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Evaluation of Crude Oil
Production Forecast Studies
Using Statistical Analysis
June,18 2009
Shinichirou Morimoto
National Institute of Advanced Industrial Science and Technology
1. Introduction
2. Objectives
3. Forecast of Crude Oil Production
(1)Optimist and Pessimist
(2) Basic criteria to distinguish the forecast
4. Method of Statistical Analysis
(1) Categorization
(2) Statistical Analysis
5. Result
6. Conclusion
Introduction
Increases of crude oil price Major factors
1. Increasing global oil demand (China, India, etc)
2. Lack of OPEC’s production capacity
3. Lack of the USA’s Major factor is
oil refinery capacity
4. Crisis of oil production which is economically feasible”
“Decline situation in the Middle East
5. Inflow of speculative capital into oil market
Cheap Oil Study Peak Oil Study
No significant economic or Decrease in the supply
social effects by introduction of oil, will cause serious
of substitute fuels economic and social effects
Objectives
What is…
1. The trend of oil peak forecast studies.
2. The basic criteria to distinguish oil peak forecasts.
3. Major factors for these basic criteria.
4. Important tasks (challenges) for oil peak forecast.
to answer these
questions…
Evaluate oil peak forecast using statistical analysis
New insights for oil peak forecast
Optimist and Pessimist
Optimist Pessimist
Reference: EIA（John.H.Wood） Reference: Colin.J.Campbell
Definitions of Optimist Definitions of Pessimist
1. Defined as “Optimist” in journal, 1. Defined as “Pessimist” in journal,
technical report, etc. technical report, etc.
2. Optimistic opinion toward reserve 2.Pessimsitic opinion toward reserve
growth. growth.
3. Criticize pessimistic opinion. 3. Criticize optimistic opinion.
4. Specialty : Economy 4. Specialty : Geology
5. Affiliation : Oil major, Oil company 5. Affiliation : Petro consultant, university
Research institute 6. Data : IHS Energy data, Petro consultant
6. Data: USGS data, BP data, OGJ data company data
Oil Peak Forecast Studies
Time of Oil peak forecast Time of Oil peak forecast
Expert Expert
forecast（y） （y） forecast（y） （y）
1956 M.King.Hubbert 2000 2000 Albert A. Bartlett 2004-2019
1969 M.King.Hubbert 2000 2001 John D. Edwards 2010-2030
1972 ESSO before 2000 2001 Kenneth.S.Deffeyes 2004-2008
1972 Rene Dubos , Barbara Ward before 2000 2001 Matｔhew.R.Simmons 2010-2015
1976 W.Marshall around 2000 2001 Richard C. Duncan 2006
1977 M.King.Hubbert 1996 2001 World Energy Council after 2010
1977 Paul Ehrlich 2000 2002 International Energy Agency after 2030
1979
1981
Shell
World Bank
before 2004
around 2000
2002
2002
Opinion
R.W.Bentley
Richard C. Duncan
2007-2012
2008
1983 Opinion
Peter R. Odell, Kenneth E. Rosing 2025 2002 Jean.H.Laherrere 2015
1989
1991
Opinion
John F. Bookout 2010
1992-1997
2002
2002
Ray C. Leonard before 2020
before 2020
1993
Colin.J.Campbell
Jean.H.Laherrere 2000
Opinion
2002
Pierre-René Bauquis
Michael R Smith 2011-2016
1993 Townes,H.L. 2010 2003 Richard Nehring
Opinion 2020-2040
1995 Opinion
Petroconsultants, '95 around 2005 2003 Walter Lewellyn Youngquist before 2013
1995 John Jennings 2025 2003 L.F.Ivanhoe 2010-2020
1995 Jean.H.Laherrere 2000 2003 Richard C. Duncan 2003-2016
1995 Franco Bernabe 2005 2003 Colin.J.Campbell around 2010
1995 Jean.H.Laherrere, Colin.J.Campbell 2005 2003 Kenneth.S.Deffeyes before 2005
1996 Wood.Mackenzie 2007-2019 (2014) 2003 Mathew.R.Simmons 2007-2009
1996 L.F.Ivanhoe 2010 2003 Ged Davis after 2025
1996 Paul Appleby
Opinion 2010
Opinion2004 A.M.Samsam.Bakhitari Opinion 2006-2007
1996 Joseph J. Romm, Charles B. Curtis 2030 2004 Opinion
Chris Skrebowski 2011 (after 2007)
1996 Richard C. Duncan 2005 2004 Cambridge Energy Research Associates after 2020
1997 Colin.J.Campbell 1998-2008 2004 David.Goodstein 2000-2010
1997 Opinion
John D. Edwards 2020 2004 Jay Hakes, John.H.Wood 2037
1997 Opinion
Richard C. Duncan, Walter Lewellyn Youngquist 2007 Opinion
2005 Energy Information Administration after 2030
1998 International Energy Agency 2010-2020 (2014) 2005 Renato Guseo 2007
1998 Energy Information Administration after 2020 2005 Kenneth.S.Deffeyes 2005
1998 Randy Udall , Steve Andrews 2013 2006 Mamdouh G.Salameh 2005-2010
1998 Wolfgang Schollnberger 2015-2020 2006 David.L.Greene 2020
1998 Franco Bernabe 2005 2006 Colin.J.Campbell around 2010
1998 Richard C. Duncan 2006 2006 Jean.H.Laherrere 2010-2020
1999 Richard C. Duncan 2005 2006 Michael R Smith 2006-2018
1999 L.G.Magoon before 2010 2006 Cambridge Energy Research Associates after 2030
2000 Richard C. Duncan 2007 2006 Pierre-René Bauquis 2015-2025
2000 L.G.Magoon 2005 2006 Wood.Mackenzie after 2025
2000 Lord Browne 2010-2020 2006 The Center for Global Energy Studies after 2020
Basic criteria
Future increase in the oil supply-demand gap
1.Technological innovation
Time lag between technological innovation (for oil extraction incl-
uding EOR and substitute fuel), and increasing of oil demand.
