Asset portfolio multi-objective optimization tools provide insight to value, risk and strategy for gas and oil decision makers

Abstract

Applying optimization algorithm to identify high-performing portfolios is an essential component of gas and oil portfolio analysis and decision making. As oil and gas companies wish to strategically optimize their performance with respect of multiple valuation and non-financial key performance indicators (KPIs) optimization algorithms that facilitate multi-objective optimization are desirable. The benefits of applying a suite of optimizers to mean versus semi-standard deviation stochastic multi-year cash flow and income models are explored with the aid of the hypothetical Portfolio X consisting of eleven upstream assets (with a summary dataset of mean values of KPI simulation distributions plus some general simulation input assumptions applied to the portfolio included as Appendix A). The methods and results of three distinct optimizer algorithms are compared, i.e., rank and cut, linear (Simplex) and evolutionary (genetic) algorithms in terms of a multi-KPI function test score, which is also used as the objective function for the genetic algorithm. The calculations described using the Portfolio X dataset are produced using Excel workbooks driven by VBA macros, and can be scaled up for use with larger asset sets. The results of variously constrained optimization runs using the three optimizers are further evaluated in terms of feasible-envelope and efficient-frontier concepts for value metrics, and for chances of achieving specified KPI targets on an annual basis. A risked portfolio value metric including a risk aversion factor is also derived in order to capture portfolio value and risk in a single metric. An overall portfolio analysis and valuation framework into which a suit of optimizers can meaningfully be deployed is described.