1 © 2016 IOP Publishing Ltd Printed in the UK

1. Introduction

The precipitation of paraffin waxes during various phases of production, processing and transportation of waxy crude oil creates challenges to the oil and gas industry and researchers have been interested to investigate the phenomenon of wax deposition under different flow conditions, particularly in the subsea pipelines [1–6]. The main motivation behind this research is the rate of formation of wax deposit as it is a critical

parameter that has to be taken into account for the design of a much more efficient and cost-effective wax control system [3, 7]. The ideal wax monitoring system should be able to pro-vide real-time and continuous visualization of the actual waxy crude oil flow in pipelines so that appropriate wax remediation techniques will be applied when a certain threshold amount of wax has been formed.

Many laboratory-scale experimental studies have been performed under both static and flow conditions to develop

Measurement Science and Technology

Real-time monitoring and measurement of wax deposition in pipelines via non-invasive electrical capacitance tomography

Irene Lock Sow Mei1, Idris Ismail2, Areeba Shafquet2 and Bawadi Abdullah1

1 Chemical Engineering Department, Universiti Teknologi PETRONAS, Bandar Sri Iskandar, 32610 Perak, Malaysia2 Electrical and Electronics Engineering Department, Universiti Teknologi PETRONAS, Bandar Sri Iskandar, 32610 Perak, Malaysia

E-mail: bawadi_abdullah@petronas.com.my

Received 24 March 2015, revised 1 November 2015Accepted for publication 5 November 2015Published 29 December 2015

AbstractTomographic analysis of the behavior of waxy crude oil in pipelines is important to permit appropriate corrective actions to be taken to remediate the wax deposit layer before pipelines are entirely plugged. In this study, a non-invasive/non-intrusive electrical capacitance tomography (ECT) system has been applied to provide real-time visualization of the formation of paraffin waxes and to measure the amount of wax fraction from the Malay Basin waxy crude oil sample under the static condition. Analogous expressions to estimate the wax fraction of the waxy crude oil across the temperatures range of 30–50 °C was obtained by using Otsu’s and Kuo’s threshold algorithms. Otsu’s method suggested that the wax fraction can be estimated by the correlation coefficient  β = − + −T T T0.0459 5.3535 200.36 2353.73 2 while Kuo’s method provides a similar correlation with  β = − + −T T T0.0741 8.4915 314.96 3721.23 2 . These correlations show good agreements with the results which are obtained from the conventional weighting method. This study suggested that Kuo’s threshold algorithm is more promising when integrated into the ECT system compared to Otsu’s algorithm because the former provides higher accuracy wax fraction measurement results below the wax appearance temperature for waxy crude oil. This study is significant because it serves as a preliminary investigation for the application of ECT in the oil and gas industry for online measurement and detection of wax fraction without causing disturbance to the process flow.

Keywords: image thresholding, wax formation, in situ visualization, tomographic images

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different methods for the detection and measurement of wax deposits. Typically, estimation of the thickness of the wax deposit layer in the laboratory scale is carried out by methods such as laser technique [8], liquid displacement-level detec-tion method (LD-LD) [9, 10], heat pulse measurement tech-nique [3], direct deposit mass measurement [11, 12], cold fingers [13], ultrasonic thickness measurement [14, 15] and the application of pressure pulse [14]. In addition, researchers have developed various reliable wax prediction models to investigate the effect of different operating parameters on the rate of wax deposition [5, 16–19].

Nevertheless, the shortcomings of the conventional meas-urement methods are most (i.e. pressure measurement and direct deposit mass measurement) are intrusive in nature and hence the accuracy of the measuring system is limited by the signal measuring process without affecting the integrity of the system [20, 21]. Furthermore, the conventional wax measure-ments have limitations on the distance of signal transmission because they have high signal attenuation and are not suited for long distance transmission. Moreover, conventional methods are not capable of providing information about the exact posi-tion in which plugging has occurred, particularly for pipelines which are heavy-walled, buried or thickly coated [21]. As for the wax deposition prediction models, the selection of proper input parameters are crucial to ensure reliable and accurate prediction results [16]. Unfortunately, the current wax deposi-tion models still exhibit severe deficiencies due to the compli-cated phase behavior of the waxy crude oil. This phenomenon occurs caused by the phase transformation of waxy crude oil from Newtonian to non-Newtonian fluid mainly due to tem-perature changes, thus leading to unsatisfactory model predic-tion results [22–24].

Hence, there is an urgency for a non-invasive and non-intrusive wax monitoring system which can not only pro-vide information of the exact location of plugging, but also offers reliable wax estimation measurements. The develop-ment of tomographic imaging presents a unique opportunity to unravel the complexities of multiphase structure without having to invade the object of interest [20, 25, 26]. The basic principle of tomography imaging is that the object of interest is interrogated by a sensor array in a cross-section from mul-tiple viewing angles, and the measured data will be processed for the reconstruction of a tomographic image representing the component distribution. Typically, a tomography system consists of three main components, which are the sensors to provide projection information, the data acquisition system (DAQ) for signal transformation and adjusting circuits, and the control computer for image reconstruction from projec-tions and displaying of tomographic images [27]. The ability of process tomography to perform direct measurement and analysis of the internal characteristics of the material of interest has attracted the attention of researchers for the appli-cation of tomography imaging to understand and to optimize the performance of multiphase flow [28–31].

