Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs
This research investigated the applicability of bagging and boosting ensemble machine learning algorithms in predicting petrophysical properties, namely porosity, permeability and water saturation which is a vital aspect in reservoir characterization. Further, an in-depth analysis was done on the ef...
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| Format: | Thesis |
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Curtin University
2025
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| Online Access: | http://hdl.handle.net/20.500.11937/97704 |
| _version_ | 1848766307985522688 |
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| author | Kohona Walawwe, Kushan Oshadi Sandunil |
| author_facet | Kohona Walawwe, Kushan Oshadi Sandunil |
| author_sort | Kohona Walawwe, Kushan Oshadi Sandunil |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This research investigated the applicability of bagging and boosting ensemble machine learning algorithms in predicting petrophysical properties, namely porosity, permeability and water saturation which is a vital aspect in reservoir characterization. Further, an in-depth analysis was done on the effects of different hyperparameter optimization algorithms. The study successfully proposed stacking-based novel ensemble models to predict petrophysical properties of sandstone reservoirs where some models performed up to 97% in prediction accuracy. |
| first_indexed | 2025-11-14T11:49:04Z |
| format | Thesis |
| id | curtin-20.500.11937-97704 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:49:04Z |
| publishDate | 2025 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-977042025-05-09T00:51:20Z Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs Kohona Walawwe, Kushan Oshadi Sandunil This research investigated the applicability of bagging and boosting ensemble machine learning algorithms in predicting petrophysical properties, namely porosity, permeability and water saturation which is a vital aspect in reservoir characterization. Further, an in-depth analysis was done on the effects of different hyperparameter optimization algorithms. The study successfully proposed stacking-based novel ensemble models to predict petrophysical properties of sandstone reservoirs where some models performed up to 97% in prediction accuracy. 2025 Thesis http://hdl.handle.net/20.500.11937/97704 Curtin University fulltext |
| spellingShingle | Kohona Walawwe, Kushan Oshadi Sandunil Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs |
| title | Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs |
| title_full | Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs |
| title_fullStr | Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs |
| title_full_unstemmed | Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs |
| title_short | Developing Ensemble Machine Learning Models to Predict Petrophysical Properties of Sandstone Reservoirs |
| title_sort | developing ensemble machine learning models to predict petrophysical properties of sandstone reservoirs |
| url | http://hdl.handle.net/20.500.11937/97704 |