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

Product Description The decision to invest in oil field development is an extremely complex problem, even in the absence of uncertainty, due to the great number of technological alternatives that may be used, to the dynamic complexity of oil reservoirs - which involves mul- phase flows (oil, gas and water) in porous media with phase change, and to the c- plicated combinatorial optimization problem of choosing the optimal oil well network, that is, choosing the number and types of wells (horizontal, vertical, directional, m- tilateral) required for draining oil from a field with a view to maximizing its economic value. This problem becomes even more difficult when technical uncertainty and e- nomic uncertainty are considered. The former are uncertainties regarding the existence, volume and quality of a reservoir and may encourage an investment in information before the field is developed, in order to reduce these uncertainties and thus optimize the heavy investments required for developing the reservoir. The economic or market uncertainties are associated with the general movements of the economy, such as oil prices, gas demand, exchange rates, etc. , and may lead decision-makers to defer - vestments and wait for better market conditions. Choosing the optimal investment moment under uncertainty is a complex problem which traditionally involves dynamic programming tools and other techniques that are used by the real options theory. 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