Due to the non-invasive behavior of tomographic imaging, its application in the oil and gas industry to understand the phenomenon of paraffin waxes deposition from waxy crude oil is gaining attention. Several research studies have proven

that the x-ray computed tomography, Nuclear Magnetic Resonance (NMR) tomography, and ATR-FTIR spectroscopy imaging are potential tomography technology which can be employed for wax detection [32–35]. Nonetheless, they are expensive ways of imaging and they utilize ionizing radiation to construct the tomographic images, making them only suit-able for laboratory-scale application but unfavorable for the application in industry.

Electrical capacitance tomography (ECT) is an imaging technique for the visualization of the instantaneous distribu-tion in a multiphase system and is composed of dielectric com-ponents of different permittivity. The measurement principle of ECT is based on the fact that the capacitance of a capacity is a function of the permittivity (ε) of the medium between the electrode plates over the entire sensing volume. The meas-ured capacitance values will be manipulated to reconstruct the tomographic images by using the appropriate image recon-struction algorithm [29, 36, 37]. The ECT technology offers the advantage of being non-invasive, not using ionizing radia-tion, providing rapid response, being scalable and robust.

However, quantification of a wax fraction by using ECT technology is challenging because it is a soft-field technique. In other words, the path over which the capacitance measure-ments are sensitive is non-linear and highly dependent on the permittivity distribution of components within the sensor [28]. The detection of wax deposits from waxy crude oil is even more challenging, given that the permittivity values of solidified paraffin waxes and crude oil are very close to each another. In addition, ECT is inherently ill-posed, in which the number of permittivity values to be estimated is greater than the number of measurements [28, 29]. For instance, for a typical 2D circular sensor with 12 electrodes, there will be 66 independent capacitance measurements. However, the tomo-graphic images will be reconstructed with 64 × 64 pixels, indicating that the problem is underspecified by a factor of approximately 50. Hence, the tomographic images by ECT is known to be of poor quality with low resolution compared to other types of advanced tomographic techniques and this will impose difficulties for wax monitoring as there will be an indistinct phase between the solid and liquid phase at the phase boundaries. The limitations of ECT technology have to be overcome so that it can be confidentially applied in industry for wax measurements and monitoring.

Image thresholding is a type of processing which divides an image into two regions, which are the object and back-ground for image enhancement, sharpening, restoration and contrast enhancement. By the application of image thresh-olding algorithms, every intensity of pixels within an image, I (x, y) will be converted into the binary value, b (x, y), which will be either 0 or 1 depending on the threshold value, T (x, y) [38, 39]. Hence, this paper describes the development of a real-time wax measurement approach by the application of the non-invasive ECT system. The novelty of this work is based on the integration of two types of image thresh-olding technique (Otsu’s and Kuo’s methods) with the image reconstruction algorithm in ECT to eliminate the indistinct phase between the solid–liquid boundaries of the tomographic images. The wax fraction of waxy crude oil is

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determined by calculating the number of black pixels within the pipe region as a function of temperature and the results are compared with the conventional wax measurement tech-nology to validate the accuracy of the wax measuring system.

2. Design and fabrication of ECT sensor

The ECT sensor used in this research work is a self-fabricated single plane 12 electrodes sensor, which has been used for the collection of raw capacitance data obtained from the dielectric materials across the cross-sectional area of the pipeline (see figure 1). The method for design of a well-functioning ECT sensor has been explained and discussed in previous research studies [36, 40, 41]. Hence, only the relevant information has been discussed and provided in this manuscript. The ECT sensor has 12 copper electrodes which have equally placed around the circumference of an acrylic pipe. This is important to ensure that there will be no mechanical interaction between the measuring planes of the electrodes. In addition, driven guard copper plates are mounted above and below the mea-suring electrodes of the sensor to ensure that the electrical field lines inside the sensor are homogenized and quasi-2D mea-surements can be achieved. Besides that, the sensing region of the ECT sensor is further protected from a grounded screen to reduce noise and the effect from external fields. The sensing region and the grounded screen are separated with thin epoxy layer to avoid contact between these layers. The specification of the fabricated ECT sensor is indicated in table 1.

3. Experimental setup

3.1. Apparatus

The experimental setup consists of three main components which include: (a) single plane 12 electrodes ECT sensor, (b) the data acquisition system and (c) the control computer (see

figure  2). The function of the sensor is to measure the raw capacitance values between all the possible combination of electrode pairs while the DAQ will perform the data collec-tion, normalization of raw capacitance measurements, channel control, digitalization and data communication with the com-puter. The data acquisition system used was an AC-based ECT (ACECT) system which is supported by an ECTGUI soft-ware. This ECT system is equipped with 16-channels and the excitation frequency applied for the current work is of 1 MHz. The measuring speed is set to be at an average speed of 140 frames per second. Next, the data are then transferred to the control computer through the DAQ system and cross-sectional images showing the permittivity distribution, and hence the component distribution over a cross-section can be displayed by selecting an appropriate image reconstruction algorithm.

