Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia
This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO2 injectiv...
| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
MDPI
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/89524 |
| _version_ | 1848765235359383552 |
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| author | Rezaee, Reza Ekundayo, Jamiu |
| author_facet | Rezaee, Reza Ekundayo, Jamiu |
| author_sort | Rezaee, Reza |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO2 injectivity in this formation. Well logs and core data were collected from 5 boreholes in the Surat Basin, where extensive core data and complete sets of conventional well logs exist for the Precipice Sandstone. Four different machine learning (ML) techniques, including Random Forest (RF), Artificial neural network (ANN), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were independently trained with a wide range of hyper-parameters to ensure that not only is the best model selected, but also the right combination of model parameters is selected. Cross-validation for 20 different combinations of the seven available input logs was used for this study. Based on the performances in the validation and blind testing phases, the ANN with all seven logs used as input was found to give the best performance in predicting permeability for the Precipice Sandstone with the coefficient of determination (R2) of about 0.93 and 0.87 for the training and blind data sets respectively. Multi-regression analysis also appears to be a successful approach to calculate reservoir permeability for the Precipice Sandstone. Models with a complete set of well logs can generate reservoir permeability with R2 of more than 90%. |
| first_indexed | 2025-11-14T11:32:01Z |
| format | Journal Article |
| id | curtin-20.500.11937-89524 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:32:01Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-895242023-01-18T06:33:20Z Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia Rezaee, Reza Ekundayo, Jamiu Science & Technology Technology Energy & Fuels permeability prediction machine learning CO2 injectivity precipice sandstone Surat Basin Australia STORAGE EFFICIENCY SELECTION POROSITY CHOICE This paper presents the results of a research project which investigated permeability prediction for the Precipice Sandstone of the Surat Basin. Machine learning techniques were used for permeability estimation based on multiple wireline logs. This information improves the prediction of CO2 injectivity in this formation. Well logs and core data were collected from 5 boreholes in the Surat Basin, where extensive core data and complete sets of conventional well logs exist for the Precipice Sandstone. Four different machine learning (ML) techniques, including Random Forest (RF), Artificial neural network (ANN), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were independently trained with a wide range of hyper-parameters to ensure that not only is the best model selected, but also the right combination of model parameters is selected. Cross-validation for 20 different combinations of the seven available input logs was used for this study. Based on the performances in the validation and blind testing phases, the ANN with all seven logs used as input was found to give the best performance in predicting permeability for the Precipice Sandstone with the coefficient of determination (R2) of about 0.93 and 0.87 for the training and blind data sets respectively. Multi-regression analysis also appears to be a successful approach to calculate reservoir permeability for the Precipice Sandstone. Models with a complete set of well logs can generate reservoir permeability with R2 of more than 90%. 2022 Journal Article http://hdl.handle.net/20.500.11937/89524 10.3390/en15062053 English http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | Science & Technology Technology Energy & Fuels permeability prediction machine learning CO2 injectivity precipice sandstone Surat Basin Australia STORAGE EFFICIENCY SELECTION POROSITY CHOICE Rezaee, Reza Ekundayo, Jamiu Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia |
| title | Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia |
| title_full | Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia |
| title_fullStr | Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia |
| title_full_unstemmed | Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia |
| title_short | Permeability Prediction Using Machine Learning Methods for the CO2 Injectivity of the Precipice Sandstone in Surat Basin, Australia |
| title_sort | permeability prediction using machine learning methods for the co2 injectivity of the precipice sandstone in surat basin, australia |
| topic | Science & Technology Technology Energy & Fuels permeability prediction machine learning CO2 injectivity precipice sandstone Surat Basin Australia STORAGE EFFICIENCY SELECTION POROSITY CHOICE |
| url | http://hdl.handle.net/20.500.11937/89524 |