5-th International ASPO Conference,
San Rossore, 18-19 July 2006, Italy
World Oil Depletion: Diffusion
Models, Price Effects, Strategic
and Technological Interventions
Renato Guseo
Department of
Statistical Sciences
Renato Guseo
University of Padova,
Italy
ASPO-5 San Rossore 18-19 July
2006, Italy
1
World Crude Oil Production
Thousand barrels daily
80000
Global
70000
60000
50000
OPEC
40000
FSU
30000
20000
CSI
USA (NGL)
10000
0
1900 10
Renato Guseo
20
30
40
50
60
ASPO-5 San Rossore 18-19 July
2006, Italy
70
80
90 2000
2
World Production and Prices
Crude Oil Production and Prices per Barrel
(X 10000)
8
80
Variables
Production (barrel x 1000)
Price (dollars, 2002)
6
60
4
40
2
20
0
0
1900
1920
1940
1960
1980
2000
2020
year
Fonte: BP Statistical Review of World Energy, 2004
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
3
Growth and Development after
World War II
-1• Cohen, J.E. (2003) Human Population: The next
Half Century, Science, 203, 1172-1175;
• Exceptional demographic expansion;
• Rural population peak in rich countries: 1950;
• Increse of world average life: 30 years in 1900
and 65 years in 2000;
• Population: 6.3 billion in 2004; United Nations
Population Division: 8.9 billion in 2050 (ex
medium variant scenario forecasting);
• Regional population contraction in 2050:
Japan -24%; Italy -22%; FSU -29%;
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
4
Growth and Development after
World War II
-2• USA dominance in oil estraction and
refining: shock 1918 (positive with local
memory);
• Decisive military advantage. Competitive
advantage after World War II;
• American way of life;
• Energetic Surplus based on cheap crude oil;
• Structural change in sustainable economic
evolution based on non-renewable
resources;
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
5
Growth and Development after
World War II
-3• Today risk: physical restrictions towards
expansion in oil production due to emergent
demand by China and India;
• Risk of a late migration towards renewable
energetic resources;
• Emerging technologies are not completely
sustainable and efficient (fuel-cells,
hydrogen, photovoltaic systems, solar
thermal systems, eolic systems, etc.).
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
6
Recent strategic researches
1.
Morse, E.L. e Jaffe, A.M. (2001). “Strategic Energy
Policy Challenges for the 21st Century”; (2000 - aprile
2001); James A. Baker III Institute for Public Policy of Rice
University, Texas; Council of Foreign Relations of USA
2.
National Energy Policy Development Group, (2001, Task
Force supervised by D. Cheney).
•
•
•
•
USA energetic policy since 1940 – security -;
Global Economic growth based on a production surplus
with cheap prices;
New emerging world oil demand;
Supply dependence (Midle East).
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
7
Non-Renewable Resources
Depletion: Hubbert and recent
developtments
• Hubbert, M.K. (1949). Energy from fossil fuels, Science, 4,
103-109.
• In 1956 Hubbert forecasts the peak of annual production
within 48-lower states in USA by year 1970;
• Campbell, C. e Laherrère, J. (1998). The End of Cheap Oil,
Scientific American, March 1998.
• Laherrère, J. (2003). Modelling future oil production,
population and the economy, ASPO 2nd international
workshop on oil and gas, Paris, 26-27.
• ASPO (Association for the Study of Peak Oil and Gas);
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
8
Economic and financial “estimation”
of oil reserves: inflated figures
• Warranties on
international longterm loans;
• Investments on
production plants;
• OPEC restrictions
overcoming: export is
proportional to the
“declared” reserves.
Renato Guseo
BP - Statistical Review of World Energy
Source: BP Statistical Review of World Energy
ASPO-5 San Rossore 18-19 July
2006, Italy
9
Ultimate Recoverable Resouce: URR
•
URR: total amount of a finite resource obtainable at the end of
extraction process;
•
Geologic estimates of Oil URR during the life- cycle of
resource extraction:
1. Production to date. Oil heterogeneity. (weight 100%);
2. Proven reserves. Ex-post recovery factor 35% is a median and
today’s variability is very large. Revision principles and
“reserves growth” (USGS) enlarge uncertainties;
3. Probable and possible reserves: undiscoverd petroleum based
on subjective assessments (probability, e.g. 5%).