3.2. Material

The sample which has been investigated in this work is a crude oil sample from the Malay Basin field in Malaysia known as waxy crude oil. The main physical properties of this waxy crude oil sample are summarized and tabulated in table 2. The investigation of this particular waxy crude oil is significant because it has high WAT, and it belongs to the heavy oil cat-egory which is very thick and has a high viscosity. This crude sample has often been reported to cause problems during the transportation process of waxy crude oil, and an effective preventive method needs to be proposed so that appropriate corrective action can be taken to remediate the wax deposit layer before the wax crystals form a strong structure within the pipeline and cannot be removed.

3.3. Calibration of ECT sensor

The ECT sensor has been calibrated by filling the body of the sensor with two reference materials in turn and by mea-suring the resultant inter-electrode capacitance values at these two extreme values of relative permittivity. The calibration of the ECT sensor is particularly important in this work because the difference of the permittivity value between the solidified paraffin waxes and liquid waxy crude oil is very small. The selection of the appropriate material to perform calibration is

Figure 1. Schematic of single plane 12 electrodes ECT sensor.

Table 1. Specification of ECT sensor with single plane and 12 electrodes.

Parameters Dimension

Outer diameter (O.D.) 50 mmInner diameter (I.D.) 46 mmThickness of pipe 2 mmNumber of electrodes 12Number of plane 1Length of pipe 200 mmLength of measurement electrodes 85 mmWidth of measurement electrodes 10 mmEnd guard width 10 mmDistance between end guard and electrodes 10 mmMaterial of pipe Acrylic

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therefore relevant since changes in the permittivity distribu-tion within the pipe section will not be sensitive enough to be detected by the sensor. For this study, the ECT sensor was calibrated by using solidified waxy crude oil at room tempera-ture for low calibration and liquid waxy crude oil at 55 °C for high calibration. For a well-calibrated sensor, a blue tomo-graphic image will be obtained for low calibration while a red tomographic image will be obtained for high calibration. The color-scale for an ECT system is used to display the varia-tion in permittivity across the cross-sectional area of the ECT sensor with the region in red color representing the region with high permittivity, and the region in blue color representing the region with low permittivity.

3.4. Experimental methodology

The experiment was conducted in the Instrumentation and Control laboratory of Universiti Teknologi PETRONAS (UTP) which has maintained a room temperature of 25 ± 2 °C. Firstly, the waxy crude oil at room temperature that appears to be in solid form has to be melt by using the hot water bath at 60 °C. Simultaneously, it was stirred continuously during the heating process to ensure homogeneity characteristics and perfect mixing between the heavy and light parts of the crude oil. When the temperature of the waxy crude oil reaches 55 °C, it was then transferred into ECT sensor for online measure-ment. The online measurement for the statically cooled waxy crude oil was recorded at various temperatures within the range of 30-50 °C.

In this work, during the measurement period, 2 electrodes were excited at any point of time while the remaining elec-trodes which are kept at the ground potential, has function as detectors until the measurement completes the full cycle or (N − 1) electrodes for a single image. For this type of ECT model, a total of 120 raw capacitance measurements were collected for a complete full cycle and for the reconstruction of a single tomographic image. The reason for selecting this measurement protocol is that it can generate a larger number of independent measurements and provide improved image resolution [40].

3.5. Wax fraction measurement

The measured raw capacitance obtained from the data acquisition system has converted to normalized capacitance measurements by using a normalization model. The normal-ization method which has been applied in this research work is the series normalization model as shown in equation  (1). The reason for selecting the series normalization method is because this model produces the most accurate tomographic results for an annular distribution [42]:

=−−

CC C

C CN

M L

H L (1)

where, CM is the measured raw capacitance, CL is the capaci-tance when the sensor is calibrated with the low permittivity material, CH is capacitance when the sensor is calibrated with the high permittivity material and CN is the normalized capac-itance measurement.

For this normalization model, the inter-electrode capaci-tance measured at the lower permittivity calibration point, CL were assigned the value of 0 while the inter-electrode capaci-tance measured at the higher permittivity calibration, CH was allotted the value of 1. All the subsequent measured raw capac-itance values, CM will be normalized to have the value CN which will be theoretically be in between ‘0’ (when the sensor is calibrated with lower permittivity material) and ‘1’(when the sensor is calibrated with high permittivity material).

Figure 2. Schematic of ECT experimental-setup for wax formation from waxy crude.

Table 2. Physical properties of Malay Basin waxy crude oil.

Physical properties Value

Density 850 kg m−3

Wax appearance temperature (WAT) 38.5 °CWax content (acetone precipitation technique—UOP method 46 to 64)

18 wt.%

Relative permittivity (liquid form) 2.66Relative permittivity (solid form) 2.30

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Subsequently, the normalized capacitance measurements has been converted into tomographic images, showing the per-mittivity distribution within the pipe across a cross-sectional area by using Landweber Iteration image reconstruction algo-rithm. The reason for selecting the Landweber Iteration algo-rithm for image reconstruction is because it can provide good tomographic images in terms of both imaging quality and imaging speed [43]. Since the imaginary part of permittivity of waxy crude oil is very low, its effect on the Landweber recon-struction algorithm is negligible. In the Landweber Iteration algorithm, the image vector (G0) is being reconstructed first using the following equation:

=G S CT0 (2)

where, ST is the sensitivity matrix, and C is the normalized capacitance measurements. In each of the subsequent steps, image Gk is corrected by combining ( )α −S C SGk

Tk to form a

new image Gk+1 with the following formulation:

Gk G S C SG G S e k1 , 0, 1, 2, 3...k kT

k k kT

k( )α α+ = + − = + = (3)In equation  (3), the term is the error between the measured capacitance C and the simulated capacitance Gk, and may be regarded as an error image used to correct the subsequent image while αk is the step length. The image is expected to progressively approach the true distribution until a certain cri-terion is met.

