Application of Open-Ended Coaxial Probes for

Detection of Sand Production from Petroleum Wells

Steven Hilgedick

Petroleum Engineering Program

Department of Geological Sciences and Engineering

Missouri University of Science and Technology

Rolla, MO 65409

sahc55@mst.edu

Jaswanth N. Vutukury and Kristen M. Donnell

Applied Microwave Nondestructive Testing Laboratory

(amntl)

Department of Electrical and Computer Engineering

Missouri University of Science and Technology

Rolla, MO 65409

<nvf4d,kmdgfd>@mst.edu

Abstract—Microwave nondestructive testing (NDT) has

shown success in many industries including aerospace and

infrastructure. Recently the potential for applying microwave

NDT methods in the petroleum industry, specifically for

detection of sand in produced petroleum fluids, was investigated.

Flowing sand grains cause erosion of many components in the

production system including tubing, piping and associated

connections, and fluids-processing equipment. Detection of

produced sand is important to planning operations and the

application of sand production mitigation techniques. To this

end, a simple, cost-effective microwave sensor design has been

considered. This paper presents simulation and measurement

results for this simplified sensor design for various production

scenarios. The results indicate the proposed sensing method is

sensitive to the presence of sand and may have the potential to

serve as a tool for detection of produced sand in the petroleum

industry.

Keywords—microwave nondestructive testing, sand production,

petroleum production monitoring, microwave sensing, open-ended

coaxial probe

I. BACKGROUND

During petroleum production from (geologically) young, unconsolidated sandstone reservoirs (such as reservoirs in offshore sedimentary basins), the reduction in reservoir pressure can cause mechanical failure of the sandstone around the wellbore, resulting in the production of sand grains (along with the produced fluids). Sand production causes the erosion of components in the production system (i.e., piping, connections, and fluids-processing equipment). This may lead to production shutdown and other economic losses, which are amplified in offshore production operations. Detection and volume quantification of produced sand is key for optimizing production rates and monitoring the effectiveness of sand production mitigation techniques [1].

During production from petroleum wells, hydrocarbons are simultaneously produced in both gas and liquid phases, along with water, known as produced water or brine (water with salt compounds such as sodium, calcium, or bromides). Sand production often coincides with water/brine production. During the life of a producing well, the gas-to-liquid (G/L) and liquid hydrocarbon-to-brine (HC/B) production rate ratios change,

along with the overall production rate of the well. Production pipelines are generally horizontal, and many different flow regimes may occur as fluid production rates vary. This may result in sand flowing along the bottom of the pipeline or being dispersed throughout the flowing fluid at very high velocities.

As the presence of sand is of concern, one method of sand production mitigation involves placing screens and other equipment inside the wellbore to prevent sand from flowing with the produced fluids. As such, sand production monitoring is used to assess the effectiveness of these screens and monitor for failure. Further, since sand production often becomes more significant as production rates increase, monitoring for sand production allows the wells to produce at the highest rate possible without resulting in formation failure and subsequent sand production.

Currently, there are two primary (established) methods used to detect the presence of sand particulate in produced fluids, namely, resistive sensors and acoustic methods [1, 2]. The resistive sensing method utilizes probes placed in-line within the flow path. Over time, these sensors erode as a result of produced sand, resulting in a change in the electrical resistivity of the probe. This change can be measured and related to the sand concentration in the produced fluid [1, 3] While this method is successful, calibration is required. Further, as the probe erodes over time, eventually it must be replaced, resulting in increased operating/equipment costs and lost production time. Ultrasonic sensors measure acoustic events resulting from sand particles impacting pipeline walls or other equipment. While these sensors have been shown to be effective at detecting and even quantifying sand production volumes, they are significantly affected by changes in volume ratios and fluid flow regimes. Therefore, they require regular recalibration (difficult to achieve in offshore production wells) [4]. Recently, fiber optic sensing methods have also shown success for sand detection, but are less common and very expensive to include if a recompletion is necessary [5].

