paint and oxide scale, then use sandpaper to make the metal surface smooth and flat. After cleaning, use butter as a coupling agent to reliably stick the sensor to the selected position. After
completing all the connections, use the broken lead core as the analog signal source to check whether each channel is working normally, measure the propagation speed of the sound wave in the blowout preventer, and check the positioning accuracy.
1.4 Loading procedure
According to the provisions of standard SY/T 6160-1995 [4], the secondary loading method is used to make the test data more complete and the results more reliable. The specific loading scheme is to load to 35Mpa for the first time and then maintain the pressure for 15 minutes, then completely unload the pressure in the blowout preventer and load for the second time. After reaching 35Mpa, the pressure is maintained for 15 minutes.
The requirements for the loading equipment are: stable and slow pressure increase, small pressure fluctuation, and no leakage during the pressure maintenance period. Each time loading starts, the acoustic emission detection system is turned on at the same time to collect data and observe the changes in the graph (data) in the display window caused by the increase in pressure. If an abnormal signal is found, the loading should be stopped immediately and the pressure should be maintained for observation. Depending on the specific situation, it is decided whether to continue to increase the pressure or reduce the pressure quickly. The test loading scheme is shown in Figure 1.
2 Determination of effective acoustic emission data
Before determining the effective acoustic emission data, let us first introduce the irreversible effect of deformation acoustic emission common to many metal materials, that is, "during the reloading of the material, no acoustic emission signal is generated before the stress value reaches the maximum stress of the last loading." [2] This irreversible phenomenon is called the "Kaiser effect".
Signal recognition is a very important link in acoustic emission detection. Due to the unique structure of the annular blowout preventer, it is composed of multiple parts. The rubber core installed inside is a casting supported by a steel block. All these parts will inevitably produce many signals when loading, which are displayed on the screen and recorded in the data file. However, the purpose of acoustic emission testing is to judge the safety of the equipment based on the presence or absence of defect expansion signals. The emergence of a large number of non-defect expansion signals makes it difficult for us to distinguish the real cracking signals. Therefore, our evaluation of the blowout preventer is not suitable for the test data collected during the loading period, but mainly based on the signal data of the blowout preventer during the pressure holding stage.
2.1 Noise signal
The source of the noise signal is mainly the friction between the shell and the internal structural parts, the deformation caused by the force of the bolts, and the load redistribution caused by the uneven force of each bolt. This is a very common signal, especially in the process of the blowout preventer's rapid pressure increase. During the pressure holding period, the above phenomenon generally does not occur, but it is not completely eliminated for the unstable structure, especially during the first pressure holding. Moreover, since the mechanism of friction is different from the mechanism of acoustic emission signals generated by a piece of metal material due to deformation, it cannot meet the Kaiser effect. Usually this type of signal has low energy and amplitude.
2.2 Effective signal
Effective signal refers to the signal generated by defect activity. This type of signal positioning source is relatively concentrated, the parameter value of the signal is large, and it will appear multiple times. During loading, there is generally no acoustic emission signal at a pressure lower than the working pressure of the blowout preventer. There will be acoustic emission signals at each stage of pressure increase and pressure maintenance above this pressure. In the second pressure increase and pressure maintenance stage after pressure reduction, the activity of the acoustic emission source is determined by whether the signal appears at the same position and its strength. For weakly active or inactive sources, there is little or no acoustic emission signal, which meets the Kaiser effect.
2.3 Test data analysis
After each test, the amplitude analysis method is used, that is, according to the change of its amplitude and acoustic emission signal parameters with time and pressure, a comprehensive analysis is performed, and then the location of the acoustic emission source is statistically analyzed. We found that there are similarities between the two tests, that is, there will be a lot of signals generated in the pressure increase stage, which is the same as our prediction before the test. Due to the structural characteristics of the blowout preventer, the internal annular rubber core and the shell will rub and the bolts will deform during the loading stage. These situations will generate a large number of noise signals. Therefore, the data we want to analyze mainly comes from the collected signals in the pressure maintenance stage. The data collected during the two loading tests are shown in Table 1.
