Quantifying the Uncertainty in Estimates of Ultimately Recoverable World Conventional Oil Resources
- Chih-Ming Tien (Texas A&M University) | Duane McVay (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Economics & Management
- Publication Date
- April 2011
- Document Type
- Journal Paper
- 79 - 92
- 2011. Society of Petroleum Engineers
- 5.6.3 Deterministic Methods, 7.4.5 Future of energy/oil and gas, 5.7.3 Deterministic Methods
- peak oil, world oil resources, uncertainty quantification
- 1 in the last 30 days
- 1,094 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 10.00|
|SPE Non-Member Price:||USD 30.00|
Since Hubbert proposed the "peak oil" concept to forecast ultimate recovery of crude oil for the US and the world, there have been countless debates over the timing of peak world conventional oil production rate and ultimate recovery. Forecasts presented in the literature can be grouped into those that are like Hubbert's, with an imminent peak, and those that do not predict an imminent peak. Although both groups have bases for their positions, viewpoints from the two groups are polarized and the debate is often heated. A big reason for the large divide between the two groups is the failure of both to acknowledge the significant uncertainty in their estimates. Although some authors attempt to quantify uncertainty, most use deterministic methods and present single values, with no ranges.
Our objective is to quantify the uncertainty in estimates of world conventional ultimately recoverable resources (URR) and time to peak oil rate. We use two different methodologies. First, we employ a mathematical modeling technique based on regression of historical production data using Hubbert's logistic model and a normal distribution model. However, we conduct the analyses probabilistically, considering errors in both the data and the model, which results in likelihood probability distributions for URR and time to peak rate. Second, we use a multiple-experts analysis to combine estimates from the multitude of papers presented in the literature, yielding an overall distribution for estimated world URR.
Both the mathematical modeling and the multiple-experts analysis indicate that there is considerable uncertainty in estimates of world conventional oil URR. Our best estimate is a P10 - P90 range of 1.8 - 4.4 trillion bbl with a mean of 2.9 trillion bbl. Because of some conservative assumptions in our analysis, we believe the uncertainty is actually greater than indicated above, and the additional uncertainty is in the upside, resulting in larger P90 and mean values. In short, we do not have enough information at this time to say with reliability what the ultimate world conventional oil recovery will be. It could peak soon, somewhere in the distant future, or somewhere in between. It would be wise to consider all of these possible outcomes in planning and making decisions regarding capital investment and formulation of energy policy.
|File Size||2 MB||Number of Pages||14|
Bardi, U. 2005. The mineral economy: a model for the shape of oil productioncurves. Energy Policy 33 (1): 53-61. doi:10.1016/S0301-4215(03)00197-6.
Bartlett, A.A. 2000. An Analysis of U.S. And World Oil Production PatternsUsing Hubbert-Style Curves. Mathematical Geology 32 (1):1-17. doi:10.1023/A:1007587132700.
Bentley, R. and Boyle, G. 2008. Global oil production: forecasts andmethodologies. Environment and Planning B: Planning and Design 35 (4): 609-626. doi:10.1068/b33063t.
BP. 2009. Statistical Review of World Energy 2009, http://www.bp.com/productlanding.do?categoryId=6929&contentId=7044622.
Brandt, A.R. 2007. Testing Hubbert. Energy Policy 35(5): 3074-3088. doi:10.1016/j.enpol.2006.11.004.
Campbell, C.J. 2003. Industry Urged to Watch for Regular Oil ProductionPeaks, Depletion Signals. Oil & Gas Journal 101 (27):38-47.
Capen, E.C. 1976. The Difficulty of Assessing Uncertainty. J PetTechnol 28 (8): 843-850. SPE-5579-PA. doi: 10.2118/5579-PA.
Clemen, R.T. 1989. Combining forecasts: A review and annotated bibliography.International Journal of Forecasting 5 (4): 559-583. doi:10.1016/0169-2070(89)90012-5.
Energy Information Administration (EIA). 1996. Annual Energy Review 1995.Annual Report No. DOE/EIA-0384(95), EIA/US DOE, Washington, DC (1 July1996).
Energy Information Administration (EIA). 2009. Annual Energy Review 2008.Annual Report No. DOE/EIA-0384(2007), EIA/US DOE, Washington, DC (26 June2009).
Energy Information Administration (EIA). 2011a. World Oil Supply, 1970-2009.International Petroleum Monthly (December 2010): Table 4.4. http://www.eia.doe.gov/emeu/ipsr/t44.xls(final edition posted 12 January 2011).
Energy Information Administration (EIA). 2011b. World Natural Gas PlantLiquids Production, 1970-2008. International Petroleum Monthly (December 2010):Table 4.3. http://www.eia.doe.gov/emeu/ipsr/t43.xls(final edition posted 12 January 2011).
Eni. 2008. World Oil and Gas Review 2008, http://www.eni.it/wogr_2008/pdf/wogr2008-oil.pdf.
Hubbert, M.K. 1956. Nuclear Energy and the Fossil Fuels. Presented at theSpring Meeting of the Southern District Division of Production, AmericanPetroleum Institute, San Antonio, Texas, USA, 7-9 March.
Hubbert, M.K. 1962. Energy Resource: A Report to the Committee on NaturalResources: National Academy of Sciences. Publication 1000-D, PB 222401,National Technical Information Service, US Department of Commerce, Springfield,Virginia.
Hubbert, M.K. 1982. Techniques of Prediction as Applied to the Productionof Oil and Gas, No. 631. Washington, DC: NBS Special Publication, USDepartment of Commerce.
Laherrere, J.H. 2007. Uncertainty of data and forecasts for fossil fuels.Report, Association for the Study of Peak Oil (ASPO) and ASPO France,Universidad de Castilla-La Mancha, Ciudad Real, Castile-La Mancha, Spain (24April 2007).
Lynch, M.C. 2003. Petroleum Resources Pessimism Debunked in Hubbert Modeland Hubbert Modelers' Assessment. Oil & Gas Journal 101(27): 38-47.
Maggio, G. and Cacciola, G. 2009. A variant of the Hubbert curve for worldoil production forecasts. Energy Policy 37 (11): 4761-4770.doi:10.1016/j.enpol.2009.06.053.
O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H.,Jenkinson, D.J., Oakley, J.E., and Rakow, T. 2006. Uncertain Judgements:Eliciting Experts' Probabilities. West Sussex, UK: Statistics in Practice,John Wiley and Sons.
Reynolds, D.B. and Kolodziej, M. 2008. Former Soviet Union oil productionand GDP decline: Granger causality and the multi-cycle Hubbert curve. EnergyEconomics 30 (2): 271-289. doi:10.1016/j.eneco.2006.05.021.
Reynolds, D.B. and Kolodziej, M. 2009. North American Natural Gas SupplyForecast: The Hubbert Method Including the Effects of Institutions.Energies 2 (2): 269-306. doi:10.3390/en20200269.
Tien, C.M. 2009. Quantifying the Uncertainty in Estimates of WorldConventional Oil Resources. MS thesis, Texas A&M University, CollegeStation, Texas.
USGS World Energy Assessment Team. 2000. World Petroleum Assessment2000--Description and Results. USGS Digital Data Series--DDS-60, http://pubs.usgs.gov/dds/dds-060/.
Verhulst, P.-F. 1845. Recherches mathématiques sur la loi d'accroissement dela population. Nouv. mém. de l'Academie Royale des Sci. et Belles-Lettres deBruxelles 18: 1-41.