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At present, the method of measuring the displacement and stroke with the dynamometer at the oil production site is based on the pull wire type. However, the method of measuring the displacement and stroke by the pull wire type has the disadvantages of heavy equipment, high breakage rate, and severe local wear, which causes the on-site operation. A lot of inconvenience. For this reason, in view of the urgent need of the oil production site, a design scheme of a miniaturization and cordless dynamometer tester was proposed to completely avoid the inherent defects of the pull-wire type.
1. Hardware circuit design The hardware block diagram of the dynamometer tester based on the accelerometer is shown in Figure 1. It is divided into four parts: the power management system, the load signal conditioning circuit, the acceleration signal conditioning circuit, and the main control and auxiliary device circuits. Among them, the acceleration sensor is responsible for collecting the acceleration signal of the up and down motion of the oil rod, and the displacement and the stroke are obtained through the integration algorithm, which is the core of the design of the dynamometer tester hardware.
1.1 Acceleration Signal Conditioning Circuit Diagram The displacement and stroke of the tester are obtained by double acceleration integration. Taking into account the characteristics of the cyclic motion of the oil rod, one cycle of the acceleration measurement is subtracted from the average to make the boundary condition zero. The acceleration is integrated over the corrected acceleration to obtain the velocity; the boundary condition is set to zero and the stroke is integrated over the corrected velocity. Because the original acceleration signal must be filtered accordingly, the double integral algorithm to remove the boundary is more complicated. Therefore, this system uses the method of the filter integration of the single-chip microcomputer program and gives up the program of the hardware integrator to realize the integral algorithm. The circuit is shown in Figure 2. Shown, is the use of acceleration sensor chip ADXL203, appropriate configuration of peripheral devices and TI's MSP430F1611 microcontroller. Among them, the COM pin is a common ground, DNC is a floating pin, Vs is a power pin, ST is a self-test pin (normally used when the pin is connected to a low level), XOUT and YOUT are horizontal and vertical acceleration Measurement signal output.
The ADXL203 is a high-precision, low-power iMEMS dual-axis accelerometer chip with a signal-adjustable voltage output that can measure both dynamic acceleration and static measurements such as gravitational acceleration. The ADXL203 has a typical measurement range of ±1.7g and withstands 3500g of ultimate acceleration. The pull-down current is less than 700mA, and the output is an analog voltage signal proportional to the acceleration with a sensitivity of 1000mV/g.
The ADXL203 accelerometer output square wave signal whose amplitude is proportional to the measured acceleration. The sensor is mainly composed of a polysilicon mechanism using surface micromachining and a differential capacitor. Under the effect of acceleration, the polysilicon structure will generate an offset, and the center plate of the differential capacitor will slide, causing the two capacitance values ​​to be different. The center plate generates a voltage and the sensor outputs a square wave. The amplitude of the output square wave is proportional to the measured acceleration. When the supply voltage changes, the sensitivity will change accordingly, affecting the measurement accuracy. For example: 5V voltage supply, output voltage scale factor 1V/g; 3V voltage supply, output voltage scale factor 0.56V/g. This system has two capacitors, 0.01mF and 10mF, connected in parallel to the ADXL203's power supply. This effectively filters out low-frequency and high-frequency noise signals, which greatly reduces the effect of power supply ripple on the output voltage of the acceleration sensor.
The ADXL203 allows the output signal bandwidth to be designed by changing the size of the filter capacitor at the XOUT and YOUT pins as required. According to the noise analysis of the actual pumping site, the filter capacitors at the XOUT and YOUT pins have selected 0.01mF, 0.1mF and 10mF, with 10mF filter capacitor as the least burr and the best effect to achieve maximum signal hardware Anti-aliasing and denoising.
1.2 MSP430 one-chip computer A/D gathers and data processing After the above acceleration level, the vertical signal passes conditioning, must input MSP430 one-chip computer A/D to carry on the analog to digital conversion. ADC12 module in MSP430 can realize 12-bit precision analog-to-digital conversion. This system uses the voltage regulator chip output voltage 3V as A/D reference power supply.
The vertical acceleration of the dynamometer tester changes greatly, and the horizontal acceleration changes little. Vertical acceleration signal range: 1~2V, horizontal acceleration signal range: 0.5~2.5V. Through the analysis of the features of the above MSP430A/D, it is determined that it can fully meet the demand for signal acquisition of the power diagram.
The dynamometer accelerometer does not get an accurate stroke after removing the integral of the boundary twice, and often two very different strokes are obtained for the same well. Because the voltage signal of the acceleration is very small, the proportional coefficient of the acceleration and voltage caused by the 3V power supply system is very small (0.56V/g), and the MCU collects the acceleration voltage signal to be severely interfered. Therefore, it is necessary to filter the collected acceleration signal and then double-integrate to obtain the stroke.
Firstly, the acceleration data collected for one cycle is stored in RAM, and singular value filtering is performed on the acceleration data; then the acceleration is filtered three times by 7-point smoothing window to filter the noise signal to the maximum; Finally, Apply the double integral of the cycle demarcation to get the displacement value of each point, correspond to the load of each point, draw the power diagram on the LCD screen, and store the oil well information and dynamometer information in the external EEPROM.
2. Composite Filtering Method of Acceleration Signal The dynamometer uses the acceleration signal to perform the double integration algorithm to obtain the displacement and stroke. However, the acceleration signal causes slight distortion of the waveform due to the influence of the power supply ripple and signal interference, and the cumulative error increases after double integration. Big. To solve the problem of measuring the displacement and stroke of the acceleration signal, this paper proposes an improved composite filtering method based on the analysis of morphological filtering and traditional smoothing filtering.
