A fuzzy inference system applied to value of information assessment for oil and gas industry.

Vilela, Martin; Oluyemi, Gbenga; Petrovski, Andrei

Abstract

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the decision reached following Boolean logic. However, human thinking logic is more complex and include the ability to process uncertainty. In addition, in value of information assessment, it is often desirable to make decisions based on multiple economic criteria, which, independently evaluated, may suggest opposite decisions. Artificial intelligence has been used successfully in several areas of knowledge, increasing and enhancing analytical capabilities. This paper aims to enrich the value of information methodology by integrating fuzzy logic into the decision-making process; this integration makes it possible to develop a human thinking assessment and coherently combine several economic criteria. To the authors’ knowledge, this is the first use of a fuzzy inference system in the domain of value of information. The methodology is successfully applied to a case study of an oil and gas subsurface assessment where the results of the standard and fuzzy methodologies are compared, leading to a more robust and complete evaluation.

Citation

VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2019. A fuzzy inference system applied to value of information assessment for oil and gas industry. Decision making: applications in management and engineering [online], 2(2), pages 1-18. Available from: https:// doi.org/10.31181/dmame1902001v

• Journal Article Type: Article
• Acceptance Date: May 21, 2019
• Online Publication Date: May 23, 2019
• Publication Date: Oct 15, 2019
• Deposit Date: May 23, 2019
• Publicly Available Date: May 24, 2019
• Journal: Decision making: applications in management and engineering
• Print ISSN: 2560-6018
• Electronic ISSN: 2620-0104
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 2
• Pages: 1-18
• DOI: https://doi.org/10.31181/dmame1902001v
• Keywords: Value of information; Fuzzy logic; Fuzzy inference system; Oil and gas industry; Uncertainty
• Public URL: https://rgu-repository.worktribe.com/output/243551
• Contract Date: May 24, 2019