An improved technique capable of achieving the desired sand detection goals would be quite beneficial to the petroleum industry. Microwave methods have shown success for flow measurements in the petroleum industry [6, 7], but have limited application as a sensing method [8]. To this end, a novel

978-1-4673-6386-0/14/$31.00 ©2014 IEEE

microwave sensor is proposed utilizing an open-ended coaxial probe (OECP).

II. MICROWAVE SENSING

At microwave frequencies, the electromagnetic behavior of non-conducting (dielectric) materials is described by their (relative-to-freespace) complex dielectric properties, εr = εr’ - j εr’’. The real part, or permittivity, εr’, indicates the ability of a material to store microwave energy, and the imaginary part or loss factor, εr’’, indicates the ability of a material to absorb microwave energy. Dielectric properties of a mixture (here, multiphase fluid) are related to the individual dielectric properties and volume fraction of each constituent through the application of mixing models [9].

To this end, a number of mixing models were considered including Maxwell-Garnet and Power Law [9]. Table I, below, contains the relative dielectric properties of the pipe constituents [6, 7, 10]. Some dielectric mixing models consider host and inclusion(s) materials differently, whereas others consider all constituents to be of equal weight. In the case of producing wells, defining a host material is not straightforward. This is further complicated by the wide range of dielectric properties of the potential pipeline contents (e.g., brine, oil, and gas). As such, the Power Law mixing model [9] was selected, since this model considers all mixtures to be of equal weight and therefore does not require a strict definition of host and inclusion materials.

TABLE I

CONSTITUENT DIELECTRIC PROPERTIES

Relative Dielectric Properties

Oil εr = 2.2-j0.1

Gas εr = 1-j0

Brine εr = 50-j40

Due to the presence of brine, the loss factor of the pipeline contents may be significant. As a result, typical microwave materials characterization methods are not ideal for this application, since the interrogating signal may not penetrate through the entire pipe volume. However, as mentioned above, horizontal flow is common and often results in sand particulate flowing along the bottom of the pipeline. In such cases, microwave reflection properties, if measured on the top and bottom of the pipeline, may indicate the presence of sand buildup.

To accomplish such measurements, a sensing system was designed consisting of two open-ended coaxial lines, herein referred to as open-ended coaxial probes, or OECP, integrated into opposing locations within a pipe. OECPs were chosen based on ease of integration into a pipeline, along with cost of the sensing elements themselves. Further, OECPs are not efficient radiators of microwave energy, thereby restricting the interrogation volume to regions very near the top and bottom (i.e., the common location of flowing sand) of the pipe. In this way, a microwave signal can interrogate the pipeline contents from opposite directions.

To illustrate this potential detection technique, simulations were conducted using CST Microwave Studio™ [11] to investigate the microwave reflection properties (defined as the ratio of the reflected signal to the incident signal) of various production scenarios (i.e., different G/L and HC/B ratios, with

and without the presence of sand). It should be noted that while the parameter of interest (e.g., reflection properties) is a complex quantity, this investigation is restricted to the magnitude of the reflection properties (representative of power), as these measurements are much easier to conduct in practice (compared to complex reflection property measurements that include phase).

Fig. 1, below, depicts the proposed system. A 2-inch diameter pipe (50.8 mm) was considered. This size was selected to represent a reasonable flow line, with the understanding that the sensing capabilities illustrated here can be adapted to fit larger flow lines. Two coaxial cables are integrated into the top and bottom of the pipe.

Fig. 1. Proposed microwave sensing model incorporating OECP’s.

To investigate the feasibly of this proposed method, simulations were conducted considering stratified flow conditions, in which a layer of gas flows over the liquids and sand in horizontal pipes. The results of these simulations are presented and discussed in the next section.

