Multisensor Data Fusion Methods for Petroleum. Engineering Applications. A. Abdelgawad, Zaher Merhi, Mohamed Elgamel and Magdy Bayoumi. The Center ...
Multisensor Data Fusion Methods for Petroleum Engineering Applications A. Abdelgawad, Zaher Merhi, Mohamed Elgamel and Magdy Bayoumi The Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA {ama1916, zmm2571, mas8520 and mab}@cacs.louisiana.edu Abstract— Small amount of sand in oil pipelines can result in significant erosion in a very short time period. This Produced sand is a serious problem in many production situations. Installation of a system to monitor and quantify sand production from a well would be valuable to assist in optimizing well productivity and to detect sand as early as possible. We present a multi-sensor framework for sand detection. Wireless acoustic sensors are applied in networked data fusion systems for sand detection. The framework is designed to collect real time data from oil pipeline using acoustic sensors and flow analyzer. Fusion was implemented using two methods; Fuzzy Art (FA) and Moving Average Filter (MAF). A test bed was established from ten acoustic sensors. The flow rate was monitored as well in order to collect the data with the same flow rate. For each acoustic sensor the average percentage error between the observed sand rate and the actual sand rate is very high and inconsistence. However, using the fusion methods, the result shows that the average percentage error of the fusion methods is decreased. Keywords-component; Multisensor Data Fusion; Moving Average Filter;Fuzzy Art
I.
INTRODUCTION
Produced sand in oil pipelines is a major problem in many production situations since small amount of sand in the produced fluid can result in significant erosion. In high velocity oil wells erosion is a serious problem since it can erode holes in the pipe in a very short time period [1]. In order to prevent a high potential incident from occurring, several fields have installed a sand detection system. Installation of a system to monitor and quantify sand production from a well would be valuable to assist in optimizing well productivity and to detect sand as early as possible. Early detection would then lead to possible remedial action that could prevent incidents due to erosion and improve production [2]. By using an efficient monitoring device with a high degree of repeatability and sensitivity the producers are capable of not only avoiding erosion-corrosion or reservoir damages, but also increase the oil and gas production. However to be able to do this you need repeatability, sensitivity and also real time measurement. Oil and gas companies are analyzing and developing Wireless Sensor Network (WSN) technology to help them increase production, streamline operations and reduce
expenses. We produced some solution to integrate the legacy sensor in the oil field with WSN. One is for remote measuring of flow rate using a MC-II Flow Analyzer sensor [3]. Another attempt to develop a framework for fast prototyping wireless sensor data acquisition (WSDA) composed of both legacy and modern sensors [4]. This work introduces how to fuse the data coming from all sensors residing in a good fault tolerant and power efficient way. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. It will be more efficient than if they were achieved by means of a single source. The term efficient in that case can mean more reliable delivery of accurate information, more complete, and more dependable. To increase the associated confidence, two or more independent sensor provides the same piece of reliable information and this is called redundant fusion [5]. II.
ENSEMBLE APPROACH FOR FUSUION
This work introduces two methods of fusions, inference method and estimation methods. Inference method is often applied in decision fusion. The decision is taken based on the knowledge of the perceived situation. A fuzzy art algorithm is introduced in this paper as an inference method. The estimation method uses the laws of probability to compute a process state vector from a measurement vector or a sequence of measurement vectors. The Moving Average Filter (MAF) is introduced as an estimation method. A platform for experimentation i.e. a test bed was established in order to collect the data. Ten acoustic sensors collect the data from the same pipe. The sand is injected in the test bed with a constant flow. The output of the acoustic sensor is modeled to calculate the sand production rate. MICAz motes are used to send the data from the sensors to a host computer. The framework is used to estimate the sand production rate from the data coming from the sensors. In order to find a good estimator, we analyze the data by calculating the distribution of the error between the actual sand production rate and the observed one. We found that the error has a normal distribution. DaF module is implemented using two methods. The first one is based on the Moving Average Filter (MAF). The second one is implemented using a Fuzzy Art (FA) method.