During the pressure-maintaining stage of the first test, a total of 10 signals were generated, of which 9 signals were from the first positioning group, mainly concentrated near sensor No. 3; and 1 signal was from the second positioning group, located between sensors No. 5 and No. 8. The signal amplitudes were very low and the energy was also very small. It was initially estimated that it was a noise signal caused by the deformation of the rubber core and the friction between the top cover. During the pressure-maintaining stage of the second test, only the first positioning group generated 1 signal, which was not in the same position as the positioning source of the first test, and the amplitude of the signal was not high, so it was determined that the first signal was an inactive acoustic emission source. That is, the blowout preventer did not show obvious signs of active defects under the test pressure of 35MPa, so it can be considered safe under working pressure.
Table 1 Statistics of data collected during the pressure holding stage of the two tests
Loading cycle positioning group Event number Ringing count Energy maximum amplitude
First time First group 9 3747 775 74
Second group 1 216 49 43
Second time First group 1 257 50 61
Second group 0 0 0 0
4 Conclusion
Through this test practice, the author believes that it is feasible to use acoustic emission technology to monitor the hydraulic test process of the blowout preventer.
From the test result data, the use of acoustic emission detection can timely and effectively reflect the stress of the test piece during the test stage, dynamically detect the activity of these defects under the action of external force, and enable the test personnel to make basic judgments on the test piece immediately after the loading program is completed, giving full play to the main advantages of acoustic emission detection. After the test, the unique functions of the software are used to analyze the data, eliminate noise signals, and draw more detailed inspection conclusions.
During the entire test process, residual stress release, friction between structures, and seal leakage can all generate a large amount of noise signals. At present, the noise signals in the boosting stage cannot be eliminated well. Further research is needed on various processing and analysis technologies of acoustic emission signals and neural network pattern recognition to identify the common reversible friction noise during loading, improve the signal acquisition level and analysis capabilities, and continuously improve signal processing methods. Combined with the mechanical properties analysis test of materials, regular acoustic emission detection of new cracks in in-service components, acoustic emission detection of fatigue crack initiation and expansion, and improve the online detection application level of pressure vessels, pressure pipelines and various petroleum equipment.
Keywords：BOP
Reference address：Application of Acoustic Emission Testing for BOP
Keywords: acoustic emission; blowout preventer; detection of acoustic emission (AE); non-destructive testing; evaluation of ultrasonic level meter ultrasonic level meter ultrasonic cleaning machine ultrasonic thickness gauge film washing machine
0 Introduction
Acoustic emission (ACOUSTIC EMISSION, referred to as AE) is a non-destructive testing (NDT) method developed in the 1960s and 1970s. In the past one or two decades, this technology has been widely used in petrochemical, aerospace, water conservancy and electricity, transportation, machinery, construction and other industries [1, 2]. And relevant detection standards have been formulated, such as references [5-7]. Its principle is: the material of the stressed component will release plastic strain energy during the initiation and expansion of damage defects, and the strain energy will propagate outward in the form of stress waves. This phenomenon is called acoustic emission. Blowout
preventers are widely used in the oil industry and are the core components of well control equipment. The quality of blowout preventers directly affects the success or failure of oil and gas well pressure control. Once a leak occurs, it will cause serious environmental pollution, bring losses and harm to the social economy, production and people's lives, and directly affect social safety. The annular blowout preventer is named because its sealing element-rubber core-is annular. When sealing the well, the annular rubber core is forced to gather toward the center of the wellbore and embrace the drilling tool. When there is no drilling tool in the well, the annular blowout preventer can be used to completely seal the wellhead. Since the function of the annular blowout preventer is relatively comprehensive and can adapt to various working conditions at the wellhead to quickly seal the well, it is widely used, so the detection of annular blowout preventers is also particularly important.