•Could we do a simple weighted sum of such components?
Probably not
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
10
Recent statistical modeling
• Guseo, R. (2004) Interventi strategici e aspetti competitivi nel
ciclo di vita di innovazioni, Working Paper Series, 11,
Department of Statistical Sciences, University of Padua.
• Guidolin, M. (2004) Cicli energetici e diffusione delle
innovazioni. Il ruolo dei modelli di Marchetti e di Bass,
Thesis, University of Padua.
• Guseo, R and Dalla Valle, A. (2005) Oil and Gas Depletion:
Diffusion Models and Forecasting under Strategic
Intervention, Statistical Methods and Applications, 14(3),
375-387;
• Guseo, R., Dalla Valle, A. and Guidolin, M. (2006) World Oil
Depletion Models: Price effects compared with strategic or
technological interventions, Technological Forecasting and
Social Change (in press);
• Guseo, R. (2006) Bass-let Detection of Automobile
Successive Generations: Evidence from the Italian Case
(submitted).
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
11
Crude Oil Production:
Diffusion of an Innovation
• Oil production is modulated by the dynamics
of international demand;
• Oil demand is a function of the diffusion
processes related to basical technologies
(transport, chemical industries, heating, etc.);
• Diffusion of technological innovations is
conditioned by social communication structure:
innovators and imitators (word-of-mouth)
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
12
Bass Equation: BM
• z’(t) = mf(t) = m[p+qF(t)][1-F(t)] or
• z’(t) = pm+(q-p)z(t) - (q/m) z(t)2 (Riccati)
• z’(t)=mf(t)
(instantaneous adoptions);
• f(t)=F’(t)
• z(t)=m F(t)
(cumulative adoptions);
F(t)=z(t)/m
• f(t)/[1-F(t)]=p+qF(t) Bass Hazard rate
• m=potential market; carrying capacity; URR
• p=innovation coefficient, p>=0
• q=imitation coefficient, q>=0
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
13
Normalized Bass models: BM and GBM
BM:
f(t)/[1-F(t)]=[p+qF(t)]
GBM:
f(t)/[1-F(t)]=[p+qF(t)] x(t)
“Standard”
“GBM”
x(t) is a quite general intervention function:
integrable, positive and “centered” around unitary
“neutral pole”: 1.
Representation of temporal price variations, of
advertising pressure, of political, strategic, legal,
environmental interventions.
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
14
Equation Solution: GBM
z
z ' (t )  ( p  q
)( m  z ) x (t )
m
t
 ( p  q )  x ( ) d
0
1 e
z (t )  m
t
q
 ( p  q )  x ( ) d
0
1
pe
b1(t a1)
b2(t a2 )
Exp. shocks
x(t )  1  c1 e
Rect. shocks
x(t )  1  c1 I (t a ) I (t b )  c2 I (t a
I
(t 
a
1
Mixed shocks x(t )  1 
Renato Guseo
b1(t a1)
ce
 c2 e
)
1
1
I
(t 
a
ASPO-5 San Rossore 18-19 July
1
2006, Italy
)
1
 c2 I (t 
I
2)
(t 
I
a2 )
( t b2 )
a2) I (t b152)
Great Britain: GBM, 2 mixed sh.
Estimation method: Marquardt
Estimation stopped after maximum iterations reached.
Number of iterations: 31
Number of function calls: 330
Estimation Results
GBbpC
Asymptotic 95,0%
Asymptotic
Confidence Interval
Parameter
Estimate Standard Error
Lower
Upper
---------------------------------------------------------------------------m
4513,39
154,806
4196,77
4830,0
p
0,0000708436
0,0000324773 0,00000441993
0,000137267
q
0,111872
0,00516425
0,10131
0,122434
c1
8,54019
1,02935
6,43493
10,6454
b1
-0,250721
0,0114596
-0,274159
-0,227284
a1
10,7677
0,458356
9,83028
11,7052
c2
-0,331417
0,0175489
-0,367309
-0,295526
a2
23,4341
0,190843
23,0438
23,8245
b2
28,6819
0,164258
28,3459
29,0178
---------------------------------------------------------------------------Analysis of Variance
Source
Sum of Squares
Df Mean Square
Model
6,52091E7
9
7,24545E6
Residual
657,546
29
22,674
(X 1000)
----------------------------------------------------3
Total
6,52097E7
38
Total (Corr.)