III. SIMULATIONS

Using the proposed sensing system (Fig. 1, above), three production scenarios assuming stratified flow, shown in Table II, were simulated. The magnitude (in dB) of the reflection properties, herein referred to as return loss (RL), for the scenarios in Table II were simulated for the bottom coaxial probe, the results of which are shown in Fig. 2-Fig. 4. For each case, a solid layer of sand (relative dielectric properties of εr = 4-j0.05) was assumed to be flowing along the bottom of the pipe. The effective dielectric properties of the produced fluid are also included in Table II. Since gas flows above the produced fluid in stratified flow, the effective dielectric properties shown in Table II are for a mixture of oil and brine only. The OECP’s were assumed to be 50-ohm RG58 standard (commercially available) coaxial lines. The simulations were conducted over a frequency range of 2 – 18 GHz (a reasonable range for a standard coaxial line).

TABLE II

SIMULATION SCENARIOS FOR STRATIFIED FLOW.

Oil (εr = 2.2-j0.1)

Gas (εr = 1-j0)

Brine (εr = 50-j40)

Effective

Dielectric

Properties*

Case 1 60% 20% 20% 5.1-j1.1

Case 2 20% 60% 20% 11.4-j4.4

Case 3 10% 30% 60% 33.2-j22.2 *The effective dielectric properties are for the fluid phase (i.e., mixture of oil

and brine).

-5

-4

-3

-2

-1

0

2 4 6 8 10 12 14 16 18

No Sand 0.3 mm 0.6 mm 0.9 mm

RL (

dB

)

Frequency (GHz)

Fig. 2. Simulated RL at the bottom OECP for Case 1.

-5

-4

-3

-2

-1

0

2 4 6 8 10 12 14 16 18

No Sand 0.3 mm 0.6 mm 0.9 mm

RL (

dB

)

Frequency (GHz)

Fig. 3. Simulated RL at the bottom OECP for Case 2.

-5

-4

-3

-2

-1

0

2 4 6 8 10 12 14 16 18

No Sand 0.3 mm 0.6 mm 0.9 mm

RL (

dB

)

Frequency (GHz)

Fig. 4. Simulated RL at the bottom OECP for Case 3.

The results of Fig. 2-Fig. 4 indicate a few important points. First, for all cases, the presence of sand (as compared to the

case of no sand) results in an immediate change in RL. However, this response varies as a function of frequency. For example, for a flow scenario with a majority of oil (Case 1, Fig. 2), the sensitivity to the presence of sand increases with operating frequency. However, once the percentage of oil is on the order of or less than the percentage of brine (Cases 2-3, Fig. 3-Fig. 4), the sensitivity decreases with frequency. This is related to the difference between the dielectric properties of sand and the liquid phase (e.g., mixture of oil and brine), and indicates that the system may be optimized if particular flow regimes are expected. Further, in all cases, as the layer of sand increased in thickness, the sensitivity of the OECP to this thickness change decreases. This is as expected, as OECPs are not efficient radiators and as the thickness of the sand layer increases, the OECP begins to sense only the presence of sand (and not the fluid in the pipeline).

A last simulation that is important to consider is the RL from the top OECP. Fig. 5 shows the simulation results for this sensor for Case 1 (since all Cases for stratified flow incorporate gas flowing in the upper section of the pipe, the response for the top OECP for all Cases is equivalent. As such, only the response for Case 1 is shown below.

-4

-3

-2

-1

0

1

2 4 6 8 10 12 14 16 18

No Sand 0.3 mm 0.6 mm 0.9 mmR

L (

dB

)

Frequency (GHz)

Fig. 5. Simulated RL at the top OECP for Case 1.

As shown by the results of Fig. 5, essentially all of the signal transmitted into the pipe is reflected back (e.g., RL of 0 dB). Further, the presence of sand on the bottom of the pipe has no effect on the RL measured from the top OECP. This is as expected, as OECPs are not efficient radiators. In addition, such a response in the top probe, when compared to the response from the bottom probe (Fig. 2-Fig. 4, with or without sand), is indicative of stratified flow.

