Energy Policy 39 (2011) 5480–5489

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Energy Policy

0301-42

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London

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journal homepage: www.elsevier.com/locate/enpol

The ACEGES laboratory for energy policy: Exploring theproduction of crude oil

Vlasios Voudouris a,b,�, Dimitrios Stasinopoulos b, Robert Rigby b, Carlo Di Maio a,c

a Centre for International Business and Sustainability, London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London EC2M 6SQ, UKb Statistics, Operational Research and Mathematics Centre, London Metropolitan University, Holloway Road, London N7 8DB, UKc Bocconi University, Via Sarfatti, 25, Milan, Italy

a r t i c l e i n f o

Article history:

Received 2 September 2010

Accepted 10 May 2011Available online 31 May 2011

Keywords:

Oil depletion

Oil scenario generation

ACEGES

15/$ - see front matter & 2011 Elsevier Ltd. A

016/j.enpol.2011.05.014

esponding author at: Centre for Internationa

Metropolitan Business School, London Metro

ndon EC2M 6SQ, UK. Tel.: þ44 20 7320 1409

ail address: v.voudouris@londonmet.ac.uk (V.

a b s t r a c t

An agent-based computational laboratory for exploratory energy policy by means of controlled

computational experiments is proposed. It is termed the ACEGES (agent-based computational

economics of the global energy system). In particular, it is shown how agent-based modelling and

simulation can be applied to understand better the challenging outlook for oil production by accounting

for uncertainties in resource estimates, demand growth, production growth and peak/decline point.

The approach emphasises the idea that the oil system is better modelled not as black-box abode of ‘the

invisible hand’ but as a complex system whose macroscopic explananda emerges from the interactions

of its constituent components. Given the estimated volumes of oil originally present before any

extraction, simulations show that on average the world peak of crude oil production may happen in the

broad vicinity of the time region between 2008 and 2027. Using the proposed petroleum market

diversity, the market diversity weakness rapidly towards the peak year.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Oil was already becoming an important commodity by the latenineteenth century, and economic activities have only becomemore dependent on the energy and products it provides(e.g., plastics, chemicals and drugs). Industrialised nations havetaken for granted an uninterrupted supply of the cheap hydro-carbon which fuels booming while volatile production and highprices plunge the world into recessions (e.g., Hamilton, 2003)or affect specific economic indicators for selected countries(e.g., Kilian, 2008). However, the exhaustible nature of oil rendersprobabilistic statements (rather than point forecasts) of oil pro-duction important to all agents (e.g., leaders in the government,business and civil society) involved in the petroleum market. So,what do we know about the future of crude oil production?

To answer the above question, models of global oil supply (eitheran econometric- or physical-centred model) are based on the conceptof representative country (also called the fallacy of division) and con-ceptualise the oil production system as consisting of several identicaland isolated components. This means that based on historicalproductions of key producers (e.g., US), the world oil production isassumed to have (more or less) the same production characteristics.

ll rights reserved.

l Business and Sustainability,

politan University, 84 Moor-

; fax: þ44 20 7320 1585.

Voudouris).

This is effectively the attribution of properties to a different levelthan where the property is observed. Even when individual countriesare modelled (e.g., Hallock et al., 2004), key uncertain variables(e.g., peak/decline point and remaining reserve/production ratio) ofthe model are assumed homogeneous for all the countries.

Resource-constrained models are the most widely used type ofmodels for long-term forecasts of oil production (see Jakobssonet al., 2009 for a defence of the resource-constrained model).Resource-constrained models are (i) the curve-fitting models or(ii) the mechanistic or heuristic models. The task of the curve-fitting

models (e.g., Caithamer, 2008; Nashawi et al., 2010) is to usecomputation to identify the best fitted curve using historicalproductions and then the best fitted curve is used to model futureproductions (see Bentley and Boyle, 2008, for a review of the curve-fitting approach). The curve-fitting models are effectively non-linearregression models. The mechanistic model (e.g., Wood et al., 2004;Campbell, 1997) assumes a production growth until an assumedminimum reserve/production, ðR=PÞmin, ratio is reached. Theassumed ðR=PÞmin is in most cases the same for all the countriesand/or is based upon the past experience of mature oil producers.After ðR=PÞmin is reached, production is determined by the ðR=PÞmin.

Based on the work of Hallock et al. (2004) who developed amechanistic model for scenarios of oil production, here we proposea computational laboratory, termed ACEGES (agent-based compu-tational economics of the global energy system), for exploratoryenergy policy by means of controlled computational experiments.The ACEGES model uses the framework proposed by Voudouris

V. Voudouris et al. / Energy Policy 39 (2011) 5480–5489 5481

(2011) and has been developed based upon the agent-basedcomputational economics (ACE) modelling paradigm.

