Although the conceptual power of the ROV methodology has been broadly recognised for a while, its practical application to optimise uncertain mineral and petroleum investment decisions has been discouraged and delayed by a perception of computational complexity. The objective of this research project is to develop practical real option models and valuation methodologies for user-friendly application to improve investment decisions in mineral and petroleum exploration and development. Recent development of decision tree software which uses dynamic programming language (DPL) makes it possible to model and evaluate complex sequential/compound real options typical of resource projects with relative ease. It also allows differentiation between the various sources of “market” and “private or project” risk leading to more comprehensive models, reliable option values and explicit optimal decision paths. Initial research results show that real option values and decision paths obtained from decision trees are consistent with those obtained using binomial lattices with ‘risk-neutral probabilities’ and/or closed-form equations such as the Black and Scholes formula. This justifies more rigorous and in-depth research designed to develop robust real option models and applications capable of addressing more realistic and complex investment optimisation problems and the related documentation to disseminate and promote their use by industry.