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.

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.

Presents applications of intelligent decision support systems to oil field development under uncertainty Includes supplementary material: sn.pub/extras

Autorentext

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

Klappentext

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.

Inhalt
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.