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Uncertainty Quantification of Reservoir Performances Using Streamline Based Inversion and Distance Based Method
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최종근 | - |
dc.contributor.author | 박지훈 | - |
dc.date.accessioned | 2017-07-14T03:18:25Z | - |
dc.date.available | 2017-07-14T03:18:25Z | - |
dc.date.issued | 2014-02 | - |
dc.identifier.other | 000000018122 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123472 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 에너지시스템공학부, 2014. 2. 최종근. | - |
dc.description.abstract | For decision makings, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since an initial model constructed with limited data has high uncertainty, it is essential to integrate both static and dynamic data for reliable prediction. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching. Multiple realizations of reservoir models should be history matched. In addition, history matching is mathematically a highly ill-posed problem.
In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline based inversion and distance based method. First, a distance between each model is defined as the norm of differences in generalized travel time vectors. Second, they are grouped according to distances and representative models are selected instead of matching all models. Third, generalized travel time inversion is applied for integration of dynamic data and a streamline simulator is adopted as a forward simulator to take advantage of computational efficiency. It is verified that the proposed method gathers models with similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances fairly well, while reducing the amount of calculations significantly by using the representative models. | - |
dc.description.tableofcontents | Abstract ⅰ
Table of Contents ⅱ List of Tables ⅲ List of Figures ⅳ 1. Introduction 1 2. Theoretical backgrounds 7 2.1 Streamline simulation 7 2.2 Generalized travel time inversion 14 2.3 Distance based method 25 2.4 Randomized maximum likelihood 28 3. Quantifying uncertainty with GTTI, RML, and distance based method 30 4. Results 35 4.1 Reference field 35 4.2 Sensitivity calculations 43 4.3 Application of distance based method, RML and GTTI 46 4.4 Misfit reduction and improvement of computational efficiency 73 5. Conclusions 76 References 78 국문초록 84 | - |
dc.format | application/pdf | - |
dc.format.extent | 1078895 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | streamline simulation | - |
dc.subject | generalized travel time inversion | - |
dc.subject | distance based method | - |
dc.subject | reservoir characterization | - |
dc.subject.ddc | 622 | - |
dc.title | Uncertainty Quantification of Reservoir Performances Using Streamline Based Inversion and Distance Based Method | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.citation.pages | ⅳ, 84 | - |
dc.contributor.affiliation | 공과대학 에너지시스템공학부 | - |
dc.date.awarded | 2014-02 | - |
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