III.
HOW TO MEASURE FLOW RATE
The flow rate was monitored in order to colleect the data with a known flow rate. We employed the NuFlo Measurement System Model MC-II Flow Analyzer [6].
A. MC-II Flow Analyzer Specifications The NuFlo Measurement System's Moddel MC-II Flow Analyzer receives an electronic pulse stream m from a turbine flow meter and provides a registration of the tootalized flow and an indication of flow rate by utilizing its microprocessor-based circuitry. The totalized flow and the flow rate are displayed on two six-digit liquid displays (LCD's). Booth displays are properly labeled with respective measuremennt units. The low current draw of its CMOS microprocessorr-based circuitry permits MC-II to run for three to five years onn a single battery. MC-II has the advantage of being batterry powered and enclosed in non-corrosive weatherproof housinng, deemed ideal for use in remote locations [7]. Fig. 1 shows the MC-II Flow Analyzer.
a
b
Figure 2. a. Intrusive device. b. Non-intrusive devices.
A. AS100 Acoustic Sensor The Senaco AS100 Sensor monitors high frequency acoustic emissions. Acoustic emissions traavel readily through solid materials such as metal, but are strongly attenuated when traveling through air. As such, the t Sensor is immune to airborne interferences and providess a non-invasive method of monitoring process activities [10]. Fig. 3 shows the AS100 acoustic sensor.
Figure 1. The MC-II Flow Analyzerr
B. Interface Circuit MDA300CA Data Crossbow Technology MICAz mote and M Acquisition Board were used to transmit collected data via a Wireless Sensor Network (WSN). The flow annalyzer generates a pulse signal whose frequency depends on thhe flow rate. The mote is used to count the number of pulses aand send it to the host computer. An amplifier lets the mote ddetect the voltage level differences and overcome signal weakneess. An amplifier circuit is designed to amplify the flow rate siignal from 1V to 2.5V before digitized. An LMC6484 CMOS S quad rail-to-rail input and output op-amp is used in the aamplifier circuit, providing a common-mode range that extendds to both supply rails [8]. IV.
CTION RATE HOW TO MEASURE SAND PRODUC
There are several techniques available in order to measure or detect sand production, and mainly we can divide them into two groups - intrusive devices and nonintrussive devices. The intrusive devices can be intrusive sensors where the gas intrudes when a hole has been eroded in the sensor and activates an alarm, a tuning fork principple working on acoustical principle, or it can be erodible resiistance probes as shown in Fig. 2a. The nonintrusive devices (clamp-on acoustic) When the flow is type monitors are installed after a bend. W passing the bend, particles will be forced out aand hit the inside of the pipe wall and generate an ultrasonicc pulse [9]. The ultrasonic signal is transmitted through thee pipe wall and picked up by the acoustic sensor itself as show wn in Fig. 2b.
Figure 3. Senaco AS S100 Sensor.
B. Interface Circuis A PIC18F8720 microcontroller iss used to determine the sand production rate using a linear regression model. The microcontroller is used also as an interface between the acoustic sensor and the MICAz mote. One of the Mote’s analog channels is used to send the sand prroduction rate values to the host computer. V.
FUSION METHODS
A. Fuzzy Art Fuzzy art is an unsupervised neu ural network where training is done online. Fuzzy art classifies data according to their t to groups. Fusion is degree of similarity and assigns them performed by assigning probabilistiic weight to each incoming data according to spatial correlaation and consensus vote. Erroneous data are detected and eliminated according to a decision tree [11]. The probabilisttic weight assignment is as follows: •
If a group or more contains more than 2 measurements and another group contains only one measurement, the neous and is eliminated by former is detected as erron assigning it a zero weight and a the latter goes through probabilistic weight computtations discussed below.
