Large-scale petroleum projects require capital investments and operating decisions that stretch out over many years. Estimating the profitability of such ventures is a complex task, usually requiring significant time and effort Firms typically produce spreadsheet-based cash-flow models that provide extensive detail on the costs associated with a project This paper asserts that decision-making about large investments is improved by focusing less on such "detail complexity"—drilling down into data to achieve greater detail and precision—and more on dynamic complexity. By dynamic complexity we mean the dynamics of how conditions will change over time and, equally important, how you can manage a venture to adapt to those changing conditions as they emerge.
The key element in planning an adaptive strategy is that you learn about major sources of uncertainty as they develop. Sources of uncertainty include oil prices, reserves levels, operating performance, and competitors' actions. Correctly evaluating adaptive strategies requires that you exploit newly acquired knowledge about evolving conditions to make better informed decisions. This paper discusses the importance of dynamic complexity in valuing petroleum projects and the vital role that learning models play in capturing such complexity. Several examples from exploration and production projects will be presented.