ACE is the computational study of processes modelled asdynamic systems of interacting and heterogeneous agents. Agentsoperate in an environment on which they live and with which theyinteract. Agents engage repeatedly in interactions over time thatgenerate emergent global regularities. ACE provides a newapproach to the explanation of complex phenomena, in whichone ‘grows’ the macro-phenomenon of interest, from sets of micro-foundations. Therefore, there is a causality between micro-beha-viours, interaction patterns and global regularities (Tesfatsion,2006). Thus, ACE proposes a research method that uses artificialsocieties as its principal scientific instrument (Epstein, 2007).

ACE can also be seen as computerised simulations of a numberof decision-makers (e.g., countries) and institutions (e.g., OPEC),which interact though prescribed rules (e.g., Fig. 1). ACE models donot rely on the assumption that the (energy) system will movetowards a predetermined state or profile (e.g., Hubbert curve).Instead, at any given time, each agent (country) acts according toits current situation (e.g., current oil production), the state of theworld around it (e.g., net unmet world demand for oil) and therules governing its behaviour. Following Farmer and Foley (2009),because ACE can handle a far wider range of non-linear behaviourthan the conventional approaches, policy-makers can simulatean artificial energy system under different policy scenarios andquantitatively explore their consequences or how likely is theenergy system to react under different policies or settings (peak/decline point, growth in oil production, growth in oil demand,estimated ultimate recovery—EUR). In the words of Buchanan(2009), we can develop computational ‘wind tunnels’ that wouldallow regulators to test policies and explore their emergent effectson the system. Therefore, if ‘wind tunnels’ and related simulationmethods work in the physical world (e.g., testing the essentialaerodynamic features of scale-model bridges), then computationalexperiments can also work for energy policy. ACE computationallaboratories are beginning to enter the policy-making process asdecision-support tools (e.g., Li and Tesfatsion, 2009).

For example, the ACEGES laboratory, as a scenario developmenttool for exploratory energy policy, uses the Model panel to set theparameters of the simulated scenario using two complementaryways, namely a user-centred approach and a mathematically centredapproach as discussed by Jefferson and Voudouris (2011). Note thatthe Model panel allows the policy-makers to modify the parametersaffecting the entire simulation and/or the parameters affecting

[oil production >0]

Equation (4)

[oil production <=0]

[cumulative oil producti

[cumulative oil producti

Fig. 1. Simplified behavioural rule for oil production. ‘Rounded rectangles’ represent

a country. A demo version of the ACEGES software is available atwww.aceges.org.

Section 2 details the ACEGES model, particularly the oil produc-tion decision rule of the agents (countries). Because the ACEGESmodel is a realistically rendered agent-based model, it also discusseshow the model is initialised with observational data and howheterogeneity is introduced. This section also discusses the GAMLSS(generalised additive models for location scale and shape) frame-work developed in Rigby and Stasinopoulos (2005) as a way ofanalysing the simulated scenarios. Section 3 presents the results ofthe high–high heterogeneity scenario and the Campbell–Heapesscenario. Section 4 concludes.

2. ACEGES model description

The ACEGES model is represented by five equations (1)–(5)given in Section 2.1 and is displayed in Fig. 1. Note that Eqs.(1)–(5) are adapted from Hallock et al. (2004). Although theACEGES model is mainly a resource-constrained model, thereare two features of the ACEGES model that represent conceptsfrom economics:

�

on <

on >

the

The model includes a variable for the domestic oil demand

growth. The domestic oil demand growth is independent fromthe domestic oil production growth.

� There is a simplified trade (interactions) between the countries.

The simplified trade is represented by Eq. (5). This simplifiedtrade is not a shortcoming of the model since it can grow theobserved macroscopic explananda (see Figs. 2 and 4). Note thatthe ACE modelling paradigm aims to grow the macroscopicregularity from simple (interacting) micro-foundations.

Following Campbell (1996), Eq. (2) represents the productiondecision of the swing countries. This decision is based on theassumption that (i) the swing countries will continue to produceoil in order to fulfill the net world demand for oil (world demand–world production) and (ii) the swing countries will not produce oilat their maximum capacity unless it is necessary. Therefore, theywill choose to produce the minimum between their productioncapacity and Eq. (2). This is effectively an approximation of theconsumers’ logic first developed by Royal Dutch Shell in the 1970s.

It is also important to clarify here that Eq. (3) is adjusted (wherenecessary) based upon a maximum allowable (country-specific)

peak point x EUR] Equation (2)

[prenp=true]

Equation (3)

[prenp=false]= peak point x EUR]

operations and the ‘diamonds’ represent the decision points of the agents.

