Contents ANEPI: Economic Analysis of Oil Field Development Projects under Uncertainty... Real Options Theory

Transcription

1 Contents 1 ANEPI: Economic Analysis of Oil Field Development Projects under Uncertainty... 1 Alexandre Anozé Emerick, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco, Marco Antonio G. Dias, 1.1 Introduction Objectives of Economic Analysis of Oil Field Development Projects under Uncetainty Why Economic Analysis of Oil Field Development Projects under Uncertainty? Characteristics and Products of Economic Analysis of Oil Field Development Projects under Uncertainty Economic Analysis Methodologies for Oil Field Development Projects under Uncertainty. 4 2 Real Options Theory , Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco 2.1 Introduction Financial Options Call Options Put Options Real Options Types of Real Options Abandonment Option Temporary Shut-Down Option Option to Exchange One Group of Commodities for Another Future Growth Option Option to Defer Investment Option to Expand Option to Contract Real Options Valuation Methods Contingent Claims Dynamic Programming

2 XIV Contents Numerical Methods Simulation Techniques European-style Options American-style Options References Decision Support Methods André Vargas Abs da Cruz, Carlos Hall Barbosa,, Karla Figueiredo, Luciana Faletti Almeida, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco, Yván Jesús Túpac Valdivia 3.1 Evolutionary Computation Genetic Algorithms Representation Evaluation Genetic Operators Evolution Parameters Cultural Algorithms Cultural Algorithm Components Representation of the Belief Space Updating the Belief Space Specialization Generalization Splitting Merging Coevolution Genetic Programming (GP) Representation Preparation for Genetic Programming Evaluation Operators Evolutionary Techniques Control Parameters Symbolic Regression Enhancement Techniques for GP Quantum-Inspired Genetic Algorithm Current Representation of the Chromosome Quantum-Inspired Evolutionary Algorithms Application to the Well Placement Problem Results Parallel Genetic Algorithms Global Parallel Genetic Algorithms Island Parallel Genetic Algorithms Parallel Cellular Genetic Algorithms Neural Networks History Definition. 55

3 Contents XV The Artificial Neuron Activation Function Artificial Neural Network Topology Type of Training Backpropagation Elman Networks Radial Basis Functions RBF Radial Basis Function Neural Networks Approximation Properties of RBF Networks The Dimensionality Problem Fuzzy Logic Fuzzy Sets Theory Fuzzy Set Singleton Set α-cut of a Fuzzy Set Fuzzy Logic Fuzzy Inference System Fuzzy Numbers Triangular Fuzzy Number Trapezoidal Fuzzy Number Interval Arithmetic Operations on Fuzzy Numbers Neuro-Fuzzy Models Hierarchical Neuro-Fuzzy BSP System Hierarchical Neuro-Fuzzy BSP Model BSP Partitioning Neuro-Fuzzy BSP Cell HNFB Architecture Learning Algorithm. 88 References Intelligent Optimization System for Selecting Alternatives for Oil Field Exploration by Means of Evolutionary Computation.. 97 Alexandre Anozé Emerick, Yván Jesús Túpac Valdivia, Luciana Faletti Almeida, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco, Ricardo Cunha Mattos Portella 4.1 Introduction System Modeling Representation of the Chromosome for the Genetic Algorithm Variable Size Chromosome Chromosome Encoding Genetic Operators Evaluation Function Chromosome Validation Hybridization of the Genetic Algorithm with a Cultural Algorithm

4 XVI Contents 4.10 Belief Space Representation Communication Protocol Voting Process Promotion Process Adjusting the Belief Space Population Modeling for Cooperative Coevolution Customizations in the Implemented Intelligent Systems Use of a Priori Available Strategic Information for Optimization by Genetic Algorithms Quality Maps Quality Map: Definition Quality Map Generation Approximation of Quality Maps through the Hierarchical Neuro-Fuzzy Model Quality Map Interpolation Experiments Using the Quality Map in the Initialization of the Initial Population Using Quality Map Information during the Genetic Algorithm Evolution Process On-Line Approximator for the Flow Simulator Flow Approximation with FF-BP, Elman and RBF Neural Networks Model Based on Curve Points Approximation Experiments: Curve Points Integration of the HNFB Model in the ANEPI-3 Module Implementation Considerations Main Interfaces Implemented in This Subsection Optimization System Interfaces On-Line Dialogs during Distributed Simulation Quality Map Generation Interface with the HNFB Module Interface of the Evaluation / Simulation Server Conclusions References Real Option Value Calculation by Monte Carlo Simulation and Approximation by Fuzzy Numbers and Genetic Algorithms.. 139, Marco Antonio G. Dias, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco 5.1 Introduction Description of the Method Proposed for Approximate Valuation of Real Options Using Fuzzy Numbers Module: Random Sampling Generator Module: Stochastic Processes for Commodity Prices Module: Fuzzy Number Module: Calculation of the Threshold Curve. 142

