Modeling and communicating the dynamics of energy market

The focus of my Ph.D. project is the investigation of the analogies between the dynamics of oil production, the economy and the physical laws that are related to every natural process. The difficulty to retrieve data on the oil market has suggested me that there is a correspondence in Science Communication: indeed, while in the magazines we find many articles that talk about climate change, for instance, we seldom met articles on related topics with keywords such as "energy resources" or "energy depletion". I started to investigate the relationship between the phase-plot of worldwide oil production (1965-2014) and its price. My idea was that the laws regulating the convective intensity and the oil production on one side, and the energy flow in human society on the other, were similar. In particular, this analogy could be true for the change from a slow diffusion process to a faster convective movement. In a first approximation, I tried to describe the phase-plot system in a theoretical way: I imagined the system without perturbations with a worldwide average EROI that varies from 60 (1965) to 15 (2014). Similarly, I picked up the value of world production in 1965 (1,567 Million ton/years) and the same value in 2014 (4,220 Million ton/years) and I assumed a constant increment of the production. Obviously, the phase-plot de-scribes the evolution of the real system and appropriately shows two evident "loops" in correspondence of the two major oil crises in recent times (in the '70s of the last century and in the '10s of the current century), although it is also apparent a "background" (or floor) price the system has never overcome. Following the idea of a possible description of the global oil market trend as a dynamical system, I investi-gated the eventual analogies between oil market and a classical dynamical system which is well known to exhibit an almost doubly cyclic behavior, i.e., the Lorentz attractor. However, this suggestion remained nothing better than that, because the major criticism is that it is rather difficult to conceive that only two loops in a phase-plot could be assimilated tout court to a Lorenz attractor: the investigated period 1965-2014 is in fact too short to validate such an analogy. Therefore, the output of the research was an article deposited it in the arXiv online repository . The relationship between natural phenomena and price-production dynamics of oil extraction, however, could be seen from another point of view. Indeed, the phase-plot, behind the "random walk" initially de-scribed as a Lorenz attractor, suggested us two peaks in correspondence with the upper part of those loops. This "swinging" (macro)behavior is rather similar to that of the theoretical model where we only have two stocks of oil and two prices for them: the first delimited by a price of 100 $/barrel, for a production that re-mains between 0 and 1,000 barrels/day; the second with a cost of 200 $ for a production in the range 1,000-1,500 barrels/day. In this ideal case, following the consumption, we expect the same swinging behavior: if the consumption is in the range between 0 and 1,000 barrels/day, the oil at the lower price will be only used, with a price (ideally) of 100 $/barrel. Since consumption generally grows, the economic system reach-es a point in which it needs to use the second stock of oil at the higher price. As the oil is a global commodi-ty, the price of all oil is determined by the marginal oil barrel, that is to say by the most expensive one (200$). In this ideal dynamics, the price suddenly jumps to the higher level instead of slowly shifting up. If the oil price increases, however, the consumption decreases and, sooner or later, the society comes back to the previous range of extraction intensity. The main characteristic of the simple oil-price dynamics here de-scribed is that there are two rapid movements on the cycle (rise and descent of the price) and two slow movements (consumption that goes up and down, to adapt to the oil price). At this level could be useful in terms of EROI instead of oil stocks (with high and low prices). Therefore, natural variables seem to be more similar to medium price and medium EROI (fast the former, slow the latter). The Lotka-Volterra equations describe the prey-predator ecological mechanisms and many cases are well known and well-studied. One of them seems to have the same features of the simple oil market behavior just described. More specifically, the analogy is between a prey, the American spruce, and a predator, the caterpillars of the species Choristoneura fumiferana that feed on the spruce. The caterpillar population is regarded as the fast variable, since there are periodically observed demographic outbreaks of this species, considered a real scourge , whereas the spruce leaf whole surface is assumed to be the slow variable be-cause the regeneration of the leaves - and not only - is a process that lasts several decades. This dynamic model requires a good evaluation of the worldwide EROI for the oil. This is, in any case, fun-damental as an index of the energy quality used by the society. This is the reason why the EROI is crucial to determine the goodness of an energy resource in general and particularly for oil and gas, that nowadays sat-isfy about 57% of primary energy demand . This dimensionless index is generally defined as the ratio between the energy extracted from a given re-source and the energy costs sustained to get that energy. In a following step of my research, I tried to set up an alternative method for the calculation of the EROI of oil companies. The difficulties to retrieve the data by the oil companies is notorious, thus the strategy consisted in using as a proxy of the energy costs, i.e. the available data about the CO2 emissions of the same oil companies, as reported in their own sustainability reports (SRs). International organizations such as IPCC and WBCSD recommend to the involved companies to compile these reports, but they are not mandatory. The second step was to use, as a proxy of the energy ex-tracted, the CO2 emissions estimate obtained by a stoichiometric conversion of the oil production declared by the oil companies. The resulting estimates of EROI are rather homogeneous and not too different from the values reported in the literature. The method could be suitable for year-by-year comparison of the time evolution of this im-portant energy quality parameter for the individual energy-producing and energy-delivering companies . I defined this parameter as the "corporate's EROI". In particular, this last work had its main difficulty in finding data to make comparisons between the different oil companies. As mentioned above, this is also reflected in the communication sector. If we look at the fol-lowing diagram, we can discover, for example, the differences between related argument ("climate change") and keywords like "energy resource" or "energy depletion". The graph includes all the terms worldwide for the last year (October 1st, 2017 - October 1st, 2018) . I am personally committed to filling the gap (thanks to my previous background in Science Communication), to sensitize the citizenship to the energy transition problem and last year, at the Bright event (European Researchers’ Night) I have developed a game about the Hubbert oil peak to play with people and explain to them the dynamics of peak and resource depletion.