2009 International Nuclear Atlantic Conference - INAC 2009 Rio de Janeiro,RJ, Brazil, September27 to October 2, 2009 ASSOCIAÇÃO BRASILEIRA DE ENERGIA NUCLEAR - ABEN ISBN: 978-85-99141-03-8 FLOW REGIME IDENTIFICATION METHODOLOGY WITH MCNP-X CODE AND ARTIFICIAL NEURAL NETWORK César M. Salgado1,2, Roberto Schirru1, Luis E. B. Brandão2, Cláudio M. N. A. Pereira2 1 Nuclear Engineering Department (PEN-DNC), COPPE-POLI Universidade Federal do Rio de Janeiro 21941-972 Rio de Janeiro, RJ [email protected] 2 Instituto de Engenharia Nuclear, DIRA/IEN/CNEN Caixa Postal 68550 21945-970 Rio de Janeiro, RJ [email protected]; [email protected]; [email protected] ABSTRACT This paper presents flow regimes identification methodology in multiphase system in annular, stratified and homogeneous oil-water-gas regimes. The principle is based on recognition of the pulse height distributions (PHD) from gamma-ray with supervised artificial neural network (ANN) systems. The detection geometry simulation comprises of two NaI(Tl) detectors and a dual-energy gamma-ray source. The measurement of scattered radiation enables the dual modality densitometry (DMD) measurement principle to be explored. Its basic principle is to combine the measurement of scattered and transmitted radiation in order to acquire information about the different flow regimes. The PHDs obtained by the detectors were used as input to ANN. The data sets required for training and testing the ANN were generated by the MCNP-X code from static and ideal theoretical models of multiphase systems. The ANN correctly identified the three different flow regimes for all data set evaluated. The results presented show that PHDs examined by ANN may be applied in the successfully flow regime identification. 1. INTRODUCTION Due to the exploration of oil wells in the sea, the measures of multiphase flows in offshore oil industry are becoming increasingly important. Techniques of gamma-ray attenuation [1,2,3,4,5] are often used in the petroleum industry because of its robustness and noninvasive nature (character), and can perform these measurements without changing the operational conditions in real time and allowing monitoring all the process. The meters are conventional gamma-ray from a source with a detector installed diametrically opposite to the source; the intention is to measure the attenuation of the beam that is influenced1 by changes in the composition of the flow. These meters are highly dependent on the flow regime. Installing other detectors around the tube, it significantly reduces the dependence on particular types of regime. The information flows in the liquid-gas system are usually obtained by subjective interpretation from visual observations (graphic illustrations) which may lead to misinterpretations [6,7]. Therefore, a noninvasive system that identifies the flow regime without subjective evaluation is very important. To this end, artificial neural network (ANN) 1 The attenuation of the gamma-ray beam depends on the flow composition, the photon energy, the tube diameter and the material and thickness of the tube wall; however, the photon energy and the diameter of the tube are constant. has been used  to interpret the PHDs obtained by detectors of gamma-ray radiation [9,10,11]. The ANNs are mathematical models inspired by the human brain that have the ability to extract knowledge from a finite set of information (patterns), and generalize2 it. ANNs have been applied successfully in several classes of problems such as pattern recognition and classification of images, voice and signals, and identification of behavior and trends. The data set (volume fraction and flow regime different) required for training and test the ANN was obtained by static and ideal mathematical models of annular, stratified and homogeneous regimes. These models were developed in the MCNP-X code  that is a simulation tool widely used in radiation transport. These models consider the main effects of the interaction of radiation with the materials involved and the PHDs obtained by NaI(Tl) detectors. Energy resolution, dimension and characteristics of a real detector are also considered . The PHDs are used directly to feed an ANN without any parameterization of the signal, which allows the use of a simplified detection scheme consisting of two NaI(Tl) detectors to record the gamma-ray radiation. Thus, this work provides an intelligent system that identifies the flow regime in multiphase flow (gas, water and oil) based on recognition