5 Contents XVII Module: Monte Carlo Simulation for Determining the Option Value Module: Option Value Description of the Methodology Proposed for Obtaining an Optimal Decision Rule Approximation Using Genetic Algorithms Module: Random Sampling Generator Module: Stochastic Processes for Commodity Prices Module: Genetic Algorithm Module: Decision Rule and Option Value Case Studies: Expansion Option Value Approximation Using Fuzzy Numbers Problem Description Solution by Stochastic Simulation Valuation of the Expansion Option Using the Traditional Method Solution Based on the Hybrid Stochastic-Fuzzy Method Experiments and Results Experiment Experiment Experiment Experiment Conclusions Case Studies: Value of the Option to Invest in Information Using Approximation by Fuzzy Numbers Problem Description Solution by Stochastic Simulation Valuation of the Option to Invest in Information Using the Traditional Methodology Solution Based on the Hybrid Stochastic-Fuzzy Method Experiments and Results Experiment Experiment Conclusions Case Studies: Obtaining an Approximation of the Optimal Decision Rule Using Genetic Algorithms Problem Description Modeling Oil Price Uncertainty Optimal Decision Rule Problem Modeling Representation of the Chromosome Evaluation of the Chromosome Results. 182 References

6 XVIII Contents 6 Analysis of Alternatives for Oil Field Development under Uncertainty , Alexandre Anozé Emerick, Dan Posternak, Thiago Souza Mendes Guimarães, Marco Aurélio Cavalcanti Pacheco, Marley Maria Bernardes Rebuzzi Vellasco 6.1 Modular System for Alternatives Analysis Problem Modeling Experiments Conclusion Analysis of Alternatives under Multiple Technical Uncertainties Introduction Gaussian Fuzzy Numbers Operations on Gaussian Fuzzy Numbers Mean and Variance of Gaussian Fuzzy Numbers Tests with Gaussian Fuzzy Numbers LogNormal-Shaped Fuzzy Numbers Operations on LogNormal-Shaped Fuzzy Numbers Tests with LogNormal-Shaped Fuzzy Numbers Methodology for Controlling Domain Growth in Fuzzy Number Operations Mapping a Probability Distribution to a Fuzzy Number Mapping a Fuzzy Number to a Probability Distribution Conclusions Option Value Approximation by Symbolic Regression Model Description Case Study Results Conclusion 223 References High-Performance Processing for E&P Yván Jesús Túpac Valdivia,, Dan Posternak 7.1 Introduction Platform for Parallel Evaluations and Simulations Optimization of Alternatives with Parallel Evaluations Distributed Simulation of 'Random' Alternatives Interface and Execution of the Parallel-Distributed Simulation Run Parallel-Distributed System for Case Simulation Requesting a Simulation Distributed Monte Carlo Simulation Introduction Modeling Model Based on the Geometric Brownian Motion Model Based on Mean Reversion References

7 Contents XIX Appendix A Types of Uncertainty in Investment Projects Reference 242 Appendix B Variance Reduction Techniques 243 References Appendix C Stochastic Processes 247 References. 253 Appendix D Grant, Vora and Weeks Algorithm for Determining the Value of an American Option 255 Reference Appendix E Calculation of the Mean Value and Variance of Fuzzy Numbers References. 264 Appendix F Real Options Valuation Methods References. 272 Appendix G Analytical Model of the Expansion Option Problem References. 275 Appendix H CORBA Architecture. 277 Yván Jesús Túpac Valdivia Reference Author Index. 287

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