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The ACEGES laboratory for energy policy: Exploring the production of crude oil
Energy Policy 39 (2011) 5480–5489
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The ACEGES laboratory for energy policy: Exploring the production 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, UK Statistics, Operational Research and Mathematics Centre, London Metropolitan University, Holloway Road, London N7 8DB, UK c Bocconi University, Via Sarfatti, 25, Milan, Italy b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 September 2010 Accepted 10 May 2011 Available online 31 May 2011
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.
Keywords: Oil depletion Oil scenario generation ACEGES
1. Introduction Oil was already becoming an important commodity by the late nineteenth century, and economic activities have only become more dependent on the energy and products it provides (e.g., plastics, chemicals and drugs). Industrialised nations have taken for granted an uninterrupted supply of the cheap hydrocarbon which fuels booming while volatile production and high prices 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 renders probabilistic statements (rather than point forecasts) of oil production 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 (either an econometric- or physical-centred model) are based on the concept of representative country (also called the fallacy of division) and conceptualise the oil production system as consisting of several identical and isolated components. This means that based on historical productions of key producers (e.g., US), the world oil production is assumed to have (more or less) the same production characteristics. Corresponding author at: Centre for International Business and Sustainability, London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London EC2M 6SQ, UK. Tel.: þ 44 20 7320 1409; fax: þ 44 20 7320 1585. E-mail address: [email protected] (V. Voudouris).
0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.05.014
This is effectively the attribution of properties to a different level than where the property is observed. Even when individual countries are modelled (e.g., Hallock et al., 2004), key uncertain variables (e.g., peak/decline point and remaining reserve/production ratio) of the model are assumed homogeneous for all the countries. Resource-constrained models are the most widely used type of models for long-term forecasts of oil production (see Jakobsson et 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 use computation to identify the best fitted curve using historical productions and then the best fitted curve is used to model future productions (see Bentley and Boyle, 2008, for a review of the curvefitting approach). The curve-fitting models are effectively non-linear regression models. The mechanistic model (e.g., Wood et al., 2004; Campbell, 1997) assumes a production growth until an assumed minimum reserve/production, ðR=PÞmin , ratio is reached. The assumed ðR=PÞmin is in most cases the same for all the countries and/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 a mechanistic model for scenarios of oil production, here we propose a computational laboratory, termed ACEGES (agent-based computational economics of the global energy system), for exploratory energy policy by means of controlled computational experiments. The ACEGES model uses the framework proposed by Voudouris
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(2011) and has been developed based upon the agent-based computational economics (ACE) modelling paradigm. ACE is the computational study of processes modelled as dynamic systems of interacting and heterogeneous agents. Agents operate in an environment on which they live and with which they interact. Agents engage repeatedly in interactions over time that generate emergent global regularities. ACE provides a new approach to the explanation of complex phenomena, in which one ‘grows’ the macro-phenomenon of interest, from sets of microfoundations. Therefore, there is a causality between micro-behaviours, interaction patterns and global regularities (Tesfatsion, 2006). Thus, ACE proposes a research method that uses artificial societies as its principal scientific instrument (Epstein, 2007). ACE can also be seen as computerised simulations of a number of decision-makers (e.g., countries) and institutions (e.g., OPEC), which interact though prescribed rules (e.g., Fig. 1). ACE models do not rely on the assumption that the (energy) system will move towards a predetermined state or profile (e.g., Hubbert curve). Instead, at any given time, each agent (country) acts according to its current situation (e.g., current oil production), the state of the world around it (e.g., net unmet world demand for oil) and the rules governing its behaviour. Following Farmer and Foley (2009), because ACE can handle a far wider range of non-linear behaviour than the conventional approaches, policy-makers can simulate an artificial energy system under different policy scenarios and quantitatively explore their consequences or how likely is the energy 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 would allow regulators to test policies and explore their emergent effects on the system. Therefore, if ‘wind tunnels’ and related simulation methods work in the physical world (e.g., testing the essential aerodynamic features of scale-model bridges), then computational experiments can also work for energy policy. ACE computational laboratories are beginning to enter the policy-making process as decision-support tools (e.g., Li and Tesfatsion, 2009). For example, the ACEGES laboratory, as a scenario development tool for exploratory energy policy, uses the Model panel to set the parameters of the simulated scenario using two complementary ways, namely a user-centred approach and a mathematically centred approach as discussed by Jefferson and Voudouris (2011). Note that the Model panel allows the policy-makers to modify the parameters affecting the entire simulation and/or the parameters affecting
[oil production >0]
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a country. A demo version of the ACEGES software is available at www.aceges.org. Section 2 details the ACEGES model, particularly the oil production decision rule of the agents (countries). Because the ACEGES model is a realistically rendered agent-based model, it also discusses how the model is initialised with observational data and how heterogeneity is introduced. This section also discusses the GAMLSS (generalised additive models for location scale and shape) framework developed in Rigby and Stasinopoulos (2005) as a way of analysing the simulated scenarios. Section 3 presents the results of the high–high heterogeneity scenario and the Campbell–Heapes scenario. 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 the ACEGES model is mainly a resource-constrained model, there are two features of the ACEGES model that represent concepts from economics:
The model includes a variable for the domestic oil demand
growth. The domestic oil demand growth is independent from the domestic oil production growth. There is a simplified trade (interactions) between the countries. The simplified trade is represented by Eq. (5). This simplified trade is not a shortcoming of the model since it can grow the observed macroscopic explananda (see Figs. 2 and 4). Note that the ACE modelling paradigm aims to grow the macroscopic regularity from simple (interacting) micro-foundations.
Following Campbell (1996), Eq. (2) represents the production decision of the swing countries. This decision is based on the assumption that (i) the swing countries will continue to produce oil in order to fulfill the net world demand for oil (world demand– world production) and (ii) the swing countries will not produce oil at their maximum capacity unless it is necessary. Therefore, they will choose to produce the minimum between their production capacity and Eq. (2). This is effectively an approximation of the consumers’ logic first developed by Royal Dutch Shell in the 1970s. It is also important to clarify here that Eq. (3) is adjusted (where necessary) based upon a maximum allowable (country-specific)
[cumulative oil production < peak point x EUR]
[prenp=true]
Equation (2)
[prenp=false] [cumulative oil production >= peak point x EUR]
Equation (3)
[oil production <=0] Equation (4)
Fig. 1. Simplified behavioural rule for oil production. ‘Rounded rectangles’ represent the operations and the ‘diamonds’ represent the decision points of the agents.
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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.
production growth from t to t þ 1. This maximum (country-specific) production growth represents both unavoidable geological limits as well as above ground factors such as activities by the movement 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 important, for example, in cases where a country (e.g., pre-peak producer) has enough reserves, but it cannot meet its domestic demand for oil because of below and/or above ground constrains or because it is uneconomical to further stimulate capacity growth (since it can be less expensive to import oil until the ‘organic’ growth in the production capacity from t to t þ1 meets the domestic demand).
