A multicriteria decision model for selecting a portfolio of oil and gas exploration projects

Abstract

ABSTRACT. As well as exploratory activity being at the heart of and guiding the future of the oil industry, it is fundamental that there be a comprehensive analysis covering the various factors and nuances that arise in the selection of exploration projects. Moreover, it is essential that a decision model enables the decisionmaker's preferences to be addressed in a structured (and methodologically correct) way, and one which is easy to understand and to apply in a real-world. Therefore, this paper proposes a multicriteria decision model which underpins using a deterministic procedure for selecting a portfolio of oil and gas exploration projects and thereafter a reality-based application is set out, based on a decision making context within Petrobras.

Analysis of MCDM methods output coherence in oil and gas portfolio prioritization

“…The advantages of using MCDM methods in project portfolio selection decision problems include the ability to address not only the two central criteria of risk and return on investment, but also any and all of the decision makers' preferences and objectives (Lopes and De Almeida 2013). However, while there are numerous methods that can be used in such decision problems, there is no evidence that one method is most effective in all applications.…”

“…Chen (2011) studied modeling E&P project portfolio optimization through multistage stochastic programs with mixed integer programming (MIP) techniques. However, E&P portfolio selection decision problems are particularly challenging due to the high stakes and significant consequences, and selection becomes a trade-off decision, where certain objectives are met at the expense of others (Lopes and De Almeida 2013). Achieving an optimum trade-off requires evaluating all alternative projects based on the same predefined quantitative and qualitative criteria using a systematic methodology (Lopes and De Almeida 2013).Traditional capital budgeting tools, such as benefitcost analysis, utility value, payback period, discounted cash flow, sensitivity analysis, preference theory, portfolio theory, and option theory are among those currently adopted by the industry.…”

“…However, E&P portfolio selection decision problems are particularly challenging due to the high stakes and significant consequences, and selection becomes a trade-off decision, where certain objectives are met at the expense of others (Lopes and De Almeida 2013). Achieving an optimum trade-off requires evaluating all alternative projects based on the same predefined quantitative and qualitative criteria using a systematic methodology (Lopes and De Almeida 2013).Traditional capital budgeting tools, such as benefitcost analysis, utility value, payback period, discounted cash flow, sensitivity analysis, preference theory, portfolio theory, and option theory are among those currently adopted by the industry. However, these techniques are mostly economics based and do not adequately consider other critical and strategic factors, such as social and environmental impacts, which cannot be captured by monetary valuations and in some cases not ethically acceptable, as argued by Cavallaro (2005).…”

“…In many cases, however, these decisions are made through improvised Intuitive approaches or using decision-aiding tools that vary in focus and commonly lack the capacity to fully address the criteria involved and accurately incorporate the decision makers' knowledge and preferences. The MCDM outranking methods have emerged as promising tools for project prioritization (Lopes and De Almeida 2013). However, they have been observed to perform differently in different applications (Cooper 2001), which also implies achieving inconsistent results by different methods if applied to the same decision problem.…”

Alternative Methods to Multiple Correspondence Analysis in Reconstructing the Relevant Information in a Burt's Table

“…In present days it may be of high interest to deal with qualitative unstructured data, whose treatment may be more complex. Studies in this framework are found in recent operational research literature [16,17,30,40]. In this context, data reduction through exploratory multidimensional scaling may contribute to clarify the data at hand, by revealing structures and factors.…”