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
factorcapacity
is
3. Lack of the USA’s Major
oil refinery
“Decline
of oil production
whichEast
is economically feasible”
4. Crisis situation
in the Middle
5. Inflow of speculative capital into oil market
Cheap Oil Study
No significant economic or
social effects by introduction
of substitute fuels
Peak Oil Study
Decrease in the supply
of oil, will cause serious
economic and social effects
Objectives
What is…
1.
2.
3.
4.
The trend of oil peak forecast studies.
The basic criteria to distinguish oil peak forecasts.
Major factors for these basic criteria.
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
Reference: EIA（John.H.Wood）
Definitions of Optimist
1. Defined as “Optimist” in journal,
technical report, etc.
2. Optimistic opinion toward reserve
growth.
3. Criticize pessimistic opinion.
4. Specialty : Economy
5. Affiliation : Oil major, Oil company
Research institute
6. Data: USGS data, BP data, OGJ data
Pessimist
Reference: Colin.J.Campbell
Definitions of Pessimist
1. Defined as “Pessimist” in journal,
technical report, etc.
2.Pessimsitic opinion toward reserve
growth.
3. Criticize optimistic opinion.
4. Specialty : Geology
5. Affiliation : Petro consultant, university
6. Data : IHS Energy data, Petro consultant
company data
Oil Peak Forecast Studies
Time of
forecast（y）
1956
1969
1972
1972
1976
1977
1977
1979
1981
1983
1989
1991
1993
1993
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
1997
1997
1997
1998
1998
1998
1998
1998
1998
1999
1999
2000
2000
2000
Oil peak forecast
（y）
Expert
M.King.Hubbert
2000
M.King.Hubbert　2000
ESSO
before 2000
Rene Dubos , Barbara Ward
before 2000
W.Marshall
around 2000
M.King.Hubbert　1996
Paul Ehrlich
2000
Shell
before 2004
World Bank
around 2000
Peter R. Odell, Kenneth E. Rosing
2025
John F. Bookout
2010
Colin.J.Campbell　1992-1997
Jean.H.Laherrere　2000
Townes,H.L.
2010
Petroconsultants, '95
around 2005
John Jennings
2025
Jean.H.Laherrere　2000
Franco Bernabe　2005
Jean.H.Laherrere, Colin.J.Campbell　2005
Wood.Mackenzie
2007-2019 (2014)
L.F.Ivanhoe　2010
Paul Appleby
2010
Joseph J. Romm, Charles B. Curtis
2030
Richard C. Duncan　2005
Colin.J.Campbell　1998-2008
John D. Edwards
2020
Richard C. Duncan, Walter Lewellyn Youngquist 2007
International Energy Agency
2010-2020 (2014)
Energy Information Administration
after 2020
Randy Udall , Steve Andrews　2013
Wolfgang Schollnberger
2015-2020
Franco Bernabe　2005
Richard C. Duncan　2006
Richard C. Duncan　2005
L.G.Magoon　before 2010
Richard C. Duncan　2007
L.G.Magoon　2005
Lord Browne
2010-2020
Opinion
Opinion
Opinion
Opinion
Opinion
Opinion
Time of
forecast（y）
Expert
Oil peak forecast
（y）
2000
2001
2001
2001
2001
2001
2002
2002
2002
2002
2002
2002
2002
2003
2003
2003
2003
2003
2003
2003
2003
2004
2004
2004
2004
2004
2005
2005
2005
2006
2006
2006
2006
2006
2006
2006
2006
2006
Albert A. Bartlett　John D. Edwards
Kenneth.S.Deffeyes　Matｔhew.R.Simmons　Richard C. Duncan　World Energy Council
International Energy Agency
R.W.Bentley　Richard C. Duncan　Jean.H.Laherrere　Ray C. Leonard Pierre-René Bauquis Michael R Smith　Richard Nehring　Walter Lewellyn Youngquist L.F.Ivanhoe　Richard C. Duncan　Colin.J.Campbell　Kenneth.S.Deffeyes　Mathew.R.Simmons　Ged Davis
A.M.Samsam.Bakhitari　Chris Skrebowski　Cambridge Energy Research Associates
David.Goodstein　Jay Hakes, John.H.Wood
Energy Information Administration
Renato Guseo　Kenneth.S.Deffeyes　Mamdouh G.Salameh　David.L.Greene
Colin.J.Campbell　Jean.H.Laherrere　Michael R Smith　Cambridge Energy Research Associates
Pierre-René Bauquis Wood.Mackenzie
The Center for Global Energy Studies
2004-2019
2010-2030
2004-2008
2010-2015
2006
after 2010
after 2030
2007-2012
2008
2015
before 2020
before 2020
2011-2016
2020-2040
before 2013
2010-2020
2003-2016
around 2010
before 2005
2007-2009
after 2025
2006-2007
2011 (after 2007)
after 2020
2000-2010
2037
after 2030
2007
2005
2005-2010
2020
around 2010
2010-2020
2006-2018
after 2030
2015-2025
after 2025
after 2020
Opinion
Opinion
Opinion
Opinion
Opinion
Opinion
Opinion
Basic criteria
Future increase in the oil supply-demand gap
1.Technological innovation
Time lag between technological innovation (for oil extraction including 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
Increasing of oil recovery rate,
100% Reserves replacement
rate
Cause serious effects
Oil peak caused in USA and UK
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 limits
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
Oil peak forecast（Year）
1.Lack of upstream or downstream investment in equipment due to political factors
2.Large-scale introduction of substitute fuels in the market
2050
R
2040
2030
2020
2
Single regression
equations
0.728
Polynomials
equation
0.721
2010
Linear increase in
relation to the time of
forecast
2000
1990
1980
1970
1980
1990
2000
Time of forecast（Year）
2010
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
Oil peak forecast（Year）
2050
R
2040
2030
2020
2
Single regression
equations
0.259
Polynomials
equation
0.351
2010
Converge around
2010 for forecasts
made at later times
2000
1990
1980
1970
1980
1990
2000
2010
Time of forecast（Year）
Figure 4. Result of Statistical Analysis (Categorization P)
2050
2040
2040
2030
2020
2010
2000
1990
1980
1970
2050
Oil peak forecast（Year）
Oil peak forecast（Year）
2050
1980
1990
2000
Time of forecast（Year）
2030
2020
2010
2000
1990
1980
1970
2010
1980
2030
2020
2010
2000
2040
2030
2020
2010
2000
1990
1990
1980
1970
1980
1970
1990
2000
Time of forecast（Year）
2010
2050
2040
1980
1990
2000
Time of forecast（Year）
Figure5-2 Result of Analysis (Categorization S)
Figure5-1 Result of Analysis (Categorization C)
Oil peak forecast（Year）
Oil peak forecast（Year）
Result (Categorization 2)
2010
Figure5-3 Result of Analysis (Categorization R)
t value is below the significance level
1980
1990
2000
2010
Time of forecast（Year）
Figure5-4 Result of Analysis (Categorization U)
No difference in the slopes of the equations
2050
2050
Variance：169.9
2040
Oil peak forecast（Year）
Oil peak forecast（Year）
Result (Categorization 3)
2030
2020
2010
2000
1990
2040
Variance ：93.8
2030
2020
2010
2000
1990
1980
1970
1980
1990
2000
Time of forecast（Year）
Figure 6-1. Result of Statistical Analysis
(Categorization E)
2010
1980
1970
1980
1990
2000
Time of forecast（Year）
Figure 6-2. Result of Statistical Analysis
(Categorization G)
Data and method do not have significant effects on oil peak forecasts
201
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.