Net-oil producing countries have the above attributes and operations in addition to those given below:
pa denotes the annual oil production of at. ca denotes the cumulative oil production of at at the beginning t
t
of time t.
ya denotes the oil yet to be produced by at at the beginning of t
2.1. Definitions and notations The ACEGES model is based upon the framework proposed by Voudouris (2011). The main building blocks of the framework are: (i) the agent (country), network of agents (e.g., OPEC) and the geoEnvironment (estimated ultimate recovery) which is represented by the Elementary_geoParticle (see Voudouris, 2010, for details about the Elementary_geoParticle). Each agent (country), the first building block, is composed of two main parts, namely the attributes and the operations. The attributes define the individual characteristics of the agents while the operations define the behavioural rules of the agents. In the current implementation of the ACEGES model, the netoil consuming agents have the following attributes (the subscript t is dropped when the variable is not dynamic):
Oil demand, da , of an agent at time t, at. Oil demand growth, ga, of a. Note that growth rate is countryt
specific but not time dynamic.
time t (or oil remaining at the end of the previous year). prenpat is a Boolean attribute that denotes if at is a pre-peak net producer. postnpat is a Boolean attribute that denotes if at is a post-peak net 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 decline phase 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 an UML (unified modelling language) activity diagram. The key idea is 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 4 0, the agent checks 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 net producer—it can cover its domestic demand given in Eq. (1). If it is a pre-peak net producer, then the following operation is selected: pat ¼ pat1 þga ndat1 þ wdat1
ð2Þ
Furthermore, the net-oil consuming agents have a single operation representing their individual demand for oil:
If the agent is not a pre-peak net producer (prenp¼ false), then Eq. (3) is selected:
dat ¼ ð1þ ga Þndat1
pat ¼ pat1 þga ndat1
ð1Þ
ð3Þ
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If the cumulative production is cat 4 ¼ ea npda , then Eq. (4) is selected: pat ¼ pat1 ðpat1 nðpat1 =yat1 ÞÞ
ð4Þ
Eq. (4) assumes pat =yat ¼ pat1 =yat1 which corresponds to the reserve to production ratio (R/P) being constant post-peak for each agent a. Eq. (2) uses wdat . This is given by wdat ¼ ðnwdt1 =nppnpt1 Þnðpat1 =mpt1 Þ
(iv)
ð5Þ
where nwdt1 is the net world demand at time t1, nppnpt1 is the total number of pre-peak net producers at t1, mpt1 is the mean production from the pre-peak net producers. Effectively, Eq. (5), which is a re-parametrisation of the equation used by Hallock et al. (2004), assumes that agents with larger pat1 would be able to increase production to meet net world demand. 2.2. Model initialisation and data Because the ACEGES model is a realistically rendered agentbased model, the initialisation of the model is based on the observational data. This requires to set a base year which in this paper is 2001. In other words, each of the 216 countries modelled in the ACEGES model is initialised with the real-world data as of 2001. It is a common practise to initialise the model with a base year that is before the ‘current’ year (2011). This is because the simulated data can be checked against the observational data using, for example, Kullback–Leibler divergence (Kullback and Leibler, 1951). Taking into account the ‘distance’ between observational and simulated data is one way to empirically validate the ACEGES model. Furthermore, historic oil production can be checked against a conceptual population (see Stuard and Ord, 1994) of simulated scenarios represented by smooth centile curves (see Figs. 2 and 4). The ACEGES model is initialised with the following data for each country (depending on the requirements of the scenario): (i) The domestic demand of oil in 2001 (total petroleum liquids—an ‘averaged proportion’ of the demand for liquefied petroleum gas), da2001 , from the United States Department of Energy (USDOE), Energy Information Administration (EIA). The ‘averaged proportion’ represents the part of the liquefied petroleum gas (LPG) consumption covered by the natural gas plant liquids (NGPL) production rather than crude oil production. (ii) The projected growth rates of oil demand, ga, using the projections of EIA International Energy Outlook 2002 (IEO02), International Energy Agency (IEA) and the three scenarios 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): Data available for 62 countries with global EUR of 1.9 trillion barrels; (b) US Geological Survey (USGS) World Petroleum Assessment 2002 (WPA02) EUR 5%-likely: Data for 52 countries with global EUR of 3 trillion barrels (excluding reserves growth); (c) Central Intelligence Agency (CIA) World Factbook 2010 (WFB10): Data available for 93 countries with global EUR of 2.4 trillion barrels. Note that CIA provides estimates of the proved reserves of oil at the beginning of 2009. Therefore, the CIA EUR is the sum of (i) the cumulative production for all the countries up to 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 the construction of EUR for 93 countries. This is important for nwdt1 used in Eq. (5). This is to say that by modelling more of the nations of the world, and having both production and
(v)
(vi) (vii)
(viii)
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demand for them, the model has a more accurate picture of the net demand for imports which is what is being apportioned among the pre-peak net producers. Having said that the CIA EUR should not be used alone as this is potentially a large underestimate of actual EUR for selected countries. The annual oil production for 2001 (crude oil including lease condensate), pa2001 , from the EIA International Energy Data, Analyses, and Forecasts. Because of the use of crude oil, we are really testing whether the EUR estimates, in the form of crude oil, generate results consistent with the empirical data. The difference between crude oil production and conventional oil production is significant for some countries such as Brazil, Angola, Canada and Venezuela. If the aim were to explore the outlook of conventional oil as defined by Campbell and Heapes (2008), we would need to adjust starting oil production, cumulative oil production, oil demand, and all production after 2001 to remove oil unconventional by their standards. 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 to 1994; (c) EIA’s International Energy Data, Analyses, and Forecasts from 1994 to 2001. Estimates of oil remaining at the start of 2001 (which is (iii) minus (v) above), ya2001 . The maximum allowable projected growth rates of oil production, gpa. This defines the constrained oil production from t to t þ1. This is defined based on literature review and our own calculations. Assumed peak/decline point (e.g., 0.5 of EUR), pda. This is defined based on literature review and our own calculations for post-peak countries.
Because the data is loaded into the ACEGES model from an external file, as new data becomes available the scenarios can be re-run to see the effect(s). Additional data (e.g., conventional oil) can also be added without changing the code of ACEGES laboratory. Therefore, the data above is just an indication of how the ACEGES model can be empirically initialised. One of the main advantages of the ACEGES model is the higher degree 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 Carlo process based on the uniform distribution Uða,bÞ. For:
MonteCarloEUR: a is the minimum of the CIA, USGS and
Campbell and Heapes estimates and b is the maximum of the 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 and WEO 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 an upper bound for production growth in Eqs. (2) and (3).
Depending on the desired degree of heterogeneity that is required for the scenario, all or some of the Monte Carlo processes can be adjusted. Note also that the random number generator for the Monte Carlo process is based on Mersenne Twister pseudorandom number generator (Matsumoto and Nishimura, 1998). Mersenne Twister is also used to randomly select the order of countries from the simulation engine. The economic reasoning of the randomly
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selected order of the countries is to ensure that no country has a permanent strategic advantage in producing its outputs. This can be turned off if a scenario is developed to explore the effects of a specific order of outputs or strategic behaviour from swing countries. It is important to note that the selection of the countries has no effect on the outcome of the simulation as reported here as the production decisions are based on ‘known’ data (information at t1) at the start of the simulation (simulation at time t). However, the reason for emphasising the use of the Mersenne Twister is only to demonstrate that if another algorithm is explored that depends on information at time t as well as t1, then the order of selecting the countries can affect the outcome as some countries will have more information at time t than others. This can happen if we change the (spatial and temporal) scale of the model by, for example, modelling individual fields on a monthly basis. Our view is that support for longterm policy might be difficult to achieve by a finer scale of analysis given the state of the publicly available data. When and if this changes, then alternatives can be explored. It is important to emphasise that despite the common practice to distinguish conventional oil from unconventional oil, there is confusion over what is measured as there is no common standard for doing so. This raises issues when different data sources are combined. For example, EIA Oil Production of Crude and Condensate (C&C) dataset includes a volume of production not included in Campbell and Heapes estimates of EUR. Furthermore, The USGS definition for the estimation of EUR differs in some ways from Campbell and Heapes definition for their estimation of the EUR. Therefore, the results of the simulated scenarios need to be interpreted as ‘approximations’ of oil production outlooks. In using crude data, we are not explicitly testing the accuracy of either USGS or Campbell and Heapes estimates of EUR. We are really testing whether those EUR estimates, in the form of crude oil, generate results consistent with the empirical data. An alternative option is to augment the above EUR estimates with estimates for oil shale, heavy oil, extra-heavy oil, deep-water oil and polar oil. As of today, the extraction of these resources of unconventional oil is difficult, costly and slow. Because these oil resources are subject to low and costly rates of extraction, their impact on the date or height of the crude oil peak is likely not to be substantial. These unconventional oil resources may smooth the post-peak decline rate.
g2 ðsi Þ ¼ h2 ðti Þ g3 ðni Þ ¼ h3 ðti Þ g4 ðti Þ ¼ h4 ðti Þ
ð7Þ
for i ¼ 1,2, . . . ,N where N is the total number of simulated observations pi, where for the SHASH distribution the parameter: (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 of the ith simulated observation pi. Note that here the two tails of the distributions are modelled separately. L specifies the smoothing hyper-parameters fl1 , l2 , l3 , l4 g.
