Production modeling in the oil and natural gas industry: An application of trend analysis

Petroleum Science and Technology

Volumn 32, Issue 5, 2014, Pages 555-564

  Aydin, G ^a  

^a KARADENIZ TECHNICAL UNIVERSITY   (Turkey)

Author keywords

modeling; natural gas; oil; trend analysis

Indexed keywords

EID: 84893970393     PISSN: 10916466     EISSN: 15322459     Source Type: Journal    
DOI: 10.1080/10916466.2013.825271     Document Type: Article

References (33)

1. American University. (2012). SPSS regressions. Retrieved from www.american.edu/ctrl/upload/SPSS Regression Spring 2010.pdf
2. Armstrong S. J. (2012). Illusions in regression analysis. Int. J. Forecast. 28:689-694.
3. Aydin G. (in press a). Modeling and projection of primary energy consumption by the sources. Energy Sources Part B.
4. Aydin G. (2014). Modeling of coal related CO2 emissions and projections into the future planning. Energ. Source. A: 36:191-201.
5. Aydin G. (in press b). Application of trend analysis for coal demand modeling. Energy Sources Part B.
6. Aydin G. Karakurt I., and Aydiner K. (2010). Evaluation of geologic storage options of CO2: Applicability, cost, storage capacity and safety. Energy Policy, 38:5072-5080.
7. BP. (2010). Statistical review of world energy. London: BP.
8. BP. (2011). Statistical review of world energy. London: BP.
9. Ceylan H. Ceylan H. Haldenbilen S., and Baskan O. (2008). Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey. Energy Policy 36:2527-2535.
10. Chedid R. Kobrosly M., and Ghajar R. (2007). A supply model for crude oil and natural gas in the Middle East. Energy Policy 35:2096-2109
11. ExxonMobil. (2013). Factors affecting future results. Retrieved from http://phx.corporate-ir.net/External.Fileitem= UGFyZW50SUQ9NTIxMjR8Q2hpbGRJRD0tMXxUeXBlPTM=&t=1.
12. Feng Y. Y., and Zhang X. L. (2012). Scenario analysis of urban energy saving and carbon abatement policies: A case study of Beijing city. China Procedia Environ. Sci. 13:632-644.
13. Freedman A. D. (2005). Statistical models: Theory and practice. New York: Cambridge University Press.
14. Geem Z.W. Kim J. H., and Loganathan G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation 76:60-68.
15. Gen M., and Cheng R. (1997). Genetic algorithms and engineering design. New York: Wiley.
16. Goldberg,D. E. (1989). Genetic algorithms in search, optimization and machine learning.Harlow england: Addison-Wesley.
17. Haldenbilen S., and Ceylan H. (2005). Genetic algorithm approach to estimate transport energy demand in Turkey. Energy Policy 33:89-98.
18. Ho S. L. Xie M., and Goh T. N. (2002). A comparative study of neural network and Box-Jenkins ARIMA modeling in time series predictions. Comput. Indust. Eng. 42:371-375.
19. Holditch A. S., and Chianelli R. R. (2008). Factors that will influence oil and gas supply and demand in the 21st century. MRS Bull. 33:317-323
20. Karakurt I. Aydin G., and Aydiner K. (2012). Sources and mitigation of methane emissions by sectors: A critical review. Renew. Energy 39:40-48.
21. Kone C. A., and Buke T. (2010). Forecasting of CO2 emissions from fuel combustion using trend analysis. Renew. Sustain. Energy Rev. 14:2906-2915.
22. Longwell J. H. (2002). The future of the oil and gas industry: past approaches, new challenges. World Energy 5:100-104.
23. Meunier F. (2007). The greenhouse effect: A new source of energy. Appl. Therm. Eng. 27:658-664.
24. Murat S. Y., and Ceylan H. (2006). Use of artificial neural networks for transport energy demand modeling. Energy Policy 34:3165-3172.
25. Notten F. W. Sleegers M. A., and Asselt A. B. M. (2005). The future shocks: on discontinuity and scenario development. Technol. Forecast. Social Change 72:175-194.
26. Shi J. Guo J., and Zheng S. (2012). Evaluation of hybrid forecasting approaches for wind speed and power generation time series. Renew. Sustain. Energy Rev. 16:3471-3480.
27. Smil,W. (2000). Perils of long-range energy forecasting: Reflections on looking far ahead. Technol. Forecast. Social Change 65:251-264.
28. Stockholm Environment Institute. (2008). User guide LEAP: long range energy alternative planning system. Boston: SEI.
29. Utgikar P. V., and Scott P. J. (2006). Energy forecasting: predictions, reality and analysis of causes of error. Energy Policy 34:3087-3092.
30. Ünler A. (2008). Improvement of energy demand forecasts using swarm intelligence. The case of Turkey with projections to 2025. Energy Policy 36:1937-1944.
31. Wolde-Rufael Y. (2010). Coal consumption and economic growth revisited. Appl. Energ. 87:160-167.
32. Yu S. Zhu K., and Zhang X. (2012). Energy demand projection of China using a path-coefficient analysis and PSO-GA approach. Energy Convers. Manage. 53:142-153.
33. Zhou P. Ang W. B., and Poh L. K. (2006). A trigonometric grey prediction approach to forecasting electricity demand. Energy 31:2839-2847.