Fig. 2. Probabilistic forecast of world oil production: H–H scenario. Centile curves of 0.1, 0.2, 0.4, 2, 10, 25, 50, 75, 90, 98, 99.6, 99.8, 99.9.

V. Voudouris et al. / Energy Policy 39 (2011) 5480–54895482

production growth from t to tþ1. This maximum (country-speci-fic) production growth represents both unavoidable geologicallimits as well as above ground factors such as activities by themovement for the emancipation of the Niger Delta (MEND),hurricanes in the Gulf of Mexico and political unrest in MENA(Middle East and North Africa). This model specification is impor-tant, for example, in cases where a country (e.g., pre-peak produ-cer) has enough reserves, but it cannot meet its domestic demandfor oil because of below and/or above ground constrains or becauseit is uneconomical to further stimulate capacity growth (since itcan be less expensive to import oil until the ‘organic’ growth in theproduction capacity from t to tþ1 meets the domestic demand).

2.1. Definitions and notations

The ACEGES model is based upon the framework proposed byVoudouris (2011). The main building blocks of the framework are:(i) the agent (country), network of agents (e.g., OPEC) and thegeoEnvironment (estimated ultimate recovery) which is repre-sented by the Elementary_geoParticle (see Voudouris, 2010, fordetails about the Elementary_geoParticle).

Each agent (country), the first building block, is composed oftwo main parts, namely the attributes and the operations. Theattributes define the individual characteristics of the agents whilethe operations define the behavioural rules of the agents.

In the current implementation of the ACEGES model, the net-oil consuming agents have the following attributes (the subscriptt is dropped when the variable is not dynamic):

�

Oil demand, dat , of an agent at time t, at. � Oil demand growth, ga, of a. Note that growth rate is country-

specific but not time dynamic.

Furthermore, the net-oil consuming agents have a singleoperation representing their individual demand for oil:

dat ¼ ð1þgaÞndat�1ð1Þ

Net-oil producing countries have the above attributes andoperations in addition to those given below:

�

pat denotes the annual oil production of at. � cat denotes the cumulative oil production of at at the beginning

of time t.

� yat denotes the oil yet to be produced by at at the beginning of

time t (or oil remaining at the end of the previous year).

� prenpat

is a Boolean attribute that denotes if at is a pre-peaknet producer.

� postnpat

is a Boolean attribute that denotes if at is a post-peaknet producer.

� ea denotes the EUR of a. � pda denotes the peak/decline point of a (i.e., the proportion of

ea cumulatively produced after which the production declinephase starts).

� isProdat is a Boolean attribute that denotes if at is a producer. � wdat is the share of world demand to be satisfied by at if it is a

net producer.

The behavioural rule for oil production is given in Fig. 1 as anUML (unified modelling language) activity diagram. The key ideais that oil production of at tends to ‘peak’ when approximately pda

of the ea has been extracted (see also Hallock et al., 2004).In particular, if pat ¼ 0 then the agent always exits with

production ¼ 0 and isProdat is set to false. If pat 40, the agentchecks if it is a pre-peak producer, i.e., if the cat is less than ea *pda. If this is true, then the agent checks if it is a pre-peak netproducer—it can cover its domestic demand given in Eq. (1). If itis a pre-peak net producer, then the following operation isselected:

pat ¼ pat�1þgandat�1

þwdat�1ð2Þ

If the agent is not a pre-peak net producer (prenp¼ false), thenEq. (3) is selected:

pat ¼ pat�1þgandat�1

ð3Þ

V. Voudouris et al. / Energy Policy 39 (2011) 5480–5489 5483

If the cumulative production is cat 4 ¼ eanpda, then Eq. (4) isselected:

pat ¼ pat�1�ðpat�1

nðpat�1=yat�1

ÞÞ ð4Þ

Eq. (4) assumes pat=yat ¼ pat�1=yat�1

which corresponds to thereserve to production ratio (R/P) being constant post-peak foreach agent a. Eq. (2) uses wdat . This is given by

wdat ¼ ðnwdt�1=nppnpt�1Þnðpat�1=mpt�1Þ ð5Þ

where nwdt�1 is the net world demand at time t�1, nppnpt�1 isthe total number of pre-peak net producers at t�1, mpt�1 is themean production from the pre-peak net producers. Effectively,Eq. (5), which is a re-parametrisation of the equation used byHallock et al. (2004), assumes that agents with larger pat�1

wouldbe able to increase production to meet net world demand.

