Publications
Detailed Information
A Prediction of Clastic Reservoir Facies Using Hidden Markov Model Combined With Viterbi Algorithm : 은닉마르코프모델과 비터비 알고리즘을 이용한 쇄설성퇴적암 저류층 암상 예측
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 강주명 | - |
dc.contributor.author | Hwa-soo Suk | - |
dc.date.accessioned | 2017-07-14T03:19:52Z | - |
dc.date.available | 2017-07-14T03:19:52Z | - |
dc.date.issued | 2015-08 | - |
dc.identifier.other | 000000071113 | - |
dc.identifier.uri | https://hdl.handle.net/10371/123498 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 에너지시스템공학부, 2015. 8. 강주명. | - |
dc.description.abstract | A facies of clastic rock affects reservoir properties such as permeability and porosity. A facies analysis in well is crucial to do a conditional facies modeling. Since a recovery of core cannot be performed through total depth due to the cost, the facies analysis is preferred to link well log data to stochastics methods.
Hidden Markov model (HMM) method, which predicts the facies from sedimentary -transition information and well log data, has been used for reservoir characterization. The conventional method is based on maximum a posteriori (MAP) | - |
dc.description.abstract | selects a facies based on only maximum probability. This method performs poor with very thin layers where probabilities are similar or input data are noisy.
In this study, Viterbi algorithm which can decrease uncertainty of decision and remove the meaningless thin layer, is included in HMM. The proposed method is applied to the three clastic reservoir including two synthetic reservoirs and a shoreface reservoir. For C field in Louisiana, USA, it performed 12% better than conventional method and the transition-consistency ratio was increased by 70% for 18 wells. | - |
dc.description.tableofcontents | Abstract i
Table of Contents iii List of Tables iv List of Figures vi 1. Introduction 1 2. Theoretical Backgrounds 7 2.1. HMM (hidden Markov model) 7 2.2. Viterbi algorithm 13 2.3. Consistency ratio parameters 16 3. HMM Method Combined with Viterbi Algorithm 19 4. Results and Discussion 24 4.1. Application to synthetic reservoir A with four lithofacies 24 4.2. Application to synthetic reservoir B with five lithofacies 39 4.3. Application to shoreface reservoir C with four facies 47 5. Conclusions 63 Reference 65 요약(국문초록) 70 | - |
dc.format | application/pdf | - |
dc.format.extent | 3249196 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | reservoir facies prediction | - |
dc.subject | well log data | - |
dc.subject | hidden Markov model | - |
dc.subject | Viterbi algorithm | - |
dc.subject.ddc | 622 | - |
dc.title | A Prediction of Clastic Reservoir Facies Using Hidden Markov Model Combined With Viterbi Algorithm | - |
dc.title.alternative | 은닉마르코프모델과 비터비 알고리즘을 이용한 쇄설성퇴적암 저류층 암상 예측 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | 석화수 | - |
dc.description.degree | Master | - |
dc.citation.pages | vii, 71 | - |
dc.contributor.affiliation | 공과대학 에너지시스템공학부 | - |
dc.date.awarded | 2015-08 | - |
- 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.