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Definition of an Offshore Petroleum Production System by Using Fuzzy Sets and Utility Functions


Tiago C. da Fonseca*, José R. P. Mendes*, Celso K. Morooka*, and Ivan R. Guilherme**


*State University of Campinas - UNICAMP, Department of Petroleum Engineering, Campinas, SP, Brazil
**Sao Paulo State University - UNESP, Department of Statistics, Applied Mathematics and Computer Science, Rio Claro, SP, Brazil


Received: March 1, 2005

Accepted: June 15, 2005


Keywords: offshore systems, field development, fuzzy sets

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.9, No.6 pp. 684-692, 2005

Abstract



Field development is a very important task in the petroleum industry. Decisions in this area may lead either to profitable success or to expensive failures, and usually involve several distinct areas in the scope of Petroleum Engineering and Science, such as Geology, Petreoleum Engineering, Offshore Engineering and Economics. Therefore, these subjects must be well understood by teams supporting the decision-making process.
This work proposes a methodology to support managers in one stage of field development: the definition of the field production system. In order to determinate the production system to be installed in an oil field, attributes such as investment, profitability, safety, environmental preservation and technological experience must be considered. A decision-making team or agent must weight these attributes in order to achieve solutions accordingly to the company strategies and objectives. Combining a few mathematical tools to represent the process, the methodology proposed herein is an approach that considers not only the financial variables involved in a field decision process, but might include other aspects, or attributes, also important to guide a decision. To this end, the application of Multi-Attribute Analysis concepts is suggested. Also, to support the decision-making agent, the approach follows Utility Functions concepts in order to numerically represent the agent trend or inclination to each option. Considering that subjectivity and imprecision are naturally involved in the decision-making process, the approach incorporates Fuzzy Sets Theory concepts as a means of formalizing the computation of this uncertainty.
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