of PHDs by ANN. 2. METHOD APPLICATION AND RESULTS 2.1. Geometry proposal The geometry simulation combines transmission (IT) and scattered (Is) radiation from gamma-ray source (dual-mode densitometry) with a fan-beam; thus, it is possible to acquire sufficient information about the flow regime and also to increase the measurement area on the cross-section of the pipe [4,14] and make the material volume fraction (MVF) prediction less dependent on the flow regime. In all simulations, a fan-beam geometry simulation has been used for the source, as well as two NaI(Tl) detectors. One of them is located aligned to the source (180°), and the other at 45°. Fig. 1 shows the measurement system. One collimated (angle beam 8.84°) gamma-ray point source (59.45 keV: 241Am and 662 keV: 137Cs) has been simulated in the MCNP-X code. In our studies, salt water was used (4% of NaCl to simulate the seawater) . The gaseous phase was substituted by air and petrol and assumed to be a hydrocarbon (molecular formula C5H10) with a 0.896 g.cm-3 density . The models for the different flow regimes are shown in Fig. 1. The mathematical model considered the NaI (Tl) scintillator detector as a homogeneous cylinder [13,16,17,18,19,20] with 31 mm (diameter) x 19 mm (thickness). The information (dimensions and materials) of a real NaI(Tl) detector was considered in the mathematical model for calculating the MCNP-X code. A special treatment is provided in the MCNP-X code: the Gaussian energy broadening3 (GEB) (card FTn) option has been used to better fit 2 3 They have the ability to appropriately respond to situations not included in the training data. The energy peaks behave like a Gaussian function. INAC 2009, Rio de Janeiro, RJ, Brazil. the full energy peak shape of PHD; GEB parameters have been set taking into account4 the resolution of the detector by means of the full width at half maximum (FWHM) provided by radioactive sources , for the parameters must be inserted in the input file (INP) of the detector’s mathematical model. b) Annular ro a) 250 mm αο ra αa α g rg c) Stra tified 5 mm 8.84º 5 mm hg α g Source ho α ο R Energy: 59.45 keV 662.00 keV Legend: Detector 1 ha αa d) Homogeneous 5 mm Detector 2 Al NaI(Tl) MgO Oil Water Gas PVC Oil+water+gas Figure 1. (a) Simulated system; Regime models; (b) annular; (c) stratified; (d) homogeneous. 2.2. Generation of simulated data for training and test the ANN. The first step in this investigation was the mathematical simulations by means of MCNP-X code in order to generate the training set for the ANN. Data for testing the ANN have also been generated to test the neural network in the working phase5. The models for the different flow regimes (annular, stratified and homogeneous regimes) had been used to get a data set in order to feed the ANN. The thickness values (rg, rw and ro - annular model, see Fig.1(b) and hg, hw e ho - stratified model see Fig.1(c)) of each material varied in the MCNP-X code, getting diverse combinations of MVF; while for the homogeneous regime, mass fraction of each one of the materials varied. For each one of these combinations, which has varied from 0% to 100%, the relative counts from transmitted and scattered beams were calculated. It is 4 This step aims to validate the response of the detector quality through energy resolution while the order quantity will be achieved by normalization of the full energy peak PHD obtained by MCNP-X. 5 The working phase (or the use of the ANN) in which the trained ANN is used to respond to new (real-world) situations. This is an on-line phase and the ANN does not need the training set any more. INAC 2009, Rio de Janeiro, RJ, Brazil. important to emphasize that the MCNP-X code considers the material in a region (cell) defined in the INP as uniform. The combinations of volume fractions which compose each of data set were uniformly distributed throughout the ternary6. Thus, a set of 354 (118x3) simulations for different combinations of MVFs and flow regimes (annular, stratified and homogeneous) were made in order to generate the training set of the ANN (195, (65x3) simulations), the test (84, (28x3) simulations) and the production (75, (25x3) simulations). The test set was used to test the neural network in training phase7 (for stopping criteria: cross validation, see Haykin, 1994) and to avoid overtraining. The production set is used for a final test after ANN’s training in order to test the ANN in the working phase. 