3,31712E7
37
R-Squared = 99,998 percent
2,5
R-Squared (adjusted for d.f.) = 99,9975 percent
Standard Error of Est. = 4,76172
Mean absolute error = 3,10566
2
Durbin-Watson statistic = 0,889298
Positive Shock
with local memory
Plot of Fitted Model
1,5
1
0,5
0
0
Renato Guseo
20
ASPO-5 San Rossore 18-19 July
2006, Italy
40
t
60
80
16
Great Britain: analysis
• The “saddle” 19871991-1999 is perfectly
absorbed by a
rectangular shock:
• a) Petroleum Reven Tax
modification;
• b) pipelines
restructuring 19861991; symmetric
behaviour confirms
ordinary regime;
• c) partial production
stall due to the
reduction of new
discoveries.
Renato Guseo
Multiple X-Y Plot
150
Variables
GBbp
DIFF(PRED)
DIFF(FOR)
120
90
60
30
0
0
20
40
60
80
t
ASPO-5 San Rossore 18-19 July
2006, Italy
17
USA: 48 lower States and Alaska,
one exponential shock
Estimation Results
---------------------------------------------------------------------------Asymptotic 95,0%
Asymptotic
Confidence Interval
Parameter
Estimate Standard Error
Lower
Upper
---------------------------------------------------------------------------m
224,885
0,784401
223,328
226,442
p
0,000445866
0,0000177788
0,000410571
0,000481162
q
0,0571941
0,000403937
0,0563922
0,057996
c1
0,682617
0,0735348
0,536632
0,828602
b1
-0,0852885
0,00948373
-0,104116
-0,0664609
a1
18,0477
0,981086
16,1
19,9954
----------------------------------------------------------------------------
Plot of Fitted Model
200
cum
160
120
80
40
0
Analysis of Variance
----------------------------------------------------Source
Sum of Squares
Df Mean Square
----------------------------------------------------Model
735809,0
6
122635,0
Residual
7,39124
95
0,0778026
----------------------------------------------------Total
735817,0
101
Total (Corr.)
352880,0
100
R-Squared = 99,9979 percent
R-Squared (adjusted for d.f.) = 99,9978 percent
Standard Error of Est. = 0,278931
Mean absolute error = 0,207909
Durbin-Watson statistic = 0,173839
Positive shock
with local memory
Renato Guseo
0
20
40
60
80
100
120
t
Multiple X-Y Plot
4
Variables
barili
DIFF(PREDb1)
DIFF(PREDbe1)
3
2
1
0
0
20
ASPO-5 San Rossore 18-19 July
2006, Italy
40
60
t
80
100
120
18
USA: 48 lower States and Alaska,
ARMAX(4,0,2) sharpening
Time Sequence Plot for barili
ARIMA(4,0,2) + 1 regressor
4
actual
forecast
95,0% limits
3
barili
Forecasting - barili
Analysis Summary
Data variable: barili
Number of observations = 101
Start index = 1,0
Sampling interval = 1,0
2
1
0
Forecast Summary
---------------Forecast model selected: ARIMA(4,0,2) + 1 regressor
Number of forecasts generated: 40
Number of periods withheld for validation: 0
-1
0
ARIMA Model Summary
Parameter
Estimate
Stnd. Error
t
P-value
---------------------------------------------------------------------------AR(1)
1,21416
0,691695
1,75534
0,082426
AR(2)
-0,140994
1,11031
-0,126986
0,899220
AR(3)
-0,146337
0,49692
-0,294488
0,769028
AR(4)
-0,132467
0,0891259
-1,48629
0,140514
MA(1)
0,591549
0,68527
0,863235
0,390183
MA(2)
0,299352
0,650254
0,460362
0,646308
DIFF(PREDbe1)
0,20426
0,0890786
2,29303
0,024052
---------------------------------------------------------------------------Backforecasting: yes
Estimated white noise variance = 0,00495321 with 95 degrees of freedom
Estimated white noise standard deviation = 0,0703791
Number of iterations: 17
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
30
60
90
120
150
Shock: 1918
19
Alaska: ARMAX(2,0,1)
sharpening
ARIMA Model Summary
Parameter
Estimate
Stnd. Error
t
P-value
---------------------------------------------------------------------------AR(1)
0,323713
0,100318
3,22686
0,002667
AR(2)
-0,172177
0,054492
-3,15967
0,003195
MA(1)
-0,818595
0,102552
-7,98224
0,000000
PREDbme1
0,847508
0,0501864
16,8872
0,000000
Mean
-0,0143829
0,0282678
-0,508809
0,613990
Constant
