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Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study

by Mohammad Ebadi, Mohammad Ali Ahmadi, Shahab Gerami, Reza Askarinezhad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 8
Year of Publication: 2012
Authors: Mohammad Ebadi, Mohammad Ali Ahmadi, Shahab Gerami, Reza Askarinezhad
10.5120/4840-7103

Mohammad Ebadi, Mohammad Ali Ahmadi, Shahab Gerami, Reza Askarinezhad . Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study. International Journal of Computer Applications. 39, 8 ( February 2012), 23-28. DOI=10.5120/4840-7103

@article{ 10.5120/4840-7103,
author = { Mohammad Ebadi, Mohammad Ali Ahmadi, Shahab Gerami, Reza Askarinezhad },
title = { Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 8 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 23-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number8/4840-7103/ },
doi = { 10.5120/4840-7103 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:55.286935+05:30
%A Mohammad Ebadi
%A Mohammad Ali Ahmadi
%A Shahab Gerami
%A Reza Askarinezhad
%T Application Fuzzy Decision Tree Analysis for Prediction Condensate Gas Ratio: Case Study
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 8
%P 23-28
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Condensate-to-gas ratio (CGR) plays a significant role in sales potential assessment of both gas and liquid, design of the required surface processing facilities, and reservoir characterization and modeling in gas-condensate reservoirs. Precise field and laboratory determination of the CGR is time and people intensive. Developing a rapid and inexpensive technique for accurate estimation of the CGR is inevitable. To tackle this issue, an intelligent model was proposed and the purpose of this paper is to illustrate how Fuzzy Decision Tree (FDT), which is an automatic method of generating fuzzy rules, can predict the condensate gas ratio, as a vital parameter in order to design the necessary wellhead production facilities in retrograde gas reservoir. As the FDT method is strongly based on applying widely and effectively the concept of ambiguity and furthermore, to do this project more accurately and less dependent on experts' knowledge, it was decided to gain from piecewise linear membership functions (MFs) whose parameters have automatically been dedicated through calculating a very special method of possibility density function (pdf). In conclusion, according to the calculated result, it is possible to exploit this method for CGR prediction field wide. The Fuzzy model was evaluated using the experimental data combined with some PVT data from the open literature. The model predictions were compared with the data from the actual field data and experimental runs. Results from this study indicate that the developed model can predict the CGR with good precision. The proposed model can serve as a reliable tool for quick and cheap but effective assessment of the CGR in absence of any adequate laboratory or field data.

