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Intelligent Systems in Oil Field Development under Uncertainty

  • Book
  • © 2009

Overview

Editors:
  1. Marco A. C. Pacheco

    1. PUC-Rio, Rio de Janeiro, Brazil

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    You can also search for this editor in PubMed   Google Scholar

  2. Marley M. B. R. Vellasco

    1. PUC-Rio, Rio de Janeiro, Brazil

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    You can also search for this editor in PubMed   Google Scholar

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

Part of the book series: Studies in Computational Intelligence (SCI, volume 183)

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Table of contents (15 chapters)

  1. Front Matter

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  2. ANEPI: Economic Analysis of Oil Field Development Projects under Uncertainty

    • Alexandre Anozé Emerick, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco, Marco Antonio G. Dias, Juan Guillermo Lazo Lazo
    Pages 1-5

  3. Real Options Theory

    • Juan Guillermo Lazo Lazo, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco
    Pages 7-22

  4. Decision Support Methods

    • André Vargas Abs da Cruz, Carlos Hall Barbosa, Juan Guillermo Lazo Lazo, Karla Figueiredo, Luciana Faletti Almeida, Marco Aurélio Cavalcanti Pacheco et al.
    Pages 23-96

  5. Intelligent Optimization System for Selecting Alternatives for Oil Field Exploration by Means of Evolutionary Computation

    • Alexandre Anozé Emerick, Yván Jesús Túpac Valdivia, Luciana Faletti Almeida, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco, Ricardo Cunha Mattos Portella
    Pages 97-137

  6. Real Option Value Calculation by Monte Carlo Simulation and Approximation by Fuzzy Numbers and Genetic Algorithms

    • Juan Guillermo Lazo Lazo, Marco Antonio G. Dias, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco
    Pages 139-186

  7. Analysis of Alternatives for Oil Field Development under Uncertainty

    • Juan Guillermo Lazo Lazo, Alexandre Anozé Emerick, Dan Posternak, Thiago Souza Mendes Guimarães, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco
    Pages 187-225

  8. High-Performance Processing for E&P

    • Yván Jesús Túpac Valdivia, Juan Guillermo Lazo Lazo, Dan Posternak
    Pages 227-239

  9. Appendix A – Types of Uncertainty in Investment Projects

    • Juan Guillermo Lazo Lazo
    Pages 241-242

  10. Appendix B – Variance Reduction Techniques

    • Juan Guillermo Lazo Lazo
    Pages 243-246

  11. Appendix C – Stochastic Processes

    • Juan Guillermo Lazo Lazo
    Pages 247-253

  12. Appendix D – Grant, Vora and Weeks Algorithm for Determining the Value of an American Option

    • Juan Guillermo Lazo Lazo
    Pages 255-259

  13. Appendix E – Calculation of the Mean Value and Variance of Fuzzy Numbers

    • Juan Guillermo Lazo Lazo
    Pages 261-264

  14. Appendix F – Real Options Valuation Methods

    • Juan Guillermo Lazo Lazo
    Pages 265-272

  15. Appendix G – Analytical Model of the Expansion Option Problem

    • Juan Guillermo Lazo Lazo
    Pages 273-275

  16. Appendix H – CORBA Architecture

    • Yván Jesús Túpac Valdivia
    Pages 277-285

  17. Back Matter

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Keywords

About this book

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.

Editors and Affiliations

  • PUC-Rio, Rio de Janeiro, Brazil

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

About the editors

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

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