Stochastic modeling and optimization of petroleum exploration portfolios

Abstract

The modern-day petroleum exploration company must employ a portfolio optimization strategy in order to determine the best combination of available prospects to pursue. Today, exploration teams can extensively model their portfolios using industry-standard platforms capable of aggregating expected volumes and risks associated with each prospect in order to obtain an overall probabilistic estimate of risks and volumes for the entire portfolio. Traditionally, exploration managers used the “rank and cut” method to select an optimal portfolio based on a general strategy. The “efficient frontier” method of portfolio optimization and preference analysis were developed with pertinence to securities trading in the mid-20th century and have become practice among petroleum exploration managers in more recent times. Because the “efficient frontier” portfolio optimization theory is limited to optimizing a single portfolio metric, a new method of “portfolio filtering” (PF) was developed to better serve exploration managers when selecting a portfolio. This study created a workflow that combines the stochastic geological models for prospects’ risks and volumetrics that can then be used in tandem with the rank and cut, the efficient frontier and the portfolio filtering selection methods in order to suggest portfolio optimization methods for specific strategies. The results of this study suggest that portfolio filtering is the most robust and configurable method of portfolio selection, though the rank and cut method and efficient frontier method are still valuable in quickly determining portfolios that maximize specific metrics aligned with a specific exploration strategy.