During the regular inspection, it is difficult to find the defects exceeding the standard by using ultrasonic and radiographic methods. If all defects are repaired, the cost will be too high. Through a large number of experiments, we found that the use of acoustic emission detection can quickly find the active defects in these defects exceeding the standard. Only these active defects need to be repaired, and the pressure vessel can be put into use again.
1 Detection method
1.1 Basic situation of test equipment A
FH28-35 cone-shaped rubber core annular blowout preventer produced in November 1993 in a certain factory has a working pressure of 35MPa. The test medium is water. Before the test, the damaged sealing rubber core, sealing ring, and unqualified bolts, nuts, etc. are replaced.
1.2 Sensor layout plan
According to the stress characteristics of the annular blowout preventer and the actual conditions of our testing equipment, two sets of plane-positioned square arrays are arranged on the annular blowout preventer, one set on the top cover and the other set in the middle of the column shell. Each array consists of four sensors, namely sensors 1-2-3-4 and sensors 5-6-7-8, to ensure the detection of key parts of the blowout preventer. The spacing between adjacent sensors in the two arrays is 560 mm and 900 mm respectively.
1.3 Testing instrument and parameter settings and preparations Host
: DISP-16 channel acoustic emission test system; Software: AEwin for DiSP Version 1.80; Threshold value: 40 dB; Sensor model: R15 piezoelectric sensor; Preamplifier: 2/4/6 type; Gain: 40 dB; Coupling agent: Butter.
On the surface of the blowout preventer where the sensor is installed, first use a grinding wheel to grind off the paint and oxide scale, then use sandpaper to make the metal surface smooth and flat. After cleaning, use butter as a coupling agent to reliably stick the sensor to the selected position. After
completing all the connections, use the broken lead core as the analog signal source to check whether each channel is working normally, measure the propagation speed of the sound wave in the blowout preventer, and check the positioning accuracy.
1.4 Loading procedure
According to the provisions of standard SY/T 6160-1995 [4], the secondary loading method is used to make the test data more complete and the results more reliable. The specific loading scheme is to load to 35Mpa for the first time and then maintain the pressure for 15 minutes, then completely unload the pressure in the blowout preventer and load for the second time. After reaching 35Mpa, the pressure is maintained for 15 minutes.
The requirements for the loading equipment are: stable and slow pressure increase, small pressure fluctuation, and no leakage during the pressure maintenance period. Each time loading starts, the acoustic emission detection system is turned on at the same time to collect data and observe the changes in the graph (data) in the display window caused by the increase in pressure. If an abnormal signal is found, the loading should be stopped immediately and the pressure should be maintained for observation. Depending on the specific situation, it is decided whether to continue to increase the pressure or reduce the pressure quickly. The test loading scheme is shown in Figure 1.
2 Determination of effective acoustic emission data
Before determining the effective acoustic emission data, let us first introduce the irreversible effect of deformation acoustic emission common to many metal materials, that is, "during the reloading of the material, no acoustic emission signal is generated before the stress value reaches the maximum stress of the last loading." [2] This irreversible phenomenon is called the "Kaiser effect".
Signal recognition is a very important link in acoustic emission detection. Due to the unique structure of the annular blowout preventer, it is composed of multiple parts. The rubber core installed inside is a casting supported by a steel block. All these parts will inevitably produce many signals when loading, which are displayed on the screen and recorded in the data file. However, the purpose of acoustic emission testing is to judge the safety of the equipment based on the presence or absence of defect expansion signals. The emergence of a large number of non-defect expansion signals makes it difficult for us to distinguish the real cracking signals. Therefore, our evaluation of the blowout preventer is not suitable for the test data collected during the loading period, but mainly based on the signal data of the blowout preventer during the pressure holding stage.
