multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity ... flow if the ratio of gas to liquid mass flow is known in advance.
A non-radioactive flow meter using a new hierarchical neural network M Meribout1, N Al-Rawahi2, A Al-Naamany2, A Al-Bimani2, K Al-Busaidi3, A Meribout4 1: Electrical Engineering Department, Petroleum Institute, UAE. 2: ECE Department, College of Engineering, SQU University, Oman. 3: Petroleum Development Oman, Oman. 4: Sonatrach Cooperation, Algeria.
ABSTRACT In this paper, a Multilayer neural network has been developed to carry out the fusion of multi-sensor information for a new multiphase flow meter (MPFM) device. The velocity and density of each phase are determined using the fluid electrical and acoustic property signals which are combined with the physical models of multiphase fluids, in addition to the venturi, differential pressure, and absolute pressure sensors. Two rings of high and low frequency ultrasonic sensors are used to overcome the uncertainties of the electrical sensors in the range of 40-60% water-cut for low and high gas fractions respectively. Experimental results on a multiphase flow loop show that real-time classification of phase flow rates for up to 90% gas fraction can be achieved with less than 10% relative error.
1 INTRODUCTION The volumetric flow rates of process fluids prior to separation are critical parameters in process control within the petrochemical industries and have achieved increased attention (1)(2)(3). Thus, significant progress has been made in the metering of multiphase fluids. Several of these meters require first homogenizing the flow in the mixture to reduce the slip velocity between all the phases of the fluid and make their individual velocities approximately equal (4)(5). This technique however works well only for flows which become liquid continuous after homogenizing the mixture. The meter addressed in (6) ignores any interaction between the gas and liquid phases and can provide the gas mass flow if the ratio of gas to liquid mass flow is known in advance. Unfortunately this method relies on inaccurate equations and gives more than twenty percent error. The solution proposed in (7) uses a water-cut meter and a volumetric flow-meter for measuring the gas and liquid phases. This invention is complicated because it requires the use of a positive displacement device so it can avoid the problem of slip between the gas and liquid phases. In addition, this system does not appear to be effective for liquid fractions below about five percent (5-10 %). Other volumetric measuring devices such as indicated in (8)(9), measure a flow of coal dust solids in a nitrogen stream. Although these types of devices use pressure measuring structures, they are not able to address the
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problem of measuring a liquid fraction in a multiphase-flow where the dispersed liquid phase is less than 10% of the overall volume. Furthermore, such devices, which typically have two pressure measuring points on the venturi throat are not sensitive to the fact that a pressure drop is caused by the interaction between the gas and liquid phases and must be calculated accordingly. Other type of multiphase flow meters uses gamma or X-rays to determine the average fluid density and have been successfully tested in several oil fields to be the most widely deployed meter worldwide (6)(10)(11)(12). However these meters are radioactive and thus present severe safety issues not suitable for oil companies. This paper presents a new non radioactive ultrasonic-based multisensor device for the determination of the volume flow rates of multiphase mixture components along a portion of a pipeline with different flow regimes and without prior separation of gas. In addition of homogenizing the flow, some properties of fluid mechanics are introduced to the pattern recognition system. This latter uses a dedicated multilayer neural network algorithm to overcome the nonlinearity and uncertainties of the sensors. The system, which is compact and easily portable, has been implemented and extensively tested on a Lab-scale multiphase flow loop with various flow regimes, fluid densities and flow rates. Experimental results indicate that the error rate of +/- 10% can be achieved for real-time classification of up to 90% gas fraction. Table 1 List of Symbols used in the paper. Symbol Q ρ Mg Qa(W) Qr(W)
Meaning Volume flow rate (in m3/hr) Fluid density (in kg/m3) Mass flow rate (in kg/hr) Volume flow rate of water measured by the proposed MPFM Reference Volume flow rate of water measured by the single phase flow meter
2 BACKGROUND AND DRIVING PARAMETERS FOR THE MPFM DESIGN In oil fields, oil is usually extracted with mixed water and gas. Therefore, real-time and accurate determination of the composition of the water and gas in the extracted oil is necessary for high level reservoir management. This remains a challenging task for several research groups and companies since it requires simultaneous and deep expertise in various technical fields of electronics, petroleum, and mechanics. A multiphase phase flow loop is usually required to test and calibrate the MPFM (Figure 1). In our Lab-scale flow loop, water, oil, and gas are extracted from two separate tanks, containing oil and water respectively, of 1 m3 each under various flow rates and flow regimes. They are then mixed to cross the MPFM device under test. A host computer measures the single phase flow rates of each of the three phases while collecting simultaneously data from all the sensors of the MPFM device. An offline calibration of the MPFM device is then performed. The goal here is to design an appropriate pattern recognition algorithm in order for the MPFM device to generate in real-time the flow rates of each phase as close as possible to the corresponding value collected from each single phase flow meter. To properly value the gas and liquid production from the wells under various flow regimes, a method relying on physical fluid equations for determining their mass flow rates is required during the MPFM design to achieve good accuracy.
