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dc.contributor.advisorChau, Dao Tran Hoang
dc.contributor.authorVi, Tran Dieu
dc.contributor.authorHelene, Anne - Marie
dc.date.accessioned2018-12-17T01:42:02Z
dc.date.available2018-12-17T01:42:02Z
dc.date.issued2017
dc.identifier.other022003932
dc.identifier.urihttp://keep.hcmiu.edu.vn:8080/handle/123456789/3021
dc.description.abstractNowadays, one of the most critical engineering problems in optimized reservoir development is Petroleum Reservoir description and characterization. There have been many reports on successful applications of Fuzzy Inference System (FIS) and Ensemble learning method in reservoir characterization. In this study, we propose an ensemble of multi-model regression framework based on FIS architecture to tackle the challenge of permeability prediction using logs data properties. During the research, the capability of the ensemble model when being tested in well log properties, which is practical data of Oligocene geological types from Cuu Long basin, is also demonstrated and these will finally be shown in section IV-RESULTS as graphs. Furthermore, empirical results also indicate that our proposed algorithm framework is efficient and has the significant improvement compared to each existing standard single model.en_US
dc.language.isoen_USen_US
dc.publisherInternational University - HCMCen_US
dc.subjectFuzzy inference system; Machine learningen_US
dc.titleA machine learning approach to reservoir characterizationen_US
dc.typeThesisen_US


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