2.1 Morphological filtering method Morphological filtering is a non-linear digital filtering technique, which is mainly applied in the field of face recognition. According to the characteristics of the acceleration signal, morphological filtering can effectively suppress the noise of the acceleration signal, and better maintain the geometric characteristics of the acceleration signal [5]. This paper uses the combination of corrosion and diffusion to achieve the effect of morphological filtering. The core algorithm is defined as:
Y={[(fΘg)⊕g](n)+[(f⊕g)Θg](n)}/2
Among them, [(fΘg)⊕g](n) denotes the morphological open operation, [(f⊕g)Θg](n) denotes the morphological closed operation, and g denotes the structural element. This paper selects {0,7.0711,10,7.0711,0 }.
Through the field test of pumping, the displacement and stroke measurement effect of the beam pumping unit is ideal. The waveform of the acceleration signal is shown in Figure 3.
However, this structural element g is designed only for the beam pumping unit and has no compatibility with the belt pumping unit, and the field test result has a large error. Because the performance of morphological filtering is closely related to the structural elements, when the prior waveform of the signal cannot be determined, the adaptive method should theoretically be used to estimate the size of the structural element. Obviously, it is not practical to use the MSP430 microcontroller to implement the adaptive algorithm.
2.2 Improved Sliding Filtering The traditional sliding smoothing filter samples only once. This time the sampled value is averaged over the past several sampled values. If the N sampled values ​​are averaged, a buffer of N data must be created in RAM. .
Since the number of strokes (the number of pump trips up and down within one minute) is calculated by judging the two highest points of acceleration (the number of points between the two highest points is multiplied by the sampling period of 50 ms to obtain the pumping cycle, the number of cycles = 60/cycle). The use of the traditional sliding filter method has a high false positive rate at the highest point, and it is difficult to obtain an accurate cycle. In this paper, an improved sliding filter method is used to solve the above problems.
The MSP430F1611 (10KRAM) is used to define an array of 1800-sized floating-point numbers used to store the 90s acceleration raw signal. After 3 sliding smoothing filters, the formula is as follows:
3-point slider: XK=(XK-1+XK+XK+1)/3(1≤K≤N-1)
7-point sliding block: XK+(XK-3+XK-2+XK-1+XK+XK+1+XK+2+XK+3)/7(3≤K≤N-3)
In the formula: XK represents the acceleration data of the K-th acquisition; N represents the number of acquisition data; K represents the serial number of the current acceleration signal.
After a 3-point slider or 7-slider block processing all accelerometer signals acquired for 90 s, the maximum value MAX is found, and then the maximum values ​​MAX1 and MAX2 (MAX-MAX1 0.01 V, MAX-MAX2 0.01 V) are found before and after MAX. Calculate the period and number of times at the 50ms sampling interval:
Where: T represents the up and down cycle of the pumping unit; nMAX1 represents the acquisition serial number of the MAX1 point; nMAX2 represents the collection serial number of the MAX2 point.
This paper compares the waveform of the 3-point sliding block and the 7-point sliding block, and compares the calculated displacement and stroke, and finds that the 7-point sliding block can better reflect the actual acceleration signal, and the repeatability of the stroke measurement of the same well is good. 4 shows.
2.3 Acceleration Double Integration Algorithm The dynamometer tester uses acceleration signals to indirectly obtain displacement and stroke information. After obtaining the measured value of the acceleration, two problems must be solved to calculate the relative displacement of the sucker rod movement: the zero point correction of the acceleration and the determination of the boundary condition when the integral speed is calculated. Because only the relative displacement of the sucker rod movement is required and the displacement is obtained from the velocity integral, the boundary condition can be set to zero.
After theoretical derivation, the algorithm for calculating displacement or stroke from acceleration can be briefly described as:
(1) Subtract the mean value of the measured acceleration from one period, make the boundary condition zero, and obtain the velocity for the corrected acceleration (using numerical integration in MSP430);
(2) Subtract the average value from the calculated velocity, make the boundary condition zero, and integrate the corrected velocity (using numerical integration in the MSP430) to obtain the relative displacement or stroke.
3. Field test data analysis The deviation of the actual placement position of the accelerometer ADXL203 on the circuit board can directly affect the size of its output signal. This design will place the ADXL203 horizontally on the circuit board. To verify that the ADXL203 was placed accurately, it was tested five times in both horizontal and vertical modes. The test results show that both X and Y axis errors are less than 0.5% regardless of horizontal or vertical placement. This shows that in the design, welding and installation process, the position of the ADXL203 is very accurate, and the specific accuracy requirements of the double-integral algorithm for removing the boundary are achieved.
For the beam pumping unit and the belt pumping unit, two different types of pumping unit wells have undergone several on-site measurements and data analysis. The data and analysis of the stroke measurement as an example are shown in Table 1. The beam pumping unit is converted into a sucker rod up and down movement by a rotary motion. The reciprocating motion rule is close to a sine wave variation and the stroke is short; the belt pumping unit directly drives the sucker rod up and down, and the movement law is close to a rectangular wave Change, and the stroke is longer.
4. Conclusion The field test results show that the displacement or stroke measurement technology studied in this paper is suitable for different pumping unit wells with strokes from 2.1m to 5m, punching times from 0.8 to 5, and high measurement accuracy; however, For heavy oil well pumping units with a stroke time <0.8, the measurement error is too large.
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Research on Displacement Measurement Technology of Oil Well Indicator Diagram Based on Acceleration Sensor
Introduction The dynamometer analysis can understand the real-time dynamics of oil wells and whether the oil pumping device is reasonable. At the same time, the dynamometer diagram is one of the important basis for fault diagnosis of pumping wells. Therefore, the quality of the dynamometer measurement has very important significance for the improvement of the working efficiency and the automation level of the entire pumping system.