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Figure 1 Block Diagram of the three Phase Flow Loop For instance, the two differential pressure sensors (p1 and p2) attached to the venturi provide an indication of the total volume flow rate passing through it according to the equation [1]: [1] 2 Q=
2( p1 − p 2 ) πD 1 . 2 . D2 4 4 ρ 1− ( ) D1
Where: D1 and D2 are the higher and lower diameters of the throats of the venturi, p1 and p2 the values of pressure at these two points, and ρ the fluid density. The above equation however is applicable only for uncompressible fluids such as liquid mixture and becomes less accurate in the presence of gas. This is because the volume of gas passing through the meter is not the same as the total volume measured by the reference gas meter. Therefore, introducing directly the above equation [1] in the pattern recognition algorithm would lead to high errors. In our case, we considered the conservation of mass instead of volume flow rate using the following Mass flow rate (Mg) equations: Mg = K ρ .( p1 − p 2 )
[2]
In another example, although the effects of pressure on the density of liquids is of little significance (liquids are considered to be incompressible), its effect on gases cannot be ignored. When a gas is compressed, the same mass is contained in a smaller space, causing a decrease in volume and an increase in density (13): V [l / min] =
k P
[3]
This relationship, which is applicable for ideal gas, is explored in our system during the calibration process to calculate the real amount of gas flow rate, V0, actually passing through the multiphase flow meter with static pressure, P0, knowing the values of the
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reference volume flow rate, V1, indicted by the gas flow meter and the corresponding nearby reference static pressure, P1. V0[l / min] = V1 ×
P1 P0
[4]
Another design factor which has been considered during the design of our MPFM is to take into consideration the slip velocity between various phases of the fluid, since this may lead to a significant uncertainty. Our solution to this issue consists to measure the actual volume flow rate of gas passing through the MPFM using the above Equation [2] as well as to provide an inline mixer (90 degrees elbow) in the MPFM in an effort to create homogenous phase distribution at the location of a positioned multiphase flow meter. Finally, in addition to use various electrical and acoustic sensors, the estimation of gas can be determined using a differential pressure sensor since the total density of the fluid passing through a vertical pipe can be described according to the following relationship: 1 ΔP = ρ gh + kv 2 2
[5]
Where h is the distance separating the two pressure sensors. This can provide useful information on the quantity of gas passing through the meter. In summary, and following the above discussions, an upstream pressure sensor, P1, was introduced in the flow loop, nearby the gas flow meter to provide the actual value of the gas flow passing through the MPFM using Equation [4]. The gas density was determined using the differential pressure and venturi sensors as indicated in Equation [5]. In addition, the Mass flow rate, Mg, was introduced as an input to the pattern recognition algorithm. The next sections will describe how the other sensors will be used to finally provide the flow rate of each phase.