The hyper-parameters fl1 , l2 , l3 , l4 g specify the amount of smoothing used in each of the smooth function fh1 ðti Þ,h2 ðti Þ, h3 ðti Þ,h4 ðti Þg. The hk’s are cubic smoothing splines. To aid the interpretation of the distribution of world production in Section 3, the following key parameter values need to be considered (see also Jones and Pewsey, 2009):
t ¼ n, the world oil production is symmetric. t ¼ n ¼ 1, the world oil production is the Gaussian distribution. t o n, the world oil production is positively skewed. t 4 n, the world oil production is negatively skewed. t o 1, the world oil production right tail is leptokurtic, t ¼ 1 mesokurtic and t 4 1 platykurtic. Note that t controls the right tail of the distribution.
n o 1, the world oil production left tail is leptokurtic, n ¼ 1 mesokurtic and n 4 1 platykurtic distribution. Note that n controls the left tail of the distribution. We also introduce the petroleum market diversity as a way of quantify the production diversity of the petroleum market. The petroleum 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 political implications (as a cheap energy source) of the increasing concentration (reduced supply diversity) of crude oil production. We measure the petroleum market diversity using: !1=k n X PMDk ðwðtÞÞ ¼ wki ðtÞ ð8Þ i
2.3. Model for analysis of simulated scenarios Once the simulated scenarios are designed and generated using the ACEGES model, a key issue is how to analyse them. Here, we propose the use of the GAMLSS (generalised additive models for location scale and shape) framework. In particular, the GAMLSS model, M ¼ fD,G,T , Lg, represents the following components:
where 0 o k o1, n is the number of oil producing countries and P wi(t) is given by pai = ni pai . Note pai , denotes the production of oil t t t by country (agent) i at time t. When PMDk increases, the petroleum market is more evenly distributed, when PMDk decreases, the petroleum market is more concentrated (reduced supply diversity). Note that Eq. (8) measures the diversity based upon the production. Quantifying the ‘export’ market diversity, requires the replacements of pai with wdai given by Eq. (5). t
t
D specifies the distribution, here a re-parametrisation of the SHASH (Sinh–Arcsinh) distribution developed in Jones and Pewsey (2009), of the simulated world oil production (response variable), p, c 2 fP ðpjm, s, n, tÞ ¼ pffiffiffiffiffiffi ez =2 2psð1 þ r 2 Þ1=2 1
ð6Þ 1
where z ¼ 12 fexp½t sinh ðrÞexp½n sinh ðrÞg, c ¼ 12 ftexp 1 1 ½t sinh ðrÞ þ nexp½n sinh ðrÞg and r ¼ ðpmÞ=s. Note 1 sinh ðrÞ ¼ logðuÞ where u ¼ r þ ðr 2 þ 1Þ1=2 . G specifies the set of link functions fg1 ðmÞ ¼ m,g2 ðsÞ ¼ logðsÞ,g3 ðnÞ ¼ logðnÞ,g4 ðtÞ ¼ logðtÞg: T specifies the predictor terms: g1 ðmi Þ ¼ h1 ðti Þ
3. Demonstrating the ACEGES model Because of the very large number of scenarios that can be developed and explored by the ACEGES model, here we present just two scenario designs to demonstrate the flexibility of the ACEGES model:
High–high heterogeneity scenario (H–H scenario): The Monte Carlo 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 interpreted as the ‘equally weighted collective view’ of the agencies of the data sources reported in Section 2.
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Campbell–Heapes scenario (C-H scenario): The Monte Carlo process for two key uncertainties (demand growth and production growth) and user-centred subjective values for EUR is based upon the Campbell and Heapes estimate of EUR and peak/decline point of 50%. Jakobsson et al. (2009) provide a critique of the implementation of the mechanistic approach (also termed the Maximum Depletion Rate Model) used by Wood et al. (2004). One of the critiques is related to the ðR=PÞmin of 10 based upon the US experience. This effectively highlights the issue with the representative country discussed in Section 1. Jakobsson et al. (2009) re-estimate the scenarios of Wood et al. (2004) by using a set of ðR=PÞmin values, namely 30, 50, 70. Using the results of the ACEGES model, the emergent ðR=PÞmin is in the ‘range’ of 15-40 (most likely range between 20-30). Therefore, if one wishes to model the world oil production explicitly, then the range of 15-40 is recommended as the ðR=PÞmin of the world oil production. Note that in the scenarios above the global ðR=PÞmin increases after it reaches a global minimum. This also demonstrates that although individual countries reach a stable global minimum of ðR=PÞmin at the peak, the emergent global ðR=PÞmin can have different dynamics than the country-specific ðR=PÞmin . These dynamics emerge because of the country-specific heterogeneity in production growth, demand growth and peak/decline point. The simulations show that the Petroleum Market Diversity reduces rapidly as we move towards the peak of the scenarios.
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What we also observe is that the diversity slightly increases in the post-peak period and then it reaches a relative equilibrium. Clearly, the degree of diversity is different in the scenarios but this difference is not wide given the designs of the scenarios. In it important to note that the dynamics of the Petroleum Market Diversity reported here are based upon the production rather than export capacity of the countries. The results of the two scenarios reported below demonstrate the design sensitivity of the scenarios and the need for controlled computational experiments for effective and exploratory energy policy. 3.1. High–high heterogeneity scenario Fig. 2 (left) also provides probabilistic forecasts associated with the world oil production. The H–H scenario shows a peak which is likely to happen between 2020 and 2030. Fig. 2 (right) are the smooth centile curves using the SHASH distribution of world oil production. The H–H scenario does not necessarily demonstrate an underlying ‘pathological’ condition. Note however that the smoothness of the centile curves (with a local and a global peak). The central 50% of projections are shown by the darkest grey area. Fig. 3 shows the fitted values of the m, s, n and t parameters of the SHASH distribution of world oil production for the H–H scenario. The m parameter, the median oil production, reaches its global maximum in the vicinity of 2012–2018. A 99% interval
Fig. 3. Dynamics of the parameters of SHASH distribution: H–H scenario.
