Modeling and prediction of geothermal reservoir temperature behavior using evolutionary design of neural networks

Geothermics

Volumn 53, Issue , 2015, Pages 320-327

  Porkhial, S ^a   Salehpour, M ^b   Ashraf, H ^c   Jamali, A ^c  

^a ISLAMIC AZAD UNIVERSITY   (Iran)

^b ISLAMIC AZAD UNIVERSITY   (Iran)

^c UNIVERSITY OF GUILAN   (Iran)

Author keywords

Genetic algorithms; Geothermal energy; GMDH; Modeling

Indexed keywords

GENETIC ALGORITHMS; GEOTHERMAL ENERGY; MODELS; SINGULAR VALUE DECOMPOSITION; DATA HANDLING; GEOTHERMAL FIELDS; GEOTHERMAL WELLS;

ANALYTIC MODELING; EVOLUTIONARY DESIGN; GEOTHERMAL RESERVOIR; GMDH; GMDH-TYPE NEURAL NETWORKS; GROUP METHOD OF DATA HANDLING; INPUT-OUTPUT DATA; MODELING AND PREDICTIONS; MODELING; MODELLING AND PREDICTIONS; NEURAL-NETWORKS; RESERVOIR TEMPERATURES; TEMPERATURE BEHAVIOR; TOP POINTS;

GEOTHERMAL FIELDS; GENETIC ALGORITHMS;

ARTIFICIAL NEURAL NETWORK; DESIGN; GENETIC ALGORITHM; GEOTHERMAL ENERGY; MODELING; TEMPERATURE PROFILE; INPUT-OUTPUT ANALYSIS; NUMERICAL MODEL; RESERVOIR; TEMPERATURE; WELL;

ARDABIL; IRAN; SABALAN GEOTHERMAL FIELD;

EID: 84907320101     PISSN: 03756505     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geothermics.2014.07.003     Document Type: Article

References (40)