3 OVERALL HARDWARE ARCHITECTURE OF THE MPFM. Our MPFM’s measurement principle involves two groups of probes (Figure 2): one group which comprises two capacitance, a conductance, and an ultrasound array sensor is used to determine the phases fractions, whereas the second group consisting of the venturi and Differential pressure sensor (DP in the figure) is used for mass flow determination (in Kg/m3). All these sensors are fixed into the flow loop by means of flange connections and interfaced to the computer through its PCI or serial buses. The capacitance and conductance sensors are used to determine the permittivity and conductivity respectively of the multiphase fluid (Hence they can provide an indication on the content of water and gas in oil), whereas the ultrasound sensor provides an indication on the fluid composition where neither the capacitance, nor conductance can respond. This hardware/software combination overcomes the inaccuracy of the capacitance and conductance sensors within the water-cut range from 40 to 60% especially with the existence of high gas fraction. This is a significant advantage over other commercially available multiphase meters which use either the conductance or capacitance sensors to determine the density
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mixture (4)(8)(10)(14)(15). In addition, two pressure sensors separated by a predefined distance are used to estimate the density of the fluid passing through the meter according to Equation 5 and to compensate for the inaccuracy of the venturi meter for the determination of the flow rate. The data collected from all these sensors is fed into a new multilayer neural network to compute the volume flow rate of each of the three phases.
Figure 2. Overall architecture of the MPFM 3.1 Ultrasound sensors High frequency ultrasound waves can be easily attenuated, especially when their wavelength is lower than the size of bubbles of gas in the liquid mixture (2). However the measurement of their time of flight is more accurate than lower frequency waves (6)(19). Thus the usage of different types of ultrasound sensors is required to cover all flow regimes: Low frequency ultrasound sensors (i.e. less than 30 KHz sensors) to determine the fluid density in case of high gas fraction containing large gas bubbles and high frequency sensors (i.e. more than 2 MHz sensors) to more accurately determine the fluid density in case of low gas fraction with very small or no gas bubbles. Hence, two ultrasound sensors arrays are deployed in our multiphase flow meter. Figure 3 shows a perspective view of the ultrasound sensors. It comprises two rings of high and low frequency sensors respectively fixed to a flanged stainless steel pipe via threaded holders. In each of these two rings, half of the sensors are emitters and the other half receivers. The low frequency sensors are fixed to the pipe by leaving a gap of air between the mixed fluid and their active surface to satisfy the adaptation of impedance since the acoustic impedance of these sensors is much higher than the liquid acoustic impedance. These two arrays of sensors (10 sensors of 4 MHz and 8 sensors of 23 KHz resonance frequency each) are coupled to the flanged multiphase pipeline and placed along different cross sectional portions of the multiphase pipeline. Each of the high frequency sensors is allocated in a round robin cycle with
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periodically 100 ms time slot to transmit a short pulse ultrasonic wave of around 200 V peak to peak amplitude in a time-multiplexed manner using a bidirectional multiplexerdemultiplexer device. The other low frequency sensors are exited in a time multiplexer manner with a burst of 10 pulses of 20 V peak to peak amplitude. The echoes are then collected by the preamplifier and narrow pass band filter to enhance the signal to noise ratio. The filtered signal is then amplified further and converted into digital form and transmitted to the RISC processor of the transmitter to perform features extraction task such as delay, amplitude, and FFT measurement of the echo signal. These vectors are then transferred to a host computer (i.e. Main Processing Unit) via its serial link for further processing together with other features which are collected from other sensors of the MPFM (e.g. capacitance and conductance sensors). Following extensive experiments it was observed that all the above input vectors are not only function of the fluid density (ρ) but also of the volume flow rate (Q) as well ( as was addressed in Section 3). The same phenomenon was also observed for both the conductance and capacitance sensors.