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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.
for the peak year of the maximum production is (2008, 2027). A 99% interval for the peak production (in million barrels per year) is from 24,831,836 to 37,336,533.2 The scale parameter s reaches 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 2050 onwards the level of uncertainty in terms of the s parameter stabilises. A local maximum of uncertainty is also observed in 2008. Towards the peak year/period, t 4 n. This means that the distribution of world oil production is negatively skewed. This effectively gives an estimate of the balance of risks around that central projection. Overall, the distribution of world oil production is positively skewed as t o n from about 2020 onwards. The right tail of the distribution of world oil production is highly leptokurtic until 2018 since t o 1 and platukurtic from 2018 onwards. The left tail is leptokurtic until 2022 since n o 1 and platukurtic after 2022. Overall, this means that there are more chances of extreme outliers of oil production in the left tail of the distribution from 2002 to 2022. This is consistent with the recent historic data of oil production. It is important to emphasise that this consistency has not been forced by the ACEGES model, which is initialised with the 2001 observational data. Therefore, from 2002 onwards the model runs without further interventions. It is possible, however, to pause the simulated scenario, change some of the key uncertain variables and continue the scenario in order to explore the effects of time-dependent shocks. 3.2. Campbell–Heapes scenario Fig. 4 (left) also provides probabilistic forecasts associated with 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 the smooth centile curves using the SHASH distribution of world oil production. The C–H scenario does not necessarily demonstrate an underlying ‘pathological’ condition. The central 50% of projections are shown by the darkest grey area. It is noticeable that the historical production is particularly well encapsulated by the central projection. It should be emphasised that the unconventional oil resources (e.g., polar oil, deep-water oil, heavy oil, extraheavy oil) are likely to ameliorate the rapid decline rate of the scenario. As discussed above because unconventional oil resources are subject to low and costly rates of extraction, the more time passes without significant technological improvements that allow unconventional oil production to increase quickly enough, the greater those developments will have to be to significantly change the peak profile of the scenario. Fig. 5 shows the fitted values of the m, s, n and t parameters of the SHASH distribution of world oil production for the C–H scenario. The m parameter, the median oil production, reaches its global maximum just between 2008 and 2015. A 99% interval for the peak year of the maximum production is (2008, 2012). A 99% interval for the peak production (in million barrels per year) is from 25,124,441 to 31,028,356. The scale parameter s reaches a local maximum around 2013 by means of a rapid increase which means a higher uncertainty of oil production as we move towards 2013. This uncertainty is rapidly reduced until 2030. After 2030, the scale increases rapidly. From 2002 until about 2020 and from 2030 onwards, the distribution of world oil production is positively skewed as n 4 t. This gives an estimate of the balance of risk around the central projection. The right tail of the distribution of world oil production is leptokurtic until 2014 and then highly platykurtic since t 41, while the left tail is platykurtic throughout since n 4 1. This means that there are less changes for extreme outliers of oil production in the tails of the distribution after the peak year. It is important to emphasise that although the C–H scenario seems to be a better fit to empirical oil production from 2002 to 2010, care is needed not to over-fit the
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Fig. 5. Dynamics of the parameters of SHASH distribution: C–H scenario.
model and penalise ‘forces in the pipeline’. For example, is the recent production figures the outcome of the great recession or the outcome of production constrains due to unavoidable constrains? The C–H scenario has been developed to demonstrate by way of comparison the outcomes of the ACEGES model when the ‘heterogeneity’ is reduced. This reduction in heterogeneity is achieved by fixing two key uncertainty variables with specific values while using country-specific distributions for the other two variables. Note that if we fix all the variables with specific values, then the ACEGES results are line forecasts rather than probability forecasts. In our view, none of the scenarios presented here demonstrate a ‘pathological condition’ and all of them are plausible portrayals of the future of crude oil production given the estimated EUR. The historical production need not be in the central projection area as every centile has an equal probability of encapsulating historical productions. The important point is that the historical production must be within the visible probability bands of the selected centile curves (see also Elder et al., 2005). For example, Fig. 6 shows that although the historical production is encapsulated well until 2008 (with a 99% interval for the peak production year between 2012 and 2034), this scenario, which uses the high USGS estimates of EUR, demonstrate signs of a ‘pathological condition’. Clearly, this needs to be interpreted with care as the recent drop in oil production might be the outcome of temporary conditions (e.g., great recession) rather than a fundamental shift in the dynamics of oil production.
4. Conclusions This paper sets out to demonstrate empirically the potential of the ACEGES decision-support tool in answering the question: So, what do we know about the future of crude oil production? We recognise that nobody can predict the future evolution of the oil market with absolute certainty. It is more realistic for analysts to recognise that uncertainty when describing their scenarios. Consequently, ACEGES has been developed to present oil scenarios in probability terms using graphical representations of those probabilities. It is demonstrated that the ACEGES model offers a new and novel way for the representation and scientific investigation of the dynamics of the oil system and more generally of the global energy system. This is achieved by bringing a number of fundamental concepts under a single umbrella such as the reconciliation of econometric and resource-constrained models (see also Kaufmann, 1991) at the agent level. It also introduces a high degree of heterogeneity in order to explore better the dynamics of the emergent explananda (world oil production) for effective energy policy. It is important to note that the ACEGES model abstracts from explicit modelling of reservoir behaviour. However, it implicitly approximates reservoir behaviour by dividing the ‘production’ profile into two different phases. Although the decline phase takes no direct account of the mechanical and chemical aids to induce artificial lift, it captures a probabilistic distribution of decline rates.
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Fig. 6. World oil production for a pathological scenario.
The ACEGES model only approximates the adaptation of new production technologies to address recovery efficiency by a probabilistic sample of publicly available EUR data. We assume the published EUR estimates (USGS and otherwise) to already include the manifestation of possible future advances in recovery rates. The ACEGES laboratory can simulate a very large number of scenarios by adjusting (interactively or using the Monte Carlo process) any of the key uncertain variables, namely resource estimates, demand growth, production growth and peak/decline point. We presented three different simulated scenarios of crude oil production. These scenarios were analysed in the GAMLSS framework by selecting the SHASH distribution. Given the estimated EUR, the simulations suggest that the peak of global production of crude oil may happen somewhere between 2008 and 2027. Using the petroleum market diversity, we also observe a reduced supply diversity in the broad vicinity of the peak year. As the research programme of economics and physical sciences progresses, the aim of building an integrated theory for exploratory energy policy by means of controlled computational experiments will be within sight. The work presented here suggests a way forward through the development of the ACEGES model.
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Campbell, C., 1996. The status of world oil depletion at the end of 1995. Energy Exploration and Exploitation 14, 63–81. Campbell, C., 1997. Depletion patterns show change due for production of conventional oil. Oil and Gas Journal 95, 33–37. Campbell, C., Heapes, S., 2008. An Atlas of Oil and Gas Depletion. Jeremy Mills Publishing ISBN-10: 1906600260. Central Intelligence Agency, 2010. World Factbook 2010. Available from: /https:// www.cia.gov/library/publications/the-world-factbookS. DeGolyer and MacNaughton inc., 2006. 20th century petroleum statistics, 2005. Technical Report. Elder, R., Kapetanios, G., Taylor, T., Yates, T., 2005. Assessing the MPC’s Fan Charts. Quarterly Bulletin, Bank of England, pp. 326–348. Available from: /http:// www.bankofengland.co.uk/publications/quarterlybulletin/qb050301.pdfS. Energy Information Administration, 2002. International Energy Outlook 2002. US Energy Information Administration. Available from: /http://tonto.eia.doe.gov/ ftproot/forecasting/0484(2002).pdfS. Epstein, J., 2007. Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press, Oxfordshire. Farmer, D., Foley, D., 2009. The economy needs agent-based modelling. Nature 460 (August), 685–686. Fernholz, R., 1999. On the diversity of equity markets. Journal of Mathematical Economics 31, 393–417. Hall, C., Tharakan, P., Hallock, J., Cleveland, C., Jefferson, M., 2003. Hydrocarbons and the evolution of human culture. Nature 426, 318–322. Hallock, J., Tharakan, P., Hall, C., Jefferson, M., Wei, W., 2004. Forecasting the limits to the availability and diversity of global conventional oil supply. Energy 29, 1673–1696. Hamilton, J., 2003. What is an oil shock? Journal of Econometrics 113, 363–398. International Energy Agency, 2009. World Energy Outlook 2009. OECD/IEA. Available from: /http://www.iea.org/W/bookshop/add.aspx?id=388S. International Energy Agency, 2010. World Energy Outlook 2010. OECD/IEA. Available from: /http://www.iea.org/W/bookshop/add.aspx?id=422S. Jakobsson, K., Soderbergh, B., Hook, M., Aleklett, K., 2009. How reasonable are oil production scenarios from public agencies? Energy Policy 37, 4809–4818. Jefferson, M., Voudouris, V., 2011, Oil Scenarios for Long-Term Business Planning: Royal Dutch Shell and Generative Explanation, 1960–2010. MPRA 27910 Paper, University Library of Munich, Germany. Available from: /http:// econpapers.repec.org/paper/pramprapa/27910.htmS. Jones, M., Pewsey, A., 2009. Sinh-arcsinh distributions. Biometrika 96, 761–780. Kaufmann, R., 1991. Oil production in the lower 48 states: reconciling curve fitting and econometric models. Resources and Energy 13, 111–127.
