• Barry Standish

Industry Size with different External Conditions and Role-players – Oil and Gas

Businesses that deal in commodities face unusual challenges. They do not control the product price. They can only adjust costs. Some industries are subject to political pressure – incentives to grow more corn or soya – a developed world experience - or government may take a larger slice of your business without much warning – an emerging market experience.


So how do you plan?


In some cases the challenges go further where there are role-players, often other organs of government, that can further hinder planning. For example, the industry would thrive if only the Transport Department would commission that new rail line. This is also an opportunity if only influence could be brought to bear on the Transport Department.


This article documents the experience of StratEcon in doing such an exercise for the oil & gas industry in South Africa. The lessons and approach could be applied in a variety of countries and commodity based industries.


To give context, South Africa has some oil & gas. It also has significant potential because of shale finds in the country, massive gas discoveries in neighbouring countries and its location on a strategic sea route used by oil & gas vessels.


The objective of the analysis was to estimate potential industry value. This is done giving cognisance to external factors over which the industry has no control and internal factors over which the industry, potentially, has control. In the process it was possible to estimate the potential role player contribution for different exogenous external environmental factors and changed internal factors. Part of the output was an estimate of the potential value of different role players under different external and internal environments. The analysis was able to quantify the influence of the different internal factors and the contribution by project.

A four-step approach was followed.


Step 1 started with identifying projects that were in the planning or conception phase. In total 38 projects were included. These projects were, in turn, aggregated into eleven relevant categories like offshore exploration; offshore extraction; onshore extraction – biogenic; and onshore extraction – shale.


Step 2 identified external and internal environmental factors.

External factors for oil & gas are political stability, international oil/gas prices and the extent of known or potential reserves. Variations were allowed for each of these three external factors and combined in different scenarios. Each of these broad factors were further disaggregated into:

  • Political stability: politically unstable; politically stable with market unfriendly policies and politically stable with market friendly policies.

  • Oil price: less than $50, $50 to $80 and greater than $80.

  • Reserves: known reserves and discovery of new reserves (for which there is some hope).

The identified internal factors are policy, strategic location, infrastructure, ease of doing business, international recognition, and the ability to grow the industry. These factors were weighted by importance, subfactors were identified and also weighted. The degree of role player influence was estimated for each detailed factor. Each of these internal factors were also disaggregated into, for example:

  • Policy to give project certainty; make FDI attractive; and promote industrial development.

  • Infrastructure was divided into ports; rail/road/telecoms/distribution networks; and addressing skills shortages.


Step 3 identified twenty-five industry role players and estimated their degree of influence over the internal factors and each of the internal sub-categories. These role players included, for example, the Department of Energy; Department of Environmental Affairs; the Ports Authority; and the Saldanha Bay Industrial Development Zone.


Step 4 reported the modelling outputs. The final model made very detailed estimates. These started at the aggregated value of the South African oil & gas industry. These were then disaggregated into:

  • Industry value under different external environmental scenarios;

  • Provincial distribution under the different scenarios;

  • Role player influence and the value of that influence by scenario. The most influential role players for a selected scenario as well as across all scenarios;

  • The most affected projects for a selected scenario as well as across all scenarios;

  • The influence by role player on projects;

  • The influence by role player on the key success factors; and

  • The influence by role player on the key success factors for selected projects.

The actual results are not relevant for this article because the focus is on the principles rather than detail. However, for the purpose of illustration the three figures below indicate the detailed output and insights that emerged from this approach.


The potential size of the South African oil & gas industry under six external scenarios is illustrated in Figure 1. The scenario detail is given in the block inside the figure. The difference between the columns is that the shorter column (orange) illustrates the industry size without any role player influence. The taller column (blue) shows the difference that active role player intervention could make.

Figure 1: Industry Value – External Environmental Scenarios and Role Player Contribution

Role player influence can be disaggregated into project categories (and individual projects – not shown here) for each external scenario as illustrated in Figure 2 for Scenario 1. In this scenario role players have a major influence on shale production, liquid natural gas (LNG) to power and down-stream fabrication, engineering, etc.

Figure 2: Value of Role Player Influence –Scenario 1 (Rm)

Finally, the potential average financial contribution (by NPV) of role players is illustrated in Figure 3.

Figure 3: Average Influence of the Top Five Role Players

The lessons are that the model gave key insights into the value of a complex industry that operates under different external constraints and internal conditions. It also identified key role players, under which scenarios they are most important, and the value of their intervention.


In the end, and at the conclusion of the analysis, was the realisation that the modelling process had uncovered a relatively simple way of cutting through a complex problem.

This conclusion, for the purpose of this short article, is that the approach could be used for many different commodity markets in many different countries.


ENDS

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