A. Effect of Sand Porosity

As mentioned above, the simulation results presented in Fig. 2-Fig. 4 assumed that the sand particulate flowing along the bottom of the pipe was a solid material (i.e., was not porous). However, in practice, sand particulate flowing along the bottom of the pipe is intermixed with produced fluids. As such, to investigate the effect of a more realistic production scenario on the OECP response, simulations were conducted for two additional production scenarios, listed in Table III (chosen to represent a common range of production). These scenarios consider the sand layer as a solid, porous material with fluid-filled pores. Furthermore, for both scenarios, the sand porosity was considered to be 50%, 70%, and 90% (representing reasonable sand concentrations). The inclusion of porosity in the sand layer will change the overall dielectric

properties of the sand. As such, the effective dielectric properties of the sand layer are given in Table IV (for different porosity values). The simulation results are provided in Fig. 6-Fig. 7.

TABLE III

SIMULATION SCENARIOS FOR POROSITY INVESTIGATION.

Oil (εr = 2.2-j0.1)

Gas (εr = 1-j0)

Brine (εr = 50-j40)

Effective

Dielectric

Properties*

Case A 17.5% 30% 52.5% 24.1-j13.9

Case B 35% 30% 35% 11.4-j4.4 *The effective dielectric properties are for the fluid phase (i.e., mixture of oil

and brine).

TABLE IV

DIELECTRIC PROPERTIES OF POROUS SAND LAYER.

50%

Porosity

70%

Porosity

90%

Porosity

Case A 10.2-j2.8 14.5-j5.7 20.4-j10.5

Case B 6.9-j1.3 8.4-j2.2 10.3-j3.6

-6

-5

-4

-3

-2

-1

0

2 4 6 8 10 12 14 16 18

No Sand 50% 70% 90%

RL (

dB

)

Frequency (GHz)

Fig. 6. Simulated RL at the bottom OECP for Case A.

-5

-4

-3

-2

-1

0

2 4 6 8 10 12 14 16 18

No Sand 50% 70% 90%

RL (

dB

)

Frequency (GHz)

Fig. 7. Simulated RL at the bottom OECP for Case B.

Clearly, the results in Fig. 6-Fig. 7 indicate that the OECP is sensitive to the presence of dispersed sand particulate. However, the response as a function of porosity differs between the two cases. When comparing the results for these two cases, it can be seen that this method is more sensitive to lower sand concentrations (e.g., porosity nearing 90%) when the brine content is reduced (Case B). This sensitivity is also a

function of frequency, as can be seen in Fig. 6, where the RL for 90% porosity and no sand is equivalent beyond 6 GHz (with minimal change between 2-6 GHz). Considering 70% porosity, this frequency dependence is also evident, and the RL is equivalent to that of no sand beyond 12 GHz. However, differences are detectable for frequencies less than 12 GHz. Similar to this, the results of Fig. 7 also show a reduced sensitivity to 90% porosity, with the sensitivity decreasing as a function of frequency (~0.5 dB for 10 GHz or lower, and ~0.25 dB or less for frequencies beyond 10 GHz). However, the sensitivity to lower porosity is improved for this case (as compared to Case B with increased brine content). Ultimately, the sensitivity to porosity (or lack thereof) relates back to the dielectric properties of the material in the near vicinity of the OECP. When the sand porosity is very high, the effective material near the OECP aperture becomes more similar to the production liquid itself. For very high porosities, the electromagnetic effect of the presence of sand on the OECP response becomes negligible, and the OECP is not able to sense the change in material. However, for production scenarios with increased sand concentrations (such as Case B), the potential of the proposed OECP sensing system for sand detection is still promising.