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Kilian, L., 2008. A comparison of the effects of exogenous oil supply shocks on output and inflation in the G7 countries. Journal of the European Economic Association 6, 78–121. Kullback, S., Leibler, R.A., 1951. On information and sufficiency. Annals of Mathematical Statistics 22, 79–86. Leclerc, G., Hall, C., 2007. Making World Development Work: Scientific Alternatives to Neoclassical Economic Theory. University of New Mexico Press ISBN-10: 0826337333. Li, B., Tesfatsion, L., 2009. Development of open source software for power market research: the ames test bed. Journal of Energy Markets 2, 111–128. Matsumoto, M., Nishimura, T., 1998. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation 8, 3–30. Nashawi, I., Malallah, A., Al-Bisharah, M., 2010. Forecasting world crude oil production using multicyclic Hubbert model. Energy Fuels 24, 1788–1800. Rigby, R.A., Stasinopoulos, D.M., 2005. Generalized additive models for location, scale and shape, (with discussion). Applied Statistics 54, 507–554.
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Contents lists available at ScienceDirect
Energy Policy journal homepage: www.elsevier.com/locate/enpol
The ACEGES laboratory for energy policy: Exploring the production 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, UK Statistics, Operational Research and Mathematics Centre, London Metropolitan University, Holloway Road, London N7 8DB, UK c Bocconi University, Via Sarfatti, 25, Milan, Italy b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 September 2010 Accepted 10 May 2011 Available online 31 May 2011
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.
Keywords: Oil depletion Oil scenario generation ACEGES
1. Introduction Oil was already becoming an important commodity by the late nineteenth century, and economic activities have only become more dependent on the energy and products it provides (e.g., plastics, chemicals and drugs). Industrialised nations have taken for granted an uninterrupted supply of the cheap hydrocarbon which fuels booming while volatile production and high prices 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 renders probabilistic statements (rather than point forecasts) of oil production 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 (either an econometric- or physical-centred model) are based on the concept of representative country (also called the fallacy of division) and conceptualise the oil production system as consisting of several identical and isolated components. This means that based on historical productions of key producers (e.g., US), the world oil production is assumed to have (more or less) the same production characteristics. Corresponding author at: Centre for International Business and Sustainability, London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London EC2M 6SQ, UK. Tel.: þ 44 20 7320 1409; fax: þ 44 20 7320 1585. E-mail address: [email protected] (V. Voudouris).
0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.05.014
This is effectively the attribution of properties to a different level than where the property is observed. Even when individual countries are modelled (e.g., Hallock et al., 2004), key uncertain variables (e.g., peak/decline point and remaining reserve/production ratio) of the model are assumed homogeneous for all the countries. Resource-constrained models are the most widely used type of models for long-term forecasts of oil production (see Jakobsson et 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 use computation to identify the best fitted curve using historical productions and then the best fitted curve is used to model future productions (see Bentley and Boyle, 2008, for a review of the curvefitting approach). The curve-fitting models are effectively non-linear regression models. The mechanistic model (e.g., Wood et al., 2004; Campbell, 1997) assumes a production growth until an assumed minimum reserve/production, ðR=PÞmin , ratio is reached. The assumed ðR=PÞmin is in most cases the same for all the countries and/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 a mechanistic model for scenarios of oil production, here we propose a computational laboratory, termed ACEGES (agent-based computational economics of the global energy system), for exploratory energy policy by means of controlled computational experiments. The ACEGES model uses the framework proposed by Voudouris
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(2011) and has been developed based upon the agent-based computational economics (ACE) modelling paradigm. ACE is the computational study of processes modelled as dynamic systems of interacting and heterogeneous agents. Agents operate in an environment on which they live and with which they interact. Agents engage repeatedly in interactions over time that generate emergent global regularities. ACE provides a new approach to the explanation of complex phenomena, in which one ‘grows’ the macro-phenomenon of interest, from sets of microfoundations. Therefore, there is a causality between micro-behaviours, interaction patterns and global regularities (Tesfatsion, 2006). Thus, ACE proposes a research method that uses artificial societies as its principal scientific instrument (Epstein, 2007). ACE can also be seen as computerised simulations of a number of decision-makers (e.g., countries) and institutions (e.g., OPEC), which interact though prescribed rules (e.g., Fig. 1). ACE models do not rely on the assumption that the (energy) system will move towards a predetermined state or profile (e.g., Hubbert curve). Instead, at any given time, each agent (country) acts according to its current situation (e.g., current oil production), the state of the world around it (e.g., net unmet world demand for oil) and the rules governing its behaviour. Following Farmer and Foley (2009), because ACE can handle a far wider range of non-linear behaviour than the conventional approaches, policy-makers can simulate an artificial energy system under different policy scenarios and quantitatively explore their consequences or how likely is the energy 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 would allow regulators to test policies and explore their emergent effects on the system. Therefore, if ‘wind tunnels’ and related simulation methods work in the physical world (e.g., testing the essential aerodynamic features of scale-model bridges), then computational experiments can also work for energy policy. ACE computational laboratories are beginning to enter the policy-making process as decision-support tools (e.g., Li and Tesfatsion, 2009). For example, the ACEGES laboratory, as a scenario development tool for exploratory energy policy, uses the Model panel to set the parameters of the simulated scenario using two complementary ways, namely a user-centred approach and a mathematically centred approach as discussed by Jefferson and Voudouris (2011). Note that the Model panel allows the policy-makers to modify the parameters affecting the entire simulation and/or the parameters affecting
[oil production >0]
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a country. A demo version of the ACEGES software is available at www.aceges.org. Section 2 details the ACEGES model, particularly the oil production decision rule of the agents (countries). Because the ACEGES model is a realistically rendered agent-based model, it also discusses how the model is initialised with observational data and how heterogeneity is introduced. This section also discusses the GAMLSS (generalised additive models for location scale and shape) framework developed in Rigby and Stasinopoulos (2005) as a way of analysing the simulated scenarios. Section 3 presents the results of the high–high heterogeneity scenario and the Campbell–Heapes scenario. 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 the ACEGES model is mainly a resource-constrained model, there are two features of the ACEGES model that represent concepts from economics:
The model includes a variable for the domestic oil demand
growth. The domestic oil demand growth is independent from the domestic oil production growth. There is a simplified trade (interactions) between the countries. The simplified trade is represented by Eq. (5). This simplified trade is not a shortcoming of the model since it can grow the observed macroscopic explananda (see Figs. 2 and 4). Note that the ACE modelling paradigm aims to grow the macroscopic regularity from simple (interacting) micro-foundations.
