This research examines the trade-off between the cost of adding complexity to a model and the value added to the results within the context of decision-making. It seeks to determine how complex a model should be in order to fit it to the purpose at hand. The report begins with a discussion on general modeling theory and model complexity. It next considers the specific case of petroleum reservoir models and the existing research that has compared modeling results with model complexity levels. Finally, it presents original results applying Monte Carlo sampling to a drilling decision scenario and to a one-dimensional reservoir model where a cylindrical oil field is represented by different numbers of cells and the results compared.