Figure 3. Hardware schematic block of the ultrasound transducer
4 PATTERN RECOGNITION ALGORITHM Prior to the design of the pattern recognition algorithm, a pattern extraction and system calibration tasks were developed following several experiments. Individual flow rates corresponding to various flow regimes in the ranges [0 to 100 % water-cut], [0 to 100%
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Water-Cut [%]
gas fraction] and [200 to 600 liters/min] have been generated in the multiphase flow loop and the corresponding outputs of the sensors compiled into the database. Various pattern recognition algorithms relying on the physical model of the fluid have then been adopted (16)(17). Figure 4 shows the database which has been built for the ultrasound sensors. As was expected from Section 4, the water-cut decreases with the increasing delay of the ultrasound waves through the mixture. However, due to the effect of the flow velocity, it was observed that in some situations, the same water-cut values are provided for slightly different ultrasound outputs. This led us to use the venturi sensor which provides an indication of the volume flow rate. The other observation from Figure 4 is that the elements of the data base are not linear and thus not suitable to be treated by only a single Feed Forward Neural Network (FNN) which can only cope with smooth responses. In Figures 4 to 6, the ‘o’ symbol represents the elements of the database, whereas the ‘+’ symbol represents the output of the multilayer FNN. The first reason of this nonlinear response is because the multiphase flow is a non-linear function of some parameters (e.g. the temperature of the mixed fluid which might affect Equation [5] and the fluid velocity). The second reason is because of the uncertainties of the ultrasonic sensors and their inability to cope with some situations. For instance the ultrasound sensors have resolution errors since they do not cover the whole ring of the pipe. The usage of the 900 bent pipe elbows reduced to some extent this effect by making the flow more homogeneous but yet this was not enough. In Figure 6 the gas fraction decreases with the differential pressure. However, similarly to the ultrasound sensors, the responses of both sensors are nonlinear but clustered about a monotonic direction and are flow dependent.
Time Delay
Figure 4. Plot showing ultrasound delay versus water-cut[%].
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Water-Cut [%]
Capacitance
Gas Fraction [%]
Figure 5. Plot showing the capacitance response versus water-cut[%].
Figure 6. Plot showing diff pressure versus gas fraction.
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4.1 Software Architecture of the MPFM The pattern recognition algorithm is based on a multilayer neural network algorithm which relies on the fluid dynamics (Equations [2] to [5] in Section 3) to compensate for various possible errors due to flow regimes variations or sensor inaccuracy. The algorithm performs in two sequential steps: At first it determines the phase fraction using the data fusion of the capacitance, conductance, ultrasound, pressure, and venturi sensors. Next it computes the flow-rate of the mixture using the estimated water-cut, in addition to the differential pressure and venturi sensors. The dependence of the flow-rate function on the water-cut is due to the fact that the density is function of the differential pressure deducted by the square of the flow velocity (as indicated in Equation 5). All sensors are fed to spatial digital filters to eliminate any possible random noise mainly due to the vibration of the pipes of the flow loop as well as to the unsteady state of the flow regime. To each sensor X, the following first order low pass filter was applied: Xaverage =
X (t + t0 ) N +1 t0 = − N / 2 N/2
∑
[6]
Where N is window size of the spatial filter. 4.2 Determination of the Fluid Composition The algorithm for phase fraction composition uses a bank of several Feed-forward neural networks which are grouped according to the value provided by the venturi sensor (Figure 7). Each bank consists of a multilayer perceptron classification networks with one single hidden layer. One single hidden layer was selected since it demonstrated to be robust enough to solve the classification problem within a relatively low computation time. In addition, the backpropagation training algorithms are much more robust with single-layer networks (7)(18)(20)(21). The first layer is fed to the output of the five sensors (ultrasound, capacitance, conductance, venturi, and differential pressure sensors).
Figure 7. Multilayer feed forward neural network for flow composition determination.
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The final layer generates the mixture density (i.e. Oil, water, and gas fractions). The nodes in a hidden layer are connected to all nodes in adjacent layers. Each connection carries a weight, wi,j. Hence, the output of a node (j) in the hidden layer can be expressed as follows: 5
u j = g j (∑ wij × xi )
[7]
i =1
Where gj is the activation function which is usually selected as non linear to enable the network to model to some extent some nonlinearities present in the problem. Following extensive experiments, the Logsig function was found to be the most appropriate in our case. For a particular input vector, the output vector of the network is determined by feedforward calculation. We progress sequentially through the network layers, from inputs to outputs, calculating the activation of each node using Eq.[7], until we calculate the activation of the output nodes. 4.3 Flow Rate Determination Similarly to the phase fractions, another multilayer neural network was used to determine the flow rate (Figure 8). It uses equations [2] to [5] (Section 3). At first, the algorithm uses Equation [2] to provide an initial estimate of the mass flow rate, Ti using as input variable θ, where θ = ρ × (k − Vent ) (Section 3, where Vent is the output of the venturi probe). The density of the mixture, for the used water (density = 1000kg/m3), used oil (density = 810 kg/ m3), and used gas (density = 0.12 kg / m3) is then computed as follows:
ρ = [Wc + 0.81(1 − Wc − GasFration ) + (1.2 × 10 −3 × GasFraction)] × 1000[ kg / m 3 ] [8] Where Wc and GasFraction are the fractions of water and gas in oil-water-gas mixture respectively and are less than 1. The above density valued is fed together with the actual differential pressure, ΔPi, into another neural network module to generate the mass flow rate residual, ΔTi, to be added to Ti. The output of the multilayer neural network algorithm is then explored by an optimizing algorithm to provide a new trial value of either the flow rates or the phase fractions components in case the actual values are not acceptable.
