Wax deposition detection in nonmetallic pipelines is an important requirement in the oil industry. In this paper, an ECT (electrical capacitance tomography) sensor is developed for wax deposition detection in nonmetallic pipelines. Four wax models with different concentrations were established for detection. These models were analyzed through simulations and practical experiments simultaneously and data were compared. A linear back projection algorithm is applied to reconstruct the image with both simulated and experimental data. A comparison of binary images with different concentration of stratified flow was demonstrated; this illustrates that the difference in concentration between the experimental results and profile distribution is less than 1.2%. The experimental results indicate that the ECT system is valid and feasible for detecting the degree of wax deposition in the nonmetallic pipelines.
Heat and pressure are widely used in oil and gas pipeline transmission to reduce the viscosity of crude oil and the deposition of wax. However, due to difference in radial temperature, radiation loss occurs rapidly. The wax is gradually separated out and deposited on the pipe wall, which can lead to condensate tube accidents [
The detection of wax deposition in the pipeline is essentially a multiphase detection, which is composed of the wax layer, oil layer, and gas. ECT (electrical capacitance tomography) is a visual procession from measurement capacitance to object distribution. The method is a noninvasive and nondestructive technique. An ECT sensor is a typical “soft” field sensor, which means that the Electromagnetic (EM) field propagates across the entire probed volume, as shown in Figure
Schematic diagram of the “soft” field.
The related research began in the 1980s at the University of Manchester and, from hardware, image construction algorithms to sensor design has made considerable progress [
Sensor optimization is an important part of the ECT system. Xie et al. presented a uniformity of sensitivity distribution as an evaluation criterion to determine the performance of the ECT sensor [
The purpose of the present paper is to develop an ECT system for wax deposition detection of a nonmetallic pipeline. The optimal sensor structure is determined based on our previous research [
In the long-range transportation process, the temperature of the crude oil decreases and wax deposition occurs due to the thermal radiation. The permittivity of wax is 1.9~2.5. The wax is deposited on the inner wall of the pipeline, and the structure could be simplified as a stratified flow pattern in a laboratory environment. A series of wax deposition models is built and the cross-sections are shown in Figure
Different thicknesses of wax deposition models. (a) Concentration is 11%. (b) Concentration is 18.5%. (c) Concentration is 52.2%. (d) Concentration is 62.5% (note: the last one is special stratified flow–heteromorphous flow).
The ECT sensor electrodes consist of copper, and earthed shielding plates are placed around the sensor electrodes. The measurement system and the sensor structure are shown in Figure
ECT system and sensor structure. (a) Diagram of measurement system and practical ECT sensor. (b) Parameters of the ECT sensor.
The shielding wire and BNC joints are used to prevent interference. In this study, the pipe consists of PVC material. The end guarding plate consists of copper, and the earthed shielding plate consists of aluminum. According to our previous research [
Parameters of the sensor.
Sensor electrodes | Pipeline | Earthed shielding plate | End guarding plate | |||||
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Materials | Copper | PVC | Aluminum | Copper | ||||
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Parameters |
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6 | 15° | 105 mm | 35 mm | 40 mm | 50 mm | 20 mm | 10 mm |
The practical stratified models are shown in Figure
Comparison of simulation results with experimental results.
Flow patterns | Empty |
Full wax |
Stratified flow (a) |
Stratified flow (b) |
Stratified flow (c) |
Stratified flow (d) | |||||||
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0% | 100% | 11% | 18.5% | 52.2% | 62.5% | |||||||
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1 | 2 | 300.9 | 288.3 | 419.8 | 382.3 | 380.0 | 335.6 | 427.6 | 350.5 | 448.9 | 383.0 | 444.8 | 385.3 |
1 | 3 | 45.9 | 47.7 | 100.9 | 100.7 | 56.0 | 53.2 | 58.7 | 56.8 | 72.1 | 71.2 | 70.2 | 71.4 |
1 | 4 | 30.8 | 30.5 | 70.3 | 69.3 | 37.2 | 35.8 | 39.0 | 37.1 | 42.7 | 42.7 | 45.8 | 50.4 |