The image reconstruction algorithm will produce gray-level tomographic images which illustrate the permittivity distribution of waxy crude oil across a cross-sectional area at different temperatures. However, measurements of wax frac-tion from these reconstructed tomographic images are difficult because there will be an indistinct phase between the mul-tiphase components. These images have further improved by applying thresholding technique because it has the ability to convert the grayscale tomographic images which are captured by the ECT system into the binary scale, in which the wax deposit formed and the liquid crude oil would be clearly sepa-rated at the phase border.

The basic concept of image thresholding is to divide the tomographic image into two regions, which are the object and background, respectively. Every intensity of pixels, I (x, y) in the grayscale will be converted into the binary value, b (x, y), which is either 0 or 1 depending on the threshold value, T (x, y) [39]:

( ) ( ( )⎧⎨⎩

=�

b x yI x y T x y

,0,     if  , )  ,1,     if otherwise

(4)

The threshold technique can be generally divided into two main categories, which are global thresholding and local thresholding. The global thresholding method only uses one optimal threshold value for the entire image while the local thresholding method divides the image into sub-windows first and the threshold value will be different for each sub-window, depending on the neighborhood pixels. In this work, Otsu’s and Kuo’s method have been selected for converting the reconstructed tomographic images into the binary mode.Otsu’s method belongs to the global thresholding group and

it selects the threshold value which will minimize the value of the within-class variance, σB

2 of the whole tomographic image. Let the level of image’s gray-scale be denoted by [1, 2, …,L] while the number of pixels at level i is represented by ni. Hence, the total number of pixels in the image can be represented by N = n1 + n2 +……nL.

In addition, the probabilities of the histogram of the images are given below,

=p n N/i i (5)

In which, �p 0i and ∑ == p 1iL

i1 . Otsu method divides the image into two classes, which are the object, X1 and back-ground, X2 at the level y. Therefore, X1 are the pixels that have the value of level [1,...,y] and X2 are the pixels that have the value of level [y + 1,….,L]. The total probabilities for each class are given below:

∑==

q pi

y

i11

(6)

∑== +

q pi y

L

i21

(7)

While, the class means for both classes are given as;

∑µ ==

ip

qi

yi

11 1

(8)

∑µ == +

ip

qi y

Li

21 2

(9)

Where, q is the class probabilities and μ is the class mean.In addition, the classes’ variances (σ2) can expressed as

below:

( )∑σ

µ=

−

=

p

q

1

i

yi2

1

12

11 (10)

( )∑σ

µ=

−

= +

p

q

1

i y

Li2

1

22

22 (11)

Otsu stated that the value of q, μ and σ2 changed when the level y was changed. Therefore,Otsu’s method which will determine the level y that will minimize the within-class vari-ance σB

2 , by following equation [44]:

( ) ( ) ( ) ( ) ( )σ σ σ= +y q y y q y yB2

1 12

2 22 (12)

On the other hand, Kuo’s method further improved Otsu’s thresholding technique by combining Otsu’s method which is global thresholding with Niblack’s and Sauvola’s method which are local thresholding technique. In this method, the first step is to determine the global threshold value from Otsu’s method and this global threshold value will be denoted by Tglobal. Next, the original ECT image with pixel size of 64 × 64 will be further divided into 256 sub-images or window, W with the size 4 × 4. The number of pixels having the gray-value less than Tglobal in W is then determined and is represented as N. Then, Kuo’s method select another value

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from the image’s histogram, which is denoted as S in which the value of S is less than Tglobal and the selection of S is to make the number of pixels falling between S and Tglobal to be a quarter of N.

Hence, the target pixel, p in window W will be binarized by:

( )( )

⩾ ( )( ) ⩽

⎧⎨⎪

⎩⎪=

>>b p

I p T

T I p S

I p S

1,

Local Thresholding,

0,

global

global (13)

Local thresholding in equation  (13) is used when the gray level of the pixels, I( p ) within W is between Tglobal and S. however, local thresholding algorithm, Tlocal for Kuo’s method is calculated using:

( ) ⩾

( )

µσ

µ

µσ

µ

=

− −

× + − − <

⎧

⎨

⎪⎪⎪

⎩

⎪⎪⎪

⎡

⎣⎢⎢

⎛

⎝⎜

⎞

⎠⎟⎤

⎦⎥⎥

TT

I pT

kT

I pT

,16

1 1 ,16

local

localglobal

localglobal

localglobal

localglobal

(14)

Figure 3. Photograph of cross-sectional view of sensor and the respective reconstructed tomogram images within the temperatures of 55–30 °C.