•
•
If a group contains only two measurements and another contains only one measurement, then we cannot rule out the possibility the group containing one measurement is erroneous. Fixed probability is then assigned by given the former a probabilistic weight of 0.25 and the latter 0.75 where the weight is divided equally across every measurement per group.
1
Similarly, at the previous time instant, k-1, the average of the latest n samples is: 1
If all measurements belong to the same group them a fixed probabilistic weight of 1 will be assigned to the group, where the weight is also divided equally across every measurement per group.
For the first case the probabilistic weight assignment is performed according to the following methodology: The higher number of measurements belonging to the same category the higher the weight should be (eq. 1). Moreover, lower standard deviation between the measurements belonging to the same group should be given also a higher probability (eq). _
_
(1)
_ _
_
_
Therefore, 1 1
_
1
_ _
This on rearrangement gives:
(3)
n k-n-1 k-n
k-2
k-1
k-n+1
, _
8
This is known as a moving average because the average at each kth instant is based on the most recent set of n values. At any time, a moving window of n values is used to calculate the average of the data sequence as shown in Fig. 5.
The final weight given to each category is the normalization of equation (3) given by (4) where n_comm is the number of committed categories: ∑
7
(2)
Where n_meas_cat is the number of measurements in a category and tot_n_input is the total number of inputs, and STD_cat is the standard deviation of the measurements in the same category and tot_STD is the total standard deviation of all categories. Furthermore since Pa and Pm are independent their joint probability is given by the following: ,
6
1
_
1
5
,
(4)
Fuzzy Art is fairly less complex than other neural network algorithm as it only require simple ADD, OR, DIVIDE and COMPARE operation, while others require complex operations like exponential and differentiation operation. The order of time complexity is O (n) where n is the number of sensors. B. Moving Average Filter (MAF) The Moving Average Filter (MAF) is broadly implemented in fusion. It is optimal for reducing random white noise at the same time as retaining a sharp step response [12]. This filter computes the arithmetic mean of a number of input measurements to produce each point of the output signal. A slight improvement in computational efficiency can be achieved if we perform the calculation of the mean in a recursive fashion. A recursive solution is one which depends on a previously calculated value. To illustrate this, consider the following development: Suppose that at any instant k, the average of the latest n samples of a data sequence, Xi, is given by:
time k
n n Figure 4. Moving window of n data.
VI.
TESTBED PLATFORM
A test bed was established using ten acoustic sensors to collect the data from the same pipe. The sand is injected in the test bed with a constant flow. A PIC18F8720 microcontroller is used as an interface between the acoustic sensor and the MICAz mote. It is also used to calculate the sand production rate using a linear regression model. The Mote’s analog channel is used to send the sand production rate to the host computer. In the host computer, a centralized fusion algorithm is used to calculate the sand production rate in the pipeline. VII. EXPERIMENTAL RESULTS Data fusion methods are simulated using the real data from ten acoustic sensors under a fixed flow rate (28 G/M (Gallon/Minute)). The average percentage error between the observed sand rate for each acoustic sensor and the actual sand rate is very high and inconsistence as shown in Table I.
TABLE I.
AVERAGE PERCENTAGE ERROR FOR 10 ACCOUSTIC SENSORS
Sensor # Avg. % Error Sensor # Avg. % Error
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Sensor 5
11%
23%
16%
16%
19%
The simulation result shows that the FA method is more accurate than MAF method. However the atomic operation of MAF method is accumulation operation only but the FA needs ADD, OR, DIVIDE and COMPARE operation.
Sensor 6
Sensor 7
Sensor 8
Sensor 9
Sensor10
VIII. CONCLUSION
28%
19%
22%
13%
14%
We present a multi-sensor data fusion framework for sand detection. A test bed was established from ten acoustic sensors. The flow rate was monitored as well in order to collect the data with a known flow rate. For each acoustic sensor the average percentage error between the observed sand production rate and the actual sand rate is very high and inconsistence. We implement two different methods of fusion, in order to increase the associated confidence. The result shows that the average percentage error of the fusion methods is decreased. The results show that the FA method is more accurate than MAF method. However the MAF method is less complex than the FA to implement, as it needs accumulation operation only but the FA needs ADD, OR, DIVIDE and COMPARE operations.