-0,0122034
---------------------------------------------------------------------------Backforecasting: yes
Estimated white noise variance = 0,00281514 with 36 degrees of freedom
Estimated white noise standard deviation = 0,0530579
Number of iterations: 20
Multiple X-Y Plot
Residual Autocorrelations for alaskac
2,4
2
1,6
1,2
0,8
ARIMA(2,0,1) with constant + 1 regressor
Autocorrelations
1
0,6
0,2
-0,2
Variables
alaskap
DIFF(FORar2ma1
)
0,4
0
-0,6
0
-1
0
3
6
9
12
20
15
40
60
80
t
lag
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
20
World Oil data:
Daily Production
Sources:
• Industriedatenbank 2001 (1900 – 1986)
• BP Statistical Review of World Energy (19872002)
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
21
GBM: x(t) pure prices control
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
22
GBM exp shocks + price effect
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
23
Guidolin (2004): GBM, 2 exp shocks
World Oil Depletion Models
(X 10000)
8
Variables
Production data
gbm2e+for
6
4
2
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
2010
2000
year
1990
1980
1970
1960
1950
0
24
GBM: 3 exp shocks
(memory persistence)
World Oil Depletion Models
(X 10000)
8
Variables
Production data
gbm3e+for
6
4
2
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
2010
2000
year
1990
1980
1970
1960
1950
0
25
GBM 3 exp shocks: estimates
(memory persistence)
q/p = 608  Qp=1%;
R2  0,999994708
F ( m. parz /  1951)  17,06
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
26
World Oil Depletion: GBM with three shocks vs
Hubbert-Bass
Oil Peak: 2007
URR=1524 Gbo
Depletion time 90% : 2019
Renato Guseo
Depletion time 95% : 2023
ASPO-5 San Rossore 18-19 July
2006, Italy
27
Oil market Operators:
prices growth
• Demand and supply self-control similar to 1973
and 1979-’83 behaviour. Extension of crude oil
economic life cycle;
• Limited techonological efficiency margins due to
the improvementes of 70’s , 80’s and 90’s;
• Savings through “life styles” modification: this is
the central dilemma in industrialized countries;
• Sluggishness of middle class whose life style is
based on an expected irreversible and indefinite
growth and development.
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
28
New Emergent Economies
• Increase in crude oil requirements by recent
emergent economies: China, India, and
other Asiatic countries;
• US EIA (Energy Information
Administration) “forecasts” a world oil
demand of 40 Gbo/year (or 109.6 milion
daily barrels) in 2020;
• Guseo, Dalla Valle, Guidolin (2006) and
Bakhtiari (2004) forecast, in 2019-20, only
55 milion daily barrels.
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
29
Crude Oil: Area consumption
Consumi medi giornalieri (in barrel X 1000)
(X 10000)
3
2,5
2
Variables
North Ame
1,5
Europe Eurasia
Asia Pacific
1
South Central Ame
0,5
0
1960
Renato Guseo
Middle East
Africa
1970
1980
1990
Anno
ASPO-5 San Rossore 18-19 July
2006, Italy
2000
2010
30
Outlook
• Nuclear fission perspective is probably a tardy
strategy with known collateral externalities. A new
plant in Italy can be launched after 13-15 years;
• Technological, political and economic efforts must
be distributed in different areas: photovoltaic,
solar thermal, bio-fuel, biomass, eolic, hydrogen,
etc.;
• Electric sector: distributed investments
(photovoltaic, micro-cogeneration, etc.).
• Individual and collective mobility.
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
31
World Oil Depletion: GBM with three shocks
vs Hubbert-Bass vs five shocks scenario
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
32
World Oil Depletion: GBM with three shocks
vs five shocks vs four shocks scenarios
Shock 2008 (sim. 1951)
Depletion time 90% : 2017
Renato Guseo
ASPO-5 San Rossore 18-19 July
2006, Italy
33