Springer Intelligent Systems in Oil Field Development under Uncertainty: 183 (Studies in Computational Intelligence, 183)

The decision to invest in oil field development is an extremely complex problem. This book is a result of about four years of research in this area. It presents applications of intelligent decision support systems to oil field development under uncertainty. From the Back Cover Intelligent Systems use a range of methodologies for analysis, pre-processing, storage, organization, enhancing and mining of operational data, turning it into useful information and knowledge for decision makers in business enterprises. These intelligent technologies for decision support have been used with success by companies and organizations that are looking for competitive advantages whenever the issues on forecast, optimization, risks analysis, fraud detection, and decision under uncertainties are presented. Intelligent Systems (IS) offer to managers and decision makers the best solutions for complex applications, normally considered difficult, very restrictive or even impossible. The use of such techniques leads to a revolutionary process which has a significant impact in the business management strategy, by providing on time, correct information, ready to use. Computational intelligence techniques, especially genetic algorithms, genetic programming, neural networks, fuzzy logic and neuro-fuzzy as well as modern finance theories, such as real options theory, are here presented and exemplified in oil and gas exploitation and production. This book is addressed to executives and students, directly involved or interested in intelligent management in different fields. About the Author Marco Aurélio Cavalcanti Pacheco • PhD in Computer Science, University College London, 1991. • MSc in Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, 1976. • BSc in Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, 1980. • Professor (Electrical Engineering Department, Catholic University of Rio de Janeiro (Brazil), PUC-Rio) • Course Load: (3) post-graduate courses per year: Evolutionary Computation, Applied Computational Intelligence, Intelligent Computational Nanotechnology ; (2) undergraduate courses per year: Computer Systems (Logic Project), Evolutionary Computation. • Currently advising (2) Ph.D. Thesis and (4) M.Sc Thesis (previously: 17 Ph.D., 30 M.Sc.) • Publications (last 5 years): 3 papers in periodicals, 1 chapter of book, 41 full papers and 7 abstracts in Conference Proceedings. Marley Maria Bernardes Rebuzzi Vellasco • PhD in Computer Science, University College London, 1992. • MSc in Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, 1987. • BSc in Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, 1985. • Professor (Electrical Engineering Department, Catholic University of Rio de Janeiro (Brazil), PUC-Rio) • Course Load: (3) post-graduate courses per year: Neural networks, Fuzzy Logic and Applied Intelligent Systems; (1) undergraduate courses per year: Applied Computational Intelligence. • Past Supervision: (19) Ph.D. Thesis and (35) M.Sc. Dissertations • Currently advising: (4) Ph.D. Thesis and (6) M.Sc Dissertations • Publications (last 5 years): 18 full papers in international periodicals, 2 book, 13 book chapters, 11 papers in Brazilian and Latin American periodicals, more than 190 full papers in Conference Proceedings. • Scientific and Technical Advisor for CAPES, CNPq and FAPERJ (Brazilian government agencies) • Member of the Computation Brazilian Society (SBC) • Member of the Sociedade Brasileira de Automática (SBA) associated to the International Federation of Automatic Control (IFAC). • Member of the IEEE Computational Intelligence Society • Member of the Systems, Man & Cybernetics Society • Senior Member of IEEE