In order to increase the associated confidence, we implement two methods of fusion for the ten acoustic sensors: Fuzzy Art (FA) and Moving Average Filter (MAF). The results show that the average percentage error is decreased by using the fusion methods. The average percentage error between the observed sand production rate and the actual sand production rate for the fusion methods was tested for different flow rate as well. Fig. 5 and Fig.6 show the average percentage error and the standard deviation for both methods at different flow rate. 4.35
ACKNOWLEDGMENT The authors acknowledge the support of this work by Dr. Tzeng, Nian-Feng, Dr. Madani, Mohammed. This material is based upon work supported by the U.S. Department of Energy (DoE) award DE-FG02-04ER46136, the Louisiana Board of Regents contract DOE/LEQSF(2004-07)-ULL, the Governor's Information Technology Initiative and the National Science Foundation under Award No. OISE-0707235.
4.3 4.25 4.2
%
4.15 4.1 4.05 4
REFERENCES
3.95 3.9
[1] 28 G/M
30 G/M
32 G/M
34 G/M
MAF Avg. % Error
4.09
FA Avg. % Error
4.06
4.14
4.2
4.31
4.11
4.16
4.27
Figure 5. The Average percentage error for both methods at different flow rate. 3.12 3.1 3.08 3.06 3.04 3.02 3 2.98
28 G/M
30 G/M
32 G/M
34 G/M
MAF Stdev
3.0615
3.0808
3.0773
3.0564
FA Stdev
3.0278
3.0758
3.1004
3.0701
Figure 6. Standard deviation for both methods at different flow rate.
I.A. Allahar. “Acoustic Signal Analysis for Sand Detection in Wells with Changing Fluid Profiles”. SPE 81002, 2003. [2] Asmund Huser and Oddmund Kvernvold “Prediction of Sand Erosion in Process and Pipe Components ”. 1st North American Conference on Multiphase Technology. Banff, Canada 1998. [3] A. Abdelgawad, Adam Lewis, Mohamed Elgamel, Fadi Esa, N.-F. Tzeng, and M. Bayoumi, "Remote Measuring of Flow Meters for Petroleum Engineering and Other Industrial Applications" International Workshop on Computer Architectures for Machine Perception and Sensing (CAMP2006), Montreal, Canada, 18-21 September 2006. [4] Adam Lewis, A. Abdelgawad, N.-F. Tzeng, and M. Bayoumi, “Wireless Sensor Data Acquisition Design for Engineering Applications" International Conference on Circuits & Systems for Communications ICCSC 2008 Shinghai, China, 26-28 May 2008. [5] F. Nakamura, A. F. Loureiro, and C. Frery “Information Fusion for Wireless Sensor Networks: Methods, Models, and Classifications “ACM Computing Surveys, Vol. 39, No. 3, Article 9, August 2007. [6] NuFlo Measurement Systems. URL – http://www.c-a-m.com/ content/products. [7] NuFlo Technologies, Inc., NuFlo MC-II flow analyzer user manual. NuFlo Technologies, Inc., 2005. [8] http://www.national.com/ds/LM/LMC6484.pdf. [9] http://www.clampon.no/doc/7.PDF. [10] http://www.lesman.com/unleashd/catalog/belt/0460-en-00.pdf. [11] Zaher Merhi, Mohamed Elgamel, Magdy Bayoumi,” Fully decentralized kalman filter for wireless sensor networks with FuzzyART neural networks”, in Procceding of the 12th IEEE Symposium on Computer and Communications (ISCC 2007), Portugal, July 2007. [12] Smith, S. W. “The Scientist and Engineer’s Guide to Digital Signal Processing”, 2nd ed. California Technical Publishing, San Diego, CA, 1999.