2.2. Model initialisation and data

Because the ACEGES model is a realistically rendered agent-based model, the initialisation of the model is based on theobservational data. This requires to set a base year which in thispaper is 2001. In other words, each of the 216 countries modelledin the ACEGES model is initialised with the real-world data as of2001. It is a common practise to initialise the model with a baseyear that is before the ‘current’ year (2011). This is because thesimulated data can be checked against the observational datausing, for example, Kullback–Leibler divergence (Kullback andLeibler, 1951). Taking into account the ‘distance’ between obser-vational and simulated data is one way to empirically validatethe ACEGES model. Furthermore, historic oil production can bechecked against a conceptual population (see Stuard and Ord,1994) of simulated scenarios represented by smooth centilecurves (see Figs. 2 and 4).

The ACEGES model is initialised with the following data foreach country (depending on the requirements of the scenario):

(i)

The domestic demand of oil in 2001 (total petroleumliquids—an ‘averaged proportion’ of the demand for liquefiedpetroleum gas), da2001

, from the United States Department ofEnergy (USDOE), Energy Information Administration (EIA).The ‘averaged proportion’ represents the part of the liquefiedpetroleum gas (LPG) consumption covered by the naturalgas plant liquids (NGPL) production rather than crude oilproduction.

(ii)

The projected growth rates of oil demand, ga, using theprojections of EIA International Energy Outlook 2002(IEO02), International Energy Agency (IEA) and the threescenarios of the World Energy Outlook 2010 (WEO10).

(iii)

The volume of oil originally present before any extraction(EUR), ea from (a) Campbell and Heapes (2008): Dataavailable for 62 countries with global EUR of 1.9 trillionbarrels; (b) US Geological Survey (USGS) World PetroleumAssessment 2002 (WPA02) EUR 5%-likely: Data for 52countries with global EUR of 3 trillion barrels (excludingreserves growth); (c) Central Intelligence Agency (CIA)World Factbook 2010 (WFB10): Data available for 93countries with global EUR of 2.4 trillion barrels. Note thatCIA provides estimates of the proved reserves of oil at thebeginning of 2009. Therefore, the CIA EUR is the sum of(i) the cumulative production for all the countries upto 2008 using the data sources discussed in (v) below and(ii) proved reserves. Note that the CIA EUR does not include‘oil-yet-to discover’. The main advantage of the CIA EUR is theconstruction of EUR for 93 countries. This is important fornwdt�1 used in Eq. (5). This is to say that by modelling more ofthe nations of the world, and having both production and

demand for them, the model has a more accurate picture ofthe net demand for imports which is what is being appor-tioned among the pre-peak net producers. Having said thatthe CIA EUR should not be used alone as this is potentially alarge underestimate of actual EUR for selected countries.

(iv)

The annual oil production for 2001 (crude oil includinglease condensate), pa2001

, from the EIA International EnergyData, Analyses, and Forecasts. Because of the use of crudeoil, we are really testing whether the EUR estimates, in theform of crude oil, generate results consistent with theempirical data. The difference between crude oil productionand conventional oil production is significant for somecountries such as Brazil, Angola, Canada and Venezuela.If the aim were to explore the outlook of conventional oil asdefined by Campbell and Heapes (2008), we would need toadjust starting oil production, cumulative oil production, oildemand, and all production after 2001 to remove oilunconventional by their standards.

(v)

The cumulative production at the start of 2001, ca2001. The

cumulative production (1859–2001) is based on (a) API -Petroleum Facts and Figures (1971) from 1964 to 1994;(b) (DeGolyer and MacNaughton inc. (1994) from 1964 to1994; (c) EIA’s International Energy Data, Analyses, andForecasts from 1994 to 2001.

(vi)

Estimates of oil remaining at the start of 2001 (which is (iii)minus (v) above), ya2001

.

(vii) The maximum allowable projected growth rates of oil

production, gpa. This defines the constrained oil productionfrom t to tþ1. This is defined based on literature review andour own calculations.

(viii)

Assumed peak/decline point (e.g., 0.5 of EUR), pda. This isdefined based on literature review and our own calculationsfor post-peak countries.

Because the data is loaded into the ACEGES model from anexternal file, as new data becomes available the scenarios can bere-run to see the effect(s). Additional data (e.g., conventional oil)can also be added without changing the code of ACEGES labora-tory. Therefore, the data above is just an indication of how theACEGES model can be empirically initialised.

