Intelligent Systems in Oil Field Development under Uncertainty / Edition 1

by Marco A. C. Pacheco, Marley M. B. R. Vellasco

Overview

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.

Product Details

ISBN-13:	9783540929994
Publisher:	Springer Berlin Heidelberg
Publication date:	04/23/2009
Series:	Studies in Computational Intelligence , #183
Edition description:	2009
Pages:	288
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

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

Table of Contents

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 – Shastic 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.