Decisions in the oil and gas industry – especially at the exploration stages are fraught with uncertainty. The complexity of the natural world renders it practically impossible to determine for certain whether oil exists in a given location yet oil companies must invest millions in the hope of discovering it. Add in the current environment of low commodity price and you are looking at a minefield of doubt. The financial consequences of making the wrong decisions can be catastrophic. How can risk and returns be optimised at a given time to inform these difficult investment decisions?
"The value of information is commonly underestimated and through a proper understanding of the true power of new information the uncertainty of major business decisions can be reduced."
We are working with companies in the oil and gas sector to optimise risk and returns in major investment decision making using a range of quantitative techniques. These techniques can and arguably should be applied to risk management across a wide variety of businesses. The value of information is commonly underestimated and through a proper understanding of the true power of new information the uncertainty of major business decisions can be reduced.
Complex business decisions will always be difficult to make and should be approached with care and attention commensurate to the scale of risks and complexity. Consider an oil and gas company in the early stages of exploration. Initial tests suggested the area identified has a 30% chance of containing a financially attractive amount of oil.
They considered the following options:
- Do they give up on the area entirely and try somewhere else?
- Do they proceed straight to drilling wells in order to extract what oil there is?
- Do they conduct further tests to reduce the uncertainty around the 30% figure?
There are many factors to be considered in making this decision. This is where a rigorous but simple to communicate methodology was used to decide on the optimal decision. Ideally, this should be illustrated in monetary terms. This involved modelling the expected outcome of each decision in financial terms and the potential cost of each decision, both of which are also subject to uncertainty.
The model must also consider:
- Option 1 will provide them with no income from the oil and it will have sunk costs in their initial investment but will have no incremental costs.
- Option 2 will be an expensive endeavour and it is still uncertain as to how much oil exists, if any.
- Option 3 in this instance, a further testing option was available which could provide an 80% chance of giving up to 70% certainty of whether sufficient oil was available but this test comes with incremental costs.
"These methods allow us to promote evidence based decision making with auditability and accountability for decision makers."
Using a range of statistical techniques, we established a decision tree based on each option with the expected net value. The decisions were then ranked in order of value and the the optimal decision was developed. The inputs to this decision tree were tested for sensitivity allowing further attention to be focused on the elements of the problem with the most financial significance.
In this example, without a thorough analysis of the potential value of each option it would have been difficult to determine that abandoning the project was in fact the best decision. These methods allow us to promote evidence based decision making with auditability and accountability for decision makers.
In reality, there are likely other risks and considerations above and beyond the cost and return of the project including, for example, company reputation, employee satisfaction and regulatory hurdles. These techniques are broad enough to allow these risks to be incorporated to build up a complete picture of the risk profile and best option. For significant business decisions, the marginal cost of conducting this analysis will pay for itself many times over in the long term so it seems nonsensical not to do it.