Publications
Detailed Information
Channel Reservoir Characterization by Ensemble Smoother with Selective Water Breakthrough Data
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 최종근 | - |
dc.contributor.author | 김관덕 | - |
dc.date.accessioned | 2018-12-03T01:36:45Z | - |
dc.date.available | 2018-12-03T01:36:45Z | - |
dc.date.issued | 2018-08 | - |
dc.identifier.other | 000000152468 | - |
dc.identifier.uri | https://hdl.handle.net/10371/143686 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 에너지시스템공학부, 2018. 8. 최종근. | - |
dc.description.abstract | Reservoir characterization is a process to figure out reservoir parameters of interest using available data. Ensemble Smoother (ES) method is one of the main methods of reservoir characterization in petroleum engineering. ES utilizes and assimilates all available data through a single global update. ES is fast but unstable with possible overshooting of parameters and distorting of parameters distribution.
In this research, a method called selective use of measurement data using ES for each well is suggested in order to improve its performances. The proposed method is to use oil production rates before water breakthrough and water cut rates after water breakthrough for each well. ES is very sensitive to the initial ensemble members. Therefore, it is necessary to select reliable models, which are similar to the reference one for better stability in the assimilation step. In this study, PCA (Principle Component Analysis) and K-means Clustering are applied to get good reservoir models. In order to check the superiority of the proposed method, 3 methods are compared in our research: ES with all data | - |
dc.description.abstract | ES with 100 initial models selected by PCA and K-means Clustering | - |
dc.description.abstract | and ES with 100 initial selected models and selective measurement data. 2D synthetic channelized reservoir models are applied with nine-spot waterflooding.
The proposed method can properly overcome the limitation of standard ES and conserve channel connectivity. this method manages high uncertainty ranges, gives the best reservoir characterization results and shows the reliable future performance estimations. | - |
dc.description.tableofcontents | Chapter 1 Introduction 1
1.1. Study Background . 1 1.2. Purpose of the Research. 7 Chapter 2 Methodology 10 2.1 Channel reservoir . 10 2.2 Principle Component Analysis 13 2.2 K-means Clustering 15 2.3 Ensemble Smoother 18 2.4 Selective Measurement Data. 20 Chapter 3 Results 24 3.1 Model description. 24 3.2 Log permeability field 28 3.3 Water and oil production rates. 40 3.4 Total oil and water productions. 45 Conclusions . 49 Bibliography 51 국문초록 55 | - |
dc.format | application/pdf | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject.ddc | 622.33 | - |
dc.title | Channel Reservoir Characterization by Ensemble Smoother with Selective Water Breakthrough Data | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Kim Gvan Dek | - |
dc.description.degree | Master | - |
dc.contributor.affiliation | 공과대학 에너지시스템공학부 | - |
dc.date.awarded | 2018-08 | - |
- Appears in Collections:
- Files in This Item:
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.