Following Campbell (1996), Eq. (2) represents the production decision of the swing countries. This decision is based on the assumption that (i) the swing countries will continue to produce oil in order to fulfill the net world demand for oil (world demand– world production) and (ii) the swing countries will not produce oil at their maximum capacity unless it is necessary. Therefore, they will choose to produce the minimum between their production capacity and Eq. (2). This is effectively an approximation of the consumers’ logic first developed by Royal Dutch Shell in the 1970s. It is also important to clarify here that Eq. (3) is adjusted (where necessary) based upon a maximum allowable (country-specific)
[cumulative oil production < peak point x EUR]
[prenp=true]
Equation (2)
[prenp=false] [cumulative oil production >= peak point x EUR]
Equation (3)
[oil production <=0] Equation (4)
Fig. 1. Simplified behavioural rule for oil production. ‘Rounded rectangles’ represent the operations and the ‘diamonds’ represent the decision points of the agents.
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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.
production growth from t to t þ 1. This maximum (country-specific) production growth represents both unavoidable geological limits as well as above ground factors such as activities by the movement 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 important, for example, in cases where a country (e.g., pre-peak producer) has enough reserves, but it cannot meet its domestic demand for oil because of below and/or above ground constrains or because it is uneconomical to further stimulate capacity growth (since it can be less expensive to import oil until the ‘organic’ growth in the production capacity from t to t þ1 meets the domestic demand).
Net-oil producing countries have the above attributes and operations in addition to those given below:
pa denotes the annual oil production of at. ca denotes the cumulative oil production of at at the beginning t
t
of time t.
ya denotes the oil yet to be produced by at at the beginning of t
2.1. Definitions and notations The ACEGES model is based upon the framework proposed by Voudouris (2011). The main building blocks of the framework are: (i) the agent (country), network of agents (e.g., OPEC) and the geoEnvironment (estimated ultimate recovery) which is represented by the Elementary_geoParticle (see Voudouris, 2010, for details about the Elementary_geoParticle). Each agent (country), the first building block, is composed of two main parts, namely the attributes and the operations. The attributes define the individual characteristics of the agents while the operations define the behavioural rules of the agents. In the current implementation of the ACEGES model, the netoil consuming agents have the following attributes (the subscript t is dropped when the variable is not dynamic):
Oil demand, da , of an agent at time t, at. Oil demand growth, ga, of a. Note that growth rate is countryt
specific but not time dynamic.
time t (or oil remaining at the end of the previous year). prenpat is a Boolean attribute that denotes if at is a pre-peak net producer. postnpat is a Boolean attribute that denotes if at is a post-peak net 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 decline phase 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 an UML (unified modelling language) activity diagram. The key idea is 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 4 0, the agent checks 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 net producer—it can cover its domestic demand given in Eq. (1). If it is a pre-peak net producer, then the following operation is selected: pat ¼ pat1 þga ndat1 þ wdat1
ð2Þ
Furthermore, the net-oil consuming agents have a single operation representing their individual demand for oil:
If the agent is not a pre-peak net producer (prenp¼ false), then Eq. (3) is selected:
dat ¼ ð1þ ga Þndat1
pat ¼ pat1 þga ndat1
ð1Þ
ð3Þ
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If the cumulative production is cat 4 ¼ ea npda , then Eq. (4) is selected: pat ¼ pat1 ðpat1 nðpat1 =yat1 ÞÞ
ð4Þ
Eq. (4) assumes pat =yat ¼ pat1 =yat1 which corresponds to the reserve to production ratio (R/P) being constant post-peak for each agent a. Eq. (2) uses wdat . This is given by wdat ¼ ðnwdt1 =nppnpt1 Þnðpat1 =mpt1 Þ
(iv)
ð5Þ
where nwdt1 is the net world demand at time t1, nppnpt1 is the total number of pre-peak net producers at t1, mpt1 is the mean production from the pre-peak net producers. Effectively, Eq. (5), which is a re-parametrisation of the equation used by Hallock et al. (2004), assumes that agents with larger pat1 would be able to increase production to meet net world demand. 2.2. Model initialisation and data Because the ACEGES model is a realistically rendered agentbased model, the initialisation of the model is based on the observational data. This requires to set a base year which in this paper is 2001. In other words, each of the 216 countries modelled in the ACEGES model is initialised with the real-world data as of 2001. It is a common practise to initialise the model with a base year that is before the ‘current’ year (2011). This is because the simulated data can be checked against the observational data using, for example, Kullback–Leibler divergence (Kullback and Leibler, 1951). Taking into account the ‘distance’ between observational and simulated data is one way to empirically validate the ACEGES model. Furthermore, historic oil production can be checked against a conceptual population (see Stuard and Ord, 1994) of simulated scenarios represented by smooth centile curves (see Figs. 2 and 4). The ACEGES model is initialised with the following data for each country (depending on the requirements of the scenario): (i) The domestic demand of oil in 2001 (total petroleum liquids—an ‘averaged proportion’ of the demand for liquefied petroleum gas), da2001 , from the United States Department of Energy (USDOE), Energy Information Administration (EIA). The ‘averaged proportion’ represents the part of the liquefied petroleum gas (LPG) consumption covered by the natural gas plant liquids (NGPL) production rather than crude oil production. (ii) The projected growth rates of oil demand, ga, using the projections of EIA International Energy Outlook 2002 (IEO02), International Energy Agency (IEA) and the three scenarios 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): Data available for 62 countries with global EUR of 1.9 trillion barrels; (b) US Geological Survey (USGS) World Petroleum Assessment 2002 (WPA02) EUR 5%-likely: Data for 52 countries with global EUR of 3 trillion barrels (excluding reserves growth); (c) Central Intelligence Agency (CIA) World Factbook 2010 (WFB10): Data available for 93 countries with global EUR of 2.4 trillion barrels. Note that CIA provides estimates of the proved reserves of oil at the beginning of 2009. Therefore, the CIA EUR is the sum of (i) the cumulative production for all the countries up to 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 the construction of EUR for 93 countries. This is important for nwdt1 used in Eq. (5). This is to say that by modelling more of the nations of the world, and having both production and
(v)
(vi) (vii)
(viii)
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demand for them, the model has a more accurate picture of the net demand for imports which is what is being apportioned among the pre-peak net producers. Having said that the CIA EUR should not be used alone as this is potentially a large underestimate of actual EUR for selected countries. The annual oil production for 2001 (crude oil including lease condensate), pa2001 , from the EIA International Energy Data, Analyses, and Forecasts. Because of the use of crude oil, we are really testing whether the EUR estimates, in the form of crude oil, generate results consistent with the empirical data. The difference between crude oil production and conventional oil production is significant for some countries such as Brazil, Angola, Canada and Venezuela. If the aim were to explore the outlook of conventional oil as defined by Campbell and Heapes (2008), we would need to adjust starting oil production, cumulative oil production, oil demand, and all production after 2001 to remove oil unconventional by their standards. 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 to 1994; (c) EIA’s International Energy Data, Analyses, and Forecasts from 1994 to 2001. Estimates of oil remaining at the start of 2001 (which is (iii) minus (v) above), ya2001 . The maximum allowable projected growth rates of oil production, gpa. This defines the constrained oil production from t to t þ1. This is defined based on literature review and our own calculations. Assumed peak/decline point (e.g., 0.5 of EUR), pda. This is defined based on literature review and our own calculations for post-peak countries.
Because the data is loaded into the ACEGES model from an external file, as new data becomes available the scenarios can be re-run to see the effect(s). Additional data (e.g., conventional oil) can also be added without changing the code of ACEGES laboratory. Therefore, the data above is just an indication of how the ACEGES model can be empirically initialised. One of the main advantages of the ACEGES model is the higher degree 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 Carlo process based on the uniform distribution Uða,bÞ. For:
MonteCarloEUR: a is the minimum of the CIA, USGS and
Campbell and Heapes estimates and b is the maximum of the 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 and WEO 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 an upper bound for production growth in Eqs. (2) and (3).