2.1 Noise signal
The source of the noise signal is mainly the friction between the shell and the internal structural parts, the deformation caused by the force of the bolts, and the load redistribution caused by the uneven force of each bolt. This is a very common signal, especially in the process of the blowout preventer's rapid pressure increase. During the pressure holding period, the above phenomenon generally does not occur, but it is not completely eliminated for the unstable structure, especially during the first pressure holding. Moreover, since the mechanism of friction is different from the mechanism of acoustic emission signals generated by a piece of metal material due to deformation, it cannot meet the Kaiser effect. Usually this type of signal has low energy and amplitude.
2.2 Effective signal
Effective signal refers to the signal generated by defect activity. This type of signal positioning source is relatively concentrated, the parameter value of the signal is large, and it will appear multiple times. During loading, there is generally no acoustic emission signal at a pressure lower than the working pressure of the blowout preventer. There will be acoustic emission signals at each stage of pressure increase and pressure maintenance above this pressure. In the second pressure increase and pressure maintenance stage after pressure reduction, the activity of the acoustic emission source is determined by whether the signal appears at the same position and its strength. For weakly active or inactive sources, there is little or no acoustic emission signal, which meets the Kaiser effect.
2.3 Test data analysis
After each test, the amplitude analysis method is used, that is, according to the change of its amplitude and acoustic emission signal parameters with time and pressure, a comprehensive analysis is performed, and then the location of the acoustic emission source is statistically analyzed. We found that there are similarities between the two tests, that is, there will be a lot of signals generated in the pressure increase stage, which is the same as our prediction before the test. Due to the structural characteristics of the blowout preventer, the internal annular rubber core and the shell will rub and the bolts will deform during the loading stage. These situations will generate a large number of noise signals. Therefore, the data we want to analyze mainly comes from the collected signals in the pressure maintenance stage. The data collected during the two loading tests are shown in Table 1.
During the pressure-maintaining stage of the first test, a total of 10 signals were generated, of which 9 signals were from the first positioning group, mainly concentrated near sensor No. 3; and 1 signal was from the second positioning group, located between sensors No. 5 and No. 8. The signal amplitudes were very low and the energy was also very small. It was initially estimated that it was a noise signal caused by the deformation of the rubber core and the friction between the top cover. During the pressure-maintaining stage of the second test, only the first positioning group generated 1 signal, which was not in the same position as the positioning source of the first test, and the amplitude of the signal was not high, so it was determined that the first signal was an inactive acoustic emission source. That is, the blowout preventer did not show obvious signs of active defects under the test pressure of 35MPa, so it can be considered safe under working pressure.
Table 1 Statistics of data collected during the pressure holding stage of the two tests
Loading cycle positioning group Event number Ringing count Energy maximum amplitude
First time First group 9 3747 775 74
Second group 1 216 49 43
Second time First group 1 257 50 61
Second group 0 0 0 0
4 Conclusion
Through this test practice, the author believes that it is feasible to use acoustic emission technology to monitor the hydraulic test process of the blowout preventer.
From the test result data, the use of acoustic emission detection can timely and effectively reflect the stress of the test piece during the test stage, dynamically detect the activity of these defects under the action of external force, and enable the test personnel to make basic judgments on the test piece immediately after the loading program is completed, giving full play to the main advantages of acoustic emission detection. After the test, the unique functions of the software are used to analyze the data, eliminate noise signals, and draw more detailed inspection conclusions.
During the entire test process, residual stress release, friction between structures, and seal leakage can all generate a large amount of noise signals. At present, the noise signals in the boosting stage cannot be eliminated well. Further research is needed on various processing and analysis technologies of acoustic emission signals and neural network pattern recognition to identify the common reversible friction noise during loading, improve the signal acquisition level and analysis capabilities, and continuously improve signal processing methods. Combined with the mechanical properties analysis test of materials, regular acoustic emission detection of new cracks in in-service components, acoustic emission detection of fatigue crack initiation and expansion, and improve the online detection application level of pressure vessels, pressure pipelines and various petroleum equipment.
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