Analysis and evaluation of input-output flexibilities in oil industry plant projects: applying the theory of real options and stochastic simulation

• Autor(a) principal: Patrício Samanez, Carlos
• Data de Publicação: 2009
• Outros Autores: A. Costa, Letícia
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: GEPROS. Gestão da Produção. Operações e Sistemas
• Texto Completo: https://revista.feb.unesp.br/gepros/article/view/747

Investments in the oil and petrochemical industries are frequently medium/long term, rich in real options (managerial and/ or operational flexibilities), with a high degree of irreversibility, and under conditions of economic and technical uncertainty. In this context, the traditional method of discounted cash flow, is very limited to deal with uncertainties and flexibilities, and frequently leads to the wrong decision. This article uses more modern techniques for economic analysis, with a real scientific approach, by modeling economic uncertainty as a stochastic process, and using stochastic simulation technics that consider the project’s managerial degrees of freedom. The modern theory of investment, under uncertainty, also named real options theory, separates the value of investment opportunity from the decision rule, so the latter maximizes the former. The present work aims at analyzing a GTL (Gas-To-Liquid) project plant using real options theory. GTL technology makes it possible to transform solids, biomass, liquids and gases into high quality liquids, such as naphtha, diesel, paraffins and high quality lubricants. In this study the real option theory is adjusted to evaluate the project’s capacity to change its input and/or output, choosing the option that maximizes payoff, in accordance with each scenario. Investments take place in an atmosphere of uncertainty, where prices (uncertainty factors) are stochastic and will follow the mean reversion stochastic process, calculated by the Monte Carlo Simulation. Keywords: Investment Project Analysis, Real Option Theory, Monte Carlo Simulation, Numerical Methods, Stochastic Processes