Depending on the desired degree of heterogeneity that is required for the scenario, all or some of the Monte Carlo processes can be adjusted. Note also that the random number generator for the Monte Carlo process is based on Mersenne Twister pseudorandom number generator (Matsumoto and Nishimura, 1998). Mersenne Twister is also used to randomly select the order of countries from the simulation engine. The economic reasoning of the randomly
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selected order of the countries is to ensure that no country has a permanent strategic advantage in producing its outputs. This can be turned off if a scenario is developed to explore the effects of a specific order of outputs or strategic behaviour from swing countries. It is important to note that the selection of the countries has no effect on the outcome of the simulation as reported here as the production decisions are based on ‘known’ data (information at t1) at the start of the simulation (simulation at time t). However, the reason for emphasising the use of the Mersenne Twister is only to demonstrate that if another algorithm is explored that depends on information at time t as well as t1, then the order of selecting the countries can affect the outcome as some countries will have more information at time t than others. This can happen if we change the (spatial and temporal) scale of the model by, for example, modelling individual fields on a monthly basis. Our view is that support for longterm policy might be difficult to achieve by a finer scale of analysis given the state of the publicly available data. When and if this changes, then alternatives can be explored. It is important to emphasise that despite the common practice to distinguish conventional oil from unconventional oil, there is confusion over what is measured as there is no common standard for doing so. This raises issues when different data sources are combined. For example, EIA Oil Production of Crude and Condensate (C&C) dataset includes a volume of production not included in Campbell and Heapes estimates of EUR. Furthermore, The USGS definition for the estimation of EUR differs in some ways from Campbell and Heapes definition for their estimation of the EUR. Therefore, the results of the simulated scenarios need to be interpreted as ‘approximations’ of oil production outlooks. In using crude data, we are not explicitly testing the accuracy of either USGS or Campbell and Heapes estimates of EUR. We are really testing whether those EUR estimates, in the form of crude oil, generate results consistent with the empirical data. An alternative option is to augment the above EUR estimates with estimates for oil shale, heavy oil, extra-heavy oil, deep-water oil and polar oil. As of today, the extraction of these resources of unconventional oil is difficult, costly and slow. Because these oil resources are subject to low and costly rates of extraction, their impact on the date or height of the crude oil peak is likely not to be substantial. These unconventional oil resources may smooth the post-peak decline rate.
g2 ðsi Þ ¼ h2 ðti Þ g3 ðni Þ ¼ h3 ðti Þ g4 ðti Þ ¼ h4 ðti Þ
ð7Þ
for i ¼ 1,2, . . . ,N where N is the total number of simulated observations pi, where for the SHASH distribution the parameter: (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 of the ith simulated observation pi. Note that here the two tails of the distributions are modelled separately. L specifies the smoothing hyper-parameters fl1 , l2 , l3 , l4 g.
The hyper-parameters fl1 , l2 , l3 , l4 g specify the amount of smoothing used in each of the smooth function fh1 ðti Þ,h2 ðti Þ, h3 ðti Þ,h4 ðti Þg. The hk’s are cubic smoothing splines. To aid the interpretation of the distribution of world production in Section 3, the following key parameter values need to be considered (see also Jones and Pewsey, 2009):
t ¼ n, the world oil production is symmetric. t ¼ n ¼ 1, the world oil production is the Gaussian distribution. t o n, the world oil production is positively skewed. t 4 n, the world oil production is negatively skewed. t o 1, the world oil production right tail is leptokurtic, t ¼ 1 mesokurtic and t 4 1 platykurtic. Note that t controls the right tail of the distribution.
n o 1, the world oil production left tail is leptokurtic, n ¼ 1 mesokurtic and n 4 1 platykurtic distribution. Note that n controls the left tail of the distribution. We also introduce the petroleum market diversity as a way of quantify the production diversity of the petroleum market. The petroleum 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 political implications (as a cheap energy source) of the increasing concentration (reduced supply diversity) of crude oil production. We measure the petroleum market diversity using: !1=k n X PMDk ðwðtÞÞ ¼ wki ðtÞ ð8Þ i
2.3. Model for analysis of simulated scenarios Once the simulated scenarios are designed and generated using the ACEGES model, a key issue is how to analyse them. Here, we propose the use of the GAMLSS (generalised additive models for location scale and shape) framework. In particular, the GAMLSS model, M ¼ fD,G,T , Lg, represents the following components:
where 0 o k o1, n is the number of oil producing countries and P wi(t) is given by pai = ni pai . Note pai , denotes the production of oil t t t by country (agent) i at time t. When PMDk increases, the petroleum market is more evenly distributed, when PMDk decreases, the petroleum market is more concentrated (reduced supply diversity). Note that Eq. (8) measures the diversity based upon the production. Quantifying the ‘export’ market diversity, requires the replacements of pai with wdai given by Eq. (5). t
t
D specifies the distribution, here a re-parametrisation of the SHASH (Sinh–Arcsinh) distribution developed in Jones and Pewsey (2009), of the simulated world oil production (response variable), p, c 2 fP ðpjm, s, n, tÞ ¼ pffiffiffiffiffiffi ez =2 2psð1 þ r 2 Þ1=2 1
ð6Þ 1
where z ¼ 12 fexp½t sinh ðrÞexp½n sinh ðrÞg, c ¼ 12 ftexp 1 1 ½t sinh ðrÞ þ nexp½n sinh ðrÞg and r ¼ ðpmÞ=s. Note 1 sinh ðrÞ ¼ logðuÞ where u ¼ r þ ðr 2 þ 1Þ1=2 . G specifies the set of link functions fg1 ðmÞ ¼ m,g2 ðsÞ ¼ logðsÞ,g3 ðnÞ ¼ logðnÞ,g4 ðtÞ ¼ logðtÞg: T specifies the predictor terms: g1 ðmi Þ ¼ h1 ðti Þ
3. Demonstrating the ACEGES model Because of the very large number of scenarios that can be developed and explored by the ACEGES model, here we present just two scenario designs to demonstrate the flexibility of the ACEGES model:
High–high heterogeneity scenario (H–H scenario): The Monte Carlo 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 interpreted as the ‘equally weighted collective view’ of the agencies of the data sources reported in Section 2.
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Campbell–Heapes scenario (C-H scenario): The Monte Carlo process for two key uncertainties (demand growth and production growth) and user-centred subjective values for EUR is based upon the Campbell and Heapes estimate of EUR and peak/decline point of 50%. Jakobsson et al. (2009) provide a critique of the implementation of the mechanistic approach (also termed the Maximum Depletion Rate Model) used by Wood et al. (2004). One of the critiques is related to the ðR=PÞmin of 10 based upon the US experience. This effectively highlights the issue with the representative country discussed in Section 1. Jakobsson et al. (2009) re-estimate the scenarios of Wood et al. (2004) by using a set of ðR=PÞmin values, namely 30, 50, 70. Using the results of the ACEGES model, the emergent ðR=PÞmin is in the ‘range’ of 15-40 (most likely range between 20-30). Therefore, if one wishes to model the world oil production explicitly, then the range of 15-40 is recommended as the ðR=PÞmin of the world oil production. Note that in the scenarios above the global ðR=PÞmin increases after it reaches a global minimum. This also demonstrates that although individual countries reach a stable global minimum of ðR=PÞmin at the peak, the emergent global ðR=PÞmin can have different dynamics than the country-specific ðR=PÞmin . These dynamics emerge because of the country-specific heterogeneity in production growth, demand growth and peak/decline point. The simulations show that the Petroleum Market Diversity reduces rapidly as we move towards the peak of the scenarios.