References
  1. Zhang H. R., Wheaton R. J., “Condensate Banking Dynamics in Gas Condensate Fields: Changes in Produced Condensate to Gas Ratios”, International Oil and Gas Conference and Exhibition, China, Beijing, 7-10 November 2000.
  2. Orangi A., Nagarajan N. R., Honarpour M. M., Rosenzweig j., “Unconventional Shale Oil and Gas-Condensate Reservoir Production, Impact of Rock, Fluid, and Hydraulic Fractures”, SPE Hydraulic Fracturing Technology Conference, USA, Texas, 24-26 January 2011.
  3. Fan Li, Harris Billy W., Jamaluddin A. J., Kamath Jairam, Mott Robert, Pope Gary A., Shandygin Alexander, Whitson Curtis Hays, “Understanding Gas-Condensate Reservoirs”, Oilfield Review, 2005, 17(4), 14-27.
  4. Savary D., “Gas Condensate Reservoir Evaluation Using an Equation of State PVT Package”, Middle East Oil Show, Bahrain, 16-19 November 1991.
  5. Eissa M., Shokri El-M., “Dewpoint Pressure Model for Gas Condensate Reservoirs Based on Genetic Programming”, CIPC/SPE Gas Technology Symposium 2008 Joint Conference, Calgary, Alberta, Canada, 16-19 June 2008.
  6. Henderson, G.D., Danesh, A., Tehrani, D. H., Al-Shaidi, S., Peden, J.M., “Measurement and Correlation of Gas Condensate Relative Permeability by the Steady-State Method”, SPE Reservoir Evaluation & Engineering, 1998, 1(2), 134-140.
  7. Thomas F. B., Andersen G., Bennion D. B., “Gas Condensate Reservoir Performance”, Journal of Canadian Petroleum Technology, July 2009, 48(7), 18-24.
  8. Luo Kai, Li Shi, Zheng Xitan, Chen Gang, Liu Ning, Sun Wenyue, “Experimental Investigation Into Revaporization of Retrograde Condensate”, SPE Production and Operations Symposium, Oklahoma, Oklahoma City, March 2001.
  9. Ovalle Adriana P., Swaco M-I, Lenn Chris P., McCain Jr. William D., “Tools To Manage Gas/Condensate Reservoirs; Novel Fluid-Property Correlations on the Basis of Commonly Available Field Data”, SPE Reservoir Evaluation & Engineering, 2007, 10(6), 687-694
  10. Alavi F.S., Mowla D., Esmaeilzadeh F., Journal of Canadian Petroleum Technology, 2010, 75(1-2), 44-53.
  11. Afidick, Deddy, Kaczorowski, N.J., “Production Performance of a Retrograde Gas Reservoir: A Case Study of the Arun Field”, SPE Asia Pacific Oil and Gas Conference, Melbourne, Australia, 7-10 November 1994.
  12. Sadeghi Boogar A., Gerami S., Masihi M., “Investigation into the capability of a modern decline curve analysis for gas condensate reservoirs”, Scientia Iranica, 2011, 18 (3), 491–501.
  13. Elsharkawy Adel M., “Predicting the dew point pressure for gas condensate reservoirs: empirical models and equations of state”, Fluid Phase Equilibria, 2002, 193(1-2) ,147–165
  14. England W.A., “Empirical correlations to predict gas/gas condensate phase behaviour in sedimentary basins”, 2002, 33(6), 665-673
  15. Nowroozi Saeed, Ranjbar Mohammad, Hashemipour Hasan, Schaffie Mahin, “Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoir”, 2009, 90(3), 452-457
  16. Ovalle Adriana P., Swaco M-I, Lenn Chris P., McCain Jr. William D., “Tools To Manage Gas/Condensate Reservoirs; Novel Fluid-Property Correlations on the Basis of Commonly Available Field Data”, SPE Reservoir Evaluation & Engineering, 2007, 10(6), 687-694
  17. M.Eissa, Shokir El-M., “Dew point Pressure Model for Gas Condensate Reservoirs Based on Genetic Programming”, Energy Fuels, 2008, 22(5), 3194–3200.
  18. Jamaluddin A.K.M., Dong C., Hermans P., Khan I.A., Carnegie A., Mullins O.C., Kurkjian A., Fujisawa G., Nighswander J., Babajan S., “Real–Time and On–Site Reservoir Fluid Characterisation using Spectral Analysis and PVT Express”, APEA Journal, 44, 2004, 605-616.
  19. Humoud A.A., Al-Marhoun M.A., “A New Correlation for Gas-condensate Dewpoint Pressure Prediction”, SPE Middle East Oil Show, Bahrain, 17-20 March 2001.
  20. Ahmed Moataz A., Muzaffar Zeeshan, "Handeling Imprecision and Uncertainty in Software Development Effort Prediction: A Type-2 Fuzzy Logic Based Framework", Information and Software Technology, 2009, 51(3), 640-654.
  21. Zadeh L. A., "Fuzzy Sets" Information and Control, 1965, 8(3), 338-353.
  22. Ghafoori M. R., Roostaeian M., Sajjadian V. A., "A State-of-the-Art Permeability Modeling Using Fuzzy Logic in a Heterogeneous Carbonate (An Iranian Carbonate Reservoir Case Study)", International Petroleum Technology Conference, Kuala Lumpur, Malaysia, 3-5 December 2008.
  23. Hameed Ibrahim A., "Using Gaussian Membership Functions for Improving the Reliability and Robustness of Students' Evaluation Systems", Expert Systems with Applications, 38(6), 7135-7142.
  24. Beynon Malcolm J., Peel Michael J., Tang Yu-Cheng, "The Application of Fuzzy Decision Tree Analysis in an Exposition of the Antecedent of Audit Fees", Omega, 2004, 32(3) 231–244.
  25. Zadeh L. A., "Fuzzy Sets as a Basis for a Theory of Possibility", Fuzzy Sets and Systems, 1978, 1(1), 3–28.
  26. Parzen E., "On Estimation of a Probability Density Function Mode", Annals of Mathematical Statistics, 1962, 33(3), 1065–1076.
  27. Thompson JR, Tapia RA. Nonparametric function estimation, modeling, and simulation. Philadelphia: Society for Industrial and Applied Mathematics; 1990.
  28. Duda RO, Hart PE. Pattern classification and scene analysis. New York: Wiley; 1973.
  29. Kaleem Awan Malik Shahrzad, Awais Mina Muhammad, "Predicting Weather Events Using Fuzzy Rule Based System", Applied Soft Computing, 2011, 11(1), 56-63.
  30. Angelov P. P, Buswell R. A, "Automatic Generation of Fuzzy Rule-based Models from Data by Genetic Algorithms", Information Sciences, 2003, 150(1-2), 17-31.
  31. Wang Lian, Yen John, "Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and Kalman filter", Fuzzy Sets and Systems, 1999, 101(3), 353-362.
  32. Park Hui-June, Lim Jong-Se, Roh Jeongyong, Kang Joo M., Min Bae-Hyun, "Prediction-System Optimization of Gas Field Using Hybrid Fuzzy/Genetic Approach", SPE Journal, 2010, 15(2), 417-425.
  33. Liu Xiaodong, Pedrycz Witold, "The Development of Fuzzy Decision Tree in the Framework of Axiomatic Fuzzy Set Logic", Applied Soft Computing, 2007, 7(1), 325-342.
  34. Wang Xi-Zhao, Zhai Jun-Hai, Lu Shu-Xia, "Induction of Multiple Fuzzy Decision Trees Based on Rough Set Technique", Information Sciences, 2008, 178(16), 3188-3202.
  35. Higashi M, Klir GJ, "Measures of Uncertainty and Information Based on Possibility Distributions", International Journal of General Systems, 9(1), 43-58.
  36. Kosko Bart, "Fuzzy Entropy and Conditioning", Information Science, 1986, 40(2), 165-174.
  37. Yuan Yufei, Shaw Michael J., "Induction of Fuzzy Decision Trees", Fuzzy Sets and Systems, 1995, 69(2), 125-139.
  38. Wang Xizhao, Chen Bin, Qian Guoliang, Ye Feng, "On the Optimization of Fuzzy Decision Trees", Fuzzy Sets and Systems, 112(1), 117-125.
  39. Roa-Sepulveda C.A., Herrera M., Pavez-Lazo B., Knight U.G., Coonick A.H., "Economic Dispatch Using Fuzzy Decision Trees", Electric Power Systems Research, 2003, 66(2), 115-122.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic Decision Tree Condensate Gas Ratio Membership Function Fuzzy Rule

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