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where, k is the preset constant, μlocal is the average gray value of all pixels in window W, and σ is the local standard devia-tion of the gray value of pixels in window W. hence, the local standard deviation can be calculated as:

( [ ( )] )∑ ∑σ

µ

=

−

×=

−

=

−

I W i j

m n

,i

m

j

n

0

1

0

1

local2

(15)

Once the value of the local threshold, Tlocal is obtained, bina-rization can be performed on the images using following for-mulation [45]:

( )(( ⩽

⎧⎨⎩

=>

b pI p TI p T

1,      ) 0,     ) 

local

local (16)

Otsu’s algorithm is selected for this work because it is the most reliable global threshold technique [44] whereas Kuo’s

Figure 4. Comparison between actual tomogram images and threshold images obtained from Otsu’s and Kuo’s algorithm.

Temperature (°C)

Tomogram Image

Threshold Image

Otsu’s Method (Benchmark)

Kuo’s Method

45

40

37

35

33

30

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method is chosen for image thresholding because it combines both of the merits of global and local thresholding method [45]. The wax deposit layer will be converted to black pixels at the binary scale, and hence the wax fraction of waxy crude oil (β) at different temperature can be determined by calcu-lating the total amount of black pixels which falls within the pipe region dividing by the total number of pixels within the pipe as shown below:

( ) ⩽( )

β =I p T

b plocal

(17)

3.6. Validation of wax deposition measurement results

The wax fraction of the waxy crude oil sample at different temperatures is determined by measuring the weight increase of the pipe during the deposition. By using this method, the ECT sensor is completely drained for remaining oil and the sensor is weighed several times during the experiment. From the weight difference as compared to the empty ECT sensor, the wax deposit thickness H can be estimated by [8]:

ρ π= − −H R R

m

L2 wax

wax (18)

where, R is the ECT sensor inner radius, mwax is the mass of the wax deposited, ρwax is the density of the wax, and L is the length of the ECT sensor

The wax fraction at different temperatures can be estimated as follows;

β =H

R (19)

with an assumption that the density of the wax deposit does not vary with temperature and the resulting wax thickness is an average over the length of the ECT sensor.

This method is selected as a benchmark to validate the wax fraction measurement results by using the ECT system because the weighing method is a simple, robust and reliable conventional technique to estimate the wax fraction of the waxy crude oil provided that the liquid waxy crude has been drained completely from the ECT sensor during the weight measurement period.

4. Results and discussion

4.1. Online measurement of waxy crude oil using single plane 12 electrodes ECT sensor

The online measurements for the waxy crude oil within the temperature range of 55–30 °C have collected after the suc-cessful calibration of an ECT sensor. The DAQ system that has been used in this work is the ACECT system which auto-matically converts these raw capacitance measurements into the normalized capacitance measurements by using the series normalization model. During the measurement period, two electrodes were excited while the other 10 electrodes, which function as detectors, were used to sense the raw capacitance measurement data. Hence, 120 raw capacitance measurements

have been collected for a single frame of image. These 120 normalized capacitance measurements have been utilized for the reconstruction of the tomographic images by using the Landweber Iteration image reconstruction algorithm. The number of iterations which is employed for the Landweber Iteration image reconstruction algorithm is 100.

4.2. Reconstruction and thresholding of tomographic images

Figure 3 displays the tomographic images which are obtained from the online measurement of waxy crude oil at different temperatures. As mentioned earlier, the blue region indicates the region of lower permittivity, which is the wax deposit formed, while the red region indicates the region of higher permittivity which is the liquid waxy crude oil. The precipita-tion of paraffin waxes and formation of a wax deposit layer can be determined from the region of lower permittivity.

Otsu’s and Kuo’s algorithms as explained in section  3.5 and performed onto the tomographic images of the waxy crude oil were used to eliminate the indistinct phase between the wax deposit and the liquid waxy crude (see figure 4). By converting the reconstructed tomographic images from the grayscale to the binary scale, the wax fraction of the waxy crude oil has been measured by calculating the total number of black pixels within the pipe region.

4.3. Comparison and validation of wax measurement results

The direct weighting method is used as a benchmark to eval-uate the accuracy of the wax fraction by using the ECT system and the results are tabulated in table 3. The wax fraction esti-mation by using the weighting method is repeated for three times to reduce the inaccuracies of the measurement results

Table 3. Wax fraction measurement results by using conventional weighting method.

Temperature (°C)

Wax fraction (%)

Trial 1 Trial 2 Trial 3 Average

45 5.30 6.45 6.22 6.0 ± 0.640 15.82 20.75 19.62 18.7 ± 2.637 63.50 66.75 69.24 66.5 ± 2.935 70.32 72.33 75.34 72.7 ± 2.533 75.12 76.34 80.10 77.2 ± 2.630 82.10 85.30 88.45 85.3 ± 3.2

Table 4. Comparison of wax fraction measurements by Otsu’s, Kuo’s and direct weighting methods.