One of the main advantages of the ACEGES model is the higherdegree of heterogeneity that can be introduced into the scenarios.This means that there is no restriction in assuming that (viii)above is the same for all the countries, as is usually assumed.Currently, heterogeneity is introduced by using a Monte Carloprocess based on the uniform distribution Uða,bÞ. For:

�

MonteCarloEUR: a is the minimum of the CIA, USGS andCampbell and Heapes estimates and b is the maximum ofthe CIA, USGS and Campbell and Heapes estimates. � MonteCarloDemandGrowth: a is the minimum of the EIA Low

and WEO estimates and b is the maximum of the EIA High andWEO estimates.

� MonteCarloPeakOilPoint: a is 0.35 and b is 0.65 (e.g., Hallock

et al., 2004 and own calculations).

� MonteCarloProductionGrowth: a is 0.05 and b is 0.15 (e.g.,

Hallock et al., 2004 and own calculations). This provides anupper bound for production growth in Eqs. (2) and (3).

Depending on the desired degree of heterogeneity that is requiredfor the scenario, all or some of the Monte Carlo processes can beadjusted. Note also that the random number generator for theMonte Carlo process is based on Mersenne Twister pseudorandomnumber generator (Matsumoto and Nishimura, 1998). Mersenne

Twister is also used to randomly select the order of countries fromthe simulation engine. The economic reasoning of the randomly

V. Voudouris et al. / Energy Policy 39 (2011) 5480–54895484

selected order of the countries is to ensure that no country has apermanent strategic advantage in producing its outputs. This canbe turned off if a scenario is developed to explore the effects ofa specific order of outputs or strategic behaviour from swingcountries. It is important to note that the selection of thecountries has no effect on the outcome of the simulation asreported here as the production decisions are based on ‘known’data (information at t�1) at the start of the simulation (simula-tion at time t). However, the reason for emphasising the use of theMersenne Twister is only to demonstrate that if another algo-rithm is explored that depends on information at time t as well ast�1, then the order of selecting the countries can affect theoutcome as some countries will have more information at timet than others. This can happen if we change the (spatial andtemporal) scale of the model by, for example, modelling indivi-dual fields on a monthly basis. Our view is that support for long-term policy might be difficult to achieve by a finer scale ofanalysis given the state of the publicly available data. When andif this changes, then alternatives can be explored.

It is important to emphasise that despite the common practiceto distinguish conventional oil from unconventional oil, there isconfusion over what is measured as there is no common standardfor doing so. This raises issues when different data sourcesare combined. For example, EIA Oil Production of Crude andCondensate (C&C) dataset includes a volume of production notincluded in Campbell and Heapes estimates of EUR. Furthermore,The USGS definition for the estimation of EUR differs in someways from Campbell and Heapes definition for their estimation ofthe EUR. Therefore, the results of the simulated scenarios need tobe interpreted as ‘approximations’ of oil production outlooks.In using crude data, we are not explicitly testing the accuracy ofeither USGS or Campbell and Heapes estimates of EUR. We arereally testing whether those EUR estimates, in the form of crudeoil, generate results consistent with the empirical data.

An alternative option is to augment the above EUR estimateswith estimates for oil shale, heavy oil, extra-heavy oil, deep-wateroil and polar oil. As of today, the extraction of these resources ofunconventional oil is difficult, costly and slow. Because these oilresources are subject to low and costly rates of extraction, theirimpact on the date or height of the crude oil peak is likely not tobe substantial. These unconventional oil resources may smooththe post-peak decline rate.

2.3. Model for analysis of simulated scenarios

Once the simulated scenarios are designed and generated usingthe ACEGES model, a key issue is how to analyse them. Here, wepropose the use of the GAMLSS (generalised additive models forlocation scale and shape) framework. In particular, the GAMLSSmodel, M¼ fD,G,T ,Lg, represents the following components:

�

D specifies the distribution, here a re-parametrisation of theSHASH (Sinh–Arcsinh) distribution developed in Jones andPewsey (2009), of the simulated world oil production(response variable), p,

fPðpjm,s,n,tÞ ¼ cffiffiffiffiffiffi2pp

sð1þr2Þ1=2

e�z2=2 ð6Þ

where z¼ 12 fexp½t sinh�1

ðrÞ��exp½�n sinh�1ðrÞ�g, c¼ 1

2 ftexp½t sinh�1

ðrÞ�þnexp½�n sinh�1ðrÞ�g and r¼ ðp�mÞ=s. Note

sinh�1ðrÞ ¼ logðuÞ where u¼ rþðr2þ1Þ1=2.