Figure 8. Flow rate determination algorithm Note that the neural network blocks FFN-1-to-n in Figure 8 which are used for flow rate determination are different from those in Figure 7 which are used to determine the fluid composition. In addition, and similarly to Figure 7, one single hidden layer with logsig activation function was provided for each individual neural network block.
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5 EXPERIMENTAL RESULTS Extensive experiments have been conducted in the three phase flow loop under various flow regimes, water-cut, and flow-rate ranges. Figures 9 and 10 show the results of several experiments for the determination of the mixed density (water and gas fractions respectively). Note here that a relatively larger number of experiment samples have been obtained from the experiments related to Figure 9 than those related to Figure 10. It can be seen from Figure 9 that both the water cut and gas fraction provided by the proposed multistage neural network could track in real-time (i.e. in less than 2 msec. sampling rate) the reference in the ranges [20 to 100% water-cut and [0 to 40% gas] respectively. The overall relative errors are provided in the next section. Figure 10 shows another experience of gas fraction determination in the range [0 to 100%]. The error (relative error) was measured in terms of percentage uncertainty relative to the flow rates of each phase. i.e. oil, gas, and water flow rates. This is the type of error we are using. The error is measured as follows: Error (W )[%] =
Qa (W ) − Qr (W ) × 100[%] Qr (W )
[9]
Where Qa(W) is the volume flow rate of water measured by the proposed MPFM, whereas Qr(W) is the reference volume flow rate of water measured by the reference single phase flow meter. 120
(a)
100 80
watercut
60
Averagefraction
40 20 0 1
3937 7873 11809 15745
Time stamp
120
(b)
100 80
GasFraction
60
GasNeuron
40 20 0 1
3611 7221 10831 14441 18051
Time stamp
Figure 9. Experimental results for density measurement for water-cut (a) and gas fraction (b).
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Figure 10. Experimental results for gas fraction determination in the range [0-100%]. The table below illustrates the overall absolute error for different experiments. Overall, the total error (which is the average relative error through all the experiments) was less than 5 % in all phases. This is an improvement over other traditional multiphase flow meters where the relative error is more than 10% in most of the cases. Table 2. Relative error of the proposed MPFM
Experiments Exp1 Exp2 Exp3 Exp4 Exp5 Exp6 Exp7 Exp8 Exp9 Exp10 Exp11 Exp12 Exp13 AVERAGE ERROR
Water Error [%] 0.14232 -5.71278 2.650998 -3.30081 2.414424 -5.02759 -5.3882 -3.09879 -1.53111 1.106282 -9.99617 -3.79356 -6.82575 3.91
Gas Error [%] 1.886367 2.123769 -1.74408 12.62836 -7.25787 3.038446 1.213958 -5.08601 -5.54731 0.032527 4.032888 13.61162 2.802357 4.68
Oil Error [%] 5.014059 6.744952 6.134537 12.61129 12.6477 4.004712 4.126266 2.774898 3.04651 9.694701 -0.34843 7.536143 6.024279 6.20
6 CONCLUSION Ultrasound-based flow measurement technology for volumetric flow was introduced successfully. The proposed flow measurement may constitute a new class of flow metering technology that is well suited for a three phase flow behavior in oil, chemical
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and petrochemical applications. One of the advantages of this model is that the ultrasound probe uses ultrasound sonar array processing technology to overcome the limitations of traditional electrical sensors to operate in the full water-cut range. In addition, It can handle various types of flow regimes (e.g. the stratified flow regime), in addition to not having a moving part and thus does not require maintenance.
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