Temperature (°C)

Wax fraction (%)

Otsu’s method Kuo’s method Direct method

45 5.6 ± 0.6 7.75 ± 0.5 6.0 ± 0.640 16.60 ± 2.5 18.05 ± 2.5 18.7 ± 2.637 64.15 ± 2.8 67.31 ± 2.7 66.5 ± 2.935 70.06 ± 2.5 81.20 ± 2.4 72.7 ± 2.533 67.53 ± 2.5 76.02 ± 2.3 77.2 ± 2.630 83.66 ± 3.2 90.98 ± 2.8 85.3 ± 3.2

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due to the improper drainage of liquid waxy crude oil from the body of the ECT sensor. The wax fraction measurement results by using Otsu’s, Kuo’s and direct weighting methods are shown in table 4 to compare and evaluate the accuracy of the thresholding algorithm.

Using experimental data from the ECT system and the conventional weighting method, the wax fraction values have plotted as a function of the corresponding temperature of the waxy crude oil as shown in figure 5. The sigmoidal nature of these curves enables the best polynomial fit to be obtained by using the experimental data to estimate the amount of wax frac-tion within the pipe across the temperature range of 45–30 °C. The expressions to estimate the amount of wax fraction for various methods are as follows:

β = − + −T T TBenchmark : 0.0866 9.893 366.78 4359.13 2

(20)

β = − + −T T TOtsu’s Method : 0.0459 5.3535 200.36 2353.73 2

(21)

T T TKuo’s Method : 0.0741 8.4915 314.96 3721.23 2β = − + −

(22)where, β is the amount of wax fraction within the pipe region and T is the temperature of the waxy crude oil. For all the three methods, the estimated wax fraction increases when the tem-perature of the waxy crude oil decreases which shows good

agreement with the theoretical understanding. As the tempera-ture of waxy crude oil decreases, a higher amount of paraffin waxes will precipitate out from the waxy crude oil solution and is deposited on the surface of the pipe wall. The rate of formation of wax deposits increases drastically when the tem-perature of the waxy crude drops below its WAT, which is estimated to be at 38.5 °C. Furthermore, this work has proven that the electrical capacitance tomography system has the capability to provide real-time visualization of the condition of the waxy crude oil within pipelines under the static condi-tion. In addition, it provides a non-intrusive and non-invasive approach to estimate the wax fraction of the waxy crude oil at different temperature by integrating both the image recon-struction algorithm and threshold image processing algorithm onto the ECT system.

The percentage error of the wax fraction measurement by using both Otsu’s method and Kuo’s method is determined by using the weighting method as the benchmark to validate the accuracy of the algorithms and the results are displayed in table 5. As can be observed from table 5, Otsu’s method provides better wax fraction measurement results compared to Kuo’s method above the WAT with the maximum difference percentage of only 1.45%. Below the WAT, Kuo’s threshold algorithm provides better wax fraction estimation results with the maximum difference percentage of 8.54% (see figure 6).This might be due to the reason that, below the WAT, the amount of wax deposited increases and the reconstructed tomographic images have higher complexity background. Otsu’s method is not suitable to threshold the tomographic images with a complicated background, and this might affect the accuracy of wax fraction measurement results below the WAT. This research work suggested that Kuo’s method is a more promising mathematical algorithm to be integrated into the ECT system because it provides higher accuracy wax frac-tion measurement results below the WAT. As the temperature drops below the WAT, paraffin waxes will start to precipitate out of the solution and therefore, it is more critical to have a

Figure 5. Wax fraction measurements using Otsu’s, Kuo’s and weighting methods.

Table 5. Error percentage (%) between conventional method with Otsu’s and Kuo’s methods for wax deposit measurements.

Temperature (°C)

Error percentage (%)

Otsu’s method Kuo’s method

45 0.37 1.7640 1.45 0.6837 2.35 0.8135 2.60 8.5433 9.66 1.1730 1.62 5.70

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higher accuracy system to estimate the amount of wax that has been formed within the pipelines.

For the Malay Basin waxy crude oil sample, the experi-mental results indicate that the ECT system tends to over-predict the wax fraction of the waxy crude oil at the WAT and when the temperature of the waxy crude drops below 31 °C. The over-prediction of the ECT system does not hinder its capability to serve as an effective preventive method to avoid the plugging of pipelines because the ECT technology ena-bles the operator to visualize the real condition of the pipeline. When the wax deposit layer starts to build up within the pipe-lines, appropriate corrective methods such as pigging or the utilization of a fused chemical reaction with controlled heat emission can be exploited immediately by the operators to remediate the wax deposit before the entire pipeline is totally clogged up with the gelled crude. The outcome of this work is significant as it serves as a preliminary investigation for the application of the ECT system in the oil and gas industry to overcome the problem of blockage of pipelines due to the phe-nomenon of wax precipitation in waxy crude oil. This method may be applied for real waxy pipelines with the following considerations; (1) A precision pumping rate (for pumping the crude oil) such as gear pump can be installed to achieve accurate pumping rate. (2) A reference instrument needs to be installed to validate the ECT measurement and to determine the wax formation and (3) An integration of all data acquisi-tion within one system to establish the real-time measurement with quantitative output.