�

G specifies the set of link functions fg1ðmÞ ¼ m,g2ðsÞ ¼logðsÞ,g3ðnÞ ¼ logðnÞ,g4ðtÞ ¼ logðtÞg: � T specifies the predictor terms:

g1ðmiÞ ¼ h1ðtiÞ

g2ðsiÞ ¼ h2ðtiÞ

g3ðniÞ ¼ h3ðtiÞ

g4ðtiÞ ¼ h4ðtiÞ ð7Þ

for i¼ 1,2, . . . ,N where N is the total number of simulatedobservations pi, where for the SHASH distribution the para-meter: (i) mi represents the median, (ii) si represents the scale,(iii) ni controls the left hand tail of the world oil production,(iv) ti controls the right hand tail of the world oil production ofthe ith simulated observation pi. Note that here the two tails ofthe distributions are modelled separately.

� L specifies the smoothing hyper-parameters fl1,l2,l3,l4g.

The hyper-parameters fl1,l2,l3,l4g specify the amount ofsmoothing used in each of the smooth function fh1ðtiÞ,h2ðtiÞ,h3ðtiÞ,h4ðtiÞg. The hk’s are cubic smoothing splines.

To aid the interpretation of the distribution of world produc-tion in Section 3, the following key parameter values need to beconsidered (see also Jones and Pewsey, 2009):

�

t¼ n, the world oil production is symmetric. � t¼ n¼ 1, the world oil production is the Gaussian distribution. � ton, the world oil production is positively skewed. � t4n, the world oil production is negatively skewed. � to1, the world oil production right tail is leptokurtic, t¼ 1

mesokurtic and t41 platykurtic. Note that t controls the righttail of the distribution.

� no1, the world oil production left tail is leptokurtic, n¼ 1

mesokurtic and n41 platykurtic distribution. Note that ncontrols the left tail of the distribution.

We also introduce the petroleum market diversity as a way ofquantify the production diversity of the petroleum market. Thepetroleum market diversity is based on the stock market diversity

(Fernholz, 1999). Hall et al. (2003) and Leclerc and Hall (2007)discuss (among other things) the strategic, economic and politicalimplications (as a cheap energy source) of the increasing con-centration (reduced supply diversity) of crude oil production. Wemeasure the petroleum market diversity using:

PMDkðwðtÞÞ ¼Xn

i

wki ðtÞ

!1=k

ð8Þ

where 0oko1, n is the number of oil producing countries andwi(t) is given by pai

t=Pn

i pait. Note pai

t, denotes the production of oil

by country (agent) i at time t. When PMDk increases, the petrol-eum market is more evenly distributed, when PMDk decreases,the petroleum market is more concentrated (reduced supplydiversity). Note that Eq. (8) measures the diversity based uponthe production. Quantifying the ‘export’ market diversity, requiresthe replacements of pai

twith wdai

tgiven by Eq. (5).

3. Demonstrating the ACEGES model

Because of the very large number of scenarios that can bedeveloped and explored by the ACEGES model, here we presentjust two scenario designs to demonstrate the flexibility of theACEGES model:

�

High–high heterogeneity scenario (H–H scenario): The MonteCarlo process is used for all the four key uncertainties: (i) EUR,(ii) demand growth, (iii) production growth and (iv) peak/decline point. The results of this scenario might be interpretedas the ‘equally weighted collective view’ of the agencies of thedata sources reported in Section 2.

V. Voudouris et al. / Energy Policy 39 (2011) 5480–5489 5485

�

Campbell–Heapes scenario (C-H scenario): The Monte Carloprocess for two key uncertainties (demand growth and pro-duction growth) and user-centred subjective values for EUR isbased upon the Campbell and Heapes estimate of EUR andpeak/decline point of 50%.

Jakobsson et al. (2009) provide a critique of the implementa-tion of the mechanistic approach (also termed the MaximumDepletion Rate Model) used by Wood et al. (2004). One of thecritiques is related to the ðR=PÞmin of 10 based upon the USexperience. This effectively highlights the issue with the repre-

sentative country discussed in Section 1. Jakobsson et al. (2009)re-estimate the scenarios of Wood et al. (2004) by using a set ofðR=PÞmin values, namely 30, 50, 70. Using the results of the ACEGESmodel, the emergent ðR=PÞmin is in the ‘range’ of 15-40 (mostlikely range between 20-30). Therefore, if one wishes to modelthe world oil production explicitly, then the range of 15-40 isrecommended as the ðR=PÞmin of the world oil production. Notethat in the scenarios above the global ðR=PÞmin increases after itreaches a global minimum. This also demonstrates that althoughindividual countries reach a stable global minimum of ðR=PÞmin

at the peak, the emergent global ðR=PÞmin can have differentdynamics than the country-specific ðR=PÞmin. These dynamicsemerge because of the country-specific heterogeneity in produc-tion growth, demand growth and peak/decline point.