2.3. Training of ANN The methodology consists of using the PHDs to train the ANN to automatically identify the flow regime of this system. The PHDs have a behaviors set that gathers the characteristics of flow regime. From there, the system will enable other ANNs trained specifically for a particular flow regime in order to predict more precisely the volume fraction . A schematic representation used for the training of ANN is shown in Fig. 2. PHD 1 1 2 3 S1 1 0 0 S2 0 1 0 S3 0 0 1 6 10 12000 PHD 1 PHD 2 10000 C20 C30 Count s 8000 C720 . . . 6000 5 10 4000 C20 C30 2000 0 C360 . . . 4 10 0 100 200 300 400 Energy (keV) 500 600 ANN 700 PHD 2 Annular Str atified Homogeneous Figure 2. Schematic representation used for training of the ANN. In this work, a 3-layer feed-forward multilayer perceptron (MLP)  has been used. The learning/training algorithm was the well-known (supervised) back-propagation algorithm [8,22,23,24]. 6 A ternary graphically depicts the ratios of the three variables as positions in an equilateral triangle. It is used in petrology and other physical sciences to show the proportions of systems composed of three species. In a ternary plot, the proportions of the three variables gas, water and oil must sum to some constant which is represented as 100%. 7 The training phase, in which, by using a learning (training) algorithm, the ANN is supposed to learn features (behavior, patterns, etc) from a previously provided finite set of examples (the training set). This is often an offline phase and training set may be carefully generated. INAC 2009, Rio de Janeiro, RJ, Brazil. The ANN training patterns are composed of the following data: - ANN inputs8 (106 neurons): PHD1: 20 to 720 keV, with steps of 10 keV (C20, C30, ..., C720 – counts from channel 2 to 71); PHD2: 20 to 360 keV, with steps of 10 keV (C20, C30, ..., C350 – counts from channel 2 to 35). - ANN outputs (3 neurons): S3, S2 and S1. The output data were classified considering three neurons in ANN output (S3, S2 and S1), so that to obtain the identification of systems it is only necessary the neural network to set the highest value among the network outputs to "1" corresponding to dominant flow regime and to "0" to other. To illustrate, suppose that the flow regime is annular, then the network should adjust the output for S3=0, S2=0 and S1=1. 2.4. Identification of regimes for ANN from PHDs To evaluate the differences between the flow regimes, for illustration, some simulated measurement of transmitted and scattered beam to the volume fraction of 30% air, 20% water and 50% oil to annular, stratified and homogeneous regimes that supplied the PHDs are presented in Fig. 3. It was possible from the differences (behavior) found between the PHDs for each one of the flow regime to train a ANN that resulted in 100% accuracy, correctly identifying all the regimes submitted for a total of 354 tests. The production set with 75 patterns was used in order to test successfully the ANN in working phase. For the interpretation of results, it was identified correctly the flow regime related to ANN’s output for response that produced the largest absolute value among the other outputs (S3, S2 and S1). 4 8.0x10 Counts (PHD1) Annular Stratified Homogeneous PHD1 4 6.0x10 5 10 4 4.0x10 PHD2 4 Counts (PHD2) 6 10 4 2.0x10 10 0 200 400 600 800 Figure 3. PHDs obtained by MCNP-X code for different flow regimes. 8 The energy range choice of each PHD used in the training took into account the value of relative error (R) below 10% in the counts, percentage acceptable according to the MCNP-X manual. INAC 2009, Rio de Janeiro, RJ, Brazil. 3. CONCLUSIONS It was possible to correctly identify, without errors, all the flow regimes from the proposed geometry simulation and with the interpretation of PHDs by ANN obtained by only two NaI(Tl) detectors. In order to provide an ideal input to the training process of ANN, a data set was simulated by MCNP-X code for annular, stratified and homogeneous models that proved to be reliable for training and for the working phase of ANN. The use of simulated data obtained by gamma-ray attenuation technique with supervised neural networks adequately trained as a reliable tool can improve the accuracy of volume fraction measures from the correct identification of the flow regime. The results are encouraging and indicate that the methodology can be used in determining the volume fraction independently of the knowledge, a priori, of the flow regime. Acknowledgments The authors would like to thank Dr. Ademir Xavier da Silva for allowing the use of the MCNP-X code of the Universidade Federal do Rio de Janeiro (UFRJ), and Claudio M. N. A. Pereira and Roberto Schirru, supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ). REFERENCES 1. M. S. A. Abouelwafa and E. J. M. Kendall, “The measurement of component ratios in multiphase systems using gamma-ray attenuation”, Journal of Physics E: Scientific Instruments. 