2.Influence of crude oil price
Correlation between crude oil price and cost of oil extraction
(New oil field discovery).
No serious problem Cause serious effects
Increasing of oil recovery rate,
100% Reserves replacement Oil peak caused in USA and UK
rate
Categorization
1.Categorization1
Categorization based on experts’ theories which support in their analysis as major
factors of the future increase in the oil supply-demand gap.
Categorization O：“Lack of upstream or downstream investment in equipment due to
political factors” or “Large-scale introduction of substitute fuels in the market”
Categorization P：“Decline in economically feasible oil production” or “Geological limit
of oil reserve growth due to increases in the cost of extracting crude oil”
2.Categorizatio2
Categorization based on experts’ organizations.
Categorization C：Oil majors or oil companies Categorization U：Universities
Categorization S：Oil consultant companies
Categorization R：International organizations or public institutions
3. Categorization3
Data and data analysis methods used by experts.
Categorization G ：IHS Energy data, Campbell data
Categorization E ：BP statistics data, OGJ data, P50 mean estimated by USGS
Statistical Analysis Method
1.Categorization1
(1) Regression analysis of oil peak forecasts is applied using
Explanatory variable x: Time of forecast
Objective variable y: Result of oil peak forecasts
(2) Coefficients of determination R2 are compared.
2.Categorization2
(1) Only simple regression equation is used for analysis.
(2) Statistical tests of differences in the slopes of the simple
regression equations are applied.
3. Categorization3
(1) Variances of the oil peak forecasts is compared
analyze the effects of the data and methods.
Result (Categorization1)
Categorization O
1.Lack of upstream or downstream investment in equipment due to political factors
2.Large-scale introduction of substitute fuels in the market
2050
2
R
Oil peak forecast（Year）
2040 Single regression
equations 0.728
2030
Polynomials
2020 equation 0.721
2010
2000 Linear increase in
relation to the time of
1990 forecast
1980
1970 1980 1990 2000 2010
Time of forecast（Year）
Figure 3. Result of Statistical Analysis (Categorization O)
Result (Categorization 1)
Categorization P
1.Decline in economically feasible oil production
2.Geological limits of oil reserve growth due to increases in cost of extracting crude oil
2050 R
2
Single regression
Oil peak forecast（Year）
2040 0.259
equations
2030 Polynomials
equation 0.351
2020
2010
Converge around
2000
2010 for forecasts
1990 made at later times
1980
1970 1980 1990 2000 2010
Time of forecast（Year）
Figure 4. Result of Statistical Analysis (Categorization P)
Result (Categorization 2)
2050 2050
2040 2040
Oil peak forecast（Year）
Oil peak forecast（Year）
2030 2030
2020 2020
2010 2010
2000 2000
1990 1990
1980 1980
1970 1980 1990 2000 2010 1970 1980 1990 2000 2010
Time of forecast（Year） Time of forecast（Year）
Figure5-1 Result of Analysis (Categorization C) Figure5-2 Result of Analysis (Categorization S)
2050 2050
2040
Oil peak forecast（Year）
2040
Oil peak forecast（Year）
2030 2030
2020 2020
2010 2010
2000 2000
1990 1990
1980 1980
1970 1980 1990 2000 2010 1970 1980 1990 2000 2010
Time of forecast（Year） Time of forecast（Year）
Figure5-3 Result of Analysis (Categorization R) Figure5-4 Result of Analysis (Categorization U)
t value is below the significance level No difference in the slopes of the equations
Result (Categorization 3)
2050 2050
2040
Variance：169.9 2040
Variance ：
93.8
Oil peak forecast（Year）
Oil peak forecast（Year）
2030 2030
2020 2020
2010 2010
2000 2000
1990 1990
1980 1980
1970 1980 1990 2000 2010 1970 1980 1990 2000 201
Time of forecast（Year） Time of forecast（Year）
Figure 6-1. Result of Statistical Analysis Figure 6-2. Result of Statistical Analysi
(Categorization E) (Categorization G)
Data and method do not have significant effects on oil peak forecasts
Conclusion
1. The basic criteria to distinguish oil peak forecasts.
The theories which support the expert’s analysis as major factors
of the future increase in the oil supply-demand gap.
2. The trend of oil peak forecast studies
Two distinct tendencies of oil peak forecasts depending on the
specialties and theories of experts.
Converge around 2010 Linear increase in relation to the time
Experts’ organizations and used data have no significant effects
on these tendencies.
3. Important tasks (challenges) for oil peak forecast
How to obtain objective insights that can contribute to formulating
energy strategies from uncertain forecasts, is likely to be ever more
important when proposing energy strategies in the future.
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