The process of portfolio optimization provides guidance to decision makers on how to manage an asset base given corporate objectives, market conditions, and organizational capability. Many applications in the oil and gas industry are based upon Markowitz's (1952) efficient-portfolio theory. In the standard implementation of this framework, an efficient portfolio is defined as one that yields the highest value given a specific degree of risk.A corporate decision maker will aim, however, to select a portfolio that meets several often-competing objectives (i.e., maximize portfolio value while minimizing capital expenditure). The optimal portfolio choice given one constraint is typically not optimal given one of the competing constraints. This requires the portfolio manager to identify and select those portfolios that best meet all corporate constraints. Deciding which portfolio to develop is often compounded by there being several portfolios having similar economic characteristics. However, these portfolios can generally be differentiated by strategy, which may depend on nonfinancial attributes such as the geographic location of the assets or on geological settings that might require different engineering expertise.In this study, a large set of exploration portfolios and their attributes have been simulated. Through applying a series of simple and transparent filters, a few portfolios can be identified that meet all the corporate constraints. After a shortlist has been created, the portfolios can easily be characterized by strategy, and the tradeoffs between them can be assessed.

This study demonstrates how portfolio insights can be created by combining well-known optimization methods that are generally used individually-genetic algorithm (GA), linear programming (LP), and portfolio filtering (PF). An integrated optimization approach combines the advantages of individual methods while mitigating their shortcomings.Effective portfolio management requires a comprehensive understanding of the tradeoffs between different portfolio choices. Given a set of constraints, portfolio-optimization techniques based on LP and GAs can be applied to identify an optimal portfolio. However, this optimal portfolio might not be the preferred portfolio. Decision makers have to understand the tradeoffs between generally conflicting objectives and constraints before one portfolio can be identified as the preferred option. Such assessment of the overall search space is not made with LP and GAs when used to identify a single best solution, and many portfolio options will have been eliminated before an understanding of these alternatives has been developed.Markowitz's mean-variance (M-V) approach and the traditional "rank and cut" approach are used typically to establish a relationship between a portfolio's value and its variance or associated development cost. Although these methods enable decision makers to compare and contrast different options, the optimization is limited to the portfolio value measure and a single other metric. This latter limitation is overcome by the more recently developed PF approach. This method is practical and transparent and allows for a quick development of strategic portfolio alternatives while considering a large number of portfolio attributes. Its main drawback is that the analyzed set of portfolios generally represents a subset of the total search space. Thus, as the number of feasible portfolio options increases, so does the chance that the optimal portfolio is not present in the population of sampled portfolios. PFPF is a transparent and practical portfolio-optimization method that can account for project dependencies and multiple decision criteria. PF, as described by Willigers and Majou (2010), consists of three main phases-portfolio generation, quantitative portfolio selection, and strategic portfolio selection.In the first phase, portfolio generation, thousands of portfolios are simulated by combining projects from a pool of assets. Portfolios are randomly created but adhere to a rule-set that reflects project dependencies and/or mutual-exclusion criteria. Then, portfolio attributes are captured for further analysis. These attributes include recoverable reserves, expected production rates, capital expenditure (Capex), operational expenditure (Opex), net present value (NPV), working interests, and operational and nonnumerical attributes such as the geographical location and geological

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Effective portfolio management requires a comprehensive understanding of the tradeoffs between different portfolio choices. Given a set of constraints, portfolio optimisation techniques based on linear programming and genetic algorithms can be applied to identify an optimal portfolio. However, this optimal portfolio might not be the preferred portfolio. Decision makers have to understand the tradeoffs between generally conflicting objectives and constraints before one portfolio can be identified as the preferred option. Such assessment of the overall search space is not made using linear programming and genetic algorithms when used to identify a single best solution and many portfolio options will have been eliminated before an understanding of these alternatives has been developed. Markowitz's mean-variance approach and the traditional "rank and cut" approach are typically used to establish a relationship between a portfolio's value and its variance or associated development cost. Although these methods enable decision makers to compare and contrast different options, the optimisation is limited to the portfolio value measure and a single other metric. This latter limitation is overcome by the more recently developed portfolio filtering approach. This method is practical and transparent and allows for a quick development of strategic portfolio alternatives while considering a large number of portfolio attributes. Its main drawback is that the analysed set of portfolios generally represents a subset of the total search space. Hence, as the number of feasible portfolio options increases, so does the chance that the optimal portfolio is not present in the population of sampled portfolios. This paper presents a portfolio optimisation approach that combines linear programming, genetic algorithms and portfolio filtering. An analysis of a realistic upstream portfolio illustrates and validates the optimisation process by demonstrating how limitations of individual methods can be mitigated by combining them.

The process of portfolio optimization provides guidance to decision makers on how to manage an asset base given corporate objectives, market conditions, and organizational capability. Many applications in the oil and gas industry are based upon Markowitz's efficient portfolio theory. In the standard implementation of this framework, an efficient portfolio is defined as one that yields the highest value given a specific degree of risk.A corporate decision maker will aim, however, to select a portfolio that meets several often competing objectives, i.e., maximize portfolio value while minimizing capital expenditure. The optimal portfolio choice given one constraint is typically not optimal given one of the competing constraints. This requires the portfolio manager to identify and select those portfolios that best meet all corporate constraints. Deciding which portfolio to develop is often compounded by there being several portfolios having similar economic characteristics. However, these portfolios can generally be differentiated by strategy, which may depend on non-financial attributes such as the geographic location of the assets or on geological settings that might require different engineering expertise.In this study, a large set of exploration portfolios and their attributes have been simulated. Through applying a series of simple and transparent filters, a few portfolios can be identified that meet all the corporate constraints. After a shortlist has been created, the portfolios can easily be characterized by strategy and the tradeoffs between them can be assessed.