NR  Vol.6 No.1 , January 2015
Forecasting Oil Production in North Dakota Using the Seasonal Autoregressive Integrated Moving Average (S-ARIMA)
ABSTRACT
North Dakota’s oil production has been rapidly increasing during the past several years. The state’s oil production in March 2013 even increased to more than twice the quantity produced in March 2011, and the estimated Bakken Formation reserves were reported very large compared with those of the United Arab Emirates. It eventually makes a question to us of how much oil will be able to be actually extracted with currently available technologies. To answer this question, this paper forecasts future oil development trend in North Dakota using the Seasonal Autoregressive Integrated Moving Average (S-ARIMA) model. Nonstationarity derived from a stochastic trend and the abrupt structural change of oil industry was a big potential problem, but through the Quandt Likelihood Ratio test, we found break points, which allowed us to select a model fitting period suitable for the S-ARIMA method to provide accurate statistical inference for the historical period. The seven major oil producing counties were investigated to determine whether the current oil boom was consistent across all oil fields in North Dakota. Empirical estimates show that North Dakota’s oil production will be more than double in the next five years. What we can predict with great certainty is that North Dakota’s influence over domestic and global oil supply systems will increase in the near future, especially over the next five to six years. This is good news for those who are concerned about domestic energy security in the USA.

Cite this paper
Choi, J. , Roberts, D. and Lee, E. (2015) Forecasting Oil Production in North Dakota Using the Seasonal Autoregressive Integrated Moving Average (S-ARIMA). Natural Resources, 6, 16-26. doi: 10.4236/nr.2015.61003.
References
[1]   USA Department of Agriculture, National Agricultural Statistics Service (2014) 2009 Agricultural Statistics Annual.
http://www.nass.usda.gov/Publications/Ag_Statistics/2009/

[2]   Mason, J. (2014) Bakken’s Maximum Potential Oil Production Rate Explored.

[3]   LeFever, J. and Helms, L. (2006) Bakken Formation Reserve Estimates.

[4]   Williams, J. (1974) Characterization of Oil Types in Williston Basin. American Association of Petroleum Geologists Bulletin, 58, 1243-1252.

[5]   Flannery, J. and Kraus, J. (2006) Integrated Analysis of the Bakken Petroleum System, USA Williston Basin. Search and Discovery Article #10105.

[6]   USA Energy Information Administration (2013) United Arab Emirates.
http://www.eia.gov/countries/cab.cfm?fips=TC

[7]   USA Geological Survey (2008) 3 to 4.3 Billion Barrels of Technically Recoverable Oil Assessed in North Dakota and Montana’s Bakken Formation.
http://www.usgs.gov/newsroom/article.asp?ID=1911#.UfKlexgo5Lg

[8]   North Dakota Industrial Commission (2014) Review of North Dakota Petroleum Council Flaring Task Force Report and Consideration of Implementation Steps.

[9]   USA Energy Information Administration (2012) North Dakota Oil Production Reaches New High in 2012, Transported by Trucks and Railroads.
http://www.eia.gov/todayinenergy/detail.cfm?id=10411

[10]   The White House (2010) Remarks by the President on Energy Security at Andrews Air Force Base.
http://www.whitehouse.gov/the-press-office/remarks-president-energy-security-andrews-air-force-base-3312010

[11]   Funke, M. (1992) Time-Series Forecasting of the German Unemployment Rate. Journal of Forecasting, 11, 111-125.
http://dx.doi.org/10.1002/for.3980110203

[12]   Ho, S. and Xie, M. (1998) The Use of ARIMA Models for Reliability Forecasting and Analysis. Computers and Industrial Engineering, 35, 213-216.
http://dx.doi.org/10.1016/S0360-8352(98)00066-7

[13]   Kelikume, I. and Salami, A. (2014) Time Series Modeling and Forecasting Inflation: Evidence from Nigeria. The International Journal of Business and Finance Research, 8, 41-51.

[14]   Koutroumanidis, T., Iliadis, L. and Sylaios, G.K. (2006) Time-Series Modeling of Fishery Landings Using ARIMA Models and Fuzzy Expected Intervals Software. Environmental Modelling and Software, 21, 1711-1721.
http://dx.doi.org/10.1016/j.envsoft.2005.09.001

[15]   Meyler, A., Kenny, G. and Quinn, T. (1998) Forecasting Irish Inflation Using ARIMA Models. Central Bank of Ireland, Dublin.

[16]   Wang, Y.J., Wang, G.Z. and Dong, Y. (2012) Application of Residual Modification Approach in Seasonal ARIMA for Electricity Demand Forecasting: A Case Study of China. Energy Policy, 48, 284-294.
http://dx.doi.org/10.1016/j.enpol.2012.05.026

[17]   Ayeni, B. and Pilat, R. (1992) Crude Oil Reserve Estimation: An Application of the Autoregressive Integrated Moving Average (ARIMA) Model. Journal of Petroleum Science and Engineering, 8, 13-28.
http://dx.doi.org/10.1016/0920-4105(92)90041-X

[18]   Ediger, V.S., Akar, S.A. and Ugurlu, B. (2006) Forecasting Production of Fossil Fuel Sources in Turkey Using a Comparative Regression and ARIMA Model. Energy Policy, 34, 3836-3846.
http://dx.doi.org/10.1016/j.enpol.2005.08.023

[19]   Ediger, V.S. and Akar, S. (2007) ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey. Energy Policy, 35, 1701-1708.
http://dx.doi.org/10.1016/j.enpol.2006.05.009

[20]   Gujarati, D. and Porter, D. (2009) Basic Econometrics. McGraw-Hill, New York.

[21]   Kadrmas, Lee & Jackson Inc. (2014) Williston Basin Oil and Gas Related Electrical Load Growth Forecast.

[22]   Stock, J. and Watson, M. (2011) Introduction to Econometrics. Addison-Wesley, Boston.

[23]   Pindyck, R.S. and Rubinfeld, D.L. (1997) Econometric Models and Economic Forecasts. McGraw-Hill, New York.

[24]   Shumway, R.H. and Stoffer, D.S. (2005) Time Series Analysis and Its Applications with R Examples. Springer Science + Business Media, Berlin.

[25]   USA Energy Information Administration (2013) Crude Oil Production.
http://www.eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_m.htm

 
 
Top