Today's economy is driven by global uncertainties such as exchange rates, oil and energy prices, layered upon local uncertainties involving individual projects. These uncertainties create an unprecedented number of interdependent risks that hamper high quality decision making. Most Oil & Gas companies lack a consistent approach to modelling these uncertainties and risks, specifically in core decisions like exploration and production portfolio selection. Instead, they typically use averages to represent uncertainties. This leads to a class of systematic errors known as the ‘flaw of averages’.
In March 2012 Stanford University professor Sam Savage and I were interviewed on managing uncertainty in portfolio decisions by Paragon. In this interview we shared our views on portfolio optimisation and how to coherently manage uncertainties and improve decision making in portfolio decisions.
As we discuss in the interview complexity of decision making in Oil & Gas has increased in the last decade. There is a need to find new sources of oil and gas as the production level of the current portfolios is diminishing. Because these new sources of oil and gas are more difficult to reach (like deep sea, arctic or shale) new and more complex techniques are required which have high investment costs. Together with the uncertainty of finding oil and the amount of oil found, uncertainty in decision making has increased a lot. As the stakes have risen, also the risks have increased.
The Oil & Gas industry has a history of failing to deliver on promised performance. A survey of Bickel and Bratvold among petroleum engineers shows that many executives are dissatisfied with their companies’ performance, citing budget and cost overruns. They therefore require more brains per barrel to understand the impact of uncertainty and improve decision making. Prof Savage and I conclude that mathematical models will be the enabling technology that will make it easier to get a grip on the increased complexity, include uncertainty in decision making and therefore improve decision quality. Also mathematical modelling will improve consistency in decision making and reduce time to reach a decision; it will provide a bicycle for the mind in portfolio decisions.
We argue that in mathematical modelling one should focus on understanding the impact of uncertainty and how it will influence the decision that needs to be made, as it doesn’t make sense to model uncertainty if it doesn’t impact your decision. Using mathematical modelling to support decision making under uncertainty is like using a paper plane to learn how to fly. It doesn’t take very long, it’s easy and although it doesn’t look much like the real thing it teaches you the basics of flying. Instead of trying to build a model that captures all the details (engineer-like) time should be spent on building paper planes. Once the basics are understood, the level of complexity can increase and we can go for the real thing, leading to better quality portfolio decisions. Enjoy the videos.