In addition, the approach proposed in this work to estimate the wax fraction of the waxy crude oil offers the advantages of being non-intrusive and non-invasive; this is practical for the application in industries, entails low fabrication cost, does not emit radiation, and provides rapid response compared to the conventional approach and other tomographic imaging tech-niques. This method provides industry with an alternative for the estimation of wax fraction from waxy crude oil without having to depend on high cost and radioactive technology. It is worthy to note that these measurements have been con-ducted in the lab using non-conducting materials; however, it is of interest to industry workers to apply this method for

conducting materials such as steel which is commonly used in real-world applications. As such, one of strategies that could be adopted is that the sensor must be completely insulated with non-conductive materials to avoid any interference with saline water.

5. Conclusion

This study has shown that the ECT system is capable of pro-viding real-time visualization of the formation of wax deposits from the waxy crude oil in subsea pipelines. This work has also proven that the integration between image reconstruc-tion algorithm and image thresholding algorithm into the ECT system has the capability of quantifying the wax fraction that has been built-up in the pipelines. The ECT system which was tested for a Malay Basin waxy crude oil sample yield results which agreed well with the results predicted by the conven-tional wax measurement technique. Otsu’s method provides higher accuracy wax fraction measurements above the WAT of the crude oil at 38.5 °C whereas Kuo’s method provides higher accuracy wax fraction measurements below the WAT. Nonetheless, Kuo’s algorithm is more suitable to be integrated into the ECT system because paraffin waxes start to precipi-tate from the waxy crude oil below the WAT and it is more critical to have a system which can provide higher accuracy wax fraction estimation results below the WAT.

References

[1] Ronningsen H P 2012 Production of waxy oils on the Norwegian continental shelf: experiences, challenges, and practices Energy Fuels 26 4124–36

[2] Sarica C and Panacharoensawad E 2012 Review of paraffin deposition research under multiphase flow conditions Energy Fuels 26 3968–78

[3] Hoffmann R, Amundsen L and Schüller R 2011 Online monitoring of wax deposition in sub-sea pipelines Meas. Sci. Technol. 22 075701

[4] Aiyejina A, Chakrabarti D P, Pilgrim A and Sastry M K S 2011 Wax formation in oil pipelines: a critical review Int. J. Multiphas. Flow 37 671–94

Figure 6. Error percentage of wax fraction measurement by Otsu’s and Kuo’s as compared to weighting method.

-7

-2

3

8

13

18

28 30 32 34 36 38 40 42 44 46

Err

or P

erce

ntag

e (%

)

Temperature of Waxy Crude (°C)

Otsu's Method

Kuo's Method

Meas. Sci. Technol. 27 (2016) 025403

I L S Mei et al

11

[5] Banki R, Hoteit H and Firoozabadi A 2008 Mathematical formulation and numerical modeling of wax deposition in pipelines from enthalpy-porosity approach and irreversible thermodynamics Int. J. Heat Mass Transfer 51 3387–98

[6] Vignati E, Piazza R, Visintin R, Lapasin R, D’Antona P and Lockhart T P 2005 Wax crystallization and aggregation in a model crude oil J. Phys. Condens. Matter 17 S3651–60

[7] Wang W, Huang Q, Huang J, Pang Q, Fu J and Wang F 2014 Study of paraffin wax deposition in seasonally pigged pipelines Chem. Technol. Fuels Oils 50 39–44

[8] Hoffmann R and Amundsen L 2009 Single-phase wax deposition experiments Energy Fuels 24 1069–80

[9] Matzain A, Apte M S, Zhang H Q, Volk M and Brill J P 2002 Investigation of paraffin deposition during multiphase flow in pipelines and wellbores—part 1: experiments J. Energy Resour. Technol. 124 180–6

[10] Chen X T, Butler T, Volk M and Brill J P 1997 Techniques for measuring wax thickness during single and multiphase flow SPE Annual Technical Conf. and Exhibition (San Antonio, Texas)

[11] Daridon J L and Dauphin C 1999 Measurement of pressure effect on wax content in partially frozen paraffinic systems Meas. Sci. Technol. 10 1309–14

[12] Pauly J, Daridon J and Coutinho J A P 2001 Measurement and prediction of temperature and pressure effect on wax content in a partially frozen paraffinic system Fluid Phase Equilibr. 187–188 71–82

[13] Correra S, Fasano A and Fusi L 2007 Modelling wax diffusion in crude oils: the cold finger device Appl. Math. Modelling 31 2286–98

[14] Creek J L, Lund H J, Brill J P and Volk M 1999 Wax deposition in single phase flow Fluid Phase Equilibr. 158–160 801–11

[15] Jiang B, Qiu L Li X, Yang S, Li K and Chen H 2014 Measurement of the wax appearance temperature of waxy oil under the reservoir condition with ultrasonic method Pet. Explor. Dev. 41 509–12

[16] Kamari A, Mohammadi A H, Bahadori A and Zendehboudi S 2014 A reliable model for estimating the wax deposition rate during crude oil production and processing Pet. Sci. Technol. 32 2837–44

[17] Kamari A, Khaksar-Manshad A, Gharagheizi F, Mohammadi A H and Ashoori S 2013 Robust model for the determination of wax deposition in oil systems Ind. Eng. Chem. Res. 52 15664–72

[18] Lock S S M, Lau K K and Shariff A M 2015 Effect of recycle ratio on the cost of natural gas processing in countercurrent hollow fiber membrane system J. Ind. Eng. Chem. 21 542–51

