Production Modeling in the Oil and Natural Gas Industry: An Application of Trend Analysis

Oil and natural gas (NG) play an important role in the world economy. Therefore, accurate forecast models for future production levels in the industry are needed for better planning and less risky business environment. The author uses trend analysis to model oil and NG production. For modeling, trends in the global oil and NG production during the period 1985–2010 are identified and linear regression analysis is employed according to the trend observed. The developed models are tested by the t distribution, the F distribution, and the residual analysis. The proposed models are used to forecast the future production trend in the industry. The results show that the proposed models can be effectively used for forecasting of future oil and NG production. The results are thought to provide a powerful tool for researchers, planners, and investors working in the energy field.

[1]  Jing Shi,et al.  Evaluation of hybrid forecasting approaches for wind speed and power generation time series , 2012 .

[2]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[3]  Vivek Utgikar,et al.  Energy forecasting: Predictions, reality and analysis of causes of error , 2006 .

[4]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[5]  Lixiao Zhang,et al.  Scenario analysis of urban energy saving and carbon abatement policies: A case study of Beijing city, China , 2012 .

[6]  G. Aydin,et al.  Sources and mitigation of methane emissions by sectors: A critical review , 2012 .

[7]  J. Armstrong,et al.  Illusions in Regression Analysis , 2011 .

[8]  Thong Ngee Goh,et al.  A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction , 2002 .

[9]  Gokhan Aydin,et al.  The Modeling and Projection of Primary Energy Consumption by the Sources , 2015 .

[10]  Gokhan Aydin,et al.  Evaluation of geologic storage options of CO2: Applicability, cost, storage capacity and safety , 2010 .

[11]  M. V. Asselt,et al.  The future shocks: On discontinuity and scenario development , 2005 .

[12]  Aylin Çiğdem Köne,et al.  Forecasting of CO2 emissions from fuel combustion using trend analysis , 2010 .

[13]  B. W. Ang,et al.  A trigonometric grey prediction approach to forecasting electricity demand , 2006 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Yemane Wolde-Rufael,et al.  Coal consumption and economic growth revisited , 2010 .

[16]  Francis Meunier.  The greenhouse effect: A new source of energy , 2007 .

[17]  Halim Ceylan,et al.  Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey , 2008 .

[18]  Soner Haldenbilen,et al.  Genetic algorithm approach to estimate transport energy demand in Turkey , 2005 .

[19]  G. Aydin,et al.  The Application of Trend Analysis for Coal Demand Modeling , 2015 .

[20]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[21]  Stephen A. Holditch,et al.  Factors That Will Influence Oil and Gas Supply and Demand in the 21st Century , 2008 .

[22]  Yetis Sazi Murat,et al.  Use of artificial neural networks for transport energy demand modeling , 2006 .

[23]  Vaclav Smil,et al.  Perils of Long-Range Energy Forecasting , 2000 .

[24]  Alper Ünler,et al.  Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025 , 2008 .

[25]  R. Chedid,et al.  A supply model for crude oil and natural gas in the Middle East , 2007 .

[26]  Kejun Zhu,et al.  Energy demand projection of China using a path-coefficient analysis and PSO–GA approach , 2012 .

[27]  Gokhan Aydin,et al.  The Modeling of Coal-related CO2 Emissions and Projections into Future Planning , 2014 .