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Optimization algorithms provide methods to explore complex solution spaces efficiently and accurately to achieve a desired outcome. Optimization problems are common in our daily lives. If planning to drive a car, one commonly decides on the best (optimum) route to the desired destination. For oil field exploration and development, optimization can take many forms, but essentially the goal is to maximize recovery, total production, or net monetary profit from the asset.

The goal of optimization is to find the optimum value of an objective function. Specifically, the primary focus of this article is on methods applicable to computationally expensive objective functions (i.e., those with long computational run times).

Another facet of many conventional optimization approaches is their need for derivative information, which is commonly not available with engineering simulators. As such, we assume throughout that such information is not available.

Valuation is somewhat more elusive as a term. It is easy to compute a simple net present value (NPV) using a spreadsheet and basic data. This type of valuation is trivial. However, valuation becomes complex when one introduces the concept of future operational decisions that materially impact the underlying value of the asset and also when uncertainty is present. This type of valuation is in the realm of “real options,” which refers to valuation under uncertainty and flexibility. Although real option valuation has been the subject of many texts, the debate continues over its usefulness in making practical engineering decisions. A detailed discussion of real options is beyond our scope here (Wilkinson et al., 2005). Nevertheless, the presence of uncertainty does impact our ability to formulate a meaningful valuation when we also have some operational flexibility, which itself is related to the optimization.

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