Intelligent Systems in Oil Field Development under Uncertainty: Studies in Computational Intelligence, cartea 183
Editat de Marco A. C. Pacheco, Marley M. B. R. Vellascoen Limba Engleză Hardback – 27 apr 2009
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Specificații
ISBN-13: 9783540929994
ISBN-10: 3540929991
Pagini: 308
Ilustrații: XX, 288 p.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.61 kg
Ediția:2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540929991
Pagini: 308
Ilustrații: XX, 288 p.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.61 kg
Ediția:2009
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Computational Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
ANEPI: Economic Analysis of Oil Field Development Projects under Uncertainty.- Real Options Theory.- Decision Support Methods.- Intelligent Optimization System for Selecting Alternatives for Oil Field Exploration by Means of Evolutionary Computation.- Real Option Value Calculation by Monte Carlo Simulation and Approximation by Fuzzy Numbers and Genetic Algorithms.- Analysis of Alternatives for Oil Field Development under Uncertainty.- High-Performance Processing for E&P.- Appendix A – Types of Uncertainty in Investment Projects.- Appendix B – Variance Reduction Techniques.- Appendix C – Stochastic Processes.- Appendix D – Grant, Vora and Weeks Algorithm for Determining the Value of an American Option.- Appendix E – Calculation of the Mean Value and Variance of Fuzzy Numbers.- Appendix F – Real Options Valuation Methods.- Appendix G – Analytical Model of the Expansion Option Problem.- Appendix H – CORBA Architecture.
Notă biografică
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
• 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
Textul de pe ultima copertă
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.
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.
Caracteristici
Presents applications of intelligent decision support systems to oil field development under uncertainty Includes supplementary material: sn.pub/extras