Investment decision-makers in the oil and gas industry are faced with an extremely complex process when attempting to decide on the optimum mix of projects to pursue. Almost all firms are faced with constraints such as capital budgets, volume commitments and/or dependent and mutually exclusive projects. Likewise, attempts to include the objectives of planning, finance and engineering can make it almost impossible to find the optimum mix of projects using traditional selection methods.

Indicators from discounted cash flow analysis such as net present value, rate of return and profit investment ratios have traditionally been used to rank projects, however these rankings will not necessarily produce the best result. In addition, unless the number of potential projects under consideration is very small it can be unfeasible to evaluate all of the possible permutations. Therefore, how does one know if the best value-creating portfolio of projects has been chosen?

A recently developed technique for solving this type of problem is the use of Genetic Algorithms. Borrowing from the biological field of evolution, algorithms have been developed that can be applied to find a combination of projects that approach the true optimum, taking in to account numerous business constraints, within an acceptable time frame.

This paper describes the theory behind genetic algorithms and their application to investment decision making in the oil and gas industry. A worked example for a hypothetical company is then used to demonstrate the potential impact of using the technique.

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