The simulations show that the Petroleum Market Diversityreduces rapidly as we move towards the peak of the scenarios.

Fig. 3. Dynamics of the parameters of

What we also observe is that the diversity slightly increases in thepost-peak period and then it reaches a relative equilibrium.Clearly, the degree of diversity is different in the scenarios butthis difference is not wide given the designs of the scenarios. In itimportant to note that the dynamics of the Petroleum MarketDiversity reported here are based upon the production ratherthan export capacity of the countries.

The results of the two scenarios reported below demonstratethe design sensitivity of the scenarios and the need for controlledcomputational experiments for effective and exploratory energypolicy.

3.1. High–high heterogeneity scenario

Fig. 2 (left) also provides probabilistic forecasts associatedwith the world oil production. The H–H scenario shows a peakwhich is likely to happen between 2020 and 2030. Fig. 2 (right)are the smooth centile curves using the SHASH distribution ofworld oil production. The H–H scenario does not necessarilydemonstrate an underlying ‘pathological’ condition. Note how-ever that the smoothness of the centile curves (with a local and aglobal peak). The central 50% of projections are shown by thedarkest grey area.

Fig. 3 shows the fitted values of the m,s,n and t parameters ofthe SHASH distribution of world oil production for the H–Hscenario. The m parameter, the median oil production, reachesits global maximum in the vicinity of 2012–2018. A 99% interval

SHASH distribution: H–H scenario.

Fig. 4. Probabilistic forecast of world oil production: C–H scenario. Centile curves of 0.1, 0.2, 0.4, 2, 10, 25, 50, 75, 90, 98, 99.6, 99.8, 99.9.

V. Voudouris et al. / Energy Policy 39 (2011) 5480–54895486

for the peak year of the maximum production is (2008, 2027).A 99% interval for the peak production (in million barrels peryear) is from 24,831,836 to 37,336,533.2 The scale parameter sreaches its global maximum around 2028 (then decreases rapidly)which means a higher uncertainty of oil production around 2028.The acceleration of higher uncertainties starts in 2013. From 2050onwards the level of uncertainty in terms of the s parameterstabilises. A local maximum of uncertainty is also observed in2008. Towards the peak year/period, t4n. This means that thedistribution of world oil production is negatively skewed. Thiseffectively gives an estimate of the balance of risks around thatcentral projection. Overall, the distribution of world oil produc-tion is positively skewed as ton from about 2020 onwards. Theright tail of the distribution of world oil production is highlyleptokurtic until 2018 since to1 and platukurtic from 2018onwards. The left tail is leptokurtic until 2022 since no1 andplatukurtic after 2022. Overall, this means that there are morechances of extreme outliers of oil production in the left tail of thedistribution from 2002 to 2022. This is consistent with the recenthistoric data of oil production. It is important to emphasise thatthis consistency has not been forced by the ACEGES model, whichis initialised with the 2001 observational data. Therefore, from2002 onwards the model runs without further interventions. It ispossible, however, to pause the simulated scenario, change someof the key uncertain variables and continue the scenario in orderto explore the effects of time-dependent shocks.

3.2. Campbell–Heapes scenario

Fig. 4 (left) also provides probabilistic forecasts associatedwith the world oil production. Compared with the H–H scenario,the C–H scenario shows a peak which is likely to happen between

2 We saved the maximum oil production and peak year for each simulation

and the sample quantiles were produced to derive the 99% interval.

2008 and 2018, particularly vicinity of 2015. Fig. 4 (right) are thesmooth centile curves using the SHASH distribution of world oilproduction. The C–H scenario does not necessarily demonstratean underlying ‘pathological’ condition. The central 50% of projec-tions are shown by the darkest grey area. It is noticeable that thehistorical production is particularly well encapsulated by thecentral projection. It should be emphasised that the unconven-tional oil resources (e.g., polar oil, deep-water oil, heavy oil, extra-heavy oil) are likely to ameliorate the rapid decline rate ofthe scenario. As discussed above because unconventional oilresources are subject to low and costly rates of extraction, themore time passes without significant technological improvementsthat allow unconventional oil production to increase quicklyenough, the greater those developments will have to be tosignificantly change the peak profile of the scenario.

