Presentation is loading. Please wait.

Presentation is loading. Please wait.

Pierre-Noël GIRAUD (CERNA, Mines ParisTech) Aline SUTTER – Timothée DENIS (EDF R&D) Hubbert oil peak and Hotelling rent revisited by a simulation model.

Similar presentations


Presentation on theme: "Pierre-Noël GIRAUD (CERNA, Mines ParisTech) Aline SUTTER – Timothée DENIS (EDF R&D) Hubbert oil peak and Hotelling rent revisited by a simulation model."— Presentation transcript:

1 Pierre-Noël GIRAUD (CERNA, Mines ParisTech) Aline SUTTER – Timothée DENIS (EDF R&D) Hubbert oil peak and Hotelling rent revisited by a simulation model OTAE 2009 July 7th, 2009, at Mines-ParisTech

2 2 Outline Questions addressed Model principles Results Single agent exploring 1 global area Single agent exploring 2 areas Stackelberg oligopoly

3 3 At stakes: the oil price trajectory on the long term Peak oil: why and when? Scarcity rent: when and how much? Gb year demand for fuel Hubbert symmetrical peak late asymmetrical peak with sharp dropping 2010 ? 2050 ? At peak oil: oil price = substitute price Marginal extraction cost $/bl

4 4 Hubbert oil peak Starting point Hubbert forecasted the 48-US oil production peak 15 years in advance (with a 1 year error!) 1956

5 5 production path of several oil wells through time Hubbert oil peak Total production of a multi-deposit region is supposed to show a peak when half of total reserves is depleted At a global scale, the symmetry of the total production profile is subjected to strong hypothesis related to the exploration strategy What happens with more realistic exploration dynamics exploration responding to price signals?

6 6 Hotelling rent Assumptions Hotelling scarcity rent random no arbitrage opportunity production of resource is optimal any time constant discounted scarcity rent over time What happens if T 0 is a random variable with a decreasing variance along time?

7 7 Model

8 8 Model type and objectives A simulation model with two representative agents: One explorer-producer representing a set of competing companies: it minimizes the cost of meeting the demand of the next time step The owner of the marginal oilfield in production who hedges between holding oil reserves or financial assets The model accounts for: The need to explore before producing oil Oil production technical constraints A learning process on the volume and cost of the remaining reserves The explorer- producer being a myopic cost minimizing agent with imperfect but improving information Oilfield owners with imperfect but improving information

9 9 Model Structure The explorer-producer explores and produces to meet the (exogenous) demand at minimal cost assess the risk of holding oil as an asset The marginal oilfield owner Oil Price marginal production costHotelling scarcity rent improves the common knowledge on the remaining reserves Exploration-Production heuristics Hotelling scarcity rent calculation Learning process about reserves

10 10 At the beginning the agent only knows the total number of oilfields: N ( number of sedimentary basins with oilfields) but it ignores the sizes ( index i) and extraction costs ( index j) of the oilfields to be discovered It will then use the outcome of its exploration campaigns to progressively update its knowledge He simply assume the actual distribution by size and extraction costs of the N deposits is homothetic to the sample already discovered. He then computes an estimated peak oil date, and knows the standard deviation of this estimate He also compute the probability of discovering an oilfield of size i and extraction cost j during the next campaign The learning process on reserves

11 11 Exploration heuristics The explorer producer agent explores as to minimize the cost of meeting the demand only for the following time steps the agent owns an oilfield portfolio inherited from his exploration/production decisions in the past it then computes for each period an exploration level which minimizes the cost of meeting the demand for the next steps: it proceeds with exploration, which randomly returns the size and production cost of the discovered oilfields E[Cost exploration ] + E[marginal Cost production (new port.)] E[marginal Cost production (old port.)] be less or equal than

12 12 Exploration heuristics The expected total cost curve shows minimum

13 13 Production constraint Demand is satisfied by putting new oilfields into production, in the increasing cost order Under a technical constraint: an oilfield yields a constant rate of production during years Profile of a producing oilfield more realistic shape Production Time

14 14 Inferring Hotelling rent Hotelling rent is computed by considering the oil deposit as a financial asset characterized by an expected level of risk and return The equilibrium rent level is then set through hedging with financial assets buying an oilfield and keeping the oil in the ground till depletion date buying a financial asset with the same risk

15 15 Current Model calibration constant and inelastic demand: D = k t 5 cost-differentiated types of oil available spread into 330 unknown oilfields of 3 different sizes (see below) constant discovery cost per oilfield randomness on both size and production cost of discovered oilfields infinitely and immediately available backstop technology at 100 $/bl Volume (Gb) / Extraction cost ($/b)

16 16 Results Single agent exploring one global area

17 Results: single actor / mono zone 1 scenario – exploration non caped

18 Results: single actor / mono zone 1 scenario – exploration caped

19 19 Results: single actor / mono zone 100 scenarii exploration caped

20 Comments No symmetric peak oil at the world level, unless exploration is caped

21 21 Results Single agent exploring 2 areas

22 22 Simulation data Area 1: larger and more competitive reserves Area 2: smaller and more expensive reserves oilfields oilfield

23 Allocating exploration between the two regions

24 24 Results: single actor / 2 areas 1 scénario exploration non caped

25 Results: single actor / 2 areas 1 scénario exploration caped in area 1 ( most favourable zone)

26 Comments A peak oil appears in region 2, the region which has progressively proved to be less favourable The case of the USA exhibited by Hubbert ? All the more when exploration is caped in the more favourable region: the middle East ?

27 27 Stackelberg oligopoly OPEC as the heart of an oligopoly with a competitive fringe (preliminary)

28 28 Introducing OPEC OPEC : Stackelberg oligopoly with a competitive fringe competitive fringe has to explore to satisfy demand minimizes its costs oligopoly owns most low cost oil reserves and knows them (no need to explore) maximises its profit has to forecast the fringe exploration strategy perfectly anticipates the fringe exploration outcome work in progress: faces the random result of exploration as the fringe does

29 29 OPEC – competitive fringe Modelling of interaction

30 30 Results Stackelberg oligopoly 1 scénario

31 Comments An intriguing result: Optimal oligopoly behaviour leads to price instability….

32 Its still a work in progress... Comments warmly welcome on: That type of model Modelling the learning process Oil fields owners behaviour Modelling the choice between the two zones

33 33 Thanks for your attention


Download ppt "Pierre-Noël GIRAUD (CERNA, Mines ParisTech) Aline SUTTER – Timothée DENIS (EDF R&D) Hubbert oil peak and Hotelling rent revisited by a simulation model."

Similar presentations


Ads by Google