13, pp. 341-345 (1980). 2. E. Åbro, G. A. Johansen and H. Opedal, “A radiation transport model as a design tool for gamma densitometers”, Nuclear Instruments and Methods in Physics Research, A431, pp. 347-355 (1998). 3. E. Åbro, V. A. Khoryakov, G. A. Johansen and L. Kocbach, “Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry”, Meas. Sci. Technol, 10, pp. 619-630 (1999). 4. S. A. Tjugum, G. A Johansen and M. B. Holstad. “The use of gamma radiation in fluid flow measurements”. Radiation Physics and Chemistry, 61, pp. 797-798 (2001). 5. S. A. Tjugum, J. Frieling and G. A Johansen, “A compact low energy multibeam gammaray densitometer for pipe-flow measurements”, Nuclear Instruments and Methods in Physics Research, B 197, pp. 301-309 (2002). 6. H. WU, F. ZHOU and Y. WU, “Intelligent identification system of flow regime of oil-gaswater multiphase flow”. International Journal of Multiphase Flow, 27, pp. 459–475 (2001). 7. N. D. Jin, X. B. Nie, Y.Y. Ren and X. B. Liu, “Characterization of oil/water two-phase flow patterns based on nonlinear time series analysis”, Flow Measurement and Instrumentation, 14, pp. 169–175 (2003). 8. S. Haykin, “Neural Networks – A Comprehensive Foundation”, Macmillian College Publishing Company (1994). INAC 2009, Rio de Janeiro, RJ, Brazil. 9. C. M. Bishop and G. D. James, “Analysis of multiphase flows using dual-energy gamma densitometry and neural networks”, Nuclear Instruments and Methods, A 327 580 (1992). 10. Y. Mi, L. H. Tsoukalas and M. Ishii., “Application of multiple self-organizing neural networks: flow pattern classification”, Trans. Am. Nucl. Soc. 77, pp. 114-116 (1997). 11. Y. Mi., M. Ishii and L. H. Tsoukalas, “Vertical two-phase flow identification using advanced instrumentation and neural networks”, Nuclear Engineering and Design, 184, pp. 409-420 (1998). 12. D. B. Pelowitz, MCNP-X TM User’s Manual, Version 2.5.0. LA-CP-05-0369. Los Alamos National Laboratory (2005). 13. C. M. Salgado, L. E. B. Brandão, R. Schirru, C. M. N. A. Pereira, R. Ramos and A. X. Silva, “Modelagem de detector NaI(Tl) usando MCNP-X”, XI Encontro de Modelagem Computacional, n 0189, Volta Redonda, RJ (2008). 14. G. A. Johansen and P. Jackson, “Salinity independent measurement of gas volume fraction in oil/gas/water pipe flows”, Applied Radiation and Isotopes, 53, pp. 595-601 (2000). 15. E. M. A. Hussein and P. Han, “Phase volume-fraction measurement in oil-water-gas flow using fast neutrons”, Nuclear Geophysics, Vol. 9, n 3, pp. 229-234 (1995). 16. M. J. Berger and S. M. Seltzer, “Response functions for sodium iodide scintillation detectors”. Nucl. Instrum. Methods, 104, pp. 317-332 (1972). 17. I. Orion and L. Wilopolski, “Limitations in the PHOTON Monte Carlo gamma transport code”, Nuclear Instruments and Methods in Physics Research. A 480, pp. 729-733 (2002). 18. K. Saito and S. Moriuchi, “Monte Carlo Calculation of accurate response functions for a NaI(Tl) detector for gamma rays”, Nuclear Instruments and Methods, 185, pp. 299-308 (1981). 19. H. X. Shi, B. X. Chen, T. Z. Li and D. I Yun., “Precise Monte Carlo simulation of gamma-ray response functions for a NaI(Tl) detector”, Applied Radiation and Isotopes, 57, pp. 517-524 (2002). 20. A. Sood and R. P. Gardner, “A new Monte Carlo assisted approach to detector response functions”, Nuclear Instruments Methods Physics. Res. B213, pp. 100-104 (2004). 21. C. M. Salgado, L. E. B. Brandão, C. M. N. A. Pereira, R. Ramos, A. X. Silva and R. Schirru, “Prediction of volume fractions in three-phase flows using nuclear technique and artificial neural network”, Applied Radiation and Isotopes, doi:10.1016/j.apradiso.2009.02.093 – Reference ARI4470 (2009). 22. Y. Chauvin and D. E. Rumelhart, Backpropagation Theory, Architectures and Applications (1995). 23. D. E. Rumelhart and J. L. Mcclelland, Parallel Distributed Processing, Vol.1, MIT Press, Cambridge, MA (1986). 24. D. E. Rumelhart, G. E. Hinton and R. J. Williams. “Learning internal representations by error propagation”. In: D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, Vol.1. MIT Press, Cambridge, MA, (1986). INAC 2009, Rio de Janeiro, RJ, Brazil.