[19] Eskin D and Ratulowski J 2014 Modelling wax deposition in oil transport pipelines Can. J. Chem. Eng. 92 973–88

[20] Ismail I, Gamio J C, Bukhari S F A and Yang W Q 2005 Tomography for multi-phase flow measurement in the oil industry Flow Meas. Instrum. 16 145–55

[21] Brower D V, Prescott C N, Zhang J and Rafferty D 2005 Real-time flow assurance monitoring with non-intrusive fiber optic technology 2005 Offshore Technology Conf. (Houston, USA)

[22] Behbahani T J 2014 A new investigation on wax precipitation in petroleum fluids influence of activity coefficient models Pet. Coal 56 157–64

[23] Singh A, Lee H S, Singh P and Sarica C 2011 Flow assurance: validation of wax deposition models using field data from a subsea pipeline 2011 Offshore Technology Conf. (Houston, USA)

[24] Gonccalves M A L, Pinho S G, Montesanti J R T, Shang W and Sarica C 2011 A case study of scale-up of wax deposition model predictions using flow loop wax deposition data for pipeline design 15th Int. Conf. on Multiphase Production Technology (Cannes, France)

[25] Yang W Q and Liu S 2000 Role of tomography in gas/solids flow measurement Flow Meas. Instrum. 11 237–44

[26] Dyakowski T, Jeanmeure L F C and Jaworski A J 2000 Application of electrical tomography for gas-solids and liquid-solids flows—a review Powder Technol. 112 174–92

[27] Beck M S and Willam R A 1996 Process tomography: a European innovation and its application Meas. Sci. Technol. 7 215–24

[28] Chandrasekera T C, Moody D, Schnellmann M A, Dennis J S and Holland D J 2015 Measurement of bubble sizes in fluidized bed using electrical capacitance tomography Chem. Eng. Sci. 126 679–87

[29] Li Y and Holland D J 2013 Fast and robust 3D electrical capacitance tomography Meas. Sci. Technol. 24 105406

[30] Olemi C, Jia J and Wang M 2013 Measurement of air distribution and void fraction of an upwards air-water flow using electrical resistance tomography and a wire-mesh sensor Meas. Sci. Technol. 24 035403

[31] Abdullah B, Dave C, Nguyen T H, Cooper C G and Adesina A A 2011 Electrical resistance tomography-assisted analysis of dispersed phase hold-up in a gas-inducing mechanically stirred vessel Chem. Eng. Sci. 66 5648–62

[32] Karacan C O, Demiral M R B and Kok M F 2000 Application of x-ray CT imaging as an alternative tool for cloud point determination Petrol. Sci. Technol. 18 835–49

[33] Miknis F P, Pauli A T, Michon L C and Netzel D A 1998 NMR imaging studies of asphaltene precipitation in asphalts Fuel 77 399–405

[34] Gabrienko A A, Morozov E V, Subramani V, Martyanov O N and Kazarian S G 2015 Chemical visualization of asphaltenes aggregation processes studied in situ wit ATR-FTIR spectroscopic imaging and NMR imaging J. Phys. Chem. C 119 2646–60

[35] Gabrienko A A, Lai C H and Kazarian S G 2014 In situ chemical imaging of asphaltene precipitation from crude oil induced by n-heptane Energy Fuels 28 964–71

[36] Yang W Q 2010 Design of electrical capacitance tomography sensors Meas. Sci. Technol. 21 042001

[37] Wang H G and Yang W Q 2011 Scale-up of electrical capacitance tomography sensor for imaging pharmaceutical fluidized beds and validation by computational fluid dynamics Meas. Sci. Technol. 22 104015

[38] Al-Azawi M A N 2013 Image thresholding using histogram fuzzy approximation Int. J. Comput. Appl. 83 36–40

[39] Lock I S M, Ismail I, Abdullah B and Shafquet A 2014 Evaluation of electrical capacitance tomography thresholding techniques for void fraction measurement of gas-liquid system Appl. Mech. Mater. 625 439–43

[40] Reinecke N and Mewes D 1996 Recent developments and industrial/research applications of capacitance tomography Meas. Sci. Technol. 7 233

[41] Li Y, Yang W Q, Xie C G, Huang S M, Wu Z P, Tsamakis D and Lenn C 2013 Gas/oil/water flow measurement by electrical capacitance tomography Meas. Sci. Technol. 24 074001

[42] Wang H G and Yang W Q 2010 Measurement of fluidised bed dryer by different frequency and different normalisation methods with electrical capacitance tomography Powder Technol. 199 60–9

[43] Li Y and Yang W Q 2008 Image reconstruction by nonlinear landweber iteration for complicated distributions Meas. Sci. Technol. 19 094014

[44] Huang D and Wang C H 2009 Optimal multi-level thresholiding using a two-stage Otsu optimization approach Pattern Recogn. 30 275–84

[45] Lock S S M, Lau K K, Ahmad F and Shariff A M 2015 Modeling, simulation and economic analysis of CO2 capture from natural gas using concurrent, countercurrent and radial crossflow hollow fiber membrane Int. J. Greenhouse Gas Control 36 114–34

Meas. Sci. Technol. 27 (2016) 025403