Fig. 5 shows the fitted values of the m,s,n and t parameters ofthe SHASH distribution of world oil production for the C–Hscenario. The m parameter, the median oil production, reachesits global maximum just between 2008 and 2015. A 99% intervalfor the peak year of the maximum production is (2008, 2012).A 99% interval for the peak production (in million barrels peryear) is from 25,124,441 to 31,028,356. The scale parameter sreaches a local maximum around 2013 by means of a rapidincrease which means a higher uncertainty of oil production aswe move towards 2013. This uncertainty is rapidly reduced until2030. After 2030, the scale increases rapidly. From 2002 untilabout 2020 and from 2030 onwards, the distribution of world oilproduction is positively skewed as n4t. This gives an estimate ofthe balance of risk around the central projection. The right tail ofthe distribution of world oil production is leptokurtic until 2014and then highly platykurtic since t41, while the left tail isplatykurtic throughout since n41. This means that there are lesschanges for extreme outliers of oil production in the tails of thedistribution after the peak year. It is important to emphasise thatalthough the C–H scenario seems to be a better fit to empirical oilproduction from 2002 to 2010, care is needed not to over-fit the

Fig. 5. Dynamics of the parameters of SHASH distribution: C–H scenario.

V. Voudouris et al. / Energy Policy 39 (2011) 5480–5489 5487

model and penalise ‘forces in the pipeline’. For example, is therecent production figures the outcome of the great recession orthe outcome of production constrains due to unavoidableconstrains?

The C–H scenario has been developed to demonstrate by wayof comparison the outcomes of the ACEGES model when the‘heterogeneity’ is reduced. This reduction in heterogeneity isachieved by fixing two key uncertainty variables with specificvalues while using country-specific distributions for the othertwo variables. Note that if we fix all the variables with specificvalues, then the ACEGES results are line forecasts rather thanprobability forecasts.

In our view, none of the scenarios presented here demonstratea ‘pathological condition’ and all of them are plausible portrayalsof the future of crude oil production given the estimated EUR. Thehistorical production need not be in the central projection area asevery centile has an equal probability of encapsulating historicalproductions. The important point is that the historical productionmust be within the visible probability bands of the selectedcentile curves (see also Elder et al., 2005). For example, Fig. 6shows that although the historical production is encapsulatedwell until 2008 (with a 99% interval for the peak production yearbetween 2012 and 2034), this scenario, which uses the high USGSestimates of EUR, demonstrate signs of a ‘pathological condition’.Clearly, this needs to be interpreted with care as the recent dropin oil production might be the outcome of temporary conditions(e.g., great recession) rather than a fundamental shift in thedynamics of oil production.

4. Conclusions

This paper sets out to demonstrate empirically the poten-tial of the ACEGES decision-support tool in answering the ques-tion: So, what do we know about the future of crude oilproduction?

We recognise that nobody can predict the future evolution ofthe oil market with absolute certainty. It is more realistic foranalysts to recognise that uncertainty when describing theirscenarios. Consequently, ACEGES has been developed to presentoil scenarios in probability terms using graphical representationsof those probabilities.

It is demonstrated that the ACEGES model offers a new andnovel way for the representation and scientific investigation ofthe dynamics of the oil system and more generally of the globalenergy system. This is achieved by bringing a number of funda-mental concepts under a single umbrella such as the reconcilia-tion of econometric and resource-constrained models (see alsoKaufmann, 1991) at the agent level. It also introduces a highdegree of heterogeneity in order to explore better the dynamics ofthe emergent explananda (world oil production) for effectiveenergy policy.

It is important to note that the ACEGES model abstracts fromexplicit modelling of reservoir behaviour. However, it implicitlyapproximates reservoir behaviour by dividing the ‘production’profile into two different phases. Although the decline phase takesno direct account of the mechanical and chemical aids to induceartificial lift, it captures a probabilistic distribution of decline rates.

Fig. 6. World oil production for a pathological scenario.

V. Voudouris et al. / Energy Policy 39 (2011) 5480–54895488

The ACEGES model only approximates the adaptation of newproduction technologies to address recovery efficiency by a prob-abilistic sample of publicly available EUR data. We assume thepublished EUR estimates (USGS and otherwise) to already includethe manifestation of possible future advances in recovery rates.

The ACEGES laboratory can simulate a very large number ofscenarios by adjusting (interactively or using the Monte Carloprocess) any of the key uncertain variables, namely resourceestimates, demand growth, production growth and peak/declinepoint. We presented three different simulated scenarios of crudeoil production. These scenarios were analysed in the GAMLSSframework by selecting the SHASH distribution. Given the esti-mated EUR, the simulations suggest that the peak of globalproduction of crude oil may happen somewhere between 2008and 2027. Using the petroleum market diversity, we also observe areduced supply diversity in the broad vicinity of the peak year.

As the research programme of economics and physical sciencesprogresses, the aim of building an integrated theory for exploratory

energy policy by means of controlled computational experiments willbe within sight. The work presented here suggests a way forwardthrough the development of the ACEGES model.

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