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An Economic Analysis Using Multi-objective Genetic Algorithm And Stochastic Process With Real Option : 다목적 유전 알고리듬과 실물옵션 모델의 확률론적 접근에 의한 경제성 분석
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 강주명 | - |
dc.contributor.author | 문동호 | - |
dc.date.accessioned | 2017-07-14T03:19:35Z | - |
dc.date.available | 2017-07-14T03:19:35Z | - |
dc.date.issued | 2015-02 | - |
dc.identifier.other | 000000026126 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123493 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 에너지시스템공학부, 2015. 2. 강주명. | - |
dc.description.abstract | Both the risk of a project and the flexibility of decision making are extremely crucial in analyzing the economic efficiency of reservoir development. Existing models cannot include operational, geological and market risks due to deterministic assumptions of future cash flow.
This study developed a model for analyzing economic efficiency stochastically using a multi-objective history matching process along with Real Option Valuation (ROV). The multi-objective genetic algorithm was introduced to generate history matching data in the reservoir. The well performance for a simulated reservoir was predicted by additional drilling sites at stages of development. ROV was employed to reflect the payback period and business risks simultaneously. The asset value of the operating field can be improved up to 6-12%. Compared to the conventional models for analysis of economic efficiency, the proposed method can flexibly determine the time for additional drilling to manage the uncertainty of the project. | - |
dc.description.tableofcontents | Abstract i.
List of Tables v List of Figures vi 1. Introduction 1 2. Theoretical Backgrounds 4 2.1 Real options 4 2.2 The Oil Price Model 16 2.3 Multi-objective optimization 18 3. Incorporating reservoir uncertainty in ROV 22 3.1 Frame work of well optimization scenario 24 3.2 History matching by multi-objective genetic algorithm 26 3.3 Construction switch option 29 4. Applications and Analysis 32 4.1 PUNQ-S3 and scenario summary 32 4.2 Result of multi-objective history matching and prediction due to each scenario 37 4.3 Decision making based on NPV 48 4.4 Decision making based on ROV 52 4.4 Sensitivity analysis and NPV vs. ROV 56 5. Conclusion 61 Bibliography 63 요약 (국문초록) 66 | - |
dc.format | application/pdf | - |
dc.format.extent | 2233139 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | economic analysis | - |
dc.subject | real option valuation | - |
dc.subject | milti-objective history matching | - |
dc.subject | well optimization | - |
dc.subject.ddc | 622 | - |
dc.title | An Economic Analysis Using Multi-objective Genetic Algorithm And Stochastic Process With Real Option | - |
dc.title.alternative | 다목적 유전 알고리듬과 실물옵션 모델의 확률론적 접근에 의한 경제성 분석 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | vii, 66 | - |
dc.contributor.affiliation | 공과대학 에너지시스템공학부 | - |
dc.date.awarded | 2015-02 | - |
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