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Research Papers: Petroleum Engineering

Management of Implementation of Nanotechnology in Upstream Oil Industry: An Analytic Hierarchy Process Analysis

[+] Author and Article Information
Pouyan Motamedi

Department of Mechanical Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: p.motamedi@ualberta.ca

Hasan Bargozin

Department of Chemical Engineering,
University of Zanjan,
Zanjan 45371-38791, Iran

Peyman Pourafshary

Department of Petroleum and Chemical
Engineering,
Sultan Qaboos University,
P.O. Box 33,
Al Khoudh, Muscat 123, Oman

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received July 19, 2017; final manuscript received December 11, 2017; published online January 31, 2018. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 140(5), 052908 (Jan 31, 2018) (7 pages) Paper No: JERT-17-1374; doi: 10.1115/1.4038846 History: Received July 19, 2017; Revised December 11, 2017

Nanotechnology has had revolutionary effects in various fields of industry such as electronics, pharmaceuticals, and biomaterials. However, upstream oil industry has been noticeably slow in adopting the emerging technologies. This is mainly due to the exceptionally large investments needed to implement novel technologies in this industry. However, the projections for the increasing global energy demand require that oil and gas industry inevitably move toward adopting the emerging technologies. The high risk associated with enormous investments required for this aim necessitates measured and well-researched energy policies, with regard to the implementation of nanotechnology in the oil and gas industry. This paper presents a concise summary of the research reported in the literature on the potential benefits of nanotechnology in upstream oil industry. These applications were categorized into ten groups, and presented to a pool of experts, who judged on their relative importance with respect to various decision-making criteria. All this information was then compiled into a single matrix, which indicates the priority of each investment alternative with respect to every criterion in the form of a raw number. Finally, using a decision-making software package, a dynamic analytic hierarchical process (AHP) analysis was performed, providing a route to customized investment policies.

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Figures

Grahic Jump Location
Fig. 1

The hierarchical network used for AHP analysis, including the top goal, the criteria, and the alternatives

Grahic Jump Location
Fig. 2

The normalized priorities of the ten investment alternatives with respect to ultimate goal

Grahic Jump Location
Fig. 3

A dynamic analysis of the effect of varying the subjective importance of (a) environmental friendliness, (b) economic attractiveness, and (c) technological attractiveness on the relative priority of various investment alternatives

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