IV. MEASUREMENTS

To further investigate the potential of the proposed OECP sensing system to detect the presence of sand in produced fluids, preliminary measurements were conducted. In order to simplify the measurement process, a pipe was created using a small plastic box (dimensions of 5 × 5 × 5 cm

3) lined at the top

and bottom with a metal plate. In this way and since OECP are inefficient radiators, a system (representative of the pipe shown in Fig. 1) but easier to use in the laboratory was created. The square pipe design is depicted in Fig. 8. In order to ensure that the square pipe design properly replicates the pipe of Fig. 1, simulations were conducted using the original (Fig. 1) and square design for a production scenario of stratified flow of 30% gas and 70% oil without sand. The results for the bottom OECP are shown in Fig. 9.

Fig. 8. Square pipe model used for preliminary measurements.

-0.5

-0.4

-0.3

-0.2

-0.1

0

2 4 6 8 10 12 14 16 18

Square Pipe Round PipeR

L (

dB

)

Frequency (GHz)

Fig. 9. Simulated RL from the bottom OECP.

Clearly, from the results of Fig. 9, the square pipe design sufficiently represents the original design of Fig. 1.

Using the square pipe, measurements were conducted using an OECP (50-Ω RG58 coaxial line) connected to a calibrated port of an HP8510C Vector Network Analyzer operating from 2 – 18 GHz. Reflection measurements (e.g., RL) were made for two different liquids, tap water and brine (15% salt content) with and without sand. Oil only was not considered since sand is usually produced along with produced water/brine. The height of the liquid was 10 mm, and the height of the sand was 1 mm. The results of these measurements from the bottom OECP are provided in Fig. 10-Fig. 11. For the brine measurements (Fig. 11), before adding the full 1 mm of sand to the square pipe, a few sand grains were placed near the aperture of the OECP to investigate whether or not the OECP is sensitive to a very small amount of sand.

-16

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-12

-10

-8

-6

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-2

0

2 4 6 8 10 12 14 16 18

No Sand 1 mm Sand

RL (

dB

)

Frequency (GHz)

Fig. 10. Measured RL at the bottom OECP for water.

-16

-14

-12

-10

-8

-6

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-2

0

2 4 6 8 10 12 14 16 18

No Sand Sand Grains 1 mm Sand

RL (

dB

)

Frequency (GHz)

Fig. 11. Measured RL at the bottom OECP for brine.

As can be seen from the results in Fig. 10-Fig. 11, the OECP is clearly sensitive to the presence of sand. This sensitivity is more significant at higher frequencies for both liquids. In addition, for both liquids, signal variation as a function of frequency is also evident, with the most significant variation occurring at frequencies greater than 10 GHz. This variation is attributed to measurement and calibration error. Nonetheless, for water, a difference of over 1 dB (on average) is evident with the introduction of sand. Similarly, for brine, nearly 2 dB (on average) of difference is evident with the introduction of a few sand grains, with an additional 1-2 dB of difference evident for the thicker (1 mm) layer of sand. This is quite important as it relates to the efficacy of this proposed sensing method, as even the presence of a sand grains (as opposed to a full layer of sand) causes a significant and measurable difference.

It is also important to note that these measurement results differ from the simulations presented above. This is not unexpected, as the results above represent more realistic production scenarios (whereas the measurements include only one type of liquid).

V. CONCLUSION

Sand production is of great concern in the petroleum industry. Sand production monitoring is necessary to detect the onset of sand production, monitor the performance of sand mitigation techniques, and optimize production rates. However, the current technology used for sand monitoring suffers from limitations. As such, a new microwave-based sensing approach is investigated in this work incorporating open-ended coaxial probes. Initial simulations indicate that this technique is promising for detecting the presence of sand flowing along the bottom of horizontal pipes for a stratified flow regimes. Simulations to investigate the effect of sand porosity were also conducted, showing that even for low sand concentrations (approaching 90% porosity), a difference in the RL is still evident. Lastly, preliminary measurements for water and brine (with and without sand) further support the potential of this method as a tool to detect the presence of sand flowing along the bottom of a pipe. Future considerations to this proposed method may include additional measurement studies incorporating multiphase fluid flow and different types of sand. Other commercially-available coaxial probes may also be considered.

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