1 | 5 | 45.3 | 44.7 | 99.7 | 85.3 | 55.1 | 44.7 | 58.4 | 47.6 | 71.4 | 60.8 | 96.4 | 90.5 |
1 | 6 | 298.0 | 264.1 | 416.5 | 351.4 | 376.0 | 317.7 | 430.4 | 335.0 | 445.3 | 358.0 | 432.0 | 346.6 |
2 | 3 | 304.4 | 307.5 | 424.4 | 405.8 | 300.6 | 301.1 | 300.3 | 297.0 | 343.7 | 331.1 | 340.3 | 338.3 |
2 | 4 | 45.2 | 48.7 | 99.4 | 100.8 | 43.1 | 47.6 | 44.7 | 48.1 | 56.8 | 59.2 | 57.0 | 66.1 |
2 | 5 | 30.9 | 29.1 | 70.7 | 68.7 | 29.0 | 29.1 | 31.0 | 32.4 | 45.8 | 44.2 | 62.9 | 72.4 |
2 | 6 | 45.1 | 46.6 | 99.3 | 99.1 | 48.3 | 60.2 | 68.0 | 77.3 | 116.0 | 110.5 | 111.9 | 104.3 |
3 | 4 | 298.1 | 297.0 | 416.4 | 397.5 | 296.8 | 294.5 | 295.6 | 295.6 | 280.2 | 285.3 | 281.3 | 274.8 |
3 | 5 | 45.3 | 44.3 | 99.5 | 96.6 | 43.9 | 43.2 | 42.5 | 43.5 | 38.3 | 38.4 | 47.9 | 62.4 |
3 | 6 | 30.7 | 30.4 | 70.2 | 67.7 | 29.0 | 30.7 | 31.1 | 33.0 | 45.8 | 47.0 | 47.1 | 45.7 |
4 | 5 | 300.4 | 295.9 | 419.6 | 390.3 | 299.6 | 296.7 | 298.7 | 284.9 | 288.1 | 285.9 | 291.7 | 334.8 |
4 | 6 | 45.2 | 41.5 | 99.5 | 90.4 | 43.6 | 41.0 | 45.2 | 42.7 | 57.9 | 54.4 | 69.9 | 72.1 |
5 | 6 | 298.6 | 171.5 | 417.2 | 268.7 | 296.9 | 168.4 | 298.7 | 173.2 | 340.3 | 209.1 | 397.1 | 273.4 |
Practical models of stratified flow wax. (a) Concentration is 11%. (b) Concentration is 18.5%. (c) Concentration is 52.2%. (d) Concentration is 62.5%.
In order to illustrate the comparison results more intuitively, the normalized capacitances are calculated according to (
Normalized capacitance trend analysis. (a) Concentration is 11%. (b) Concentration is 18.5%. (c) Concentration is 52.2%. (d) Concentration is 62.5%.
According to Figure
In order to examine the effects of the design factors, it was necessary to perform the image reconstructions. Large numbers of algorithms have been developed to derive reconstructed images over the past several decades, such as linear back projection (LBP), Landweber iteration, and the sparsity-inspired image reconstruction method. In [ relative image error, relative capacitance residual, correlation coefficient between the test object and the reconstruction.
Consider
Different concentrations of stratified flow image results. (a) Simulation results. (b) Experimental results.
The imaging results of the experiments are consistent with the reconstructed images of the simulations, which signifies that the designed ECT system is valid and feasible. For different concentrations of the wax, the reconstructed images are varied. Especially for the heteromorphous flow pattern, the shape of the semicircular convex is difficult to identify. The boundary of the heteromorphous flow pattern is difficult to determine, and the stratified flow resembles an inclined plane. According to [
Flowchart of the linear back projection threshold processing method.
In Figure
The threshold value takes the minimum pixel value as a reference; the increment is the product of pixel value difference and threshold increment. In this paper, the threshold values are 0.705, 0.78, 0.38, and 0.395, respectively, for the four reconstructed images.
The binary images of different concentrations of stratified flows are shown in Figure
Contrast between standard images and reconstructed images.
Model 1 | Model 2 | Model 3 | Model 4 | |
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Concentration of standard images | 11% | 18.5% | 52.5% | 62.5% |
Concentration of reconstructed images | 12.2% | 19.2% | 52.97% | 62.9% |
Correlation coefficient | 93.79% | 92.78% | 98.01% | 87.04% |
Binary images of different concentrations of stratified flow. (a) Profile distribution. (b) Threshold processing image based on experimental data.
As we can see from Table
For wax deposition detection in nonmetallic pipelines, the use of an ECT sensor was demonstrated. LBP algorithm was applied to reconstruct images. Four models were tested, and the reconstructed images showed that the sensor is feasible. However, due to the limitations of the algorithm, it was difficult to identify the special stratified model. The boundary of the heteromorphous flow pattern was recognized as an incline in the reconstructed images. For the concentrations of 11%, 18.5%, 52.5%, and 62.5%, the differences between the profile and reconstructed image were 1.2%, 0.7%, 0.47%, and 0.4%, respectively. The correlation coefficients between standard images and reconstructed images were 93.79%, 92.78%, 98.01%, and 87.04%, separately. This proves that the system designed in this paper can be applied to wax deposition detection of nonmetallic pipelines.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is financially supported by the National Natural Science Foundation of China (Grants nos. 51475013, 51505013, and 51405381).