1. Álvarez del Castillo A., Santoyo E., García-Valladares O. A new void fraction correlation inferred from artificial neural networks for modeling two-phase flows in geothermal wells. Comput. Geosci. 2012, 41:25-39.
2. Astrom K.J., Eykhoff P. System identification, a survey. Automatica 1971, 7-123-62.
3. Atashkari K., Nariman-Zadeh N., Gölcü M., Khalkhali A., Jamali A. Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Convers. Manage. 2007, 48(3):1029-1041.
4. Bassam A., Santoyo E., Andaverde J., Hernández J.A., Espinoza-Ojeda O.M. Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach. Comput. Geosci. 2010, 36(9):1191-1199.
5. Bayram A.F. Application of an artificial neural network model to a Na-K geothermometer. J. Volcanol. Geotherm. Res. 2001, 112:75-81.
6. Bogie I., Cartwright A.J., Khosrawi K., Talebi B., Sahabi F. The Meshkin Shahr geothermal prospect, Iran. Proceedings of the World Geothermal Congress 2000 2000, 997-1002.
7. Can I. A new improved Na/K geothermometer by artificial neural networks. Geothermics 2002, 31:751-760.
8. Díaz-González L., Santoyo E., Reyes J. In Spanish: Tres nuevos geotermómetros mejorados de Na/K usando herramientas computacionales y geoquimiométricas: aplicación a la predicción de temperaturas de sistemas geotérmicos. Rev. Mex. Cienc. Geol. 2008, 25(3):465-482.
9. Esen H., Inalli M., Sengur A., Esen M. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system. Energy Build. 2008, 40(6):1074-1083.
10. Esen H., Inalli M., Sengur A., Esen M. Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing. Int. J. Therm. Sci. 2008, 47(4):431-441.
11. Esen H., Inalli M., Sengur A., Esen M. Performance prediction of a ground-coupled heat pump system using artificial neural networks. Expert Syst. Appl. 2008, 35(4):1940-1948.
12. Esen H., Ozgen F., Esen M., Sengur A. Artificial neural network and wavelet neural network approaches for modeling of a solar air heater. Expert Syst. Appl. 2009, 36(8):11240-11248.
13. Espinoza-Ojeda O.M., Santoyo E., Andaverde J. A new look at the statistical assessment of approximate and rigorous methods for the estimation of stabilized formation temperatures in geothermal and petroleum wells. J. Geophys. Eng. 2011, 8:233-258.
14. Farlow S.J. Self-Organizing Method in Modeling: GMDH Type Algorithm 1984, Marcel Dekker Inc.
15. Fonseca C.M., Fleming P.J. Nonlinear system identification with multiobjective genetic algorithms. Proceedings of the 13th World Congress of the International Federation of Automatic Control 1996, 187-192. Pergamon Press, San Francisco, California.
16. Fotouhi M. Iran's geothermal potential. Electric Power Research Center. Geotherm. Resour. Counc. Bull. 1994, 23(8). August 1994, p. 280 and IGA News, p. 5.
17. Iba H., Kuita T., deGaris H., Sator T. System identification using structured genetic algorithms. Proc. of 5th Int. Conf. On Genetic Algorithms, ICGA'93 1993.
18. Iba H., deGaris H., Sato T. A numerical approach to genetic programming for system identification. Evol. Comput. 1996, 3(4):417-452.
19. Ivakhnenko A.G. Polynomial theory of complex systems. IEEE Trans. Syst. Man Cybernet. 1971, SMC-1:364-378.
20. Jamali A., Nariman-zadeh N., Darvizeh A., Masoumi A., Hamrang S. Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Eng. Appl. Artif. Intell. 2009, 22(4-5):676-687.
21. Koza J. Genetic Programming, On the Programming of Computers by Means of Natural Selection 1992, MIT Press, Cambridge, MA.
22. Kristinson K., Dumont G. System identification and control using genetic algorithms. IEEE Trans. Syst. Man Cybernet. 1992, 22(5):1033-1046.
23. Liu G.P., Kadirkamanathan V. Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms. IEE Proc. Contr. Theor. Appl. 1999, 146(5):373-382.
24. Madandoust R., Ghavidel R., Nariman-zadeh N. Evolutionary design of generalized GMDH-type neural network for prediction of concrete compressive strength using UPV. Comput. Mater. Sci. 2010, 49(3):556-567.
25. McKenzie W.F., Truesdell A.H. Geothermal reservoir temperatures estimated from the oxygen isotope compositions of dissolved sulfate and water from hot springs and shallow drillholes. Geothermics 1977, 5(1-4):51-61.
26. Mueller J.A., Lemke F. Self-Organising Data Mining: An Intelligent Approach to Extract Knowledge from Data 2000, Pub. Libri, Hamburg.
27. Nariman-zadeh N., Darvizeh A., Darvizeh M., Gharababaei H. Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition. J. Mater. Process. Technol. 2002, 128(1-3):80-87.
28. Nariman-Zadeh N., Darvizeh A., Felezi M.E., Gharababaei H. Polynomial modelling of explosive compaction process of metallic powders using GMDH-type neural networks and singular value decomposition. Modell. Simul. Mater. Sci. Eng. 2002, 10(6):727-744.
29. Nariman-Zadeh N., Darvizeh A., Ahmad-Zadeh R. Hybrid genetic design of GMDH-type neural networks using singular value decomposition for modelling and prediction of the explosive cutting process. Proc. Inst. Mech. Eng. B: J. Eng. Manuf. 2003, 217:779-790.
30. Nariman-zadeh N., Atashkari K., Jamali A., Pilechi A., Yao X. Inverse modelling of multi-objective thermodynamically optimized turbojet engines using GMDH-type neural networks and evolutionary algorithms. Eng. Optim. 2005, 37(5):437-462.
31. Nariman-Zadeh N., Darvizeh A., Jamali A., Moeini A. Evolutionary design of generalized polynomial neural networks for modelling and prediction of explosive forming process. J. Mater. Process. Technol. 2005, 164-165:1561-1571.
32. Porkhial S., Ghomshei M.M., Yousefi P. Geothermal energy in Iran. Proceedings World Geothermal Congress 2010, 25-29.
33. Porto V.W. Evolutionary computation approaches to solving problems in neural computation. Handbook of Evolutionary Computation 1997, Institute of Physics Publishing/Oxford University Press, New York, pp. D1.2:1-D1.2:6. T. Back, D.B. Fogel, Z. Michalewicz (Eds.).
34. Rodríguez-Vázquez K. Multiobjective Evolutionary Algorithms in Non-Linear System Identification 1999, PhD thesis, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK.
35. Sanchez E., Shibata T., Zadeh L.A. Genetic Algorithms and Fuzzy Logic Systems 1997, World Scientific, River Edge, NJ.
36. Sakai H., Matsubaya O. Isotopic geochemistry of the thermal waters of Japan and its bearing on the Kuroko ore solutions. Econ. Geol. 1974, 69:974-991.
37. Vasechkina E.F., Yarin V.D. Evolving polynomial neural network by means of genetic algorithm: some application examples. Comp. Int. 2001, 9.
38. Verma S.P., Kailasa P., Santoyo E. SolGeo: a new computer program for solute geothermometers and application in Mexican geothermal fields. Geothermics 2008, 37(6):597-621.
39. Wong-Loya J.A., Andaverde J., Santoyo E. A new practical method for the determination of static formation temperatures in geothermal and petroleum wells using a numerical method based on rational polynomial functions. J. Geophys. Eng. 2012, 9:711-728.
40. Yao X. Evolving artificial neural networks. Proc. IEEE 1999, 87(9):1423-1447.