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What we also observe is that the diversity slightly increases in the post-peak period and then it reaches a relative equilibrium. Clearly, the degree of diversity is different in the scenarios but this difference is not wide given the designs of the scenarios. In it important to note that the dynamics of the Petroleum Market Diversity reported here are based upon the production rather than export capacity of the countries. The results of the two scenarios reported below demonstrate the design sensitivity of the scenarios and the need for controlled computational experiments for effective and exploratory energy policy. 3.1. High–high heterogeneity scenario Fig. 2 (left) also provides probabilistic forecasts associated with the world oil production. The H–H scenario shows a peak which is likely to happen between 2020 and 2030. Fig. 2 (right) are the smooth centile curves using the SHASH distribution of world oil production. The H–H scenario does not necessarily demonstrate an underlying ‘pathological’ condition. Note however that the smoothness of the centile curves (with a local and a global peak). The central 50% of projections are shown by the darkest grey area. Fig. 3 shows the fitted values of the m, s, n and t parameters of the SHASH distribution of world oil production for the H–H scenario. The m parameter, the median oil production, reaches its global maximum in the vicinity of 2012–2018. A 99% interval
Fig. 3. Dynamics of the parameters of SHASH distribution: H–H scenario.
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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.
for the peak year of the maximum production is (2008, 2027). A 99% interval for the peak production (in million barrels per year) is from 24,831,836 to 37,336,533.2 The scale parameter s reaches 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 2050 onwards the level of uncertainty in terms of the s parameter stabilises. A local maximum of uncertainty is also observed in 2008. Towards the peak year/period, t 4 n. This means that the distribution of world oil production is negatively skewed. This effectively gives an estimate of the balance of risks around that central projection. Overall, the distribution of world oil production is positively skewed as t o n from about 2020 onwards. The right tail of the distribution of world oil production is highly leptokurtic until 2018 since t o 1 and platukurtic from 2018 onwards. The left tail is leptokurtic until 2022 since n o 1 and platukurtic after 2022. Overall, this means that there are more chances of extreme outliers of oil production in the left tail of the distribution from 2002 to 2022. This is consistent with the recent historic data of oil production. It is important to emphasise that this consistency has not been forced by the ACEGES model, which is initialised with the 2001 observational data. Therefore, from 2002 onwards the model runs without further interventions. It is possible, however, to pause the simulated scenario, change some of the key uncertain variables and continue the scenario in order to explore the effects of time-dependent shocks. 3.2. Campbell–Heapes scenario Fig. 4 (left) also provides probabilistic forecasts associated with 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 the smooth centile curves using the SHASH distribution of world oil production. The C–H scenario does not necessarily demonstrate an underlying ‘pathological’ condition. The central 50% of projections are shown by the darkest grey area. It is noticeable that the historical production is particularly well encapsulated by the central projection. It should be emphasised that the unconventional oil resources (e.g., polar oil, deep-water oil, heavy oil, extraheavy oil) are likely to ameliorate the rapid decline rate of the scenario. As discussed above because unconventional oil resources are subject to low and costly rates of extraction, the more time passes without significant technological improvements that allow unconventional oil production to increase quickly enough, the greater those developments will have to be to significantly change the peak profile of the scenario. Fig. 5 shows the fitted values of the m, s, n and t parameters of the SHASH distribution of world oil production for the C–H scenario. The m parameter, the median oil production, reaches its global maximum just between 2008 and 2015. A 99% interval for the peak year of the maximum production is (2008, 2012). A 99% interval for the peak production (in million barrels per year) is from 25,124,441 to 31,028,356. The scale parameter s reaches a local maximum around 2013 by means of a rapid increase which means a higher uncertainty of oil production as we move towards 2013. This uncertainty is rapidly reduced until 2030. After 2030, the scale increases rapidly. From 2002 until about 2020 and from 2030 onwards, the distribution of world oil production is positively skewed as n 4 t. This gives an estimate of the balance of risk around the central projection. The right tail of the distribution of world oil production is leptokurtic until 2014 and then highly platykurtic since t 41, while the left tail is platykurtic throughout since n 4 1. This means that there are less changes for extreme outliers of oil production in the tails of the distribution after the peak year. It is important to emphasise that although the C–H scenario seems to be a better fit to empirical oil production from 2002 to 2010, care is needed not to over-fit the
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Fig. 5. Dynamics of the parameters of SHASH distribution: C–H scenario.
model and penalise ‘forces in the pipeline’. For example, is the recent production figures the outcome of the great recession or the outcome of production constrains due to unavoidable constrains? The C–H scenario has been developed to demonstrate by way of comparison the outcomes of the ACEGES model when the ‘heterogeneity’ is reduced. This reduction in heterogeneity is achieved by fixing two key uncertainty variables with specific values while using country-specific distributions for the other two variables. Note that if we fix all the variables with specific values, then the ACEGES results are line forecasts rather than probability forecasts. In our view, none of the scenarios presented here demonstrate a ‘pathological condition’ and all of them are plausible portrayals of the future of crude oil production given the estimated EUR. The historical production need not be in the central projection area as every centile has an equal probability of encapsulating historical productions. The important point is that the historical production must be within the visible probability bands of the selected centile curves (see also Elder et al., 2005). For example, Fig. 6 shows that although the historical production is encapsulated well until 2008 (with a 99% interval for the peak production year between 2012 and 2034), this scenario, which uses the high USGS estimates of EUR, demonstrate signs of a ‘pathological condition’. Clearly, this needs to be interpreted with care as the recent drop in oil production might be the outcome of temporary conditions (e.g., great recession) rather than a fundamental shift in the dynamics of oil production.
4. Conclusions This paper sets out to demonstrate empirically the potential of the ACEGES decision-support tool in answering the question: So, what do we know about the future of crude oil production? We recognise that nobody can predict the future evolution of the oil market with absolute certainty. It is more realistic for analysts to recognise that uncertainty when describing their scenarios. Consequently, ACEGES has been developed to present oil scenarios in probability terms using graphical representations of those probabilities. It is demonstrated that the ACEGES model offers a new and novel way for the representation and scientific investigation of the dynamics of the oil system and more generally of the global energy system. This is achieved by bringing a number of fundamental concepts under a single umbrella such as the reconciliation of econometric and resource-constrained models (see also Kaufmann, 1991) at the agent level. It also introduces a high degree of heterogeneity in order to explore better the dynamics of the emergent explananda (world oil production) for effective energy policy. It is important to note that the ACEGES model abstracts from explicit modelling of reservoir behaviour. However, it implicitly approximates reservoir behaviour by dividing the ‘production’ profile into two different phases. Although the decline phase takes no direct account of the mechanical and chemical aids to induce artificial lift, it captures a probabilistic distribution of decline rates.
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Fig. 6. World oil production for a pathological scenario.
The ACEGES model only approximates the adaptation of new production technologies to address recovery efficiency by a probabilistic sample of publicly available EUR data. We assume the published EUR estimates (USGS and otherwise) to already include the manifestation of possible future advances in recovery rates. The ACEGES laboratory can simulate a very large number of scenarios by adjusting (interactively or using the Monte Carlo process) any of the key uncertain variables, namely resource estimates, demand growth, production growth and peak/decline point. We presented three different simulated scenarios of crude oil production. These scenarios were analysed in the GAMLSS framework by selecting the SHASH distribution. Given the estimated EUR, the simulations suggest that the peak of global production of crude oil may happen somewhere between 2008 and 2027. Using the petroleum market diversity, we also observe a reduced supply diversity in the broad vicinity of the peak year. As the research programme of economics and physical sciences progresses, the aim of building an integrated theory for exploratory energy policy by means of controlled computational experiments will be within sight. The work presented here suggests a way forward through the development of the ACEGES model.
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